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arXiv 2023年

作者： Huang, Chung-Ju Wang, Leye Han, Xiao Key Laboratory of High Confidence Software Technologies Peking University Ministry of Education Beijing China School of Computer Science Peking University Beijing China School of Information Management and Engineering Shanghai University of Finance and Economics Shanghai China

Collaboration between healthcare institutions can significantly lessen the imbalance in medical resources across various geographic areas. However, directly sharing diagnostic information between institutions is typically not permitted due to the protection of patients' highly sensitive privacy. As a novel privacy-preserving machine learning paradigm, federated learning (FL) makes it possible to maximize the data utility among multiple medical institutions. These feature-enrichment FL techniques are referred to as vertical FL (VFL). Traditional VFL can only benefit multi-parties' shared samples, which strongly restricts its application scope. In order to improve the information-sharing capability and innovation of various healthcare-related institutions, and then to establish a next-generation open medical collaboration network, we propose a unified framework for vertical federated knowledge transfer mechanism (VFedTrans) based on a novel cross-hospital representation distillation component. Specifically, our framework includes three steps. First, shared samples' federated representations are extracted by collaboratively modeling multi-parties' joint features with current efficient vertical federated representation learning methods. Second, for each hospital, we learn a local-representation-distilled module, which can transfer the knowledge from shared samples' federated representations to enrich local samples' representations. Finally, each hospital can leverage local samples' representations enriched by the distillation module to boost arbitrary downstream machine learning tasks. The experiments on real-life medical datasets verify the knowledge transfer effectiveness of our framework. © 2023, CC BY-SA.

关键词： Distillation

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Augmented weighted K-means grey wolf optimizer: An enhanced metaheuristic algorithm for data clustering problems (vol 14, 5434, 2024)

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SCIENTIFIC REPORTS 2024年第1期14卷 1-33页

作者： Premkumar, Manoharan Sinha, Garima Ramasamy, Manjula Devi Sahu, Santhoshini Subramanyam, Chithirala Bala Sowmya, Ravichandran Abualigah, Laith Derebew, Bizuwork Department of Electrical & Electronics Engineering Dayananda Sagar College of Engineering Kumaraswamy Layout Bengaluru Karnataka 560078 India Department of Computer Science and Engineering Jain University Ramanagaram Bengaluru Karnataka India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore Tamil Nadu India Department of Computer Science & Engineering GMR Institute of Technology Rajam Srikakulam Andhra Pradesh India Department of Electrical and Electronics Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk 71491 Tabuk Saudi Arabia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan MEU Research Unit Middle East University Amman 11831 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos 13-5053 Lebanon School of Engineering and Technology Sunway University Malaysia 27500 Petaling Jaya Malaysia College of Engineering Yuan Ze University Taoyuan Taiwan Department of Statistics College of Natural and Computational Science Mizan-Tepi University Tepi Bushira Ethiopia Applied science research center Applied science private university Amman 11931 Jordan Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India

This study presents the K-means clustering-based grey wolf optimizer, a new algorithm intended to improve the optimization capabilities of the conventional grey wolf optimizer in order to address the problem of data clustering. The process that groups similar items within a dataset into non-overlapping groups. Grey wolf hunting behaviour served as the model for grey wolf optimizer, however, it frequently lacks the exploration and exploitation capabilities that are essential for efficient data clustering. This work mainly focuses on enhancing the grey wolf optimizer using a new weight factor and the K-means algorithm concepts in order to increase variety and avoid premature convergence. Using a partitional clustering-inspired fitness function, the K-means clustering-based grey wolf optimizer was extensively evaluated on ten numerical functions and multiple real-world datasets with varying levels of complexity and dimensionality. The methodology is based on incorporating the K-means algorithm concept for the purpose of refining initial solutions and adding a weight factor to increase the diversity of solutions during the optimization phase. The results show that the K-means clustering-based grey wolf optimizer performs much better than the standard grey wolf optimizer in discovering optimal clustering solutions, indicating a higher capacity for effective exploration and exploitation of the solution space. The study found that the K-means clustering-based grey wolf optimizer was able to produce high-quality cluster centres in fewer iterations, demonstrating its efficacy and efficiency on various datasets. Finally, the study demonstrates the robustness and dependability of the K-means clustering-based grey wolf optimizer in resolving data clustering issues, which represents a significant advancement over conventional techniques. In addition to addressing the shortcomings of the initial algorithm, the incorporation of K-means and the innovative weight factor into the gre

关键词： Computational intelligence Data mining Grey wolf optimizer K-means clustering Optimization algorithm

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Assessing the Impact of Tanzania Tourism Classification on the Economy: A Study on the Implementation and Effectiveness of Tourism Grading System in Tanzania 3

Assessing the Impact of Tanzania Tourism Classification on t...

