This study explores the impact of hyperparameter optimization on machine learning models for predicting cardiovascular disease using data from an IoST(Internet of Sensing Things)device.Ten distinct machine learning ap...This study explores the impact of hyperparameter optimization on machine learning models for predicting cardiovascular disease using data from an IoST(Internet of Sensing Things)device.Ten distinct machine learning approaches were implemented and systematically evaluated before and after hyperparameter tuning.Significant improvements were observed across various models,with SVM and Neural Networks consistently showing enhanced performance metrics such as F1-Score,recall,and precision.The study underscores the critical role of tailored hyperparameter tuning in optimizing these models,revealing diverse outcomes among algorithms.Decision Trees and Random Forests exhibited stable performance throughout the evaluation.While enhancing accuracy,hyperparameter optimization also led to increased execution time.Visual representations and comprehensive results support the findings,confirming the hypothesis that optimizing parameters can effectively enhance predictive capabilities in cardiovascular disease.This research contributes to advancing the understanding and application of machine learning in healthcare,particularly in improving predictive accuracy for cardiovascular disease management and intervention strategies.展开更多
“Flying Ad Hoc Networks(FANETs)”,which use“Unmanned Aerial Vehicles(UAVs)”,are developing as a critical mechanism for numerous applications,such as military operations and civilian services.The dynamic nature of F...“Flying Ad Hoc Networks(FANETs)”,which use“Unmanned Aerial Vehicles(UAVs)”,are developing as a critical mechanism for numerous applications,such as military operations and civilian services.The dynamic nature of FANETs,with high mobility,quick node migration,and frequent topology changes,presents substantial hurdles for routing protocol development.Over the preceding few years,researchers have found that machine learning gives productive solutions in routing while preserving the nature of FANET,which is topology change and high mobility.This paper reviews current research on routing protocols and Machine Learning(ML)approaches applied to FANETs,emphasizing developments between 2021 and 2023.The research uses the PRISMA approach to sift through the literature,filtering results from the SCOPUS database to find 82 relevant publications.The research study uses machine learning-based routing algorithms to beat the issues of high mobility,dynamic topologies,and intermittent connection in FANETs.When compared with conventional routing,it gives an energy-efficient and fast decision-making solution in a real-time environment,with greater fault tolerance capabilities.These protocols aim to increase routing efficiency,flexibility,and network stability using ML’s predictive and adaptive capabilities.This comprehensive review seeks to integrate existing information,offer novel integration approaches,and recommend future research topics for improving routing efficiency and flexibility in FANETs.Moreover,the study highlights emerging trends in ML integration,discusses challenges faced during the review,and discusses overcoming these hurdles in future research.展开更多
Network enabled digital technologies are offering new and exciting opportunities to increase the connectivity of devices for the purpose of home and office automation. ZigBee (IEEE 802.15.4) is such a digital wireless...Network enabled digital technologies are offering new and exciting opportunities to increase the connectivity of devices for the purpose of home and office automation. ZigBee (IEEE 802.15.4) is such a digital wireless technology that is used for personal area networks. In this paper, an office automation network using the combination of fixed and mobile IEEE 802.15.4 has been deployed and analyzed. The QoS parameters of the network as the performance metrics like throughput, MAC delay and data dropped rate have been investigated. Finally the network has been finalized with the optimized QoS parameters.展开更多
One exciting area within computer vision is classifying human activities, which has diverse applications like medical informatics, human-computer interaction, surveillance, and task monitoring systems. In the healthca...One exciting area within computer vision is classifying human activities, which has diverse applications like medical informatics, human-computer interaction, surveillance, and task monitoring systems. In the healthcare field, understanding and classifying patients’ activities is crucial for providing doctors with essential information for medication reactions and diagnosis. While some research methods already exist, utilizing machine learning and soft computational algorithms to recognize human activity from videos and images, there’s ongoing exploration of more advanced computer vision techniques. This paper introduces a