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International Conference on Contemporary Computing and informatics (IC3I)

作者： Bediga Sharan Umar Mubarak Mulani R. Naveenkumar Sujit Kumar Badodia Hemant Makawana Joshuva Arockia Dhanraj School of Computer Science & Artificial Intelligence SR University Warangal Telangana India Computer Engineering KJ College of Engineering and Management Research Pune Maharashtra India Department of Computer Science and Engineering Chandigarh Colleges of Engineering Chandigarh Group of Colleges Jhanjeri Mohali Punjab India Devi Ahilya Vishvavidyalaya Institute of Engineering and Technology Indore Madhya Pradesh India Department of Computer Science and Engineering (AI&ML) School of Engineering Dayananda Sagar University Devarakaggalahalli Bengaluru Karnataka India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Synthetic news production, language translation, and text summarization have all seen significant success with sophisticated text generating techniques. Although neural model-generated false news detection has advanced recently, the results are not easily transferable to the efficient identification of misinformation created by humans. The significant contrasts between fake news produced by machines and those written by humans, including style and underlying intentions, are what prevents them from successfully transferring. In light of this, we provide a brand-new framework for creating training cases that draw inspiration from the well-known techniques and styles of propaganda written by people. But these methods might be misused to spread false information. researchers create superior news material using a unique generation technique called FACTGEN in order to gain a greater understanding the possible risks associated with synthetic news. Because most text generation techniques now in use either provide very little more information or break continuity across the inputs and outputs, the synthetic news becomes less reliable. The results of experiments conducted on real-world datasets show that FACTGEN generates news items that are bursting with data and accurate.

关键词： Training Translation Accuracy Text summarization Production Natural language processing Reliability Fake news informatics

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Bilingual Chatbot Powered by Artificial Intelligence for academic Advice

Bilingual Chatbot Powered by Artificial Intelligence for Aca...

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Information Technologies and Communications (GCITC), Global Conference on

作者： Fred Torres-Cruz Leonel Coyla-Idme Juan C. Juarez-Vargas Rajesh Singh Maloth Upender Dhiraj Kapila Academic Department of Statistics and Computer Engineering Universidad Nacional del Altiplano de Puno Puno Perú Uttaranchal Institute of Technology Uttaranchal University India Department of English Faculty of Science and Technology (IcfaiTech) ICFAI Foundation for Higher Education Hyderabad India Department of Computer Science & Engineering Lovely Professional University Phagwara Punjab India

The role of academic advising in advancing institutions' educational goals is becoming more widely recognised. Utilizing technology tools like Canvas and Degree Works, universities have made considerable investments in assisting instructional advisers and student success measures. The demand for innovation that can support academic advisors in their duty to direct students toward academic achievement is growing, and AI tools that encourage the effective use of human capital are well-placed to fill that requirement. Functions for chatbots can be used as technology-mediated assistance to give knowledge and engaging content to help students succeed. This project set out to create, build, and assess an automated, AI-enabled advising resource (the AVA chatbot) for use in an honour’s college setting to provide academic counselling and university-related information. The researchers came to the conclusion that chatbots could assist educational universities in efficiently supporting their student populations by giving 24/7 advice, following and evaluating total assistance, and identifying data perceptions in real time. The consequences of this research can be helpful for chat application design and manufacturing as well as technology adoption in higher education.

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A Development of Object Detection System based on Deep Learning Approach to Support the Laparoscope Manipulating Robot (LMR)

A Development of Object Detection System based on Deep Learn...

