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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Yuvaraj, S. Chenni Kumaran, J. Vinston Raja, R. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology School of Computing Department of Computational Intelligence Kattankulathur Chennai India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529246

The investigation aims to evaluate the performance of machine learning techniques, particularly the XGBoost regression method, for stock price prediction with the help of technical indicators. The research targets the adjusted closing price to reduce the autocorrelation problem in time series. The Five Days and Ten Days Exponential Moving Averages (EMA-5 and EMA-10) are involved in refining feature selection. The performance of the model was evaluated using different error metrics, namely, Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R2). The results indicate that the XGBoost model, combined with EMA-5 and EMA-10, achieves high predictive performance, obtaining an r2 value of 0.98. It implies that incorporating short-term EMAs into advanced machine learning techniques drastically improves stock price forecasting. © 2025 IEEE.

关键词： Time series

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Community Hiding Algorithm Based on Likelihood Analysis Method in Link Prediction 22

Community Hiding Algorithm Based on Likelihood Analysis Meth...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Wang, Ya-Si Shi, Lei-Lei Han, Zi-Xuan Liu, Lu Song, Siyang Chen, Liming Jiangsu University School of Computer Science and Communication Engineering China University of Leicester School of Computing and Mathematical Sciences United Kingdom Dalian University of Technology School of Computer Science and Technology China

ISBN: (纸本)9798331509712

In recent years, the rapid development and widespread applications of non-overlapping community discovery have led to a growing issue of privacy leakage. The core of this problem lies in the community discovery algorithms themselves. Researchers have shifted their focus toward developing community hiding algorithms. However, existing non-overlapping community hiding algorithms have mainly been studied in static networks, overlooking the prevalence of dynamic networks in real life. In this paper, we introduce a community hiding algorithm called LALH, which employs the link prediction likelihood analysis method. The algorithm consists of two parts: first, the OLPL algorithm calculates a list of link probabilities in the subsequent time scale of the network, allowing observation of important link distribution. Second, the LALH algorithm selects these crucial links for the community hiding operation. We validate the effectiveness of the LALH algorithm through experiments on three public datasets and a real Twitter dataset. © 2024 IEEE.

关键词： Consensus algorithm

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Weighted Fuzzy System for Identifying DNA N4-Methylcytosine Sites With Kernel Entropy Component Analysis

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第2期5卷 895-903页

作者： Wang, Leyao Tiwari, Prayag Ding, Yijie Guo, Fei College of Intelligence and Computing Tianjin University Tianjin300350 China School of Information Technology Halmstad University Halmstad30118 Sweden University of Electronic Science and Technology of China Quzhou324000 China School of Computer Science and Engineering Central South University Changsha410083 China

N4-methylcytosine (4mC) is a common DNA methylation that has been implicated in epigenetic regulation and host defense. Accurate prediction of 4mC sites in DNA sequences will help to better explore the biological processes and mechanisms. For this problem, computational methods based on machine learning and deep learning are faster, less complex, and less expensive than experimental detection methods. However, the existing computational methods are still unsatisfactory in terms of prediction accuracy, so we propose a new method with better performance. In this work, we propose a weighted fuzzy system for identifying DNA 4mC sites by kernel entropy component analysis (KECA). We named it as W-TSK-FS-KECA. This model is improved based on the Takagi-Sugeuo-Kang fuzzy system (TSK-FS). We use position-specific trinucleotide propensity to construct feature vectors on representative benchmark datasets. Then we use KECA to get the reconstruct error. Finally, we put the calculated reconstruction error add to the regular term of TSK-FS as the weights to enhance the model performance. Comparative experiments with other methods show that it has good classification perfor- mance. © 2023 IEEE.

关键词： Entropy

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Large Language Model for Medical Images:A Survey of Taxonomy,Systematic Review,and Future Trends

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Big Data Mining and Analytics 2025年第2期8卷 496-517页

作者： Peng Wang Wenpeng Lu Chunlin Lu Ruoxi Zhou Min Li Libo Qin School of Computer Science and Engineering Central South UniversityChangsha 410083China Key Laboratory of Computing Power Network and Information Security Affiliated with Ministry of Education Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353China

The advent of Large Language Models(LLMs)has sparked considerable interest in the medical image domain,as they can generalize to multiple tasks and offer outstanding *** LLMs achieve promising results,there is currently a lack of a comprehensive summary of medical images,making it challenging for researchers to understand the progress within this *** fill this gap,we make the first attempt to present a comprehensive survey for LLM on medical *** addition,to better summarize the current progress comprehensively,we further introduce a novel x-stage tuning paradigm for summarization,including zero-stage tuning,one-stage tuning,and multi-stage tuning,offering a unified perspective on LLMs for medical ***,we discuss challenges and future directions in this domain,aiming to spur more breakthroughs in the *** hope this work can pave the way for the broad application of LLMs in medical images and provide a valuable resource for this domain.

