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Advance Computing and Innovative technologies in Engineering (ICACITE), International Conference on

作者： Jamvant Omkar S. Menaka J Praveena Neeraj Varshney B. Arunkumar G Vinoda Reddy Department of Electronics & Communication Engineering IES College Of Technology Bhopal M. P India Nehru Institute of Information Technology and Management Coimbatore Department of Science and hummanities Prince Shri Venkateshwara Padmavathy Engineering College TN Department of Computer Engineering and Applications GLA University Mathura Computer Science Engineering department Karpagam Academy of Higher Education Department CSE (AI&ML) CMR Technical Campus Medchal Hyderabad

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

The IIoT is the most important element in the 6th generation networks, or 6G, so it greatly depends on effective data processing and storage. For the cause of ensuring smooth node-to-node communication, 6G provides ultra-low delays, high bandwidth, and higher-level QoS. Intelligent diagnosis, numerous in-hospital, and distant healthcare services are prerequisites to enable healthcare institutions to provide patientcentered care. What is essential is that the system hosting an array of uses, ranging from ambient appropriate living settings with wearable technology-enabled remote patient monitoring to robotic procedures powered by an intelligent networking platform. Therefore, this work addresses the needs of the healthcare sector by providing a reference-layered contact architecture for locations and devices in real-time communication. The unique 6G technology can, therefore, be utilized with great foresight to the future in the healthcare sector, enabling effective treatment and intelligent service integration. Because several networking nodes, including terminal devices, are integrated into effective health service delivery, a backbone structure is thus required for the services that take much time to complete. Therefore, a backbone structure for managing services is required to enable smooth communication by the framework, developed to enable this kind of 6G-enabled advanced healthcare application.

关键词： 6G mobile communication Patient monitoring Surgery Medical services Quality of service computer architecture Transforms

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AI-Enabled Motion Detection System for Smart Healthcare Environment 2

AI-Enabled Motion Detection System for Smart Healthcare Envi...

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2nd IEEE Global Conference on Communications and Information technologies, GCCIT 2024

作者： Dhabliya, Dharmesh Ravivarman, G. Kapoor, Tarun Keerthika, A. Garg, Anvesha Mathur, Sanjiv Vishwakarma Institute of Technology Department of Information Technology Maharashtra Pune India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore64102 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Quantum University Research Center Quantum University Uttarakhand Roorkee247667 India School of Mangement - Pg Management Karnataka Bangalore India

ISBN: (纸本)9798350388916

Humans' bad habits are a leading cause of many chronic illnesses. Preventative medicines and telemedicine courses for treating these conditions are proliferating. The availability of secure and pleasurable alternatives continues to be of paramount importance. When it comes to monitoring and studying human health, inertial measurement units (IMU) are frequently viewed as the most objective and least intrusive alternative due to their ability to identify motion patterns with relative ease. Machine learning is commonly regarded as an effective method for analysing IMU data and identifying motion patterns. As part of a smart healthcare learning tool, we describe a deep learning-based technique for detecting human movements. An innovative hybrid adjectives-based on before-classification & multi-features analysis technique is created to classify human motion for usage in healthcare e-learning. To begin, a Kalman filter is used to do preliminary processing on the raw data from the IMUs. An experiment is conducted in which minimum and average gravity reduction techniques are used to the acceleration data. Signal segmentation has been used to data from a variety of time periods to establish which kind of segmentation is most effective. Then, we identify active as well as passive motion patterns to do a preliminary categorization. Both static and dynamic motion patterns have been analysed to recover characteristics. The Convolutional Neural Network (CNN) has been used to categorise the active and passive motion components of healthcare e-learning. We accomplished this using the wearable computing and REALDISP datasets. In experimental comparisons, our proposed method for intelligent health care learning surpassed the current state-of-the-art systems. Implementing the technique as recommended led to an accuracy of 87.35% for the REALDISP dataset & 85.18 on the wearable computers dataset. Furthermore, a smart healthcare advisor is provided with access to the classified motion s

关键词： Convolutional neural networks

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DearLLM: Enhancing Personalized Healthcare via Large Language Models-Deduced Feature Correlations 39

