Pub Date : 2024-04-05DOI: 10.53759/7669/jmc202404038
Prabhu M, Sathishkumar A, Sasi G, Lau Chee Yong, Shanker M C, Selvakumarasamy K
Despite the global COVID-19 pandemic, public health professionals are also concerned about a possible new monkeypox epidemic. Similar to vaccinia, cowpox, and variola, the orthopoxvirus that causes monkeypox has two strands that are double-stranded. Many people have propagated the current pandemic through sexual means, particularly those who identify as bisexual or gay. The speed with which monkeypox was detected is the most important element here. In order to catch monkeypox before it infects more people, machine learning could be a huge help in making a quick and accurate diagnosis. Finding a solution is the driving force behind this project, which aims to develop a model for detecting monkeypox using deep learning and image processing. For optimal cluster selection during photo segmentation, the Chameleon Swarm Algorithm (CSA) employs K-means clustering. Examining the accuracy with which the Swin Transformer model identified instances of monkeypox was the driving force for this study. The proposed techniques are evaluated on two datasets: Kaggle Monkeypox Skin Lesion Dataset (MSLD) besides the Monkeypox Skin Image Dataset (MSID). We assessed the outcomes of various deep learning models using sensitivity, specificity, and balanced accuracy. Positive results from the projected process raise the possibility of its widespread application in monkeypox detection. This ingenious and cheap method can be put to good use in economically deprived communities that may not have access to proper laboratory facilities.
{"title":"Monkeypox Detection using CSA Based K-Means Clustering with Swin Transformer Model","authors":"Prabhu M, Sathishkumar A, Sasi G, Lau Chee Yong, Shanker M C, Selvakumarasamy K","doi":"10.53759/7669/jmc202404038","DOIUrl":"https://doi.org/10.53759/7669/jmc202404038","url":null,"abstract":"Despite the global COVID-19 pandemic, public health professionals are also concerned about a possible new monkeypox epidemic. Similar to vaccinia, cowpox, and variola, the orthopoxvirus that causes monkeypox has two strands that are double-stranded. Many people have propagated the current pandemic through sexual means, particularly those who identify as bisexual or gay. The speed with which monkeypox was detected is the most important element here. In order to catch monkeypox before it infects more people, machine learning could be a huge help in making a quick and accurate diagnosis. Finding a solution is the driving force behind this project, which aims to develop a model for detecting monkeypox using deep learning and image processing. For optimal cluster selection during photo segmentation, the Chameleon Swarm Algorithm (CSA) employs K-means clustering. Examining the accuracy with which the Swin Transformer model identified instances of monkeypox was the driving force for this study. The proposed techniques are evaluated on two datasets: Kaggle Monkeypox Skin Lesion Dataset (MSLD) besides the Monkeypox Skin Image Dataset (MSID). We assessed the outcomes of various deep learning models using sensitivity, specificity, and balanced accuracy. Positive results from the projected process raise the possibility of its widespread application in monkeypox detection. This ingenious and cheap method can be put to good use in economically deprived communities that may not have access to proper laboratory facilities.","PeriodicalId":516221,"journal":{"name":"Journal of Machine and Computing","volume":"16 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140737542","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}
Pub Date : 2024-04-05DOI: 10.53759/7669/jmc202404039
Pagidela Yamuna, Visali N
Currently, this work lays the ground work for sophisticated control methods and decision support systems in hybrid microgrid operations by providing insightful information about integrating artificial intelligence for improved microgrid control. In this work, a neural network (NN) method is proposed for power flow analysis in an IEEE 12-bus-based Hybrid AC/DC Microgrid. The study optimizes power dispatch, minimizes expenses, and minimizes losses in both AC and DC components. Simulation is carried using MATLAB software and the results are presented and analysed. The accuracy of the NN’s predictions of active power flows is demonstrated by training it on historical data and validating it on real-time observations. Regression plots comparing anticipated and real values demonstrate the effectiveness of NN-based analysis in reaching the ideal power distribution.
