An important feature of image analysis is texture, seen in all images, from aerial and satellite images to microscopic images in biomedical research. A chest X-ray is the most common and effective method for diagnosing severe lung diseases such as cancer, pneumonia, and tuberculosis. The lungs are the largest X-ray object. The correct separation of the shapes and sizes of the contours of the lungs is an important reason for diagnosis, because of which an intelligent information environment can be created. Despite the use of X-rays, to identify the diagnosis, there is a chance that the disease will not be detected. In this sense, there is a risk of development, which may be fatal. The article deals with the problems of pneumonia clustering using the autocorrelation function to obtain the most accurate result. This provides a reliable tool for diagnosing lung radiographs. Image pre-processing and data shaping play an important role in revealing a well-functioning basis of the nervous system. Therefore, images from two classes were selected for the task: healthy and with pneumonia. This paper demonstrates the applicability of the autocorrelation function for highlighting interest in lung radiographs based on the fineness of textural features and k-means extraction.
Classification of pathologies on digital chest radiographs using machine lear...IJECEIAES
This article is devoted to the research and development of methods for classifying pathologies on digital chest radiographs using two different machine learning approaches: the eXtreme gradient boosting (XGBoost) algorithm and the deep convolutional neural network residual network (ResNet50). The goal of the study is to develop effective and accurate methods for automatically classifying various pathologies detected on chest X-rays. The study collected an extensive dataset of digital chest radiographs, including a variety of clinical cases and different classes of pathology. Developed and trained machine learning models based on the XGBoost algorithm and the ResNet50 convolutional neural network using preprocessed images. The performance and accuracy of both models were assessed on test data using quality metrics and a comparative analysis of the results was carried out. The expected results of the article are high accuracy and reliability of methods for classifying pathologies on chest radiographs, as well as an understanding of their effectiveness in the context of clinical practice. These results may have significant implications for improving the diagnosis and care of patients with chest diseases, as well as promoting the development of automated decision support systems in radiology.
Detection of lung pathology using the fractal methodIJECEIAES
Currently, the detection of pathology of lung cavities and their digitalization is one of the urgent problems of the healthcare industry in Kazakhstan. In this paper, the method of fractal analysis was considered to solve the task set. Diagnosis of lung pathology based on fractal analysis is an actively developing area of medical research. Conducted experiments on a set of clinical data confirm the effectiveness of the proposed methodology. The results obtained show that fractal analysis can be a useful tool for early detection of lung pathologies. It allows you to detect even minor changes in the structure and texture of lung tissues, which may not be obvious during visual analysis. The article deals with images of pathology of the pulmonary cavity, taken from an open data source. Based on the analysis of fractal objects, they were pre-assembled. Software algorithms for the operation of the information system for screening diagnostics have been developed. Based on the information contained in the fractal image of the lungs, mathematical models have been developed to create a diagnostic rule. A reference set of information features has been created that allows you to create algorithms for diagnosing the lungs: healthy and with pathologies of tuberculosis
A comparative analysis of chronic obstructive pulmonary disease using machin...IJECEIAES
Chronic obstructive pulmonary disease (COPD) is a general clinical issue in numerous countries considered the fifth reason for inability and the third reason for mortality on a global scale within 2021. From recent reviews, a deep convolutional neural network (CNN) is used in the primary analysis of the deadly COPD, which uses the computed tomography (CT) images procured from the deep learning tools. Detection and analysis of COPD using several image processing techniques, deep learning models, and machine learning models are notable contributions to this review. This research aims to cover the detailed findings on pulmonary diseases or lung diseases, their causes, and symptoms, which will help treat infections with high performance and a swift response. The articles selected have more than 80% accuracy and are tabulated and analyzed for sensitivity, specificity, and area under the curve (AUC) using different methodologies. This research focuses on the various tools and techniques used in COPD analysis and eventually provides an overview of COPD with coronavirus disease 2019 (COVID-19) symptoms.
Evaluation of SVM performance in the detection of lung cancer in marked CT s...nooriasukmaningtyas
This paper concerns the development/analysis of the IQ-OTH/NCCD lung cancer dataset. This CT-scan dataset includes more than 1100 images of diagnosed healthy and tumorous chest scans collected in two Iraqi hospitals. A computer system is proposed for detecting lung cancer in the dataset by using image-processing/computer-vision techniques. This includes three preprocessing stages: image enhancement, image segmentation, and feature extraction techniques. Then, support vector machine (SVM) is used at the final stage as a classification technique for identifying the cases on the slides as one of three classes: normal, benign, or malignant. Different SVM kernels and feature extraction techniques are evaluated. The best accuracy achieved by applying this procedure on the new dataset was 89.8876%.
Classification of pathologies on digital chest radiographs using machine lear...IJECEIAES
This article is devoted to the research and development of methods for classifying pathologies on digital chest radiographs using two different machine learning approaches: the eXtreme gradient boosting (XGBoost) algorithm and the deep convolutional neural network residual network (ResNet50). The goal of the study is to develop effective and accurate methods for automatically classifying various pathologies detected on chest X-rays. The study collected an extensive dataset of digital chest radiographs, including a variety of clinical cases and different classes of pathology. Developed and trained machine learning models based on the XGBoost algorithm and the ResNet50 convolutional neural network using preprocessed images. The performance and accuracy of both models were assessed on test data using quality metrics and a comparative analysis of the results was carried out. The expected results of the article are high accuracy and reliability of methods for classifying pathologies on chest radiographs, as well as an understanding of their effectiveness in the context of clinical practice. These results may have significant implications for improving the diagnosis and care of patients with chest diseases, as well as promoting the development of automated decision support systems in radiology.
Detection of lung pathology using the fractal methodIJECEIAES
Currently, the detection of pathology of lung cavities and their digitalization is one of the urgent problems of the healthcare industry in Kazakhstan. In this paper, the method of fractal analysis was considered to solve the task set. Diagnosis of lung pathology based on fractal analysis is an actively developing area of medical research. Conducted experiments on a set of clinical data confirm the effectiveness of the proposed methodology. The results obtained show that fractal analysis can be a useful tool for early detection of lung pathologies. It allows you to detect even minor changes in the structure and texture of lung tissues, which may not be obvious during visual analysis. The article deals with images of pathology of the pulmonary cavity, taken from an open data source. Based on the analysis of fractal objects, they were pre-assembled. Software algorithms for the operation of the information system for screening diagnostics have been developed. Based on the information contained in the fractal image of the lungs, mathematical models have been developed to create a diagnostic rule. A reference set of information features has been created that allows you to create algorithms for diagnosing the lungs: healthy and with pathologies of tuberculosis
A comparative analysis of chronic obstructive pulmonary disease using machin...IJECEIAES
Chronic obstructive pulmonary disease (COPD) is a general clinical issue in numerous countries considered the fifth reason for inability and the third reason for mortality on a global scale within 2021. From recent reviews, a deep convolutional neural network (CNN) is used in the primary analysis of the deadly COPD, which uses the computed tomography (CT) images procured from the deep learning tools. Detection and analysis of COPD using several image processing techniques, deep learning models, and machine learning models are notable contributions to this review. This research aims to cover the detailed findings on pulmonary diseases or lung diseases, their causes, and symptoms, which will help treat infections with high performance and a swift response. The articles selected have more than 80% accuracy and are tabulated and analyzed for sensitivity, specificity, and area under the curve (AUC) using different methodologies. This research focuses on the various tools and techniques used in COPD analysis and eventually provides an overview of COPD with coronavirus disease 2019 (COVID-19) symptoms.