DearLLM: Enhancing Personalized Healthcare via Large Languag...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xu, Yongxin Jiang, Xinke Chu, Xu Qiu, Rihong Feng, Yujie Ding, Hongxin Zhao, Junfeng Wang, Yasha Xie, Bing School of Computer Science School of Software & Microelectronics Peking University Beijing China Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China National Engineering Research Center For Software Engineering Peking University Beijing China Center on Frontiers of Computing Studies Peking University Beijing China Peking University Information Technology Institute Binhai Tianjin China Nanhu Laboratory Jiaxing China Department of Computing The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)157735897X

Exploring the correlations between medical features is essential for extracting patient health patterns from electronic health records (EHR) data, and strengthening medical predictions and decision-making. To constrain the hypothesis space of pure data-driven deep learning in the context of limited annotated data, a common trend is to incorporate external knowledge, especially knowledge priors related to personalized health contexts, to optimize model training. However, most existing methods lack flexibility and are constrained by the uncertainties brought about by fixed feature correlation priors. In addition, in utilizing knowledge, these methods overlook the knowledge informative for personalized healthcare. To this end, we propose DearLLM, a novel and effective framework that leverages feature correlations deduced by large language models (LLMs) to enhance personalized healthcare. Concretely, DearLLM captures and learns quantitative correlations between medical features by calculating the conditional perplexity of LLMs’ deduction based on personalized patient backgrounds. Then, DearLLM enhances healthcare predictions by emphasizing knowledge that carries unique patient information through a feature-frequency-aware graph pooling method. Extensive experiments on two real-world benchmark datasets show significant performance gains brought by DearLLM. Furthermore, the discovered findings align well with medical literature, offering meaningful clinical interpretations. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Electronic health record

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NeuroPCA: Enhancing Alzheimer’s disorder Disease Detection through Optimized Feature Reduction and Machine Learning

NeuroPCA: Enhancing Alzheimer’s disorder Disease Detection ...

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Electrical, Electronics, Information and Communication technologies (ICEEICT), International Conference on

作者： Sreenivas Reddy Sagili Senthilnathan Chidambaranathan Nagarjuna Nallametti Harana M. Bodele Laxmi Raja P G. Gayathri Department of Computer Science Rochester Institute of Technology United States 20 Corporate Pl South Virtusa Corporation Piscataway United States Department of CSIT CVR College of Engineering Hyderabad Telangana India Department of Electronics and Telecommunications J D College of Engineering & Management Nagpur India Department of Computer Science and Engineering (Cyber Security) Karpagam Academy of Higher Education Coimbatore India Department of Artificial Intelligence and Data Science Kongunadu College of Engineering and Technology India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369090

Alzheimer’s (memory loss disorder) disorder Disease (AD) poses a significant diagnostic challenge due to its complex etiology and subtle onset of clinical symptoms. The Early and accurate detection will be a crucial for effective management and intervention. The integration of machine Learning-Driven (ML) techniques based on neuro imaging and clinical data may offers a promising avenue for enhancing of AD detection. However, the highest dimensionality of such data could be impede the efficacy and accuracy of ML models due to curse of dimensionality and over fitting. This research will introduces the "NeuroPCA System," a novelist diagnostic framework by employing Principal Components based Analysis (PCA) for features reduction, tailored specifically for Alzheimer’s (memory loss disorder) disorder Disease detection. The NeuroPCA System will systematically reduces the dimensionality of input health data (e.g., MRI, PET scans, and genetic information) while by preserving the most informative variance components crucial for distinguish between AD patients and healthy controls. The proposed methodology may encompasses preprocessing of the multi-modal dataset to normalized feature scales, followed by the application of PCA to extracts the principal components that capturing of the maximum variance in the dataset. The reduced feature set will then utilized to train and validate a suite of machine Learning-Driven models like Supportive Vector Machines (SVM) for classify subjects into AD and non-AD groups. The effectiveness of the NeuroPCA System may evaluated depends on classification accuracy, sensitivity, specificity, and computational efficiency, by using a cross- validated dataset of diagnosed AD patients and a control group. The NeuroPCA System also demonstrates a significant improvement in model performance and computational efficiency, by highlighting the potential of PCA for feature reduction in the context of Alzheimer’s (memory loss disorder) disorder Disease detec

