Pub Date : 2024-02-09DOI: 10.2174/0126662558258394231228080539
Keren Lois Daniel, R. C. Poonia
Path planning is a way to define the motion of an autonomous surface vehicle (ASV) in any existing obstacle environment to enable the vehicle's movement by setting directions to avoid that can react to the obstacles in the vehicle's path. A good, planned path perceives the environment to the extent of uncertainty and tries to build or adapt its change in the path of motion. Efficient path planning algorithms are needed to alleviate deficiencies, which are to be modified using the deterministic path that leads the ASV to reach a goal or a desired location while finding an optimal solution has become a challenge in the field of optimization along with a collision-free path, making path planning a critical thinker. The traditional algorithms have a lot of training and computation, making it difficult in a realistic environment. This review paper explores the different techniques available in path planning and collision avoidance of ASV in a dynamic environment. The objective of good path planning and collision avoidance for a dynamic environment is compared effectively with the existing obstacle’s movement of different vehicles. Different path planning technical approaches are compared with their performance and collision avoidance for unmanned vehicles in marine environments by early researchers. This paper gives us a clear idea for developing an effective path planning technique to overcome marine accidents in the dynamic ocean environment while choosing the shortest, obstacle-free path for Autonomous Surface Vehicles that can reduce risk and enhance the safety of unmanned vehicle movement in a harsh ocean environment.
路径规划是一种在任何现有障碍物环境中确定自主水面飞行器（ASV）运动的方法，通过设置可对飞行器路径上的障碍物做出反应的避让方向来实现飞行器的运动。良好的规划路径能感知环境的不确定性，并尝试建立或调整运动路径的变化。我们需要高效的路径规划算法来缓解不足之处，这些不足之处需要使用确定性路径进行修改，以引导 ASV 到达目标或理想位置，而寻找最佳解决方案已成为优化领域的一项挑战，同时还要实现无碰撞路径，这使得路径规划成为一个关键的思考者。传统算法需要大量的训练和计算，在现实环境中难以实现。本文探讨了动态环境中 ASV 路径规划和碰撞规避的不同技术。本文探讨了动态环境中 ASV 路径规划和碰撞规避的不同技术，并将动态环境中良好的路径规划和碰撞规避目标与不同车辆的现有障碍物探测技术进行了有效比较。早期研究人员比较了不同路径规划技术方法的性能和在海洋环境中无人驾驶飞行器的防撞性能。本文给出了一个清晰的思路，即开发一种有效的路径规划技术，以克服动态海洋环境中的海上事故，同时为自主水面飞行器选择最短、无障碍的路径，从而降低风险，提高无人飞行器在恶劣海洋环境中的运动安全性。
{"title":"An In-Depth Analysis of Collision Avoidance Path Planning Algorithms in\u0000Autonomous Vehicles","authors":"Keren Lois Daniel, R. C. Poonia","doi":"10.2174/0126662558258394231228080539","DOIUrl":"https://doi.org/10.2174/0126662558258394231228080539","url":null,"abstract":"\u0000\u0000Path planning is a way to define the motion of an autonomous surface vehicle\u0000(ASV) in any existing obstacle environment to enable the vehicle's movement by setting directions to avoid that can react to the obstacles in the vehicle's path. A good, planned path perceives the environment to the extent of uncertainty and tries to build or adapt its change in the\u0000path of motion. Efficient path planning algorithms are needed to alleviate deficiencies, which\u0000are to be modified using the deterministic path that leads the ASV to reach a goal or a desired\u0000location while finding an optimal solution has become a challenge in the field of optimization\u0000along with a collision-free path, making path planning a critical thinker. The traditional algorithms have a lot of training and computation, making it difficult in a realistic environment.\u0000This review paper explores the different techniques available in path planning and collision\u0000avoidance of ASV in a dynamic environment. The objective of good path planning and collision avoidance for a dynamic environment is compared effectively with the existing obstacle’s\u0000movement of different vehicles. Different path planning technical approaches are compared\u0000with their performance and collision avoidance for unmanned vehicles in marine environments\u0000by early researchers. This paper gives us a clear idea for developing an effective path planning\u0000technique to overcome marine accidents in the dynamic ocean environment while choosing the\u0000shortest, obstacle-free path for Autonomous Surface Vehicles that can reduce risk and enhance\u0000the safety of unmanned vehicle movement in a harsh ocean environment.\u0000","PeriodicalId":506582,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":" 31","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139790458","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 : 2024-02-09DOI: 10.2174/0126662558258394231228080539
Keren Lois Daniel, R. C. Poonia
Path planning is a way to define the motion of an autonomous surface vehicle (ASV) in any existing obstacle environment to enable the vehicle's movement by setting directions to avoid that can react to the obstacles in the vehicle's path. A good, planned path perceives the environment to the extent of uncertainty and tries to build or adapt its change in the path of motion. Efficient path planning algorithms are needed to alleviate deficiencies, which are to be modified using the deterministic path that leads the ASV to reach a goal or a desired location while finding an optimal solution has become a challenge in the field of optimization along with a collision-free path, making path planning a critical thinker. The traditional algorithms have a lot of training and computation, making it difficult in a realistic environment. This review paper explores the different techniques available in path planning and collision avoidance of ASV in a dynamic environment. The objective of good path planning and collision avoidance for a dynamic environment is compared effectively with the existing obstacle’s movement of different vehicles. Different path planning technical approaches are compared with their performance and collision avoidance for unmanned vehicles in marine environments by early researchers. This paper gives us a clear idea for developing an effective path planning technique to overcome marine accidents in the dynamic ocean environment while choosing the shortest, obstacle-free path for Autonomous Surface Vehicles that can reduce risk and enhance the safety of unmanned vehicle movement in a harsh ocean environment.
