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

作者： Urinbaev Sharofiddin S. Gopi Muntather Almusawi Rakesh Kumar Manjinder Kaur Wratch Amarjeet Kumar Ghosh Department of Physics and Chemistry Tashkent Institute of Irrigation and Agricultural Mechanization Engineers" National Research University Tashkent Uzbekistan Western Caspian University Scientific Researcher Baku Azerbaijan Department od CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Computer Engineering and Applications GLA University Mathura Department of Computer Science Engineering Chandigarh Engineering College Chandigarh Group of Colleges Jhanjeri Mohali Punjab India Department of Electronics & Communication Engineering IES College of Technology Bhopal M. P. India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

Intelligent buildings and intelligent asset monitoring on tenants comfort (ICT for enhanced tenant comfort) Nonetheless, present systems are failing the SFM in connecting so many IoT devices as they tend to have poor connection quality and long latency. 5G technology offers ultra-reliable and low latency, high-speed network infrastructure with slicing features for providing real-time economics. It is also expected to make possible a more sustainable future by reducing energy use and directing new applications in ways that meet ever-tougher sustainability targets. In this blog, he shows the benefits and what is possible now and then explains where 5G has not been fully realised just yet on his SFM applications for various use cases. This document also provides an illustrative example of the Singapore 5G deployment plans based on SUSS and a SFM use case development timeline to be undertaken by the 5G Advanced BIM Lab in alignment with Singapore's 5G deployment strategy. In addition to the 5G forest project, we innovate and co-create with a range of industry partners in order to benefit our students in learning new ways of using this technology across fields and sectors (and also as part of their education/training for entry-level professionals; you read more about it here). Their abilities are supported by development. Facilitates the Utilization of 5G Networks This paper sets a benchmark for future moves on SFM methodology and directs training activities aiming to exploit 5G networks in response to growing sustainability demands, compatible with improved enterprise ***- Improved Extreme Learning Machine, Prediction, Human Scenarios, Smart Homes, Healthcare Support, Monitoring, Recurrent Formulation, Feedback Mechanisms, Precision, Swiftness.

关键词： Training Industries 5G mobile communication Government Transportation Smart homes Internet of Things Sustainable development Monitoring Facilities management

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Research on a Trustworthy Digital Learning Roll Call System

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International Journal of Network Security 2022年第4期24卷 681-688页

作者： Liao, Anthony Y. H. Hsieh, Yu-Ying Yang, Cheng-Ying Hwang, Min-Shiang Department of M-Commerce and Multimedia Applications Asia University No. 500 Lioufeng Rd. Taichung41354 Taiwan Department of Computer Science and Information Engineering Asia University No. 500 Lioufeng Rd. Taichung41354 Taiwan Department of Computer Science University of Taipei Taipei10048 Taiwan Department of Medical Research China Medical University Hospital China Medical University No. 91 Xueshi Rd. Taichung40402 Taiwan

Since the New Coronavirus Pneumonia (COVID-19) outbreak in 2019, it has seriously threatened people’s health, life, and lifestyle. As a result, universities and colleges worldwide have changed their teaching methods from physical to digital teaching. However, this digital teaching model has generated many problems that have never existed before. For example, how to effectively ensure student participation in learning and monitor student attendance. In particular, the reliability of the digital roll call results has been questioned by many. In order to understand the above problems and explore ways to improve, this paper organizes and analyzes a large number of literature and research materials and attempts to propose a specification of a trusted digital learning roll call system that meets expectations. We also propose a guideline for developing and implementing the trusted digital teaching system. © Institute of Mathematical Statistics, 2022

关键词： Coronavirus

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Examining the Intersection of Artificial Intelligence and Biosensors in Healthcare: Complex Neurodegenerative Issues

Examining the Intersection of Artificial Intelligence and Bi...

