As the cause of the breast cancer disease has not yet clearly identified and a method to prevent its occurrence has not yet been developed, its early detection has a significant role in enhancing survival rate. In fact, artificial intelligent approaches have been playing an important role to enhance the diagnosis process of breast cancer. This work has selected eight classification models that are mostly used to predict breast cancer to be under investigation. These classifiers include single and ensemble classifiers. A trusted dataset has been enhanced by applying five different feature selection methods to pick up only weighted features and to neglect others. Accordingly, a dataset of only 17 features has been developed. Based on our experimental work, three classifiers, multi-layer perceptron (MLP), support vector machine (SVM) and stack are competing with each other by attaining high classification accuracy compared to others. However, SVM is ranked on the top by obtaining an accuracy of 97.7% with classification errors of 0.029 false negative (FN) and 0.019 false positive (FP). Therefore, it is noteworthy to mention that SVM is the best classifier and it outperforms even the stack classier.
Breast Tumor Detection Using Efficient Machine Learning and Deep Learning Tec...mlaij
Breast cancer tissues grow when cells in the breast expand and divide uncontrollably, resulting in a lump of tissue commonly called and named tumor. Breast cancer is the second most prevalent cancer among women, following skin cancer. While it is more commonly diagnosed in women aged 50 and above, it can affect individuals of any age. Although it is rare, men can also develop breast cancer, accounting for less than 1% of all cases, with approximately 2,600 cases reported annually in the United States. Early detection of breast tumors is crucial in reducing the risk of developing breast cancer. A publicly available dataset containing features of breast tumors was utilized to identify breast tumors using machine learning and deep learning techniques. Various prediction models were constructed, including logistic regression (LR), decision tree (DT), random forest (RF), support vector machine (SVM), Gradient Boosting (GB), Extreme Gradient Boosting (XGB), Light GBM, and a recurrent neural network (RNN) model. These models were trained to classify and predict breast tumor cases based on the provided features.
BREAST TUMOR DETECTION USING EFFICIENT MACHINE LEARNING AND DEEP LEARNING TEC...mlaij
Breast cancer tissues grow when cells in the breast expand and divide uncontrollably, resulting in a lump
of tissue commonly called and named tumor. Breast cancer is the second most prevalent cancer among
women, following skin cancer. While it is more commonly diagnosed in women aged 50 and above, it can
affect individuals of any age. Although it is rare, men can also develop breast cancer, accounting for less
than 1% of all cases, with approximately 2,600 cases reported annually in the United States. Early
detection of breast tumors is crucial in reducing the risk of developing breast cancer. A publicly available
dataset containing features of breast tumors was utilized to identify breast tumors using machine learning
and deep learning techniques. Various prediction models were constructed, including logistic regression
(LR), decision tree (DT), random forest (RF), support vector machine (SVM), Gradient Boosting (GB),
Extreme Gradient Boosting (XGB), Light GBM, and a recurrent neural network (RNN) model. These
models were trained to classify and predict breast tumor cases based on the provided features.
Breast Tumor Detection Using Efficient Machine Learning and Deep Learning Tec...mlaij
Machine Learning and Applications: An International Journal (MLAIJ) is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the machine learning. The journal is devoted to the publication of high quality papers on theoretical and practical aspects of machine learning and applications.The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on machine learning advancements, and establishing new collaborations in these areas. Original research papers, state-of-the-art reviews are invited for publication in all areas of machine learning.
Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of machine learning.
Breast cancer diagnosis: a survey of pre-processing, segmentation, feature e...IJECEIAES
Machine learning methods have been an interesting method in the field of medical for many years, and they have achieved successful results in various fields of medical science. This paper examines the effects of using machine learning algorithms in the diagnosis and classification of breast cancer from mammography imaging data. Cancer diagnosis is the identification of images as cancer or non-cancer, and this involves image preprocessing, feature extraction, classification, and performance analysis. This article studied 93 different references mentioned in the previous years in the field of processing and tries to find an effective way to diagnose and classify breast cancer. Based on the results of this research, it can be concluded that most of today’s successful methods focus on the use of deep learning methods. Finding a new method requires an overview of existing methods in the field of deep learning methods in order to make a comparison and case study.
Breast Tumor Detection Using Efficient Machine Learning and Deep Learning Tec...mlaij
Breast cancer tissues grow when cells in the breast expand and divide uncontrollably, resulting in a lump of tissue commonly called and named tumor. Breast cancer is the second most prevalent cancer among women, following skin cancer. While it is more commonly diagnosed in women aged 50 and above, it can affect individuals of any age. Although it is rare, men can also develop breast cancer, accounting for less than 1% of all cases, with approximately 2,600 cases reported annually in the United States. Early detection of breast tumors is crucial in reducing the risk of developing breast cancer. A publicly available dataset containing features of breast tumors was utilized to identify breast tumors using machine learning and deep learning techniques. Various prediction models were constructed, including logistic regression (LR), decision tree (DT), random forest (RF), support vector machine (SVM), Gradient Boosting (GB), Extreme Gradient Boosting (XGB), Light GBM, and a recurrent neural network (RNN) model. These models were trained to classify and predict breast tumor cases based on the provided features.
BREAST TUMOR DETECTION USING EFFICIENT MACHINE LEARNING AND DEEP LEARNING TEC...mlaij
Breast cancer tissues grow when cells in the breast expand and divide uncontrollably, resulting in a lump
of tissue commonly called and named tumor. Breast cancer is the second most prevalent cancer among
women, following skin cancer. While it is more commonly diagnosed in women aged 50 and above, it can
affect individuals of any age. Although it is rare, men can also develop breast cancer, accounting for less
than 1% of all cases, with approximately 2,600 cases reported annually in the United States. Early
detection of breast tumors is crucial in reducing the risk of developing breast cancer. A publicly available
dataset containing features of breast tumors was utilized to identify breast tumors using machine learning
and deep learning techniques. Various prediction models were constructed, including logistic regression
(LR), decision tree (DT), random forest (RF), support vector machine (SVM), Gradient Boosting (GB),
Extreme Gradient Boosting (XGB), Light GBM, and a recurrent neural network (RNN) model. These
models were trained to classify and predict breast tumor cases based on the provided features.
Breast Tumor Detection Using Efficient Machine Learning and Deep Learning Tec...mlaij
Machine Learning and Applications: An International Journal (MLAIJ) is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the machine learning. The journal is devoted to the publication of high quality papers on theoretical and practical aspects of machine learning and applications.The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on machine learning advancements, and establishing new collaborations in these areas. Original research papers, state-of-the-art reviews are invited for publication in all areas of machine learning.
Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of machine learning.
Breast cancer diagnosis: a survey of pre-processing, segmentation, feature e...IJECEIAES
Machine learning methods have been an interesting method in the field of medical for many years, and they have achieved successful results in various fields of medical science. This paper examines the effects of using machine learning algorithms in the diagnosis and classification of breast cancer from mammography imaging data. Cancer diagnosis is the identification of images as cancer or non-cancer, and this involves image preprocessing, feature extraction, classification, and performance analysis. This article studied 93 different references mentioned in the previous years in the field of processing and tries to find an effective way to diagnose and classify breast cancer. Based on the results of this research, it can be concluded that most of today’s successful methods focus on the use of deep learning methods. Finding a new method requires an overview of existing methods in the field of deep learning methods in order to make a comparison and case study.
Predictive modeling for breast cancer based on machine learning algorithms an...IJECEIAES
Breast cancer is one of the leading causes of death among women worldwide. However, early prediction of breast cancer plays a crucial role. Therefore, strong needs exist for automatic accurate early prediction of breast cancer. In this paper, machine learning (ML) classifiers combined with features selection methods are used to build an intelligent tool for breast cancer prediction. The Wisconsin diagnostic breast cancer (WDBC) dataset is used to train and test the model. Classification algorithms, including support vector machine (SVM), light gradient boosting machine (LightGBM), random forest (RF), logistic regression (LR), k-nearest neighbors (k-NN), and naïve Bayes, were employed. Performance measures for each of them were obtained, namely: accuracy, precision, recall, F-score, Kappa, Matthews correlation coefficient (MCC), and time. The results indicate that without feature selection, LightGBM achieves the highest accuracy at 95%. With minimum redundancy maximum relevance (mRMR) feature selection (15 features), LightGBM outperforms other classifiers, achieving an accuracy of 98%. For Pearson correlation coefficient feature selection (15 features), LightGBM also excels with a 95% accuracy rate. Lasso feature selection (5 features) produces varied results across classifiers, with logistic regression achieving the highest accuracy at 96%. These findings underscore the importance of feature selection in refining model performance and in improving detection for breast cancer.