目前，这项工作为混合微电网运行中的复杂控制方法和决策支持系统奠定了基础，提供了有关集成人工智能以改进微电网控制的深刻信息。本研究提出了一种神经网络（NN）方法，用于基于 IEEE 12 总线的交直流混合微电网的功率流分析。该研究优化了电力调度，最大限度地减少了开支，并最大限度地降低了交流和直流部分的损耗。仿真使用 MATLAB 软件进行，并对结果进行了展示和分析。通过对历史数据的训练和实时观测的验证，证明了有功功率流 NN 预测的准确性。比较预期值和实际值的回归图显示了基于 NN 的分析在实现理想功率分布方面的有效性。
{"title":"Optimal Power Flow in Hybrid AC/DC Microgrid using ANN for Cost Minimization","authors":"Pagidela Yamuna, Visali N","doi":"10.53759/7669/jmc202404039","DOIUrl":"https://doi.org/10.53759/7669/jmc202404039","url":null,"abstract":"Currently, this work lays the ground work for sophisticated control methods and decision support systems in hybrid microgrid operations by providing insightful information about integrating artificial intelligence for improved microgrid control. In this work, a neural network (NN) method is proposed for power flow analysis in an IEEE 12-bus-based Hybrid AC/DC Microgrid. The study optimizes power dispatch, minimizes expenses, and minimizes losses in both AC and DC components. Simulation is carried using MATLAB software and the results are presented and analysed. The accuracy of the NN’s predictions of active power flows is demonstrated by training it on historical data and validating it on real-time observations. Regression plots comparing anticipated and real values demonstrate the effectiveness of NN-based analysis in reaching the ideal power distribution.","PeriodicalId":516221,"journal":{"name":"Journal of Machine and Computing","volume":"16 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140737541","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}
Pub Date : 2024-04-05DOI: 10.53759/7669/jmc202404034
Karrar S. Mohsin, Jhansilakshmi Mettu, Chinnam Madhuri, Gude Usharani, Silpa N, P. Yellamma
Traffic congestion has made city planning and citizen well-being difficult due to fast city growth and the increasing number of vehicles. Traditional traffic management fails to solve urban transportation's ever-changing issues. Traffic prediction and control systems are vital for enhancing Traffic Flow (TF) and minimizing congestion. Smart cities need advanced prediction models to regulate urban TF as traffic management becomes more complex. This paper introduces a hybrid Convolutional Neural Networks (CNN) and Graph Neural Networks (GNN) model for better real-time traffic management. The hybrid model combines CNNs' spatial feature extraction with GNNs' structural and relational data processing to analyze and predict traffic conditions. Traffic camera images are pre-processed to extract spatial characteristics. Traffic network graph construction is used for structural research. The model accurately captures traffic topology and space. The proposed method sequentially processes spatial data with CNNs and integrates them with GNNs. The final hybrid model is trained on one year of traffic data from diverse circumstances and events. The hybrid model is compared to CNN, GNN, and traditional Traffic Prediction Models (TPM) like ARIMA and SVM utilizing MAE, RMSE, and MAPE. The hybrid GNN+CNN model outperforms benchmark models with lower MAE, RMSE, and MAPE across several prediction intervals.