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2022 International Electrical engineering Congress, iEECON 2022

作者： Sonsilphong, Suphachoke Sonsilphong, Amornthep Hormdee, Daranee Khampitak, Kovit Khon Kaen University Data Science Health Informatics Program Academic Affairs Faculty of Medicine Khon Kaen Thailand Rajamangala University of Technology Isan Department of Electrical Engineering Khon Kean Thailand Khon Kaen University Embedded System RD Group Department of Computer Engineering Faculty of Engineering Khon Kean Thailand Khon Kean University Faculty of Medicine Department of Obstetrics and Gynecology Khon Kean Thailand

ISBN: (纸本)9781665402064

This paper presents the development of an object detection system based on the deep learning approach of computer vision to support the laparoscopic surgical robotic position control system. The system comprises two main phases, the training phase, and the real-time operating phase. In the training phase, a modified YOLOv4 algorithm is proposed to be adopted within the Darknet framework for training the weights of detection models. The best detection model is then selected to be assigned to the object detector in the real-time operating phase. The validation result of the detection model across the experiment datasets is 90% accuracy on average during the training phase. To validate the viability of real-time object detection, the proposed system was implemented in two test-cases of gynecologic surgery with the soft-body cadavers. © 2022 IEEE.

关键词： Laparoscopy

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University department Recommendations Using Subject-Score-Based Skyline Queries

University Department Recommendations Using Subject-Score-Ba...

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Information technology and Computing (ICITCOM), International Conference on

作者： Cahya Damarjati Galang Wicaksana Slamet Suripto Heri Wijayanto Haris Setyawan Hsing-Chung Chen Department of Information Technology Universitas Muhammadiyah Yogyakarta Yogyakarta Indonesia Department of Electrical Engineering Universitas Muhammadiyah Yogyakarta Yogyakarta Indonesia Department of Informatics Engineering Universitas Mataram Mataram Indonesia Department of Computer Science and Information Engineering Department of Medical Research Asia University China Medical University Taichung Taiwan

ISBN: (数字)9798350379839

ISBN: (纸本)9798350379846

Selecting the right university department is a critical decision-making problem for high school graduates, significantly influenced by limited guidance and personal preferences. Traditional counseling methods often do not fully utilize available academic data, resulting in less effective recommendations. This study proposed a Subject-Score-Based Skyline Queries Algorithm to enhance decision-making by recommending university departments based on candidates' high school subject scores. It used the score differences and minimum score rule in addition to the native Skyline Queries. The Algorithm identifies departments where a candidate's grades meet or exceed minimum requirements across multiple academic subjects, thereby suggesting optimal matches. This approach aims to mitigate uncertainty and enhance decision confidence among prospective students, potentially reducing dropout rates and improving academic outcomes. Experimental results demonstrate the method's effectiveness in providing personalized recommendations, underscoring its applicability beyond university admissions to various decision-making contexts. Future research could extend this approach by incorporating non-academic factors for more comprehensive guidance.

关键词： Employee welfare Economics Uncertainty Accuracy Decision making Optimal matching Information technology

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Prediction of Heat Energy Release Rate for Ammonia Combustion in a Constant Volume Combustion Chamber: A Machine Learning Approach 15

Prediction of Heat Energy Release Rate for Ammonia Combustio...

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15th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2023

作者： Fang, Lijia Singh, Hardeep Ichiyanagi, Mitsuhisa Suzuki, Takashi Raboaca, Maria Simona Filote, Constantin Verma, Chaman Sophia University Department of Engineering and Applied Sciences Tokyo Japan University of Windsor Turbulence and Energy Laboratory Windsor Canada Institute for Cryogenics and Isotopic Technologies Icsi Energy Department National Research and Development Ramnicu Valcea Romania Stefan Cel Mare University of Suceava Faculty of Electrical Engineering and Computer Science Suceava Romania Eötvös Loránd University Faculty of Informatics Budapest Hungary

ISBN: (纸本)9798350321388

Addressing global warming requires immediate and concerted action. Transitioning to non-conventional energy sources, such as solar and Aeolian power, reducing emissions from transportation and industry, and implementing sustainable land-use practices are crucial steps. Therefore, Ammonia attracts researchers when used as a fuel, and offers several potential benefits such as higher energy density. However, due to poor combustion speed of Ammonia offers slower energy release. To enhance the average flame velocity of Ammonia, the current investigation employed the exhaust gas expelled from the secondary chamber to compress the mixture within the primary chamber and facilitate its HCCI combustion. The main goal of research is the optimization on air-fuel ER to contribute to the development of more efficient and sustainable constant volume combustion. However, the time required for constructing physical prototypes and conducting experiments can be considerable, thus, to mitigate these challenges, the authors turned to machine learning tools as an alternative approach. In the proposed research, results were evaluated through Support Vector Regression. However, due to poor prediction accuracy, authors tried KNeighborsRegressor model for predicting and validating the results. Finally, it is revealed from the KNN model that data sets are highly accurate. © 2023 IEEE.

