Alzheimer’s disease (AD) is an irremediable neurodegenerative illness developed by the fast deterioration of brain cells. AD is mostly common in elder people and it extremely disturbs the physical and mental health of patients, therefore early detection is essential to prevent AD development. However, the precise detection of AD and mild cognitive impairment (MCI) is difficult during classification. In this paper, the Residual network i.e., ResNet-18 is used for extracting the features, and the proposed improved marine predators algorithm (IMPA) is developed for choosing the optimum features to perform an effective classification of AD. The multi-verse optimizer (MVO) used in the IMPA helps to balance exploration and exploitation, which leads to the selection of optimal relevant features. Further, the classification of AD is accomplished using the multiclass support vector machine (MSVM). Open access series of imaging studies-1 (OASIS-1) and Alzheimer disease neuroimaging initiative (ADNI) datasets are used to evaluate the IMPA-MSVM method. The performance of the IMPA-MSVM method is analyzed using accuracy, sensitivity, specificity, positive predictive value (PPV) and matthews correlation coefficient (MCC). The existing methods such as the deep learning-based segmenting method using SegNet (DLSS), mish activation function (MAF) with spatial transformer network (STN) and BrainNet2D are used to evaluate the IMPA-MSVM method. The accuracy of IMPA-MSVM for the ADNI dataset is 98.43% which is more when compared to the DLSS and MAF-STN.
The document proposes a pixel-level fusion approach using vision transformer for early detection of Alzheimer's disease from MRI and PET scans. It applies discrete wavelet transform to decompose MRI and PET images into frequency bands. It then uses a pre-trained VGG16 model to optimize the decomposition. The frequency bands are fused using inverse discrete wavelet transform. Finally, a pre-trained vision transformer model classifies the fused images and achieves 81.25% accuracy for AD/EMCI and AD/LMCI classification on MRI data and 93.75% accuracy on PET data, outperforming existing methods on PET data classification.
IRJET - Detection of Heamorrhage in Brain using Deep LearningIRJET Journal
This document presents a method for detecting hemorrhage in brain CT scans using deep learning. It begins with an introduction to brain hemorrhage and the need for automated detection. Previous related work using various segmentation and classification methods is summarized. Deep learning is identified as a promising technique due to its ability to extract complex features from images. The proposed method uses a convolutional neural network model with several convolutional, max pooling, dropout and dense layers to classify brain CT scans as either normal or hemorrhagic. The model is trained on 180 images and tested on 20 images, achieving an accuracy of 94.4% at predicting hemorrhage. The method provides a fast and automated way to detect hemorrhage in brain CT scans to help
Ensemble learning approach for multi-class classification of Alzheimer’s stag...TELKOMNIKA JOURNAL
The document presents an ensemble learning approach for multi-class classification of Alzheimer's disease stages using magnetic resonance imaging (MRI). The proposed algorithm concatenates the output layers of three pre-trained models - Xception, InceptionV3, and MobileNet. The algorithm is tested on MRI images from the Alzheimer's Disease Neuroimaging Initiative database. The middle slice is extracted from each 3D MRI and data augmentation is applied. The ensemble model provides 85% accuracy for multi-class classification of Alzheimer's, mild cognitive impairment convertible, mild cognitive impairment non-convertible, and cognitively normal. It also achieves 85% accuracy for classifying mild cognitive impairment convertible vs non-convertible, 94% for Alzheimer's vs
Brain Tumor Detection and Segmentation using UNETIRJET Journal
This document discusses brain tumor detection and segmentation using the UNET model. It analyzes previous research on brain tumor segmentation techniques and their limitations. The proposed method uses the BraTS 2020 dataset containing 369 MRI images for training and 125 for testing. It develops a 3D UNET model for multimodal brain tumor segmentation. The model generates 3D outputs and achieves 98.5% accuracy in segmenting whole, core and enhancing tumors.
IRJET- Machine Learning Techniques for Brain Stroke using MRIIRJET Journal
This document discusses the use of machine learning techniques for detecting brain strokes using MRI scans. It provides an overview of machine learning and its applications in neuroimaging and brain stroke detection. Specifically, it reviews several studies that have used techniques like random forests, artificial neural networks, support vector machines, and convolutional neural networks to accurately classify MRI scans and detect strokes with accuracies ranging from 70% to 98%. The document concludes that machine learning algorithms have great potential to assist doctors in diagnosing and treating strokes by automating analysis of MRI scans.
The IoT and registration of MRI brain diagnosis based on genetic algorithm an...IJEECSIAES
The technology of the multimodal brain image registration is the key method for accurate and rapid diagnosis and treatment of brain diseases. For achieving high-resolution image registration, a fast sub pixel registration algorithm is used based on single-step discrete wavelet transform (DWT) combined with phase convolution neural network (CNN) to classify the registration of brain tumors. In this work apply the genetic algorithm and CNN clasifcation in registration of magnetic resonance imaging (MRI) image. This approach follows eight steps, reading the source of MRI brain image and loading the reference image, enhencment all MRI images by bilateral filter, transforming DWT image by applying the DWT2, evaluating (fitness function) each MRI image by using entropy, applying the genetic algorithm, by selecting the two images based on rollout wheel and crossover of the two images, the CNN classify the result of subtraction to normal or abnormal, “in the eighth one,” the Arduino and global system for mobile (GSM) 8080 are applied to send the message to patient. The proposed model is tested on MRI Medical City Hospital in Baghdad database consist 550 normal and 350 abnormal and split to 80% training and 20 testing, the proposed model result achieves the 98.8% accuracy.
The IoT and registration of MRI brain diagnosis based on genetic algorithm an...nooriasukmaningtyas
The document describes a proposed model for MRI brain diagnosis using genetic algorithms, convolutional neural networks, and the Internet of Things. The model has eight steps: loading MRI images, enhancing images, applying discrete wavelet transform, evaluating images using entropy, applying genetic algorithm for registration, subtracting images and using CNN to classify results as normal or abnormal, and sending messages to patients using Arduino and GSM. The model was tested on 550 normal and 350 abnormal MRI images, achieving 98.8% accuracy in classification.
The document proposes a pixel-level fusion approach using vision transformer for early detection of Alzheimer's disease from MRI and PET scans. It applies discrete wavelet transform to decompose MRI and PET images into frequency bands. It then uses a pre-trained VGG16 model to optimize the decomposition. The frequency bands are fused using inverse discrete wavelet transform. Finally, a pre-trained vision transformer model classifies the fused images and achieves 81.25% accuracy for AD/EMCI and AD/LMCI classification on MRI data and 93.75% accuracy on PET data, outperforming existing methods on PET data classification.
IRJET - Detection of Heamorrhage in Brain using Deep LearningIRJET Journal
This document presents a method for detecting hemorrhage in brain CT scans using deep learning. It begins with an introduction to brain hemorrhage and the need for automated detection. Previous related work using various segmentation and classification methods is summarized. Deep learning is identified as a promising technique due to its ability to extract complex features from images. The proposed method uses a convolutional neural network model with several convolutional, max pooling, dropout and dense layers to classify brain CT scans as either normal or hemorrhagic. The model is trained on 180 images and tested on 20 images, achieving an accuracy of 94.4% at predicting hemorrhage. The method provides a fast and automated way to detect hemorrhage in brain CT scans to help
Ensemble learning approach for multi-class classification of Alzheimer’s stag...TELKOMNIKA JOURNAL
The document presents an ensemble learning approach for multi-class classification of Alzheimer's disease stages using magnetic resonance imaging (MRI). The proposed algorithm concatenates the output layers of three pre-trained models - Xception, InceptionV3, and MobileNet. The algorithm is tested on MRI images from the Alzheimer's Disease Neuroimaging Initiative database. The middle slice is extracted from each 3D MRI and data augmentation is applied. The ensemble model provides 85% accuracy for multi-class classification of Alzheimer's, mild cognitive impairment convertible, mild cognitive impairment non-convertible, and cognitively normal. It also achieves 85% accuracy for classifying mild cognitive impairment convertible vs non-convertible, 94% for Alzheimer's vs
Brain Tumor Detection and Segmentation using UNETIRJET Journal
This document discusses brain tumor detection and segmentation using the UNET model. It analyzes previous research on brain tumor segmentation techniques and their limitations. The proposed method uses the BraTS 2020 dataset containing 369 MRI images for training and 125 for testing. It develops a 3D UNET model for multimodal brain tumor segmentation. The model generates 3D outputs and achieves 98.5% accuracy in segmenting whole, core and enhancing tumors.
IRJET- Machine Learning Techniques for Brain Stroke using MRIIRJET Journal
This document discusses the use of machine learning techniques for detecting brain strokes using MRI scans. It provides an overview of machine learning and its applications in neuroimaging and brain stroke detection. Specifically, it reviews several studies that have used techniques like random forests, artificial neural networks, support vector machines, and convolutional neural networks to accurately classify MRI scans and detect strokes with accuracies ranging from 70% to 98%. The document concludes that machine learning algorithms have great potential to assist doctors in diagnosing and treating strokes by automating analysis of MRI scans.