{"title":"Enhancing Urban Traffic Management Through Hybrid Convolutional and Graph Neural Network Integration","authors":"Karrar S. Mohsin, Jhansilakshmi Mettu, Chinnam Madhuri, Gude Usharani, Silpa N, P. Yellamma","doi":"10.53759/7669/jmc202404034","DOIUrl":"https://doi.org/10.53759/7669/jmc202404034","url":null,"abstract":"Traffic congestion has made city planning and citizen well-being difficult due to fast city growth and the increasing number of vehicles. Traditional traffic management fails to solve urban transportation's ever-changing issues. Traffic prediction and control systems are vital for enhancing Traffic Flow (TF) and minimizing congestion. Smart cities need advanced prediction models to regulate urban TF as traffic management becomes more complex. This paper introduces a hybrid Convolutional Neural Networks (CNN) and Graph Neural Networks (GNN) model for better real-time traffic management. The hybrid model combines CNNs' spatial feature extraction with GNNs' structural and relational data processing to analyze and predict traffic conditions. Traffic camera images are pre-processed to extract spatial characteristics. Traffic network graph construction is used for structural research. The model accurately captures traffic topology and space. The proposed method sequentially processes spatial data with CNNs and integrates them with GNNs. The final hybrid model is trained on one year of traffic data from diverse circumstances and events. The hybrid model is compared to CNN, GNN, and traditional Traffic Prediction Models (TPM) like ARIMA and SVM utilizing MAE, RMSE, and MAPE. The hybrid GNN+CNN model outperforms benchmark models with lower MAE, RMSE, and MAPE across several prediction intervals.","PeriodicalId":516221,"journal":{"name":"Journal of Machine and Computing","volume":"162 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140740338","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}
Pub Date : 2024-04-05DOI: 10.53759/7669/jmc202404040
Venugopal Rao A, Santosh Kumar Vishwakarma, Shakti Kundu, Varun Tiwari
Human activity recognition (HAR) is an active research area in computer vision from past several years and research is still continuing in this field due to the unavailability of perfect recognition system. The human activity recognition system it covers e-health, patient monitoring, assistive daily living activities, video surveillance, security and behaviour analysis, and sports analysis. Many researchers have suggested techniques that use visual perception to detect human activities. Researchers will need to address problems including light variations in human activity detection, interclass similarity between scenes, the surroundings and recording setting, and temporal variation in order to construct an efficient vision-based human activity recognition system. However, a significant drawback of many deep learning models is their inability to achieve satisfactory results in real-world scenarios due to the conflicts mentioned above. To address this challenge, we developed a hybrid HAR-CNN classifier aimed at enhancing the learning outcomes of Deep CNNs by combining two models: Improved CNN and VGG-19. Using the KTH dataset, we collected 6,000 images for training, validation, and testing of our proposed technique. Our research findings indicate that the Hybrid HAR-CNN model, which combines Improved CNN with VGG-19 Net, outperforms individual deep learning models such as Improved CNN and VGG-19 Net.
{"title":"Hybrid HAR-CNN Model: A Hybrid Convolutional Neural Network Model for Predicting and Recognizing the Human Activity Recognition","authors":"Venugopal Rao A, Santosh Kumar Vishwakarma, Shakti Kundu, Varun Tiwari","doi":"10.53759/7669/jmc202404040","DOIUrl":"https://doi.org/10.53759/7669/jmc202404040","url":null,"abstract":"Human activity recognition (HAR) is an active research area in computer vision from past several years and research is still continuing in this field due to the unavailability of perfect recognition system. The human activity recognition system it covers e-health, patient monitoring, assistive daily living activities, video surveillance, security and behaviour analysis, and sports analysis. Many researchers have suggested techniques that use visual perception to detect human activities. Researchers will need to address problems including light variations in human activity detection, interclass similarity between scenes, the surroundings and recording setting, and temporal variation in order to construct an efficient vision-based human activity recognition system. However, a significant drawback of many deep learning models is their inability to achieve satisfactory results in real-world scenarios due to the conflicts mentioned above. To address this challenge, we developed a hybrid HAR-CNN classifier aimed at enhancing the learning outcomes of Deep CNNs by combining two models: Improved CNN and VGG-19. Using the KTH dataset, we collected 6,000 images for training, validation, and testing of our proposed technique. Our research findings indicate that the Hybrid HAR-CNN model, which combines Improved CNN with VGG-19 Net, outperforms individual deep learning models such as Improved CNN and VGG-19 Net.","PeriodicalId":516221,"journal":{"name":"Journal of Machine and Computing","volume":"4 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140738265","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}
Pub Date : 2024-04-05DOI: 10.53759/7669/jmc202404046
Eun-Gi Kim
This paper proposes and analyzes a ticket-based OCSP protocol for efficient certificate revocation checking in vehicle communication systems. The IEEE WAVE standard for vehicular networks requires real-time processing of Basic Safety Messages (BSMs) exchanged between vehicles. Traditional OCSP revocation checking can introduce delays. The proposed approach distributes OCSP responses as tickets valid for a road section. Vehicles use shorter keys extracted from the tickets for faster cryptographic processing. Experiments compare processing times for signature generation and verification with different elliptic curves. The results show the proposed technique provides faster revocation checking. This allows time-critical vehicle-to-vehicle message processing at high rates under computational constraints. The ticket-based OCSP mechanism enhances security while meeting real-time requirements in vehicular networks.
