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6th International Conference on Advancements in computing, ICAC 2024

作者： Aflal, Saroon Mohammed Shamugarajah, Sutharshan Thiruthanigesan, Kanagasabai Balasubramaniam, Balarajan Samarakoon, Uthpala Ragel, Roshan G. Sri Lanka Institute of Information Technology Northern Uni Faculty of Computing Jaffna Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka University of Peradeniya Department of Computer Engineering Sri Lanka

ISBN: (纸本)9798331517878

This study investigates the impact of artificial intelligence (AI) tools on undergraduate education in Sri Lanka, focusing on various categories of AI tools, including chatbots, grammar checkers, CV generators, and more. Through a comprehensive survey conducted across multiple higher education institutions in Sri Lanka, the study assesses the frequency of AI tool usage and students' interest levels. The research utilizes k-means clustering to identify distinct segments of students based on their AI tool usage patterns and interest levels. Findings reveal high adoption rates of AI tools among Sri Lankan undergraduates, with significant reliance on chatbots like ChatGPT. The study highlights the diverse usage of AI tools, their impact on academic activities, and the varying levels of student engagement. The results suggest that while AI tools are widely used, targeted interventions and additional support are necessary to maximize their benefits. The study provides valuable insights for policymakers and educators, emphasizing the need to strategically integrate AI tools in higher education to enhance learning outcomes and prepare students for a technology-driven future. © 2024 IEEE.

关键词： Students

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Deep Learning Algorithm for Person Re-Identification Based on Dual Network Architecture

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computers, Materials & Continua 2025年第5期83卷 2889-2905页

作者： Meng Zhu Xingyue Wang Honge Ren Abeer Hakeem Linda Mohaisen College of Information Engineering Harbin UniversityHarbin150086China Heilongjiang Provincial Key Laboratory of the Intelligent Perception and Intelligent Software Harbin UniversityHarbin150086China College of Computer and Control Engineering Northeast Forestry UniversityHarbin150040China Heilongjiang Forestry Intelligent Equipment Engineering Research Center Northeast Forestry UniversityHarbin150040China Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia

Changing a person’s posture and low resolution are the key challenges for person re-identification(ReID)in various deep learning *** this paper,we introduce an innovative architecture using a dual attention network that includes an attentionmodule and a joint measurement module of spatial-temporal *** proposed approach can be classified into two main ***,the spatial attention feature map is formed by aggregating features in the spatial ***,the same operation is carried out on the channel dimension to formchannel attention ***,the receptive field size is adjusted adaptively tomitigate the changing person posture ***,we use a joint measurement method for the spatial-temporal information to fully harness the data,and it can also naturally integrate the information into the visual features of supervised ReID and hence overcome the low resolution *** experimental results indicate that our proposed algorithm markedly improves the accuracy in addressing changing human postures and low-resolution issues compared with contemporary leading *** proposed method shows superior outcomes on widely recognized benchmarks,which are the Market-1501,MSMT17,and DukeMTMC-reID ***,the proposed algorithmattains a Rank-1 accuracy of 97.4% and 94.9% mAP(mean Average Precision)on the Market-1501 ***,it achieves a 94.2% Rank-1 accuracy and 91.8% mAP on the DukeMTMC-reID dataset.

关键词： Person reidentification ReID computer vision self-attention spatial-temporal information

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Predicting of Photovoltaic Power Generation Using Similarity-Based Forecasting Models 24

Predicting of Photovoltaic Power Generation Using Similarity...

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24th International Middle East Power System Conference, MEPCON 2023

作者： Aboelmagd, Mohamed R. Selim, Ali Abdel-Akher, Mamdouh Aswan University Electrical Engineering Dept Aswan Egypt Almaaqal University Control and Computer Engineering Dept Basra Iraq Aswan University Faculty of Engineering Department of Electrical Engineering Aswan Egypt

ISBN: (纸本)9798350358469

Accurate forecasting in photovoltaic energy is one of the most essential integrated and influential events for managing the system of new and renewable energy sources in the power grid. The paper presents a practical approach using neural networks to predict photovoltaic energy generation. Applying neural networks in PV power generation forecasting is challenging due to unpredictable environmental changes such as solar radiation, temperature, humidity, wind speed, etc. Neural network methods are distinguished by achieving high performance in prediction compared to traditional mathematical models that have difficulties dealing with complex nonlinear relationships, resulting in substandard prediction. The proposed neural network-based forecasting model offers an effective way to improve the precision of estimating PV generation. It has a great deal of promise to enhance the integration and dependability of solar energy in the current electricity landscape because of its capacity to capture complex linkages in the data and adapt to changing circumstances. © 2023 IEEE.