The IoT and registration of MRI brain diagnosis based on genetic algorithm an...IJEECSIAES
The technology of the multimodal brain image registration is the key method for accurate and rapid diagnosis and treatment of brain diseases. For achieving high-resolution image registration, a fast sub pixel registration algorithm is used based on single-step discrete wavelet transform (DWT) combined with phase convolution neural network (CNN) to classify the registration of brain tumors. In this work apply the genetic algorithm and CNN clasifcation in registration of magnetic resonance imaging (MRI) image. This approach follows eight steps, reading the source of MRI brain image and loading the reference image, enhencment all MRI images by bilateral filter, transforming DWT image by applying the DWT2, evaluating (fitness function) each MRI image by using entropy, applying the genetic algorithm, by selecting the two images based on rollout wheel and crossover of the two images, the CNN classify the result of subtraction to normal or abnormal, “in the eighth one,” the Arduino and global system for mobile (GSM) 8080 are applied to send the message to patient. The proposed model is tested on MRI Medical City Hospital in Baghdad database consist 550 normal and 350 abnormal and split to 80% training and 20 testing, the proposed model result achieves the 98.8% accuracy.
The IoT and registration of MRI brain diagnosis based on genetic algorithm an...nooriasukmaningtyas
The document describes a proposed model for MRI brain diagnosis using genetic algorithms, convolutional neural networks, and the Internet of Things. The model has eight steps: loading MRI images, enhancing images, applying discrete wavelet transform, evaluating images using entropy, applying genetic algorithm for registration, subtracting images and using CNN to classify results as normal or abnormal, and sending messages to patients using Arduino and GSM. The model was tested on 550 normal and 350 abnormal MRI images, achieving 98.8% accuracy in classification.
DIRECTIONAL CLASSIFICATION OF BRAIN TUMOR IMAGES FROM MRI USING CNN-BASED DEE...IRJET Journal
This document presents research on using a convolutional neural network (CNN) model for the detection and classification of brain tumors from MRI images. The CNN model improves the accuracy of tumor detection and can serve as a useful tool for physicians. The researchers trained and tested several CNN architectures, including CNN, ResNet50, MobileNetV2, and VGG19 on an MRI brain image database. Their proposed model uses a modified Residual U-Net architecture with residual blocks and attention gates to better segment tumors and extract local features from MRI images. Evaluation results found their model achieved better accuracy than existing methods like U-Net and CNN for brain tumor segmentation tasks.
A Review On Methods For Feature Extraction And Classification For The Automat...Heather Strinden
This document reviews various feature extraction and classification methods that have been used for the automated detection of Alzheimer's disease from magnetic resonance imaging (MRI) scans. It summarizes several studies that used different feature extraction techniques like voxel-based, vertex-based, and region of interest-based methods. Popular classification algorithms discussed include support vector machines, linear discriminant analysis, Bayesian classifiers and artificial neural networks. The document concludes that selecting relevant features extracted from MRI scans can yield accurate classification of Alzheimer's disease.
Primary challenges are the identification, segmentation, and extraction of the afflicted area from the scanning of magnetic resonance. However, it is a time-consuming and tiresome for clinical specialists. In this paper,
an automated brain tumor system is proposed. The proposed system employs hybrid image processing techniques such as contrast correction, histogram normalization, thresholding techniques, arithmetic, and morphological operations to quarantine nearby organs and other tissue from the brain for improving the localization of the affected region. At first, the skull stripping process is proposed to segregate the non-designated regions to extract the designated brain regions. Those resultant brain region images are further subjected to discover the brain tumor. The planned scheme is studied on the magnetic resonance (MR) images with the use of T1, T2, T1c, and fluid-attenuated inversion recovery (FLAIR). The proposed hybrid method employed. The results reveal that the proposed method is quite efficient to extract the tumor region. The accuracy rate for segmentation and separation of area of interest in brain tumor reached to 95%. Finally, the significance of the proposed procedure is confirmed using the real image clinical dataset got from ten patients were diagnosed as begin, malignant, and metastatic brain tumors in Al-Yarmouk and Baghdad teaching hospital in Baghdad, Iraq.
Survey of various methods used for integrating machine learning into brain tu...Drjabez
This document surveys various machine learning methods used for integrating machine learning into brain tumor detection and classification from MRI images. It discusses preprocessing techniques like median filtering, Gaussian high pass filtering, and morphology dilation to enhance images. Segmentation techniques covered include thresholding, edge detection, region-based, watershed, Berkeley wavelet transform, K-means clustering, and neural networks. Feature extraction calculates correlation, skewness. Classification algorithms discussed are multi-layer perceptron, naive Bayes, and support vector machines. The document provides an overview of key steps and methods for machine learning-based brain tumor detection and segmentation from MRI images.
An ensemble deep learning classifier of entropy convolutional neural network ...BASMAJUMAASALEHALMOH
This document summarizes a research paper that proposes an ensemble deep learning classifier for efficient disease prediction of heart disease and breast cancer. The classifier combines two models: an Entropy Convolutional Neural Network (ECNN) and a Divergence Weight Bidirectional LSTM (DWBi-LSTM). Feature selection is first performed on the dataset using Bayesian Network imputation and Mode Fuzzy Weight Canonical Polyadic decomposition. An Adaptive Mean Chaotic Grey Wolf Optimization algorithm is then used for wrapper-based feature selection. The results of the ECNN and DWBi-LSTM classifiers are combined using bootstrap aggregation and majority voting. Performance is evaluated using various metrics and compared to existing classifiers. The proposed ensemble classifier aims to improve disease prediction accuracy over single classifiers.
Automatic Diagnosis of Abnormal Tumor Region from Brain Computed Tomography I...ijcseit
The research work presented in this paper is to achieve the tissue classification and automatically
diagnosis the abnormal tumor region present in Computed Tomography (CT) images using the wavelet
based statistical texture analysis method. Comparative studies of texture analysis method are performed
for the proposed wavelet based texture analysis method and Spatial Gray Level Dependence Method
(SGLDM). Our proposed system consists of four phases i) Discrete Wavelet Decomposition (ii)
Feature extraction (iii) Feature selection (iv) Analysis of extracted texture features by classifier. A
wavelet based statistical texture feature set is derived from normal and tumor regions. Genetic Algorithm
(GA) is used to select the optimal texture features from the set of extracted texture features. We construct
the Support Vector Machine (SVM) based classifier and evaluate the performance of classifier by
comparing the classification results of the SVM based classifier with the Back Propagation Neural network
classifier(BPN). The results of Support Vector Machine (SVM), BPN classifiers for the texture analysis
methods are evaluated using Receiver Operating Characteristic (ROC) analysis. Experimental results
show that the classification accuracy of SVM is 96% for 10 fold cross validation method. The system
has been tested with a number of real Computed Tomography brain images and has achieved satisfactory
results.
Alzheimer Disease Prediction using Machine Learning AlgorithmsIRJET Journal
This document summarizes research on using machine learning algorithms to predict Alzheimer's disease. It discusses collecting data from the Alzheimer's Disease Neuroimaging Initiative database and preprocessing MRI images. Feature extraction is performed using techniques like volume and thickness measurements. Machine learning models like CNNs and SVMs are trained on the data and tested to distinguish between patient groups and predict progression. The research aims to more accurately diagnose Alzheimer's at earlier stages by combining clinical assessments with structural neuroimaging data and machine learning. Accuracy of over 90% was achieved in some cases at distinguishing between patient classifications like AD vs normal control.
The biomedical profession has gained importance due to the rapid and accurate diagnosis of clinical patients using computer-aided diagnosis (CAD) tools.
The diagnosis and treatment of Alzheimer’s disease (AD) using complementary multimodalities can improve the quality of life and mental state of patients.
In this study, we integrated a lightweight custom convolutional neural network
(CNN) model and nature-inspired optimization techniques to enhance the performance, robustness, and stability of progress detection in AD. A multi-modal
fusion database approach was implemented, including positron emission tomography (PET) and magnetic resonance imaging (MRI) datasets, to create a fused
database. We compared the performance of custom and pre-trained deep learning models with and without optimization and found that employing natureinspired algorithms like the particle swarm optimization algorithm (PSO) algorithm significantly improved system performance. The proposed methodology,
which includes a fused multimodality database and optimization strategy, improved performance metrics such as training, validation, test accuracy, precision, and recall. Furthermore, PSO was found to improve the performance of
pre-trained models by 3-5% and custom models by up to 22%. Combining different medical imaging modalities improved the overall model performance by
2-5%. In conclusion, a customized lightweight CNN model and nature-inspired
optimization techniques can significantly enhance progress detection, leading to
better biomedical research and patient care.
IRJET- Brain Tumor Detection and Classification with Feed Forward Back Propag...IRJET Journal
This document presents a method for detecting and classifying brain tumors in MRI images using a feed forward back propagation neural network. It first preprocesses MRI images by dividing them into blocks and applying Haar transforms for noise removal and edge preservation. Statistical, GLCM, morphological and edge features are then extracted from each block. These features are used to identify abnormal areas. The blocks are then classified as normal or tumor using a feed forward back propagation neural network, which can model nonlinear relationships and is trained to reduce error rates. The method achieves 98% classification accuracy on a benchmark MRI dataset. It results in high accuracy tumor detection with less iterations, reducing computation time compared to previous methods.