{"title":"Verifying Certificate Revocation Status for Short Key Lengths in Vehicle Communication Systems","authors":"Eun-Gi Kim","doi":"10.53759/7669/jmc202404046","DOIUrl":"https://doi.org/10.53759/7669/jmc202404046","url":null,"abstract":"This paper proposes and analyzes a ticket-based OCSP protocol for efficient certificate revocation checking in vehicle communication systems. The IEEE WAVE standard for vehicular networks requires real-time processing of Basic Safety Messages (BSMs) exchanged between vehicles. Traditional OCSP revocation checking can introduce delays. The proposed approach distributes OCSP responses as tickets valid for a road section. Vehicles use shorter keys extracted from the tickets for faster cryptographic processing. Experiments compare processing times for signature generation and verification with different elliptic curves. The results show the proposed technique provides faster revocation checking. This allows time-critical vehicle-to-vehicle message processing at high rates under computational constraints. The ticket-based OCSP mechanism enhances security while meeting real-time requirements in vehicular networks.","PeriodicalId":516221,"journal":{"name":"Journal of Machine and Computing","volume":"10 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140737155","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}
Pub Date : 2024-04-05DOI: 10.53759/7669/jmc202404035
Vincent Arokiam Arul Raja V, Senthamarai C
The most significant method is intrusion detection, which improves privacy concerns about client authentication and authorization. No matter what is done to enhance security intelligence, vulnerability has also increased in the modern era. The major role is to predict those vulnerabilities and improve security enhancements by using blockchain methods to enhance privacy concerns. In the corporation, banking, or healthcare system, the major issues are data spoofing, cyber security issues, and viruses affecting confidential data or breaking the shield of data protection. Enhance authorization and authentication by connecting the fog cloud and using the blockchain to protect privacy. In the transition of data, attackers may increase their attacks using various forms. Even if the data is transformed, attackers can easily access it and break the confidentiality of the entire massive database. FCBS (Fog Cloud Blockchain Server) will prevent data vulnerability by using FCS (Fog Cloud Server) modalities for data access. It consists of two segments, AuC (Authentication) and AuT (authorization) during the processing of data. BC (blockchain) addresses the data functionality and enhances the FCS security intelligence in two parts. By preventing the vulnerability earlier, no FC (Fog Cloud) data will be affected. To ensure data protection is reliable and accurate by handing over the AuC and AuT.