路径规划是一种在任何现有障碍物环境中确定自主水面飞行器（ASV）运动的方法，通过设置可对飞行器路径上的障碍物做出反应的避让方向来实现飞行器的运动。良好的规划路径能感知环境的不确定性，并尝试建立或调整运动路径的变化。我们需要高效的路径规划算法来缓解不足之处，这些不足之处需要使用确定性路径进行修改，以引导 ASV 到达目标或理想位置，而寻找最佳解决方案已成为优化领域的一项挑战，同时还要实现无碰撞路径，这使得路径规划成为一个关键的思考者。传统算法需要大量的训练和计算，在现实环境中难以实现。本文探讨了动态环境中 ASV 路径规划和碰撞规避的不同技术。本文探讨了动态环境中 ASV 路径规划和碰撞规避的不同技术，并将动态环境中良好的路径规划和碰撞规避目标与不同车辆的现有障碍物探测技术进行了有效比较。早期研究人员比较了不同路径规划技术方法的性能和在海洋环境中无人驾驶飞行器的防撞性能。本文给出了一个清晰的思路，即开发一种有效的路径规划技术，以克服动态海洋环境中的海上事故，同时为自主水面飞行器选择最短、无障碍的路径，从而降低风险，提高无人飞行器在恶劣海洋环境中的运动安全性。
{"title":"An In-Depth Analysis of Collision Avoidance Path Planning Algorithms in\u0000Autonomous Vehicles","authors":"Keren Lois Daniel, R. C. Poonia","doi":"10.2174/0126662558258394231228080539","DOIUrl":"https://doi.org/10.2174/0126662558258394231228080539","url":null,"abstract":"\u0000\u0000Path planning is a way to define the motion of an autonomous surface vehicle\u0000(ASV) in any existing obstacle environment to enable the vehicle's movement by setting directions to avoid that can react to the obstacles in the vehicle's path. A good, planned path perceives the environment to the extent of uncertainty and tries to build or adapt its change in the\u0000path of motion. Efficient path planning algorithms are needed to alleviate deficiencies, which\u0000are to be modified using the deterministic path that leads the ASV to reach a goal or a desired\u0000location while finding an optimal solution has become a challenge in the field of optimization\u0000along with a collision-free path, making path planning a critical thinker. The traditional algorithms have a lot of training and computation, making it difficult in a realistic environment.\u0000This review paper explores the different techniques available in path planning and collision\u0000avoidance of ASV in a dynamic environment. The objective of good path planning and collision avoidance for a dynamic environment is compared effectively with the existing obstacle’s\u0000movement of different vehicles. Different path planning technical approaches are compared\u0000with their performance and collision avoidance for unmanned vehicles in marine environments\u0000by early researchers. This paper gives us a clear idea for developing an effective path planning\u0000technique to overcome marine accidents in the dynamic ocean environment while choosing the\u0000shortest, obstacle-free path for Autonomous Surface Vehicles that can reduce risk and enhance\u0000the safety of unmanned vehicle movement in a harsh ocean environment.\u0000","PeriodicalId":506582,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":"30 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139850477","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 : 2024-02-07DOI: 10.2174/0126662558284886240130154414
Manjunath Ramanna Lamani, Julian Benadit P
Autism Spectrum Disorder (ASD) is a multifaceted neurodevelopmental condition with significant heterogeneity in its clinical presentation. Timely and precise identification of ASD is crucial for effective intervention and assistance. Recent advances in deep learning techniques have shown promise in enhancing the accuracy of ASD detection. This comprehensive review aims to provide an overview of various deep learning methods employed in detecting ASD, utilizing diverse neuroimaging modalities. We analyze a range of studies that use resting-state functional Magnetic Resonance Imaging (rsfMRI), structural MRI (sMRI), task-based fMRI (tfMRI), and electroencephalography (EEG). This paper aims to assess the effectiveness of these techniques based on criteria such as accuracy, sensitivity, specificity, and computational efficiency. We systematically review studies investigating ASD detection using deep learning across different neuroimaging modalities. These studies utilize various preprocessing tools, atlases, feature extraction techniques, and classification algorithms. The performance metrics of interest include accuracy, sensitivity, specificity, precision, F1-score, recall, and area under the curve (AUC). The review covers a wide range of studies, each with its own dataset and methodology. Notable findings include a study employing rsfMRI data from ABIDE that achieved an accuracy of 80% using LeNet. Another study using rsfMRI data from ABIDE-II achieved an impressive accuracy of 95.4% with the ASGCN deep learning model. Studies utilizing different modalities, such as EEG and sMRI, also reported high accuracies ranging from 74% to 95%. Deep learning-based approaches for ASD detection have demonstrated significant potential across multiple neuroimaging modalities. These methods offer a more objective and data-driven approach to diagnosis, potentially reducing the subjectivity associated with clinical evaluations. However, challenges remain, including the need for larger and more diverse datasets, model interpretability, and clinical validation. The field of deep learning in ASD diagnosis continues to evolve, holding promise for early and accurate identification of individuals with ASD, which is crucial for timely intervention and support.
