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2024 International Conference on Control, Automation and Diagnosis, ICCAD 2024

作者： Ahmed, Saim Azar, Ahmad Taher Fekik, Arezki Wang, Haoping Njima, Chakib Ben Prince Sultan University College of Computer and Information Sciences Riyadh Saudi Arabia Riyadh11586 Saudi Arabia Benha University Faculty of Computers and Artificial Intelligence Benha Egypt University Akli Mohand Oulhadj-Bouria Department of Electrical Engineering Rue Drissi Yahia Bouira 10000 Algeria Nantes Université École Centrale Nantes Cnrs LS2N Umr 6004 NantesF-44000 France Nanjing University of Science and Technology School of Automation China University of Sousse Tunisia

ISBN: (纸本)9798350361025

Time delay estimation using fixed-time terminal sliding mode control (TDEFxSMC) for unknown nonlinear systems with uncertainty and external disturbances is presented in this study. Firstly, the notion of fixed-time terminal sliding mode control (FxSMC) is outlined and thoroughly examined. Using the FxSMC scheme, nonsingular fixed-time control, non-chatter control inputs, and high performance tracking positions have been achieved. To estimate the unknown dynamics, proposed method incorporates a TDE scheme. The Lyapunov theorem is applied to find whether the proposed system can achieve its stability in a fixed-time. The effectiveness of the suggested method is evaluated and demonstrated by presenting the results of the proposed method implemented to the nonlinear robot dynamics. © 2024 IEEE.

关键词： Dynamical systems

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Smart Class on Android: A Handy Classroom Scheduling Solution

Smart Class on Android: A Handy Classroom Scheduling Solutio...

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Next Generation Communication & Information Processing (INCIP), International Conference on

作者： Vishnuraja Palanisamy M. Thiyagarajan R. Bhavani Arun Anthonisamy K. Manivannan S. Vaishnavi Department of Artificial Intelligence and Data Science Builders Engineering College Kangeyam Tamil Nadu India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamil Nadu India Department of Electronics and Communication Engineering K. Ramakrishnan College of Technology Trichy Tamil Nadu India Department of Computer Science and Business System Panimalar Engineering College Chennai Tamil Nadu India Department of Information Science and Engineering School of Computer Science & Engineering JAIN (Deemed-to-be) University Bengaluru Karnataka India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education (MAHE) Manipal India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

Classroom scheduling is vital but difficult for educational institutions, especially those with large student populations and diverse needs. Traditional methods using outdated software or manual processes often cause inefficiencies, disagreements, and administrative workload. In low-resource regions without access to cutting-edge technology and infrastructure, these issues are severe. This paper introduces Android-based SmartClass Mobile to transform classroom scheduling. The program's SOA-based functionality offers flexibility, adaptability, and integration with institution-specific procedures. The Heuristic algorithm and Solution Space Navigation (SSN) tackle scheduling problems such as room availability, multimedia needs, and seating capacities. SSN dynamically resolves conflicts by exploring alternate configurations, while the Heuristic Algorithm assigns time slots and classrooms depending on availability and priority. SmartClass Mobile analysis indicates benefits. The solution reduces conflicts, increases user satisfaction, and decreases scheduling time by 50% compared to manual methods with 85% test user approval. Due to its mobile-first approach, the initiative is open to urban and rural institutions. Dependence on internet access for some functions is a downside, especially in resource-constrained contexts. The findings suggest that SmartClass Mobile provides a scalable, effective, and simple classroom scheduling solution. Mobile technology, advanced algorithms, and SOA concepts make it valuable for school administrators. Additions like AI and machine learning will boost its potential.

关键词： Navigation Heuristic algorithms Software algorithms Manuals Transforms Scheduling Software Real-time systems Mobile applications Reliability

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The Effect of English Text Readability on Speech Duration of Second Language Learners

The Effect of English Text Readability on Speech Duration of...