{"title":"Security Intelligence Enhanced by Blockchain Data Transitions and Effective Handover Authentication","authors":"Vincent Arokiam Arul Raja V, Senthamarai C","doi":"10.53759/7669/jmc202404035","DOIUrl":"https://doi.org/10.53759/7669/jmc202404035","url":null,"abstract":"The most significant method is intrusion detection, which improves privacy concerns about client authentication and authorization. No matter what is done to enhance security intelligence, vulnerability has also increased in the modern era. The major role is to predict those vulnerabilities and improve security enhancements by using blockchain methods to enhance privacy concerns. In the corporation, banking, or healthcare system, the major issues are data spoofing, cyber security issues, and viruses affecting confidential data or breaking the shield of data protection. Enhance authorization and authentication by connecting the fog cloud and using the blockchain to protect privacy. In the transition of data, attackers may increase their attacks using various forms. Even if the data is transformed, attackers can easily access it and break the confidentiality of the entire massive database. FCBS (Fog Cloud Blockchain Server) will prevent data vulnerability by using FCS (Fog Cloud Server) modalities for data access. It consists of two segments, AuC (Authentication) and AuT (authorization) during the processing of data. BC (blockchain) addresses the data functionality and enhances the FCS security intelligence in two parts. By preventing the vulnerability earlier, no FC (Fog Cloud) data will be affected. To ensure data protection is reliable and accurate by handing over the AuC and AuT.","PeriodicalId":516221,"journal":{"name":"Journal of Machine and Computing","volume":"49 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140736511","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}
Pub Date : 2024-04-05DOI: 10.53759/7669/jmc202404042
Joseph Michael Jerard V, Sarojini Yarramsetti, Vennira Selvi G, Natteshan N V S
The through-wall capability, device-free detection of radar-based human activity recognition are drawing a lot of interest from both academics and industry. The majority of radar-based systems do not yet combine signal analysis and feature extraction in the frequency domain and the time domain. Applications like smart homes, assisted living, and monitoring rely on human identification and activity recognition (HIAR). Radar has a number of advantages over other sensing modalities, such as the ability to shield users' privacy and conduct contactless sensing. The article introduces a new human tracking system that uses radar and a classifier called Dual Spatial Convolution Gated Recurrent Unit (DSC-GRU) to identify the subject and their behavior. The system follows the person and identifies the type of motion whenever it detects movement. One important feature is the integration of the GRU with the DSC unit, which allows the model to simultaneously capture the spatiotemporal dependence. Present prediction models just take into account spatial features that are immediately adjacent to each other, disregarding or just superimposing global spatial features when taking spatial correlation into account. A new dependency graph is created by calculating the correlation among nodes using the correlation coefficient; this graph represents the global spatial dependence, while the classic static graph represents the neighboring spatial dependence in the DSC unit. The DSC unit goes a step further by using a modified gated mechanism to quantify the various contributions of both local and global spatial correlation. While previous models performed worse, the suggested model outperformed them with an accuracy of 99.45 percent and a precision of 97.15 percent.
{"title":"Micro-Doppler based Human Activity Recognition using ABOA based Dual Spatial Convolution with Gated Recurrent Unit","authors":"Joseph Michael Jerard V, Sarojini Yarramsetti, Vennira Selvi G, Natteshan N V S","doi":"10.53759/7669/jmc202404042","DOIUrl":"https://doi.org/10.53759/7669/jmc202404042","url":null,"abstract":"The through-wall capability, device-free detection of radar-based human activity recognition are drawing a lot of interest from both academics and industry. The majority of radar-based systems do not yet combine signal analysis and feature extraction in the frequency domain and the time domain. Applications like smart homes, assisted living, and monitoring rely on human identification and activity recognition (HIAR). Radar has a number of advantages over other sensing modalities, such as the ability to shield users' privacy and conduct contactless sensing. The article introduces a new human tracking system that uses radar and a classifier called Dual Spatial Convolution Gated Recurrent Unit (DSC-GRU) to identify the subject and their behavior. The system follows the person and identifies the type of motion whenever it detects movement. One important feature is the integration of the GRU with the DSC unit, which allows the model to simultaneously capture the spatiotemporal dependence. Present prediction models just take into account spatial features that are immediately adjacent to each other, disregarding or just superimposing global spatial features when taking spatial correlation into account. A new dependency graph is created by calculating the correlation among nodes using the correlation coefficient; this graph represents the global spatial dependence, while the classic static graph represents the neighboring spatial dependence in the DSC unit. The DSC unit goes a step further by using a modified gated mechanism to quantify the various contributions of both local and global spatial correlation. While previous models performed worse, the suggested model outperformed them with an accuracy of 99.45 percent and a precision of 97.15 percent.","PeriodicalId":516221,"journal":{"name":"Journal of Machine and Computing","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140737136","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}
