Salliah Shafi Bhat, Venkatesan Selvam, Gufran Ahmad Ansari
A branch of artificial intelligence called Machine Learning (ML) enables machines to learn without having to be emphatically instructed. Machine Learning Techniques (MLT) have been used to forecast a variety of chronic diseases in the healthcare sector. Improvement in clinical approaches is necessary for early diabetes prediction to prevent complications and prolong the diagnosis of diabetes. Diabetes is growing fast in this world. In this paper MLT based Framework is recommended for early prediction of Diabetes Mellitus (DM). In this Paper the authors make use of PIDD data set. Different MLTs are used including Support Vector Classification (SVC), Logistic Regression (LR), K Nearest Neighbor (KNN) and Random Forest (RF). Data analysis is the first step in our method after which the information is transferred for data pre-processing and feature selection methods. RF performed better than other models with a 92.85 % accuracy rate followed by SVC (91.5%), LR (83.11) and KNN (89.6). K-fold cross-validation technique is utilized to verify the outcomes. The contribution of lifestyle characteristics is calculated using a feature engineering process. As a result, comprehensive overall comparative assessments of all the algorithms are performed taking into account variables such as accuracy, precision, sensitivity, recall, F1 score and ROC-AUC. The medical field can use the proposed framework to make early diabetes predictions. Additionally, it can be applied to other datasets that have data in common with diabetes.
{"title":"Predicting Life Style of Early Diabetes Mellitus using Machine Learning Technique","authors":"Salliah Shafi Bhat, Venkatesan Selvam, Gufran Ahmad Ansari","doi":"10.47839/ijc.22.3.3230","DOIUrl":"https://doi.org/10.47839/ijc.22.3.3230","url":null,"abstract":"A branch of artificial intelligence called Machine Learning (ML) enables machines to learn without having to be emphatically instructed. Machine Learning Techniques (MLT) have been used to forecast a variety of chronic diseases in the healthcare sector. Improvement in clinical approaches is necessary for early diabetes prediction to prevent complications and prolong the diagnosis of diabetes. Diabetes is growing fast in this world. In this paper MLT based Framework is recommended for early prediction of Diabetes Mellitus (DM). In this Paper the authors make use of PIDD data set. Different MLTs are used including Support Vector Classification (SVC), Logistic Regression (LR), K Nearest Neighbor (KNN) and Random Forest (RF). Data analysis is the first step in our method after which the information is transferred for data pre-processing and feature selection methods. RF performed better than other models with a 92.85 % accuracy rate followed by SVC (91.5%), LR (83.11) and KNN (89.6). K-fold cross-validation technique is utilized to verify the outcomes. The contribution of lifestyle characteristics is calculated using a feature engineering process. As a result, comprehensive overall comparative assessments of all the algorithms are performed taking into account variables such as accuracy, precision, sensitivity, recall, F1 score and ROC-AUC. The medical field can use the proposed framework to make early diabetes predictions. Additionally, it can be applied to other datasets that have data in common with diabetes.","PeriodicalId":37669,"journal":{"name":"International Journal of Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135458402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alzheimer's disease (AD) is a degenerative progressive disorder that affects the brain's neurons and nerve cells, causing behavioral changes, memory loss, language skills, and thinking. It is a neurological condition with an exponentially increasing incidence rate, primarily affecting adults over 65. Contrary to popular belief, AD is not a normal aspect of aging and is the most prevalent type of dementia. In this work, CNN, Densenet169, and the Hybrid convolution recurrent neural network approach are used to detect Alzheimer's disease at an early stage. Data augmentation is utilized at preprocessing step to handle the small size of the dataset. The Hybrid CNN-RNN network design comprises convolution layers followed by a recurrent neural network (RNN). The combined model uses the RNN to extract relationships from MRI images and to account for temporal dependencies of the images during classification. Three algorithms are used for classifying AD and comparing their results. We have tested the model on MRI dataset. According to the results, the proposed CNN algorithm achieved higher accuracy than the Densenet169 and the hybrid Convolution-Recurrent Neural Network.