Classification AlgorithmBased Analysis of Breast Cancer DataIIRindia
The classification algorithms are very frequently used algorithms for analyzing various kinds of data available in different repositories which have real world applications. The main objective of this research work is to find the performance of classification algorithms in analyzing Breast Cancer data via analyzing the mammogram images based its characteristics.Different attribute values of cancer affected mammogram images are considered for analysis in this work. The Patients food habits, age of the patients, their life styles, occupation, their problem about the diseases and other information are taken into account for classification. Finally, performance of classification algorithms J48, CART and ADTree are given with its accuracy. The accuracy of taken algorithms is measured by various measures like specificity, sensitivity and kappa statistics (Errors).
Image processing and machine learning techniques used in computer-aided dete...IJECEIAES
This paper aims to review the previously developed Computer-aided detection (CAD) systems for mammogram screening because increasing death rate in women due to breast cancer is a global medical issue and it can be controlled only by early detection with regular screening. Till now mammography is the widely used breast imaging modality. CAD systems have been adopted by the radiologists to increase the accuracy of the breast cancer diagnosis by avoiding human errors and experience related issues. This study reveals that in spite of the higher accuracy obtained by the earlier proposed CAD systems for breast cancer diagnosis, they are not fully automated. Moreover, the false-positive mammogram screening cases are high in number and over-diagnosis of breast cancer exposes a patient towards harmful overtreatment for which a huge amount of money is being wasted. In addition, it is also reported that the mammogram screening result with and without CAD systems does not have noticeable difference, whereas the undetected cancer cases by CAD system are increasing. Thus, future research is required to improve the performance of CAD system for mammogram screening and make it completely automated.
SVM &GA-CLUSTERING BASED FEATURE SELECTION APPROACH FOR BREAST CANCER DETECTIONijscai
Mortality leading among women in developed countries is breast cancer. Breast cancer is women's second
most prominent cause of cancer mortality worldwide. In recent decades, women's high prevalence of breast
cancer has risen dramatically. This paper discussed several data analysis methods used to detect breast
cancer early. Breast cancer diagnosis distinguishes benign and malignant breast lumps. Using data
processing tools, we tackled this disease analysis. Data mining is an important step of library discovery
where intelligent methods are used to detect patterns. Several clinical breast cancer studies were
conducted using soft computing and machine learning techniques. Sometimes their algorithms are easier,
easier, or more comprehensive than others. This research is focused on genetic programming and machine
learning algorithms to reliably identify benign and malignant breast cancer. This study aimed to optimise
the testing algorithm. We used genetic programming methods to choose classification machines' best
features and parameter values. Data mining is an important step of library discovery where intelligent
methods are used to detect patterns. We are analysing data accessible from the U.C.I. deep-learning data
set in Wisconsin. In this experiment, we equate four Weka clustering strategies with genetic clustering. A
comparison of results reveals that sequential minimal optimization (S.M.O.) is better than I.B.K. and B.F.
Tree processes, i.e. 97.71%.
SVM &GA-CLUSTERING BASED FEATURE SELECTION APPROACH FOR BREAST CANCER DETECTIONijscai
Mortality leading among women in developed countries is breast cancer. Breast cancer is women's second most prominent cause of cancer mortality worldwide. In recent decades, women's high prevalence of breast cancer has risen dramatically. This paper discussed several data analysis methods used to detect breast
cancer early. Breast cancer diagnosis distinguishes benign and malignant breast lumps. Using data processing tools, we tackled this disease analysis. Data mining is an important step of library discovery where intelligent methods are used to detect patterns. Several clinical breast cancer studies were conducted using soft computing and machine learning techniques. Sometimes their algorithms are easier, easier, or more comprehensive than others. This research is focused on genetic programming and machine learning algorithms to reliably identify benign and malignant breast cancer. This study aimed to optimise
the testing algorithm. We used genetic programming methods to choose classification machines' best features and parameter values. Data mining is an important step of library discovery where intelligent methods are used to detect patterns. We are analysing data accessible from the U.C.I. deep-learning data
set in Wisconsin. In this experiment, we equate four Weka clustering strategies with genetic clustering. A comparison of results reveals that sequential minimal optimization (S.M.O.) is better than I.B.K. and B.F. Tree processes, i.e. 97.71%
SVM &GA-CLUSTERING BASED FEATURE SELECTION APPROACH FOR BREAST CANCER DETECTIONijscai
Mortality leading among women in developed countries is breast cancer. Breast cancer is women's second most prominent cause of cancer mortality worldwide. In recent decades, women's high prevalence of breast cancer has risen dramatically. This paper discussed several data analysis methods used to detect breast cancer early. Breast cancer diagnosis distinguishes benign and malignant breast lumps. Using data processing tools, we tackled this disease analysis. Data mining is an important step of library discovery where intelligent methods are used to detect patterns. Several clinical breast cancer studies were conducted using soft computing and machine learning techniques. Sometimes their algorithms are easier, easier, or more comprehensive than others. This research is focused on genetic programming and machine learning algorithms to reliably identify benign and malignant breast cancer. This study aimed to optimise the testing algorithm. We used genetic programming methods to choose classification machines' best features and parameter values. Data mining is an important step of library discovery where intelligent methods are used to detect patterns. We are analysing data accessible from the U.C.I. deep-learning data set in Wisconsin. In this experiment, we equate four Weka clustering strategies with genetic clustering. A comparison of results reveals that sequential minimal optimization (S.M.O.) is better than I.B.K. and B.F. Tree processes, i.e. 97.71%.
An approach of cervical cancer diagnosis using class weighting and oversampli...TELKOMNIKA JOURNAL
Globally, cervical cancer caused 604,127 new cases and 341,831 deaths in 2020, according to the global cancer observatory. In addition, the number of cervical cancer patients who have no symptoms has grown recently. Therefore, giving patients early notice of the possibility of cervical cancer is a useful task since it would enable them to have a clear understanding of their health state. The use of artificial intelligence (AI), particularly in machine learning, in this work is continually uncovering cervical cancer. With the help of a logit model and a new deep learning technique, we hope to identify cervical cancer using patient-provided data. For better outcomes, we employ Keras deep learning and its technique, which includes class weighting and oversampling. In comparison to the actual diagnostic result, the experimental result with model accuracy is 94.18%, and it also demonstrates a successful logit model cervical cancer prediction.
SVM &GA-CLUSTERING BASED FEATURE SELECTION APPROACH FOR BREAST CANCER DETECTIONijscai
Mortality leading among women in developed countries is breast cancer. Breast cancer is women's second most prominent cause of cancer mortality worldwide. In recent decades, women's high prevalence of breast cancer has risen dramatically. This paper discussed several data analysis methods used to detect breast cancer early. Breast cancer diagnosis distinguishes benign and malignant breast lumps. Using data processing tools, we tackled this disease analysis. Data mining is an important step of library discovery where intelligent methods are used to detect patterns. Several clinical breast cancer studies were conducted using soft computing and machine learning techniques. Sometimes their algorithms are easier, easier, or more comprehensive than others. This research is focused on genetic programming and machine
learning algorithms to reliably identify benign and malignant breast cancer. This study aimed to optimise the testing algorithm. We used genetic programming methods to choose classification machines' best features and parameter values. Data mining is an important step of library discovery where intelligent methods are used to detect patterns. We are analysing data accessible from the U.C.I. deep-learning data
set in Wisconsin. In this experiment, we equate four Weka clustering strategies with genetic clustering. A comparison of results reveals that sequential minimal optimization (S.M.O.) is better than I.B.K. and B.F. Tree processes, i.e. 97.71%.
A Review on Data Mining Techniques for Prediction of Breast Cancer RecurrenceDr. Amarjeet Singh
The most common type of cancer in women
worldwide is the Breast Cancer. Breast cancer may be
detected early using Mammograms, probably before it's
spread. Recurrent breast cancer could occur months or years
after initial treatment. The cancer could return within the
same place because the original cancer (local recurrence), or it
may spread to different areas of your body (distant
recurrence). Early stage treatment is done not only to cure
breast cancer however additionally facilitate in preventing its
repetition/recurrence. Data mining algorithms provide
assistance in predicting the early-stage breast cancer that
continually has been difficult analysis drawback. The
projected analysis can establish the most effective algorithm
that predicts the recurrence of the breast cancer and improve
the accuracy the algorithms. Large information like Clump,
Classification, Association Rules, Prediction and Neural
Networks, Decision Trees can be analyzed using data mining
applications and techniques.
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.