Evaluation of SVM performance in the detection of lung cancer in marked CT s...nooriasukmaningtyas
This paper concerns the development/analysis of the IQ-OTH/NCCD lung cancer dataset. This CT-scan dataset includes more than 1100 images of diagnosed healthy and tumorous chest scans collected in two Iraqi hospitals. A computer system is proposed for detecting lung cancer in the dataset by using image-processing/computer-vision techniques. This includes three preprocessing stages: image enhancement, image segmentation, and feature extraction techniques. Then, support vector machine (SVM) is used at the final stage as a classification technique for identifying the cases on the slides as one of three classes: normal, benign, or malignant. Different SVM kernels and feature extraction techniques are evaluated. The best accuracy achieved by applying this procedure on the new dataset was 89.8876%.
MARIE: VALIDATION OF THE ARTIFICIAL INTELLIGENCE MODEL FOR COVID-19 DETECTIONijma
Lung X-ray images, if processed using statistical and computational methods, can distinguish pneumonia from COVID-19. The present work shows that it is possible to extract lung X-ray characteristics to improve the methods of examining and diagnosing patients with suspected COVID-19, distinguishing them from malaria, tuberculosis, and Streptococcus pneumonia. More precisely, an intelligent computational model was developed to process lung X-ray images and classify whether the image is of a patient with COVID-19. In partnership with the municipality of Itapeva, Minas Gerais, we carried out patient analysis and, at the same time, we evolved the algorithms to meet the needs of health professionals and also expand support in tracking COVID-19 in the municipality. In this project we will describe cases and even signs and symptoms that were similar to the clinical performed by the doctor. The average recognition accuracy of COVID-19 was 0.97,4 ± 0.043.
In this proposed work, we identified the significant research issues on lung cancer risk factors. Capturing and defining symptoms at an early stage is one of the most difficult phases for patients. Based on the history of patients records, we reviewed a number of current research studies on lung cancer and its various stages. We identified that lung cancer is one of the significant research issues in predicting the early stages of cancer disease. This research aimed to develop a model that can detect lung cancer with a remarkably high level of accuracy using the deep learning approach (convolution neural network). This method considers and resolves significant gaps in previous studies. We compare the accuracy levels and loss values of our model with VGG16, InceptionV3, and Resnet50. We found that our model achieved an accuracy of 94% and a minimum loss of 0.1%. Hence physicians can use our convolution neural network models for predicting lung cancer risk factors in the real world. Moreover, this investigation reveals that squamous cell carcinoma, normal, adenocarcinoma, and large cell carcinoma are the most significant risk factors. In addition, the remaining attributes are also crucial for achieving the best performance.
A deep learning framework for accurate diagnosis of colorectal cancer using h...IJECEIAES
Colorectal cancer (CRC) is one of the most prevalent malignancies worldwide, with high mortality and incidence rates. Early detection of the disease may increase the probability of survival, making it critical to develop effective procedures for precise treatment. In the past few years, there has been an increased use of deep learning techniques in image classification that aid in the detection of various types of cancer. In this study, convolutional neural network (CNN) models were used to classify colorectal cancer into benign and malignant. After applying various data preprocessing techniques to the image dataset, we evaluated our prototypes using three distinct subsets of testing data, representing 20%, 30%, and 40% of the total dataset. Additionally, four pre-trained CNN models (ResNet-18, ResNet-50, GoogLeNet, and MobileNetV2) were trained, and the network architectural techniques were compared by applying the Adam optimizer. Finally, we assessed the performance of algorithms in terms of accuracy, sensitivity, specificity, precision, F1-score, and area under the receiver operating characteristic (ROC) curve (AUC). In this research, deep learning approaches demonstrated high efficacy in accurately diagnosing colorectal cancer. This indicates that these techniques have an important and significant value for advancing medical research.
Recognition of Corona virus disease (COVID-19) using deep learning network IJECEIAES
Corona virus disease (COVID-19) has an incredible influence in the last few months. It causes thousands of deaths in round the world. This make a rapid research movement to deal with this new virus. As a computer science, many technical researches have been done to tackle with it by using image processing algorithms. In this work, we introduce a method based on deep learning networks to classify COVID-19 based on x-ray images. Our results are encouraging to rely on to classify the infected people from the normal. We conduct our experiments on recent dataset, Kaggle dataset of COVID-19 X-ray images and using ResNet50 deep learning network with 5 and 10 folds cross validation. The experiments results show that 5 folds gives effective results than 10 folds with accuracy rate 97.28%.
Insomnia analysis based on internet of things using electrocardiography and e...TELKOMNIKA JOURNAL
Insomnia is a disorder to start, maintain, and wake up from sleep, has many sufferers in the world. For patients in remote locations who suffer from insomnia, which requires testing, the gold standard performed requires patients to take the time and travel to the health care center. By making alternatives to remote sleep insomnia testing using electrocardiography and electromyography connected to the internet of things can solve the problem of patients' access to treatment. Delivery of patient data to the server is done to make observations from the visualization of patient data in real-time. Furthermore, using artificial neural networks was used to classify EMG, ECG, and combine patient data to determine patients who have Insomnia get resulted in patient classification errors around 0.2% to 2.7%.
PREDICTIVE ANALYTICS IN HEALTHCARE SYSTEM USING DATA MINING TECHNIQUEScscpconf
The health sector has witnessed a great evolution following the development of new computer technologies, and that pushed this area to produce more medical data, which gave birth to multiple fields of research. Many efforts are done to cope with the explosion of medical data on one hand, and to obtain useful knowledge from it on the other hand. This prompted researchers to apply all the technical innovations like big data analytics, predictive analytics, machine learning and learning algorithms in order to extract useful knowledge and help in making decisions. With the promises of predictive analytics in big data, and the use of machine learning
algorithms, predicting future is no longer a difficult task, especially for medicine because predicting diseases and anticipating the cure became possible. In this paper we will present an overview on the evolution of big data in healthcare system, and we will apply a learning algorithm on a set of medical data. The objective is to predict chronic kidney diseases by using Decision Tree (C4.5) algorithm.
Involving machine learning techniques in heart disease diagnosis: a performan...IJECEIAES
Artificial intelligence is a science that is growing at a tremendous speed every day and has become an essential part of many domains, including the medical domain. Therefore, countless artificial intelligence applications can be seen in the medical domain at various levels, which are employed to enhance early diagnosis and prediction and reduce the risks associated with many diseases, including heart diseases. In this article, machine learning techniques (logistic regression, random forest, artificial neural network, support vector machines, and k-nearest neighbors) are utilized to diagnose heart disease from the Cleveland Clinic dataset got from the University of California Irvine machine learning (UCL) repository and Kaggle platform then create a comparison between the performance of these techniques. In addition, some literature related to machine learning and deep learning techniques that aim to provide reasonable solutions in monitoring, detecting, diagnosing, and predicting heart disease and how these technologies assist in making health decisions are reviewed. Ten studies are selected and summarized by the authors published between 2017 and 2022 are illustrated. After executing a series of tests, it is seen that the most profitable performance in diagnosing heart disease is the support vector machines, with a diagnostic accuracy of 96%. This article has concluded that these techniques play a significant and influential role in assisting physicians and health care workers in analyzing heart patients' data, making health decisions, and saving patients' lives.
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.
Early detection of breast cancer using mammography images and software engine...TELKOMNIKA JOURNAL
The breast cancer has affected a wide region of women as a particular case. Therefore, different researchers have focused on the early detection of this disease to overcome it in efficient way. In this paper, an early breast cancer detection system has been proposed based on mammography images. The proposed system adopts deep-learning technique to increase the accuracy of detection. The convolutional neural network (CNN) model is considered for preparing the datasets of training and test. It is important to note that the software engineering process model has been adopted in constructing the proposed algorithm. This is to increase the reliably, flexibility and extendibility of the system. The user interfaces of the system are designed as a website used at country side general purpose (GP) health centers for early detection to the disease under lacking in specialist medical staff. The obtained results show the efficiency of the proposed system in terms of accuracy up to more than 90% and decrease the efforts of medical staff as well as helping the patients. As a conclusion, the proposed system can help patients by early detecting the breast cancer at far places from hospital and referring them to nearest specialist center.