{"title":"A Thorough Review of Deep Learning in Autism Spectrum Disorder Detection: From Data to Diagnosis","authors":"Manjunath Ramanna Lamani, Julian Benadit P","doi":"10.2174/0126662558284886240130154414","DOIUrl":"https://doi.org/10.2174/0126662558284886240130154414","url":null,"abstract":"\u0000\u0000Autism Spectrum Disorder (ASD) is a multifaceted neurodevelopmental\u0000condition with significant heterogeneity in its clinical presentation. Timely and precise\u0000identification of ASD is crucial for effective intervention and assistance. Recent advances in\u0000deep learning techniques have shown promise in enhancing the accuracy of ASD detection.\u0000\u0000\u0000\u0000This comprehensive review aims to provide an overview of various deep learning\u0000methods employed in detecting ASD, utilizing diverse neuroimaging modalities. We analyze a\u0000range of studies that use resting-state functional Magnetic Resonance Imaging (rsfMRI), structural\u0000MRI (sMRI), task-based fMRI (tfMRI), and electroencephalography (EEG). This paper\u0000aims to assess the effectiveness of these techniques based on criteria such as accuracy, sensitivity,\u0000specificity, and computational efficiency.\u0000\u0000\u0000\u0000We systematically review studies investigating ASD detection using deep learning\u0000across different neuroimaging modalities. These studies utilize various preprocessing tools, atlases,\u0000feature extraction techniques, and classification algorithms. The performance metrics of\u0000interest include accuracy, sensitivity, specificity, precision, F1-score, recall, and area under the\u0000curve (AUC).\u0000\u0000\u0000\u0000The review covers a wide range of studies, each with its own dataset and methodology.\u0000Notable findings include a study employing rsfMRI data from ABIDE that achieved an accuracy\u0000of 80% using LeNet. Another study using rsfMRI data from ABIDE-II achieved an impressive\u0000accuracy of 95.4% with the ASGCN deep learning model. Studies utilizing different\u0000modalities, such as EEG and sMRI, also reported high accuracies ranging from 74% to 95%.\u0000\u0000\u0000\u0000Deep learning-based approaches for ASD detection have demonstrated significant\u0000potential across multiple neuroimaging modalities. These methods offer a more objective and\u0000data-driven approach to diagnosis, potentially reducing the subjectivity associated with clinical\u0000evaluations. However, challenges remain, including the need for larger and more diverse datasets,\u0000model interpretability, and clinical validation. The field of deep learning in ASD diagnosis\u0000continues to evolve, holding promise for early and accurate identification of individuals\u0000with ASD, which is crucial for timely intervention and support.\u0000","PeriodicalId":506582,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":"38 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139797292","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 : 2024-02-07DOI: 10.2174/0126662558284886240130154414
Manjunath Ramanna Lamani, Julian Benadit P
Autism Spectrum Disorder (ASD) is a multifaceted neurodevelopmental condition with significant heterogeneity in its clinical presentation. Timely and precise identification of ASD is crucial for effective intervention and assistance. Recent advances in deep learning techniques have shown promise in enhancing the accuracy of ASD detection. This comprehensive review aims to provide an overview of various deep learning methods employed in detecting ASD, utilizing diverse neuroimaging modalities. We analyze a range of studies that use resting-state functional Magnetic Resonance Imaging (rsfMRI), structural MRI (sMRI), task-based fMRI (tfMRI), and electroencephalography (EEG). This paper aims to assess the effectiveness of these techniques based on criteria such as accuracy, sensitivity, specificity, and computational efficiency. We systematically review studies investigating ASD detection using deep learning across different neuroimaging modalities. These studies utilize various preprocessing tools, atlases, feature extraction techniques, and classification algorithms. The performance metrics of interest include accuracy, sensitivity, specificity, precision, F1-score, recall, and area under the curve (AUC). The review covers a wide range of studies, each with its own dataset and methodology. Notable findings include a study employing rsfMRI data from ABIDE that achieved an accuracy of 80% using LeNet. Another study using rsfMRI data from ABIDE-II achieved an impressive accuracy of 95.4% with the ASGCN deep learning model. Studies utilizing different modalities, such as EEG and sMRI, also reported high accuracies ranging from 74% to 95%. Deep learning-based approaches for ASD detection have demonstrated significant potential across multiple neuroimaging modalities. These methods offer a more objective and data-driven approach to diagnosis, potentially reducing the subjectivity associated with clinical evaluations. However, challenges remain, including the need for larger and more diverse datasets, model interpretability, and clinical validation. The field of deep learning in ASD diagnosis continues to evolve, holding promise for early and accurate identification of individuals with ASD, which is crucial for timely intervention and support.