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International Conference on Universal Village (UV)

作者： Tomoko Nariai Shiroh Itai Hiroaki Kojima Faculty of Business Administration Takachiho University Tokyo Japan Faculty of Engineering Hiroshima Institute of Technology Hiroshima Japan Artificial Intelligence Research Center The National Institute of Advanced Industrial Science and Technology Tsukuba Japan

In order to facilitate active communication among people of various cultural and linguistic backgrounds, it is necessary to learn a second language that is linguistically different from one’s native language. The difficulty in learning second language is thought to depend on readability. Most research on the validity of readability assessment has focused on native speakers. Moreover, most of the few studies of readability with second language learners have focused on textual data. This study analyzed actual speech data to determine the effects of word frequencies, syllable structures, and grammatical functions used to estimate the relation with readability on word duration. We compared English word durations in native English speakers and native Japanese speakers. It is hypothesized that highly readable words, i.e., those that are simple and familiar, would be easier for Japanese to utter and shorter in duration, but the opposite was found in the experiment. This suggests that the features adopted for native English speakers as readability indicators are not fully compatible with Japanese speakers.

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The Role of Edge Computing in Enhancing Cloud Performance and Reducing Latency

The Role of Edge Computing in Enhancing Cloud Performance an...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Puneet Sharma J Shyamala Devi K Manikandan R Ani Minisha R Harini S. Karthik School of CS & AI SR University Warangal Telangana India Rajalakshmi Institute of Technology Chennai School of Computer Science & Engineering (SCOPE) Vellore Institute of Technology (VIT) Vellore Tamilnadu India Department of Artificial Intelligence and Data Science R.M. K. Engineering College Department of Computer Science and Engineering Veltech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Mathematics Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamilnadu India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

The flexibility of implementing edge computing over cloud infrastructures has become essential for responding to the need for reducing latency and improving data processing in a distributed environment. This research aims to determine the use of edge computing in enhancing the effectiveness of cloud utilization through conducting machine learning investigations utilizing Random Forest in the determination of the optimum contribution of resources. The work also set up and applied real-world datasets from Kaggle to illustrate that edge computing can alleviate the latency issue mainly by offloading computationally intensive tasks to edge nodes away from the central cloud. The results show that edge computing with the help of Machine learning is the best solution for cutting-edge cloud environments as it is fast and does not aggravate the problems of excessive use of resources. Therefore, this research will be a great addition to the existing literature on the integration of cloud and edge computing and is hoped to offer useful information for future developments in the field of cloud computing.

关键词： Cloud computing Machine learning algorithms Job shop scheduling Smart cities Processor scheduling Security Reliability Low latency communication Random forests Edge computing

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CINNAMON: A hybrid approach to change point detection and parameter estimation in single-particle tracking data

arXiv

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arXiv 2025年

作者： Malinowski, Jakub Kostrzewa, Marcin Balcerek, Michal Tomczuk, Weronika Szwabiński, Janusz Hugo Steinhaus Center Faculty of Pure and Applied Mathematics Wroclaw University of Science and Technology Wybrzeże Stanislawa Wyspiańskiego 27 Wroclaw50-370 Poland Department of Artificial Intelligence Faculty of Information and Communication Technology Wroclaw University of Science and Technology Wybrzeże Stanislawa Wyspiańskiego 27 Wroclaw50-370 Poland Dioscuri Centre in Topological Data Analysis Mathematical Institute Polish Academy of Sciences ul. Śniadeckich 8 Warsaw00-656 Poland

Change point detection has become an important part of the analysis of the single-particle tracking data, as it allows one to identify moments, in which the motion patterns of observed particles undergo significant changes. The segmentation of diffusive trajectories based on those moments may provide insight into various phenomena in soft condensed matter and biological physics. In this paper, we propose CINNAMON, a hybrid approach to classifying single-particle tracking trajectories, detecting change points within them, and estimating diffusion parameters in the segments between the change points. Our method is based on a combination of neural networks, feature-based machine learning, and statistical techniques. It has been benchmarked in the second Anomalous Diffusion Challenge. The method offers a high level of interpretability due to its analytical and feature-based components. A potential use of features from topological data analysis is also discussed. Copyright © 2025, The Authors. All rights reserved.

关键词： Change detection

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Autonomous Vehicle Traffic Flow Optimization Using AI and 5G Communication Networks

Autonomous Vehicle Traffic Flow Optimization Using AI and 5G...