Pub Date : 2024-04-05DOI: 10.53759/7669/jmc202404045
Abhijit Das, Shobha N, Natesh M, Gyanendra Tiwary, Karthik V
Recently, we have noticed tremendous growth in the field of Information Technology. This increased growth has proliferated the use of new technologies and continued advancement of networking systems. These systems are widely adopted for real-time online and offline tasks. Due to this growth in information technology, maintaining security has gained huge attention as these systems are vulnerable to various attacks. In this context, an Intrusion Detection System (IDS) plays an important role in ensuring security by detecting and preventing suspicious activities within the network. However, as technology is overgrowing, malicious activities are also increasing. Moreover, legacy IDS methods cannot handle new threats, such as traditional signature-based methods requiring a predefined rule set to detect malicious activity. Also, several new methods have been proposed earlier to address security-related issues; however, the performance of these methods is limited due to poor attack detection accuracy and increased false positive rates. In this work, we propose and compare different deep-learning (DL) models that can be used to construct IDSs to provide network security. Details on convolutional neural networks (CNNs), Multilayer Perceptron (MLP), and long short-term memories (LSTMs) are introduced. A discussion of the outcomes achieved follows an assessment of the proposed DL model known as the FOA-CNN-LSTM technique. Comparisons are made between the suggested models and other machine-learning methods. This work presents a deep-learning approach based on hybrid CNN-LSTM with Fruit fly Optimization Algorithm (FOA) by ensemble techniques to distinguish between normal and abnormal behaviors.
{"title":"An Enhanced Hybrid Deep Learning Model to Enhance Network Intrusion Detection Capabilities for Cybersecurity","authors":"Abhijit Das, Shobha N, Natesh M, Gyanendra Tiwary, Karthik V","doi":"10.53759/7669/jmc202404045","DOIUrl":"https://doi.org/10.53759/7669/jmc202404045","url":null,"abstract":"Recently, we have noticed tremendous growth in the field of Information Technology. This increased growth has proliferated the use of new technologies and continued advancement of networking systems. These systems are widely adopted for real-time online and offline tasks. Due to this growth in information technology, maintaining security has gained huge attention as these systems are vulnerable to various attacks. In this context, an Intrusion Detection System (IDS) plays an important role in ensuring security by detecting and preventing suspicious activities within the network. However, as technology is overgrowing, malicious activities are also increasing. Moreover, legacy IDS methods cannot handle new threats, such as traditional signature-based methods requiring a predefined rule set to detect malicious activity. Also, several new methods have been proposed earlier to address security-related issues; however, the performance of these methods is limited due to poor attack detection accuracy and increased false positive rates. In this work, we propose and compare different deep-learning (DL) models that can be used to construct IDSs to provide network security. Details on convolutional neural networks (CNNs), Multilayer Perceptron (MLP), and long short-term memories (LSTMs) are introduced. A discussion of the outcomes achieved follows an assessment of the proposed DL model known as the FOA-CNN-LSTM technique. Comparisons are made between the suggested models and other machine-learning methods. This work presents a deep-learning approach based on hybrid CNN-LSTM with Fruit fly Optimization Algorithm (FOA) by ensemble techniques to distinguish between normal and abnormal behaviors.","PeriodicalId":516221,"journal":{"name":"Journal of Machine and Computing","volume":"5 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140738091","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}
Pub Date : 2024-04-05DOI: 10.53759/7669/jmc202404026
Thiyagu T, Krishnaveni S
In the realm of IoT-based Intensive Care Unit (ICU) healthcare, the quest for precision and reliability in patient monitoring and treatment optimization is paramount. This study delves into the realm of advanced algorithms, particularly focusing on the Pelican Optimization Algorithm Long Short-Term Memory (POA-LSTM), known for its remarkable accuracy rates exceeding 95%. The POA-LSTM algorithm, fine-tuned through the Pelican Optimization Algorithm, emerges as a beacon of accuracy in ICU healthcare. By optimizing hyperparameters and leveraging the Pelican Optimization Algorithm's optimization prowess, POA-LSTM surpasses industry standards, offering unparalleled precision and recall rates. Its ability to make informed predictions and provide real-time insights significantly enhances the quality of patient care and clinical decision-making in ICU settings. Additionally, the study explores Context-Oriented Attention LSTM (COA-LSTM) and Particle Swarm Optimization Long Short-Term Memory (PSO-LSTM) algorithms, each contributing unique strengths to the landscape of IoT-based ICU healthcare. COA-LSTM's attention mechanism and PSO-LSTM's hyperparameter optimization further enrich the capabilities of predictive modeling and anomaly detection in critical care scenarios. Through the integration of these advanced algorithms, healthcare providers can harness the power of data-driven insights to revolutionize ICU healthcare, ensuring optimal patient outcomes and advancing the frontier of medical care in the digital age.