Overview of convolutional neural networks architectures for brain tumor segm...IJECEIAES
Due to the paramount importance of the medical field in the lives of people, researchers and experts exploited advancements in computer techniques to solve many diagnostic and analytical medical problems. Brain tumor diagnosis is one of the most important computational problems that has been studied and focused on. The brain tumor is determined by segmentation of brain images using many techniques based on magnetic resonance imaging (MRI). Brain tumor segmentation methods have been developed since a long time and are still evolving, but the current trend is to use deep convolutional neural networks (CNNs) due to its many breakthroughs and unprecedented results that have been achieved in various applications and their capacity to learn a hierarchy of progressively complicated characteristics from input without requiring manual feature extraction. Considering these unprecedented results, we present this paper as a brief review for main CNNs architecture types used in brain tumor segmentation. Specifically, we focus on researcher works that used the well-known brain tumor segmentation (BraTS) dataset.
Automatic brain tumor detection using adaptive region growing with thresholdi...IAESIJAI
Brain cancer affects many people around the world. It's not just limited to the elderly; it is also recognized in children. With the development of image processing, early detection of mental development is possible. Some designers suggest deformable models, histogram averaging, or manual division. Due to constant manual intervention, these cycles can be uncomfortable and tiring. This research introduces a high-level system for the removal of malignant tumors from attractive reverberation images, based on a programmed and rapid distribution strategy for surface extraction and recreation for clinicians. To test the proposed system, acquired tomography images from the Cancer Imaging Archive were used. The results of the study strongly demonstrate that the intended structure is viable in brain tumor detection.
This document summarizes a study that used artificial neural networks (ANN) to segment MRI brain images into gray matter, white matter, and cerebrospinal fluid in order to analyze and classify three neurodegenerative diseases: Alzheimer's disease, Parkinson's disease, and epilepsy. Real MRI data from patients with these diseases was preprocessed, features were extracted using Gabor filters, and ANN was used to classify tissues. The ANN approach achieved 96.13% accuracy for Alzheimer's classification, 93.26% for Parkinson's, and 91.33% for epilepsy. The study demonstrated that ANN is effective for automated brain tissue segmentation and shows potential for assisting in diagnosis of neurological diseases.
IRJET- Brain Tumor Detection using Convolutional Neural NetworkIRJET Journal
This document summarizes research on using convolutional neural networks (CNNs) to detect brain tumors from MRI images. It begins with an abstract describing how earlier tumor detection was done manually by doctors, which took more time and was sometimes inaccurate. CNN models provide quicker and more precise results. The document then reviews several existing techniques for brain tumor segmentation and classification, noting their advantages and limitations. It proposes using a CNN-based classifier to overcome these limitations by comparing trained and test data to get the best results. Key steps in tumor detection using image processing techniques are described as image pre-processing, segmentation, feature extraction, and classification.
IRJET- A Novel Segmentation Technique for MRI Brain Tumor ImagesIRJET Journal
This document summarizes several research papers on techniques for segmenting brain tumors in MRI images. It discusses challenges in brain tumor segmentation and describes various approaches that have been proposed, including methods using feature selection, kernel sparse representation, multiple kernel learning (MKL), and post-processing techniques. The document also reviews state-of-the-art segmentation, registration, and modeling methods for brain tumor images and their performance.
Mri brain tumour detection by histogram and segmentationiaemedu
This document summarizes a research paper on detecting brain tumors in MRI images using a combination of histogram thresholding, modified gradient vector field (GVF), and morphological operators. The non-brain regions are removed using morphological operators. Histogram thresholding is then used to detect if the brain is normal or abnormal/contains a tumor. If abnormal, the modified GVF is used to detect the tumor contour. The proposed method aims to be computationally efficient by only performing segmentation if a tumor is detected. It was tested on many MRI brain images and performance was validated against human expert segmentation.
Mri brain tumour detection by histogram and segmentationiaemedu
This document summarizes a research paper on detecting brain tumors in MRI images using a combination of histogram thresholding, modified gradient vector field (GVF), and morphological operators. The non-brain regions are removed using morphological operators. Histogram thresholding is then used to detect if the brain is normal or abnormal/contains a tumor. If abnormal, the modified GVF is used to detect the tumor contour. The proposed method aims to be computationally efficient by only performing segmentation if a tumor is detected. It was tested on many MRI brain images and performance was validated against human expert segmentation.
Mri brain tumour detection by histogram and segmentation by modified gvf model 2IAEME Publication
The document describes a new method for detecting and segmenting brain tumors in MRI images. It combines histogram thresholding, modified gradient vector flow, and morphological operators. Histogram thresholding is used to detect if the brain is normal or abnormal. If abnormal, modified GVF is used to segment the tumor contour. Otherwise, segmentation is skipped to minimize computation time. The method was tested on many MRI brain images and tumor detection and dimensions were validated against expert segmentation. It provides an efficient and computationally inexpensive approach for brain tumor detection and segmentation in MRI images.
Mri brain tumour detection by histogram and segmentation by modified gvf model 2iaemedu
The document summarizes a proposed method for detecting brain tumors in MRI images in 4 steps:
1. Skull stripping and smoothing are performed to isolate the brain region.
2. Histogram thresholding is used to detect if the brain is normal or abnormal by comparing histograms of halves of the brain image.
3. For abnormal brains, a modified gradient vector flow (GVF) model is used to create a force field and detect the tumor contour.
4. The area of the tumor is then calculated. The method aims to minimize segmentation time by skipping segmentation for normal brains detected in Step 2. Validation with expert segmentation is performed.
Hybrid model for detection of brain tumor using convolution neural networksCSITiaesprime
The development of aberrant brain cells, some of which may turn cancerous, is known as a brain tumor. Magnetic resonance imaging (MRI) scans are the most common technique for finding brain tumors. Information about the aberrant tissue growth in the brain is discernible from the MRI scans. In numerous research papers, machine learning, and deep learning algorithms are used to detect brain tumors. It takes extremely little time to forecast a brain tumor when these algorithms are applied to MRI pictures, and better accuracy makes it easier to treat patients. The radiologist can make speedy decisions because of this forecast. The proposed work creates a hybrid convolution neural networks (CNN) model using CNN for feature extraction and logistic regression (LR). The pre-trained model visual geometry group 16 (VGG16) is used for the extraction of features. To reduce the complexity and parameters to train we eliminated the last eight layers of VGG16. From this transformed model the features are extracted in the form of a vector array. These features fed into different machine learning classifiers like support vector machine (SVM), naïve bayes (NB), LR, extreme gradient boosting (XGBoost), AdaBoost, and random forest for training and testing. The performance of different classifiers is compared. The CNN-LR hybrid combination outperformed the remaining classifiers. The evaluation measures such as recall, precision, F1-score, and accuracy of the proposed CNN-LR model are 94%, 94%, 94%, and 91% respectively.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
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DIRECTIONAL CLASSIFICATION OF BRAIN TUMOR IMAGES FROM MRI USING CNN-BASED DEE...IRJET Journal
This document presents research on using a convolutional neural network (CNN) model for the detection and classification of brain tumors from MRI images. The CNN model improves the accuracy of tumor detection and can serve as a useful tool for physicians. The researchers trained and tested several CNN architectures, including CNN, ResNet50, MobileNetV2, and VGG19 on an MRI brain image database. Their proposed model uses a modified Residual U-Net architecture with residual blocks and attention gates to better segment tumors and extract local features from MRI images. Evaluation results found their model achieved better accuracy than existing methods like U-Net and CNN for brain tumor segmentation tasks.
A Review On Methods For Feature Extraction And Classification For The Automat...Heather Strinden
This document reviews various feature extraction and classification methods that have been used for the automated detection of Alzheimer's disease from magnetic resonance imaging (MRI) scans. It summarizes several studies that used different feature extraction techniques like voxel-based, vertex-based, and region of interest-based methods. Popular classification algorithms discussed include support vector machines, linear discriminant analysis, Bayesian classifiers and artificial neural networks. The document concludes that selecting relevant features extracted from MRI scans can yield accurate classification of Alzheimer's disease.
Primary challenges are the identification, segmentation, and extraction of the afflicted area from the scanning of magnetic resonance. However, it is a time-consuming and tiresome for clinical specialists. In this paper,
an automated brain tumor system is proposed. The proposed system employs hybrid image processing techniques such as contrast correction, histogram normalization, thresholding techniques, arithmetic, and morphological operations to quarantine nearby organs and other tissue from the brain for improving the localization of the affected region. At first, the skull stripping process is proposed to segregate the non-designated regions to extract the designated brain regions. Those resultant brain region images are further subjected to discover the brain tumor. The planned scheme is studied on the magnetic resonance (MR) images with the use of T1, T2, T1c, and fluid-attenuated inversion recovery (FLAIR). The proposed hybrid method employed. The results reveal that the proposed method is quite efficient to extract the tumor region. The accuracy rate for segmentation and separation of area of interest in brain tumor reached to 95%. Finally, the significance of the proposed procedure is confirmed using the real image clinical dataset got from ten patients were diagnosed as begin, malignant, and metastatic brain tumors in Al-Yarmouk and Baghdad teaching hospital in Baghdad, Iraq.