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

作者： Sandeep Bisht Dharmesh Dhabalia Ahmed Al-Ansari Angeline Benita Waleed Sadeq Jaaywel Ali Abdulkareem Mezaal Muntather Almusawi Ban Sefeer School of Management Graphic Era Hill University Bhimtal Uttarakhand India Department of Information Technology Vishwakarma Institute of Information Technology Pune India Dental Department Al-kunooze University College Basra Iraq Department of computer applications Hindustan institute of technology and science College of Technical Engineering National University of Science and Technology DhiQar Iraq Business Administration Department Al kut Kut University College wasit Iraq Department of computers Techniques engineering College of technical engineering The Islamic university Najaf Iraq Al-Farahidi university Baghdad Iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

This paper delves into the transformative capability of artificial intelligence (AI) and biosensor technologies in revolutionizing healthcare, with a selected attention on dealing with complex neurodegenerative sicknesses. In recent years, the healthcare industry has witnessed a significant inflow of records from wearable sensors, medical imaging, and health records, necessitating advanced analytical methods. Leveraging AI, we advocate a framework that integrates biosensor facts to enhance diagnostic accuracy, screen ailment development in actual time, and tailor treatments to person affected person needs. Our evaluation covers the trendy advancements in device mastering algorithms for medical imaging, precision medicinal drug, and the mixing of Internet of Things (IoT) biosensors. The dialogue extends to the challenges of records-driven AI, consisting of privateness worries and the reliability of biosensor gadgets, while also exploring future possibilities in part computing and federated mastering. This paper highlights the critical position of AIpushed biosensor technology in personalizing healthcare and enhancing effects for sufferers with neurodegenerative conditions.

关键词： Industries Drugs Accuracy Medical services Biosensors Internet of Things Reliability

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Hiding critical transactions using a modified un-realisation approach

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International Journal of Electronic Business 2020年第3期15卷 223-234页

作者： Satyanarayana Murthy, T. Gopalan, N.P. Athira, T.R. Department of Computer Applications National Institute of Technology Tiruchirapalli India Department of Computer Science and Engineering National Institute of Technology Puducherry India

Huge amount of data generated from the hospitals, social media sites and online departmental stores contains sensitive data. Nowadays finding association rules among these data may lead to privacy issues, in turn to leakage of sensitive information that may reveal the facts about an individual. In this paper an algorithm is proposed for hiding the sensitive association rules by minimising the ghost rules and lost rules. This maximum association rule (MAR) hiding performs better than GA and PSO algorithms. These traditional algorithms may lack in hiding the association rules due to its computational complexity, hiding failure, lost rules and ghost rules. The performance of this approach has been improved by using a modified un-realisation algorithm for hiding the sensitive association rules. Copyright © 2020 Inderscience Enterprises Ltd.

关键词： Association rules

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A PCA-based fuzzy tensor evaluation model for multiple-criteria group decision making

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Applied Soft Computing 2023年 132卷

作者： Singh, Meenu Pant, Millie Kong, Lingping Alijani, Zahra Snášel, Václav Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Roorkee Uttarakhand247667 India Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Roorkee Uttarakhand247667 India Faculty of Electrical Engineering and Computer Science VŠB-TU Ostrava Czech Republic Institute for Research and Applications of Fuzzy Modeling University of Ostrava 30. dubna 22 701 03 Ostrava Czech Republic

Multiple-criteria group decision-making (MCGDM) problems mainly consist of multiple factors and multiple Decision Makers (DMs) or Users, for which dimension extension is necessary when considering all the entries of DMs together. Tensor, a generalized form of a matrix, displays a multi-way array item, which is the most suitable and practical way to represent high-dimensional data without losing any information. In this paper, we first reduce the dimension through Principal Component Analysis (PCA), which helps consider the most-informative criteria. Then, we reintroduced the tensor as a fuzzy-form tensor for the MCGDM problem because of user information uncertainty. We choose Interval-valued Neutrosophic Fuzzy numbers (IVNFNs) as the basis for the tensor form because of their ability to distinguish between truth, indeterminacy, and falsity in the data. Lastly, a Generalized Interval-valued Neutrosophic Fuzzy Weighted Geometric (GIVNFWG) operator is defined. Moreover, a generalized framework for fuzzy-form tensors for high-dimensional MCGDM problems is proposed. The feasibility and efficiency of this proposed process is illustrated for a real-world MCGDM problem of ranking the most efficient Third Party Reverse Logistics Partners (3PRLPs), i.e., recycled fiber-based paper mills for the packaging industry. The obtained results are according to the experts and are validated using sensitivity analysis. This analysis facilitates in assessing the impact on the overall ranking performance of 3PRLPs by considering various combinations of environment and technological sub-criteria. © 2022 Elsevier B.V.