关键词： Ammonia

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Medical Diagnostic System using Embedding JADE in jCOLIBRI

Medical Diagnostic System using Embedding JADE in jCOLIBRI

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Electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： Jagjit Singh Dhatterwal Kiran Malik Purushottam Das Kuldeep Singh Kaswan Department of Artificial Intelligence & Data Science Koneru Lakshmaiah Education Foundation Vaddeswaram AP India Department of Computer Science & Engineering (AIML) GL Bajaj Institute of Technology & Management Greater Noida India Department of Computer Science & Engineering Graphic Era Hill University Research Scholar Graphic Era Deemed to be University Dehradun India Department of Computer Science & Engineering Galgotias University Greater Noida India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

Case-based reasoning (CBR) methodology presents a foundation for a new technology of building intelligent computer-aided diagnoses systems. This technology directly addresses the problems found in the traditional Artificial Intelligence (AI) techniques, e.g., the problems of knowledge acquisition, remembering, robustness and maintenance. This paper discusses the CBR methodology, the research issues and the technical aspects of implementing intelligent medical diagnoses systems. It develops a celebrative framework of MAS-CBR to criticize the medical problem to get the best treatment against dengue disease. This paper also embedding the framework of JADE and jCOLIBRI to improve the quality of decision- making in the medical diagnosis field.

关键词： Knowledge acquisition Knowledge based systems System integration Cognition Robustness Maintenance Medical diagnosis informatics Medical diagnostic imaging Viruses (medical)

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Accelerating Stable Matching between Workers and Spatial-Temporal Tasks for Dynamic MCS: A Stagewise Service Trading Approach

arXiv

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arXiv 2025年

作者： Qi, Houyi Liwang, Minghui Wang, Xianbin Fu, Liqun Hong, Yiguang Li, Li Cheng, Zhipeng School of Informatics Xiamen University Fujian China Department of Control Science and Engineering Canada Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China Department of Electrical and Computer Engineering Western University ON Canada School of Future Science and Engineering Soochow University Jiangsu China

Designing proper incentives in mobile crowdsensing (MCS) networks represents a critical mechanism in engaging distributed mobile users (workers) to contribute heterogeneous data for diverse applications (tasks). We develop a novel stagewise trading framework to reach efficient and stable matching between tasks and workers, upon considering the diversity of tasks and the dynamism of MCS networks. This framework integrates futures and spot trading stages, where in the former, we propose futures trading-driven stable matching and pre-path-planning (FT-SMP3) for long-term task-worker assignment and pre-planning of workers’ paths based on historical statistics and risk analysis. While in the latter, we investigate spot trading-driven DQN path planning and onsite worker recruitment (ST-DP2WR) mechanism to enhance workers’ and tasks’ practical utilities by facilitating temporary worker recruitment. We prove that our proposed mechanisms support crucial properties such as stability, individual rationality, competitive equilibrium, and weak Pareto optimality theoretically. Also, comprehensive evaluations confirm the satisfaction of these properties in practical network settings, demonstrating our commendable performance in terms of service quality, running time, and decision-making overheads. Copyright © 2025, The Authors. All rights reserved.