关键词： Forecasting

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Diversity and Freshness-Aware Regret Minimizing Set Queries 29th

Diversity and Freshness-Aware Regret Minimizing Set Queries

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29th International computing and Combinatorics Conference, COCOON 2023

作者： Guo, Hongjie Li, Jianzhong Shen, Fangyao Gao, Hong Faculty of Computing Harbin Institute of Technology Heilongjiang Harbin China School of Computer Science and Technology Zhejiang Normal University Zhejiang Jinhua China Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9783031491924

Multi-criteria decision-making often involves selecting a small representative set from a database. A recently proposed method is the regret minimization set (RMS) queries. It aims to rectify the shortcomings of needing a utility function in top-k queries and the overly large result size of skyline queries. However, the existing definition of RMS only ensures one result under any utility function, and do not consider the diversity and freshness of the returned results. In this paper, we define a strong regret set, which guarantees the utility value error of k data points under any utility function. Given this new definition, we propose two problems, namely the Minimum Size problem and the Max-sum Diversity and Freshness problem. Both proposed problems have been proven to be NP-hard. Correspondingly, we devise approximation algorithms for them, and analyze algorithms’ time complexities and the approximation ratios of the solutions obtained. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Approximation algorithms

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An Empirical Investigation on Employing Machine Learning for Balancing Home Agent Loads in Next Generation IP Mobility 2

An Empirical Investigation on Employing Machine Learning for...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Khatri, Anshu Mathi, Senthilkumar Ashiq Sheriff, A. Deepthika, R. Naveen Raaghavendran, G. Ramalingam, Venkadeshan Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India Faculty in Information Technology Department University of Technology and Applied Sciences-Shinas Oman

Mobile IPv6 is a key technology to enable the mobility of devices on next-generation internet protocol networks. Home agents provide simple services to registered mobile nodes. In addition, the use of multiple domestic operators for load-balancing devices faces problems of synchronization and signalling overhead. Thus, most of the existing load balancing methods present a significant delay in the transmission of data packets. The machine learning approach provides an efficient hardware storage solution for balancing the load of the home agent by improving the state-work schedule. It provides a method for dynamic load balancing based on the research-consumption system. Thus, the present paper investigates the use of machine learning, specifically reinforcement learning, to enhance home agent load balancing in next-generation IP traffic. The reinforcement learning-based home agent decisions, guided by mobile node speed and registration queries, reduce latency, improve network performance, and provide dynamic solutions. The proposed method significantly reduces the delay for packet delivery by balancing the home agent load with the incorporation of reward parameters. The empirical investigation found a remarkable improvement in network reliability, as manifested by a 2.31% increase in packet delivery ratio compared to systems with existing methods. © 2024 Elsevier B.V.. All rights reserved.

关键词： Reinforcement learning

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A New Approach to Road Incident Detection Leveraging Live Traffic Data: An Empirical Investigation 2

A New Approach to Road Incident Detection Leveraging Live Tr...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Kumar Gannina, Aswin Ram Jaffarullah, Aadhil Ahamed Reddy, Tiyyagura Mohit Subba Reddy, Sabbella Manoj Vikas, Ambati Sai Mathi, Senthilkumar Ramalingam, Venkadeshan Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India Faculty in Information Technology Department University of Technology and Applied Sciences-Shinas Oman

Accidents can have a significant impact on road safety and the efficiency of transport. With the ever-increasing number of vehicles on the road, it is crucial to quickly detect and respond to such events to decrease the impact. The current paper presents a road accident and incident detection solution utilizing traffic API monitoring and continuously analyzing real-time traffic data from various sources like GPS-enabled devices. Consequently, the proposed approach can identify abnormal patterns in the movement of vehicles, such as unexpected road closures or an accident. It numerically analyzes the traffic pattern and identifies incidents and accidents using live traffic data collected from the map service. When the approach identifies any abnormality, it can immediately let the user know, allowing them to act accordingly. It can facilitate authorities in identifying accident-prone areas and deploy safety measures. This can help law enforcement agencies to identify drivers at risk of causing accidents and implement education and enforcement programs targeted towards them. Hence, the proposed approach can aid in reducing the negative impact of traffic incidents, and the public can respond to these incidents more preparedly. © 2024 Elsevier B.V.. All rights reserved.

关键词： Accidents

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EQUILIBRIA AND STABILITY OF ONE MESSENGER RNA AND TWO MICRO RNA DYNAMICS

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2022年第4期84卷 3-8页

作者： Olteanu, Mircea Ştefan, Radu Department of Mathematical Methods and Models Faculty of Applied Sciences University "Politehnica" of Bucharest Romania Department of Automatic Control and Systems Engineering Faculty of Automatic Control and Computer Science University "Politehnica" of Bucharest Romania

In this paper we study an ODE model on the microRNA-mRNA dynamics. We prove the existence of two equilibrium points (one with strictly positive compo-nents) and obtain a biologically consistent, sufficient asymptotic stability condition for the strictly positive equilibrium. © 2022, Politechnica University of Bucharest. All rights reserved.