{"title":"Designing an Intelligent System for Predicting Alzheimer’s Disease","authors":"Wasan Ahmed Ali","doi":"10.47839/ijc.22.3.3238","DOIUrl":"https://doi.org/10.47839/ijc.22.3.3238","url":null,"abstract":"Alzheimer's disease (AD) is a degenerative progressive disorder that affects the brain's neurons and nerve cells, causing behavioral changes, memory loss, language skills, and thinking. It is a neurological condition with an exponentially increasing incidence rate, primarily affecting adults over 65. Contrary to popular belief, AD is not a normal aspect of aging and is the most prevalent type of dementia. In this work, CNN, Densenet169, and the Hybrid convolution recurrent neural network approach are used to detect Alzheimer's disease at an early stage. Data augmentation is utilized at preprocessing step to handle the small size of the dataset. The Hybrid CNN-RNN network design comprises convolution layers followed by a recurrent neural network (RNN). The combined model uses the RNN to extract relationships from MRI images and to account for temporal dependencies of the images during classification. Three algorithms are used for classifying AD and comparing their results. We have tested the model on MRI dataset. According to the results, the proposed CNN algorithm achieved higher accuracy than the Densenet169 and the hybrid Convolution-Recurrent Neural Network.","PeriodicalId":37669,"journal":{"name":"International Journal of Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135457769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Viktor O. Makarichev, Vladimir V. Lukin, Vyacheslav S. Kharchenko
Digital images are a particular type of data. They have numerous applications. Taking into account current challenges and trends, image compression and protection have to be ensured. Data format, which provides fast analysis of the image compressed, is needed. In order to satisfy a combination of these requirements, an appropriate information system should be developed. In this paper, we design such a system based on atomic functions (AF) that are solutions of special functional differential equations and, in terms of function theory, are as good constructive tools as trigonometric polynomials. AF-based image processing system (AFIPS), which satisfies the requirements considered, is developed. A core of this system is discrete atomic transform (DAT). Data protection feature of AFIPS is provided by the possibility to vary a structure of the procedure DAT. Constructive approximation properties of AF ensure high lossy and lossless image compression, as well as good image representation by DAT-coefficients. Software implementation of AFIPS is investigated. The results of test data processing are given.
{"title":"Image Compression and Protection Systems Based on Atomic Functions","authors":"Viktor O. Makarichev, Vladimir V. Lukin, Vyacheslav S. Kharchenko","doi":"10.47839/ijc.22.3.3222","DOIUrl":"https://doi.org/10.47839/ijc.22.3.3222","url":null,"abstract":"Digital images are a particular type of data. They have numerous applications. Taking into account current challenges and trends, image compression and protection have to be ensured. Data format, which provides fast analysis of the image compressed, is needed. In order to satisfy a combination of these requirements, an appropriate information system should be developed. In this paper, we design such a system based on atomic functions (AF) that are solutions of special functional differential equations and, in terms of function theory, are as good constructive tools as trigonometric polynomials. AF-based image processing system (AFIPS), which satisfies the requirements considered, is developed. A core of this system is discrete atomic transform (DAT). Data protection feature of AFIPS is provided by the possibility to vary a structure of the procedure DAT. Constructive approximation properties of AF ensure high lossy and lossless image compression, as well as good image representation by DAT-coefficients. Software implementation of AFIPS is investigated. The results of test data processing are given.","PeriodicalId":37669,"journal":{"name":"International Journal of Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135458220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Analysis of scientific research on the development of adaptive and self-adaptive software systems is conducted. It is established that the use of machine learning methods and feedback diagrams is an effective way to design and develop adaptive software. It is determined that the existing methods do not fully provide the possibility of dynamic changes and expansion of functional and graphic characteristics. The software adaptation process is designed based on the ontological model using the semantic decision-making mechanism. The proposed method allows us to dynamically determine the necessary system characteristics and perform software adaptation. Modification process takes into account the information about currently active device based on data about the needs and requirements of the user. Using the results of designing an abstract approach to software configuration modification, an experimental study of the speed of generating optimal system settings is conducted. According to the results of the experiment, it is established that the new method demonstrates 20% better indicators of the speed of generating software settings compared to classical approaches.
