Pub Date : 2024-01-03DOI: 10.2174/0126662558277382231204074443
S. Pandey, Buddha Singh
Wireless Sensor Networks (WSNs) have gained significant attention due to their diverse applications, including border area security, earthquake detection, and fire detection. WSNs utilize compact sensors to detect environmental events and transmit data to a Base Station (BS) for analysis. Energy consumption during data transmission is a critical issue, which has led to the exploration of additional energy-saving techniques, such as clustering. The primary objective is to propose an algorithm that selects optimal Cluster Heads (CHs) through a fuzzy-based genetic approach. This algorithm aims to address energy consumption concerns, enhance load balancing, and improve routing efficiency within WSNs. The proposed algorithm employs a fuzzy-based genetic approach to optimize the selection of CHs for data transmission. Four key parameters are considered: the average remaining energy of CHs, the average distance between CHs and the BS, the average distance between member nodes and CHs, and the standard deviation of the distance between member nodes and CHs. The algorithm's effectiveness is demonstrated through simulation results. When compared to popular models like LEACH, MOEES, and FEEC, it demonstrates an 8-20% improvement in the lifetime of WSNs. The proposed approach achieves enhanced efficiency, lifetime extension, and improved performance in CH selection, load balancing, and routing. In conclusion, this study introduces a novel algorithm that utilizes fuzzy-based genetic techniques to optimize CH selection in WSNs. By considering four key parameters and addressing energy consumption challenges, the proposed algorithm offers significant improvements in efficiency, lifespan, and overall network performance, as validated through simulation results.
{"title":"Optimized Multi-Objective Clustering using Fuzzy Based Genetic\u0000Algorithm for Lifetime Maximization of WSN","authors":"S. Pandey, Buddha Singh","doi":"10.2174/0126662558277382231204074443","DOIUrl":"https://doi.org/10.2174/0126662558277382231204074443","url":null,"abstract":"\u0000\u0000Wireless Sensor Networks (WSNs) have gained significant attention\u0000due to their diverse applications, including border area security, earthquake detection, and fire\u0000detection. WSNs utilize compact sensors to detect environmental events and transmit data to a\u0000Base Station (BS) for analysis. Energy consumption during data transmission is a critical issue,\u0000which has led to the exploration of additional energy-saving techniques, such as clustering.\u0000\u0000\u0000\u0000The primary objective is to propose an algorithm that selects optimal Cluster Heads\u0000(CHs) through a fuzzy-based genetic approach. This algorithm aims to address energy consumption concerns, enhance load balancing, and improve routing efficiency within WSNs.\u0000\u0000\u0000\u0000The proposed algorithm employs a fuzzy-based genetic approach to optimize the selection of CHs for data transmission. Four key parameters are considered: the average remaining energy of CHs, the average distance between CHs and the BS, the average distance between member nodes and CHs, and the standard deviation of the distance between member\u0000nodes and CHs.\u0000\u0000\u0000\u0000The algorithm's effectiveness is demonstrated through simulation results. When compared to popular models like LEACH, MOEES, and FEEC, it demonstrates an 8-20% improvement in the lifetime of WSNs. The proposed approach achieves enhanced efficiency, lifetime extension, and improved performance in CH selection, load balancing, and routing.\u0000\u0000\u0000\u0000In conclusion, this study introduces a novel algorithm that utilizes fuzzy-based\u0000genetic techniques to optimize CH selection in WSNs. By considering four key parameters and\u0000addressing energy consumption challenges, the proposed algorithm offers significant improvements in efficiency, lifespan, and overall network performance, as validated through simulation results.\u0000","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":"8 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139388455","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 : 2024-01-01DOI: 10.2174/266625581701240209144756
{"title":"Patent Selections","authors":"","doi":"10.2174/266625581701240209144756","DOIUrl":"https://doi.org/10.2174/266625581701240209144756","url":null,"abstract":"","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":"30 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140519804","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 : 2024-01-01DOI: 10.2174/266625581701240209152105
Sandeep Kumar
The special issue contains research papers elaborating advancements in computational intelligence. Computational intelligence mimics the extraordinary capacity of the human intellect to assert and understand in an environment of uncertainty and imprecision. Computational intelligence is new-age multidisciplinary artificial intelligence. The main goal of computational intelligence is to develop intelligent systems to solve real-world problems that are not modelled or too hard to model mathematically.