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3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Tiwari, Raj Gaurang Maheshwari, Himani Srivastava, Vishal Srivastava, Manisha Agarwal, Ambuj Kumar Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computing Graphic Era Hill University Uttrakhand Dehradun India International Institute for Special Education Uttar Pradesh Lucknow India Sherwood College of Professional Management Uttar Pradesh Lucknow India School of Engineering and Technology Sharda University Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350342338

The tourism industry is crucial to Tanzania's economy. In recent years, the government has taken steps to improve the quality of its tourist products by enforcing a rating system. The purpose of this research is to investigate the contribution of Tanzania's tourism classification system to the national economy. It also explores the possibility of using machine learning to classify tourist spending and then using explainable machine learning to identify the key determinants of such spending. Studying the effectiveness and impact of Tanzania's tourism rating system on the country's economy. This paper also shows that the tourism grading system has helped raise the number of visitors visiting Tanzania, which has been good for the country's economy. It also suggests that the government should provide additional assistance to the tourist industry's players in order to sustain the sector's rapid expansion. © 2023 IEEE.

关键词： expenditure grading explainable machine learning machine learning Tanzania travel and tourism

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Older Adults' Views on the use of In-Home Ambient Sensors and Assistive Robotic Agents in Greece. Data from the ASPiDA Project

Older Adults' Views on the use of In-Home Ambient Sensors an...

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IEEE International Workshop on Cellular Nanoscale Networks and their Applications, (CNNA)

作者： Archontissa Kanavaki Ioannis Kansizoglou Maria Michalopoulou Georgios Ch. Sirakoulis Athanasios Gkrekidis Evgenia Kouli Dimitrios Menychtas Helen Douda Ilias Smilios Vassilios Gourgoulis Antonios Gasteratos Nikolaos Aggelousis Department of Physical Education and Sports Science Democritus University of Thrace Komotini Greece Department of Production and Management Engineering Democritus University of Thrace Xanthi Greece Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi Greece

ISBN: (数字)9798350308921

ISBN: (纸本)9798350308938

The present study aimed to to map the views on the use of ambient sensors and robotic assistance for falls detection and prevention of a sample of older adults in Greece. Data were collected from 100 older adults as part of a wider observational study via an interview-administered questionnaire and were analysed using descriptive statistics and thematic analysis. Findings show that the vast majority of participants appeared to have positive views on the use of ambient sensors for falls prevention, whereas the views on robotic agents, although still positive in majority, were more equivocal. Positive views do not necessarily translate into intention to use assistive technologies. User needs assessment and tailored solutions are important steps for the adoption of these innovative technologies by older adults.

关键词： Prevention and mitigation Conferences Assistive technologies Robot sensing systems Nanoscale devices Sensors Older adults

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Intrusion Detection and Prevention System for DDoS Attacks using Hybrid CNN Architecture

Intrusion Detection and Prevention System for DDoS Attacks u...

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Communications and Information technologies (GCCIT), Global Conference on

作者： S. Rathika Diksha Aggarwal Xavier V K P. R. Rege N. Thangarasu Prabhjot Kaur Department of Business Administration Prince Shri Venkateshwara Padmavathy Engineering College Chennai Quantum University Research Center Quantum University Roorkee Uttarakhand India School of Management - PG Management JAIN (Deemed to be University) Bangalore Karnataka Computer Vishwakarma Institute of Technology Pune Department of Computer Science Karpagam Academy of Higher Education Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

Machine-based intrusion detection systems are crucial for network protection and data security in this age of widespread Internet connectivity and consistent growth in data quantities. However, current methods of both shallow and deep learning fall short when trying to capture the intricate incursion patterns associated with data transmission. To combat this, we offer a large data-based hierarchy deep learning system to further improve the effectiveness of IDS-based artificial intelligence. Both network traffic as well as content information may be captured by the proposed system thanks to its behavioral and content-functional features. Each framework deep learning model is dedicated to understanding the data structure of a specific cluster. For this reason, we advocated the use of a convolutional Neural Network (CNN) using a Min-Max optimization approach. When compared to earlier single-learning models, the suggested method increases the categorization rate of disruptive attacks. Incorporating a larger number of devices also speeds up the system's model-building process.