{"title":"Approach to Implementation of Configuration Process for Adaptive Software Systems based on Ontologies","authors":"Dmytro Fedasyuk, Illia Lutsyk","doi":"10.47839/ijc.22.3.3234","DOIUrl":"https://doi.org/10.47839/ijc.22.3.3234","url":null,"abstract":"Analysis of scientific research on the development of adaptive and self-adaptive software systems is conducted. It is established that the use of machine learning methods and feedback diagrams is an effective way to design and develop adaptive software. It is determined that the existing methods do not fully provide the possibility of dynamic changes and expansion of functional and graphic characteristics. The software adaptation process is designed based on the ontological model using the semantic decision-making mechanism. The proposed method allows us to dynamically determine the necessary system characteristics and perform software adaptation. Modification process takes into account the information about currently active device based on data about the needs and requirements of the user. Using the results of designing an abstract approach to software configuration modification, an experimental study of the speed of generating optimal system settings is conducted. According to the results of the experiment, it is established that the new method demonstrates 20% better indicators of the speed of generating software settings compared to classical approaches.","PeriodicalId":37669,"journal":{"name":"International Journal of Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135459001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kamilla G. Sadvakassova, Azhar Z. Nurmagambetova, Gulmira E. Kassenova, Zhanar S. Kazbekova, Dariyoush Jamshidi
For Kazakh airlines, the issue of using information technologies (IT) is relevant and complex, since the increased competition and partially identical business practices by companies in the same industry force the accelerated implementation of such technologies in their activities. It is necessary to consider and systematize information technologies and systems used by leading air carriers in order to structure them and determine the formats of their use in airlines. The methodological framework of the study consists of the dialectical, system, and historical approaches, fundamental provisions of economic theory, the theory of information economy and innovative development, and studies conducted by scientists-economists devoted to the development of the information society and the problems of company functioning in the information economy. As a result, the investment project assessment for the implementation of information technologies was calculated, which clearly demonstrated the capabilities of such systems as a tool for improving competitiveness, and proved their fast payback period and positive impact on the company.
{"title":"Development of an Investment Management Model for Air Carriers","authors":"Kamilla G. Sadvakassova, Azhar Z. Nurmagambetova, Gulmira E. Kassenova, Zhanar S. Kazbekova, Dariyoush Jamshidi","doi":"10.47839/ijc.22.3.3226","DOIUrl":"https://doi.org/10.47839/ijc.22.3.3226","url":null,"abstract":"For Kazakh airlines, the issue of using information technologies (IT) is relevant and complex, since the increased competition and partially identical business practices by companies in the same industry force the accelerated implementation of such technologies in their activities. It is necessary to consider and systematize information technologies and systems used by leading air carriers in order to structure them and determine the formats of their use in airlines. The methodological framework of the study consists of the dialectical, system, and historical approaches, fundamental provisions of economic theory, the theory of information economy and innovative development, and studies conducted by scientists-economists devoted to the development of the information society and the problems of company functioning in the information economy. As a result, the investment project assessment for the implementation of information technologies was calculated, which clearly demonstrated the capabilities of such systems as a tool for improving competitiveness, and proved their fast payback period and positive impact on the company.","PeriodicalId":37669,"journal":{"name":"International Journal of Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135458717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Dyvak, R. Pasichnyk, A. Melnyk, A. Dyvak, Frank Otoo
Mathematical models of complex objects in the form of interval difference equations are built on the basis of the obtained experimental interval data within the limits of the inductive approach. At the same time, interpretation of physical properties of the object on the base of such model is complex enough. A method of transformation of a mathematical model in the form of interval differential equations was proposed in the article. The proposed method is based on the formulas for representing the values of the function at the node of the difference grid in the Taylor series in the neighborhood of the base node, as well as the differential representation of the derivatives in the same neighborhood. The developed approach creates opportunities for the identification of interval models of complex objects based on the analysis of interval data with further interpretation of the physical properties of the modeled object according to the classical scheme.