关键词： Training Support vector machines Accuracy Computational modeling Feature extraction Genetics Vectors Computational efficiency Alzheimer's disease Principal component analysis

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African Union Convention on Cyber Security and Personal Data Protection: Challenges and Future Directions

arXiv

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arXiv 2023年

作者： Bouke, Mohamed Aly Abdullah, Azizol Alshatebi, Sameer Hamoud El Atigh, Hayate Cengiz, Korhan Department of Communication Technology and Network Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang43400 Malaysia Department of Computer Engineering Istinye University Istanbul34010 Turkey Department of Information Technologies Faculty of Informatics and Management University of Hradec Kralove Kralove500 03 Czech Republic Departement of Computer Engineering Faculty of Computer Engineering Bandirma Onyedi Eylul University Balikesir10200 Turkey

This paper investigates the challenges and opportunities of implementing the African Union Convention on Cyber Security and Personal Data Protection (AUDPC) across Africa. Focusing on legal, regulatory, technical, infrastructural, capacity building, awareness, Harmonization, and cross-border cooperation challenges, the paper identifies key findings that highlight the diverse legal systems and traditions, the lack of comprehensive data protection laws, the need to balance national security and data privacy, the digital divide, cybersecurity threats, implications of emerging technologies on data privacy, limited resources for data protection authorities, and the need for capacity building in data privacy and protection. The paper also emphasizes the importance of Harmonization and cross-border cooperation in aligning data protection frameworks and collaborating with international partners and global organizations. To address these challenges and facilitate the successful implementation of the AUDPC, the paper proposes a set of recommendations, including strengthening legal and regulatory frameworks, enhancing technical and infrastructural capacities, fostering capacity-building and awareness initiatives, promoting Harmonization and cross-border cooperation, and engaging with global data protection trends and developments. © 2023, CC BY.

关键词： Cybersecurity

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Investigating the Effectiveness of Internet Key Exchange (IKE) Protocol in Wireless Network Security

Investigating the Effectiveness of Internet Key Exchange (IK...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Dipannita Mondal N. Thangarasu Preetha. M Yashwant S. Ingle Ashutosh Saxena Jambi Ratna Raja Kumar Artificial Intelligence and Data Science Department Dr.D.Y Patil College of Engineering and Innovation Talegaon Department of Computer Science Karpagam Academy of Higher Education Coimbatore Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India Department of Management Mangalayatan University Jabalpur India Department of Computer Engineering Genba Sopanrao Moze College of Engineering Balewadi Pune Maharashtra India

The Internet Key alternate (IKE) protocol is a widely used safety protocol for organizing comfy tunnels and encrypting visitors in wireless networks. The protocol is designed to authenticate and set up a relaxed conversation channel between two devices, usually a Wi-Fi entry to-point and a personal device. This paper researches the effectiveness of the IKE protocol in imparting security for wireless networks. The IKE protocol is primarily based on a mixture of crucial trade protocols, specifically the Internet Security Association and Key Control Protocol (ISAKMP) and Oakley Protocol. Those protocols paint together to establish a cozy tunnel and exchange keys for Encryption. The main advantage of using IKE is its ability to offer key management and authentication offerings for a couple of protection protocols, making it a surprisingly flexible and flexible solution for wireless community protection. One of the primary effectiveness of the IKE protocol lies in its potential to negotiate and set up at-ease connections among Wi-Fi gadgets, even within the presence of network address translation (NAT) gadgets. This feature suits it nicely for supplying protection in Wi-Fi networks where multiple gadgets might also have specific IP addresses. Moreover, the protocol supports diverse authentication strategies, digital certificates, and pre-shared keys, making it adaptable to exclusive security necessities. Moreover, the IKE protocol presents green essential trade through the usage of symmetric vital algorithms consisting of advanced Encryption widespread (AES)

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AModified Search and Rescue Optimization Based Node Localization Technique inWSN