{"title":"A Thorough Review of Deep Learning in Autism Spectrum Disorder Detection: From Data to Diagnosis","authors":"Manjunath Ramanna Lamani, Julian Benadit P","doi":"10.2174/0126662558284886240130154414","DOIUrl":"https://doi.org/10.2174/0126662558284886240130154414","url":null,"abstract":"\u0000\u0000Autism Spectrum Disorder (ASD) is a multifaceted neurodevelopmental\u0000condition with significant heterogeneity in its clinical presentation. Timely and precise\u0000identification of ASD is crucial for effective intervention and assistance. Recent advances in\u0000deep learning techniques have shown promise in enhancing the accuracy of ASD detection.\u0000\u0000\u0000\u0000This comprehensive review aims to provide an overview of various deep learning\u0000methods employed in detecting ASD, utilizing diverse neuroimaging modalities. We analyze a\u0000range of studies that use resting-state functional Magnetic Resonance Imaging (rsfMRI), structural\u0000MRI (sMRI), task-based fMRI (tfMRI), and electroencephalography (EEG). This paper\u0000aims to assess the effectiveness of these techniques based on criteria such as accuracy, sensitivity,\u0000specificity, and computational efficiency.\u0000\u0000\u0000\u0000We systematically review studies investigating ASD detection using deep learning\u0000across different neuroimaging modalities. These studies utilize various preprocessing tools, atlases,\u0000feature extraction techniques, and classification algorithms. The performance metrics of\u0000interest include accuracy, sensitivity, specificity, precision, F1-score, recall, and area under the\u0000curve (AUC).\u0000\u0000\u0000\u0000The review covers a wide range of studies, each with its own dataset and methodology.\u0000Notable findings include a study employing rsfMRI data from ABIDE that achieved an accuracy\u0000of 80% using LeNet. Another study using rsfMRI data from ABIDE-II achieved an impressive\u0000accuracy of 95.4% with the ASGCN deep learning model. Studies utilizing different\u0000modalities, such as EEG and sMRI, also reported high accuracies ranging from 74% to 95%.\u0000\u0000\u0000\u0000Deep learning-based approaches for ASD detection have demonstrated significant\u0000potential across multiple neuroimaging modalities. These methods offer a more objective and\u0000data-driven approach to diagnosis, potentially reducing the subjectivity associated with clinical\u0000evaluations. However, challenges remain, including the need for larger and more diverse datasets,\u0000model interpretability, and clinical validation. The field of deep learning in ASD diagnosis\u0000continues to evolve, holding promise for early and accurate identification of individuals\u0000with ASD, which is crucial for timely intervention and support.\u0000","PeriodicalId":506582,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139857000","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 : 2024-02-06DOI: 10.2174/0126662558285372240109113226
Devasis Pradhan, Manjusha Behera, Mehdi Gheisari
The rapid integration of distributed cloud systems in the healthcare industry has profoundly impacted the management of valuable medical data. While this advancement has significantly improved data handling, protecting sensitive healthcare information in such a complex environment remains daunting. This comprehensive study explores the crucial intersection between dynamic data placement strategies and network security concerns in distributed cloud environments, particularly healthcare. After establishing the significance and context of this research, the survey delves into the growing need to safeguard medical data within the everevolving landscape of cloud-based healthcare systems. It lays out fundamental concepts, such as dynamic data placement and network security, highlighting their unique implications in the medical domain. Ultimately, this survey sheds light on the most effective approaches for balancing dynamic data placement and network security in the healthcare sector. This research delves into examining many tactics, evaluating their effectiveness in handling delicate medical information, and presenting tangible use cases. A key focus of this investigation is the fusion of data organization and network safety within the healthcare industry. It investigates the adaptability of dynamic data positioning techniques in fortifying network security and safeguarding against potential threats unique to the healthcare sector. Case studies of the successful implementation of these strategies in healthcare establishments are also included.