{"title":"Computational Intelligence for Solving Contemporary Problems","authors":"Sandeep Kumar","doi":"10.2174/266625581701240209152105","DOIUrl":"https://doi.org/10.2174/266625581701240209152105","url":null,"abstract":"\u0000\u0000The special issue contains research papers elaborating advancements in computational intelligence. Computational intelligence\u0000mimics the extraordinary capacity of the human intellect to assert and understand in an environment of uncertainty and imprecision.\u0000Computational intelligence is new-age multidisciplinary artificial intelligence. The main goal of computational intelligence is\u0000to develop intelligent systems to solve real-world problems that are not modelled or too hard to model mathematically.\u0000","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":"40 1-2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140517062","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 : 2023-12-19DOI: 10.2174/0126662558285074231120063921
Ramalingam M, Manish Paliwal, R. Patibandla, Pooja Shah, B. T. Rao, D. G, S. Parvathavarthini, Gokul Yenduri, R. Jhaveri
The Internet of Medical Things (IoMT), a growing field, involves the interconnection of medical devices and data sources. It connects smart devices with data and optimizes patient data with real time insights and personalized solutions. It is mandatory to hold the development of IoMT and join the evolution of healthcare. This integration of Transfer Learning and Explainable AI for IoMT is considered to be an essential advancement in healthcare. By making use of knowledge transfer between medical domains, Transfer Learning enhances diagnostic accuracy while reducing data necessities. This makes IoMT applications more efficient which is considered to be a mandate in today’s healthcare. In addition, explainable AI techniques offer transparency and interpretability to AI driven medical decisions. This can foster trust among healthcare professionals and patients. This integration empowers personalized medicine, supports clinical decision making, and confirms the responsible handling of sensitive patient data. Therefore, this integration promises to revolutionize healthcare by merging the strengths of AI driven insights with the requirement for understandable, trustworthy, and adaptable systems in the IoMT ecosystem.
{"title":"Amalgamation of Transfer Learning and Explainable AI for Internet of\u0000Medical Things","authors":"Ramalingam M, Manish Paliwal, R. Patibandla, Pooja Shah, B. T. Rao, D. G, S. Parvathavarthini, Gokul Yenduri, R. Jhaveri","doi":"10.2174/0126662558285074231120063921","DOIUrl":"https://doi.org/10.2174/0126662558285074231120063921","url":null,"abstract":"\u0000\u0000The Internet of Medical Things (IoMT), a growing field, involves the interconnection of medical devices and data sources. It connects smart devices with data and optimizes patient data with real time insights and personalized solutions. It is mandatory to hold the development of IoMT and join the evolution of healthcare. This integration of Transfer Learning\u0000and Explainable AI for IoMT is considered to be an essential advancement in healthcare. By\u0000making use of knowledge transfer between medical domains, Transfer Learning enhances diagnostic accuracy while reducing data necessities. This makes IoMT applications more efficient which is considered to be a mandate in today’s healthcare. In addition, explainable AI\u0000techniques offer transparency and interpretability to AI driven medical decisions. This can foster trust among healthcare professionals and patients. This integration empowers personalized\u0000medicine, supports clinical decision making, and confirms the responsible handling of sensitive\u0000patient data. Therefore, this integration promises to revolutionize healthcare by merging the\u0000strengths of AI driven insights with the requirement for understandable, trustworthy, and\u0000adaptable systems in the IoMT ecosystem.\u0000","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":" 46","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138994547","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 : 2023-12-15DOI: 10.2174/0126662558286756231206062720
K. G. Vani, M. P. K. Reddy
