Dementia is a brain disease that stays in the seventh position of death rate as per the report of the World Health Organization (WHO). Among the various types of dementia, Alzheimer’s disease has more than 70% of cases of dementia. The objective is to predict dementia disease from the open access series of imaging studies (OASIS) dataset using machine learning techniques. Also, the performance of the machine learning model is analyzed to improve the performance of the model using the cuckoo algorithm. In this paper, feature engineering has been focused and the prediction of dementia has been done using the OASIS dataset with the help of data mining techniques. Feature engineering is followed by prediction using the machine learning model Gaussian naïve Bayes (NB), support vector machine, and linear regression. Also, the best prediction model has been selected and done the validation. The evaluation metrics considered for validating the models are accuracy, precision, recall, and F1-Score and the highest values are 95%, 97%, 95%, and 95%. The Gaussian NB has been given these best results. The accuracy of the machine learning models has been increased by eliminating the factors which affect the performance of the models using the cuckoo algorithm
EFFICIENT USE OF HYBRID ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM COMBINED WITH N...csandit
This research study proposes a novel method for automatic fault prediction from foundry data
introducing the so-called Meta Prediction Function (MPF). Kernel Principal Component
Analysis (KPCA) is used for dimension reduction. Different algorithms are used for building the
MPF such as Multiple Linear Regression (MLR), Adaptive Neuro Fuzzy Inference System
(ANFIS), Support Vector Machine (SVM) and Neural Network (NN). We used classical
machine learning methods such as ANFIS, SVM and NN for comparison with our proposed
MPF. Our empirical results show that the MPF consistently outperform the classical methods.
Ataxic person prediction using feature optimized based on machine learning modelIJECEIAES
Ataxic gait monitoring and assessment of neurological disorders belong to important areas that are supported by digital signal processing methods and artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) techniques. This paper uses spatio-temporal data from Kinect sensor to optimize machine learning model to distinguish between ataxic and normal gait. Existing ML-based methodologies fails to establish feature correlation between different gait parameters; thus, exhibit very poor performance. Further, when data is imbalanced in nature the existing ML-based methodologies induces higher false positive. In addressing the research issues this paper introduces an extreme gradient boost (XGBoost)based classifier and enhanced feature optimization (EFO) by modifying the standard cross validation (SCV) mechanism. Experiment outcome shows the proposed ataxic person identification model achieves very good result in comparison with existing ML-based and DL-based ataxic person identification methodologies.
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.
EFFICIENT USE OF HYBRID ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM COMBINED WITH N...csandit
This research study proposes a novel method for automatic fault prediction from foundry data
introducing the so-called Meta Prediction Function (MPF). Kernel Principal Component
Analysis (KPCA) is used for dimension reduction. Different algorithms are used for building the
MPF such as Multiple Linear Regression (MLR), Adaptive Neuro Fuzzy Inference System
(ANFIS), Support Vector Machine (SVM) and Neural Network (NN). We used classical
machine learning methods such as ANFIS, SVM and NN for comparison with our proposed
MPF. Our empirical results show that the MPF consistently outperform the classical methods.
Ataxic person prediction using feature optimized based on machine learning modelIJECEIAES
Ataxic gait monitoring and assessment of neurological disorders belong to important areas that are supported by digital signal processing methods and artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) techniques. This paper uses spatio-temporal data from Kinect sensor to optimize machine learning model to distinguish between ataxic and normal gait. Existing ML-based methodologies fails to establish feature correlation between different gait parameters; thus, exhibit very poor performance. Further, when data is imbalanced in nature the existing ML-based methodologies induces higher false positive. In addressing the research issues this paper introduces an extreme gradient boost (XGBoost)based classifier and enhanced feature optimization (EFO) by modifying the standard cross validation (SCV) mechanism. Experiment outcome shows the proposed ataxic person identification model achieves very good result in comparison with existing ML-based and DL-based ataxic person identification methodologies.
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.
A new model for large dataset dimensionality reduction based on teaching lear...TELKOMNIKA JOURNAL
One of the human diseases with a high rate of mortality each year is breast cancer (BC). Among all the forms of cancer, BC is the commonest cause of death among women globally. Some of the effective ways of data classification are data mining and classification methods. These methods are particularly efficient in the medical field due to the presence of irrelevant and redundant attributes in medical datasets. Such redundant attributes are not needed to obtain an accurate estimation of disease diagnosis. Teaching learning-based optimization (TLBO) is a new metaheuristic that has been successfully applied to several intractable optimization problems in recent years. This paper presents the use of a multi-objective TLBO algorithm for the selection of feature subsets in automatic BC diagnosis. For the classification task in this work, the logistic regression (LR) method was deployed. From the results, the projected method produced better BC dataset classification accuracy (classified into malignant and benign). This result showed that the projected TLBO is an efficient features optimization technique for sustaining data-based decision-making systems.
Exploring the performance of feature selection method using breast cancer dat...nooriasukmaningtyas
Breast cancer is the most common type of cancer occurring mostly in females. In recent years, many researchers have devoted to automate diagnosis of breast cancer by developing different machine learning model. However, the quality and quantity of feature in breast cancer diagnostic dataset have significant effect on the accuracy and efficiency of predictive model. Feature selection is effective method for reducing the dimensionality and improving the accuracy of predictive model. The use of feature selection is to determine feature required for training model and to remove irrelevant and duplicate feature. Duplicate feature is a feature that is highly correlated to another feature. The objective of this study is to conduct experimental research on three different feature selection methods for breast cancer prediction. Sequential, embedded and chi-square feature selection are implemented using breast cancer diagnostic dataset. The study compares the performance of sequential embedded and chi-square feature selection on test set. The experimental result evidently shows that sequential feature selection outperforms as compared to chi-square (X2) statistics and embedded feature selection. Overall, sequential feature selection achieves better accuracy of 98.3% as compared to chi-square (X2) statistics and embedded feature selection.
A hybrid approach to medical decision-making: diagnosis of heart disease wit...IJECEIAES
Heart disease is one of the most widely spreading and deadliest diseases across the world. In this study, we have proposed hybrid model for heart disease prediction by employing random forest and support vector machine. With random forest, iterative feature elimination is carried out to select heart disease features that improves predictive outcome of support vector machine for heart disease prediction. Experiment is conducted on the proposed model using test set and the experimental result evidently appears to prove that the performance of the proposed hybrid model is better as compared to an individual random forest and support vector machine. Overall, we have developed more accurate and computationally efficient model for heart disease prediction with accuracy of 98.3%. Moreover, experiment is conducted to analyze the effect of regularization parameter (C) and gamma on the performance of support vector machine. The experimental result evidently reveals that support vector machine is very sensitive to C and gamma.
A new model for iris data set classification based on linear support vector m...IJECEIAES
Data mining is known as the process of detection concerning patterns from essential amounts of data. As a process of knowledge discovery. Classification is a data analysis that extracts a model which describes an important data classes. One of the outstanding classifications methods in data mining is support vector machine classification (SVM). It is capable of envisaging results and mostly effective than other classification methods. The SVM is a one technique of machine learning techniques that is well known technique, learning with supervised and have been applied perfectly to a vary problems of: regression, classification, and clustering in diverse domains such as gene expression, web text mining. In this study, we proposed a newly mode for classifying iris data set using SVM classifier and genetic algorithm to optimize c and gamma parameters of linear SVM, in addition principle components analysis (PCA) algorithm was use for features reduction.
A F AULT D IAGNOSIS M ETHOD BASED ON S EMI - S UPERVISED F UZZY C-M EANS...IJCI JOURNAL
Machine learning approaches are generally adopted i
n many fields including data mining, image
processing, intelligent fault diagnosis etc. As a c
lassic unsupervised learning technology, fuzzy C-me
ans
cluster analysis plays a vital role in machine lear
ning based intelligent fault diagnosis. With the ra
pid
development of science and technology, the monitori
ng signal data is numerous and keeps growing fast.
Only typical fault samples can be obtained and labe
led. Thus, how to apply semi-supervised learning
technology in fault diagnosis is significant for gu
aranteeing the equipment safety. According to this,
a novel
fault diagnosis method based on semi-supervised fuz
zy C-means(SFCM) cluster analysis is proposed.
Experimental results on Iris data set and the steel
plates faults data set show that this method is su
perior to
traditional fuzzy C-means clustering analysis
Early Detection of Lung Cancer Using Neural Network TechniquesIJERA Editor
Effective identification of lung cancer at an initial stage is an important and crucial aspect of image processing. Several data mining methods have been used to detect lung cancer at early stage. In this paper, an approach has been presented which will diagnose lung cancer at an initial stage using CT scan images which are in Dicom (DCM) format. One of the key challenges is to remove white Gaussian noise from the CT scan image, which is done using non local mean filter and to segment the lung Otsu’s thresholding is used. The textural and structural features are extracted from the processed image to form feature vector. In this paper, three classifiers namely SVM, ANN, and k-NN are applied for the detection of lung cancer to find the severity of disease (stage I or stage II) and comparison is made with ANN, and k-NN classifier with respect to different quality attributes such as accuracy, sensitivity(recall), precision and specificity. It has been found from results that SVM achieves higher accuracy of 95.12% while ANN achieves 92.68% accuracy on the given data set and k-NN shows least accuracy of 85.37%. SVM algorithm which achieves 95.12% accuracy helps patients to take remedial action on time and reduces mortality rate from this deadly disease.