COVID-19 knowledge-based system for diagnosis in Iraq using IoT environmentnooriasukmaningtyas
The importance and benefits of healthcare mobile applications is increasing rapidly, especially when such applications are connected to the internet of things (IoT). This paper describes a smart knowledge-based system (KBS) that helps patients showing symptoms of Influenza verify being infected with Coronavirus, commonly known as COVID-19. In addition to the systems’ diagnostic functionality, it helps these patients get medical assistance fast by notifying medical authorities using the IoT. This system displays patient’s location, phone number, date and time of examination. During the applications’ development, the developers used Twilio, short message service (SMS), WhatsApp, and Google map applications.
A new procedure for lung region segmentation from computed tomography imagesIJECEIAES
Lung cancer is the leading cause of cancer death among people worldwide. The primary aim of this research is to establish an image processing method for lung cancer detection. This paper focuses on lung region segmentation from computed tomography (CT) scan images. In this work, a new procedure for lung region segmentation is proposed. First, the lung CT scan images will undergo an image thresholding stage before going through two morphological reconstruction and masking stages. In between morphological and masking stages, object extraction, border change, and object elimination will occur. Finally, the lung field will be annotated. The outcomes of the proposed procedure and previous lung segmentation methods i.e., the modified watershed segmentation method is compared with the ground truth images for performance evaluation that will be carried out both in qualitative and quantitative manners. Based on the analyses, the new proposed procedure for lung segmentation, denotes better performance, an increment by 0.02% to 3.5% in quantitative analysis. The proposed procedure produced better-segmented images for qualitative analysis and became the most frequently selected method by the 22 experts. This study shows that the outcome from the proposed method outperforms the existing modified watershed segmentation method.
deep learning applications in medical image analysis brain tumorVenkat Projects
The tremendous success of machine learning algorithms at image recognition tasks in recent years intersects with a time of dramatically increased use of electronic medical records and diagnostic imaging. This review introduces the machine learning algorithms as applied to medical image analysis, focusing on convolutional neural networks, and emphasizing clinical aspects of the _eld. The advantage of machine learning in an era of medical big data is that signi_cant hierarchal relationships within the data can be discovered algorithmically without laborious hand-crafting of features. We cover key research areas and applications of medical image classi_cation, localization, detection, segmentation, and registration. We conclude by discussing research obstacles, emerging trends, and possible future directions.
Design AI platform using fuzzy logic technique to diagnose kidney diseasesTELKOMNIKA JOURNAL
Artificial intelligence (AI) is an advanced scientific technology that can provide strong ability to assist in analysis and diagnosis of almost every type of data, therefore; AI widely used in medical fields, which is applied in the diagnosis and early detection of diseases. Kidney disease is one of the common diseases that are diagnosed and the necessary treatments are suggested by artificial intelligence. In this research, a logic system was used. The fuzzy logic system (FLS) is one of the artificial intelligence systems for diagnosing kidney diseases, where the fuzzy logic system divided into five variable inputs, namely urea, creatinine, glucose, bun, and uric acid, and they represented laboratory tests of the patients, this variables and also three outputs were identified, which are chronic inflammation and kidney failure, stones and salts, acute inflammation of the kidneys and bladder, which is the result of the medical diagnosis of the disease. Five memberships for inputs and three memberships for outputs are used in FLS. Diseases are concluded based on the values of the inputs, and thus the system proved its effectiveness and accuracy in diagnosis and this system is considered an aid to the specialized doctors in the field of kidney diseases.
Proposed Model for Chest Disease Prediction using Data Analyticsvivatechijri
Chest diseases if not properly diagnosed in early stages can be fatal. Because of lack of skilled
knowledge or experiences of real life practitioners, many a times one chest disease is wrongly diagnosed for the
other, which leads to wrong treatment. Due to this the actual disease keeps on growing and become fatal. For
example, muscular chest pains can be treated for the heart disease or COPD is treated for Asthma. Early
prediction of chest disease is crucial but is not an easy task. Consequently, the computer based prediction system
for chest disease may play a significant role as a pre-stage detection to take proper actions with a view to recover
from it. However the choice of the proper Data Mining classification method can effectively predict the early
stage of the disease for being cured from it. In this paper, the three mostly used classification techniques such as
support vector machine (SVM), k-nearest neighbour (KNN) and artificial neural network (ANN) have been studied
with a view to evaluating them for chest disease prediction.
Machine learning approach for predicting heart and diabetes diseases using da...IAESIJAI
Environmental changes and food habits affect people's health with numerous diseases in today's life. Machine learning is a technique that plays a vital role in predicting diseases from collected data. The health sector has plenty of electronic medical data, which helps this technique to diagnose various diseases quickly and accurately. There has been an improvement in accuracy in medical data analysis as data continues to grow in the medical field. Doctors may have a hard time predicting symptoms accurately. This proposed work utilized Kaggle data to predict and diagnose heart and diabetic diseases. The diseases heart and diabetes are the foremost cause of higher death rates for people. The dataset contains target features for the diagnosis of heart disease. This work finds the target variable for diabetic disease by comparing the patient's blood sugars to normal levels. Blood pressure, body mass index (BMI), and other factors diagnose these diseases and disorders. This work justifies the filter method and principal component analysis for selecting and extracting the feature. The main aim of this work is to highlight the implementation of three ensemble techniques-Adaptive boost, Extreme Gradient boosting, and Gradient boosting-as well as the emphasis placed on the accuracy of the results.
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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Lung X-ray images, if processed using statistical and computational methods, can distinguish pneumonia from COVID-19. The present work shows that it is possible to extract lung X-ray characteristics to improve the methods of examining and diagnosing patients with suspected COVID-19, distinguishing them from malaria, tuberculosis, and Streptococcus pneumonia. More precisely, an intelligent computational model was developed to process lung X-ray images and classify whether the image is of a patient with COVID-19. In partnership with the municipality of Itapeva, Minas Gerais, we carried out patient analysis and, at the same time, we evolved the algorithms to meet the needs of health professionals and also expand support in tracking COVID-19 in the municipality. In this project we will describe cases and even signs and symptoms that were similar to the clinical performed by the doctor. The average recognition accuracy of COVID-19 was 0.97,4 ± 0.043.
In this proposed work, we identified the significant research issues on lung cancer risk factors. Capturing and defining symptoms at an early stage is one of the most difficult phases for patients. Based on the history of patients records, we reviewed a number of current research studies on lung cancer and its various stages. We identified that lung cancer is one of the significant research issues in predicting the early stages of cancer disease. This research aimed to develop a model that can detect lung cancer with a remarkably high level of accuracy using the deep learning approach (convolution neural network). This method considers and resolves significant gaps in previous studies. We compare the accuracy levels and loss values of our model with VGG16, InceptionV3, and Resnet50. We found that our model achieved an accuracy of 94% and a minimum loss of 0.1%. Hence physicians can use our convolution neural network models for predicting lung cancer risk factors in the real world. Moreover, this investigation reveals that squamous cell carcinoma, normal, adenocarcinoma, and large cell carcinoma are the most significant risk factors. In addition, the remaining attributes are also crucial for achieving the best performance.