Survey of various methods used for integrating machine learning into brain tu...Drjabez
This document surveys various machine learning methods used for integrating machine learning into brain tumor detection and classification from MRI images. It discusses preprocessing techniques like median filtering, Gaussian high pass filtering, and morphology dilation to enhance images. Segmentation techniques covered include thresholding, edge detection, region-based, watershed, Berkeley wavelet transform, K-means clustering, and neural networks. Feature extraction calculates correlation, skewness. Classification algorithms discussed are multi-layer perceptron, naive Bayes, and support vector machines. The document provides an overview of key steps and methods for machine learning-based brain tumor detection and segmentation from MRI images.
An ensemble deep learning classifier of entropy convolutional neural network ...BASMAJUMAASALEHALMOH
This document summarizes a research paper that proposes an ensemble deep learning classifier for efficient disease prediction of heart disease and breast cancer. The classifier combines two models: an Entropy Convolutional Neural Network (ECNN) and a Divergence Weight Bidirectional LSTM (DWBi-LSTM). Feature selection is first performed on the dataset using Bayesian Network imputation and Mode Fuzzy Weight Canonical Polyadic decomposition. An Adaptive Mean Chaotic Grey Wolf Optimization algorithm is then used for wrapper-based feature selection. The results of the ECNN and DWBi-LSTM classifiers are combined using bootstrap aggregation and majority voting. Performance is evaluated using various metrics and compared to existing classifiers. The proposed ensemble classifier aims to improve disease prediction accuracy over single classifiers.
Automatic Diagnosis of Abnormal Tumor Region from Brain Computed Tomography I...ijcseit
The research work presented in this paper is to achieve the tissue classification and automatically
diagnosis the abnormal tumor region present in Computed Tomography (CT) images using the wavelet
based statistical texture analysis method. Comparative studies of texture analysis method are performed
for the proposed wavelet based texture analysis method and Spatial Gray Level Dependence Method
(SGLDM). Our proposed system consists of four phases i) Discrete Wavelet Decomposition (ii)
Feature extraction (iii) Feature selection (iv) Analysis of extracted texture features by classifier. A
wavelet based statistical texture feature set is derived from normal and tumor regions. Genetic Algorithm
(GA) is used to select the optimal texture features from the set of extracted texture features. We construct
the Support Vector Machine (SVM) based classifier and evaluate the performance of classifier by
comparing the classification results of the SVM based classifier with the Back Propagation Neural network
classifier(BPN). The results of Support Vector Machine (SVM), BPN classifiers for the texture analysis
methods are evaluated using Receiver Operating Characteristic (ROC) analysis. Experimental results
show that the classification accuracy of SVM is 96% for 10 fold cross validation method. The system
has been tested with a number of real Computed Tomography brain images and has achieved satisfactory
results.
Alzheimer Disease Prediction using Machine Learning AlgorithmsIRJET Journal
This document summarizes research on using machine learning algorithms to predict Alzheimer's disease. It discusses collecting data from the Alzheimer's Disease Neuroimaging Initiative database and preprocessing MRI images. Feature extraction is performed using techniques like volume and thickness measurements. Machine learning models like CNNs and SVMs are trained on the data and tested to distinguish between patient groups and predict progression. The research aims to more accurately diagnose Alzheimer's at earlier stages by combining clinical assessments with structural neuroimaging data and machine learning. Accuracy of over 90% was achieved in some cases at distinguishing between patient classifications like AD vs normal control.
The biomedical profession has gained importance due to the rapid and accurate diagnosis of clinical patients using computer-aided diagnosis (CAD) tools.
The diagnosis and treatment of Alzheimer’s disease (AD) using complementary multimodalities can improve the quality of life and mental state of patients.
In this study, we integrated a lightweight custom convolutional neural network
(CNN) model and nature-inspired optimization techniques to enhance the performance, robustness, and stability of progress detection in AD. A multi-modal
fusion database approach was implemented, including positron emission tomography (PET) and magnetic resonance imaging (MRI) datasets, to create a fused
database. We compared the performance of custom and pre-trained deep learning models with and without optimization and found that employing natureinspired algorithms like the particle swarm optimization algorithm (PSO) algorithm significantly improved system performance. The proposed methodology,
which includes a fused multimodality database and optimization strategy, improved performance metrics such as training, validation, test accuracy, precision, and recall. Furthermore, PSO was found to improve the performance of
pre-trained models by 3-5% and custom models by up to 22%. Combining different medical imaging modalities improved the overall model performance by
2-5%. In conclusion, a customized lightweight CNN model and nature-inspired
optimization techniques can significantly enhance progress detection, leading to
better biomedical research and patient care.
IRJET- Brain Tumor Detection and Classification with Feed Forward Back Propag...IRJET Journal
This document presents a method for detecting and classifying brain tumors in MRI images using a feed forward back propagation neural network. It first preprocesses MRI images by dividing them into blocks and applying Haar transforms for noise removal and edge preservation. Statistical, GLCM, morphological and edge features are then extracted from each block. These features are used to identify abnormal areas. The blocks are then classified as normal or tumor using a feed forward back propagation neural network, which can model nonlinear relationships and is trained to reduce error rates. The method achieves 98% classification accuracy on a benchmark MRI dataset. It results in high accuracy tumor detection with less iterations, reducing computation time compared to previous methods.
Overview of convolutional neural networks architectures for brain tumor segm...IJECEIAES
Due to the paramount importance of the medical field in the lives of people, researchers and experts exploited advancements in computer techniques to solve many diagnostic and analytical medical problems. Brain tumor diagnosis is one of the most important computational problems that has been studied and focused on. The brain tumor is determined by segmentation of brain images using many techniques based on magnetic resonance imaging (MRI). Brain tumor segmentation methods have been developed since a long time and are still evolving, but the current trend is to use deep convolutional neural networks (CNNs) due to its many breakthroughs and unprecedented results that have been achieved in various applications and their capacity to learn a hierarchy of progressively complicated characteristics from input without requiring manual feature extraction. Considering these unprecedented results, we present this paper as a brief review for main CNNs architecture types used in brain tumor segmentation. Specifically, we focus on researcher works that used the well-known brain tumor segmentation (BraTS) dataset.
Automatic brain tumor detection using adaptive region growing with thresholdi...IAESIJAI
Brain cancer affects many people around the world. It's not just limited to the elderly; it is also recognized in children. With the development of image processing, early detection of mental development is possible. Some designers suggest deformable models, histogram averaging, or manual division. Due to constant manual intervention, these cycles can be uncomfortable and tiring. This research introduces a high-level system for the removal of malignant tumors from attractive reverberation images, based on a programmed and rapid distribution strategy for surface extraction and recreation for clinicians. To test the proposed system, acquired tomography images from the Cancer Imaging Archive were used. The results of the study strongly demonstrate that the intended structure is viable in brain tumor detection.
This document summarizes a study that used artificial neural networks (ANN) to segment MRI brain images into gray matter, white matter, and cerebrospinal fluid in order to analyze and classify three neurodegenerative diseases: Alzheimer's disease, Parkinson's disease, and epilepsy. Real MRI data from patients with these diseases was preprocessed, features were extracted using Gabor filters, and ANN was used to classify tissues. The ANN approach achieved 96.13% accuracy for Alzheimer's classification, 93.26% for Parkinson's, and 91.33% for epilepsy. The study demonstrated that ANN is effective for automated brain tissue segmentation and shows potential for assisting in diagnosis of neurological diseases.
IRJET- Brain Tumor Detection using Convolutional Neural NetworkIRJET Journal
This document summarizes research on using convolutional neural networks (CNNs) to detect brain tumors from MRI images. It begins with an abstract describing how earlier tumor detection was done manually by doctors, which took more time and was sometimes inaccurate. CNN models provide quicker and more precise results. The document then reviews several existing techniques for brain tumor segmentation and classification, noting their advantages and limitations. It proposes using a CNN-based classifier to overcome these limitations by comparing trained and test data to get the best results. Key steps in tumor detection using image processing techniques are described as image pre-processing, segmentation, feature extraction, and classification.
IRJET- A Novel Segmentation Technique for MRI Brain Tumor ImagesIRJET Journal
This document summarizes several research papers on techniques for segmenting brain tumors in MRI images. It discusses challenges in brain tumor segmentation and describes various approaches that have been proposed, including methods using feature selection, kernel sparse representation, multiple kernel learning (MKL), and post-processing techniques. The document also reviews state-of-the-art segmentation, registration, and modeling methods for brain tumor images and their performance.
Mri brain tumour detection by histogram and segmentationiaemedu
This document summarizes a research paper on detecting brain tumors in MRI images using a combination of histogram thresholding, modified gradient vector field (GVF), and morphological operators. The non-brain regions are removed using morphological operators. Histogram thresholding is then used to detect if the brain is normal or abnormal/contains a tumor. If abnormal, the modified GVF is used to detect the tumor contour. The proposed method aims to be computationally efficient by only performing segmentation if a tumor is detected. It was tested on many MRI brain images and performance was validated against human expert segmentation.