An efficient convolutional neural network-based classifier for an imbalanced ...IAESIJAI
Imbalanced datasets pose a major challenge for the researchers while addressing machine learning tasks. In these types of datasets, samples of different classes are not in equal proportion rather the gap between the numbers of individual class samples is significantly large. Classification models perform better for datasets having equal proportion of data tuples in both the classes. But, in reality, the medical image datasets are skewed and hence are not always suitable for a model to achieve improved classification performance. Therefore, various techniques have been suggested in the literature to overcome this challenge. This paper applies oversampling technique on an imbalanced dataset and focuses on a customized convolutional neural network model that classifies the images into two categories: diseased and non-diseased. Outcome of the proposed model can assist the health experts in the detection of oral cancer. The proposed model exhibits 99% accuracy after data augmentation. Performance metrics such as precision, recall and F1-score values are very close to 1. In addition, statistical test is performed to validate the statistical significance of the model. It has been found that the proposed model is an optimised classifier in terms of number of network layers and number of neurons.
AN EFFICIENT PSO BASED ENSEMBLE CLASSIFICATION MODEL ON HIGH DIMENSIONAL DATA...ijsc
As the size of the biomedical databases are growing day by day, finding an essential features in the disease prediction have become more complex due to high dimensionality and sparsity problems. Also, due to the
availability of a large number of micro-array datasets in the biomedical repositories, it is difficult to analyze, predict and interpret the feature information using the traditional feature selection based classification models. Most of the traditional feature selection based classification algorithms have computational issues such as dimension reduction, uncertainty and class imbalance on microarray datasets. Ensemble classifier is one of the scalable models for extreme learning machine due to its high efficiency, the fast processing speed for real-time applications. The main objective of the feature selection
based ensemble learning models is to classify the high dimensional data with high computational efficiency
and high true positive rate on high dimensional datasets. In this proposed model an optimized Particle swarm optimization (PSO) based Ensemble classification model was developed on high dimensional microarray
datasets. Experimental results proved that the proposed model has high computational efficiency compared to the traditional feature selection based classification models in terms of accuracy , true positive rate and error rate are concerned.
An Efficient PSO Based Ensemble Classification Model on High Dimensional Data...ijsc
As the size of the biomedical databases are growing day by day, finding an essential features in the disease prediction have become more complex due to high dimensionality and sparsity problems. Also, due to the availability of a large number of micro-array datasets in the biomedical repositories, it is difficult to
analyze, predict and interpret the feature information using the traditional feature selection based classification models. Most of the traditional feature selection based classification algorithms have computational issues such as dimension reduction, uncertainty and class imbalance on microarray datasets. Ensemble classifier is one of the scalable models for extreme learning machine due to its high efficiency, the fast processing speed for real-time applications. The main objective of the feature selection based ensemble learning models is to classify the high dimensional data with high computational efficiency and high true positive rate on high dimensional datasets. In this proposed model an optimized Particle swarm optimization (PSO) based Ensemble classification model was developed on high dimensional microarray datasets. Experimental results proved that the proposed model has high computational efficiency compared to the traditional feature selection based classification models in terms of accuracy , true positive rate and error rate are concerned.
Simplified Knowledge Prediction: Application of Machine Learning in Real LifePeea Bal Chakraborty
Machine learning is the scientific study of algorithms and statistical models that is used by the machines to perform a specific task depending on patterns and inference rather than explicit instructions. This research and analysis aims to observe how precisely a machine can predict that a patient suspected of breast cancer is having malignant or benign cancer.In this paper the classification of cancer type and prediction of risk levels is done by various model of machine learning and is pictorially depicted by various tools of visual analytics.
A novel ensemble modeling for intrusion detection system IJECEIAES
Vast increase in data through internet services has made computer systems more vulnerable and difficult to protect from malicious attacks. Intrusion detection systems (IDSs) must be more potent in monitoring intrusions. Therefore an effectual Intrusion Detection system architecture is built which employs a facile classification model and generates low false alarm rates and high accuracy. Noticeably, IDS endure enormous amounts of data traffic that contain redundant and irrelevant features, which affect the performance of the IDS negatively. Despite good feature selection approaches leads to a reduction of unrelated and redundant features and attain better classification accuracy in IDS. This paper proposes a novel ensemble model for IDS based on two algorithms Fuzzy Ensemble Feature selection (FEFS) and Fusion of Multiple Classifier (FMC). FEFS is a unification of five feature scores. These scores are obtained by using feature-class distance functions. Aggregation is done using fuzzy union operation. On the other hand, the FMC is the fusion of three classifiers. It works based on Ensemble decisive function. Experiments were made on KDD cup 99 data set have shown that our proposed system works superior to well-known methods such as Support Vector Machines (SVMs), K-Nearest Neighbor (KNN) and Artificial Neural Networks (ANNs). Our examinations ensured clearly the prominence of using ensemble methodology for modeling IDSs, and hence our system is robust and efficient.
Enhancing feature selection with a novel hybrid approach incorporating geneti...IJECEIAES
Computing advances in data storage are leading to rapid growth in large-scale datasets. Using all features increases temporal/spatial complexity and negatively influences performance. Feature selection is a fundamental stage in data preprocessing, removing redundant and irrelevant features to minimize the number of features and enhance the performance of classification accuracy. Numerous optimization algorithms were employed to handle feature selection (FS) problems, and they outperform conventional FS techniques. However, there is no metaheuristic FS method that outperforms other optimization algorithms in many datasets. This motivated our study to incorporate the advantages of various optimization techniques to obtain a powerful technique that outperforms other methods in many datasets from different domains. In this article, a novel combined method GASI is developed using swarm intelligence (SI) based feature selection techniques and genetic algorithms (GA) that uses a multi-objective fitness function to seek the optimal subset of features. To assess the performance of the proposed approach, seven datasets have been collected from the UCI repository and exploited to test the newly established feature selection technique. The experimental results demonstrate that the suggested method GASI outperforms many powerful SI-based feature selection techniques studied. GASI obtains a better average fitness value and improves classification performance.
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 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.
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.
More Related Content
Similar to Prediction of dementia using machine learning model and performance improvement with cuckoo algorithm
A new model for large dataset dimensionality reduction based on teaching lear...TELKOMNIKA JOURNAL
One of the human diseases with a high rate of mortality each year is breast cancer (BC). Among all the forms of cancer, BC is the commonest cause of death among women globally. Some of the effective ways of data classification are data mining and classification methods. These methods are particularly efficient in the medical field due to the presence of irrelevant and redundant attributes in medical datasets. Such redundant attributes are not needed to obtain an accurate estimation of disease diagnosis. Teaching learning-based optimization (TLBO) is a new metaheuristic that has been successfully applied to several intractable optimization problems in recent years. This paper presents the use of a multi-objective TLBO algorithm for the selection of feature subsets in automatic BC diagnosis. For the classification task in this work, the logistic regression (LR) method was deployed. From the results, the projected method produced better BC dataset classification accuracy (classified into malignant and benign). This result showed that the projected TLBO is an efficient features optimization technique for sustaining data-based decision-making systems.
Exploring the performance of feature selection method using breast cancer dat...nooriasukmaningtyas
Breast cancer is the most common type of cancer occurring mostly in females. In recent years, many researchers have devoted to automate diagnosis of breast cancer by developing different machine learning model. However, the quality and quantity of feature in breast cancer diagnostic dataset have significant effect on the accuracy and efficiency of predictive model. Feature selection is effective method for reducing the dimensionality and improving the accuracy of predictive model. The use of feature selection is to determine feature required for training model and to remove irrelevant and duplicate feature. Duplicate feature is a feature that is highly correlated to another feature. The objective of this study is to conduct experimental research on three different feature selection methods for breast cancer prediction. Sequential, embedded and chi-square feature selection are implemented using breast cancer diagnostic dataset. The study compares the performance of sequential embedded and chi-square feature selection on test set. The experimental result evidently shows that sequential feature selection outperforms as compared to chi-square (X2) statistics and embedded feature selection. Overall, sequential feature selection achieves better accuracy of 98.3% as compared to chi-square (X2) statistics and embedded feature selection.