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Device intelligence, Computing and Communication Technologies, (DICCT), International Conference on

作者： Jayant Shekhar Rama Prabha K. P S. Prashanth Umamaheswari K Rahul S G K. Gayathri Department of CSE Sharda School of Engineering and Technology Sharda University Greater Noida UP India School of Computer Science and Engineering Vellore Institute of Technology Chennai Tamil Nadu India Department of Aeronautical Engineering Excel Engineering College Namakkal Tamil Nadu India Department of Information Technology V.S.B. Engineering College Karur Tamilnadu India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science St. Joseph's Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

Real-time optimization of autonomous vehicle (AV) traffic flow using 5G communication networks and AI algorithms could overcome the limitations of static traffic enforcement, which uses outdated technologies to manage fluctuating traffic demands. It allows dynamic flow. Such commodities cannot adapt in real time because to their rigorous handling requirements, which disrupts traffic dynamics and creates construction delays and traffic congestion. The study suggests transmitting real-time vehicle status data to nearest intelligent crossings via vehicular wireless networks (VWNs). These intersections enhance traffic flow and reduce 5G network congestion using AI algorithms. Predictive analytics will help AVs change routes while on the road to maximize travel efficiency. It finds that peak congestion, average route duration, and fuel use reduce vehicle CO2 emissions by 66.7%, 22%, and 15%, respectively. It determined that the proposed system was a viable design route for a deployable smart city transportation solution because it outperformed traditional techniques in economic and environmental criteria.

关键词： 5G mobile communication Smart cities Heuristic algorithms Real-time systems artificial intelligence Vehicle dynamics Autonomous vehicles Optimization Traffic congestion Intelligent sensors

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Enhancing Lifetime in Wireless Sensor Networks through Image Processing-Based Clustering with Genetic Algorithm Routing

Enhancing Lifetime in Wireless Sensor Networks through Image...

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Soft Computing for Security Applications (ICSCSA), International Conference on

作者： M. Ilampari M. Amina Begum N Mahesh Babu S. Gopi M. Mythreyee Shanmugavel Deivasigamani Saveetha School of Law SIMATS Department of ECE Oxford College of Engineering Chennai India Department of Information Technology Anurag Engineering College Suryapet Kodad Telangana India Department of CSE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Department of EEE Dr. MGR Educational and Research Institute Chennai Department of Artificial Intelligence and Data Science Jeppiaar Institute of Technology Chennai India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

Wireless Sensor Networks (WSNs) are a very important technology widely used in diverse application domains of environmental monitoring, disaster management, health care, industrial automation etc. These are networks in which spatially distributed sensor nodes monitor physical or environmental conditions and communications data are processed through a base station. Here, we introduced a novel method for enhancing the efficiency and lifetime of WSNs by using the WSN to process images data and genetic programming (GA) to find the best route. A system model was developed to balance a network consisting of homogenous sensor nodes placed randomly in a particular geographical area, representing realistic scenarios. Each sensor node has limited energy resources, hence the operation of the network was partitioned into clustering phase and routing phase to optimal utilization of energy. To improve both the accuracy and efficiency of clustering, the genetic algorithm then identified the optimal path through these clusters using the best combination of pivot nodes for transmission. The proposed method applies image processing techniques to process sensor data, preprocesses sensor data, and then improves the classification process, clustering results, and the genetic algorithm obtains the more energy-efficient paths. Our extensive evaluation results prove substantial optimizations in network performance, energy efficiency and lifetime over known protocols. This method is expected to be effective in such applications as environmental monitoring, disaster management, and other applications that will utilize reliable and sustainable WSNs.

关键词： Wireless sensor networks Protocols Image processing Disaster management Routing Energy efficiency Environmental monitoring Security Reliability Genetic algorithms

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Bridging Knowledge Gap Between Image Inpainting and Large-Area Visible Watermark Removal

arXiv

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arXiv 2025年

作者： Leng, Yicheng Fang, Chaowei Chen, Junye Fang, Yixiang Li, Sheng Li, Guanbin School of Artificial Intelligence Xidian University Xi’an China School of Data Science The Chinese University of Hong Kong Shenzhen China School of Computer Science and Engineering Research Institute of Sun Yat-sen University in Shenzhen Sun Yat-sen University Guangzhou China Afirstsoft Shenzhen China GuangDong Province Key Laboratory of Information Security Technology China