{"title":"Transformation of Mathematical Model for Complex Object in Form of Interval Difference Equations to a Differential Equation","authors":"M. Dyvak, R. Pasichnyk, A. Melnyk, A. Dyvak, Frank Otoo","doi":"10.47839/ijc.22.2.3091","DOIUrl":"https://doi.org/10.47839/ijc.22.2.3091","url":null,"abstract":"Mathematical models of complex objects in the form of interval difference equations are built on the basis of the obtained experimental interval data within the limits of the inductive approach. At the same time, interpretation of physical properties of the object on the base of such model is complex enough. A method of transformation of a mathematical model in the form of interval differential equations was proposed in the article. The proposed method is based on the formulas for representing the values of the function at the node of the difference grid in the Taylor series in the neighborhood of the base node, as well as the differential representation of the derivatives in the same neighborhood. The developed approach creates opportunities for the identification of interval models of complex objects based on the analysis of interval data with further interpretation of the physical properties of the modeled object according to the classical scheme.","PeriodicalId":37669,"journal":{"name":"International Journal of Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81654792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Joshua Gutierrez-Ojeda, V. Ponomaryov, J. Almaraz-Damian, R. Reyes-Reyes, Clara Cruz-Ramos
Cardiovascular diseases are the leading cause of death worldwide, claiming approximately 17.9 million lives each year. In this study, a novel CAD system to detect and classify electrocardiogram (ECG) signals is presented. Designed system employs the recurrence plot (RP) approach that transforms a ECG signal into a 2D representative colour image, finally performing their classifications via employment of Deep Learning architecture (ResNet-18). Novel system includes two steps, where the first step is the preprocessing one, which performs segmentation of the data into two-second intervals, finally forming images via the RP approach; following, in the second step, the RP images are classified by the ResNet- 18 network. The proposed method is evaluated on the MIT-BIH arrhythmia database where 5 principal types of arrhythmias that have medical relevance should be classified. Novel system can classify the before-mentioned quantity of diseases according to the AAMI Standard and appears to demonstrate good performance in terms of criteria: overall accuracy of 97.62%, precision of 95.42%, recall of 95.42%, F1-Score of 95.06%, and AUC of 95.7% that are competitive with better state-of-the-art systems. Additionally. the method demonstrated the ability in mitigating the problem of imbalanced samples.
{"title":"ECG Arrhythmia Classification Using Recurrence Plot and ResNet-18","authors":"Joshua Gutierrez-Ojeda, V. Ponomaryov, J. Almaraz-Damian, R. Reyes-Reyes, Clara Cruz-Ramos","doi":"10.47839/ijc.22.2.3083","DOIUrl":"https://doi.org/10.47839/ijc.22.2.3083","url":null,"abstract":"Cardiovascular diseases are the leading cause of death worldwide, claiming approximately \u000017.9 million lives each year. In this study, a novel CAD system to detect and classify electrocardiogram (ECG) signals is presented. Designed system employs the recurrence plot (RP) approach that transforms a ECG signal into a 2D representative colour image, finally performing their classifications via employment of Deep Learning architecture (ResNet-18). Novel system includes two steps, where the first step is the preprocessing one, which performs segmentation of the data into two-second intervals, finally forming images via the RP approach; following, in the second step, the RP images are classified by the ResNet- 18 network. The proposed method is evaluated on the MIT-BIH arrhythmia database where 5 principal types of arrhythmias that have medical relevance should be classified. Novel system can classify the before-mentioned quantity of diseases according to the AAMI Standard and appears to demonstrate good performance in terms of criteria: overall accuracy of 97.62%, precision of 95.42%, recall of 95.42%, F1-Score of 95.06%, and AUC of 95.7% that are competitive with better state-of-the-art systems. Additionally. the method demonstrated the ability in mitigating the problem of imbalanced samples.","PeriodicalId":37669,"journal":{"name":"International Journal of Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75068796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Since the discovery of COVID-19, the