A hybrid approach to medical decision-making: diagnosis of heart disease wit...IJECEIAES
Heart disease is one of the most widely spreading and deadliest diseases across the world. In this study, we have proposed hybrid model for heart disease prediction by employing random forest and support vector machine. With random forest, iterative feature elimination is carried out to select heart disease features that improves predictive outcome of support vector machine for heart disease prediction. Experiment is conducted on the proposed model using test set and the experimental result evidently appears to prove that the performance of the proposed hybrid model is better as compared to an individual random forest and support vector machine. Overall, we have developed more accurate and computationally efficient model for heart disease prediction with accuracy of 98.3%. Moreover, experiment is conducted to analyze the effect of regularization parameter (C) and gamma on the performance of support vector machine. The experimental result evidently reveals that support vector machine is very sensitive to C and gamma.
A new model for iris data set classification based on linear support vector m...IJECEIAES
Data mining is known as the process of detection concerning patterns from essential amounts of data. As a process of knowledge discovery. Classification is a data analysis that extracts a model which describes an important data classes. One of the outstanding classifications methods in data mining is support vector machine classification (SVM). It is capable of envisaging results and mostly effective than other classification methods. The SVM is a one technique of machine learning techniques that is well known technique, learning with supervised and have been applied perfectly to a vary problems of: regression, classification, and clustering in diverse domains such as gene expression, web text mining. In this study, we proposed a newly mode for classifying iris data set using SVM classifier and genetic algorithm to optimize c and gamma parameters of linear SVM, in addition principle components analysis (PCA) algorithm was use for features reduction.
A F AULT D IAGNOSIS M ETHOD BASED ON S EMI - S UPERVISED F UZZY C-M EANS...IJCI JOURNAL
Machine learning approaches are generally adopted i
n many fields including data mining, image
processing, intelligent fault diagnosis etc. As a c
lassic unsupervised learning technology, fuzzy C-me
ans
cluster analysis plays a vital role in machine lear
ning based intelligent fault diagnosis. With the ra
pid
development of science and technology, the monitori
ng signal data is numerous and keeps growing fast.
Only typical fault samples can be obtained and labe
led. Thus, how to apply semi-supervised learning
technology in fault diagnosis is significant for gu
aranteeing the equipment safety. According to this,
a novel
fault diagnosis method based on semi-supervised fuz
zy C-means(SFCM) cluster analysis is proposed.
Experimental results on Iris data set and the steel
plates faults data set show that this method is su
perior to
traditional fuzzy C-means clustering analysis
Early Detection of Lung Cancer Using Neural Network TechniquesIJERA Editor
Effective identification of lung cancer at an initial stage is an important and crucial aspect of image processing. Several data mining methods have been used to detect lung cancer at early stage. In this paper, an approach has been presented which will diagnose lung cancer at an initial stage using CT scan images which are in Dicom (DCM) format. One of the key challenges is to remove white Gaussian noise from the CT scan image, which is done using non local mean filter and to segment the lung Otsu’s thresholding is used. The textural and structural features are extracted from the processed image to form feature vector. In this paper, three classifiers namely SVM, ANN, and k-NN are applied for the detection of lung cancer to find the severity of disease (stage I or stage II) and comparison is made with ANN, and k-NN classifier with respect to different quality attributes such as accuracy, sensitivity(recall), precision and specificity. It has been found from results that SVM achieves higher accuracy of 95.12% while ANN achieves 92.68% accuracy on the given data set and k-NN shows least accuracy of 85.37%. SVM algorithm which achieves 95.12% accuracy helps patients to take remedial action on time and reduces mortality rate from this deadly disease.
An efficient convolutional neural network-based classifier for an imbalanced ...IAESIJAI
Imbalanced datasets pose a major challenge for the researchers while addressing machine learning tasks. In these types of datasets, samples of different classes are not in equal proportion rather the gap between the numbers of individual class samples is significantly large. Classification models perform better for datasets having equal proportion of data tuples in both the classes. But, in reality, the medical image datasets are skewed and hence are not always suitable for a model to achieve improved classification performance. Therefore, various techniques have been suggested in the literature to overcome this challenge. This paper applies oversampling technique on an imbalanced dataset and focuses on a customized convolutional neural network model that classifies the images into two categories: diseased and non-diseased. Outcome of the proposed model can assist the health experts in the detection of oral cancer. The proposed model exhibits 99% accuracy after data augmentation. Performance metrics such as precision, recall and F1-score values are very close to 1. In addition, statistical test is performed to validate the statistical significance of the model. It has been found that the proposed model is an optimised classifier in terms of number of network layers and number of neurons.
AN EFFICIENT PSO BASED ENSEMBLE CLASSIFICATION MODEL ON HIGH DIMENSIONAL DATA...ijsc
As the size of the biomedical databases are growing day by day, finding an essential features in the disease prediction have become more complex due to high dimensionality and sparsity problems. Also, due to the
availability of a large number of micro-array datasets in the biomedical repositories, it is difficult to analyze, predict and interpret the feature information using the traditional feature selection based classification models. Most of the traditional feature selection based classification algorithms have computational issues such as dimension reduction, uncertainty and class imbalance on microarray datasets. Ensemble classifier is one of the scalable models for extreme learning machine due to its high efficiency, the fast processing speed for real-time applications. The main objective of the feature selection
based ensemble learning models is to classify the high dimensional data with high computational efficiency
and high true positive rate on high dimensional datasets. In this proposed model an optimized Particle swarm optimization (PSO) based Ensemble classification model was developed on high dimensional microarray
datasets. Experimental results proved that the proposed model has high computational efficiency compared to the traditional feature selection based classification models in terms of accuracy , true positive rate and error rate are concerned.
An Efficient PSO Based Ensemble Classification Model on High Dimensional Data...ijsc
As the size of the biomedical databases are growing day by day, finding an essential features in the disease prediction have become more complex due to high dimensionality and sparsity problems. Also, due to the availability of a large number of micro-array datasets in the biomedical repositories, it is difficult to
analyze, predict and interpret the feature information using the traditional feature selection based classification models. Most of the traditional feature selection based classification algorithms have computational issues such as dimension reduction, uncertainty and class imbalance on microarray datasets. Ensemble classifier is one of the scalable models for extreme learning machine due to its high efficiency, the fast processing speed for real-time applications. The main objective of the feature selection based ensemble learning models is to classify the high dimensional data with high computational efficiency and high true positive rate on high dimensional datasets. In this proposed model an optimized Particle swarm optimization (PSO) based Ensemble classification model was developed on high dimensional microarray datasets. Experimental results proved that the proposed model has high computational efficiency compared to the traditional feature selection based classification models in terms of accuracy , true positive rate and error rate are concerned.
Simplified Knowledge Prediction: Application of Machine Learning in Real LifePeea Bal Chakraborty
Machine learning is the scientific study of algorithms and statistical models that is used by the machines to perform a specific task depending on patterns and inference rather than explicit instructions. This research and analysis aims to observe how precisely a machine can predict that a patient suspected of breast cancer is having malignant or benign cancer.In this paper the classification of cancer type and prediction of risk levels is done by various model of machine learning and is pictorially depicted by various tools of visual analytics.
A novel ensemble modeling for intrusion detection system IJECEIAES
Vast increase in data through internet services has made computer systems more vulnerable and difficult to protect from malicious attacks. Intrusion detection systems (IDSs) must be more potent in monitoring intrusions. Therefore an effectual Intrusion Detection system architecture is built which employs a facile classification model and generates low false alarm rates and high accuracy. Noticeably, IDS endure enormous amounts of data traffic that contain redundant and irrelevant features, which affect the performance of the IDS negatively. Despite good feature selection approaches leads to a reduction of unrelated and redundant features and attain better classification accuracy in IDS. This paper proposes a novel ensemble model for IDS based on two algorithms Fuzzy Ensemble Feature selection (FEFS) and Fusion of Multiple Classifier (FMC). FEFS is a unification of five feature scores. These scores are obtained by using feature-class distance functions. Aggregation is done using fuzzy union operation. On the other hand, the FMC is the fusion of three classifiers. It works based on Ensemble decisive function. Experiments were made on KDD cup 99 data set have shown that our proposed system works superior to well-known methods such as Support Vector Machines (SVMs), K-Nearest Neighbor (KNN) and Artificial Neural Networks (ANNs). Our examinations ensured clearly the prominence of using ensemble methodology for modeling IDSs, and hence our system is robust and efficient.
Enhancing feature selection with a novel hybrid approach incorporating geneti...IJECEIAES
Computing advances in data storage are leading to rapid growth in large-scale datasets. Using all features increases temporal/spatial complexity and negatively influences performance. Feature selection is a fundamental stage in data preprocessing, removing redundant and irrelevant features to minimize the number of features and enhance the performance of classification accuracy. Numerous optimization algorithms were employed to handle feature selection (FS) problems, and they outperform conventional FS techniques. However, there is no metaheuristic FS method that outperforms other optimization algorithms in many datasets. This motivated our study to incorporate the advantages of various optimization techniques to obtain a powerful technique that outperforms other methods in many datasets from different domains. In this article, a novel combined method GASI is developed using swarm intelligence (SI) based feature selection techniques and genetic algorithms (GA) that uses a multi-objective fitness function to seek the optimal subset of features. To assess the performance of the proposed approach, seven datasets have been collected from the UCI repository and exploited to test the newly established feature selection technique. The experimental results demonstrate that the suggested method GASI outperforms many powerful SI-based feature selection techniques studied. GASI obtains a better average fitness value and improves classification performance.