Visible watermark removal which involves watermark cleaning and background content restoration is pivotal to evaluate the resilience of watermarks. Existing deep neural network (DNN)-based models still struggle with large-area watermarks and are overly dependent on the quality of watermark mask prediction. To overcome these challenges, we introduce a novel feature adapting framework that leverages the representation modeling capacity of a pre-trained image inpainting model. Our approach bridges the knowledge gap between image inpainting and watermark removal by fusing information of the residual background content beneath watermarks into the inpainting backbone model. We establish a dual-branch system to capture and embed features from the residual background content, which are merged into intermediate features of the inpainting backbone model via gated feature fusion modules. Moreover, for relieving the dependence on high-quality watermark masks, we introduce a new training paradigm by utilizing coarse watermark masks to guide the inference process. This contributes to a visible image removal model which is insensitive to the quality of watermark mask during testing. Extensive experiments on both a large-scale synthesized dataset and a real-world dataset demonstrate that our approach significantly outperforms existing state-of-the-art methods. The source code is available in the supplementary materials. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep neural networks

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Co-evolution-based Metal-binding Residue Prediction with Graph Neural Networks

arXiv

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arXiv 2025年

作者： Rastegari, Sayedmohammadreza Tabakhi, Sina Liu, Xianyuan Sang, Wei Lu, Haiping Faculty of Computer Engineering University of Isfahan Isfahan Iran School of Computer Science Centre for Machine Intelligence University of Sheffield Sheffield United Kingdom School of Basic Medical Sciences Institute of Medical Technology Shanxi Medical University Taiyuan China Institute of Big Data Science and Industry Shanxi University Taiyuan China

In computational structural biology, predicting metal-binding sites and their corresponding metal types is challenging due to the complexity of protein structures and interactions. Conventional sequence- and structure-based prediction approaches cannot capture the complex evolutionary relationships driving these interactions to facilitate understanding, while recent co-evolution-based approaches do not fully consider the entire structure of the co-evolved residue network. In this paper, we introduce MBGNN (Metal-Binding Graph Neural Network) that utilizes the entire co-evolved residue network and effectively captures the complex dependencies within protein structures via graph neural networks to enhance the prediction of co-evolved metal-binding residues and their associated metal types. Experimental results on a public dataset show that MBGNN outperforms existing co-evolution-based metal-binding prediction methods, and it is also competitive against recent sequence-based methods, showing the potential of integrating co-evolutionary insights with advanced machine learning to deepen our understanding of protein-metal interactions. The MBGNN code is publicly available at https://***/SRastegari/MBGNN. Copyright © 2025, The Authors. All rights reserved.

关键词： Graph neural networks

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Sentiment Analysis on Drug Reviews Using Diverse Classification Techniques

Sentiment Analysis on Drug Reviews Using Diverse Classificat...

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Communication, Computing and Industry 4.0 (C2I4), International Conference on

作者： Premkumar Duraisamy Yuvaraj Natarajan K.R.Sri Preethaa K. Mouthami Department of Artificial Intelligence and Data Science KPR Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India Research Professor Intelligent Construction Automation Center Kyungpook National University Deagu South Korea

ISBN: (数字)9781665474979

ISBN: (纸本)9781665474986

Free text reviews are abundantly distributed over the internet among the standard population of the world. Although online doctors were consulted during the pandemic, many drugs are taken without prescription by ordinary people. Reviews are distributed about the medicines through online feeds, posts and blogs, which are merely helpful in identifying the side effects and their adverse reactions in combination with another drug or standalone adverse reactions. Spelling mistakes, jargon and short names are normalized throughout the medical terminologies using Natural language processing (NLP) and RxNorm from the reviews extracted from these sources. The concept of Bidirectional Encoder Representations from Transformers (BERT) is used to mine the association rules for the drug interactions when used in conjugation with another drug, and the idea of Adaptive Fuzzy logic neural networks is used to validate the results of association rules derived from the BERT. Inconsistencies identified by the regulations generated were rejected efficiently by the Support vector machines (SVM), Radial Basis Function (RBF)and the system shows 60.11 percentage of accuracy with 10 cross-fold validation, whereas the results are equivalent to the accuracy obtained using SVM with RBF kernel. The data set is reduced from its dimension using Linear discriminant analysis (LDA) by applying the mining process and BERT on two drugs, namely “Azithromycin” in conjugation with Buprenorphine has Type ‘A’ side effects, and Prozac in conjugation with Metronidazole shows different side effects. Deplin and Zoloft give very positive symptoms and are independent of dosage interactions.

关键词： Support vector machines Drugs Analytical models Terminology Bit error rate Support vector machine classification Transformers

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