{"title":"Dynamic Data Placement Strategy with Network Security Issues in Distributed Cloud Environment for Medical Issues: An Overview","authors":"Devasis Pradhan, Manjusha Behera, Mehdi Gheisari","doi":"10.2174/0126662558285372240109113226","DOIUrl":"https://doi.org/10.2174/0126662558285372240109113226","url":null,"abstract":"\u0000\u0000The rapid integration of distributed cloud systems in the healthcare industry has profoundly\u0000impacted the management of valuable medical data. While this advancement has significantly\u0000improved data handling, protecting sensitive healthcare information in such a complex\u0000environment remains daunting. This comprehensive study explores the crucial intersection\u0000between dynamic data placement strategies and network security concerns in distributed cloud\u0000environments, particularly healthcare. After establishing the significance and context of this\u0000research, the survey delves into the growing need to safeguard medical data within the everevolving\u0000landscape of cloud-based healthcare systems. It lays out fundamental concepts, such\u0000as dynamic data placement and network security, highlighting their unique implications in the\u0000medical domain. Ultimately, this survey sheds light on the most effective approaches for balancing\u0000dynamic data placement and network security in the healthcare sector. This research\u0000delves into examining many tactics, evaluating their effectiveness in handling delicate medical\u0000information, and presenting tangible use cases. A key focus of this investigation is the fusion\u0000of data organization and network safety within the healthcare industry. It investigates the\u0000adaptability of dynamic data positioning techniques in fortifying network security and safeguarding\u0000against potential threats unique to the healthcare sector. Case studies of the successful\u0000implementation of these strategies in healthcare establishments are also included.\u0000","PeriodicalId":506582,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":"60 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139799998","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 : 2024-02-06DOI: 10.2174/0126662558285372240109113226
Devasis Pradhan, Manjusha Behera, Mehdi Gheisari
The rapid integration of distributed cloud systems in the healthcare industry has profoundly impacted the management of valuable medical data. While this advancement has significantly improved data handling, protecting sensitive healthcare information in such a complex environment remains daunting. This comprehensive study explores the crucial intersection between dynamic data placement strategies and network security concerns in distributed cloud environments, particularly healthcare. After establishing the significance and context of this research, the survey delves into the growing need to safeguard medical data within the everevolving landscape of cloud-based healthcare systems. It lays out fundamental concepts, such as dynamic data placement and network security, highlighting their unique implications in the medical domain. Ultimately, this survey sheds light on the most effective approaches for balancing dynamic data placement and network security in the healthcare sector. This research delves into examining many tactics, evaluating their effectiveness in handling delicate medical information, and presenting tangible use cases. A key focus of this investigation is the fusion of data organization and network safety within the healthcare industry. It investigates the adaptability of dynamic data positioning techniques in fortifying network security and safeguarding against potential threats unique to the healthcare sector. Case studies of the successful implementation of these strategies in healthcare establishments are also included.