{"title":"IoT Based ICU Healthcare: Optimizing Patient Monitoring and Treatment with Advanced Algorithms","authors":"Thiyagu T, Krishnaveni S","doi":"10.53759/7669/jmc202404026","DOIUrl":"https://doi.org/10.53759/7669/jmc202404026","url":null,"abstract":"In the realm of IoT-based Intensive Care Unit (ICU) healthcare, the quest for precision and reliability in patient monitoring and treatment optimization is paramount. This study delves into the realm of advanced algorithms, particularly focusing on the Pelican Optimization Algorithm Long Short-Term Memory (POA-LSTM), known for its remarkable accuracy rates exceeding 95%. The POA-LSTM algorithm, fine-tuned through the Pelican Optimization Algorithm, emerges as a beacon of accuracy in ICU healthcare. By optimizing hyperparameters and leveraging the Pelican Optimization Algorithm's optimization prowess, POA-LSTM surpasses industry standards, offering unparalleled precision and recall rates. Its ability to make informed predictions and provide real-time insights significantly enhances the quality of patient care and clinical decision-making in ICU settings. Additionally, the study explores Context-Oriented Attention LSTM (COA-LSTM) and Particle Swarm Optimization Long Short-Term Memory (PSO-LSTM) algorithms, each contributing unique strengths to the landscape of IoT-based ICU healthcare. COA-LSTM's attention mechanism and PSO-LSTM's hyperparameter optimization further enrich the capabilities of predictive modeling and anomaly detection in critical care scenarios. Through the integration of these advanced algorithms, healthcare providers can harness the power of data-driven insights to revolutionize ICU healthcare, ensuring optimal patient outcomes and advancing the frontier of medical care in the digital age.","PeriodicalId":516221,"journal":{"name":"Journal of Machine and Computing","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140736014","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}
Pub Date : 2024-04-05DOI: 10.53759/7669/jmc202404032
Ramesh T, Santhi V
Osteoporosis and osteopenia, prevalent bone diseases affecting millions of people globally, necessitate accurate early diagnosis for effective treatment and fracture prevention. This paper proposes a novel hybrid optimization algorithm tailored for classifying these conditions based on Bone Mineral Density (BMD) measurements. The algorithm, a customized Mini-Batch Gradient Descent (MBGD), blends the advantages of Gradient Descent (GD) and Stochastic Gradient Descent (SGD), addressing specific needs for osteoporosis and osteopenia classification. Utilizing a dataset comprising BMD measurements and clinical risk factors from the Osteoporotic Fractures in Men (MrOS), Study of Osteoporotic Fractures (SOF), and Fracture Risk Assessment (FRAX), the model achieves an impressive accuracy of 99.01%. The proposed model outperforms existing methods, demonstrating superior accuracy compared to the accuracy obtained in Gradient Descent of 97.26%, Stochastic Gradient Descent of 97.23%, and other optimization algorithms such as Adam of 96.45% and the RMSprop of 96.23%. This hybrid model presents a robust framework for early diagnosis of Osteoporosis and osteopenia, and hence there is an enhancement in quality of life.
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