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 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.
Similar to Prediction of dementia using machine learning model and performance improvement with cuckoo algorithm (20)
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%.
Developing a smart system for infant incubators using the internet of things ...IJECEIAES
This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R 2 value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R 2 value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.
A review on internet of things-based stingless bee's honey production with im...IJECEIAES
Honey is produced exclusively by honeybees and stingless bees which both are well adapted to tropical and subtropical regions such as Malaysia. Stingless bees are known for producing small amounts of honey and are known for having a unique flavor profile. Problem identified that many stingless bees collapsed due to weather, temperature and environment. It is critical to understand the relationship between the production of stingless bee honey and environmental conditions to improve honey production. Thus, this paper presents a review on stingless bee's honey production and prediction modeling. About 54 previous research has been analyzed and compared in identifying the research gaps. A framework on modeling the prediction of stingless bee honey is derived. The result presents the comparison and analysis on the internet of things (IoT) monitoring systems, honey production estimation, convolution neural networks (CNNs), and automatic identification methods on bee species. It is identified based on image detection method the top best three efficiency presents CNN is at 98.67%, densely connected convolutional networks with YOLO v3 is 97.7%, and DenseNet201 convolutional networks 99.81%. This study is significant to assist the researcher in developing a model for predicting stingless honey produced by bee's output, which is important for a stable economy and food security.
A trust based secure access control using authentication mechanism for intero...IJECEIAES
The internet of things (IoT) is a revolutionary innovation in many aspects of our society including interactions, financial activity, and global security such as the military and battlefield internet. Due to the limited energy and processing capacity of network devices, security, energy consumption, compatibility, and device heterogeneity are the long-term IoT problems. As a result, energy and security are critical for data transmission across edge and IoT networks. Existing IoT interoperability techniques need more computation time, have unreliable authentication mechanisms that break easily, lose data easily, and have low confidentiality. In this paper, a key agreement protocol-based authentication mechanism for IoT devices is offered as a solution to this issue. This system makes use of information exchange, which must be secured to prevent access by unauthorized users. Using a compact contiki/cooja simulator, the performance and design of the suggested framework are validated. The simulation findings are evaluated based on detection of malicious nodes after 60 minutes of simulation. The suggested trust method, which is based on privacy access control, reduced packet loss ratio to 0.32%, consumed 0.39% power, and had the greatest average residual energy of 0.99 mJoules at 10 nodes.
Fuzzy linear programming with the intuitionistic polygonal fuzzy numbersIJECEIAES
In real world applications, data are subject to ambiguity due to several factors; fuzzy sets and fuzzy numbers propose a great tool to model such ambiguity. In case of hesitation, the complement of a membership value in fuzzy numbers can be different from the non-membership value, in which case we can model using intuitionistic fuzzy numbers as they provide flexibility by defining both a membership and a non-membership functions. In this article, we consider the intuitionistic fuzzy linear programming problem with intuitionistic polygonal fuzzy numbers, which is a generalization of the previous polygonal fuzzy numbers found in the literature. We present a modification of the simplex method that can be used to solve any general intuitionistic fuzzy linear programming problem after approximating the problem by an intuitionistic polygonal fuzzy number with n edges. This method is given in a simple tableau formulation, and then applied on numerical examples for clarity.
The performance of artificial intelligence in prostate magnetic resonance im...IJECEIAES
Prostate cancer is the predominant form of cancer observed in men worldwide. The application of magnetic resonance imaging (MRI) as a guidance tool for conducting biopsies has been established as a reliable and well-established approach in the diagnosis of prostate cancer. The diagnostic performance of MRI-guided prostate cancer diagnosis exhibits significant heterogeneity due to the intricate and multi-step nature of the diagnostic pathway. The development of artificial intelligence (AI) models, specifically through the utilization of machine learning techniques such as deep learning, is assuming an increasingly significant role in the field of radiology. In the realm of prostate MRI, a considerable body of literature has been dedicated to the development of various AI algorithms. These algorithms have been specifically designed for tasks such as prostate segmentation, lesion identification, and classification. The overarching objective of these endeavors is to enhance diagnostic performance and foster greater agreement among different observers within MRI scans for the prostate. This review article aims to provide a concise overview of the application of AI in the field of radiology, with a specific focus on its utilization in prostate MRI.
Seizure stage detection of epileptic seizure using convolutional neural networksIJECEIAES
According to the World Health Organization (WHO), seventy million individuals worldwide suffer from epilepsy, a neurological disorder. While electroencephalography (EEG) is crucial for diagnosing epilepsy and monitoring the brain activity of epilepsy patients, it requires a specialist to examine all EEG recordings to find epileptic behavior. This procedure needs an experienced doctor, and a precise epilepsy diagnosis is crucial for appropriate treatment. To identify epileptic seizures, this study employed a convolutional neural network (CNN) based on raw scalp EEG signals to discriminate between preictal, ictal, postictal, and interictal segments. The possibility of these characteristics is explored by examining how well timedomain signals work in the detection of epileptic signals using intracranial Freiburg Hospital (FH), scalp Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) databases, and Temple University Hospital (TUH) EEG. To test the viability of this approach, two types of experiments were carried out. Firstly, binary class classification (preictal, ictal, postictal each versus interictal) and four-class classification (interictal versus preictal versus ictal versus postictal). The average accuracy for stage detection using CHB-MIT database was 84.4%, while the Freiburg database's time-domain signals had an accuracy of 79.7% and the highest accuracy of 94.02% for classification in the TUH EEG database when comparing interictal stage to preictal stage.
Analysis of driving style using self-organizing maps to analyze driver behaviorIJECEIAES
Modern life is strongly associated with the use of cars, but the increase in acceleration speeds and their maneuverability leads to a dangerous driving style for some drivers. In these conditions, the development of a method that allows you to track the behavior of the driver is relevant. The article provides an overview of existing methods and models for assessing the functioning of motor vehicles and driver behavior. Based on this, a combined algorithm for recognizing driving style is proposed. To do this, a set of input data was formed, including 20 descriptive features: About the environment, the driver's behavior and the characteristics of the functioning of the car, collected using OBD II. The generated data set is sent to the Kohonen network, where clustering is performed according to driving style and degree of danger. Getting the driving characteristics into a particular cluster allows you to switch to the private indicators of an individual driver and considering individual driving characteristics. The application of the method allows you to identify potentially dangerous driving styles that can prevent accidents.
Hyperspectral object classification using hybrid spectral-spatial fusion and ...IJECEIAES
Because of its spectral-spatial and temporal resolution of greater areas, hyperspectral imaging (HSI) has found widespread application in the field of object classification. The HSI is typically used to accurately determine an object's physical characteristics as well as to locate related objects with appropriate spectral fingerprints. As a result, the HSI has been extensively applied to object identification in several fields, including surveillance, agricultural monitoring, environmental research, and precision agriculture. However, because of their enormous size, objects require a lot of time to classify; for this reason, both spectral and spatial feature fusion have been completed. The existing classification strategy leads to increased misclassification, and the feature fusion method is unable to preserve semantic object inherent features; This study addresses the research difficulties by introducing a hybrid spectral-spatial fusion (HSSF) technique to minimize feature size while maintaining object intrinsic qualities; Lastly, a soft-margins kernel is proposed for multi-layer deep support vector machine (MLDSVM) to reduce misclassification. The standard Indian pines dataset is used for the experiment, and the outcome demonstrates that the HSSF-MLDSVM model performs substantially better in terms of accuracy and Kappa coefficient.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
Prediction of dementia using machine learning model and performance improvement with cuckoo algorithm
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 4, August 2023, pp. 4623~4632
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i4.pp4623-4632 4623
Journal homepage: http://ijece.iaescore.com
Prediction of dementia using machine learning model and
performance improvement with cuckoo algorithm
Sivakani Rajayyan1
, Syed Masood Mohamed Mustafa2
1
Department of Computer Science and Engineering, B. S. Abdur Rahman Crescent Institute of Science and Technology, Vandalur, India
2
Department of Computer Applications, B. S. Abdur Rahman Crescent Institute of Science and Technology, Vandalur, India
Article Info ABSTRACT
Article history:
Received Jun 30, 2022
Revised Sep 16, 2022
Accepted Oct 1, 2022
Dementia is a brain disease that stays in the seventh position of death rate as
per the report of the World Health Organization (WHO). Among the various
types of dementia, Alzheimer’s disease has more than 70% of cases of dementia.