关键词： Large Language Model(LLM) x-stage tuning medical images

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Multiuser Computation Offloading for Long-Term Sequential Tasks in Mobile Edge computing Environments

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Tsinghua Science and technology 2023年第1期28卷 93-104页

作者： Huanhuan Xu Jingya Zhou Wenqi Wei Baolei Cheng School of Computer Science and Technology Soochow UniversitySuzhou 215006China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214125China Provincial Key Laboratory for Computer Information Processing Technology Soochow UniversitySuzhou 215006China School of Computer Science Georgia Institute of TechnologyAtlantaGA 30332USA

Mobile edge computing has shown its potential in serving emerging latency-sensitive mobile applications in ultra-dense 5G networks via offloading computation workloads from the remote cloud data center to the nearby network ***,current computation offloading studies in the heterogeneous edge environment face multifaceted challenges:Dependencies among computational tasks,resource competition among multiple users,and diverse long-term *** applications typically consist of several functionalities,and one huge category of the applications can be viewed as a series of sequential *** this study,we first proposed a novel multiuser computation offloading framework for long-term sequential ***,we presented a comprehensive analysis of the task offloading process in the framework and formally defined the multiuser sequential task offloading ***,we decoupled the long-term offloading problem into multiple single time slot offloading problems and proposed a novel adaptive method to solve *** further showed the substantial performance advantage of our proposed method on the basis of extensive experiments.

关键词： mobile edge computing sequential tasks computation offloading dependency

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LF-PGVIO: A Visual-Inertial-Odometry Framework for Large Field-of-View Cameras using Points and Geodesic Segments

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-13页

作者： Wang, Ze Yang, Kailun Shi, Hao Zhang, Yufan Xu, Zhijie Gao, Fei Wang, Kaiwei State Key Laboratory of Extreme Photonics and Instrumentation School of Robotics and the National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha China School of Computing and Engineering University of Huddersfield Huddersfield U.K State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China

In this paper, we propose LF-PGVIO, a Visual-Inertial-Odometry (VIO) framework for large Field-of-View (FoV) cameras with a negative plane using points and geodesic segments. The purpose of our research is to unleash the potential of point-line odometry with large-FoV omnidirectional cameras, even for cameras with negative-plane FoV. To achieve this, we propose an Omnidirectional Curve Segment Detection (OCSD) method combined with a camera model which is applicable to images with large distortions, such as panoramic annular images, fisheye images, and various panoramic images. The geodesic segment is sliced into multiple straight-line segments based on the radian and descriptors are extracted and recombined. Descriptor matching establishes the constraint relationship between 3D line segments in multiple frames. In our VIO system, line feature residual is also extended to support large-FoV cameras. Extensive evaluations on public datasets demonstrate the superior accuracy and robustness of LF-PGVIO compared to state-of-the-art methods. The source code will be made publicly available at https://***/flysoaryun/LF-PGVIO. IEEE

关键词： Feature extraction

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Efficient Intelligent Based Compliance, Detection, Tracking, and Proximity Model for Traffic System

Efficient Intelligent Based Compliance, Detection, Tracking,...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Revadala, Abhinay Reddy, Sri Ram Charan Ram Kumar, J. Kumar, Manoj School of Computing SRM Institute of Science and Technology Department of Computing Technologies Chennai India University of Wollongong Faculty of Engineering and Information Sciences Dubai United Arab Emirates

ISBN: (纸本)9798350375237

The Intelligent Traffic Management System (ITMS) is a cutting-edge solution designed to address multiple aspects of traffic management and safety. This advanced system incorporates various technologies to tackle issues such as helmet compliance, traffic signal violations, number plate identification, cell phone usage tracking, and proximity-based police station alerts. By leveraging the power of artificial intelligence and machine learning algorithms, the ITMS can accurately detect and enforce helmet compliance among motorcyclists, ultimately reducing the risk of head injuries in road accidents. Furthermore, the system utilizes advanced video analytics to detect violations of traffic signals, ensuring that motorists adhere to traffic rules and regulations. Additionally, the ITMS employs automatic number plate recognition (ANPR) technology to quickly and efficiently identify vehicles with illegal or expired license plates, enabling law enforcement agencies to take appropriate action. Moreover, the system includes a cell phone usage tracking feature which uses computer vision techniques to identify drivers who are using their phones while driving, contributing to a decrease in distracted driving incidents. Lastly, by introducing proximity-based police station alerts, the ITMS enables prompt response from law enforcement agencies by automatically notifying the nearest police station in the event of a traffic violation or accident. In summary, the Intelligent Traffic Management System provides a comprehensive and holistic approach to traffic management and safety through its features of helmet compliance, traffic signal violation detection, number plate identification, cell phone usage tracking, and proximity-based police station alerts. © 2024 IEEE.