Internet of Medical Things (IoMT) is a technology that encompasses medical devices, wearable sensors, and applications connected to the Internet. In road accidents, it plays a crucial role in enhancing emergency response and reducing the impact of accidents on victims. Smart Transportation uses this technology to improve the efficiency and safety of transportation systems. The current Artificial Intelligence applications lack transparency and interpretability which is of utmost importance in critical transportation scenarios, such as autonomous vehicles, air traffic control systems, and traffic management systems. Explainable Artificial Intelligence (XAI) provides a clear, transparent explanation and actions. Traditional Machine Learning techniques have enabled Intelligent Transportation systems by performing centralized vehicular data training at the server where data sharing is needed, thus introducing privacy issues. To reduce transmission overhead and achieve privacy, a collaborative and distributed machine learning approach called Federated Learning (FL) is used. Here only model updates are transmitted instead of the entire dataset. This paper provides a comprehensive survey on the prediction of traffic using Machine Learning, Deep Learning, and FL. Among these, FL can predict traffic accurately without compromising privacy. We first present the overview of XAI and FL in the introduction. Then, we discuss the basic concepts of FL and its related work, the FL-IoMT framework, and motivations for using FL in transportation. Subsequently, we discuss the applications of using FL in transportation and open-source projects. Finally, we highlight several research challenges and their possible directions in FL
{"title":"An IoMT-based Federated Learning Survey in Smart Transportation","authors":"K. G. Vani, M. P. K. Reddy","doi":"10.2174/0126662558286756231206062720","DOIUrl":"https://doi.org/10.2174/0126662558286756231206062720","url":null,"abstract":"\u0000\u0000Internet of Medical Things (IoMT) is a technology that encompasses medical devices, wearable sensors, and applications connected to the Internet. In road accidents, it plays a\u0000crucial role in enhancing emergency response and reducing the impact of accidents on victims.\u0000Smart Transportation uses this technology to improve the efficiency and safety of transportation systems. The current Artificial Intelligence applications lack transparency and interpretability which is of utmost importance in critical transportation scenarios, such as autonomous\u0000vehicles, air traffic control systems, and traffic management systems. Explainable Artificial Intelligence (XAI) provides a clear, transparent explanation and actions. Traditional Machine\u0000Learning techniques have enabled Intelligent Transportation systems by performing centralized\u0000vehicular data training at the server where data sharing is needed, thus introducing privacy issues. To reduce transmission overhead and achieve privacy, a collaborative and distributed\u0000machine learning approach called Federated Learning (FL) is used. Here only model updates\u0000are transmitted instead of the entire dataset. This paper provides a comprehensive survey on the\u0000prediction of traffic using Machine Learning, Deep Learning, and FL. Among these, FL can\u0000predict traffic accurately without compromising privacy. We first present the overview of XAI\u0000and FL in the introduction. Then, we discuss the basic concepts of FL and its related work, the\u0000FL-IoMT framework, and motivations for using FL in transportation. Subsequently, we discuss\u0000the applications of using FL in transportation and open-source projects. Finally, we highlight\u0000several research challenges and their possible directions in FL\u0000","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":"53 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138999890","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 : 2023-12-15DOI: 10.2174/0126662558268841231123112855
Zeng Qinwu, Yu Wanbo, Zeng Qingjian
Considering that some image encryption algorithms have the disadvantages of complex structure and high computational cost, and there are not many commonly used chaotic systems, which are easy to crack by attacks, to solve these problems, this paper proposes an image encryption algorithm based on three-dimensional nonlinear functions to solve these problems. The algorithm mainly combines the sinusoidal chaotic map with the ternary nonlinear function system to encrypt the image. Firstly, multiple ternary nonlinear function chaotic systems are designed. Then, the function iteration system is changed to invoke the computation of a specific expression under a random number; it is a chaotic sequence generated according to a chaotic mapping such as sine, and then the value of this chaotic sequence is used to select a ternary nonlinear function for iteration to obtain a chaotic sequence. Finally, the chaotic sequence performs the XOR and scrambling operations on the grey image The algorithm has a simple structure, a better encryption effect, and more incredible difficulty deciphering. Moreover, through the phase diagram and bifurcation diagram, it can be seen that the system has good chaotic characteristics The method in this paper is novel; this method is a random variable order composite operation, which can not only be applied to image encryption but also can be used for fractal map generation and so on, and in some other chaotic fields will have a wide range of applications. It has essential research value.