关键词： Deep learning Radio frequency Prevention and mitigation Neural networks Intrusion detection Telecommunication traffic Minimization Convolutional neural networks Protection Optimization

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Patient Engagement and Satisfaction in Ai-Enhanced Healthcare management

Patient Engagement and Satisfaction in Ai-Enhanced Healthcar...

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Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI), International Conference on

作者： Yogesh Rathore Vandana Mishra Chaturvedi Khadilkar Sujay madhukar Vijay Suresh Karwande Anil Haribhau Rokade Yogesh Nagargoje Dept of Computer Science & Engineering Shri Shankaracharya Institute of Professional Management and Technology Raipur Chhattisgarh India D Y Patil Deemed to be University Navi Mumbai KITsIMER KIT's Institute of Management Education & Research Kolhapur Department of Computer science and Engineering Everest Educational Society Group of Institutions College of engineering and technology Chatrapati Sambhaji nagar CSE(AI&ML) Deogiri Institute of Engineering and Management Studies Sambhajinagar Department of Computer science and Engineering CSMSS Chh. Shahu College of Engineering Chhatrapati Sambhajinagar (Aurangabad)

This study looks into how patient involvement and satisfaction are affected by AI-enhanced healthcare administration. Using a descriptive methodology and secondary data gathered from reliable sources, the research takes an interpretive approach within a deductive paradigm. The findings of the investigation indicate that artificial intelligence (AI)-driven solutions, such as chatbots and virtual assistants, enhance patient engagement by providing easy access to healthcare knowledge and customized interactions. Additionally, patients are happy with the effectiveness and customized care that AI systems offer. Nonetheless, consumer happiness levels are influenced by the caliber of contacts and worries about the confidentiality of data. Transparency, dependability, data privacy, provider expertise, and education for patients all influence people's trust in AI technologies. For artificial intelligence integration to be successful, a technical assessment emphasizes the significance of algorithm efficiency, scalability, seamless integration, data protection, and continuous maintenance. Patient-centered design should be prioritized, stakeholders should be informed, and AI systems should be continuously monitored. Prospective investigations ought to concentrate on extended periods, tailored treatment schemes, psychological assistance, reducing bias, economies of scale, and innovative artificial intelligence approaches.

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Convolutional Neural Network Model With Hybrid Feature Learning For Sematic Text Mining

Convolutional Neural Network Model With Hybrid Feature Learn...

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Communications and Information technologies (GCCIT), Global Conference on

作者： Shrinivas Patil V.K. Kolekar K. Ranjith Singh Pratibha Sharma Hariharasudhan S Shubhi Goyal School of Management - PG Management JAIN Deemed to be University Bangalore Karnataka Computer Engineering Vishwakarma Institute of Technology Pune Department of Computer Science Karpagam Academy of Higher Education Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Information Technology Prince Shri Venkateshwara Padmavathy Engineering College Chennai Quantum University Research Center Quantum University Roorkee Uttarakhand India

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

In this article, we present a generic inference hybrid structure for Convolutional Recurrent Neural Network (conv-RNN) text semantic modelling, combining the strengths of convolutional and recurrent neural network structures in extracting distinct linguistic features. In addition to the conv-RNN-based sentence matching as well as classification models, we also offer a new sentence classification model as well as an attention-based response selection model with improved performance. Two difficult answer selection (AS) tasks & five standard datasets for sentence classification (SC) are used to verify the suggested models. As far as we are aware, these are the most comprehensive comparison findings in AS and SC to date. We demonstrate the superiority of conv-RNN over other state-of-the-art methods experimentally on a variety of hard tasks and benchmark datasets. Deep training-based architectures outperform ensemble learning methods and supervised training approaches for the sentiment analysis issue in educational data mining. Overall classification accuracy was 95.80% for long-term short-term memory networks using representations built upon the GloVe word-embedding method, making them the most efficient prediction arrangement.

关键词： Training Text mining Representation learning Sentiment analysis Recurrent neural networks Semantics Memory management Feature extraction Convolutional neural networks Standards

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Fake Currency Detection using Ensemble Learning 10

Fake Currency Detection using Ensemble Learning

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10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer Engineering, UPCON 2023

作者： Kumar, Ashok Sachi, Savya Bhatia, Anshoo Belwal, Pravesh Kumar, Santosh Bhatnagar, Vivek Koneru Lakshmaiah Education Foundation Department of Computer Science & Engineering AP Vaddeswaram India Lalit Narayan Mishra College of Business Management Department of Information Technology Bihar Muzaffarpur842001 India Coer University Department of Cyber Security U.K. Roorkee India Dev Bhoomi Uttarakhand University Department of Electronics & Communication U.K. Dehradun India Noida Institute of Engineering and Technology CSE-IOT Greater Noida India Haryana Ambala India