关键词： Risk analysis

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What Improves the Generalization of Graph Transformers? A Theoretical Dive into the Self-attention and Positional Encoding

arXiv

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arXiv 2024年

作者： Li, Hongkang Wang, Meng Ma, Tengfei Liu, Sijia Zhang, Zaixi Chen, Pin-Yu Department of Electrical Computer and System Engineering Rensselaer Polytechnic Institute TroyNY United States Department of Biomedical Informatics Stony Brook University Stony Brook NY United States Department of Computer Science and Engineering Michigan State University East LansingMI United States MIT-IBM Watson AI Lab IBM Research MA United States Department of Computer Science and Engineering University of Science and Technology of China Anhui Hefei China IBM Thomas J. Watson Research Center Yorktown Heights NY United States

Graph Transformers, which incorporate self-attention and positional encoding, have recently emerged as a powerful architecture for various graph learning tasks. Despite their impressive performance, the complex non-convex interactions across layers and the recursive graph structure have made it challenging to establish a theoretical foundation for learning and generalization. This study introduces the first theoretical investigation of a shallow Graph Transformer for semi-supervised node classification, comprising a self-attention layer with relative positional encoding and a two-layer perceptron. Focusing on a graph data model with discriminative nodes that determine node labels and non-discriminative nodes that are class-irrelevant, we characterize the sample complexity required to achieve a desirable generalization error by training with stochastic gradient descent (SGD). This paper provides the quantitative characterization of the sample complexity and number of iterations for convergence dependent on the fraction of discriminative nodes, the dominant patterns, and the initial model errors. Furthermore, we demonstrate that self-attention and positional encoding enhance generalization by making the attention map sparse and promoting the core neighborhood during training, which explains the superior feature representation of Graph Transformers. Our theoretical results are supported by empirical experiments on synthetic and real-world benchmarks. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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Non-Orthogonal Multiple Access Based Multi-Objective Optimization in Emergency Task Offloading

Non-Orthogonal Multiple Access Based Multi-Objective Optimiz...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Chen, Caijuan Zhang, Chaofeng Li, Gaolei Liu, Yuchen National Institute of Informatics The Information Systems Architecture Science Research Division Tokyo Japan Advanced Institute of Industrial Technology Tokyo Japan School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Nc State University Department of Computer Science NC United States

ISBN: (纸本)9798350368369

The escalating frequency of geological disasters, local conflicts, and public health crises necessitates robust emergency communication systems within Internet of Things (IoT) frameworks to ensure public safety. Employing Power-Domain NOMA (PD-NOMA) and Successive Interference Cancellation (SIC) enables effective multi-device communication that is crucial for emergency responses. However, the practical implementation of NOMA technologies in emergency scenarios is hindered by existing resource constraints and the requirement of prioritizing critical emergency information. To address these limitations, we propose a multi-objective optimization-based NOMA framework to maximize network throughput while ensuring quality of service (QoS) and energy efficiency. This framework incorporates a novel communication grouping algorithm based on queue penalty optimization and an Actor-Critic-based dynamic updating algorithm for multiple resource allocation. Our study proposes a practical approach to enhance emergency communication in disaster areas, ensuring efficient and reliable network performance under various constraints. © 2025 IEEE.

关键词： Multiobjective optimization

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Resource Provisioning Techniques in Multi-Access Edge Computing Environments: Outlook, Expression, and Beyond

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Mobile Information Systems 2022年第1期2022卷

作者： Durga, S. Daniel, Esther Onesimu, J. Andrew Sei, Yuichi Department of Information Technology Sri Krishna College of Engineering and Technology Coimbatore641008 India Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore641114 India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal576104 India Department of Informatics The University of Electro-Communications Chofu1828585 Japan

Mobile cloud computing promises a research foundation in information and communication technology (ICT). Multi-access edge computing is an intermediate solution that reduces latency by delivering cloud computing services close to IoT and mobile clients (MCs), hence addressing the performance issues of mobile cloud computing. However, the provisioning of resources is a significant and challenging process in mobile cloud-based environments as it organizes the heterogeneous sensing and processing capacities to provide the customers with an elastic pool of resources. Resource provisioning techniques must meet quality of service (QoS) considerations such as availability, responsiveness, and reliability to avoid service-level agreement (SLA) breaches. This investigation is essential because of the unpredictable change in service demands from diverse regions and the limits of MEC's available computing resources. In this study, resource provisioning approaches for mobile cloud computing are thoroughly and comparatively studied and classified as taxonomies of previous research. The paper concludes with an insightful summary that gives recommendations for future enhancements. © 2022 S. Durga et al.

关键词： Quality of service

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