straightforward and effective automated approach that involves five key steps: preprocessing, feature extraction technique, feature selection, feature fusion, and finally classification. To evaluate the proposed approach, two commonly used benchmark datasets KTH and Weizmann are employed for training, validation, and testing of ML classifiers. The study’s findings show that the first and second datasets had remarkable accuracy rates of 99.94% and 99.80%, respectively. When compared to existing methods, our approach stands out in terms of sensitivity, accuracy, precision, and specificity evaluation metrics. In essence, this paper demonstrates a practical method for automatically classifying human activities using an optimal feature fusion and deep learning approach, promising a great result that could benefit various fields, particularly in healthcare.展开更多
A novel coronavirus(SARS-CoV-2) is an unusual viral pneumonia in patients, first found in late December 2019, latter it declared a pandemic by World Health Organizations because of its fatal effects on public health. ...A novel coronavirus(SARS-CoV-2) is an unusual viral pneumonia in patients, first found in late December 2019, latter it declared a pandemic by World Health Organizations because of its fatal effects on public health. In this present, cases of COVID-19 pandemic are exponentially increasing day by day in the whole world. Here, we are detecting the COVID-19 cases, i.e., confirmed, death, and cured cases in India only. We are performing this analysis based on the cases occurring in different states of India in chronological dates. Our dataset contains multiple classes so we are performing multi-class classification. On this dataset, first, we performed data cleansing and feature selection, then performed forecasting of all classes using random forest, linear model, support vector machine,decision tree, and neural network, where random forest model outperformed the others, therefore, the random forest is used for prediction and analysis of all the results. The K-fold cross-validation is performed to measure the consistency of the model.展开更多
Wireless Sensor Network (WSN) is a special type of communication medium through distributed sensor nodes. Popular wireless sensor nodes like ZigBee have splendid interoperability after IEEE 802.15.4 standardization in...Wireless Sensor Network (WSN) is a special type of communication medium through distributed sensor nodes. Popular wireless sensor nodes like ZigBee have splendid interoperability after IEEE 802.15.4 standardization in the domain of wireless personal area network (WPAN). ZigBee has another great feature mobility that makes the ZigBee network more versatile. The mobility feature of ZigBee mobile nodes has a greater impact on network performance than fixed nodes. This impact sometimes turns into more severe because of network structure and mobility model. This study mainly focuses on the performance analysis of the ZigBee mobile node under Random and Octagonal mobility management model with the Tree routing method. The Riverbed academic modeler is used to design, implement and simulate the ZigBee network under certain conditions. This study also presents a competitive performance analysis based on ZigBee mobile nodes transmitter and receiver characteristics under the observation of the mobility model. This indicates that Octagonal mobility model exhibits better performance than the Random mobility model. This study will constitute a new way for further designing and planning a reliable and efficient ZigBee network.展开更多
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU),Grant Number IMSIU-RG23151.
文摘This study explores the impact of hyperparameter optimization on machine learning models for predicting cardiovascular disease using data from an IoST(Internet of Sensing Things)device.Ten distinct machine learning approaches were implemented and systematically evaluated before and after hyperparameter tuning.Significant improvements were observed across various models,with SVM and Neural Networks consistently showing enhanced performance metrics such as F1-Score,recall,and precision.The study underscores the critical role of tailored hyperparameter tuning in optimizing these models,revealing diverse outcomes among algorithms.Decision Trees and Random Forests exhibited stable performance throughout the evaluation.While enhancing accuracy,hyperparameter optimization also led to increased execution time.Visual representations and comprehensive results support the findings,confirming the hypothesis that optimizing parameters can effectively enhance predictive capabilities in cardiovascular disease.This research contributes to advancing the understanding and application of machine learning in healthcare,particularly in improving predictive accuracy for cardiovascular disease management and intervention strategies.
基金support the findings of this study are openly available in(Scopus database)at www.scopus.com(accessed on 07 January 2025).