关键词： Asymptotic stability

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Swallowing Assessment using High-Resolution Cervical Auscultations and Transformer-based Neural Networks 46

Swallowing Assessment using High-Resolution Cervical Auscult...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Anwar, Ayman Khalifa, Yassin Lucatorto, Erin Coyle, James L. Sejdic, Ervin University of Toronto Faculty of Applied Science & Engineering Department of Electrical and Computer Engineering TorontoON Canada University of Pittsburgh Center for Research Computing and Information Technology Analytics PittsburghPA United States Cairo University Faculty of Engineering Systems and Biomedical Engineering Department Giza Egypt University of Pittsburgh School of Health and Rehabilitation Sciences Department of Communication Science and Disorders PittsburghPA United States University of Toronto North York General Hospital Faculty of Applied Science & Engineering Department of Electrical and Computer Engineering TorontoON Canada

ISBN: (纸本)9798350371499

Swallowing assessment is a crucial task to reveal swallowing abnormalities. There are multiple modalities to analyze swallowing kinematics, such as videofluoroscopic swallow studies (VFSS), which is the gold standard method, and high-resolution cervical auscultation (HRCA), which is a noninvasive technique that uses a triaxial accelerometer attached to the patient's neck. Deep learning models play an essential role in data driven analysis of swallowing landmarks using VFSS and/or HRCA as input data. Most of these models utilize convolutional and recurrent neural networks. Here, we investigate the ability of transformers to analyze swallowing kinematics;specifically upper esophageal sphincter opening and laryngeal vestibule closure using HRCA signals. We tested the model using an independent test dataset to assess the generalizability of the proposed network. The proposed network achieved an average detection accuracy higher than 90% and 85% for both segmentation tasks, which outperform the hybrid neural networks from the literature, and the model obtained high-performance measures for the independent dataset, showing the transformers' ability to generalize on unseen data. © 2024 IEEE.

关键词： Recurrent neural networks

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Detection of Copy-Move Forgery in Digital Images Using Singular Value Decomposition

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computers, Materials & Continua 2023年第2期74卷 4135-4147页

作者： Zaid Nidhal Khudhair Farhan Mohamed Amjad Rehman Tanzila Saba Saeed Ali bahaj Faculty of Engineering School of ComputingUniversity Technology of MalaysiaJohar BahruMalaysia UTM-IRDA MaGICX Institute of Human Centered EngineeringUniversiti Teknologi MalaysiaMalaysia Artificial Intelligence&Data Analytics Lab CCIS Prince Sultan University Riyadh Saudi Arabia Computer Techniques Engineering Department Faculty of Information TechnologyImam Ja’afar Al-Sadiq UniversityBaghdadIraq

This paper presents an improved approach for detecting copy-move forgery based on singular value decomposition(SVD).It is a block-based method where the image is scanned from left to right and top to down by a sliding window with a determined *** each step,the SVD is ***,the diagonal matrix’s maximum value(norm)is selected(representing the scaling factor for SVD and a fixed value for each set of matrix elements even when rotating thematrix or scaled).Then,the similar norms are grouped,and each leading group is separated into many subgroups(elements of each subgroup are neighbors)according to 8-adjacency(the subgroups for each leading group must be far from others by a specific distance).After that,a weight is assigned for each subgroup to classify the image as forgery or ***,the F1 score of the proposed system is measured,reaching 99.1%.This approach is robust against rotation,scaling,noisy images,and illumination *** is compared with other similarmethods and presents very promised results.

关键词： Forgery image forensic image processing region duplication SVD transformation technological development

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Fuzzy Set-Membership Filtering for Discrete-Time Nonlinear Systems

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IEEE/CAA Journal of Automatica Sinica 2022年第6期9卷 1026-1036页

作者： Jingyang Mao Xiangyu Meng Derui Ding Department of Control Science and Engineering University of Shanghai for Science and TechnologyShanghai 200093China Division of Electrical and Computer Engineering Louisiana State UniversityBaton RougeLA70803 USA IEEE School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia

In this article,the problem of state estimation is addressed for discrete-time nonlinear systems subject to additive unknown-but-bounded noises by using fuzzy set-membership ***,an improved T-S fuzzy model is introduced to achieve highly accurate approximation via an affine model under each fuzzy ***,compared to traditional prediction-based ones,two types of fuzzy set-membership filters are proposed to effectively improve filtering performance,where the structure of both filters consists of two parts:prediction and *** the locally Lipschitz continuous condition of membership functions,unknown membership values in the estimation error system can be treated as multiplicative noises with respect to the estimation ***-time recursive algorithms are given to find the minimal ellipsoid containing the true ***,the proposed optimization approaches are validated via numerical simulations of a one-dimensional and a three-dimensional discrete-time nonlinear systems.

关键词： Affine model membership functions set-membership filtering stability Takagi-Sugeno fuzzy modeling

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