The objective is to predict dementia disease from the open access series of
imaging studies (OASIS) dataset using machine learning techniques. Also, the
performance of the machine learning model is analyzed to improve the
performance of the model using the cuckoo algorithm. In this paper, feature
engineering has been focused and the prediction of dementia has been done
using the OASIS dataset with the help of data mining techniques. Feature
engineering is followed by prediction using the machine learning model
Gaussian naïve Bayes (NB), support vector machine, and linear regression.
Also, the best prediction model has been selected and done the validation. The
evaluation metrics considered for validating the models are accuracy,
precision, recall, and F1-Score and the highest values are 95%, 97%, 95%,
and 95%. The Gaussian NB has been given these best results. The accuracy
of the machine learning models has been increased by eliminating the factors
which affect the performance of the models using the cuckoo algorithm.
Keywords:
Classification
Dementia
Gaussian naïve Bayes
Linear regression
Support vector machine
This is an open access article under the CC BY-SA license.
Corresponding Author:
Syed Masood Mohamed Mustafa
Department of Computer Applications, B. S. Abdur Rahman Crescent Institute of Science and Technology
Vandalur, Chennai, India
Email: ms.masood@crescent.education
1. INTRODUCTION
The process of converting the raw data into useful data as required is called data mining. The paper gives
the contribution of feature engineering and the prediction of dementia using machine learning models [1]. The
features are very important for a good prediction. The data analyzer should decide what kind of data and in which
format it is required; for this, feature engineering is the focus. Feature engineering is the process of making the
dataset in such a way that is required for processing using machine learning models [2], so that the prediction
done using the machine learning model will be the best and most accurate. The process of creating new features
from the existing raw data for processing is called feature creation [3]. The required feature for good prediction
can be created using data mining tools. The process of changing the data to another required format is called
transformation. Data encoding is applied to convert the text to numeric values for the instances in the dataset. The
process of extracting the required feature from the dataset is called feature extraction. Each and every data will be
processed and based on the importance of the data the feature will be extracted. The process of selecting the
required feature is called feature selection [4]. Feature selection is an important process in feature engineering.
A machine learning model is a model constructed using the machine learning algorithm for
the prediction of results using an input dataset. The machine learning model can be mainly classified into
supervised and unsupervised learning models [5]. The supervised learning model trains the labeled data
2. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 13, No. 4, August 2023: 4623-4632
4624
and predicts the result. The unsupervised learning model trains the unlabeled data and predicts the results.
The electrocardiogram (ECG) beats of the arrhythmia have been classified using the machine learning
classifiers decision trees, support vector machine (SVM), and random forest [6]. The novel feature engineering
method was introduced using the deep learning method along with the K-nearest neighbor (KNN). A 5-fold
cross-validation has been implemented for the validation and the accuracy obtained was 99.77%.
The advantage is featuring extraction gives a high balance of sensitivity and specificity [7]. Feature selection
was done using the chi2, information gain score, and backward elimination method. The feature selection
process makes the distributed denial-of-service (DDoS) process faster. The model has been constructed
using the machine learning methods KNN, SVM, naïve Bayes (NB), artificial neural network (ANN), and
cross-validation has been applied to find the better performance of the algorithms [8]. A two-classifier
pool has been created with four classifiers in each pool. Using the ensemble method, the best classifier
has been selected in this multi-subspace elastic network (MSEN) proposed model. The missing values are
generated using the KNN algorithms and the outliers are removed using the isolation forest method. Feature
selection has been done using the recursive feature elimination method and the model has been validated using
the real-world dataset [9]. An efficient blockchain secure healthcare system for predicting disease using fog
computing was created. The data are collected using fog nodes and stored in a blockchain; the rule-based
cluster method has been applied to diabetes and cardio disease data and the feature selection was done using
the forward selection-adaptive neuro-fuzzy inference system (FS-ANFIS) method. The accuracy of the
prediction was 81% [10]. An ensemble model has been constructed for the classification of hepatitis-C medical
record patients with 75 records [11].
A new method was introduced for breast cancer detection by applying machine learning algorithms
to the clinical dataset. Feature selection was done using the auto-encoder and principal component analysis
(PCA) algorithm. The relief-SVM has been applied to diagnose the disease [12]. Multi-classification was done
on the cleaned and selected features. Machine learning models are used for the classification and K-fold cross-
validation has been applied for the evaluation of the dataset [13]. The weighted ensemble method was
introduced to predict Alzheimer’s disease (AD) early stage. The feature selection methods applied were
recursive feature elimination (RFE) and L1 regularization [14]. The linear discriminant analysis (LDA) model
along with the RF classifier was constructed for the classification of the neurodegenerative problem. For the
feature extraction, the LDA method is applied and for the classification, an RF classifier is applied [15]. A
machine learning (ML) classifier model was introduced for the detection of AD. The feature selection method
applied was RFE and the 10-fold cross-validation was applied for the evaluation of the dataset [16]. A machine
learning model was introduced for the AD classification and for the feature selection elaboration likelihood
model (ELM) method was applied and cross-validation was done on the Alzheimer's disease neuroimaging
initiative (ADNI) data set [17]. Various machine learning algorithms are used for generating the missing values
in the open access series of imaging studies (OASIS) Alzheimer dataset [18]. A framework has been developed
for the prediction of Alzheimer’s [19]. A multipath delay commutator has been proposed for enhancing the
throughput and speed [20], [21]. Cooperative routing using the fresher encounter algorithm to improve
efficiency [22].
A new injury metric has been introduced for computing the risk due to the injury in the head [23]. A
hybrid algorithm has been introduced using the combination of Harris Hawks optimizer (HHO) and cuckoo
search (CS) and chaotic maps. Feature selection is also implemented for better performance. The CS maintains
the vector’s position and HHO balances the exploitation and exploration process. Experimental and statistical
analysis has been done [24]. A modified cuckoo search algorithm has been introduced along with the
variational parameter and logistic map (VLCS) to solve the dimension problems [25]. Three new cuckoo search
algorithms have been developed by using various parameters and the performance has been tested using the
mathematical model. Finally, concluded that the cuckoo algorithm improves the performance by changing the
parameters [26]. A modified cuckoo search algorithm has been used for tournament selection in robot path
planning [27]. A new hybrid model has been developed using the conditional mutual information maximization
algorithm and the cuckoo search algorithm for the prediction of the disease [28]. The particle swarm
optimization method tolerates uncertainty and imprecision to a maximum extent [29]. ANN controller for load
frequency control of a four-area interconnected power system. This controller is designed by optimal control
theory to defeat the issue of load frequency control. A feed-forward neural network with multi-layers and
Bayesian regularization backpropagation training function is utilized [30]. The literature shows that various
machine learning methods are used for the prediction and classification of the disease but no performance
improvement has been done. So, the prediction of dementia has been done using the machine learning model
and the performance of the model has been improved using the cuckoo algorithm. The result of the proposed
model has been compared with [31], [32], and the proposed shows the best result. In the proposed framework
the Gaussian NB, SVM, and linear regression (LR) classifiers are used for the machine learning model
construction. These classifiers belong to the supervised learning model. The validation of the result has been
3. Int J Elec & Comp Eng ISSN: 2088-8708
Prediction of dementia using machine learning model and performance improvement … (Sivakani Rajayyan)
4625
done using the hold-out cross-validation. The performance of the model has improved using the cuckoo
algorithm. Outlines of the contributions are OASIS dataset for processing, feature engineering concept
implementation, constructing the machine learning model, prediction of the dementia disease, validating and
analyzing the results obtained, and performance improvement of the model using the cuckoo algorithm.
The prediction of dementia disease has been done using machine learning techniques and the result
has been validated. The improvement in the result has been done using the cuckoo algorithm. This paper
is organized with an introduction followed by the proposed framework, method, result and discussion,
and conclusion.
2. PROPOSED FRAMEWORK
The proposed model has the dataset and is subjected to feature engineering, and the selected features
are given to the machine learning model for prediction and the validation has been done. Figure 1 describes
the proposed model; the feature engineering concept has been applied to the OASIS dataset for the prediction.
The machine learning model has been constructed and the result has been validated. Finally, the performance
of the machine learning model has been improved using the cuckoo algorithm. In [33]–[39], feature
engineering, feature creature feature transformation, feature selection, and feature extraction process have been
implemented. Gaussian NB, SVM, and LR classifiers are used for the construction of the machine learning
model and the validation has been done using the hold-out validation technique.
Figure 1. A framework of the proposed model
2.1. Dataset
The OASIS dataset has been taken for validation of the proposed model; the execution has been done
using the WEKA and python tools. The input features considered are specified in the dataset section. The
dataset has been subjected to a feature engineering process and the processed data has been given for the
machine learning model and the validation has been done. The performance of the model has been improved
by using the cuckoo algorithm. OASIS longitudinal dataset is a freely available dataset in the Washington
University Alzheimer’s Disease Research Center, the attributes and the description are given in Table 1.