A deep learning framework for accurate diagnosis of colorectal cancer using h...IJECEIAES
Colorectal cancer (CRC) is one of the most prevalent malignancies worldwide, with high mortality and incidence rates. Early detection of the disease may increase the probability of survival, making it critical to develop effective procedures for precise treatment. In the past few years, there has been an increased use of deep learning techniques in image classification that aid in the detection of various types of cancer. In this study, convolutional neural network (CNN) models were used to classify colorectal cancer into benign and malignant. After applying various data preprocessing techniques to the image dataset, we evaluated our prototypes using three distinct subsets of testing data, representing 20%, 30%, and 40% of the total dataset. Additionally, four pre-trained CNN models (ResNet-18, ResNet-50, GoogLeNet, and MobileNetV2) were trained, and the network architectural techniques were compared by applying the Adam optimizer. Finally, we assessed the performance of algorithms in terms of accuracy, sensitivity, specificity, precision, F1-score, and area under the receiver operating characteristic (ROC) curve (AUC). In this research, deep learning approaches demonstrated high efficacy in accurately diagnosing colorectal cancer. This indicates that these techniques have an important and significant value for advancing medical research.
Recognition of Corona virus disease (COVID-19) using deep learning network IJECEIAES
Corona virus disease (COVID-19) has an incredible influence in the last few months. It causes thousands of deaths in round the world. This make a rapid research movement to deal with this new virus. As a computer science, many technical researches have been done to tackle with it by using image processing algorithms. In this work, we introduce a method based on deep learning networks to classify COVID-19 based on x-ray images. Our results are encouraging to rely on to classify the infected people from the normal. We conduct our experiments on recent dataset, Kaggle dataset of COVID-19 X-ray images and using ResNet50 deep learning network with 5 and 10 folds cross validation. The experiments results show that 5 folds gives effective results than 10 folds with accuracy rate 97.28%.
Insomnia analysis based on internet of things using electrocardiography and e...TELKOMNIKA JOURNAL
Insomnia is a disorder to start, maintain, and wake up from sleep, has many sufferers in the world. For patients in remote locations who suffer from insomnia, which requires testing, the gold standard performed requires patients to take the time and travel to the health care center. By making alternatives to remote sleep insomnia testing using electrocardiography and electromyography connected to the internet of things can solve the problem of patients' access to treatment. Delivery of patient data to the server is done to make observations from the visualization of patient data in real-time. Furthermore, using artificial neural networks was used to classify EMG, ECG, and combine patient data to determine patients who have Insomnia get resulted in patient classification errors around 0.2% to 2.7%.
PREDICTIVE ANALYTICS IN HEALTHCARE SYSTEM USING DATA MINING TECHNIQUEScscpconf
The health sector has witnessed a great evolution following the development of new computer technologies, and that pushed this area to produce more medical data, which gave birth to multiple fields of research. Many efforts are done to cope with the explosion of medical data on one hand, and to obtain useful knowledge from it on the other hand. This prompted researchers to apply all the technical innovations like big data analytics, predictive analytics, machine learning and learning algorithms in order to extract useful knowledge and help in making decisions. With the promises of predictive analytics in big data, and the use of machine learning
algorithms, predicting future is no longer a difficult task, especially for medicine because predicting diseases and anticipating the cure became possible. In this paper we will present an overview on the evolution of big data in healthcare system, and we will apply a learning algorithm on a set of medical data. The objective is to predict chronic kidney diseases by using Decision Tree (C4.5) algorithm.
Involving machine learning techniques in heart disease diagnosis: a performan...IJECEIAES
Artificial intelligence is a science that is growing at a tremendous speed every day and has become an essential part of many domains, including the medical domain. Therefore, countless artificial intelligence applications can be seen in the medical domain at various levels, which are employed to enhance early diagnosis and prediction and reduce the risks associated with many diseases, including heart diseases. In this article, machine learning techniques (logistic regression, random forest, artificial neural network, support vector machines, and k-nearest neighbors) are utilized to diagnose heart disease from the Cleveland Clinic dataset got from the University of California Irvine machine learning (UCL) repository and Kaggle platform then create a comparison between the performance of these techniques. In addition, some literature related to machine learning and deep learning techniques that aim to provide reasonable solutions in monitoring, detecting, diagnosing, and predicting heart disease and how these technologies assist in making health decisions are reviewed. Ten studies are selected and summarized by the authors published between 2017 and 2022 are illustrated. After executing a series of tests, it is seen that the most profitable performance in diagnosing heart disease is the support vector machines, with a diagnostic accuracy of 96%. This article has concluded that these techniques play a significant and influential role in assisting physicians and health care workers in analyzing heart patients' data, making health decisions, and saving patients' lives.
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.
Early detection of breast cancer using mammography images and software engine...TELKOMNIKA JOURNAL
The breast cancer has affected a wide region of women as a particular case. Therefore, different researchers have focused on the early detection of this disease to overcome it in efficient way. In this paper, an early breast cancer detection system has been proposed based on mammography images. The proposed system adopts deep-learning technique to increase the accuracy of detection. The convolutional neural network (CNN) model is considered for preparing the datasets of training and test. It is important to note that the software engineering process model has been adopted in constructing the proposed algorithm. This is to increase the reliably, flexibility and extendibility of the system. The user interfaces of the system are designed as a website used at country side general purpose (GP) health centers for early detection to the disease under lacking in specialist medical staff. The obtained results show the efficiency of the proposed system in terms of accuracy up to more than 90% and decrease the efforts of medical staff as well as helping the patients. As a conclusion, the proposed system can help patients by early detecting the breast cancer at far places from hospital and referring them to nearest specialist center.
COVID-19 knowledge-based system for diagnosis in Iraq using IoT environmentnooriasukmaningtyas
The importance and benefits of healthcare mobile applications is increasing rapidly, especially when such applications are connected to the internet of things (IoT). This paper describes a smart knowledge-based system (KBS) that helps patients showing symptoms of Influenza verify being infected with Coronavirus, commonly known as COVID-19. In addition to the systems’ diagnostic functionality, it helps these patients get medical assistance fast by notifying medical authorities using the IoT. This system displays patient’s location, phone number, date and time of examination. During the applications’ development, the developers used Twilio, short message service (SMS), WhatsApp, and Google map applications.
A new procedure for lung region segmentation from computed tomography imagesIJECEIAES
Lung cancer is the leading cause of cancer death among people worldwide. The primary aim of this research is to establish an image processing method for lung cancer detection. This paper focuses on lung region segmentation from computed tomography (CT) scan images. In this work, a new procedure for lung region segmentation is proposed. First, the lung CT scan images will undergo an image thresholding stage before going through two morphological reconstruction and masking stages. In between morphological and masking stages, object extraction, border change, and object elimination will occur. Finally, the lung field will be annotated. The outcomes of the proposed procedure and previous lung segmentation methods i.e., the modified watershed segmentation method is compared with the ground truth images for performance evaluation that will be carried out both in qualitative and quantitative manners. Based on the analyses, the new proposed procedure for lung segmentation, denotes better performance, an increment by 0.02% to 3.5% in quantitative analysis. The proposed procedure produced better-segmented images for qualitative analysis and became the most frequently selected method by the 22 experts. This study shows that the outcome from the proposed method outperforms the existing modified watershed segmentation method.
deep learning applications in medical image analysis brain tumorVenkat Projects
The tremendous success of machine learning algorithms at image recognition tasks in recent years intersects with a time of dramatically increased use of electronic medical records and diagnostic imaging. This review introduces the machine learning algorithms as applied to medical image analysis, focusing on convolutional neural networks, and emphasizing clinical aspects of the _eld. The advantage of machine learning in an era of medical big data is that signi_cant hierarchal relationships within the data can be discovered algorithmically without laborious hand-crafting of features. We cover key research areas and applications of medical image classi_cation, localization, detection, segmentation, and registration. We conclude by discussing research obstacles, emerging trends, and possible future directions.