{"title":"Dynamic Data Placement Strategy with Network Security Issues in Distributed Cloud Environment for Medical Issues: An Overview","authors":"Devasis Pradhan, Manjusha Behera, Mehdi Gheisari","doi":"10.2174/0126662558285372240109113226","DOIUrl":"https://doi.org/10.2174/0126662558285372240109113226","url":null,"abstract":"\u0000\u0000The rapid integration of distributed cloud systems in the healthcare industry has profoundly\u0000impacted the management of valuable medical data. While this advancement has significantly\u0000improved data handling, protecting sensitive healthcare information in such a complex\u0000environment remains daunting. This comprehensive study explores the crucial intersection\u0000between dynamic data placement strategies and network security concerns in distributed cloud\u0000environments, particularly healthcare. After establishing the significance and context of this\u0000research, the survey delves into the growing need to safeguard medical data within the everevolving\u0000landscape of cloud-based healthcare systems. It lays out fundamental concepts, such\u0000as dynamic data placement and network security, highlighting their unique implications in the\u0000medical domain. Ultimately, this survey sheds light on the most effective approaches for balancing\u0000dynamic data placement and network security in the healthcare sector. This research\u0000delves into examining many tactics, evaluating their effectiveness in handling delicate medical\u0000information, and presenting tangible use cases. A key focus of this investigation is the fusion\u0000of data organization and network safety within the healthcare industry. It investigates the\u0000adaptability of dynamic data positioning techniques in fortifying network security and safeguarding\u0000against potential threats unique to the healthcare sector. Case studies of the successful\u0000implementation of these strategies in healthcare establishments are also included.\u0000","PeriodicalId":506582,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":"63 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139859855","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}
Cross-domain Sentiment Classification is a well-researched field in sentiment analysis. The biggest challenge in CDSC arises from the differences in domains and features, which cause a decrease in model performance when applying source domain features to predict sentiment in the target domain. To address this challenge, several feature selection methods can be employed to identify the most relevant features for training and testing in CDSC. The primary objective of this study is to perform a comparative analysis of different feature selection methods on the various CDSC tasks. In this study, statistical test-based feature selection methods using 18 classifiers for the CDSC task has been implemented. The impact of these feature selection methods on Amazon product reviews, specifically those in the DVD, Electronics, Kitchen, and TV domains, has been compared. Total 12x18 experiments were conducted for each feature selection method by varying source and target domain pairs from the Amazon product reviews dataset and by using 18 classifiers. Performance evaluation measures are accuracy and f-score. From the experiments, it has been inferred that the CSDC task depends on various factors for a good performance, from the right domain selection to the right feature selection method. We have concluded that the best training dataset is Electronics as it gives more precise results while testing in either domain selected for our study. Cross-domain sentiment analysis is a dynamic and interdisciplinary field that offers valuable insights for understanding how sentiment varies across different domains.
{"title":"A Comparative Analysis of Feature Selection Algorithms in Cross Domain\u0000Sentiment Classification","authors":"Lipika Goel, Sonam Gupta, Avdhesh Gupta, Neha Nandal, Siddhi Nath Ranjan, Pradeep Gupta","doi":"10.2174/0126662558276889240125062857","DOIUrl":"https://doi.org/10.2174/0126662558276889240125062857","url":null,"abstract":"\u0000\u0000Cross-domain Sentiment Classification is a well-researched field in\u0000sentiment analysis. The biggest challenge in CDSC arises from the differences in domains and\u0000features, which cause a decrease in model performance when applying source domain features\u0000to predict sentiment in the target domain. To address this challenge, several feature selection\u0000methods can be employed to identify the most relevant features for training and testing in\u0000CDSC.\u0000\u0000\u0000\u0000The primary objective of this study is to perform a comparative analysis of different\u0000feature selection methods on the various CDSC tasks. In this study, statistical test-based feature\u0000selection methods using 18 classifiers for the CDSC task has been implemented. The impact\u0000of these feature selection methods on Amazon product reviews, specifically those in the\u0000DVD, Electronics, Kitchen, and TV domains, has been compared. Total 12x18 experiments\u0000were conducted for each feature selection method by varying source and target domain pairs\u0000from the Amazon product reviews dataset and by using 18 classifiers. Performance evaluation\u0000measures are accuracy and f-score.\u0000\u0000\u0000\u0000From the experiments, it has been inferred that the CSDC task depends on various factors\u0000for a good performance, from the right domain selection to the right feature selection\u0000method. We have concluded that the best training dataset is Electronics as it gives more precise\u0000results while testing in either domain selected for our study.\u0000\u0000\u0000\u0000Cross-domain sentiment analysis is a dynamic and interdisciplinary field that offers\u0000valuable insights for understanding how sentiment varies across different domains.\u0000","PeriodicalId":506582,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139809444","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}
Cross-domain Sentiment Classification is a well-researched field in sentiment analysis. The biggest challenge in CDSC arises from the differences in domains and features, which cause a decrease in model performance when applying source domain features to predict sentiment in the target domain. To address this challenge, several feature selection methods can be employed to identify the most relevant features for training and testing in CDSC. The primary objective of this study is to perform a comparative analysis of different feature selection methods on the various CDSC tasks. In this study, statistical test-based feature selection methods using 18 classifiers for the CDSC task has been implemented. The impact of these feature selection methods on Amazon product reviews, specifically those in the DVD, Electronics, Kitchen, and TV domains, has been compared. Total 12x18 experiments were conducted for each feature selection method by varying source and target domain pairs from the Amazon product reviews dataset and by using 18 classifiers. Performance evaluation measures are accuracy and f-score. From the experiments, it has been inferred that the CSDC task depends on various factors for a good performance, from the right domain selection to the right feature selection method. We have concluded that the best training dataset is Electronics as it gives more precise results while testing in either domain selected for our study. Cross-domain sentiment analysis is a dynamic and interdisciplinary field that offers valuable insights for understanding how sentiment varies across different domains.