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Breast cancer is one of the leading causes of death among women worldwide. However, early prediction of breast cancer plays a crucial role. Therefore, strong needs exist for automatic accurate early prediction of breast cancer. In this paper, machine learning (ML) classifiers combined with features selection methods are used to build an intelligent tool for breast cancer prediction. The Wisconsin diagnostic breast cancer (WDBC) dataset is used to train and test the model. Classification algorithms, including support vector machine (SVM), light gradient boosting machine (LightGBM), random forest (RF), logistic regression (LR), k-nearest neighbors (k-NN), and naïve Bayes, were employed. Performance measures for each of them were obtained, namely: accuracy, precision, recall, F-score, Kappa, Matthews correlation coefficient (MCC), and time. The results indicate that without feature selection, LightGBM achieves the highest accuracy at 95%. With minimum redundancy maximum relevance (mRMR) feature selection (15 features), LightGBM outperforms other classifiers, achieving an accuracy of 98%. For Pearson correlation coefficient feature selection (15 features), LightGBM also excels with a 95% accuracy rate. Lasso feature selection (5 features) produces varied results across classifiers, with logistic regression achieving the highest accuracy at 96%. These findings underscore the importance of feature selection in refining model performance and in improving detection for breast cancer.
Classification AlgorithmBased Analysis of Breast Cancer DataIIRindia
The classification algorithms are very frequently used algorithms for analyzing various kinds of data available in different repositories which have real world applications. The main objective of this research work is to find the performance of classification algorithms in analyzing Breast Cancer data via analyzing the mammogram images based its characteristics.Different attribute values of cancer affected mammogram images are considered for analysis in this work. The Patients food habits, age of the patients, their life styles, occupation, their problem about the diseases and other information are taken into account for classification. Finally, performance of classification algorithms J48, CART and ADTree are given with its accuracy. The accuracy of taken algorithms is measured by various measures like specificity, sensitivity and kappa statistics (Errors).
Image processing and machine learning techniques used in computer-aided dete...IJECEIAES
This paper aims to review the previously developed Computer-aided detection (CAD) systems for mammogram screening because increasing death rate in women due to breast cancer is a global medical issue and it can be controlled only by early detection with regular screening. Till now mammography is the widely used breast imaging modality. CAD systems have been adopted by the radiologists to increase the accuracy of the breast cancer diagnosis by avoiding human errors and experience related issues. This study reveals that in spite of the higher accuracy obtained by the earlier proposed CAD systems for breast cancer diagnosis, they are not fully automated. Moreover, the false-positive mammogram screening cases are high in number and over-diagnosis of breast cancer exposes a patient towards harmful overtreatment for which a huge amount of money is being wasted. In addition, it is also reported that the mammogram screening result with and without CAD systems does not have noticeable difference, whereas the undetected cancer cases by CAD system are increasing. Thus, future research is required to improve the performance of CAD system for mammogram screening and make it completely automated.
SVM &GA-CLUSTERING BASED FEATURE SELECTION APPROACH FOR BREAST CANCER DETECTIONijscai
Mortality leading among women in developed countries is breast cancer. Breast cancer is women's second
most prominent cause of cancer mortality worldwide. In recent decades, women's high prevalence of breast
cancer has risen dramatically. This paper discussed several data analysis methods used to detect breast
cancer early. Breast cancer diagnosis distinguishes benign and malignant breast lumps. Using data
processing tools, we tackled this disease analysis. Data mining is an important step of library discovery
where intelligent methods are used to detect patterns. Several clinical breast cancer studies were
conducted using soft computing and machine learning techniques. Sometimes their algorithms are easier,
easier, or more comprehensive than others. This research is focused on genetic programming and machine
learning algorithms to reliably identify benign and malignant breast cancer. This study aimed to optimise
the testing algorithm. We used genetic programming methods to choose classification machines' best
features and parameter values. Data mining is an important step of library discovery where intelligent
methods are used to detect patterns. We are analysing data accessible from the U.C.I. deep-learning data
set in Wisconsin. In this experiment, we equate four Weka clustering strategies with genetic clustering. A
comparison of results reveals that sequential minimal optimization (S.M.O.) is better than I.B.K. and B.F.
Tree processes, i.e. 97.71%.
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Mortality leading among women in developed countries is breast cancer. Breast cancer is women's second most prominent cause of cancer mortality worldwide. In recent decades, women's high prevalence of breast cancer has risen dramatically. This paper discussed several data analysis methods used to detect breast
cancer early. Breast cancer diagnosis distinguishes benign and malignant breast lumps. Using data processing tools, we tackled this disease analysis. Data mining is an important step of library discovery where intelligent methods are used to detect patterns. Several clinical breast cancer studies were conducted using soft computing and machine learning techniques. Sometimes their algorithms are easier, easier, or more comprehensive than others. This research is focused on genetic programming and machine learning algorithms to reliably identify benign and malignant breast cancer. This study aimed to optimise
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Mortality leading among women in developed countries is breast cancer. Breast cancer is women's second most prominent cause of cancer mortality worldwide. In recent decades, women's high prevalence of breast cancer has risen dramatically. This paper discussed several data analysis methods used to detect breast cancer early. Breast cancer diagnosis distinguishes benign and malignant breast lumps. Using data processing tools, we tackled this disease analysis. Data mining is an important step of library discovery where intelligent methods are used to detect patterns. Several clinical breast cancer studies were conducted using soft computing and machine learning techniques. Sometimes their algorithms are easier, easier, or more comprehensive than others. This research is focused on genetic programming and machine learning algorithms to reliably identify benign and malignant breast cancer. This study aimed to optimise the testing algorithm. We used genetic programming methods to choose classification machines' best features and parameter values. Data mining is an important step of library discovery where intelligent methods are used to detect patterns. We are analysing data accessible from the U.C.I. deep-learning data set in Wisconsin. In this experiment, we equate four Weka clustering strategies with genetic clustering. A comparison of results reveals that sequential minimal optimization (S.M.O.) is better than I.B.K. and B.F. Tree processes, i.e. 97.71%.
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learning algorithms to reliably identify benign and malignant breast cancer. This study aimed to optimise the testing algorithm. We used genetic programming methods to choose classification machines' best features and parameter values. Data mining is an important step of library discovery where intelligent methods are used to detect patterns. We are analysing data accessible from the U.C.I. deep-learning data
set in Wisconsin. In this experiment, we equate four Weka clustering strategies with genetic clustering. A comparison of results reveals that sequential minimal optimization (S.M.O.) is better than I.B.K. and B.F. Tree processes, i.e. 97.71%.
A Review on Data Mining Techniques for Prediction of Breast Cancer RecurrenceDr. Amarjeet Singh
The most common type of cancer in women
worldwide is the Breast Cancer. Breast cancer may be
detected early using Mammograms, probably before it's
spread. Recurrent breast cancer could occur months or years
after initial treatment. The cancer could return within the
same place because the original cancer (local recurrence), or it
may spread to different areas of your body (distant
recurrence). Early stage treatment is done not only to cure
breast cancer however additionally facilitate in preventing its
repetition/recurrence. Data mining algorithms provide
assistance in predicting the early-stage breast cancer that
continually has been difficult analysis drawback. The
projected analysis can establish the most effective algorithm
that predicts the recurrence of the breast cancer and improve
the accuracy the algorithms. Large information like Clump,
Classification, Association Rules, Prediction and Neural
Networks, Decision Trees can be analyzed using data mining
applications and techniques.
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.
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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.
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.
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.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
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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.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptx
Breast cancer detection using machine learning approaches: a comparative study
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 1, February 2023, pp. 736~745
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i1.pp736-745 736
Journal homepage: http://ijece.iaescore.com
Breast cancer detection using machine learning approaches: a
comparative study
Muawia A. Elsadig1
, Abdelrahman Altigani2
, Huwaida T. Elshoush3
1
Deanship of Scientific Research, Imam Abdulrahman Bin Faisal University, Dammam, Kingdom of Saudi Arabia
2
Department of Computer Science, College of Computer Science and Information Technology,
Imam Abdulrahman Bin Faisal University, Dammam, Kingdom of Saudi Arabia
3
Department of Computer Science, Faculty of Mathematical Sciences and Informatics, University of Khartoum, Khartoum, Sudan
Article Info ABSTRACT
Article history:
Received Nov 6, 2021
Revised Jul 21, 2022
Accepted Aug 14, 2022
As the cause of the breast cancer disease has not yet clearly identified and a
method to prevent its occurrence has not yet been developed, its early
detection has a significant role in enhancing survival rate. In fact, artificial
intelligent approaches have been playing an important role to enhance the
diagnosis process of breast cancer. This work has selected eight
classification models that are mostly used to predict breast cancer to be
under investigation. These classifiers include single and ensemble classifiers.