{"title":"Research on Image Encryption Method based on the Chaotic Iteration of a\u0000Ternary Nonlinear Function","authors":"Zeng Qinwu, Yu Wanbo, Zeng Qingjian","doi":"10.2174/0126662558268841231123112855","DOIUrl":"https://doi.org/10.2174/0126662558268841231123112855","url":null,"abstract":"\u0000\u0000Considering that some image encryption algorithms have the disadvantages\u0000of complex structure and high computational cost, and there are not many commonly used chaotic\u0000systems, which are easy to crack by attacks, to solve these problems, this paper proposes an image encryption algorithm based on three-dimensional nonlinear functions to solve these problems.\u0000\u0000\u0000\u0000The algorithm mainly combines the sinusoidal chaotic map with the ternary nonlinear\u0000function system to encrypt the image. Firstly, multiple ternary nonlinear function chaotic systems\u0000are designed. Then, the function iteration system is changed to invoke the computation of a specific expression under a random number; it is a chaotic sequence generated according to a chaotic\u0000mapping such as sine, and then the value of this chaotic sequence is used to select a ternary nonlinear function for iteration to obtain a chaotic sequence. Finally, the chaotic sequence performs\u0000the XOR and scrambling operations on the grey image\u0000\u0000\u0000\u0000The algorithm has a simple structure, a better encryption effect, and more incredible difficulty deciphering. Moreover, through the phase diagram and bifurcation diagram, it can be seen\u0000that the system has good chaotic characteristics\u0000\u0000\u0000\u0000The method in this paper is novel; this method is a random variable order composite\u0000operation, which can not only be applied to image encryption but also can be used for fractal map\u0000generation and so on, and in some other chaotic fields will have a wide range of applications. It\u0000has essential research value.\u0000","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":"1 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138996740","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 : 2023-12-15DOI: 10.2174/0126662558273325231201051141
Mahdi Rezapour
Reddit comments are a valuable source of natural language data where emotion plays a key role in human communication. However, emotion recognition is a difficult task that requires understanding the context and sentiment of the texts. In this paper, we aim to compare the effectiveness of four recurrent neural network (RNN) models for classifying the emotions of Reddit comments. We use a small dataset of 4,922 comments labeled with four emotions: approval, disapproval, love, and annoyance. We also use pre-trained Glove.840B.300d embeddings as the input representation for all models. The models we compare are SimpleRNN, Long ShortTerm Memory (LSTM), bidirectional LSTM, and Gated Recurrent Unit (GRU). We experiment with different text preprocessing steps, such as removing stopwords and applying stemming, removing negation from stopwords, and the effect of setting the embedding layer as trainable on the models. We find that GRU outperforms all other models, achieving an accuracy of 74%. Bidirectional LSTM and LSTM are close behind, while SimpleRNN performs the worst. We observe that the low accuracy is likely due to the presence of sarcasm, irony, and complexity in the texts. We also notice that setting the embedding layer as trainable improves the performance of LSTM but increases the computational cost and training time significantly. We analyze some examples of misclassified texts by GRU and identify the challenges and limitations of the dataset and the models In our study GRU was found to be the best model for emotion classification of Reddit comments among the four RNN models we compared. We also discuss some future directions for research to improve the emotion recognition task on Reddit comments. Furthermore, we provide an extensive discussion of the applications and methods behind each technique in the context of the paper.
