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International Conference for Convergence of Technology (I2CT)

作者： Laxmaiah Kocharla Rashika Tiwari E N Vijaya Kumari D. Shankar Ghafar Ahmed Al-Salman Sai Kumar K Department of Computer Science and Engineering Vignana Bharathi Institute of Technology Aushapur(v) Ghtkesar(M)MedchalDist Telangana Geetanjali Olympiad School Vartur Hobli Bengaluru Department of Computer Science and Information Technology MLR Institute of Technology Hyderabad Telangana India Department of CSE Vishnu Institute of Technology Vishnupur Bhimavaram Andhra Pradesh Scientific Research Center Al-Ayen University Thi-Qar Iraq Computer Science Koneru Lakshmaiah Education Foundation Green Fields Vaddeswaram Andhra Pradesh

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

Accidents prevent injured people from communicating in emergencies. They may use IoT to send accident data to an emergency unit as soon as possible, depending on severity. IoT networks use standard models to categorize situation severity and report it to the control unit. These obsolete models operate poorly. Logistic regression was presented to overcome embedded, cloud, and server model restrictions. The suggested approach is applied to a previous accident database and provided to the local emergency unit for quick testing. Unlike a human review, the suggested technology instantly discovers accident data and delivers it to the local cloud database. A nearby guardian receives accident data from the user's Sensors. IoT networking & emergency alerting to local guardians benefit from SVM with Logistic regression (LR). The suggested application got 96.74% accuracy, 98.67% sensitivity, 96.86% recall, 96.54% throughput, and 95.87% F1 score, which was good.

关键词： Support vector machines Logistic regression Databases Vehicle safety Throughput Sensors Servers

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Regression Models for Predicting Gamified Educational Outcomes

Regression Models for Predicting Gamified Educational Outcom...

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Telecommunications Conference (ITC-Egypt), International

作者： Abdelhameed Ibrahim Christos Gatzoulis Marwa M. Eid Faris H. Rizk Laith Abualigah El-Sayed M. El-kenawy Faculty of Engineering Design and Information & Communications Technology (EDICT) School of ICT Bahrain Polytechnic Isa Town Bahrain Faculty of Artificial Intelligence Delta University for Science and Technology Mansoura Egypt Computer Science and Intelligent Systems Research Center Blacksburg Virginia USA Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Computer Science Department Al al-Bayt University Mafraq Jordan MEU Research Unit Middle East University Amman Jordan Applied Science Research Center Applied Science Private University Amman Jordan Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura Egypt

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

This paper focuses on improving learning and involvement in studies by employing game elements and considering online learning platforms to deliver statistics curriculum. According to the problem statement, the research examines gamification methods to transform studying from a routine to having enjoyment while improving academic results. This area of investigation is being extended to predict academic outcomes by applying different machine learning models to student performance data received from gamified. The strategy entails using a pedagogy based on collecting data from students who participated in a gamified educational platform; the data comprises indicators like pre-and post-platform exam grades, grades on quizzes, and indicators of platform usage. Multiple regression algorithms, including Random Forest and Gradient Boosting, are applied to student performance. In the experiments, the output results indicate that the model of Random Forest Regressor outperforms other models with the least Mean Squared Error (MSE) of 0.052. This model performs the best in terms of prediction accuracy with competence level in predicting students' results in gamified environments. The findings can support decision-making in designing educational activities. The Random Forest Regressor model can be identified as the best predictive model and may help train gamified learning platforms, which can encourage student engagement that correlates to performance.

关键词： Machine learning algorithms Transforms Predictive models Nearest neighbor methods Prediction algorithms Boosting Data models

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The Advanced Encryption Standard with a Dynamic S-Box Using the RC4 Key Schedule

The Advanced Encryption Standard with a Dynamic S-Box Using ...

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Electrical, computer and Energy Technologies (ICECET), International Conference on

作者： Abdelrahman Altigani Muhammad Rukunuddin Ghalib Huwaida T. Elshoush Muawia A. Elsadig Nashat Alrefai Shiraz Naserelden Computer Information Science Higher Colleges of Technology Al Ain UAE School of Engineering & Computing De Monfort University Dubai UAE Computer Science Department Faculty of Mathematical Sciences and Informatics University of Khartoum Khartoum Sudan College of Computer Science and Information Technology Imam Abdulrahman bin Faisal University Dammam KSA Department of Mathematics College of Science Imam Abdulrahman Bin Faisal University Basic and Applied Scientific Research Center Imam Abdulrahman Bin Faisal University Dammam KSA Department of Mathematical Sciences Faculty of Science Universiti Teknologi Malaysia Johor Malaysia