Table 1. Data description
Sl. No. Data Attributes Data Description
1 SubjectID Identification of the subjects
2 MRIID Identification of the MRI
3 MF Gender
4 Hand Dominant hand
5 Age Age of the patient
6 Educ Education of the patient
7 SES Socio-economic status of the patient
8 MMSE Mini mental state examination
9 CDR Clinical dementia rating
10 eTIV Estimated total intra-cranial volume
11 nWBV Normalize whole brain volume
12 Visit Visiting of the patient
13 MRDelay Time delay
14 ASF Atlas scale factor of the patient
15 Group Class of the patient
4. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 13, No. 4, August 2023: 4623-4632
4626
2.2. Feature engineering
Feature engineering has a major role in good prediction. In feature engineering, the following tasks
are implemented for the prediction of dementia. Feature engineering has feature creation, feature
transformation, feature selection, and feature extraction. The feature creation process of creating the features
as required for the processing from the raw dataset is called feature creation. It contains two types: data creation
and data imputation.
Data creation, in which data required for processing, can be created from the raw data and is called
data creation. In this dataset, we have 15 data attributes for processing. The process of replacing the missing
value with any other data is called data imputation. There are many types of imputation techniques. In this
dataset, SES and MMSE attributes have some missing values. The mean imputation method has been applied
for generating the missing values.
The process of converting the data from one format to another required format is called feature
transformation. Label encoding has been implemented for converting the text to numeric format. The process
of selecting the required features for the processing is called feature selection. i) correlation-based feature
selection (CFS) subset evaluator and ii) info gain attribute evaluator
CFS subset evaluator is one of the feature selection techniques. CFS means based on the correlation
between the attributes the features are selected along with the best search method. Info gain attributes evaluator
means information gain-based attribute evaluator; the features are selected based on the entropy method along
with the Ranker search method. This is one of the feature selection techniques. The required features that can
be extracted from the given features for the best prediction are called feature extraction. The PCA method has
been applied for the extraction of the features. Using the co-variance principle, the features are extracted in
PCA.
2.3. Machine learning model construction
The machine learning model is the model which processes the data which we can feed and generate
the result by learning the data. The data can be split into training data and testing data. The training data is
given to train the model and for the validation testing data can be given. Various machine learning algorithms
are used for constructing machine learning models.
− Gaussian NB: a type of NB classifier; also called normal distribution. In this classifier, each feature is
continuously distributed.
− SVM: a supervised learning classifier where the classification can be done using the margin maximization
principle.
− LR: a predictor, used for predicting the result using the relationship between the continuous data.
2.4. Validation
Validation is the process of evaluating the machine learning models and finding the error rate using
the dataset. 80-20 data splitting has been done for the training and testing; this type of validating is called as
hold-out validation technique. The result is predicted and the evaluation is done using the confusion matrix and
selecting the best prediction model from the given models.
2.5. Performance improvement of the machine learning model
To improve the performance of the machine learning models the mathematical model has been done
using the cuckoo algorithm. Generally, the performance can be improved by applying any one of the given
methods such as, adding more data or handling the missing values and the outliers or proper use of feature
engineering or parameter turning methods or using any algorithms. Hypothesis testing can be also done for
finding a better solution. Hypothesis testing can be of two types of null hypothesis H0 and alternate hypothesis
HA. H0 shows that there will change in the accuracy and HA shows that there will be alternate accuracy for the
models. The pseudo-code for the proposed model is given below.
Input oasis longitudinal dataset
Feature Creation
DDataset_creation
D1mean_imputationD
Feature Transformation
D2Label_EncodingD1
Feature Extraction
D3PCAD2
Feature Selection (CFS Subset Evaluator & Best First search, Info Gain AttributeEvaluator&
Ranker)
D4Best_AttributesD3
Classification
5. Int J Elec & Comp Eng ISSN: 2088-8708
Prediction of dementia using machine learning model and performance improvement … (Sivakani Rajayyan)
4627
Classification D4
Best_ModelGaussian NB, SVM, LR
Validation
Hold-out cross validationD4
Output Resultdementia_predictionBest_Model
3. RESULTS AND DISCUSSION
In OASIS longitudinal dataset, 373 instances are available among which 146 data records come under
the class demented, 190 data records come under non-demented, and 37 data records come under the converted
class. Figure 2 shows the class distribution in the dataset. Figure 3 represented the missing data in the dataset.
The OASIS dataset is an incomplete dataset. Among the 15 data attributes SES and MMSE attributes have
incomplete data values, in the feature engineering task, the missing data has been generated and converted to
a complete dataset.
Figure 2. Graphical representation for the comparison of
all the models
Figure 3. Graphical representation of the
accuracy for all the models
The main motivation of this paper is to focus on feature engineering and to predict dementia. Also,
the accuracy result is to be improved using the cuckoo algorithm. Feature engineering has been organized as
feature creation followed by feature transformation, feature extraction, and feature selection. The OASIS
dataset has 15 attributes and it has been analyzed that there are some missing values in SES and MMSE
attributes. The mean imputation technique has been applied to generate the missing values. The missing values
are represented in Figure 3. Label encoding has been applied for the SubjectID, MRIID, M/F, Hand, and Group
in the dataset for converting the text to the numeric values in the data transformation task.
Feature extraction and feature selection have been implemented for reducing the dimensionality of
the dataset. The CfsSubsetEval function has been applied along with the Best First search method and this
function selected 12 attributes. Also, the info gain attribute evaluator along with the Ranker search method has
been applied and it also selected the same 12 attributes. For the best prediction, the attributes have a major role.
In the feature selection, the major attributes are selected for prediction. The machine learning model has been
constructed for multi-class classification. Gaussian naive Bayes, SVM, and LR are the classifiers used for the
processing. Table 2 shows the evaluation metrics for the Gaussian NB classifier.
Figure 4 represents the graphical representation of the precision, recall, and F1-score value for the
Gaussian NB. Figure 5 represents the confusion matrix for the Gaussian NB. The classification of the three
classes has been done and the evaluation metrics predicted have been tabulated. The performance has been
analyzed and represented in graphical form. The accuracy for the NB classifier has been 95%. Table 3 shows
the evaluation metrics for the SVM classifier. Figure 6 represents the graphical representation of the precision,
recall, and F1-score values for the SVM. Figure 7 represents the confusion matrix for the SVM. The classification
of the 3 classes has been done and the evaluation metrics predicted have been tabulated. The performance has
been analyzed and represented in graphical form. The accuracy for the SVM classifier has been 89%.
Table 2. Evaluation metrics of Gaussian NB
Sl. No. Class Precision Recall F1-Score
1 Converted 1 0.40 0.57
2 Dementia 0.91 1 0.95
3 Non-Dementia 0.92 1 0.96
Table 3. Evaluation metrics of SVM
Sl. No. Class Precision Recall F1-Score
1 Converted 0.67 0.25 0.36
2 Dementia 0.96 0.93 0.95
3 Non-Dementia 0.86 1 0.93
0
50
100
150
200
Demented Non-demented Converted
Instances
Classes
Distribution of data
No. of Patients
0
5
10
15
20
SES MMSE
Missing
data
Features
Missing data in the dataset
No. of Missing data
6. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 13, No. 4, August 2023: 4623-4632
4628
Figure 4. Graphical representation for the evaluation metrics for
Gaussian NB
Figure 5. Confusion matrix for
Gaussian NB
Figure 6. Graphical representation of evaluation metrics for SVM Figure 7. Confusion matrix for SVM
Figure 8 represents the graphical representation of the precision, recall, and F1-score values for the
LR. Figure 9 represents the confusion matrix for the LR. The classification of the 3 classes has been done and
the evaluation metrics predicted have been tabulated. The performance has been analyzed and represented in
graphical form. The accuracy for the LR classifier has been 63%. Table 4 shows the evaluation metrics for the
LR classifier. Table 5 shows the comparison of all the models and Figure 10 shows the graphical representation
of the comparison chart. Table 6 shows the comparison of the accuracy of all the classifiers. Table 6 shows the
comparison of the accuracy of all the models, and Figure 11, shows the graphical representation of the accuracy
comparison chart. The Gaussian NB, SVM, and LR machine learning models have been used for the prediction
of dementia. The validation has been done using the hold-out cross-validation technique. By comparing the
evaluation metrics and the accuracy it is identified that the Gaussian NB classifier is the best model among
these models.