A trusted dataset has been enhanced by applying five different feature
selection methods to pick up only weighted features and to neglect others.
Accordingly, a dataset of only 17 features has been developed. Based on our
experimental work, three classifiers, multi-layer perceptron (MLP), support
vector machine (SVM) and stack are competing with each other by attaining
high classification accuracy compared to others. However, SVM is ranked
on the top by obtaining an accuracy of 97.7% with classification errors of
0.029 false negative (FN) and 0.019 false positive (FP). Therefore, it is
noteworthy to mention that SVM is the best classifier and it outperforms
even the stack classier.
Keywords:
Breast cancer detection
Deep learning
Machine learning
Classification algorithms
Breast cancer diagnosis
This is an open access article under the CC BY-SA license.
Corresponding Author:
Muawia A. Elsadig
Cybersecurity, Deanship of Scientific Research, Imam Abdulrahman Bin Faisal University
Dammam, Kingdom of Saudi Arabia
Email: muawiasadig@yahoo.com
1. INTRODUCTION
The rapid spread of breast cancer is clearly noticeable, especially in some developed countries. It is
considered to be one of the most significant reasons of death among females [1], [2]. In other words, in most
cancer-affected women, breast cancer is the most frequent cancer [3] and the primary cause of death [4]. In
2018, about two million new cases were reported [5]. The complications of the breast cancer diagnosis
process and its late discovery are the reasons behind the low survival rate. Its early detection prevents its
progression and thus reduces the risk. The early detection and treatment of this disease improves the survival
rate [6]–[10]. However, the absence of the breast cancer symptoms at the beginning stage of this disease
makes its early discovery harder [11]. Therefore, the computer-aided diagnosis (CAD) methods are highly
required as they are very effective in prediction process. CAD methods have the potential of being valuable
tools to help radiologists [12].
In comparison with other types of cancer and regarding the number of new cases, a recent report has
shown that breast cancer among females received the highest number and it is the leading cause of death
[13]. Figures 1 and 2 show the mortality and incidence respectively for female breast cancer in 2018. It may
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be clearly noticed that female breast cancer dominates in both cases, number of new cases and death.
Moreover, other sources indicated that woman’s breast cancer is said to be the second largest cause of death
worldwide [14]; therefore, computer-aided detection techniques which help radiologists in detecting
abnormal behavior are much needed to enrich survival rate through early detection of this deadly disease.
Early detection and treatment are important for patients with breast cancer. It is possible for 95% of patients
with early breast cancer to be cured completely [15].
Figure 1. The death rate of some common cancers among females worldwide for the year 2018 [13]
Moreover, to this end, a method to prevent the occurrence of breast cancer has not yet been
presented therefore cancer tumor in the beginning periods could be treated effectively [16], [17]. This reflects
the need to develop adequate methods to support the early diagnosis of this disease. Developing such
methods is mainly based on taking benefits of advanced computing techniques to enrich the diagnostic
capabilities.
Generally, CAD assists radiologists to screen patient images and therefore increase the detection
accuracy. It has been widely used to diagnose different kinds of disease [18]. Interested readers are referred
to the study in [19] which sheds light on CAD methods that use mammograms to diagnosis or detect breast
cancer.
Figure 2. The rate of new cases of some common cancers among females worldwide for the year 2018 [13]
In this paper an enhanced version of Wisconsin breast cancer diagnosis (WBDC) dataset has been
used to train and test eight popular machine learning models that are commonly used in medical image
classification. One of these classifiers is an ensemble classifier based on stacking techniques. It takes the
output of all other classifiers to form an ensemble classification model. The dataset is thoroughly improved
through applying five feature selection methods that include Chi-square (X2), ReliefF, Analysis of variance
(ANOVA), Gini index, and Gain ratio. The purpose of applying these efficient feature selection methods is to
reduce the data dimensionality and therefore neglect all unweighted features and consider only features that
have considerable impact in the classification process. When a model learns from irrelevant features, this will
affect its accuracy, so an important key of success is to have only the relevant features and ignore others.
Using the relevant features reduces the computational cost and improves model performance.
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A thorough review on the common classification models to predict breast cancer occurrence has
been given by this paper. These classifiers are carefully selected, taking into consider their capabilities to
attain high prediction accuracy. In addition, these classifiers include two classifiers that are based on an
ensemble approach; one uses bagging technique and the other uses stacking technique. These variations are
being considered to base our work on different techniques and scenarios to enrich our findings. These
classifiers will be trained and tested under an enhanced version of a common dataset (which is another
contribution of this work) to pick the classifiers that are capable of achieving high detection accuracy, and
this is the main aim of this paper. Generally, we can conclude that many phases of enhancement have been
applied to reflect trusted and reliable findings, and these phases will be thoroughly illustrated throughout this
paper.
The paper is organized: the spread of breast cancer worldwide has been thoroughly discussed in the
first section and many statistics supporting that have been given. In addition, this section sheds lights on
computer-aided techniques and their important role in detection and diagnosis capabilities. The following
section provides a review of relevant classification models that are commonly used to predict breast cancer.
The limitations and achievements of these models are given. Mainly, this section gives more focus on eight
classification methods that have ensured their capabilities in obtaining high performance to predict breast
cancer tumors. Our proposed method is given in section 3 which gives a clear description of the method and
highlights its effectiveness in achieving the expected goals. Comprehensive details about our enhanced
dataset, the applied feature selection methods, implementation tool and validation techniques are fully
illustrated by this section. Section 4 shows the experimental work and thoroughly discusses the results
obtained. Finally, the conclusion is summarized in section 5.
2. RELATED WORK
It is noteworthy that computer aided techniques such as machine learning and deep learning
methods are widely becoming common techniques in the medical field. The way to exam and evaluate
patient data is one of the most important factors that influences the diagnostic process, so the automation of
this process can greatly help to gain accurate results. Machine learning classification models have a great
influence in minimizing the errors that can be caused by inexperienced physicians and they are capable of
giving accurate results in a short time. However, some problems arise with machine learning techniques that
may happen due to using inefficient validation techniques, inadequate classification models, and redundant
unweighted features. In addition, and due to the complexities of breast tissues, classification and prediction of
breast cancer in medical imaging is said to be a critical task [20]. Therefore, utilizing machine learning and
deep learning techniques is a vital part in improving the diagnosis process, if these techniques are
implemented perfectly considering all phases of work flow starting from using efficient tools for image
reprocessing and selecting proper features. up to applying adequate validation methods. The rest of this
section gives a comprehensive review of the classifiers that are selected to be under test by this work.
Gao et al. [21] pointed out the potential solutions that CAD techniques can offer compared to
traditional methods. Generally, the advanced development of computer techniques in machine learning, data
mining, and deep learning, has playing a huge role in improving clinical care systems and supporting early
diagnosis of many diseases and therefore the survival rate.
Segmentation is one of the essential CAD system components [22] that paly important role in
classification accuracy. A comparative analysis for three segmentation techniques that include: spatial fuzzy
c-means (SFCM) with level set, selective level set, and spatial neutrosophic distance regularized level set
(SNDRLS) was conducted in [23]. The performance of these techniques was evaluated on a dataset of breast
cancer images. Their results showed that SFCM with level set works effectively to extract cancerous cells
compared to other techniques. Kaushal and Singla [24] proposed an automated segmentation technique for
breast cancer images. The authors listed some advantages of this technique that lead to enhance segmentation
process and therefore the proposed technique can identify cancerous cells correctly. The proposed technique
was evaluated and showed its effectiveness.
An experiment that uses the logistic regression (LR) classifier model has been carried out. The
dataset was used in this experiment is Wisconsin diagnosis breast cancer (WDBC), and the authors highlight
that the proper selection for feature combination is important to improve classification accuracy. Their
findings show that the logistic classifier accuracy can reach up to 96.5% in case of selecting maximum
perimeter and maximum texture as two characteristics [25]. On the other hand, another study has compared
LR and decision tree classifiers to point out that the decision tree (DT) algorithm has performed slightly
better than LR; however, both classifiers obtained high accuracy rate [26].
A study was conducted to compare two classifiers-multi-layer perceptron (MLP) and radial basis
function (RBR). Both are neural network classifiers. Their results reflect that MLP has outperformed ring-
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between-ring (RBR) by attaining high accuracy compared to RBR [27]. Zheng et al. [28] outlined that neural
network classifiers have become a popular method to classify cancer data. In addition to that, another study
pointed out that MLP is the most effective classifier [29]. This result is based on their results obtained by
comparing three classifiers that include MLP, naïve Bayes (NB) and C4.5 tree.