ISBN: (纸本)9798350382471

Fake currency is one of the most serious issues that may arise during financial transactions. For a developing nation like India., this is becoming a significant obstacle. Because of advancements in printing and scanning technology., it is now feasible for anybody to create counterfeit banknotes with the use of the most up-to-date hardware equipment. Identifying false notes manually is a procedure that is both time-consuming and messy;hence., there is a need for automated approaches that make fake money detection a more effective and efficient process. For this machine learning model i.e. ensemble learning is proposed in this research for detecting the forgery currency. A data set consisting of forgery and original currency images have been chosen for the analysis. The proposed model is compared in accordance with performance parameters like accuracy., precision., recall., and f1-score. With other existing models like naive bayes., random forest., and decision tree to prove efficiency of the proposed algorithm. The evaluation of these algorithms prove that the proposed algorithm ensemble learning is highly efficient with 99% accuracy in detecting forgery currency. This can be used in some of the automated machines. © 2023 IEEE.

关键词： deep learning Ensemble learning fake currency Machine learning matlab

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Real-Time Water Quality Tracking and Alert System with IoT Integration

Real-Time Water Quality Tracking and Alert System with IoT I...

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Pervasive Computing and Social Networking (ICPCSN), International Conference on

作者： Mohan Kumar Ch Masthan Siva Krishna Munaga Chandra Sekhar Kolli Suresh Kumar Maddila Department of Computer Science & Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Department of Information Technology Aditya College of Engineering and Technology Surampalem Andhra Pradesh Department of Information Technology Shri Vishnu Engineering College for Women Bhimavaram Andhra Pradesh Department of Computer Science School of computing Gandhi Institute of Technology and Management Visakhapatnam Andhra Pradesh

Water is an essential resource for all living beings, and given the limited availability of natural resources, it is crucial to use water efficiently. Monitoring and analysing water quality parameters have become imperative in today's scenario to ensure good health and prevent excessive usage and wastage of water. The traditional approach of manually collecting water samples from different sources and sending them to research labs for analysis is neither feasible nor cost-effective. Moreover,frequent manual sampling and analysis could be more practical. IoT technology can be leveraged to monitor and alert people in real time about water quality in specific areas. The proposed system comprises sensors to measure essential water characteristics in real time, enabling decision-making without external intervention. The system can control all the critical parameters to ensure accurate measurements, and the readings can be stored in a cloud environment for future analysis. This proposed system can be used for real-time water quality analysis.

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Smartcharge: Reducing Power Usage in Intelligent Homes

Smartcharge: Reducing Power Usage in Intelligent Homes

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Communication, Computing and Energy Efficient technologies (I3CEET), International Conference on

作者： A. Sonya Neha Chhabra Abhishek Kumar Gupta P. Lakshmi Harika B. Aishwarya R. Karthika Department of Information Technology B. S. Abdur Rahman Crescent Institute of Science and Technology Chennai Department of Information Technology Jagannath International Management School New Delhi India Department of Information Technology Jagannath International Management School New Delhi Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Bowrampet Hyderabad Telangana Department of Computer Science and Engineering(Cyber Security) Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi India Department of EEE Theni Kammavar Sangam College of Technology Theni India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

Smart homes oriented towards reminders and monitoring for healthcare must be reasoned, definitely centered on matured home or disability in a house. Accurately predicting the movements of people would be essential to offer such level support for residents in this situation. Our study helps to cater this requirement by proposing an improved forecasting model which is both accurate and has faster learning. For doing that, we here develop the recurrent version of an existing model; i.e., Extreme Learning Machine [11] to keep using temporal dependency of inputs into network in different time steps. This model is different since it can be constantly adapted to the needs of people in a smart home environment, especially those who anticipate needing medical support. So, finally this Recurrent Extreme Learning Machine model validates that for smart home technology evolutions as the time progresses continuously evolved intelligent are required to solve when it comes health care critical challenges. This type of learning model is not only apt in accuracy and speed which should be enough during the learning phase though; but it also allows machine to learn more about human behavior making them generally intelligent because now you have a pattern how humans behave. Advantages Of Sexual Health These are certainly rewarding Smart Home Systems..

关键词： Training Adaptation models Accuracy Extreme learning machines Smart homes Medical services Predictive models Telecommunication computing Forecasting Monitoring

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