Figure 8. Graphical representation for the evaluation metrics for LR Figure 9. Confusion matrix for LR
0
0.2
0.4
0.6
0.8
1
1.2
Precision Recall F1-Score
Predicted
values
Evaluation Metrics
Metrics of Gaussian NB
Converted
Dementia
Non-
Dementia
0
0.2
0.4
0.6
0.8
1
1.2
Precision Recall F1-Score
Predicted
Values
Evaluation Metrics
Metrics of SVM
Converted
Dementia
Non-Dementia
0
0.2
0.4
0.6
0.8
1
Precision Recall F1-Score
Predicted
Values
Evaluation Metrics
Metrics of LR
Converted
Dementia
Non-Dementia
7. Int J Elec & Comp Eng ISSN: 2088-8708
Prediction of dementia using machine learning model and performance improvement … (Sivakani Rajayyan)
4629
Table 4. Evaluation metrics of LR
Sl. No. Class Precision Recall F1-Score
1 Converted 0.00 0.00 0.00
2 Dementia 0.88 0.89 0.87
3 Non-Dementia 0.62 0.82 0.70
Table 5. Comparison of the classifiers
Sl. No. Class Precision Recall F1-Score
1 Gaussian NB 0.93 0.92 0.90
2 SVM 0.64 0.55 0.59
3 LR 0.56 0.59 0.75
Table 6. Comparison of the accuracy for the classifiers
Sl. No. Classifiers Accuracy %
1 Gaussian NB 95
2 SVM 89
3 LR 63
Figure 10. Graphical representation for the comparison
of all the models
Figure 11. Graphical representation of the
accuracy for all the models
3.1. Performance improvement
The performance improvement has been done using the cuckoo algorithm. The LR model has an
accuracy of 63%; it is very less when compared to the other two models, so the performance of this model is
to be improved. This is done using the mathematical model, along with this cuckoo algorithm is implemented
to show better performance.
𝑌 = 𝛽0 + 𝛽1𝑥1 + … + 𝛽𝑛𝑥𝑛 (1)
The LR equation is represented by (1), where Y is the predicted output, β0, β1, …, βn are
the weighted input and are considered as the co-efficient of the x terms and x, x2, …, xn are the inputs.
The parameter which affects the performance of the machine learning classifiers is considered as the noise
and specified as given in (2),
𝑌𝑛 = 𝑓𝑋𝑛 + 𝑁 (2)
where fXn is the function of input and N is the noise. In this, the parameters considered as noise are missing
values MV and feature encoding FE (3). Using mean imputation and feature transformation both problems are
solved here (4).
𝑌𝑛 = 𝑓(𝑋𝑛) + 𝑀𝑉 + 𝐹𝐸 (3)
𝑌𝑛 = 𝑓(𝑋𝑛) + 𝑖𝑚𝑝𝑢𝑡𝑎𝑡𝑖𝑜𝑛 + 𝐹𝑒𝑎𝑡𝑢𝑟𝑒𝑇𝑟𝑎𝑛𝑠𝑓𝑜𝑟𝑚𝑎𝑡𝑖𝑜𝑛 (4)
Also, the cuckoo algorithm is used to remove the noise. The concept behind this algorithm is the
cuckoo bird lays the egg by choosing the other bird’s nest. Sometimes the other birds identify the egg, and they
will drop the egg so that the count of the cuckoo in the next generation will be reduced. If it does not identify,
the egg will be hatched and the cuckoo in the next generation will be increased. In this case, there is a chance
of an increase or decrease in the cuckoo in the next generation. The same concept is implemented here to reduce
the noise. As the birds drop the egg the count of the cuckoo birds will be decreased similarly the parameters
which affect the performance of the machine learning models will be dropped and better performance will be
given by the models. Figure 12 represents the steps involved in the performance improvement in the machine
0
0.5
1
Precision Recall F1-Score
Predicted
values
Evaluation Metrics
Comparison of the Models
Gaussian
NB
SVM
LR
0
20
40
60
80
100
Gaussian
NB
SVM LR
Predicted
Values
Classifiers
Comparison of Accuracy
Accuracy
8. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 13, No. 4, August 2023: 4623-4632
4630
learning model. The performance improvement has been done in three steps; they are the machine learning
model, the cuckoo algorithm implementation, and the performance improvement. Using the concept of the
cuckoo algorithm the performance has been increased for the machine learning models by eliminating the
parameters which affect the performance of the models.
Figure 12. Steps in the performance improvement
4. CONCLUSION
Dementia is a brain disorder disease. This paper focused on feature engineering and the prediction of
dementia disease using the machine learning models Gaussian NB, SVM, and LR. The feature engineering
focus tasks are feature creation, feature transformation, feature selection, and feature extraction. In feature
creation, data creation and data imputation have been done; mean imputation has been applied for generating
the missing values. Label encoding has been done for feature transformation. CFS Subset Evaluator along with
the best first search method and the info gain attribute evaluator along with the ranker method has been applied
for selecting the best attributes. Initially, there are 15 features in the oasis dataset and 12 features are selected
as the best attributes for the prediction. The evaluation parameters considered for validating the models are
precision, recall, F1-score, and accuracy; for all the metrics the Gaussian NB was given the highest values as
given 95%, 97%, 95%, and 95%. So, from these metrics, it is concluded that the Gaussian NB is the best
classifier. Hold-out cross-validation has been done for validating the models. The accuracy of the algorithms
has been improved by eliminating the parameters which affect the performance of the machine learning models
using the cuckoo algorithm. In the future, the plan is to predict dementia using other machine learning models.
REFERENCES
[1] T. Khatibi and N. Rabinezhadsadatmahaleh, “Proposing feature engineering method based on deep learning and K-NNs for ECG
beat classification and arrhythmia detection,” Physical and Engineering Sciences in Medicine, vol. 43, no. 1, pp. 49–68, Mar. 2020,
doi: 10.1007/s13246-019-00814-w.
[2] M. Aamir and S. M. A. Zaidi, “DDoS attack detection with feature engineering and machine learning: the framework and
performance evaluation,” International Journal of Information Security, vol. 18, no. 6, pp. 761–785, Dec. 2019, doi:
10.1007/s10207-019-00434-1.
[3] J. Dhar, “Multistage ensemble learning model with weighted voting and genetic algorithm optimization strategy for detecting
chronic obstructive pulmonary disease,” IEEE Access, vol. 9, pp. 48640–48657, 2021, doi: 10.1109/ACCESS.2021.3067949.
[4] P. G. Shynu, V. G. Menon, R. L. Kumar, S. Kadry, and Y. Nam, “Blockchain-based secure healthcare application for diabetic-
cardio disease prediction in fog computing,” IEEE Access, vol. 9, pp. 45706–45720, 2021, doi: 10.1109/ACCESS.2021.3065440.
[5] D. Chicco and G. Jurman, “An ensemble learning approach for enhanced classification of patients with hepatitis and cirrhosis,”
IEEE Access, vol. 9, pp. 24485–24498, 2021, doi: 10.1109/ACCESS.2021.3057196.
[6] A. U. Haq et al., “Detection of breast cancer through clinical data using supervised and unsupervised feature selection techniques,”
IEEE Access, vol. 9, pp. 22090–22105, 2021, doi: 10.1109/ACCESS.2021.3055806.
[7] V. K. Gupta, A. Gupta, D. Kumar, and A. Sardana, “Prediction of COVID-19 confirmed, death, and cured cases in India using
random forest model,” Big Data Mining and Analytics, vol. 4, no. 2, pp. 116–123, Jun. 2021, doi: 10.26599/BDMA.2020.9020016.
[8] A. H. Syed, T. Khan, A. Hassan, N. A. Alromema, M. Binsawad, and A. O. Alsayed, “An ensemble-learning based application to
predict the earlier stages of Alzheimer’s disease (AD),” IEEE Access, vol. 8, pp. 222126–222143, 2020, doi:
10.1109/ACCESS.2020.3043715.
[9] C.-H. Lin, S.-I. Chiu, T.-F. Chen, J.-S. R. Jang, and M.-J. Chiu, “Classifications of neurodegenerative disorders using a multiplex
blood biomarkers-based machine learning model,” International Journal of Molecular Sciences, vol. 21, no. 18, Sep. 2020, doi:
10.3390/ijms21186914.
9. Int J Elec & Comp Eng ISSN: 2088-8708
Prediction of dementia using machine learning model and performance improvement … (Sivakani Rajayyan)
4631
[10] J. Zhou and O. G. Troyanskaya, “Predicting effects of noncoding variants with deep learning–based sequence model,” Nature
Methods, vol. 12, no. 10, pp. 931–934, Oct. 2015, doi: 10.1038/nmeth.3547.
[11] N. Takahashi, N. Goswami, and Y. Mitsufuji, “Mmdenselstm: An efficient combination of convolutional and recurrent neural
networks for audio source separation,” in 2018 16th International Workshop on Acoustic Signal Enhancement (IWAENC), Sep.
2018, pp. 106–110. doi: 10.1109/IWAENC.2018.8521383.
[12] S. Shataee, S. Kalbi, A. Fallah, and D. Pelz, “Forest attribute imputation using machine-learning methods and ASTER data:
comparison of kNN, SVR and random forest regression algorithms,” International Journal of Remote Sensing, vol. 33, no. 19,
pp. 6254–6280, Oct. 2012, doi: 10.1080/01431161.2012.682661.
[13] R. Sivakani and G. A. Ansari, “Machine learning framework for implementing Alzheimer’s disease,” in 2020 International
Conference on Communication and Signal Processing (ICCSP), Jul. 2020, pp. 0588–0592. doi:
10.1109/ICCSP48568.2020.9182220.