Mri brain tumour detection by histogram and segmentationiaemedu
This document summarizes a research paper on detecting brain tumors in MRI images using a combination of histogram thresholding, modified gradient vector field (GVF), and morphological operators. The non-brain regions are removed using morphological operators. Histogram thresholding is then used to detect if the brain is normal or abnormal/contains a tumor. If abnormal, the modified GVF is used to detect the tumor contour. The proposed method aims to be computationally efficient by only performing segmentation if a tumor is detected. It was tested on many MRI brain images and performance was validated against human expert segmentation.
Mri brain tumour detection by histogram and segmentation by modified gvf model 2IAEME Publication
The document describes a new method for detecting and segmenting brain tumors in MRI images. It combines histogram thresholding, modified gradient vector flow, and morphological operators. Histogram thresholding is used to detect if the brain is normal or abnormal. If abnormal, modified GVF is used to segment the tumor contour. Otherwise, segmentation is skipped to minimize computation time. The method was tested on many MRI brain images and tumor detection and dimensions were validated against expert segmentation. It provides an efficient and computationally inexpensive approach for brain tumor detection and segmentation in MRI images.
Mri brain tumour detection by histogram and segmentation by modified gvf model 2iaemedu
The document summarizes a proposed method for detecting brain tumors in MRI images in 4 steps:
1. Skull stripping and smoothing are performed to isolate the brain region.
2. Histogram thresholding is used to detect if the brain is normal or abnormal by comparing histograms of halves of the brain image.
3. For abnormal brains, a modified gradient vector flow (GVF) model is used to create a force field and detect the tumor contour.
4. The area of the tumor is then calculated. The method aims to minimize segmentation time by skipping segmentation for normal brains detected in Step 2. Validation with expert segmentation is performed.
Hybrid model for detection of brain tumor using convolution neural networksCSITiaesprime
The development of aberrant brain cells, some of which may turn cancerous, is known as a brain tumor. Magnetic resonance imaging (MRI) scans are the most common technique for finding brain tumors. Information about the aberrant tissue growth in the brain is discernible from the MRI scans. In numerous research papers, machine learning, and deep learning algorithms are used to detect brain tumors. It takes extremely little time to forecast a brain tumor when these algorithms are applied to MRI pictures, and better accuracy makes it easier to treat patients. The radiologist can make speedy decisions because of this forecast. The proposed work creates a hybrid convolution neural networks (CNN) model using CNN for feature extraction and logistic regression (LR). The pre-trained model visual geometry group 16 (VGG16) is used for the extraction of features. To reduce the complexity and parameters to train we eliminated the last eight layers of VGG16. From this transformed model the features are extracted in the form of a vector array. These features fed into different machine learning classifiers like support vector machine (SVM), naïve bayes (NB), LR, extreme gradient boosting (XGBoost), AdaBoost, and random forest for training and testing. The performance of different classifiers is compared. The CNN-LR hybrid combination outperformed the remaining classifiers. The evaluation measures such as recall, precision, F1-score, and accuracy of the proposed CNN-LR model are 94%, 94%, 94%, and 91% respectively.
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Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Design and optimization of ion propulsion dronebjmsejournal
Electric propulsion technology is widely used in many kinds of vehicles in recent years, and aircrafts are no exception. Technically, UAVs are electrically propelled but tend to produce a significant amount of noise and vibrations. Ion propulsion technology for drones is a potential solution to this problem. Ion propulsion technology is proven to be feasible in the earth’s atmosphere. The study presented in this article shows the design of EHD thrusters and power supply for ion propulsion drones along with performance optimization of high-voltage power supply for endurance in earth’s atmosphere.
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
artificial intelligence and data science contents.pptxGauravCar
What is artificial intelligence? Artificial intelligence is the ability of a computer or computer-controlled robot to perform tasks that are commonly associated with the intellectual processes characteristic of humans, such as the ability to reason.
› ...
Artificial intelligence (AI) | Definitio
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
Rainfall intensity duration frequency curve statistical analysis and modeling...bijceesjournal
Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
An effective feature selection using improved marine predators algorithm for Alzheimer’s disease classification
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 5, October 2023, pp. 5126~5134
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i5.pp5126-5134 5126
Journal homepage: http://ijece.iaescore.com
An effective feature selection using improved marine predators
algorithm for Alzheimer’s disease classification
Preeti Sadanand Topannavar1
, Dinkar M. Yadav2
1
Department of Electronics and Telecommunication, G. H. Raisoni College of Engineering and Management, Pune, India
2
Department of Electronics and Telecommunication, S. N. D. College of Engineering and Research Center, Yeola, India
Article Info ABSTRACT
Article history:
Received Dec 19, 2022
Revised Mar 27, 2023
Accepted Apr 7, 2023
Alzheimer’s disease (AD) is an irremediable neurodegenerative illness
developed by the fast deterioration of brain cells. AD is mostly common in
elder people and it extremely disturbs the physical and mental health of
patients, therefore early detection is essential to prevent AD development.
However, the precise detection of AD and mild cognitive impairment (MCI)
is difficult during classification. In this paper, the Residual network i.e.,
ResNet-18 is used for extracting the features, and the proposed improved
marine predators algorithm (IMPA) is developed for choosing the optimum
features to perform an effective classification of AD. The multi-verse
optimizer (MVO) used in the IMPA helps to balance exploration and
exploitation, which leads to the selection of optimal relevant features. Further,
the classification of AD is accomplished using the multiclass support vector
machine (MSVM). Open access series of imaging studies-1 (OASIS-1) and
Alzheimer disease neuroimaging initiative (ADNI) datasets are used to
evaluate the IMPA-MSVM method. The performance of the IMPA-MSVM
method is analyzed using accuracy, sensitivity, specificity, positive predictive
value (PPV) and matthews correlation coefficient (MCC). The existing
methods such as the deep learning-based segmenting method using SegNet
(DLSS), mish activation function (MAF) with spatial transformer network
(STN) and BrainNet2D are used to evaluate the IMPA-MSVM method. The
accuracy of IMPA-MSVM for the ADNI dataset is 98.43% which is more
when compared to the DLSS and MAF-STN.
Keywords:
Alzheimer’s disease
Feature selection
Improved marine predators
algorithm
Multiclass support vector
Machine
ResNet-18
This is an open access article under the CC BY-SA license.
Corresponding Author:
Preeti Sadanand Topannavar
Department of Electronics and Telecommunication, G. H. Raisoni College of Engineering and
Management
Wagholi, Pune, Maharashtra, India
Email: topannavarp@gmail.com
1. INTRODUCTION
Alzheimer’s disease (AD) is a common neurodegenerative disease characterized by hidden onset and
continuous expansion. AD causes changes in memory, behavior, and cognitive functioning, as well as the
patient’s risk of dying after about 3 to 10 years [1]–[3]. AD slowly develops from mild to moderate, to serious
stages of dementia. This happens because of the irregular development of proteins like tau tangles and amyloid
plaques in the brain [4]. The World Alzheimer’s Report of 2020 stated that there were 5.8 million patients over
65 years of age affected by Alzheimer’s in the United States, with AD being the sixth main reason leading to
deaths in 2018. Further, the number of dementia patients may probably reach up to 150 million in the year
2050 [5]. The people identified with mild cognitive impairment (MCI) noticeably have a higher risk of
developing AD, as MCI is observed as a transitional phase between healthy cognitive aging and dementia. 60%
2. Int J Elec & Comp Eng ISSN: 2088-8708
An effective feature selection using improved marine predators algorithm … (Preeti Sadanand Topannavar)
5127
of patients having MCI develop dementia within ten years of diagnosis, others however endure cognitive
stablility or recover their normal cognitive (NC) functions [6]–[8].
Still, the efficiency of drugs for AD treatment is restricted to data and no treatment is stated to reverse
or avoid AD development [9], [10]. Conventionally, the precise identification of AD, MCI and NC mainly
depend on neuropsychological tests. However, neuropsychological test is subjective and appropriate for a
person having certain clinical symptoms. Further, magnetic resonance imaging (MRI), essential role in brain
disease identification along with the growth of medical imaging modalities [11], positron emission tomography,
and single-photon emission computed tomography perform an [12]. MRI is an effective approach for
nonaggressive in vivo imaging of the human brain. It is preferred over other technologies because of its ability to
detect the structural variations that occur in neurodegenerative diseases, and hence has more importance in AD
analysis and prediction [13]. The dimension of extracted features is comparatively higher than the sample size in
neuroimaging. Subsequently, the classification performance is affected due to irrelevant features. Hence, feature
selection that eliminates irrelevant features is considered a primary step while diagnosing AD [14], [15].