wearing of a face mask has been recognized as an effective means of curbing the spread of most infectious respiratory diseases. A face mask must completely enclose the lips and nose properly for effective prevention of the disease. Some people still refuse to wear the mask, either out of annoyance or difficulty, or they are just wearing it incorrectly, which diminishes the mask's effectiveness and renders it worthless. The deep learning models described in this research provide a mechanism for assessing whether a face mask is being worn correctly or incorrectly using images. For both training and testing, the suggested method makes use of MaskedFace-Net dataset that contains annotated photos of an individual's face with proper and improper masks. Threshold optimizations are applied to produce significant results of prediction when comparing ResNet50, MobileNetV2 and DenseNet121 models. It is observed that better performance can be achieved with having accuracy as the target evaluation metric and reaching accuracy levels of 97.6%, 99.0%, and 99.8% for ResNet50, DenseNet121, and MobileNetV2, respectively after threshold optimization. As an outcome, DenseNet121 outperformed the other evaluated models when accuracy, recall, and precision metrics were used to assess the testing set. The face mask categorization can be used to automatically monitor face masks in real-time in public locations like hospitals, airports, shopping complexes and congested spaces to verify compliance with the published guidelines by the higher authorities in a country, making the results valuable for future use.
{"title":"Real-Time Face Mask Classification with Convolutional Neural Network for Proper and Improper Face Mask Wearing","authors":"Fatin Amanina Azis, Hazwani Suhaimi, E. Abas","doi":"10.47839/ijc.22.2.3087","DOIUrl":"https://doi.org/10.47839/ijc.22.2.3087","url":null,"abstract":"Since the discovery of COVID-19, the wearing of a face mask has been recognized as an effective means of curbing the spread of most infectious respiratory diseases. A face mask must completely enclose the lips and nose properly for effective prevention of the disease. Some people still refuse to wear the mask, either out of annoyance or difficulty, or they are just wearing it incorrectly, which diminishes the mask's effectiveness and renders it worthless. The deep learning models described in this research provide a mechanism for assessing whether a face mask is being worn correctly or incorrectly using images. For both training and testing, the suggested method makes use of MaskedFace-Net dataset that contains annotated photos of an individual's face with proper and improper masks. Threshold optimizations are applied to produce significant results of prediction when comparing ResNet50, MobileNetV2 and DenseNet121 models. It is observed that better performance can be achieved with having accuracy as the target evaluation metric and reaching accuracy levels of 97.6%, 99.0%, and 99.8% for ResNet50, DenseNet121, and MobileNetV2, respectively after threshold optimization. As an outcome, DenseNet121 outperformed the other evaluated models when accuracy, recall, and precision metrics were used to assess the testing set. The face mask categorization can be used to automatically monitor face masks in real-time in public locations like hospitals, airports, shopping complexes and congested spaces to verify compliance with the published guidelines by the higher authorities in a country, making the results valuable for future use.","PeriodicalId":37669,"journal":{"name":"International Journal of Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72859905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Breast Cancer is found as the most dangerous and most commonly affecting diseases in the world by WHO. The severity of breast cancer and early diagnosis of it has gained the attention of researchers to save humankind from such devastating disease. Early prediction of breast cancer has geared up its journey after the introduction to machine learning supervised algorithms. In the paper, the use of various machine learning algorithms along with the ensemble algorithms is shown. The results obtained are highly accurate to help one correctly predict cancer. The paper aims at early diagnosis of breast cancer with a humble motto of saving patients suffering from the disease by allowing them to know whether the diagnosed tumor is cancerous or non-cancerous, being Malignant and Benign respectively. This paper would be useful and aiding for those who are novel researchers in prediction and diagnosis of breast cancer using machine learning.