关键词： Principal component analysis

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Efficient Route Map Selection Using Cat Swarm Optimization with Multi-Hop Communication in Vehicular Adhoc Network

Efficient Route Map Selection Using Cat Swarm Optimization w...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： Valluri Venkata Gopala Rao Suhail Mubarak S. Bharath N. Nalini G. Karthikeyan F. Sangeetha Francelin Vinnarasi Department of Computer Applications Aditya University Andhra Pradesh Research Institute of Humanities and Social Sciences University of Sharjah Sharjah United Arab Emirates Department of ECE Rajalakshmi Institute of Technology Chennai Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Department of Information Technology St. Joseph's Institute of Technology Chennai

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

VANETs are a popular method for communication between nearby and stationary vehicles. VANETs enhance intelligent communication technology significantly, yet they face challenges like vehicle speed, dynamic topology, and routing complexities leading to communication disruptions. Various devices such as RSUs, OBUs, and dedicated short-range systems form the network, relying on inter-vehicular and infrastructure-based communication. This model introduces a unique two-layer vehicle node organization, allowing cars and buses to transmit, receive, and relay packets. The system emphasizes reduced node density and consistent path selection, employing AODV for multi-path routing. Inspired by cat behavior, the SCCSO method streamlines the optimization process, ensuring efficient energy utilization and routing path optimization. This paper presents a novel ERCSO for VANETs, focusing on energy efficiency and network stability. The proposed model employs multi-path routing using AODV and SCCSO to optimize paths based on node density and consistency. Through ns3 simulations, ERCSO is compared against existing methods, showcasing superior energy efficiency, consumption, delay reduction, throughput, and data delivery performance.

关键词： Energy consumption Vehicular ad hoc networks Routing Throughput Energy efficiency Stability analysis Delays Particle swarm optimization Optimization AODV

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A hybrid approach for mortality prediction for heart patients using ACO-HKNN

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Journal of Ambient Intelligence and Humanized Computing 2020年第5期12卷 5405-5412页

作者： Sowmiya, C. Sumitra, P. Department of Computer Science and Applications Vivekananda College of Arts and Sciences for Women (Autonomous) Tiruchengode India

Heart disease is the major cause of mortality in the world. The heart disease prediction from the clinical data is deliberate as the most important subject in clinical data analysis. Especially the size of data in health care is vast. Data mining (DM) assists decision and prediction from the raw health care data. DM converts the large collection into useful information. Several existing studies utilize the data mining approaches in heart disease prediction. There is only little research focused on selecting the important features which play a significant role in predicting heart disease is less. The aim of this study is to provide an enhanced approach with novel feature selection and classification technique to predict mortality in congestive heart failure patients. Through this approach the death rate due to heart disease will be decreased gradually. The ant colony optimization (ACO) algorithm is utilized for selecting the best feature for hybrid K-nearest neighbor (KNN) classifier. The proposed approach is compared with the prior classification techniques such as the Support vector machine, Naïve Bayes, KNN, C4.5, and decision tree. UCI Cleveland dataset is utilized for our implementation. Using the Netbeans IDE an experimental was conducted and the result shows that the heart disease prediction model provides a better result with accuracy of 99.2%.The present study shows the efficiency of the HKNN in heart disease prediction system. Initially important features are analyzed and then classification is utilized to obtain a better result.

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A study on computer aided automatic leaf disease classification techniques

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AIP Conference Proceedings 2023年第1期2904卷

作者： Swapna Jamal J. E. Judith Department of Computer Applications Noorul Islam Centre for Higher Education Kumaracoil Kanyakumari India Department of Computer Science and Engineering NoorulIslam Centre for Higher Education Kumaracoil Kanyakumari India

As a source of human energy, plants have been seen as highly significant. Multiple diseases have the potential to drastically impair crop productivity and seriously jeopardize food security. Agricultural production is important to the global economy, but the sad fact is that damage from plant diseases all over the world has a negative impact on it. Therefore, it is crucial to spot disease affected o r infected leaves. Farmers can then use this information to protect their crops from the time they are sown until they are harvested, potentially reducing their financial loss. Traditional categorization techniques such as visual inspection and experimental tests have many drawbacks including the need for a lot of time and subjectivity. However, physical labor in this aspect will be a severe load on labor. As a result, the automatic diagnosis of diseases will be a supplement to agricultural yield and will also aid in increasing crop production. Convolutional Neural Networks (CNN) can classify plant diseases based on Deep Learning (DL) techniques. Plant leaf classification seemed to perform better when a variety of pre-processing methods were used, as well as typical parameters in feature extraction. This study reviews DL methods for detecting, classifying, and diagnosing plant leaf diseases. Before leaf recognition and validation, the machine learning algorithm must be established using a trustworthy source of leaf database since the quality of leaf images plays a significant role.