文摘“Flying Ad Hoc Networks(FANETs)”,which use“Unmanned Aerial Vehicles(UAVs)”,are developing as a critical mechanism for numerous applications,such as military operations and civilian services.The dynamic nature of FANETs,with high mobility,quick node migration,and frequent topology changes,presents substantial hurdles for routing protocol development.Over the preceding few years,researchers have found that machine learning gives productive solutions in routing while preserving the nature of FANET,which is topology change and high mobility.This paper reviews current research on routing protocols and Machine Learning(ML)approaches applied to FANETs,emphasizing developments between 2021 and 2023.The research uses the PRISMA approach to sift through the literature,filtering results from the SCOPUS database to find 82 relevant publications.The research study uses machine learning-based routing algorithms to beat the issues of high mobility,dynamic topologies,and intermittent connection in FANETs.When compared with conventional routing,it gives an energy-efficient and fast decision-making solution in a real-time environment,with greater fault tolerance capabilities.These protocols aim to increase routing efficiency,flexibility,and network stability using ML’s predictive and adaptive capabilities.This comprehensive review seeks to integrate existing information,offer novel integration approaches,and recommend future research topics for improving routing efficiency and flexibility in FANETs.Moreover,the study highlights emerging trends in ML integration,discusses challenges faced during the review,and discusses overcoming these hurdles in future research.
文摘Network enabled digital technologies are offering new and exciting opportunities to increase the connectivity of devices for the purpose of home and office automation. ZigBee (IEEE 802.15.4) is such a digital wireless technology that is used for personal area networks. In this paper, an office automation network using the combination of fixed and mobile IEEE 802.15.4 has been deployed and analyzed. The QoS parameters of the network as the performance metrics like throughput, MAC delay and data dropped rate have been investigated. Finally the network has been finalized with the optimized QoS parameters.
文摘One exciting area within computer vision is classifying human activities, which has diverse applications like medical informatics, human-computer interaction, surveillance, and task monitoring systems. In the healthcare field, understanding and classifying patients’ activities is crucial for providing doctors with essential information for medication reactions and diagnosis. While some research methods already exist, utilizing machine learning and soft computational algorithms to recognize human activity from videos and images, there’s ongoing exploration of more advanced computer vision techniques. This paper introduces a straightforward and effective automated approach that involves five key steps: preprocessing, feature extraction technique, feature selection, feature fusion, and finally classification. To evaluate the proposed approach, two commonly used benchmark datasets KTH and Weizmann are employed for training, validation, and testing of ML classifiers. The study’s findings show that the first and second datasets had remarkable accuracy rates of 99.94% and 99.80%, respectively. When compared to existing methods, our approach stands out in terms of sensitivity, accuracy, precision, and specificity evaluation metrics. In essence, this paper demonstrates a practical method for automatically classifying human activities using an optimal feature fusion and deep learning approach, promising a great result that could benefit various fields, particularly in healthcare.
文摘A novel coronavirus(SARS-CoV-2) is an unusual viral pneumonia in patients, first found in late December 2019, latter it declared a pandemic by World Health Organizations because of its fatal effects on public health. In this present, cases of COVID-19 pandemic are exponentially increasing day by day in the whole world. Here, we are detecting the COVID-19 cases, i.e., confirmed, death, and cured cases in India only. We are performing this analysis based on the cases occurring in different states of India in chronological dates. Our dataset contains multiple classes so we are performing multi-class classification. On this dataset, first, we performed data cleansing and feature selection, then performed forecasting of all classes using random forest, linear model, support vector machine,decision tree, and neural network, where random forest model outperformed the others, therefore, the random forest is used for prediction and analysis of all the results. The K-fold cross-validation is performed to measure the consistency of the model.
文摘Wireless Sensor Network (WSN) is a special type of communication medium through distributed sensor nodes. Popular wireless sensor nodes like ZigBee have splendid interoperability after IEEE 802.15.4 standardization in the domain of wireless personal area network (WPAN). ZigBee has another great feature mobility that makes the ZigBee network more versatile. The mobility feature of ZigBee mobile nodes has a greater impact on network performance than fixed nodes. This impact sometimes turns into more severe because of network structure and mobility model. This study mainly focuses on the performance analysis of the ZigBee mobile node under Random and Octagonal mobility management model with the Tree routing method. The Riverbed academic modeler is used to design, implement and simulate the ZigBee network under certain conditions. This study also presents a competitive performance analysis based on ZigBee mobile nodes transmitter and receiver characteristics under the observation of the mobility model. This indicates that Octagonal mobility model exhibits better performance than the Random mobility model. This study will constitute a new way for further designing and planning a reliable and efficient ZigBee network.