ISBN: (数字)9798350395914

ISBN: (纸本)9798350395921

Since the inception of secure communication, safeguarding data confidentiality has been paramount. With the increased reliance on technology, the need for robust security mechanisms is more critical than ever. This paper explores confidentiality in cybersecurity, focusing on symmetric encryption algorithms. While symmetric encryption, particularly the Advanced Encryption Standard (AES), is in wide use, concerns arise regarding its static nature. Therefore, the paper introduces a dynamic cipher, augmenting AES security by introducing a key-dependent S-Box. By leveraging the RC4 key schedule, the proposed cipher generates unique S-Boxes for each encryption key, significantly increasing security against non-generic attacks. Theoretical analysis demonstrates the proposed cipher is capable of resisting brute force, linear cryptanalysis, and differential cryptanalysis. Furthermore, its adaptability to existing AES implementations ensures practicality and ease of adoption.

关键词： Ciphers Schedules Heuristic algorithms Dynamics Force Dynamic scheduling Encryption computer security Time complexity Standards

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MEO: Martial Eagle Optimizer Algorithm

Research Square

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Research Square 2024年

作者： Salehnia, Taybeh Ahmadi, Mahmood Abualigah, Laith Department of Computer Engineering and Information Technology Razi University Kermanshah Iran Computer Science Department Prince Hussein Bin Abdullah Faculty for Information Technology Al al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Faculty of Information Technology Middle East University Amman11831 Jordan Applied science research center Applied science private university Amman11931 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang 11800 Malaysia

In this paper, the method of flight and movement of Martial Eagle (Polemaetus Bellicosus) for hunting prey is used to design and Meta-Heuristic (MH) algorithm called Martial Eagle Optimizer (MEO). Martial Eagle is a large hunting bird of the falcon category, which lives in southern Africa. Its prey is very diverse and varies according to its habitat, including birds, reptiles and mammals. The Martial Eagle is even capable of hunting young pythons, baboons and other monkeys, and small deer. The optimization process by the proposed MEO is performed in three stages, two stages are related to the ExploRation Phase (ERP) and one stage is related to the ExploItation Phase (EIP). These steps are selecting the Search Space (SS) by soaring high, flying and moving along the horizon, and bending vertically in the ERP. Logarithmic spiral movement, vertical bending attack, vertical landing, and grabbing the prey are mechanism to achieve EIP the proficiency of MEO is tested using test functions as well as several engineering issues. The comparative results demonstrate that the MEO performs better than the existing algorithms, both in terms of achieving the final solution and reducing the number of Objective Function recalls. © 2024, CC BY.

关键词： Optimization

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IoT Based Health Monitoring System with AI-Powered Disease Prediction

IoT Based Health Monitoring System with AI-Powered Disease P...

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International Conference on science Technology engineering and Management (ICONSTEM)

作者： S. Radha Mahendran Mohammad Shahbaz Khan Hari Priya M P Ahmed J. Obaid Karrar Shareef Mohsen Ashraf Mohammed Shareef Department of Bioinformatics School of Life Sciences Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai India operations and regulatory affairs Children's National Hospital Washington DC USA Department of Computer Science Kristu Jayanti College (Autonomous) Bangalore Karnataka India Najaf Iraq Department of Computer Technical Engineering Faculty of Computer Science and Mathematics University of Kufa Technical Engineering College Al-Ayen University Thi-Qar Iraq Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Thi-Qar Iraq National University of Science and Technology DhiQar Iraq

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

Healthcare monitoring systems have advanced significantly in emergency rooms and many other health settings. Today, many nations are deeply concerned about the rise of small healthcare monitoring systems. In-person advising and telemedicine are two different ways that the Internet of Things (IoT) is further developing healthcare. The Internet of Things makes things and individuals reliably available, and its relationship with the cloud improves our personal satisfaction. Proactive clinical exploration can help change a responsive healthcare approach into a proactive one, as man-made reasoning (AI) and high-level human insight become increasingly more coordinated into the healthcare area. Within the field of machine learning, deep learning holds significant potential in precisely and swiftly analysing vast quantities of data, producing perceptive experiences, and resolving intricate issues. Preventive treatment and early intervention for persons who are at risk depend on accurate and useful illness prediction. The constant inflow of electronic medical information necessitates the need for more accurate prediction models in recurrent neural network versions of deep learning that handle successive time-series data. Devices connected to the Internet of Things provide data to the proposed system, and future analysis will be conducted on cloud-stored electronic clinical records pertaining to patient histories.