NB classification model is built on Bayes theorem. A comparison study was conducted on three
classifiers to indicate that MLP is more effective; however, it has poor interpretability. In spite of violation of
one of NB’s assumption, NB has attained good performance with good interpretability [29].
Karabatak [30] indicates that NB is one of the most powerful classification models; however, it has
some drawbacks. To overcome these drawbacks, Karabatak proposed a new NB classifier (weighted NB).
The author claimed that, the conducted experiments showed that weighted NB has obtained better accuracy
than the regular NB. On the other hand, the researcher stated some drawbacks for this new classifier to be
overcome in future research.
Showrov et al. [31] conducted a comparative study among three classifiers-artificial neuron network
(ANN), support vector machine (SVM), and NB-using WDBC dataset of nine features for breast cancer
prediction. In terms of classification accuracy, their results reflected that linear SVM topped the other
classifiers while radius basis function neural network (RBF NN) comes next and then Gaussian naïve Bayes.
Moreover, another study has shown that support vector machine has precise diagnosis capacity [32].
Using extracted features from mammography images-after applying some image processing
techniques Al-Hadidi et al. [33] trained and tested two classification algorithms using MATLAB software
and reported their results accordingly. These two classifiers are logistic regression (LR) and back propagation
neural network (BPNN). Their observation reflects that a good regression value exceeding 93% has been
obtained using BPNN with no more than 240 features.
The random forest (RF) classifier is an ensemble approach which contains multiple algorithms. Each
one can be implemented in a decision tree. The combined result of these multiple decision trees leads to
enhanced classification accuracy [34]. In other words, the RF is a combination of multiple decision trees that
represent an ensemble classifier to promote performance and prediction accuracy. A classifier that is based
on RF was developed in [35]. Their model has been trained using two different datasets and has obtained
promising results with high accuracy.
Using the WDBC, a comparison study has been carried out for three classifiers, including NB, RF
and k-nearest-neighbor (KNN). These classifiers were trained and tested to examine their prediction accuracy
of breast cancer tumors based on the aforementioned dataset. Accordingly, the authors conducting this
comparison observed that KNN outperformed the other classifiers as it has obtained higher accuracy
compared to NB and RF classifiers and generally all classifiers have obtained detection accuracy rates above
94%. The KNN classifier does just obtain the best accuracy among the others, it also outperforms them in
terms of precision and F1 score values [36]. Price and Lindqvist indicate that ANN classifier has performed
well when applying feature selection methods compared to SVM, NB, and decision trees classifiers. It
attained considerable improvement in its performance that reaches 51% increase [37].
Using a small dataset of 275 instances, the authors in [38] compared the performance of two
machine learning classifier models, extreme gradient boosting (XGBoost) and RF. Their results show that RF
has performed better than XGBoost in terms of detection accuracy for breast cancer; however, using a small
dataset may reflect unreliable results, so the authors stated that a large dataset is required to support their
findings. A recent study compared nine classification models that include LR, Gaussian NB, RBF SVM,
linear SVM, DT, RF, XGBoost, KNN and gradient boosting. These models were trained and tested using
Wisconsin diagnosis cancer dataset. The obtained results indicate that KNN for supervised learning and LR
for semi-supervised learning achieved the highest accuracy [39].
Ensemble learning approach is one of the most partial ways to offer a trade-off between variance
and bias. Many studies show that combining single classifiers to build an aggregated classification model can
improve classification performance compared to the performance that can be obtained by any one of these
classifiers. The three basic techniques of ensemble classification are stacking, boosting and bagging. In the
stacking approach, the outputs from different classification models are to be aggregated into a new dataset
[40]. Readers interested for more information are referred to [41]–[46].
Hosni et al. [47] based on their review study, highlighted that most classifiers that are frequently
used to build ensemble classification models include artificial neural networks, support vector machines, and
trees. In addition, the dataset most frequently used by researchers is WBCD. The results of this thorough
review have motivated us to base our work on WBCD which is a trusted dataset by the research community.
According to our mentioned survey work, seven classification models have been chosen to be under
investigation throughout this work. Additionally, an ensemble classifier based on stacking technique will be
used to include all the aforementioned seven classifiers. Therefore, our study will include eight classifiers:
RF, SVM, logistic regression, DT, KNN, MLP, NB and stack.
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3. RESEARCH METHOD
Eight classification models have been selected carefully to be under test throughout this work. These
classifiers are: LR, ANN, RF, SVM, MLP, NB, DT, and stack. Two of these classifiers are ensemble
classifiers that are based on different ensemble techniques: RF classifier and stack classifier. RF depends on
bagging technique. It is a collection of tree-structured classifiers. While the stack classifier is based on
stacking technique, it takes the outputs of different classification models. This variation of choosing different
classifiers that are based on different techniques and scenarios is to boost up our findings and therefore lead
to trusted results. Each classifier has been trained and tested under four different train-test sets that taken
from the enhanced dataset that described below.
3.1. Dataset
In this work an enhanced dataset has been used to train and test the classifiers that are under test.
The enhanced dataset has been developed based on the WBCD, the well-known dataset that available in UCI
machine learning repository website [48]. The WBDC include 569 instances with 30 features. The enhanced
process that applied to this dataset has reduced the number of features to only 17 features. The features have
been reduced by applying five feature selection techniques and accordingly the top 17 features that have
considerable wight have been selected and the other features (redundant and unweighted features) have been
neglected. This process is considered a huge contribution of this paper which results in improving the
classification accuracy and at the same time reduces the classification process overheads. It is so smart to
gain high accuracy and to reduce the computation overhead simultaneously. Figure 3 shows the revised
dataset with 17 features.
Four different sets of our revised dataset have been used to train and test all classifiers. These sets
are: set 1: 60% to train and 40% for test; set 2: 70% to train and 30% for test; set 3: 80% to train and 20% for
test; and set 4: 90% to train and 10% for test. It is commonly known that applying different training sizes
leads to having in-depth experiments that give ensured trusted results.
Figure 3. The enhanced dataset
3.2. The feature selection approach
Using an adequate feature selection approach is an important step to improving classification
accuracy. This process reduces the features by taking into consideration the features with impact weight into
the classification process and ignoring other features that have not. It has two benefits: it boosts classifier
predictability and reduces its computational overheads. There are many feature selection methods that are
commonly known and have great impact in improving classification performance. In this paper five selection
methods are applied to our dataset to take only features that received considerable wights by these methods.
These feature selection methods include Chi-square (X2) [49]–[51], ReliefF [52], [53], ANOVA [54], Gini
index [55], [56], and Gain ratio [57]. Choosing these feature selection methods allows these methods to use
different metrics to select optimal features. For example, Chi-square (X2) computes the chi-score to rank the
features, and information gain is ranking the features depend on their value.
Using these five feature selection methods, the weight for any given feature has been calculated, and
then the feature has been ranked accordingly. A feature that received high rank by all feature selection
methods will be selected automatically, while for the other features that received variant ranking, the average
F1: texture_worst F2: radius_worst F3: perimeter_worst F4: perimeter_mean
F5: radius_mean F6: concave points_worst F7: concave points_mean F8: area_worst
F9: area_mean F10: concavity_mean F11: concavity_worst F12: radius_se
F13: area_se F14: perimeter_se F15: compactness_mean F16: compactness_worst
F17: texture_mean
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of the weights will be calculated to select only those have considerable average of weights. By applying this
scenario, only 17 features have received the best ranking by the five features selection methods and the other
feature received poor weights and therefore has been neglected. In other words, the average is calculated
when the features selection methods give variant weights, but in case they all agreed to give high rank for a
given feature, then this feature will be selected automatically. Table 1 shows a part of the calculated weights
that were given by the five selection feature methods for two features of the dataset. According to this
important phase of enhancement, the following 17 features were selected as they received the top weights
compared to the rest of the features: texture_worst, radius_worst, perimeter_worst, perimeter_mean,
radius_mean, concave points_worst, concave points_mean, area_worst, area_mean, concavity_mean,
concavity_worst, radius_se, area_se, perimeter_se, compactness_mean, compactness_worst, texture_mean.
This method perfectly enhances our dataset and therefore reflects a high degree of detection accuracy.