{"title":"Emotion Recognition in Reddit Comments Using Recurrent Neural\u0000Networks","authors":"Mahdi Rezapour","doi":"10.2174/0126662558273325231201051141","DOIUrl":"https://doi.org/10.2174/0126662558273325231201051141","url":null,"abstract":"\u0000\u0000Reddit comments are a valuable source of natural language data\u0000where emotion plays a key role in human communication. However, emotion recognition is a\u0000difficult task that requires understanding the context and sentiment of the texts. In this paper,\u0000we aim to compare the effectiveness of four recurrent neural network (RNN) models for classifying the emotions of Reddit comments.\u0000\u0000\u0000\u0000We use a small dataset of 4,922 comments labeled with four emotions: approval,\u0000disapproval, love, and annoyance. We also use pre-trained Glove.840B.300d embeddings as\u0000the input representation for all models. The models we compare are SimpleRNN, Long ShortTerm Memory (LSTM), bidirectional LSTM, and Gated Recurrent Unit (GRU). We experiment with different text preprocessing steps, such as removing stopwords and applying stemming, removing negation from stopwords, and the effect of setting the embedding layer as\u0000trainable on the models.\u0000\u0000\u0000\u0000We find that GRU outperforms all other models, achieving an accuracy of 74%. Bidirectional LSTM and LSTM are close behind, while SimpleRNN performs the worst. We observe that the low accuracy is likely due to the presence of sarcasm, irony, and complexity in\u0000the texts. We also notice that setting the embedding layer as trainable improves the performance of LSTM but increases the computational cost and training time significantly. We analyze some examples of misclassified texts by GRU and identify the challenges and limitations\u0000of the dataset and the models\u0000\u0000\u0000\u0000In our study GRU was found to be the best model for emotion classification of\u0000Reddit comments among the four RNN models we compared. We also discuss some future directions for research to improve the emotion recognition task on Reddit comments. Furthermore, we provide an extensive discussion of the applications and methods behind each technique in the context of the paper.\u0000","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":"49 44","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138995875","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 : 2023-12-15DOI: 10.2174/0126662558271024231122045127
Song Li, Yuxin Yang, Liping Zhang
Knowledge representation learning aims at mapping entity and relational data in knowledge graphs to a low-dimensional space in the form of vectors. The existing work has mainly focused on structured information representation of triples or introducing only one additional kind of information, which has large limitations and reduces the representation efficiency. This study aims to combine entity description information and textual relationship description information with triadic structure information, and then use the linear mapping method to linearly transform the structure vector and text vector to obtain the joint representation vector. A knowledge representation learning (DRKRL) model that fuses external information for semantic enhancement is proposed, which combines entity descriptions and textual relations with a triadic structure. For entity descriptions, a vector representation is performed using a bi-directional long- and short-term memory network (Bi-LSTM) model and an attention mechanism. For the textual relations, a convolutional neural network is used to vectorially encode the relations between entities, and then an attention mechanism is used to obtain valuable information as complementary information to the triad. Link prediction and triadic group classification experiments were conducted on the FB15K, FB15K-237, WN18, WN18RR, and NELL-995 datasets. Theoretical analysis and experimental results show that the DRKRL model proposed in this paper has higher accuracy and efficiency compared with existing models. Combining entity description information and textual relationship description information with triadic structure information can make the model have better performance and effectively improve the knowledge representation learning ability.
{"title":"Knowledge Representation Learning Method Based on Semantic\u0000Enhancement of External Information","authors":"Song Li, Yuxin Yang, Liping Zhang","doi":"10.2174/0126662558271024231122045127","DOIUrl":"https://doi.org/10.2174/0126662558271024231122045127","url":null,"abstract":"\u0000\u0000Knowledge representation learning aims at mapping entity and relational data in knowledge graphs to a low-dimensional space in the form of vectors. The existing work has mainly focused on structured information representation of triples or introducing\u0000only one additional kind of information, which has large limitations and reduces the representation efficiency.\u0000\u0000\u0000\u0000This study aims to combine entity description information and textual relationship\u0000description information with triadic structure information, and then use the linear mapping\u0000method to linearly transform the structure vector and text vector to obtain the joint representation vector.\u0000\u0000\u0000\u0000A knowledge representation learning (DRKRL) model that fuses external information for semantic enhancement is proposed, which combines entity descriptions and textual\u0000relations with a triadic structure. For entity descriptions, a vector representation is performed\u0000using a bi-directional long- and short-term memory network (Bi-LSTM) model and an attention mechanism. For the textual relations, a convolutional neural network is used to vectorially\u0000encode the relations between entities, and then an attention mechanism is used to obtain valuable information as complementary information to the triad.