关键词： Deep learning Cloud computing Accuracy Telemedicine Medical services Internet of Things Reliability

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Non-Hermitian Chiral Heat Transport

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Physical Review Letters 2023年第26期130卷 266303-266303页

作者： Guoqiang Xu Xue Zhou Ying Li Qitao Cao Weijin Chen Yunfeng Xiao Lan Yang Cheng-Wei Qiu Department of Electrical and Computer Engineering National University of Singapore Kent Ridge 117583 Republic of Singapore School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing 400067 China Interdisciplinary Center for Quantum Information State Key Laboratory of Extreme Photonics and Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou 310027 China International Joint Innovation Center Key Lab of Advanced Micro/Nano Electronic Devices and Smart Systems of Zhejiang The Electromagnetics Academy at Zhejiang University Zhejiang University Haining 314400 China State Key Laboratory for Mesoscopic Physics and Frontiers Science Center for Nano-optoelectronics School of Physics Peking University100871 Beijing China Department of Electrical and Systems Engineering Washington University St. Louis Missouri 63130 USA

Exceptional point (EP) has been captivated as a concept of interpreting eigenvalue degeneracy and eigenstate exchange in non-Hermitian physics. The chirality in the vicinity of EP is intrinsically preserved and usually immune to external bias or perturbation, resulting in the robustness of asymmetric backscattering and directional emission in classical wave fields. Despite recent progress in non-Hermitian thermal diffusion, all state-of-the-art approaches fail to exhibit chiral states or directional robustness in heat transport. Here we report the first discovery of chiral heat transport, which is manifested only in the vicinity of EP but suppressed at the EP of a thermal system. The chiral heat transport demonstrates significant robustness against drastically varying advections and thermal perturbations imposed. Our results reveal the chirality in heat transport process and provide a novel strategy for manipulating mass, charge, and diffusive light.

关键词： Diffusion Heat transfer Metamaterials Non-Hermitean systems

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A Coarse-to-fine Cascaded Evidence-Distillation Neural Network for Explainable Fake News Detection

arXiv

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arXiv 2022年

作者： Yang, Zhiwei Ma, Jing Chen, Hechang Lin, Hongzhan Luo, Ziyang Chang, Yi College of Computer Science and Technology Jilin University Changchun China Department of Computer Science Hong Kong Baptist University Hong Kong School of Artificial Intelligence Jilin University China International Center of Future Science Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China

Existing fake news detection methods aim to classify a piece of news as true or false and provide veracity explanations, achieving remarkable performances. However, they often tailor automated solutions on manual fact-checked reports, suffering from limited news coverage and debunking delays. When a piece of news has not yet been fact-checked or debunked, certain amounts of relevant raw reports are usually disseminated on various media outlets, containing the wisdom of crowds to verify the news claim and explain its verdict. In this paper, we propose a novel Coarse-to-fine Cascaded Evidence-Distillation (CofCED) neural network for explainable fake news detection based on such raw reports, alleviating the dependency on fact-checked ones. Specifically, we first utilize a hierarchical encoder for web text representation, and then develop two cascaded selectors to select the most explainable sentences for verdicts on top of the selected top-K reports in a coarse-to-fine manner. Besides, we construct two explainable fake news datasets, which are publicly available. Experimental results demonstrate that our model significantly outperforms state-of-the-art baselines and generates high-quality explanations from diverse evaluation perspectives. Copyright © 2022, The Authors. All rights reserved.

关键词： Distillation

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Evidence-Aware Multi-Modal Data Fusion and its Application to Total Knee Replacement Prediction

Evidence-Aware Multi-Modal Data Fusion and its Application t...