Table 1. Example of the wights given by the five features selection methods for two features
Feature Chi-square (X2) ReliefF ANOVA Gini index Gain ratio
Concave points_worst 279.705 0.089 964.385 0.308 0.293
Radius _worst 290.486 0.086 860.782 0.320 0.310
3.3. Validation methods
Validation is an essential phase for any model to gain acceptance. In our experiments and for
attaining realistic and reliable results, the random sampling validation technique has been repeated 10 times
for each classification model. The classification accuracy for each model has been calculated through the
common accuracy in (1):
Accuracy =
(TP+TN)
(TP+TN+FP+FN)
(1)
where a true positive (TP) refers to positive instances that are predicted correctly by a classification model. A
true negative (TN) refers to negative cases that are predicted correctly by a classification model. A false
positive (FP) refers to negative cases that are predicted incorrectly by a classification model. A false negative
(FN) refers to positive cases that are predicted incorrectly by a classification model. Then the confusion
matrix has been used to evaluate our models’ performance. The confusion matrix measures the classification
error in terms of false negatives (FN) and false positives (FP).
3.4. Models implementation tool
Orange data mining software has been chosen to conduct all the experiments throughout this work.
It is a very powerful tool that has huge capabilities to visualize data efficiently and professionally. It is open
source that is very useful to implement different machine learning classification algorithms.
4. RESULTS AND DISCUSSION
After a thorough investigation on eight classifiers that are mostly used in classification of medical
images, this paper comes up with different findings that include nominating the two classifiers as the best in
diagnosing breast cancer; these classifiers are SVM and MLP, while SVM is dominant. SVM has attained the
highest classification accuracy followed by MLP. The results illustrated in Table 2 show the accuracy of each
classifier. The accuracy is read after investigating each classifier under four different training-testing sets.
SVM has outperformed all classifiers under all testing scenarios by obtaining classification accuracies of
97.7%, 97.5%, 97% and 97% over the four sets 80%-20%, 90%-10%, 60%-40%, and 70%-30% respectively.
It noticed that the highest accuracy is obtained when the training size is 80%. SVM outperforms two types of
ensemble classifiers-stack and RF. Both stack and RF use different ensemble techniques, bagging and
stacking respectively. It is noteworthy to mention that SVM has outperformed Stack classifier in spite of the
fact that stack classifier takes the outputs of all other classifiers-under our investigation including SVM-to
enhance prediction accuracy. MLP and stack come next, next to each other in the second position after SVM.
Moreover, Table 3 shows the classification errors, false positives (FP) and false negatives (FN) for
all classifiers under four training-test sets. These results indicate that SVM outperformed other classifiers by
casing the least classification errors, 0.029 FN and 0.019 FP. Generally, the classifiers under our
investigation can be classified into three groups: Group 1 which include SVM, MLP, and stack (ensemble
classifier); this group obtain an excellent prediction accuracy and topped the other groups. Group 2 which
include LR and RF. These classifiers achieved very good accuracy while Group 3, which includes KNN, DT
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and NB, gained good prediction accuracy and took the bottom level of this classification. Figure 4 clearly
reflects that. Based on our findings and this thorough evaluation, the paper concludes that SVM ranks at the
top; however, MLP is the closest competitor. SVM outperformed even the stack classifier which is an
ensemble classifier that is based on the stacking technique.
Table 2. Comparison of eight classifiers’ accuracy over four training-testing sets
Classifier Accuracy
Training testing size
60%-40%
Training testing size
70%-30%
Training testing size
80%-20%
Training testing size
90%-10%
KNN 93.6% 92.9% 9.35% 94.0%
DT 94.2% 92.3% 93.2% 93.9%
SVM 97.0% 97.0% 97.7% 97.5%
Random forest 95.7% 95.5% 95.4% 95.8%
MLP (neural network) 96.9% 96.5% 96.8% 96.8%
NB 93.8% 93.0% 93.8% 93.2%
Logistic regression 95.0% 94.4% 95.1% 96.0%
Stack 97.0% 96.3% 96.4% 97.0%
Table 3. Classification errors, false positives (FP) and false negatives (FN) for all classifies under four
training-testing sets
Training set 60% 70% 80% 90%
FP FN FP FN FP FN FP FN
DC 0.036 0.095 0.050 0.123 0.054 0.090 0.036 0.105
LR 0.034 0.076 0.036 0.089 0.036 0.071 0.028 0.062
SVM 0.020 0.045 0.027 0.034 0.019 0.029 0.017 0.038
RF 0.029 0.068 0.026 0.077 0.033 0.069 0.028 0.067
NB 0.055 0.074 0.064 0.080 0.060 0.067 0.061 0.080
MLP 0.027 0.038 0.034 0.036 0.028 0.038 0.017 0.057
KNN 0.035 0.112 0.036 0.130 0.042 0.105 0.036 0.100
STACK 0.023 0.042 0.028 0.053 0.031 0.045 0.025 0.038
Figure 4. The classification performance in terms of accuracy obtained by each classifier over four training
sets: 60%, 70%, 80% and 90%
5. CONCLUSION
It is noteworthy to mention that machine learning techniques have ensured their efficiency to
discover and define patterns from large amount of medical images and therefore this will assist to classify
and sort out these images accordingly. In return this will highly enrich the detection process. However,
choosing a trusted dataset, an adequate machine learning approach, a proper feature selection method and
accurate validation technique is a key factor to introduce a reliable and efficient detection scheme. This paper
applies weighty enhancements to enrich its findings. Many phases of improvements have been implemented
that include, but are not not limited to: i) using an enhanced version of publicly trusted dataset by applying
five different feature selection methods and, accordingly, the features which have the greatest influence on
the prediction process have been selected to improve the prediction accuracy while the other features that
0.89
0.9
0.91
0.92
0.93
0.94
0.95
0.96
0.97
0.98
0.99
KNN DT SVM RF MLP NB LR Stack
Classification
Accuracy
Rate
Classifiers
60%-40% 70%-30% 80%-20% 90%-10%
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have no weighted influence are discarded. Decreasing the number of the features reduces the computational
overheads. Therefore, this phase of enhancement has two important improvements: i) it enriches
classification accuracy and decreases the model computation cost; ii) applying four sets of training-test
scenarios to boost the trustworthiness; and iii) using variant evaluation methods.
The main contribution of this paper is ranking the SVM as the best classifier. It obtained a
classification accuracy reaching 97.7% with the least classification errors 0.029 false negatives (FN) and
0.019 false positives (FP). This was followed by MLP and stack classifiers. Stack is an ensemble classifier
based on stacking technique. Also, the paper presented a comparative study that classifies these classifiers
into three levels or groups based on their performance. While excluding the stack classifier-as it is an
ensemble classifier that is based on all other classifiers-the top group includes SVM and MLP followed by
the next group that includes LR and RF and then the last group that includes DT, KNN and NB. On the other
hand, the paper contributes by introducing an enhanced version of the dataset that has been improved by
applying the five-feature selection methods to improve prediction accuracy and reduce computational cost.
Generally, SVM has topped all classifiers, even the stack classifier which is an ensemble classifier of all
classifiers under investigation of this paper.
ACKNOWLEDGEMENTS
The author would like to extend his gratitude and appreciation to the Deanship of Scientific
Research of Imam Abdulrahman bin Faisal University to their support for the project No. 2019348.
REFERENCES
[1] V. Lahoura et al., “Cloud computing-based framework for breast cancer diagnosis using extreme learning machine,” Diagnostics,
vol. 11, no. 2, Feb. 2021, doi: 10.3390/diagnostics11020241.
[2] A. B. Nassif, M. A. Talib, Q. Nasir, Y. Afadar, and O. Elgendy, “Breast cancer detection using artificial intelligence techniques: A
systematic literature review,” Artificial Intelligence in Medicine, vol. 127, May 2022, doi: 10.1016/j.artmed.2022.102276.
[3] C. Kaushal and A. Singla, “Analysis of breast cancer for histological dataset based on different feature extraction and classification
algorithms,” in Advances in Intelligent Systems and Computing, Springer Singapore, 2021, pp. 821–833.
[4] G. Meenalochini and S. Ramkumar, “Survey of machine learning algorithms for breast cancer detection using mammogram images,”
Materials Today: Proceedings, vol. 37, pp. 2738–2743, 2021, doi: 10.1016/j.matpr.2020.08.543.
[5] N. Kumari, “A survey on various machine learning approaches used for breast cancer detection,” International Journal of Advanced
Research in Computer Science, vol. 10, no. 3, pp. 76–79, Jun. 2019, doi: 10.26483/ijarcs.v10i3.6438.
[6] D. Sharma, R. Kumar, and A. Jain, “A systematic review of risk factors and risk assessment models for breast cancer,” in Mobile
Radio Communications and 5G Networks, Springer Singapore, 2021, pp. 509–519.
[7] O. Ginsburg et al., “Breast cancer early detection: A phased approach to implementation,” Cancer, vol. 126, no. S10, pp. 2379–2393,
May 2020, doi: 10.1002/cncr.32887.