[14] S. Rowson and S. M. Duma, “Brain injury prediction: Assessing the combined probability of concussion using linear and rotational
head acceleration,” Annals of Biomedical Engineering, vol. 41, no. 5, pp. 873–882, May 2013, doi: 10.1007/s10439-012-0731-0.
[15] E. H. Houssein, M. E. Hosney, M. Elhoseny, D. Oliva, W. M. Mohamed, and M. Hassaballah, “Hybrid Harris hawks optimization
with cuckoo search for drug design and discovery in chemoinformatics,” Scientific Reports, vol. 10, no. 1, Sep. 2020, doi:
10.1038/s41598-020-71502-z.
[16] L. Liu, X. Liu, N. Wang, and P. Zou, “Modified cuckoo search algorithm with variational parameters and logistic map,” Algorithms,
vol. 11, no. 3, Mar. 2018, doi: 10.3390/a11030030.
[17] M. Mareli and B. Twala, “An adaptive cuckoo search algorithm for optimisation,” Applied Computing and Informatics, vol. 14,
no. 2, pp. 107–115, Jul. 2018, doi: 10.1016/j.aci.2017.09.001.
[18] A. S. Joshi, O. Kulkarni, G. M. Kakandikar, and V. M. Nandedkar, “Cuckoo search optimization- a review,” Materials Today:
Proceedings, vol. 4, no. 8, pp. 7262–7269, 2017, doi: 10.1016/j.matpr.2017.07.055.
[19] M. Phogat and D. Kumar, “Classification of complex diseases using an improved binary cuckoo search and conditional mutual
information maximization,” Computación y Sistemas, vol. 24, no. 3, Sep. 2020, doi: 10.13053/cys-24-3-3354.
[20] S. Murugan, G. Babu T R, and S. C, “Underwater object recognition using KNN classifier,” International Journal of MC Square
Scientific Research, vol. 9, no. 3, Dec. 2017, doi: 10.20894/IJMSR.117.009.003.007.
[21] M. M. Ismail, M. Subbiah, and S. Chelliah, “Design of pipelined radix-2, 4 and 8 based multipath delay commutator (MDC) FFt,”
Indian Journal of Public Health Research & Development, vol. 9, no. 3, 2018, doi: 10.5958/0976-5506.2018.00380.7.
[22] A. Unnikrishnan and V. Das, “Cooperative routing for improving the lifetime of wireless ad-hoc networks,” International Journal
of Advances in Signal and Image Sciences, vol. 8, no. 1, pp. 17–24, Jan. 2022, doi: 10.29284/IJASIS.8.1.2022.17-24.
[23] T. Bock, “What is feature engineering?,” Displayr. https //www.displayr.com/what-is-feature-engineering (accessed January 18,
2022)
[24] Y. Xiong and Y. Lu, “Deep feature extraction from the vocal vectors using sparse autoencoders for Parkinson’s classification,”
IEEE Access, vol. 8, pp. 27821–27830, 2020, doi: 10.1109/ACCESS.2020.2968177.
[25] D. C. Yadav and S. Pal, “Prediction of heart disease using feature selection and Random forest ensemble method,” International
Journal of Pharmaceutical Research, vol. 12, no. 04, pp. 56–66, Jun. 2020, doi: 10.31838/ijpr/2020.12.04.013.
[26] H. A. Helaly, M. Badawy, and A. Y. Haikal, “Deep learning approach for early detection of Alzheimer’s disease,” Cognitive
Computation, vol. 14, no. 5, pp. 1711–1727, Sep. 2022, doi: 10.1007/s12559-021-09946-2.
[27] M. Akhtaruzzaman, M. K. Hasan, S. R. Kabir, S. N. H. S. Abdullah, M. J. Sadeq, and E. Hossain, “HSIC bottleneck based distributed
deep learning model for load forecasting in smart grid with a comprehensive survey,” IEEE Access, vol. 8, pp. 222977–223008,
2020, doi: 10.1109/ACCESS.2020.3040083.
[28] R. Mazloumi, S. R. Abazari, F. Nafarieh, A. Aghsami, and F. Jolai, “Statistical analysis of blood characteristics of COVID-19
patients and their survival or death prediction using machine learning algorithms,” Neural Computing and Applications, vol. 34,
no. 17, pp. 14729–14743, Sep. 2022, doi: 10.1007/s00521-022-07325-y.
[29] K. Kalegowda, A. D. Iyengar Srinivasan, and N. Chinnamadha, “Particle swarm optimization and Taguchi algorithm-based power
system stabilizer-effect of light loading condition,” International Journal of Electrical and Computer Engineering (IJECE),
vol. 12, no. 5, pp. 4672–4679, Oct. 2022, doi: 10.11591/ijece.v12i5.pp4672-4679.
[30] B. S. Rameshappa and N. M. Shadaksharappa, “An optimal artificial neural network controller for load frequency control of a four-
area interconnected power system,” International Journal of Electrical and Computer Engineering (IJECE), vol. 12, no. 5,
pp. 4700–4711, Oct. 2022, doi: 10.11591/ijece.v12i5.pp4700-4711.
[31] G. Castellazzi et al., “A machine learning approach for the differential diagnosis of Alzheimer and vascular dementia Fed by MRI
selected features,” Frontiers in Neuroinformatics, vol. 14, no. 25, Jun. 2020, doi: 10.3389/fninf.2020.00025.
[32] H. Wang et al., “Develop a diagnostic tool for dementia using machine learning and non-imaging features,” Frontiers in Aging
Neuroscience, vol. 14, Aug. 2022, doi: 10.3389/fnagi.2022.945274.
[33] K. Sivanantham, “Deep learning-based convolutional neural network with cuckoo search optimization for MRI brain tumour
segmentation,” in Computational Intelligence Techniques for Green Smart Cities, 2022, pp. 149–168. doi: 10.1007/978-3-030-
96429-0_7.
[34] M. Sudharsan and G. Thailambal, “A hybrid learning approach for early-stage prediction and classification of Alzheimer’s disease
using multi-features,” in 2022 6th International Conference on Trends in Electronics and Informatics (ICOEI), Apr. 2022,
pp. 1373–1380. doi: 10.1109/ICOEI53556.2022.9776843.
[35] C. Kavitha, V. Mani, S. R. Srividhya, O. I. Khalaf, and C. A. Tavera Romero, “Early-stage Alzheimer’s disease prediction using
machine learning models,” Frontiers in Public Health, vol. 10, Mar. 2022, doi: 10.3389/fpubh.2022.853294.
[36] J. Sheng, Y. Xin, Q. Zhang, L. Wang, Z. Yang, and J. Yin, “Predictive classification of Alzheimer’s disease using brain imaging
and genetic data,” Scientific Reports, vol. 12, no. 1, Feb. 2022, doi: 10.1038/s41598-022-06444-9.
[37] K. Balasubramanian, N. Ananthamoorthy, and K. Ramya, “Prediction of neuro-degenerative disorders using sunflower optimisation
algorithm and Kernel extreme learning machine: A case-study with Parkinson’s and Alzheimer’s disease,” Proceedings of the
Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, vol. 236, no. 3, pp. 438–453, Mar. 2022, doi:
10.1177/09544119211060989.
[38] F. Zhang, M. Petersen, L. Johnson, J. Hall, and S. E. O’Bryant, “Accelerating hyperparameter tuning in machine learning for
Alzheimer’s disease with high performance computing,” Frontiers in Artificial Intelligence, vol. 4, Dec. 2021, doi:
10.3389/frai.2021.798962.
[39] Q. Pan, K. Ding, and D. Chen, “Multi-classification prediction of Alzheimer’s disease based on fusing multi-modal features,” in
2021 IEEE International Conference on Data Mining (ICDM), Dec. 2021, pp. 1270–1275. doi: 10.1109/ICDM51629.2021.00156.
10. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 13, No. 4, August 2023: 4623-4632
4632
BIOGRAPHIES OF AUTHORS
Sivakani Rajayyan received her B.Tech. degree from Anna university in
Information Technology. She also received her M.E. degree from Sathyabama University in
computer science and engineering. She is currently pursuing her Ph.D. degree in B.S. Abdur
Rahman Crescent Institute of Science and Technology, Chennai, Tamilnadu, India. She can be
contacted at sivakani13@gmail.com.
Syed Masood Mohamed Mustafa is an associate professor at B.S. Abdur
Rahman Crescent Institute of Science and Technology, Chennai, Tamilnadu, India. He
received his M.E., from Anna University in Computer Science and Engineering. He received
his Ph.D. degree from B.S. Abdur Rahman Crescent Institute of Science. He has more than
25 years of experience in teaching. He has contributed more than 25 research articles in
reputed journals and conferences. His research areas are computer networks, software
engineering, information system, management information system, decision support system,
cyber security, artificial intelligence, machine learning, data mining, and data security. He
can be contacted at ms.masood@crescent.education.