The related works about the classification of AD are given as follows: Buvaneswari and Gayathri [16]
developed deep learning-based segmenting namely SegNet to identify the features related to AD. Further, the
classification of accurate AD was obtained using ResNet-101. The strong features of the labels which included
the graded features were established by the properties of every segmented image. Therefore, these graded
features were used to enhance the classification. The developed SegNet did not consider entire brain regions
as the region of interest (ROI) while performing the feature extraction. Sun et al. [17] presented a model for
early identification of AD using a deep learning model depending on ResNet-50. In ResNet, the mish activation
function (MAF) was chosen instead of the Relu function, followed by spatial transformer network (STN)
incorporated into the input layer and the improved ResNet-50. The incorporation of STN was used to improve
the spatial invariance that was used to enhance the feature-extracting capacity. An appropriate feature selection
was required to choose the optimum features from the feature vector. Saratxaga et al. [18] developed 2D and
3D networks in either a slice-level approach or a subject-level method for predicting AD. Custom networks
i.e., BrainNet2D and BrainNet3D were developed together with a popular architecture and transfer learning
methods such as fine-tuning along with ImageNet weights. A huge set of examples were used in the 2D network
which was then used to decrease the overfitting issue. The accuracy of the BrainNet3D was less, even though
it was processed with all slices.
Cui et al. [19] presented adaptive least absolute shrinkage and selection operator (LASSO) logistic
regression according to the particle swarm optimization (PSO) for detecting AD. Initially, the PSO was utilized
for a global search for removing the irrelevant features and decrease the operational time. Later, the adaptive
LASSO was performed in the local search for selecting the optimal features to classify AD. Here, the
classification accuracy was sensitive to the tuning parameter. Suresha and Parthasarathy [20] developed the
detection of AD using grey wolf optimization based clustering algorithm (GWOCA) and deep neural network
(DNN). The adaptive histogram equalization and GWOCA were developed to denoise and segment the brain
tissues. Next, the local ternary pattern, dual-tree complex wavelet transform and Tamura feature extraction
were used to obtain the features. Subsequently, the ReliefF was used to select the optimal features for a precise
classification of AD’s abnormality and normality. The ReliefF used in this AD detection was not operated
based on a learning basis, hence there was no assurance of acquiring an efficient feature subset.
The contributions are summarized as follows: i) for improving the intensity of pixels, normalization
is performed over the input images. The ResNet-18 is used for feature extraction because each layer learns
from the residual functions by taking reference to its input layer and ii) improved marine predators algorithm
(IMPA) is proposed for feature selection because of its effective balance among the exploration and
exploitation processes developed using the multi-verse optimizer (MVO). Further, the classification of AD is
done by using multiclass support vector machine (MSVM), because it can handle high-dimension spaces and
control nonlinear problems.
The remaining paper is arranged as follows: section 2 delivers a detailed explanation of the IMPA-
MSVM. The outcomes of IMPA-MSVM are provided in section 3. Further, the conclusion is presented in
section 4.
2. IMPA-MSVM METHOD
In this research, the classification of brain images is enhanced to take early action for patients who
are suffering from AD and MCI. The classification accuracy of different stages of AD is enhanced using IMPA
based feature selection. The important process of IMPA-MSVM method is data acquisition, preprocessing,
feature extraction, feature selection, and classification. The block diagram of the overall IMPA-MSVM method
is shown in Figure 1.
3. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 13, No. 5, October 2023: 5126-5134
5128
Figure 1. Block diagram of overall IMPA-MSVM method
2.1. Data acquisition
The classification of Alzheimer’s disease using the proposed method is analyzed with two different
datasets namely Open access series of imaging studies-1 (OASIS-1) [21] and Alzheimer disease neuroimaging
initiative (ADNI) [22]. OASIS-1 is a neuroimaging dataset that has a cross-sectional collection of 416 subjects.
Among 416 subjects, 100 subjects are over 60 years of age and identified with very light or moderate
Alzheimer’s using clinical dementia rating (CDR) value. OASIS-1 has a T1-weighted MRI scan for each
subject along with 20 subjects without dementia. These 20 subjects' information is scanned twice on continuous
visits, and used for a baseline evaluation with a list of explanations. On the other hand, the ADNI was launched
in 2003 which is a multisite, longitudinal study intended to create genetic, clinical, imaging and biospecimen
biomarkers to achieve an early prediction of Alzheimer's. ADNI consists of T1 weighted structural MRI data
for three different classes such as: 1,425 scans for AD, 1,021 scans for cognitive normal (CN) and 1,479 scans
for mild cognitive impairment (MCI).
2.2. Preprocessing using normalization
The normalization is applied over the input images from the OASIS-1 and ADNI datasets. The input
image’s pixel intensity is enhanced by changing the pixel range through normalization as expressed in (1).
𝐷′ = (𝐷 − 𝑚𝑖𝑛)
𝑛𝑒𝑤𝑚𝑎𝑥−𝑛𝑒𝑤𝑚𝑖𝑛
𝑚𝑎𝑥−𝑚𝑖𝑛
+ 𝑛𝑒𝑤𝑚𝑖𝑛 (1)
where, 𝐷 denotes the input image; the minimum and maximum intensity values of input are denoted as 𝑚𝑖𝑛
and 𝑚𝑎𝑥; the preprocessed image is denoted as 𝐷′ and it has intensity values of 𝑛𝑒𝑤𝑚𝑖𝑛 and 𝑛𝑒𝑤𝑚𝑎𝑥.
2.3. Feature extraction using ResNet-18
In feature extraction, the pre-trained network of ResNet-18 [23] is used to extract the features.
The architecture of ResNet18 is shown in Figure 2. The preprocessed image of 𝐷′ is given as input to the
ResNet-18 architecture. The developed ResNet-18 has 16 convolution layers, 2 downsampling layers and few
fully connected layers. The eigenvector i.e., the feature map of the last convolution layer is obtained, once the
average pooling is done in ResNet-18. The obtained eigenvector has numerous probabilities. From the
convolutional layers, the extracted features are obtained and it is further processed under the feature selection
process.
2.4. Feature selection using IMPA
In the proposed method, the IMPA is utilized to extract the optimum features out of the the overall
features extracted from ResNet-18. This IMPA based feature selection removes the irrelevant information from
the extracted features of ResNet-18 to enhance the accuracy. Generally, the MPA [24] imitates the foraging
strategy namely Levy and Brownian movements in ocean predators, and also the optimal searching in the
biological communication between Predator and Prey. The IMPA-based feature selection has four main phases
according to the different velocity ratios and it replicates the normal behavior of predators and prey.
4. Int J Elec & Comp Eng ISSN: 2088-8708
An effective feature selection using improved marine predators algorithm … (Preeti Sadanand Topannavar)
5129
Phase 1: The first phase is initialized when there is a high-velocity ratio (velocity 𝑣 ≥ 10) in the event of the
prey traveling faster, as the appropriate policy of the predator is to be stationary. The exploration is essential
during the initial iteration of optimization. The mathematical expression for this 1st phase is expressed in (2).
𝑊ℎ𝑖𝑙𝑒 𝐼𝑡𝑒𝑟 <
1
3
max _𝐼𝑡𝑒𝑟
𝑠𝑡𝑒𝑝𝑠𝑖𝑧𝑒
⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ 𝑖 = 𝑅
⃗ 𝐵⨂(𝐸𝑙𝑖𝑡𝑒𝑖 − 𝑅
⃗ 𝐵⨂𝑃𝑟𝑒𝑦
⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ 𝑖) 𝑖 = 1,2, … , 𝑛
𝑃𝑟𝑒𝑦
⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ 𝑖 = 𝑃𝑟𝑒𝑦
⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ 𝑖 + 𝑃. 𝑅
⃗ ⨂𝑠𝑡𝑒𝑝𝑠𝑖𝑧𝑒
⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ 𝑖 (2)
where, 𝑃𝑟𝑒𝑦
⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ 𝑖 is considered as the feature vectors extracted using ResNet-18.
According to the Brownian motion’s normal distribution, the 𝑅
⃗ 𝐵 contains the random values. The
motion of prey is replicated by multiplying the 𝑅𝐵 with prey; 𝑃 is a constant number that is equal to 0.5 and
the uniform random number in the range of [0, 1] is denoted as 𝑅. This phase is accomplished in 1/3 of
iterations when the motion velocity is higher for allowing higher exploration levels.
Figure 2. Architecture of ResNet18 for feature extraction
Phase 2: The second phase is accomplished when both the predator and prey are concurrently traveling in the
search space. This kind of motion is accomplished when the exploration tends to get transformed into
exploitation. This phase comprises both the exploration and exploitation features, while the predator is
accountable for exploration and the prey is accountable for exploitation.
An appropriate approach for predator is Brownian and for prey is levy, when there is a unit velocity
ratio (𝑣 ≈ 1) which is expressed in (3).
𝑊ℎ𝑖𝑙𝑒
1
3
𝑚𝑎𝑥 _𝐼𝑡𝑒𝑟 < 𝐼𝑡𝑒𝑟 <
2
3
𝑚𝑎𝑥 _𝐼𝑡𝑒𝑟
𝑠𝑡𝑒𝑝𝑠𝑖𝑧𝑒
⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ 𝑖 = 𝑅
⃗ 𝐿⨂(𝐸𝑙𝑖𝑡𝑒
⃗⃗⃗⃗⃗⃗⃗⃗ 𝑖 − 𝑅
⃗ 𝐿⨂𝑃𝑟𝑒𝑦
⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ 𝑖) 𝑖 = 1,2, … , 𝑛/2
𝑃𝑟𝑒𝑦
⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ 𝑖 = 𝑃𝑟𝑒𝑦
⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ 𝑖 + 𝑃. 𝑅
⃗ ⨂𝑠𝑡𝑒𝑝𝑠𝑖𝑧𝑒
⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ 𝑖 (3)
5. ISSN: 2088-8708
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where, the random number vectors according to the levy are denoted as 𝑅
⃗ 𝐿 that used for 1st half of the
population. The multiplication of 𝑅
⃗ 𝐿 is used to replicate the prey’s motion in levy manner, whereas the
incorporation of step size to the prey’s location replicates the prey’s movement. A huge amount of step sizes
in levy are small. The mathematical expression for 2nd
half of the population is shown in (4).