[8] Y. Amethiya, P. Pipariya, S. Patel, and M. Shah, “Comparative analysis of breast cancer detection using machine learning and
biosensors,” Intelligent Medicine, vol. 2, no. 2, pp. 69–81, May 2022, doi: 10.1016/j.imed.2021.08.004.
[9] N. M. Hassan, S. Hamad, and K. Mahar, “Mammogram breast cancer CAD systems for mass detection and classification: a review,”
Multimedia Tools and Applications, vol. 81, no. 14, pp. 20043–20075, Jun. 2022, doi: 10.1007/s11042-022-12332-1.
[10] A. Ahmed and O. M. B. El Sadig, “Heterogeneous multi-classifier method based on weighted voting for breast cancer detection,”
International Journal of Advances in Science Engineering and Technology, vol. 7, no. 4, pp. 36–41, 2019.
[11] S. A. Medjahed, T. Ait Saadi, and A. Benyettou, “Breast cancer diagnosis by using k-nearest neighbor with different distances and
classification rules,” International Journal of Computer Applications, vol. 62, no. 1, pp. 1–5, Jan. 2013, doi: 10.5120/10041-4635.
[12] K. Drukker, C. A. Sennett, and M. L. Giger, “Automated method for improving system performance of computer-aided diagnosis in
breast ultrasound,” IEEE Transactions on Medical Imaging, vol. 28, no. 1, pp. 122–128, Jan. 2009, doi: 10.1109/TMI.2008.928178.
[13] F. Bray, J. Ferlay, I. Soerjomataram, R. L. Siegel, L. A. Torre, and A. Jemal, “Global cancer statistics 2018: GLOBOCAN estimates
of incidence and mortality worldwide for 36 cancers in 185 countries,” CA: A Cancer Journal for Clinicians, vol. 68, no. 6,
pp. 394–424, Nov. 2018, doi: 10.3322/caac.21492.
[14] E. H. Houssein, M. M. Emam, A. A. Ali, and P. N. Suganthan, “Deep and machine learning techniques for medical imaging-based
breast cancer: A comprehensive review,” Expert Systems with Applications, vol. 167, Apr. 2021, doi: 10.1016/j.eswa.2020.114161.
[15] A. Hizukuri, R. Nakayama, M. Nara, M. Suzuki, and K. Namba, “Computer-aided diagnosis scheme for distinguishing between
benign and malignant masses on breast DCE-MRI images using deep convolutional neural network with Bayesian optimization,”
Journal of Digital Imaging, vol. 34, no. 1, pp. 116–123, Feb. 2021, doi: 10.1007/s10278-020-00394-2.
[16] H. Zerouaoui and A. Idri, “Reviewing machine learning and image processing based decision-making systems for breast cancer
imaging,” Journal of Medical Systems, vol. 45, no. 1, Jan. 2021, doi: 10.1007/s10916-020-01689-1.
[17] H. Fang, H. Fan, S. Lin, Z. Qing, and F. R. Sheykhahmad, “Automatic breast cancer detection based on optimized neural network
using whale optimization algorithm,” International Journal of Imaging Systems and Technology, vol. 31, no. 1, pp. 425–438, Mar.
2021, doi: 10.1002/ima.22468.
[18] Y. Wang, L. Zhang, X. Shu, Y. Feng, Y. Zhang, and Q. Lv, “Feature-sensitive deep convolutional neural network for multi-instance
breast cancer detection,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, p. 1, 2021, doi:
10.1109/TCBB.2021.3060183.
[19] S. Z. Ramadan, “Methods used in computer-aided diagnosis for breast cancer detection using mammograms: a review,” Journal of
Healthcare Engineering, pp. 1–21, Mar. 2020, doi: 10.1155/2020/9162464.
[20] I. Hirra et al., “Breast cancer classification from histopathological images using patch-based deep learning modeling,” IEEE Access,
vol. 9, pp. 24273–24287, 2021, doi: 10.1109/ACCESS.2021.3056516.
[21] Y. Gao, K. J. Geras, A. A. Lewin, and L. Moy, “New frontiers: an update on computer-aided diagnosis for breast imaging in the age
of artificial intelligence,” American Journal of Roentgenology, vol. 212, no. 2, pp. 300–307, Feb. 2019, doi: 10.2214/AJR.18.20392.
9. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 13, No. 1, February 2023: 736-745
744
[22] C. Kaushal, S. Bhat, D. Koundal, and A. Singla, “Recent trends in computer assisted diagnosis (CAD) system for breast cancer
diagnosis using histopathological images,” IRBM, vol. 40, no. 4, pp. 211–227, Aug. 2019, doi: 10.1016/j.irbm.2019.06.001.
[23] C. Kaushal, D. Koundal, and A. Singla, “Comparative analysis of segmentation techniques using histopathological images of breast
cancer,” in 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC), Mar. 2019, pp. 261–266,
doi: 10.1109/ICCMC.2019.8819659.
[24] C. Kaushal and A. Singla, “Automated segmentation technique with self‐driven post‐processing for histopathological breast cancer
images,” CAAI Transactions on Intelligence Technology, vol. 5, no. 4, pp. 294–300, Dec. 2020, doi: 10.1049/trit.2019.0077.
[25] L. Liu, “Research on logistic regression algorithm of breast cancer diagnose data by machine learning,” in 2018 International
Conference on Robots and Intelligent System (ICRIS), May 2018, pp. 157–160, doi: 10.1109/ICRIS.2018.00049.
[26] P. P. Sengar, M. J. Gaikwad, and A. S. Nagdive, “Comparative study of machine learning algorithms for breast cancer prediction,” in
2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT), Aug. 2020, pp. 796–801, doi:
10.1109/ICSSIT48917.2020.9214267.
[27] A. M. S. C. Meenakshi, M. Govindarajan, “Detection of breast cancer using MLP and RBF classifiers,” IMS Manthan, vol. 5, no. 1,
2010.
[28] J. Zheng, D. Lin, Z. Gao, S. Wang, M. He, and J. Fan, “Deep learning assisted efficient AdaBoost algorithm for breast cancer
detection and early diagnosis,” IEEE Access, vol. 8, pp. 96946–96954, 2020, doi: 10.1109/ACCESS.2020.2993536.
[29] D. Soria, J. M. Garibaldi, E. Biganzoli, and I. O. Ellis, “A comparison of three different methods for classification of breast cancer
data,” in 2008 Seventh International Conference on Machine Learning and Applications, 2008, pp. 619–624, doi:
10.1109/ICMLA.2008.97.
[30] M. Karabatak, “A new classifier for breast cancer detection based on Naïve Bayesian,” Measurement, vol. 72, pp. 32–36, Aug. 2015,
doi: 10.1016/j.measurement.2015.04.028.
[31] M. I. H. Showrov, M. T. Islam, M. D. Hossain, and M. S. Ahmed, “Performance comparison of three classifiers for the classification
of breast cancer dataset,” in 2019 4th International Conference on Electrical Information and Communication Technology (EICT),
Dec. 2019, pp. 1–5, doi: 10.1109/EICT48899.2019.9068816.
[32] M. F. Akay, “Support vector machines combined with feature selection for breast cancer diagnosis,” Expert Systems with
Applications, vol. 36, no. 2, pp. 3240–3247, Mar. 2009, doi: 10.1016/j.eswa.2008.01.009.
[33] M. R. Al-Hadidi, A. Alarabeyyat, and M. Alhanahnah, “Breast cancer detection using k-nearest neighbor machine learning
algorithm,” in 2016 9th International Conference on Developments in eSystems Engineering (DeSE), Aug. 2016, pp. 35–39, doi:
10.1109/DeSE.2016.8.
[34] B. Dai, R.-C. Chen, S.-Z. Zhu, and W.-W. Zhang, “Using random forest algorithm for breast cancer diagnosis,” in 2018 International
Symposium on Computer, Consumer and Control (IS3C), Dec. 2018, pp. 449–452, doi: 10.1109/IS3C.2018.00119.
[35] C. Nguyen, Y. Wang, and H. N. Nguyen, “Random forest classifier combined with feature selection for breast cancer diagnosis and
prognostic,” Journal of Biomedical Science and Engineering, vol. 6, no. 5, pp. 551–560, 2013, doi: 10.4236/jbise.2013.65070.
[36] S. Sharma, A. Aggarwal, and T. Choudhury, “Breast cancer detection using machine learning algorithms,” in 2018 International
Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS), Dec. 2018, pp. 114–118, doi:
10.1109/CTEMS.2018.8769187.
[37] T. Price and N. Lindqvist, “Evaluation of feature selection methods for machine learning classification of breast cancer,” Degree
Project, Kth Royal Institute of Technology, School of Electrical Engineering and Computer Science. Stockholm, Sweden, 2018.