{"title":"Early Detection of Breast Cancer Using Machine Learning and Ensemble Techniques","authors":"Disha H. Parekh, Vishal Dahiya","doi":"10.47839/ijc.22.2.3093","DOIUrl":"https://doi.org/10.47839/ijc.22.2.3093","url":null,"abstract":"Breast Cancer is found as the most dangerous and most commonly affecting diseases in the world by WHO. The severity of breast cancer and early diagnosis of it has gained the attention of researchers to save humankind from such devastating disease. Early prediction of breast cancer has geared up its journey after the introduction to machine learning supervised algorithms. In the paper, the use of various machine learning algorithms along with the ensemble algorithms is shown. The results obtained are highly accurate to help one correctly predict cancer. The paper aims at early diagnosis of breast cancer with a humble motto of saving patients suffering from the disease by allowing them to know whether the diagnosed tumor is cancerous or non-cancerous, being Malignant and Benign respectively. This paper would be useful and aiding for those who are novel researchers in prediction and diagnosis of breast cancer using machine learning.","PeriodicalId":37669,"journal":{"name":"International Journal of Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91379799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
IoT has attracted a diverse range of applications due to its adaptability, flexibility, and scalability. However, the most significant barriers to IoT adoption are security, privacy, interoperability, and a lack of standards. Due to the persistent online connectivity and lack of security measures, adversaries can quickly attack IoT systems for various adversarial operations, financial gain, and access to sensitive data. We conducted a massive vulnerability scan on IoT devices using Shodan, the IoT search engine. The discovered vulnerabilities are analyzed using the Octave Allegro risk assessment method to determine the risk level (Critical, High, Moderate, Low, None), and the results are classified based on the vulnerabilities. The research findings are intriguing, shocking, and alarming, revealing the bitter reality that IoT devices are rapidly increasing while simultaneously eroding users' privacy on a never-before-seen scale. Our search discovered 13,558 webcams with outdated components, 11,090 devices disclosing NAT-PMP information, and 16,356 connected devices responding to remote telnet access. Around 2,456 IoT devices were found with the Heartbleed vulnerability, 674 with the Ticketbleed vulnerability, and 9,241 with expired SSL certificates. Nearly 18,638 IoT consumer devices are configured with insecure default settings; 11,481 devices with default SNMP agent community names; 4,987 devices running on non-standard ports; and 4,425 Cisco devices are configured with generic or default passwords.
{"title":"A Study on Internet of Things Devices Vulnerabilities using Shodan","authors":"V. Rajasekar, S. Rajkumar","doi":"10.47839/ijc.22.2.3084","DOIUrl":"https://doi.org/10.47839/ijc.22.2.3084","url":null,"abstract":"IoT has attracted a diverse range of applications due to its adaptability, flexibility, and scalability. However, the most significant barriers to IoT adoption are security, privacy, interoperability, and a lack of standards. Due to the persistent online connectivity and lack of security measures, adversaries can quickly attack IoT systems for various adversarial operations, financial gain, and access to sensitive data. We conducted a massive vulnerability scan on IoT devices using Shodan, the IoT search engine. The discovered vulnerabilities are analyzed using the Octave Allegro risk assessment method to determine the risk level (Critical, High, Moderate, Low, None), and the results are classified based on the vulnerabilities. The research findings are intriguing, shocking, and alarming, revealing the bitter reality that IoT devices are rapidly increasing while simultaneously eroding users' privacy on a never-before-seen scale. Our search discovered 13,558 webcams with outdated components, 11,090 devices disclosing NAT-PMP information, and 16,356 connected devices responding to remote telnet access. Around 2,456 IoT devices were found with the Heartbleed vulnerability, 674 with the Ticketbleed vulnerability, and 9,241 with expired SSL certificates. Nearly 18,638 IoT consumer devices are configured with insecure default settings; 11,481 devices with default SNMP agent community names; 4,987 devices running on non-standard ports; and 4,425 Cisco devices are configured with generic or default passwords.","PeriodicalId":37669,"journal":{"name":"International Journal of Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73173735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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