{"title":"A Comparative Analysis of Feature Selection Algorithms in Cross Domain\u0000Sentiment Classification","authors":"Lipika Goel, Sonam Gupta, Avdhesh Gupta, Neha Nandal, Siddhi Nath Ranjan, Pradeep Gupta","doi":"10.2174/0126662558276889240125062857","DOIUrl":"https://doi.org/10.2174/0126662558276889240125062857","url":null,"abstract":"\u0000\u0000Cross-domain Sentiment Classification is a well-researched field in\u0000sentiment analysis. The biggest challenge in CDSC arises from the differences in domains and\u0000features, which cause a decrease in model performance when applying source domain features\u0000to predict sentiment in the target domain. To address this challenge, several feature selection\u0000methods can be employed to identify the most relevant features for training and testing in\u0000CDSC.\u0000\u0000\u0000\u0000The primary objective of this study is to perform a comparative analysis of different\u0000feature selection methods on the various CDSC tasks. In this study, statistical test-based feature\u0000selection methods using 18 classifiers for the CDSC task has been implemented. The impact\u0000of these feature selection methods on Amazon product reviews, specifically those in the\u0000DVD, Electronics, Kitchen, and TV domains, has been compared. Total 12x18 experiments\u0000were conducted for each feature selection method by varying source and target domain pairs\u0000from the Amazon product reviews dataset and by using 18 classifiers. Performance evaluation\u0000measures are accuracy and f-score.\u0000\u0000\u0000\u0000From the experiments, it has been inferred that the CSDC task depends on various factors\u0000for a good performance, from the right domain selection to the right feature selection\u0000method. We have concluded that the best training dataset is Electronics as it gives more precise\u0000results while testing in either domain selected for our study.\u0000\u0000\u0000\u0000Cross-domain sentiment analysis is a dynamic and interdisciplinary field that offers\u0000valuable insights for understanding how sentiment varies across different domains.\u0000","PeriodicalId":506582,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":"18 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139869407","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 : 2024-02-01DOI: 10.2174/0126662558268321231231065419
Yangbing Zheng, Xiaohan Tu
Unmanned aerial vehicles (UAVs) are being widely used in many fields, such as national economy, social development, national defense, and security. Currently, the number of registered UAVs in China is far less than that of flying UAVs-the frequent occurrence of unsafe incidents. The phenomenon of UAVs flying undeclared and unapproved has caused more serious troubles to social public order and people's production and life. In this paper, to assist the public security department in detecting the phenomenon of UAV black flying, our team conducts a series of research based on the deep learning YOLOv5 (You Only Look Once) algorithm. Firstly, the Vision Transformer mechanism is integrated to enhance the robustness of the model. Secondly, depth-separable convolution is introduced to reduce parameter redundancy. Finally, the SimAM attention-free mechanism and CBAM attention-free mechanism are combined to enhance the attention of small target UAVs. Through the analysis of UAV targets in video surveillance, the rapid identification of black-flying UAVs can be realized, the monitoring and early warning ability of UAVs in a specific area can be improved, and the loss of life and property of people can be reduced or saved as much as possible.