Design AI platform using fuzzy logic technique to diagnose kidney diseasesTELKOMNIKA JOURNAL
Artificial intelligence (AI) is an advanced scientific technology that can provide strong ability to assist in analysis and diagnosis of almost every type of data, therefore; AI widely used in medical fields, which is applied in the diagnosis and early detection of diseases. Kidney disease is one of the common diseases that are diagnosed and the necessary treatments are suggested by artificial intelligence. In this research, a logic system was used. The fuzzy logic system (FLS) is one of the artificial intelligence systems for diagnosing kidney diseases, where the fuzzy logic system divided into five variable inputs, namely urea, creatinine, glucose, bun, and uric acid, and they represented laboratory tests of the patients, this variables and also three outputs were identified, which are chronic inflammation and kidney failure, stones and salts, acute inflammation of the kidneys and bladder, which is the result of the medical diagnosis of the disease. Five memberships for inputs and three memberships for outputs are used in FLS. Diseases are concluded based on the values of the inputs, and thus the system proved its effectiveness and accuracy in diagnosis and this system is considered an aid to the specialized doctors in the field of kidney diseases.
Proposed Model for Chest Disease Prediction using Data Analyticsvivatechijri
Chest diseases if not properly diagnosed in early stages can be fatal. Because of lack of skilled
knowledge or experiences of real life practitioners, many a times one chest disease is wrongly diagnosed for the
other, which leads to wrong treatment. Due to this the actual disease keeps on growing and become fatal. For
example, muscular chest pains can be treated for the heart disease or COPD is treated for Asthma. Early
prediction of chest disease is crucial but is not an easy task. Consequently, the computer based prediction system
for chest disease may play a significant role as a pre-stage detection to take proper actions with a view to recover
from it. However the choice of the proper Data Mining classification method can effectively predict the early
stage of the disease for being cured from it. In this paper, the three mostly used classification techniques such as
support vector machine (SVM), k-nearest neighbour (KNN) and artificial neural network (ANN) have been studied
with a view to evaluating them for chest disease prediction.
Machine learning approach for predicting heart and diabetes diseases using da...IAESIJAI
Environmental changes and food habits affect people's health with numerous diseases in today's life. Machine learning is a technique that plays a vital role in predicting diseases from collected data. The health sector has plenty of electronic medical data, which helps this technique to diagnose various diseases quickly and accurately. There has been an improvement in accuracy in medical data analysis as data continues to grow in the medical field. Doctors may have a hard time predicting symptoms accurately. This proposed work utilized Kaggle data to predict and diagnose heart and diabetic diseases. The diseases heart and diabetes are the foremost cause of higher death rates for people. The dataset contains target features for the diagnosis of heart disease. This work finds the target variable for diabetic disease by comparing the patient's blood sugars to normal levels. Blood pressure, body mass index (BMI), and other factors diagnose these diseases and disorders. This work justifies the filter method and principal component analysis for selecting and extracting the feature. The main aim of this work is to highlight the implementation of three ensemble techniques-Adaptive boost, Extreme Gradient boosting, and Gradient boosting-as well as the emphasis placed on the accuracy of the results.
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Developing a smart system for infant incubators using the internet of things ...IJECEIAES
This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R 2 value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R 2 value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.
A review on internet of things-based stingless bee's honey production with im...IJECEIAES
Honey is produced exclusively by honeybees and stingless bees which both are well adapted to tropical and subtropical regions such as Malaysia. Stingless bees are known for producing small amounts of honey and are known for having a unique flavor profile. Problem identified that many stingless bees collapsed due to weather, temperature and environment. It is critical to understand the relationship between the production of stingless bee honey and environmental conditions to improve honey production. Thus, this paper presents a review on stingless bee's honey production and prediction modeling. About 54 previous research has been analyzed and compared in identifying the research gaps. A framework on modeling the prediction of stingless bee honey is derived. The result presents the comparison and analysis on the internet of things (IoT) monitoring systems, honey production estimation, convolution neural networks (CNNs), and automatic identification methods on bee species. It is identified based on image detection method the top best three efficiency presents CNN is at 98.67%, densely connected convolutional networks with YOLO v3 is 97.7%, and DenseNet201 convolutional networks 99.81%. This study is significant to assist the researcher in developing a model for predicting stingless honey produced by bee's output, which is important for a stable economy and food security.
A trust based secure access control using authentication mechanism for intero...IJECEIAES
The internet of things (IoT) is a revolutionary innovation in many aspects of our society including interactions, financial activity, and global security such as the military and battlefield internet. Due to the limited energy and processing capacity of network devices, security, energy consumption, compatibility, and device heterogeneity are the long-term IoT problems. As a result, energy and security are critical for data transmission across edge and IoT networks. Existing IoT interoperability techniques need more computation time, have unreliable authentication mechanisms that break easily, lose data easily, and have low confidentiality. In this paper, a key agreement protocol-based authentication mechanism for IoT devices is offered as a solution to this issue. This system makes use of information exchange, which must be secured to prevent access by unauthorized users. Using a compact contiki/cooja simulator, the performance and design of the suggested framework are validated. The simulation findings are evaluated based on detection of malicious nodes after 60 minutes of simulation. The suggested trust method, which is based on privacy access control, reduced packet loss ratio to 0.32%, consumed 0.39% power, and had the greatest average residual energy of 0.99 mJoules at 10 nodes.
Fuzzy linear programming with the intuitionistic polygonal fuzzy numbersIJECEIAES
In real world applications, data are subject to ambiguity due to several factors; fuzzy sets and fuzzy numbers propose a great tool to model such ambiguity. In case of hesitation, the complement of a membership value in fuzzy numbers can be different from the non-membership value, in which case we can model using intuitionistic fuzzy numbers as they provide flexibility by defining both a membership and a non-membership functions. In this article, we consider the intuitionistic fuzzy linear programming problem with intuitionistic polygonal fuzzy numbers, which is a generalization of the previous polygonal fuzzy numbers found in the literature. We present a modification of the simplex method that can be used to solve any general intuitionistic fuzzy linear programming problem after approximating the problem by an intuitionistic polygonal fuzzy number with n edges. This method is given in a simple tableau formulation, and then applied on numerical examples for clarity.
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.
Fuzzy logic method-based stress detector with blood pressure and body tempera...IJECEIAES
In this study, using the fuzzy logic method, a stress detection tool was created with body temperature and blood pressure parameters as indicators to determine a person's stress level. This tool uses the LM35DZ sensor to detect body temperature, the MPX5100GP sensor to read blood pressure values, and Arduino Uno as a data processor from sensor readings which are then calculated using the fuzzy logic method as a stress level decisionmaker. The resulting output measures blood pressure, body temperature, and the stress level experienced by a person, which will be displayed on the liquid crystal display. Based on the results of testing the body temperature parameter, the highest error generated was 1.17%, and for the blood pressure parameter, the highest error was 2.5% for systole and 0.93% for diastole. Furthermore, testing the stress level displayed on the tool is compared to the depression, anxiety, and stress scales 42 (DASS 42), a psychological stress measuring instrument. From the results of testing the tool with the questionnaire, the average conformity level is 74%.
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Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
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Detection of chest pathologies using autocorrelation functions
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 4, August 2023, pp. 4526~4534
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i4.pp4526-4534 4526
Journal homepage: http://ijece.iaescore.com
Detection of chest pathologies using autocorrelation functions
Gulzira Abdikerimova1
, Ainur Shekerbek1
, Murat Tulenbayev2
, Svetlana Beglerova2
,
Elena Zakharevich2
, Gulmira Bekmagambetova3
, Zhanat Manbetova4
, Makbal Baibulova1
1
Department of Information Systems, Faculty of Information Technology, L.N. Gumilyov Eurasian National University, Astana,
Republic of Kazakhstan
2
Department of Information Systems, Faculty of Information Technology, M. Kh. Dulaty Taraz Regional University, Taraz,
Republic of Kazakhstan
3
Department of Information technology, Faculty of Technology, Kazakh University of Technology and Business, Astana,
Republic of Kazakhstan
4
Department of Radio Engineering, Electronics and Telecommunications, Faculty of Energy, Saken Seifullin Kazakh Agrotechnical
University, Astana, Republic of Kazakhstan
Article Info ABSTRACT
Article history:
Received Nov 14, 2022
Revised Dec 8, 2022
Accepted Jan 14, 2023
An important feature of image analysis is texture, seen in all images, from
aerial and satellite images to microscopic images in biomedical research. A
chest X-ray is the most common and effective method for diagnosing severe
lung diseases such as cancer, pneumonia, and tuberculosis. The lungs are the
largest X-ray object. The correct separation of the shapes and sizes of the
contours of the lungs is an important reason for diagnosis, because of which
an intelligent information environment can be created. Despite the use of
X-rays, to identify the diagnosis, there is a chance that the disease will not be
detected. In this sense, there is a risk of development, which may be fatal.