𝑠𝑡𝑒𝑝𝑠𝑖𝑧𝑒
⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ 𝑖 = 𝑅
⃗ 𝐵⨂(𝑅
⃗ 𝐵⨂𝐸𝑙𝑖𝑡𝑒
⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ 𝑖 − 𝑃𝑟𝑒𝑦
⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ 𝑖) 𝑖 =
𝑛
2
, … , 𝑛
𝑃𝑟𝑒𝑦
⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ 𝑖 = 𝐸𝑙𝑖𝑡𝑒
⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ 𝑖 + 𝑃. 𝐶𝐹⨂𝑠𝑡𝑒𝑝𝑠𝑖𝑧𝑒
⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ 𝑖 (4)
where, 𝐶𝐹 = (1 −
𝐼𝑡𝑒𝑟
max _𝐼𝑡𝑒𝑟
)
2
𝐼𝑡𝑒𝑟
max _𝐼𝑡𝑒𝑟 is the adaptive parameter used to handle the step size for predator
motion. The multiplication of 𝑅
⃗ 𝐵 is used to stimulate the predator motion in a Brownian manner. According to
the predator motion, the prey motion is simulated using Elite for updating its location.
Phase 3: If the predator’s movement is faster than the prey's low-velocity ratio, the third phase is initialized
and connected with higher exploitation ability. If the low-velocity ratio 𝑣 = 0.1, then the appropriate approach
for Predator is a levy. The updation of step size and prey for third phase is expressed in (5).
𝑊ℎ𝑖𝑙𝑒 𝐼𝑡𝑒𝑟 < 𝑚𝑎𝑥 _𝐼𝑡𝑒𝑟
𝑠𝑡𝑒𝑝𝑠𝑖𝑧𝑒
⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ 𝑖 = 𝑅
⃗ 𝐿⨂(𝑅
⃗ 𝐿⨂𝐸𝑙𝑖𝑡𝑒
⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ 𝑖 − 𝑃𝑟𝑒𝑦
⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ 𝑖) 𝑖 = 1,2, … , 𝑛/2
𝑃𝑟𝑒𝑦
⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ 𝑖 = 𝐸𝑙𝑖𝑡𝑒
⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ 𝑖 + 𝑃. 𝐶𝐹⨂𝑠𝑡𝑒𝑝𝑠𝑖𝑧𝑒
⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ 𝑖 (5)
Phase 4: The operators of MVO are included in IMPA for obtaining the better solution (i.e., optimal features).
The prey is updated based on the first half of phase 2, when 𝑟3 ≥ 𝑊𝐴𝑃; Otherwise, the second half is executed,
when 𝑟3 < 𝑊𝐴𝑃 and the 𝑊𝐴𝑃 is defined in (6).
𝑊𝐴𝑃 = 𝑚𝑖𝑛 + 𝐼𝑡𝑒𝑟 × (
𝑚𝑖𝑛−𝑚𝑎𝑥
𝐼𝑡𝑒𝑟
) (6)
The location update of prey is expressed in (7).
𝑃𝑟𝑒𝑦
⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ 𝑖 = {
{
𝑃𝑟𝑒𝑦
⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ 𝑖 + 𝑇𝐷𝑅 × ((𝑢𝑏𝑖 − 𝑙𝑏𝑖) × 𝑟5 + 𝑙𝑏𝑖) 𝑟4 < 0.5
𝑃𝑟𝑒𝑦
⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ 𝑖 − 𝑇𝐷𝑅 × ((𝑢𝑏𝑖 − 𝑙𝑏𝑖) × 𝑟5 + 𝑙𝑏𝑖) 𝑟4 ≥ 0.5
𝑃𝑟𝑒𝑦
⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ 𝑖
(7)
where, 𝑟3, 𝑟4 and 𝑟5 denote the random numbers and 𝑇𝐷𝑅 is expressed in (8).
𝑇𝐷𝑅 = 1 −
𝐼𝑡𝑒𝑟1/𝑃1
𝑚𝑎𝑥 _𝐼𝑡𝑒𝑟1/𝑃1 (8)
where, 𝑃1 is the constant value that is set as 6 for controlling the exploitation accuracy. Further, the remaining
point which causes the variation in marine predator’s behavior is eddy formation or fish aggregating devices
(FADs) effects that are expressed in (9).
𝑃𝑟𝑒𝑦
⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ 𝑖 = {
𝑃𝑟𝑒𝑦
⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ 𝑖 + 𝐶𝐹[𝑋𝑚𝑖𝑛 + 𝑅
⃗ ⊗ (𝑋
̅𝑚𝑎𝑥 − 𝑋
̅𝑚𝑖𝑛)] ⊗ 𝑈
⃗
⃗ 𝑖𝑓 𝑟 ≤ 𝐹𝐴𝐷𝑠
𝑃𝑟𝑒𝑦
⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ 𝑖 + [𝐹𝐴𝐷𝑠(1 − 𝑟) + 𝑟](𝑃𝑟𝑒𝑦
⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ 𝑟1 − 𝑃𝑟𝑒𝑦
⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ 𝑟2) 𝑖𝑓 𝑟 > 𝐹𝐴𝐷𝑠
(9)
where, 𝑟 is the uniform random number generated in the range of [0,1]; 𝑋
̅𝑚𝑖𝑛 and 𝑋
̅𝑚𝑎𝑥 are the lower and
upper boundary of the dimensions respectively; 𝐹𝐴𝐷𝑠 is equal to 0.2 which creates the impact in the
optimization process; the binary vector which has 0 and 1 is denoted as 𝑈 and it is defined by creating a random
vector in [0, 1]. If the array is less than 0.2, the array is changed to 0; otherwise, the array is changed to 1 when
the array is greater than 0.2.
The IMPA selects the optimal features according to two distinct fitness functions namely the
classification accuracy and the number of features, as expressed in (10).
𝐹𝑖𝑡𝑛𝑒𝑠𝑠(𝑃𝑟𝑒𝑦
⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ 𝑗) = 𝜗𝐴𝑐𝑐(𝑃𝑟𝑒𝑦
⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ 𝑖) + 𝛿(1 |𝑃𝑟𝑒𝑦
⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ 𝑖|
⁄ ) (10)
where, the 𝑃𝑟𝑒𝑦
⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ 𝑖 defines the chosen random feature subset; 𝐴𝑐𝑐(𝑃𝑟𝑒𝑦
⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ 𝑖) is the classification accuracy of 𝑃𝑟𝑒𝑦
⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ 𝑖;
|𝑃𝑟𝑒𝑦
⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ 𝑖| defines the number of features; 𝜗 and 𝛿 is the random number generated in the range of [0, 1] that is
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used to define the relation among the accuracy and number of the selected subset. The fitness of each 𝑃𝑟𝑒𝑦
⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ 𝑗
i.e., feature subset is computed for each iteration and the 𝑃𝑟𝑒𝑦
⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ 𝑗 with best fitness is saved as 𝑃𝑟𝑒𝑦
⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ 𝑏𝑒𝑠𝑡. Further,
this 𝑃𝑟𝑒𝑦
⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ 𝑏𝑒𝑠𝑡 is used in the classification for detecting AD.
2.5. MSVM based classification
MSVM is used to classify the AD, NC, and MCI according to the features (𝑃𝑟𝑒𝑦
⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ 𝑏𝑒𝑠𝑡) selected from
IMPA. Generally, the conventional support vector machine (SVM) is generated for binary classification, hence
the SVM is transformed into MSVM [25] to classify different types of Alzheimer’s disease. The one-against-
one approach is used in MSVM for multi class classification. Further, the radial basis function is used as the
kernel in MSVM for nonlinear issues.
3. RESULTS AND DISCUSSION
The implementation and simulation of the IMPA-MSVM method is performed using MATLAB
R2020a software where the system is configured with an i5 processor with 8GB of RAM. The datasets used to
analyze the IMPA-MSVM method is OASIS-1 and ADNI, where training takes 80% and testing takes 20% of
data from each dataset. Here, the data is randomly taken for training and testing based on the iterations. The
IMPA-MSVM method is examined using accuracy, sensitivity, specificity, PPV and MCC which are expressed
in (11) to (15).
𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 =
𝑇𝑃+𝑇𝑁
𝑇𝑁+𝑇𝑃+𝐹𝑁+𝐹𝑃
× 100 (11)
𝑆𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦 =
𝑇𝑃
𝑇𝑃+𝐹𝑁
× 100 (12)
𝑆𝑝𝑒𝑐𝑖𝑓𝑖𝑐𝑖𝑡𝑦 =
𝑇𝑁
𝑇𝑁+𝐹𝑃
× 100 (13)
𝑃𝑃𝑉 =
𝑇𝑃
𝑇𝑃+𝐹𝑃
× 100 (14)
𝑀𝐶𝐶 =
𝑇𝑃×𝑇𝑁−𝐹𝑃×𝐹𝑁
√(𝑇𝑁+𝐹𝑁)(𝑇𝑃+𝐹𝑃)(𝑇𝑁+𝐹𝑃)(𝑇𝑃+𝐹𝑁)
× 100 (15)
where, 𝑇𝑃, 𝑇𝑁, 𝐹𝑃 and 𝐹𝑁 denote true positive, true negative, false positive and false negative.
The OASIS-1 dataset has different classes such as with different CDR values. The classes of
OASIS-1 are cognitive normal with 336 samples and 𝐶𝐷𝑅 = 0, very-mild dementia with 70 samples and
𝐶𝐷𝑅 = 0.5, mild dementia with 28 samples and 𝐶𝐷𝑅 = 1 and moderate dementia with 2 samples and𝐶𝐷𝑅 = 2.
Subsequently, this OASIS-1 is labeled as three class problem i.e., 𝐶𝐷𝑅 = 0 is first class, 𝐶𝐷𝑅 = 0.5 is second
class and 𝐶𝐷𝑅 = 1 and 𝐶𝐷𝑅 = 2 is third class. Because, the class of 𝐶𝐷𝑅 = 2 has only 2 samples and 𝐶𝐷𝑅 = 1
has 28 samples, it creates inaccuracy in classification because of under-representation. On the other hand,
randomly 1,000 scans are taken from AD, NC and MCI to avoid the data imbalance issue during classification.
3.1. Performance analysis of the IMPA-MSVM method
In this section, the IMPA-MSVM method is analyzed with various classifiers and with various feature
selection approaches. The different classifiers used to evaluate the IMPA-MSVM method are random forest
classifier (RFC), K-nearest neighbour (KNN) and decision tree (DE). The performance analyses of IMPA-
MSVM & different classifiers with feature selection (WFS) and without feature selection (WOFS) are shown
in Table 1. The graphically illustrated results of IMPA-MSVM with ADNI dataset for WOFS and WFS are
shown in Figures 3 and 4 respectively. From the examination, it is concluded that the MSVM achieves better
classification performances in WOFS as well as WFS. For example, the accuracy of IMPA-MSVM with WFS
for the ADNI dataset is 98.43%, which is higher when compared to the RFC, KNN, and DE classifiers. Besides,
the accuracy of IMPA-MSVM with WFS for OASIS-1 dataset is 94.65%, which is higher in relation with RFC,
KNN and DE. The MSVM with IMPA provides better classification among the multiple classifiers taken for
evaluation, because it can handle high-dimension spaces and control the non linear problems. Further, the
optimal selection of features eliminates the irrelevant features that help to improve the accuracy.
Additionally, the different feature selection approaches such as bat optimization algorithm (BOA),
grey wolf optimization (GWO) and conventional MPA are used to evaluate the performance of IMPA-MSVM.
Table 2 shows the performance analyses of IMPA-MSVM and different feature selection approaches where
the performance is evaluated using ADNI and OASIS-1 datasets. An example of a graphical illustration of
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IMPA-MSVM for the ADNI dataset is shown in Figure 5. This analysis shows that the IMPA provides better
classification accuracy for both the OASIS-1 and ADNI datasets. For example, the accuracy of IMPA-MSVM
method for ADNI dataset is 98.43% which is higher compared to the BOA, GWO and MPA. The reason for
IMPA with better performance is the incorporation of MVO parameters, which creates an effective balance
between the exploration and exploitation processes that help to search for an optimal feature subset.
Table 1. Performance analysis of IMPA-MSVM for different classifiers
Data set Feature selection Classifier Accuracy (%) Sensitivity (%) Specificity (%) PPV (%) MCC (%)
ADNI WOFS RFC 87.4 84.69 84.53 80.51 87.36
KNN 92.71 91.99 90.32 93.97 90.52
DE 90.25 89.26 88.51 90.45 87.28
MSVM 95.7 94.51 92.36 94.93 93.17
WFS RFC 90.08 92.98 89.86 89.43 90.61
KNN 96.67 95.96 95.07 92.82 91.87
DE 93.33 95.92 94.41 92.65 92.35
MSVM 98.43 98.86 98.02 97.04 96.07
OASIS-1 WOFS RFC 82.7 84.65 82.31 79.87 79.32
KNN 82.6 84.96 85.8 80.33 79.55
DE 85.47 87.38 83.9 80.89 81.74
MSVM 85.73 89.34 89.91 83.14 84.04
WFS RFC 88.89 86.78 86.86 87.45 82.33
KNN 90.08 93.03 92.6 92.4 91.42
DE 92.76 89.07 90.12 90.26 88.04
MSVM 94.65 94.32 95.81 95.69 94.06
Figure 3. Graphical comparison of classifiers WOFS for ADNI dataset
Figure 4. Graphical comparison of classifiers WFS for ADNI dataset
Table 2. Performance analysis of IMPA-MSVM for different feature selection approaches
Data set Feature selection Accuracy (%) Sensitivity (%) Specificity (%) PPV (%) MCC (%)
ADNI BOA 89.62 90.87 89.08 89.7 91.03
GWO 90.6 91.65 91.02 89.49 89.91
MPA 95.63 92.56 94.06 94.26 92.02
IMPA 98.43 98.86 98.02 97.04 96.07
OASIS-1 BOA 77.49 84.79 83.09 75.6 77.97
GWO 75.11 82.58 82.41 73.18 73.39
MPA 79.72 81.07 65.98 73.24 76.07
IMPA 94.65 94.32 95.81 95.69 94.06
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Figure 5. Graphical comparison of feature selection approaches for ADNI dataset
3.2. Comparative analysis
Existing researches such as deep learning-based segmenting method using SegNet (DLSS) [16],
MAF-STN [17] and BrainNet2D [18] are used to evaluate the efficiency of the IMPA-MSVM method.
Table 3 shows the comparative analysis of IMPA-MSVM with DLSS [16], MAF-STN [17] and BrainNet2D
[18]. From Table 3, it is concluded that the IMPA-MSVM achieves improved performance than the existing
researches. The accuracy of IMPA-MSVM for the OASIS-1 dataset is 94.65% which is higher than the
BrainNet2D [18]. The optimal feature selection using IMPA helps to improve the multi-class classification of
AD.
Table 3. Comparative analysis of IMPA-MSVM
Dataset Methods Accuracy (%) Sensitivity (%) Specificity (%) PPV (%)
ADNI DLSS [16] 96.3 96.7 93.9 NA
MAF-STN [17] 97.1 95.3 NA 95.5
IMPA-MSVM 98.43 98.86 98.02 97.04
OASIS-1 BrainNet2D [18] 84 NA NA NA
IMPA-MSVM 94.65 94.32 95.81 95.69
4. CONCLUSION
The precise diagnosis of AD in its initial stage such as the condition of MCI, is necessary for timely
treatment and to slow down the AD development. In this paper, the ResNet-18-based feature extraction and
IMPA are used to eliminate the inappropriate features during the classification. The MVO operators used in
the IMPA provide balance among exploration and exploitation processes. Further, the MSVM is used to
classify AD according to the features selected from the IMPA. The performance evaluation shows that the
IMPA-MSVM provides better classification results in both the OASIS-1 and ADNI datasets when compared
to DLSS, MAF-STN, and BrainNet2D. The accuracy of IMPA-MSVM for the ADNI dataset is 98.43% which
is relatively higher compared to DLSS and MAF-STN. In the future, deep learning classifiers can be used to
improve the detection of AD classes.
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BIOGRAPHIES OF AUTHORS
Preeti Sadanand Topannavar is currently a research scholar in the electronics and
telecommunication department at G. H. Raisoni College of Engineering and Management, Pune,
India. She is having total teaching experience of 17 years and currently working as Assistant
professor at JSPM’s Imperial College of Engineering and Research, Pune India. She has received
a Bachelor’s degree in electronics and telecommunication engineering from Amaravati
University and M.E. degree from Sinhgad College of Engineering Pune in 2002 and 2010
respectively. Her current research interests include biomedical image processing, human-
computer interactions, machine learning. She has published various research papers in different
international and national journals and conferences. She can be contacted at
topannavarp@gmail.com.
Dinkar M. Yadav received Bachelor’s degree, Master’s Degree and Ph.D. in
electronics and telecommunication engineering. He is having total teaching experience of over
28 years and research Experience of 8 years. He is currently working as professor and principal
at S. N. D. college of engineering and research center, Yeola, Maharashtra, India. His research
interests include biomedical image processing, communication engineering, digital signal
processing, neural networks. He has published several research papers in various SCI/Scopus
listed journals and more than 20 research papers in peer reviewed international conferences. He
is recognised Ph.D. guide for various repute universities such as Savitribai Phule Pune
University, Dr. Babasaheb Ambedkar Marathwada University Aurangabad JJTU Rajasthan,
Modi University, Jaipur He has guided more than 15 students for Ph.D. He can be contacted at
dineshyadav800@gmail.com.