无人驾驶飞行器（UAV）正广泛应用于国民经济、社会发展、国防安全等诸多领域。目前，我国登记在册的无人机数量远远少于飞行的无人机数量，不安全事件频发。无人机未经申报和批准飞行的现象给社会公共秩序和人民群众的生产生活带来了较为严重的困扰。本文团队基于深度学习 YOLOv5（You Only Look Once）算法进行了一系列研究，以协助公安部门检测无人机黑飞现象。其次，引入深度分离卷积以减少参数冗余。通过对视频监控中无人机目标的分析，可以实现对黑飞无人机的快速识别，提高无人机在特定区域的监控和预警能力，最大限度地减少或挽救人们的生命和财产损失。
{"title":"Target Signal Communication Detection of Black Flying UAVs Based on\u0000Deep Learning Algorithm","authors":"Yangbing Zheng, Xiaohan Tu","doi":"10.2174/0126662558268321231231065419","DOIUrl":"https://doi.org/10.2174/0126662558268321231231065419","url":null,"abstract":"\u0000\u0000Unmanned aerial vehicles (UAVs) are being widely used in many\u0000fields, such as national economy, social development, national defense, and security. Currently, the number of registered UAVs in China is far less than that of flying UAVs-the frequent\u0000occurrence of unsafe incidents.\u0000\u0000\u0000\u0000The phenomenon of UAVs flying undeclared and unapproved has caused more serious troubles to social public order and people's production and life.\u0000\u0000\u0000\u0000In this paper, to assist the public security department in detecting the phenomenon of\u0000UAV black flying, our team conducts a series of research based on the deep learning YOLOv5\u0000(You Only Look Once) algorithm.\u0000\u0000\u0000\u0000Firstly, the Vision Transformer mechanism is integrated to enhance the robustness of\u0000the model. Secondly, depth-separable convolution is introduced to reduce parameter redundancy. Finally, the SimAM attention-free mechanism and CBAM attention-free mechanism are\u0000combined to enhance the attention of small target UAVs.\u0000\u0000\u0000\u0000Through the analysis of UAV targets in video surveillance, the rapid identification of black-flying UAVs can be realized, the monitoring and early warning ability of UAVs\u0000in a specific area can be improved, and the loss of life and property of people can be reduced or\u0000saved as much as possible.\u0000","PeriodicalId":506582,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":"15 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139891395","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 : 2024-02-01DOI: 10.2174/0126662558268321231231065419
Yangbing Zheng, Xiaohan Tu
Unmanned aerial vehicles (UAVs) are being widely used in many fields, such as national economy, social development, national defense, and security. Currently, the number of registered UAVs in China is far less than that of flying UAVs-the frequent occurrence of unsafe incidents. The phenomenon of UAVs flying undeclared and unapproved has caused more serious troubles to social public order and people's production and life. In this paper, to assist the public security department in detecting the phenomenon of UAV black flying, our team conducts a series of research based on the deep learning YOLOv5 (You Only Look Once) algorithm. Firstly, the Vision Transformer mechanism is integrated to enhance the robustness of the model. Secondly, depth-separable convolution is introduced to reduce parameter redundancy. Finally, the SimAM attention-free mechanism and CBAM attention-free mechanism are combined to enhance the attention of small target UAVs. Through the analysis of UAV targets in video surveillance, the rapid identification of black-flying UAVs can be realized, the monitoring and early warning ability of UAVs in a specific area can be improved, and the loss of life and property of people can be reduced or saved as much as possible.
无人驾驶飞行器（UAV）正广泛应用于国民经济、社会发展、国防安全等诸多领域。目前，我国登记在册的无人机数量远远少于飞行的无人机数量，不安全事件频发。无人机未经申报和批准飞行的现象给社会公共秩序和人民群众的生产生活带来了较为严重的困扰。本文团队基于深度学习 YOLOv5（You Only Look Once）算法进行了一系列研究，以协助公安部门检测无人机黑飞现象。其次，引入深度分离卷积以减少参数冗余。通过对视频监控中无人机目标的分析，可以实现对黑飞无人机的快速识别，提高无人机在特定区域的监控和预警能力，最大限度地减少或挽救人们的生命和财产损失。
{"title":"Target Signal Communication Detection of Black Flying UAVs Based on\u0000Deep Learning Algorithm","authors":"Yangbing Zheng, Xiaohan Tu","doi":"10.2174/0126662558268321231231065419","DOIUrl":"https://doi.org/10.2174/0126662558268321231231065419","url":null,"abstract":"\u0000\u0000Unmanned aerial vehicles (UAVs) are being widely used in many\u0000fields, such as national economy, social development, national defense, and security. Currently, the number of registered UAVs in China is far less than that of flying UAVs-the frequent\u0000occurrence of unsafe incidents.\u0000\u0000\u0000\u0000The phenomenon of UAVs flying undeclared and unapproved has caused more serious troubles to social public order and people's production and life.\u0000\u0000\u0000\u0000In this paper, to assist the public security department in detecting the phenomenon of\u0000UAV black flying, our team conducts a series of research based on the deep learning YOLOv5\u0000(You Only Look Once) algorithm.\u0000\u0000\u0000\u0000Firstly, the Vision Transformer mechanism is integrated to enhance the robustness of\u0000the model. Secondly, depth-separable convolution is introduced to reduce parameter redundancy. Finally, the SimAM attention-free mechanism and CBAM attention-free mechanism are\u0000combined to enhance the attention of small target UAVs.\u0000\u0000\u0000\u0000Through the analysis of UAV targets in video surveillance, the rapid identification of black-flying UAVs can be realized, the monitoring and early warning ability of UAVs\u0000in a specific area can be improved, and the loss of life and property of people can be reduced or\u0000saved as much as possible.\u0000","PeriodicalId":506582,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":"781 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139831429","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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