The article deals with the problems of pneumonia clustering using the
autocorrelation function to obtain the most accurate result. This provides a
reliable tool for diagnosing lung radiographs. Image pre-processing and data
shaping play an important role in revealing a well-functioning basis of the
nervous system. Therefore, images from two classes were selected for the
task: healthy and with pneumonia. This paper demonstrates the applicability
of the autocorrelation function for highlighting interest in lung radiographs
based on the fineness of textural features and k-means extraction.
Keywords:
Chest radiograph
Clustering
Medical imaging
Pathology
Texture
This is an open access article under the CC BY-SA license.
Corresponding Author:
Ainur Shekerbek
Department of Information Systems, Faculty Information Technology, L. N. Gumilyov Eurasian National
University
010000 Astana, Republic of Kazakhstan
Email: shekerbek80@mail.ru
1. INTRODUCTION
With the spread of radiation diagnostic methods, the volume of workload on radiologists has
increased significantly. The constant process of digitalization in healthcare allows the use of more and more
new technologies in various areas of medicine. Currently, the Government of the Republic of Kazakhstan is
actively involved in the digitalization of all sectors and areas of the economy. On December 26, 2019, the
Ministry of Health of the Republic of Kazakhstan presented the “State Program for the Development of
Healthcare of the Republic of Kazakhstan for 2020-2025”, which contains several indicators to improve the
quality of life of the population, the achievement of which is planned by 2025. One of the main focuses of the
program will be strengthening the health of children, adolescents, and young people through the prevention
of diseases, the provision of timely assistance, and full rehabilitation taking into account the best
2. Int J Elec & Comp Eng ISSN: 2088-8708
Detection of chest pathologies using autocorrelation functions (Gulzira Abdikerimova)
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international practice, following the approaches in the Health Strategy for 2016-2030 of UNICEF. The
epidemiological situation for infectious diseases in the Republic of Kazakhstan for 2018 is stable. A decrease
in the incidence of 34 infectious and parasitic diseases has been achieved.
The study aims to develop an instrumental environment for the automatic classification of
radiograph images based on autocorrelation functions. With the goal, to realize the capabilities of the
software being created, it was necessary to solve the following tasks: to analyze the existing methods and
develop algorithmic and mathematical software for highlighting a homogeneous area with pathology in an
image. For the study, the data were reviewed from [1] and verified by mathematical methods to identify
pathologies, to further assess the possibility of introducing artificial intelligence into the working practice of
a radiologist, as well as to optimize work with fluorograms to reduce workload and resource costs. Health-
related digital data is expanding from the more obvious and traditional, such as records in a medical record,
to sometimes less obvious information about our daily lives, as well as a wide range of data describing the
environment in which we live. The constant process of digitalization in healthcare allows the use of more and
more new technologies in various areas of medicine [2]. With the spread of radiation diagnostic methods, the
volume of workload on radiologists has increased significantly. In 2019, it was estimated that the average
radiologist must interpret a fluorogram, radiograph, or one computed tomography (CT) or magnetic
resonance imaging (MRI) image every 3–4 seconds during an 8-hour workday to meet demand. This
increasing number of images requiring interpretation means that the amount of work has increased
significantly. With technological advances, radiologists are processing more and more images in a single
exam. Reducing the time to perform work is of great interest not only to reduce the burden on the radiologist
but also to reduce resource costs, thereby improving the economic situation in the health care of the Republic
of Kazakhstan [2]. Chest fluorography is one of the most commonly used X-ray methods in the world,
currently available to researchers for the digitalization of pathology. Improving the quality of medical
services and digitalization of the detection of infectious diseases is one of the topical issues within the
framework of the State Program for the Development of the Healthcare Sector of the Republic of Kazakhstan
for 2020-2025.
Two approaches to the problem of classifying chest radiographs for pneumonia diagnosis are
compared in [3]. The first one is based on the use of neural networks, and the second one uses normalized
compression distance. High values of classification quality metrics in both cases convincingly confirm the
reliable differentiation of chest radiographs in healthy people from patients with pneumonia. The advantages
of the first approach are obvious for large sets of training samples, and the second approach allows us to
solve the same problem in the presence of a small number of classified images when the first approach does
not work. This opens good prospects for the development of computational methods for pneumonia
diagnosis, combining both approaches. In [4], a new shape-dependent feature descriptor based on Fibonacci-
p patterns is proposed using a machine learning approach. Computer simulations show that the presented
system improves the efficiency of differentiating coronavirus disease-19 (COVID-19), viral pneumonia, and
normal conditions is efficient on small datasets and have faster inference time compared to deep learning
methods with comparable performance.
Miroshnychenko et al. [5] considered the possibility of using digital X-ray tomosynthesis for
automatic computer diagnostics of lung pathologies, including the detection of early stages of pneumonia
(including those caused by COVID-19) and tuberculosis. The advantages of this method in comparison with
digital radiography and computed tomography are shown. Digital tomosynthesis is considered an additional
option, which is recommended to be installed on all existing basic radiographic systems with their transfer to
digital technologies.
Ramnarine [6] showed advances in modern technology, which allow ordinary home computers to
study the anatomical and pathological features that distinguish the healthy from the sick, with the accuracy of
highly specialized, trained doctors. Computer vision artificial intelligence (AI) applications use medical
imaging such as lung and chest X-rays (LCXRs) to facilitate diagnosis by providing a “second opinion” in
addition to physician or radiologist interpretation. Deep learning methods such as convolutional neural
networks (CNNs) can select features that distinguish between healthy and diseased states in other lung
pathologies. This study aims to use this body of literature to apply image transformations that would help
compensate for the lack of data on LCXR COVID-19.
Chandra et al. [7] shows a system of automatic computer diagnostics (CAD). However, such a
system still lacks clinical acceptability and trust due to a gap in integration between patient metadata,
radiologist feedback, and the CAD system. Three integration frameworks have been proposed in this article,
namely direct integration (DI), rule-based integration (RBI), and weight-based integration (WBI). The
proposed framework helps clinicians diagnose pneumonia and provides an end-to-end robust diagnostic
system. The performance of the proposed method is evaluated using a private dataset consisting of 70 chest
x-rays (CXR) (31 COVID-19, 14 other diseases, and 25 normal). The results show that the proposed WBI
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achieved the highest classification scores (accuracy=98.18%, F1 score=97.73%, and Matthew correlation
coefficient=0.969) compared to DI and RI.
Flores et al. [8] used a generative adversarial network (GAN), which provides a method for training
generative models for data augmentation. The synthesized images can be used to improve the reliability of
automated diagnostic systems. However, GANs are difficult to train due to the unstable learning dynamics
that can occur during training, such as mode collapse and vanishing gradients. This article focuses on
Lipizzaner, a GAN learning system that combines spatial co-evolution with gradient-based learning that has
been used to mitigate GAN learning pathologies. Lipizzaner improves productivity by taking advantage of its
distributed nature and working at scale. Thus, the Lipizzaner algorithm and implementation robustness can be
scaled to high-performance computing (HPC) to provide more accurate generative models. Experimental
analysis shows improvement in performance by scaling up Lipizzaner GAN training.