[38] S. Kabiraj et al., “Breast cancer risk prediction using XGBoost and random forest algorithm,” in 2020 11th International Conference
on Computing, Communication and Networking Technologies (ICCCNT), 2020, pp. 1–4, doi: 10.1109/ICCCNT49239.2020.9225451.
[39] N. Al-Azzam and I. Shatnawi, “Comparing supervised and semi-supervised machine learning models on diagnosing breast cancer,”
Annals of Medicine and Surgery, vol. 62, pp. 53–64, Feb. 2021, doi: 10.1016/j.amsu.2020.12.043.
[40] S. Wang, Y. Wang, D. Wang, Y. Yin, Y. Wang, and Y. Jin, “An improved random forest-based rule extraction method for breast
cancer diagnosis,” Applied Soft Computing, vol. 86, Jan. 2020, doi: 10.1016/j.asoc.2019.105941.
[41] R. Nagarajan and M. Upreti, “An ensemble predictive modeling framework for breast cancer classification,” Methods, vol. 131,
pp. 128–134, Dec. 2017, doi: 10.1016/j.ymeth.2017.07.011.
[42] X. Tang, L. Cai, Y. Meng, C. Gu, J. Yang, and J. Yang, “A novel hybrid feature selection and ensemble learning framework for
unbalanced cancer data diagnosis with transcriptome and functional proteomic,” IEEE Access, vol. 9, pp. 51659–51668, 2021, doi:
10.1109/ACCESS.2021.3070428.
[43] V. Chaurasia and S. Pal, “Stacking-based ensemble framework and feature selection technique for the detection of breast cancer,” SN
Computer Science, vol. 2, no. 2, Apr. 2021, doi: 10.1007/s42979-021-00465-3.
[44] X. Zhang et al., “Deep learning based analysis of breast cancer using advanced ensemble classifier and linear discriminant analysis,”
IEEE Access, vol. 8, pp. 120208–120217, 2020, doi: 10.1109/ACCESS.2020.3005228.
[45] N. Arya and S. Saha, “Multi-modal classification for human breast cancer prognosis prediction: Proposal of deep-learning based
stacked ensemble model,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2020, doi:
10.1109/TCBB.2020.3018467.
[46] S. J. Malebary and A. Hashmi, “Automated breast mass classification system using deep learning and ensemble learning in digital
mammogram,” IEEE Access, vol. 9, pp. 55312–55328, 2021, doi: 10.1109/ACCESS.2021.3071297.
[47] M. Hosni, I. Abnane, A. Idri, J. M. Carrillo de Gea, and J. L. Fernández Alemán, “Reviewing ensemble classification methods in
breast cancer,” Computer Methods and Programs in Biomedicine, vol. 177, pp. 89–112, Aug. 2019, doi: 10.1016/j.cmpb.2019.05.019.
[48] D. Dua and C. Graff, “UCI machine learning repository,” Irvine, CA: University of California, School of Information and Computer
Science. 2019.
[49] X. Jin, A. Xu, R. Bie, and P. Guo, “Machine learning techniques and chi-square feature selection for cancer classification using SAGE
gene expression profiles,” in Lecture Notes in Computer Science, Springer Berlin Heidelberg, 2006, pp. 106–115.
[50] Z. Mushtaq, A. Yaqub, S. Sani, and A. Khalid, “Effective K-nearest neighbor classifications for Wisconsin breast cancer data sets,”
Journal of the Chinese Institute of Engineers, vol. 43, no. 1, pp. 80–92, Jan. 2020, doi: 10.1080/02533839.2019.1676658.
[51] G. I. Salama, M. Abdelhalim, and M. A. Zeid, “Breast cancer diagnosis on three different datasets using multi-classifiers,” Breast
Cancer (WDBC), vol. 32, no. 569, 2012.
[52] R. J. Urbanowicz, M. Meeker, W. La Cava, R. S. Olson, and J. H. Moore, “Relief-based feature selection: Introduction and review,”
Journal of Biomedical Informatics, vol. 85, pp. 189–203, Sep. 2018, doi: 10.1016/j.jbi.2018.07.014.
[53] M. R. Mahmood, “Two feature selection methods comparison chi-square and relief-F for facial expression recognition,” Journal of
Physics: Conference Series, vol. 1804, no. 1, Feb. 2021, doi: 10.1088/1742-6596/1804/1/012056.
[54] N. Seedat and V. Aharonson, “Machine learning discrimination of Parkinson’s disease stages from walker-mounted sensors data,” in
Explainable AI in Healthcare and Medicine, Springer International Publishing, 2021, pp. 37–44.
10. Int J Elec & Comp Eng ISSN: 2088-8708
Breast cancer detection using machine learning approaches: a comparative study (Muawia A. Elsadig)
745
[55] T. Li, C. Zhang, and M. Ogihara, “A comparative study of feature selection and multiclass classification methods for tissue
classification based on gene expression,” Bioinformatics, vol. 20, no. 15, pp. 2429–2437, Oct. 2004, doi:
10.1093/bioinformatics/bth267.
[56] Y. R. Tabar, K. B. Mikkelsen, M. L. Rank, M. C. Hemmsen, and P. Kidmose, “Investigation of low dimensional feature spaces for
automatic sleep staging,” Computer Methods and Programs in Biomedicine, vol. 205, Jun. 2021, doi: 10.1016/j.cmpb.2021.106091.
[57] A. Thakkar and R. Lohiya, “Attack classification using feature selection techniques: a comparative study,” Journal of Ambient
Intelligence and Humanized Computing, vol. 12, no. 1, pp. 1249–1266, Jan. 2021, doi: 10.1007/s12652-020-02167-9.
BIOGRAPHIES OF AUTHORS
Muawia A. Elsadig received the bachelor degree in Computer Engineering, the
MSc degree in computer networks, and the Ph.D. degree in computer science (information
security). Currently, he is an assistant professor of cybersecurity and researcher, Deanship of
Scientific Research, Imam Abdulrahman Bin Faisal University (IAU), Dammam KSA. In
addition, he has engaged in teaching different computer courses in the college of Computer
Science and Information Technology (CSIT) and the college of applied studies. He worked for
different accredited international universities and has a rich record of publications at
recognized international journals and conferences. He received many awards for his research
achievements. Dr. Elsadig has many years of teaching experience and considerable industry
contributions. He contributed as a reviewer for many international reputable journals. His
research interests include the area of information security, network security, cybersecurity,
wireless sensor networks, bioinformatics, and information extraction; ranging from theory to
design to implementation. He can be contacted at email: muawiasadig@yahoo.com.
Abdelrahman Altigani received the bachelor’s degree in mathematics and the
master’s degree in computer science from the Faculty of Mathematical Sciences, University of
Khartoum, Sudan. He is currently pursuing the Ph.D. degree. He has 11 publications in
international refereed journals and conferences. He received the award of the best research
article in the computing track at the ICCEEE2013 conference. During his studies, he received
the International Ph.D. Fellowship scholarship thrice, in recognition of his outstanding
academic performance. He participated in reviewing a number of research articles for many
journals. His area of interest includes symmetric encryption. He can be contacted at email:
aaaltigani@iau.edu.sa.
Huwaida T. Elshoush received the bachelor’s degree in computer science
(division 1), the master’s degree in computer science, and the Ph.D. degree in information
security from the Faculty of Mathematical Sciences and Informatics, University of Khartoum,
Sudan, in 1994, 2001, and 2012, respectively. Her M.Sc. dissertation dealt with Frame Relay
Security. She is currently an Associate Professor within the Computer Science Department,
Faculty of Mathematical Sciences and informatics, University of Khartoum, where she is also
acting as the Head of Research office. She has more than 26 publications and some of her
publications appeared in Applied Soft Computing Journal (Elsevier), PLOS One Journal,
IEEE Access, Multimedia Tools and Applications, PeerJ Computer Science, Journal of
Information Hiding and Multimedia Signal Processing and Springer book chapters. Her
research interests include the information security, cryptography, steganography, and intrusion
detection systems. She is a reviewer of many international reputable journals related to her
fields, including Applied Soft Computing Journal (Elsevier). Dr. Elshoush’s awards and
honors include the second-place prize in the ACM Student Research Competition SRC-SAC
in 2013 in Coimbra, Portugal. Her article entitled “An Improved Framework for Intrusion
Alert Correlation” has been awarded the Best Student Paper Award of the 2012 International
Conference of Information Security and Internet Engineering (ICISIE) in WCE 2012. Other
prizes were the best student during the five years of her undergraduate study. She can be
contacted at email: htelshoush@hotmail.com.