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Work-in-Progress: Using the PerFECt Framework to Design and Implement Blended Learning Activities to Introduce the Binary System in Primary School Students

Work-in-Progress: Using the PerFECt Framework to Design and ...

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9th International Conference on Interactive Collaborative and Blended Learning, ICBL 2020

作者： Moumoutzis, Nektarios Rigas, Nikolaos Apostolos Xanthaki, Chara Maragkoudakis, Yiannis Christodoulakis, Christina Paneva-Marinova, Desislava Pavlova, Lilia Laboratory of Distributed Multimedia Information Systems and Applications School of Electrical and Computer Engineering Technical University of Crete Chania Crete73100 Greece Institute of Mathematics and Informatics Bulgarian Academy of Sciences Sofia1113 Bulgaria Directorate of Secondary Education Chania Crete Greece Department of Computer Science University of Toronto Toronto Canada Laboratory of Telematics Bulgarian Academy of Sciences Sofia1113 Bulgaria

ISBN: (纸本)9783030672089

This paper presents how the PerFECt framework is employed to enable innovative teaching and learning mathematics in schools using drama-based approaches. The topic addressed lies in the cross-section between mathematics and computer science: teaching and learning the binary system of arithmetic that constitutes the basis of modern computing. The work builds on previous research efforts targeting gamification of mathematics and is seen under new light offered by the Theatre in Mathematics (TIM) Methodology. The result is an engaging game to teach the binary system to children using their bodies and simple rules for interacting with each other. Following these simple rules the participants enact the operation of a calculator that is able to transform integers into their binary representations and compute the result of the four arithmetic operations (addition, subtraction, multiplication and division). The game can be combined with online activities using a simulator of the game, so that learners can explore it for themselves and discover how the arithmetic operations are done, even when they are not in the same physical space with their peers. This way, an effective blended learning approach is ultimately offered combining face-to-face and online activities. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Learning systems

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Collusion Detection in Team-Based Multiplayer Games

Collusion Detection in Team-Based Multiplayer Games

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AAAI 2022 Spring Symposium on Machine Learning and Knowledge Engineering for Hybrid Intelligence, AAAI-MAKE 2022

作者： Greige, Laura De Mesentier Silva, Fernando Trotter, Meredith Lawrence, Chris Chin, Peter Varadarajan, Dilip Department of Computer Science Boston University BostonMA United States EA Digital Platform Electronic Arts Redwood CityCA United States Artifical Intelligence and Machine Learning GlaxoSmithKline San FranciscoCA United States Respawn Entertainment Los AngelesCA United States Center of Mathematical Sciences and Applications Harvard University CambridgeMA United States Center for Brains Minds and Machines MIT CambridgeMA United States

In the context of competitive multiplayer games, collusion happens when two or more teams decide to collaborate towards a common goal, with the intention of gaining an unfair advantage from this cooperation. The task of identifying colluders from the player population is however infeasible to game designers due to the sheer size of the player population. In this paper, we propose a system that detects colluding behaviors in team-based multiplayer games and highlights the players that most likely exhibit colluding behaviors. The game designers then proceed to analyze a smaller subset of players and decide what action to take. For this reason, it is important and necessary to be extremely careful with false positives when automating the detection. The proposed method analyzes the players' social relationships paired with their in-game behavioral patterns and, using tools from graph theory, infers a feature set that allows us to detect and measure the degree of collusion exhibited by each pair of players from opposing teams. We then automate the detection using Isolation Forest, an unsupervised learning technique specialized in highlighting outliers, and show the performance and efficiency of our approach on two real datasets, each with over 170,000 unique players and over 100,000 different matches. © 2022 Copyright for this paper by its authors

关键词： Anomaly detection

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