Ortiz-Toro et al. [9] assesses the potential of three methods for characterizing texture images:
radionics, fractal dimension, and engineered histone based on super pixels as biomarkers that have been used
to train artificial intelligence (AI) models to detect pneumonia on chest x-rays. Models generated by three
different AI algorithms are shown: K-nearest neighbors, support vector machines, and random forest. The
results of this work confirmed the validity of the tested methods as reliable and easy-to-implement automatic
means of diagnosing pneumonia.
In [10], an effective model for pneumonia recognition from computerized chest x-rays was
developed and proposed. Several methods are appropriately used to extend the dataset preparation process.
Therefore, it is more advantageous to manufacture a computerized indicator for predicting pneumonia using
big information deep learning strategies. Among the wide range of different procedures, convolutional neural
networks rank high in this prediction along with various classifiers. A convolutional brain network consisting
of convolution and pooling layers and fully connected softmax layers towards the end to give the final
prediction. In this paper, it is proposed to solve the problem of clustering using an autocorrelation function
for the most accurate selection of diseases based on the results of X-ray image processing. As a result of the
experiment, the autocorrelation function identified small homogeneous regions in X-ray images.
2. METHOD
Correlation analysis makes it possible to get an idea in practice about some properties of the image,
for example, about the rate of change of intensity along the coordinates, about the length of homogeneous
sections without decomposing them into harmonic components. The meaning of correlation analysis is to
quantitatively measure the degree of similarity of various signals. For this, correlation functions are used,
whose value characterizes the size of the main primitives, which, in turn, determines the uniformity of the
texture, and which, in turn, as a result of experiments, is identified with pathology. Homogeneous areas in the
images are considered to be texture areas that are related to the lung lobes. In this approach, the texture is
related to the spatial size of the tone non-derivative elements of the image (the tone non-derivative element is
an area of the image with certain pathological features). The value of the autocorrelation function is just the
sign that characterizes the size of tone non-derivative elements of pathology. The spatial arrangement is
characterized by a correlation coefficient, which is a measure of the linear dependence of the brightness of
one image element on the brightness of another [11]. The autocorrelation function 𝑅𝑥,𝑦
𝐼(𝛼.𝛽)
, considered as a
statistical and global measure, is computed along the horizontal and vertical axes of the analysis window I of
an image according to:
𝑅𝑥,𝑦
𝐼(𝛼.𝛽)
= ∑ ∑ 𝐼(𝑥, 𝑦)𝐼(𝑥 + 𝛼, 𝑦 + 𝛽) = 𝐹𝐹𝑇−1
([𝐹𝐹𝑇[𝐼(𝑥, 𝑦)]𝐹𝐹𝑇∗
[𝐼(𝑥, 𝑦)]])
𝛽𝜖Ω
𝛼∈Ω
where 𝐼(𝑥, 𝑦)𝐼(𝑥 + 𝛼, 𝑦 + 𝛽) is the translation of the analysis window of an image 𝐼(𝑥, 𝑦) by α and pixels
along the horizontal and vertical axes respectively, defined on the plane Ω. FFT, (.)∗ , and (.)−1 denote
respectively the fast Fourier transform, the complex conjugate, and the inverse transform [12].
Analysis of images of radiation diagnostics. Radiation diagnostics is the science of using radiation
to study the structure and function of normal and pathologically altered human organs and systems to prevent
and recognize diseases [13], [14]. One of the main directions of state policy in the field of healthcare is
improving the quality of medical care. The relevance of the fact that modern medicine actively uses
information support, digital technologies and telemedicine is due to the need to provide the population with
highly qualified medical care. One of the conditions for improving the quality of medical services is the
introduction of an e-health system [15]–[18]. Radiation diagnostics includes X-ray diagnostics, ultrasound
diagnostics, X-ray computed tomography, radionuclide diagnostics, and magnetic resonance imaging
4. Int J Elec & Comp Eng ISSN: 2088-8708
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4529
[19], [20]. In addition, interventional radiology adjoins it. X-ray methods of research are methods of studying
organs using x-rays. The method of X-ray diagnostics is based on different permeability of tissues for X-rays
[21]. Each of the X-ray methods has its advantages and disadvantages, and hence certain limits of diagnostic
capabilities. But all X-ray methods are characterized by high information content, ease of implementation,
accessibility, and the ability to complement each other. X-ray methods occupy one of the leading places in
medical diagnostics: in more than 50% of cases, diagnosis is impossible without the use of X-ray diagnostics
[22], [23]. The most commonly used X-ray diagnostic methods are radiography, fluoroscopy, and X-ray
fluorography. Currently, film fluorography is increasingly being replaced by digital. Health information
resources are currently being actively developed. In modern conditions of the dominance of information
technologies, the target states of the industry are called “digital medicine” and “digital healthcare” [24], [25].
E-health uses modern digital technologies. Thanks to this, the treatment and diagnostic process is now
moving to a new, high-tech level of development in the field of obtaining and implementing diagnostic and
therapeutic information, accounting, and reporting data. Digital technologies have opened up opportunities
for the remote exchange of medical information [26].
X-rays are currently the most suitable detector for fluorographic studies. The need to improve the
reliability of registration and identification of defects in the structure of lung radiographs requires the
elimination of factors that make it difficult to decipher the contrast and identification of the analyzed images,
increasing the information content of methods, presenting images in a form that is more convenient for their
identification, developing effective algorithms and programs for digital processing of X-ray images, solving
problems, related to the nature of the disease. The X-ray image is characterized by the fact that the
perturbations present on it are small, but, at the same time, the average description of the considered, for
example, fluorogram, quite well reflects the local changes in the intensity values associated with the disease.
In the case of converting an X-ray image into a digital form of representation, the resulting digital array is a
stochastic distribution of radiation intensity in a given plane, and the solution of diagnostic problems, from
the standpoint of statistical analysis of information, is already possible. The digital image is presented in the
form of a digital matrix, these are numerical lines and columns. To display images, the digital matrix is
transformed into a matrix of visible image elements-pixels. Each pixel, following the value of the digital
matrix, has one of the shades of grayscale.
Application of an algorithm for the detection of pathologies in chest radiographs. The program was
implemented in Python. The research work carried out on the algorithm in Figure 1 aims to determine the
pathology of the database images. During the calculation, images from the database [4] were considered.
During the work, the original images were processed. The selected lung region in the original image was
selected for the study. The contrast has been increased as shown in Figures 2(a) and 2(b).
Figure 1. Stages of determining the pathology of the chest
(a) (b)
Figure 2. Chest X-ray with pathology: (a) edited image of the original and (b) the contrast was enhanced after
preprocessing
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“Abnormal” texture areas were identified in each image; of the identified areas in Figure 3(a) and
their percentage was determined in Figure 3(b). The percentage ratio was revealed through clustering. When
comparing the processing data of the autocorrelation function and the data of the conclusions of radiologists,
it was found that the results of the program and doctors completely coincide. The results obtained indicate the
high diagnostic accuracy of the method used, as well as the possibility of using the method to automate the
work of a radiologist. The deep application of the mathematical method to medical images as a matrix in
digital image processing was considered.
(a)
(b)
Figure 3. The result of the experiment: (a) contrast-enhanced image clustering result, (b) percentage of lung
clustering result
3. RESULTS AND DISCUSSION
During the study, 15 X-ray images from an open database were considered as the "norm". 18
X-rays-with chest pathologies. The autocorrelation function was applied to them, and the results of their
values are shown in Table 1. To verify the effectiveness of the autocorrelation function method, the k-means
method was considered, the graph of which is shown Figure 4. For this we used 33 X-ray images. Of these,
15 were normal and 18 with pathology.