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computers, Materials & Continua 2022年第1期70卷 1229-1245页

作者： Suma Sira Jacob K.Muthumayil M.Kavitha Lijo Jacob Varghese M.Ilayaraja Irina V.Pustokhina Denis A.Pustokhin Department of Computer Science and Engineering Christian College of Engineering and TechnologyOddanchatram624619India Department of Information Technology PSNA College of Engineering and TechnologyDindigul624622India Department of Electronics and Communication Engineering K.Ramakrishnan College of TechnologyTiruchirappalli621112India Department of Electrical and Electronics Engineering Christian College of Engineering and TechnologyOddanchatram624619India School of Computing Kalasalingam Academy of Research and EducationKrishnankoil626128India Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia

Wireless sensor network(WSN)is an emerging technology which find useful in several application areas such as healthcare,environmentalmonitoring,border surveillance,*** issues that exist in the designing of WSN are node localization,coverage,energy efficiency,security,and so *** spite of the issues,node localization is considered an important issue,which intends to calculate the coordinate points of unknown nodes with the assistance of *** efficiency of the WSN can be considerably influenced by the node localization ***,this paper presents a modified search and rescue optimization based node localization technique(MSRONLT)*** major aim of theMSRO-NLT technique is to determine the positioning of the unknown nodes in *** the traditional search and rescue optimization(SRO)algorithm suffers from the local optima problemwith an increase in number of iterations,MSRO algorithm is developed by the incorporation of chaotic maps to improvise the diversity of the *** application of the concept of chaotic map to the characteristics of the traditional SRO algorithm helps to achieve better exploration ability of the MSRO *** order to validate the effective node localization performance of the MSRO-NLT algorithm,a set of simulations were performed to highlight the supremacy of the presented model.A detailed comparative results analysis showcased the betterment of the MSRO-NLT technique over the other compared methods in terms of different measures.

关键词： Node localization WSN chaotic map search and rescue optimization algorithm localization error

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A novel cascade heat integration configuration for electricity/freshwater/hydrogen outputs using SOFC-GT, multi-effect desalination, and PEM electrolysis using machine learning optimization

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Case Studies in Thermal Engineering 2025年 72卷

作者： Zuo, Zhaoyang Wang, Junhua Samad, Sarminah Othman, Nashwan Adnan Almadhor, Ahmad Ghandour, Raymond Alsayer, Ibrahim A Abduvalieva, Dilsora Alkhalaf, Salem Babiker, Samah G. School of Mechanical Engineering Xijing University Shaanxi Xi'an710123 China School of Computer Science South China Business College Guangdong University of Foreign Studies Gangzhou Guangdong510545 China Department of Management College of Business Administration Princess Nourah Bint Abdulrahman University Riyadh 11671 Saudi Arabia Department of Computer Engineering College of Engineering Knowledge University Erbil44001 Iraq Department of Computer Engineering Al-Kitab University Altun Kupri Iraq Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Saudi Arabia College of Engineering and Technology American University of the Middle East Egaila54200 Kuwait Department of Chemical and Materials Engineering Engineering College Northern Border University Arar91431 Saudi Arabia Department of Mathematics and Information Technologies Tashkent State Pedagogical University Bunyodkor avenue 27 Tashkent100070 Uzbekistan Department of Computer Engineering College of Computer Qassim University Buraydah Saudi Arabia Department of Electronic Physics Faculty of Applied Science Red Sea University Port Sudan Sudan

The current study presents an innovative integrated multi-generation system tailored for residential communities based on a novel cascade heat integration configuration. The proposed system is designed for scalability, making it adaptable for various residential communities, from small housing complexes to larger urban developments. Its modular architecture allows flexible integration with existing infrastructure, enabling widespread deployment in diverse geographic and economic conditions. Utilizing a real-Time machine learning framework, the system dynamically optimizes energy distribution and operational efficiency, making it feasible for large-scale implementation while maintaining economic viability and environmental benefits. Hence, a real-Time machine learning framework is implemented to optimize key operational parameters dynamically, ensuring efficient fuel utilization, energy distribution, and load balancing across the system. The ML model continuously processes sensor data to adjust SOFC current density, GT pressure ratio, and ORC steam pressure, maximizing exergy efficiency while minimizing operational costs and CO2 emissions. Additionally, it enhances system reliability through predictive maintenance and demand-based power allocation, enabling a more adaptive and cost-effective multi-generation process. Noteworthy outcomes highlight an exergy efficiency improvement from 48 % to 60 % and a power output increase from 6.1 MW to 11.6 MW. Additionally, the levelized cost of electricity is notably reduced from 8 to 3 cents/kWh. The system also provides extra benefits by generating freshwater and hydrogen, addressing various societal challenges. © 2025 The Authors.