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Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Xinwen Liu Jing Wang S. Kevin Zhou Craig Engstrom Shekhar S. Chandra School of Electrical Engineering and Computer Science The University of Queensland Brisbane Australia The Commonwealth Scientific and Industrial Research Organisation Canberra Australia Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) School of Biomedical Engineering & Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China School of Human Movement and Nutrition Sciences The University of Queensland Brisbane Australia

ISBN: (数字)9798350379037

ISBN: (纸本)9798350379044

Deep neural networks have been widely studied to predict a medical condition, such as total knee replacement (TKR). It has shown that data of different modalities, such as imaging data, clinical variables, and demographic information, provide complementary information and thus can improve the prediction accuracy together. However, the data sources of various modalities may not always be of high quality, and each modality may have only partial information of medical condition. Thus, predictions from different modalities can be in conflict, and the final prediction may fail in the presence of such a conflict. Therefore, it is important to account for the reliability of each source data and the prediction output when making a final decision. In this paper, we propose an evidence-aware multimodal data fusion framework based on the Dempster-Shafer theory (DST). The backbone models contain an image branch, a non-image branch and a fusion branch. For each branch, there is an evidence network that takes the extracted features as input and outputs an evidence score, which is designed to represent the reliability of the output from the current branch. The output probabilities along with the evidence scores from multiple branches are combined with the Dempster's combination rule to make a final prediction. Experimental results on the public OA initiative (OAI) dataset for the TKR prediction task show that the proposed method has better performance by accounting for conflicts from various modalities.

关键词： Medical conditions Soft sensors Digital images Data integration Imaging Artificial neural networks Reliability theory Feature extraction Reliability engineering Knee replacement

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A Group Decision-Making and Optimization Method Based on Relative Inverse Number

SSRN

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SSRN 2023年

作者： Liu, Chuanbin Yu, Lean Liu, Bin Wang, Dan Yang, Jianan School of Economics and Management Harbin Engineering University Harbin150000 China Center for Scientific Research and Development in Higher Education Institutes Ministry of Education Beijing10010 China College of Computer Science Chongqing University Chongqing40044 China School of Public Policy and Administration Chongqing University Chongqing40044 China

Group decision-making is an important branch of decision-making theory, and choosing the most appropriate decision-making theory under the subjective cognition of individuals has become a research hotspot. The Borda rule and Copeland rule are common ranking rules in group decision-making. This paper demonstrates that the ranked output of these two methods is unreasonable and do not accurately reflect the inner structure of the ranked input. In order to objectively quantify the difference between different rules, the concept of the relative inverse number (RIN) is proposed. A ranking decision algorithm, called the voting rule, based on relative inversions is then proposed. The RIN rule reflects differences in the ranked input structure and the ranked output more effectively than the Borda and Copeland rules. However, the complexity of the RIN rule increases exponentially with the number of alternatives. Fortunately, algorithms have been designed, combined with the Borda rule, to solve the RIN rule output. What's more, the applications of the RIN rule in science and technology evaluation are introduced. © 2023, The Authors. All rights reserved.

关键词： Inverse problems

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SELF-DIFFUSIVE SPATIOTEMPORAL PATTERNS IN A FOOD CHAIN MODEL THROUGH WEAKLY NONLINEAR ANALYSIS

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Fractals 2023年第9期31卷 2350098-2350098页

作者： Shivam Singh, Teekam Kumar, Mukesh Hsieh, Sun-Yuan Singh, Kamred Udham Uttarakhand Dehradun248002 India 566/6 Bell Road Clement Town Uttarakhand Dehradun248002 India Center for Innovative FinTech Business Models International Center for the Scientific Development of Shrimp Aquaculture Department of Computer Science and Information Engineering Institute of Medical Information Institute of Manufacturing Information and Systems National Cheng Kung University Tainan701 Taiwan Department of Computer Science and Information Engineering National Cheng Kung University Tainan701 Taiwan School of Computing Graphic Era Hill University Uttarakhand Dehradun248002 India

Density distributions of populations with self-diffusion and interaction in a spatial domain are dynamically visualized with coupled nonlinear reaction–diffusion equations. Incorporating self-diffusion terms creates a more pragmatic modeling paradigm and provides meaningful descriptions of influences on spatiotemporal pattern formation phenomena. This paper examines the effect of self-diffusion in a food chain system with a Holling type-IV functional response and the type of spatial structures forms on a geographical scale due to the random movement of species. We discussed the existence and uniqueness of a positive equilibrium solution and obtained the Turing instability conditions for the self-diffusive food chain model. Moreover, weakly nonlinear analysis close to the Turing bifurcation boundary is used to derive the amplitude equations. The stability of the amplitude equations and sufficient conditions for the emanation of spatiotemporal patterns (such as spots, stripes, and blended patterns) are investigated. The analytical results are verified with numerical simulations. The results are applicable to all environments and can be used to understand the effects of self-diffusion in other food chain models both qualitatively and quantitatively. © World scientific Publishing Company.

关键词： Nonlinear analysis

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