Figure 5 shows a graph of the deviation of the normal state of the chest. The appearance of
pathology as a result of the application of the autocorrelation function method as shown in Figure 5. The
graph shows the values of the percentage deviation of the autocorrelation function of norm and pathology.
Below is a graph of the deviation of the normal state of the chest cavity and the appearance of pathology as a
result of applying the method of autocorrelation functions.
6. Int J Elec & Comp Eng ISSN: 2088-8708
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Table 1. The meaning of the results of using the autocorrelation function and the k-means method
Images title Autocorrelation function method K-means method
Normal-57.png 9 22
person76_bacteria_371.jpeg 21 8
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person77_bacteria_377.jpeg 20 9
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person80_virus_150.jpeg 25 12
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person82_virus_154.jpeg 23 21
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person83_virus_156.jpeg 21 20
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person88_virus_163.jpeg 19 20
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person88_virus_165.jpeg 30 15
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person89_virus_168.jpeg 22 17
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person95_virus_177.jpeg 19 17
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person96_virus_178.jpeg 25 18
Normal-80.png 13 17
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person97_virus_181.jpeg 22 15
person98_virus_182.jpeg 23 16
person99_virus_183.jpeg 21 17
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person100_virus_184.jpeg 20 13
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person102_virus_189.jpeg 19 18
person105_virus_192.jpeg 27 23
person106_virus_194.jpeg 21 8
person106_virus_195.jpeg 21 13
Figure 4. The result of using the K-means method
Figure 5. Graph of percentage deviation values of the autocorrelation function of norm and pathology
0
5
10
15
20
25
30
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
percentage
ratio
Number of source images
The norm after using the K-means method is the percentage value of the lungst
The percentage of pathology in the lungs after using the K-means method
0
5
10
15
20
25
30
35
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
percentage
ratio
Number of source images
the percentage deviation value of the normal lung after applying the autocorrelation function
the percentage deviation value of the pathology in the lungs after using the autocorrelation function
7. ISSN: 2088-8708
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The Figure 5 shows that the percentage of the norm and pathology have a significant difference,
which will allow a high degree of reliability to establish the boundary for determining the result of the norm
and pathology. Studies have shown that this limit lies at a value of 18%. Results above this limit are highly
likely to refer to images with pathology. Experiments were carried out using the k-means method to test the
effectiveness of the autocorrelation function used in determining lung pathology. Based on the obtained
values, the k-means method incorrectly identified 9 images as normal out of 18 pathological images, i.e.,
44%, and 9 images as pathological out of 15 healthy lungs, i.e., 60%. From the result after using the
autocorrelation function, 18 showed 99% accuracy for pathological images and 100% accuracy for normal
images.
4. CONCLUSION
The developed algorithms for preliminary segmentation of radiographs and methods based on the
use of autocorrelation functions make it possible to achieve an accuracy of recognizing pathologies of about
98%. This accuracy is determined by a small training set, therefore, in future work, it is planned to carry out
differentiation on a larger number of radiographs and the neural network will be tuned. Thus, in the future,
this technique will speed up the process of diagnosing diseases and reduce the proportion of repeated studies.
To test the described research method, 33 fluorographic images were selected. One-half of the images had
various pathologies with pneumonia, and the other half were images of healthy lungs. Radiologists play a key
role in solving some current challenges of the digitalization of health care in the Republic of Kazakhstan,
such as creating high-quality data sets for training, determining the clinical problem that needs to be solved,
and interpreting the results. Many studies are needed for the further implementation of artificial intelligence
in the practice of a radiologist, but now we can say that an automated medicine system can take over part of
the workload, facilitating the work of a doctor, as well as improve the economic situation by reducing the
costs of the healthcare resource base.
ACKNOWLEDGMENT
Authors express gratitude to the state utility company on the right of economic management of the
city polyclinic 4 and radiologist Nurusheva Kunsulu for helping to work together as an expert.
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BIOGRAPHIES OF AUTHORS
Gulzira Abdikerimova received her Ph.D. in 2020 in Information Systems from
L.N. Gumilyov Eurasian National University, Kazakhstan. Currently, she is an associate
professor of the Department of Information Systems at the same university. Her research
interests include image processing, computer vision, satellite imagery, artificial intelligence
and machine learning. You can contact her by e-mail: gulzira1981@mail.ru.
Ainur Shekerbek received a bachelor's degree in computer science in 2002 from
Taraz State University named after M. Kh. Dulaty. In 2005, she received a master's degree in
applied mathematics from the South Kazakhstan State University named after M. Auezov,
Kazakhstan, Shymkent. Currently, she is a doctoral student at the Department of Information
Systems of the Eurasian National University. L.N. Gumilev. Her research interests include
image processing, computer vision, radiography, artificial intelligence and machine learning.
You can contact her by e-mail: shekerbek80@mail.ru.
Murat Tulenbayev Doctor of Technical Sciences, Professor, Academician of
MAIN. Currently he works at the Department of Information Systems of the Taraz Regional
University named after M. Kh. Dulati. He has more than 45 years of scientific and pedagogical
experience and more than 100 scientific papers, including 2 articles on the Scopus database, 7
copyright certificates for programs and developments. You can contact him by e-mail:
mtulenbaev@mail.ru.
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Int J Elec & Comp Eng, Vol. 13, No. 4, August 2023: 4526-4534
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Svetlana Beglerova candidate of technical sciences, acting Associate Professor,
Department of Information Systems, Taraz Regional University named after M. Kh. Dulati.
Currently, he is the head of the distance department of the same university. She has more than
25 years of scientific and pedagogical experience and more than 70 scientific papers, including
4 articles on the Scopus database, 6 copyright certificates for programs and developments. She
can be contacted by e-mail: sbeglerova@mail.ru.
Elena Zakharevich received in 2011 an engineer's degree in Information Systems
and Technologies from the Novosibirsk State University of Architecture and Civil Engineering
(Sibstrin), Russia, Novosibirsk and a master's degree in technical sciences in 2015 from Taraz
State University named after M. Kh. Dulati, Kazakhstan, Taraz. Currently she works at the
Taraz Regional University named after M. Kh. Dulati in the Department of Distance
Education. Her research interests include organizing distance learning technology. You can
contact her by e-mail: elenatea@mail.ru.
Gulmira Bekmagambetova Ph.D., Associated Professor. Currently, she works at
the Kazakhstan University of Technology and Business at the Department of Information
Technology. She has more than 20 years of scientific and pedagogical experience and more
than 20 scientific papers, including 1 article based on Scopus, 2 textbooks with ISBN, 5
manuals and developments. Member of the "Modern education and research institute",
Registration No. 0660.570.790 Brussels, Belgium. She can be contacted at email:
gulmirabekmagam@gmail.com.
Zhanat Manbetova in 1999, she graduated from the Korkyt-Ata Kyzylorda State
University with a degree in Physics and Additional Mathematics. In 2014, she graduated with a
master's degree in "Radio Engineering, Electronics and Telecommunications" from Kazakh
Agrotechnical University named after S. Seifullin. In 2022, she defended her doctoral
dissertation in the specialty "Radio engineering, electronics and telecommunications". From
2021 to the present, he is a Doctor of Philosophy Ph.D. of the Department of "Radio
Engineering, Electronics and Telecommunications" of the Kazakh Agrotechnical University
named after S. Seifullin. She is the author of more than 40 works. His research interests
include wireless communications, mobile communication systems, GSM and mobile systems
management, as well as mobile communication technologies. She can be contacted at email:
zmanbetova@inbox.ru.
Makbal Baibulova received a bachelor's degree information systems in 2010
from Taraz State University named after M. Kh. Dulaty and a master's degree in applied
Computer science in 2012 from Taraz State University. Her research interests include
databases, programming, and artificial intelligence. You can contact her by e-mail:
m.gabbasovnaa@gmail.com.