\u0000\u0000\u0000\u0000Link prediction and triadic group classification experiments were conducted on the\u0000FB15K, FB15K-237, WN18, WN18RR, and NELL-995 datasets. Theoretical analysis and experimental results show that the DRKRL model proposed in this paper has higher accuracy and\u0000efficiency compared with existing models.\u0000\u0000\u0000\u0000Combining entity description information and textual relationship description information with triadic structure information can make the model have better performance and\u0000effectively improve the knowledge representation learning ability.\u0000","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":"60 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138996308","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 : 2023-12-12DOI: 10.2174/0126662558266152231128060222
C. G, Ramalingam M, Gokul Yenduri, D. G, Dasari Bhulakshmi, Dasaradharami Reddy K, Y. Supriya, T. G., Rajkumar Singh Rathore, R. Jhaveri
The Internet of Medical Things (IoMT) has emerged as a paradigm shift in healthcare, integrating the Internet of Things (IoT) with medical devices, sensors, and healthcare systems. From peripheral devices that monitor vital signs to remote patient monitoring systems and smart hospitals, IoMT provides a vast array of applications that empower healthcare professionals. However, the integration of IoMT presents numerous obstacles, such as data security, privacy concerns, interoperability, scalability, and ethical considerations. For the successful integration and deployment of IoMT, addressing these obstacles is essential. Federated Learning (FL) permits collaborative model training while maintaining data privacy in distributed environments like IoMT. By incorporating Explainable Artificial Intelligence (XAI) techniques, the resulting models become more interpretable and transparent, enabling healthcare professionals to comprehend the underlying decision-making processes. This integration not only improves the credibility of Artificial Intelligence models but also facilitates the detection of biases, errors, and peculiar patterns in the data. The combination of FL and XAI contributes to the development of more privacy-preserving, trustworthy, and explainable AI systems, which are essential for the development of dependable and ethically sound IoMT applications. Hence, the aim of this paper is to conduct a literature review on the amalgamation of FL and XAI for IoMT.
{"title":"The Amalgamation of Federated Learning and Explainable Artificial\u0000Intelligence for the Internet of Medical Things: A Review","authors":"C. G, Ramalingam M, Gokul Yenduri, D. G, Dasari Bhulakshmi, Dasaradharami Reddy K, Y. Supriya, T. G., Rajkumar Singh Rathore, R. Jhaveri","doi":"10.2174/0126662558266152231128060222","DOIUrl":"https://doi.org/10.2174/0126662558266152231128060222","url":null,"abstract":"\u0000\u0000The Internet of Medical Things (IoMT) has emerged as a paradigm shift in healthcare,\u0000integrating the Internet of Things (IoT) with medical devices, sensors, and healthcare systems.\u0000From peripheral devices that monitor vital signs to remote patient monitoring systems and smart\u0000hospitals, IoMT provides a vast array of applications that empower healthcare professionals. However, the integration of IoMT presents numerous obstacles, such as data security, privacy concerns,\u0000interoperability, scalability, and ethical considerations. For the successful integration and deployment of IoMT, addressing these obstacles is essential. Federated Learning (FL) permits collaborative model training while maintaining data privacy in distributed environments like IoMT. By incorporating Explainable Artificial Intelligence (XAI) techniques, the resulting models become\u0000more interpretable and transparent, enabling healthcare professionals to comprehend the underlying\u0000decision-making processes. This integration not only improves the credibility of Artificial Intelligence models but also facilitates the detection of biases, errors, and peculiar patterns in the data.\u0000The combination of FL and XAI contributes to the development of more privacy-preserving, trustworthy, and explainable AI systems, which are essential for the development of dependable and\u0000ethically sound IoMT applications. Hence, the aim of this paper is to conduct a literature review on\u0000the amalgamation of FL and XAI for IoMT.\u0000","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":"19 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139008566","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 : 2023-12-12DOI: 10.2174/0126662558246531231121115514
Divya Singla, Neetu Verma
Data authentication is vital nowadays, as the development of the internet and its applications allow users to have all-time data availability, attracting attention towards security and privacy and leading to authenticating legitimate users. We have diversified means to gain access to our accounts, like passwords, biometrics, and smartcards, even by merging two or more techniques or various factors of authentication. This paper presents a systematic literature review of papers published from 2010 to 2022 and gives an overview of all authentication techniques available in the market. Our study provides a comprehensive overview of all three authentication techniques with all performance metrics (Accuracy, Equal Error Rate (EER), False Acceptance Rate (FAR)), security, privacy, memory requirements, and usability (Acceptability by user)) that will help one choose a perfect authentication technique for an application. In addition, the study also explores the performance of multimodal and multifactor authentication and the application areas of authentication.
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