关键词： Predictive maintenance

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Coding Like a Data Miner: A Sandbox Approach to Computing-Based Data science for High School Student Learning

Coding Like a Data Miner: A Sandbox Approach to Computing-Ba...

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Frontiers in education (FIE) Conference

作者： Justice T. Walker Amanda Barany Alex Acquah Sayed Mohsin Reza Alan Barrera Karen Del Rio Guzman Michael A. Johnson Department of Education University of Texas at El Paso El Paso Texas Graduate School of Education University of Pennsylvania Philadelphia Pennsylvania Department of Computer Science Pennsylvania State University Harrisburg Middletown Pennsylvania Department of Learning Technologies University of North Texas Denton Texas

Personal health tracking devices and internet-based digital platforms with the capacity to collect, aggregate, and store data at massive scales are examples of tools that have broadened priorities in computing to include data science. In response, there has been growing attention in research and practice emphasizing pre-college groups. This is partly because of the growing recognition-reflected in initiatives like CS4ALL, ***, Bootstrap: Data science, Exploring computer science-that learning experiences before college are consequential in sustaining a robust pipeline of computer scientists and engineers. Despite these inroads, there is justifiable concern that existing efforts might not fully support learner development in the necessary conceptual, epistemological, and heuristic styles needed to productively parse and understand “big data.” This is because computing-based curricula that include data science often involve data curated by others (rather than learners directly), which results in simulated versions of practice instead of engagement that is realistically discursive and messy. This is further complicated by the persistent shortage of K-12 computer science teachers in general and even fewer who can design and implement curricula that support authentic engagement with data science. To address these issues, we leverage culturally relevant and constructionist perspectives in a sandbox (i.e., open-ended) science where tools like Scratch and electronic textiles (E-textiles) have had success expanding possibilities in computing to also include activities where learners can engage broadly along varied pursuits-and encounter challenges that spur computational thinking and problem-solving. The literature suggests that learning activities framed in this way encourage knowledge construction, practice literacies, and seriously impact learner attitudes, interest, and perceptions of growth in the field. This latter set of self-concept measures represents a few of m

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An Enhanced Semantic Analysis of Potential Recommender Systems for Big Data management

An Enhanced Semantic Analysis of Potential Recommender Syste...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Rahul Mishra Sachin S. Pund Omprakash Dewangan Ruchi Chandrakar Nuresh Kr Khunte Veeraswamy Ammisetty Kalinga University Naya Raipur Department of Industrial Engineering Shri Ramdeobaba College of Engineering and Management Nagpur Maharashtra India Department of Computer Science and Information technology Kalinga University Raipur Chhattisgarh Department Faculty of Technology Kalinga University Naya Raipur Chattishgarh Department of Computer Science & Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India

this paper proposes a more desirable semantic analysis for capacity recommender structures for large facts management. The proposed approach is primarily based on ontologies, text-mining, and semantic relatedness processes. The proposed technique uses a site particular ontology to perceive relationships among exceptional objects of the domain, as well as semantically and syntactically analyzing text from literature. The goals of the proposed technique are to: (I) identify the relationships among special objects (places, sports, and attendances; (ii) perceive the effect of each courting on the advice performance; (iii) and discover feasible styles from the relationships. to evaluate the proposed method, the effects are in comparison with current methods. The comparison results display that the proposed technique plays higher than other strategies in both efficiency and accuracy. Moreover, the proposed approach can be applied to other domains, supplying new opportunities in recommendation gadget layout.

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