关键词： Traffic signals

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Siamese capsule gorilla troops network-based multimodal sentiment analysis for car reviews

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Soft computing 2024年第13-14期28卷 7627-7647页

作者： Kothuri, Sri Raman RajaLakshmi, N.R. Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R & ampD Institute of Science and Technology Avadi Tamil Nadu Chennai600062 India

Sentiment analysis of online car reviews is a significant process in natural language processing. Numerous car enthusiasts and influencers create video reviews of automobiles on platforms for example YouTube. In realistic settings for sentiment analysis within this context, the task includes evaluating spoken reviews, the video content, and the emotions expressed by both the content creators and the viewers as reflected in the comments section. The vulnerability to facing circumstances in real-world scenarios where critical components are necessary for performing sentiment analysis on multimedia car reviews may unexpectedly be absent. To overcome this issue, siamese capsule gorilla troops network-based multimodal sentiment analysis for car reviews is proposed in this research work. Initially, pre-processing and feature extraction are performed on the audio, video and text information. The feature fusion is performed by enhancing spiking neural networks with geometric attention fusion network. The fused feature is fed as an input to siamese capsule gorilla troops network which classifies the emotion states as valence, arousal, and trustworthiness. Also, the algorithm called gorilla troop optimization (GTO) optimizes the classification error of the proposed siamese capsule network. Finally, this research work assesses high- and low-level features across modalities, introduces a novel enhancing spiking neural networks with geometric attention fusion network, enhances classification accuracy through new audio, video, and text embeddings, and boosts system robustness by integrating incomplete multimodal data. The achieved accuracy, precision, recall, and F-score values are 99%, 98.55%, 98.75%, and 98.02%, respectively, for the active multimodal sentimental analysis in car review (MuSe-CaR) dataset. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Sentiment analysis

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Survey on AI-Enabled Resource Management for 6G Heterogeneous Networks:Recent Research,Challenges,and Future Trends

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Computers, Materials & Continua 2025年第6期83卷 3585-3622页

作者： Hayder Faeq Alhashimi Mhd Nour Hindia Kaharudin Dimyati Effariza Binti Hanafi Feras Zen Alden Faizan Qamar Quang Ngoc Nguyen Centre of Advanced Communication Research and Innovation(ACRI)Department of Electrical EngineeringFaculty of EngineeringUniversity of MalayaKuala Lumpur50603Malaysia School of Computing Sciences College of ComputingInformatics and MathematicsUniversiti TeknologiMaraShah Alam40450Malaysia Centre for Cyber Security Faculty of Information Science and Technology(FTSM)Universiti KebangsaanMalaysia(UKM)Bangi43600Malaysia Faculty of Science and Engineering Waseda UniversityTokyo169-8555Japan Faculty of Telecommunications Posts and Telecommunications Institute of TechnologyHanoi11518Vietnam

The forthcoming 6G wireless networks have great potential for establishing AI-based networks that can enhance end-to-end connection and manage massive data of real-time *** Intelligence(AI)advancements have contributed to the development of several innovative technologies by providing sophisticated specific AI mathematical models such as machine learning models,deep learning models,and hybrid ***,intelligent resource management allows for self-configuration and autonomous decision-making capabilities of AI methods,which in turn improves the performance of 6G ***,6G networks rely substantially on AI methods to manage *** paper comprehensively surveys the recent work of AI methods-based resource management for 6G ***,the AI methods are categorized into Deep Learning(DL),Federated Learning(FL),Reinforcement Learning(RL),and Evolutionary Learning(EL).Then,we analyze the AI approaches according to optimization issues such as user association,channel allocation,power allocation,and mode ***,we provide appropriate solutions to the most significant problems with the existing approaches of AI-based resource ***,various open issues and potential trends related to AI-based resource management applications are *** summary,this survey enables researchers to understand these advancements thoroughly and quickly identify remaining challenges that need further investigation.

关键词： Artificial intelligence(AI) resource management deep learning(DL) federated learning(FL) reinforcement learning(RL) evolutionary learning(EL)

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Design and Development of User-friendly Bi-lingual Translation System Employing Machine Translation 5 Base Deep Learning Neural Network Framework Based NLP 2

Design and Development of User-friendly Bi-lingual Translati...

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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Raman Deep, S. Nagar, Sanjay Shrinivas Upendra, Raje Siddiraju Karthik, R. REVA University School of Computing and Information Technology Karnataka Bengaluru India REVA University School of Electronics and Communication Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350349221

Natural language Processing (NLP) is the fledgling field of computer comprehension and analysis of human language. One of the world's most multilingual and multicultural countries is India. Given that Indians live in different regions and speak different regional languages, the country has the second-highest number of languages in the world. Whereas in India English is offered as the second language. But India uses little English, which creates a communication barrier. It is nearly difficult for the people to fill this language gap by converting one language into another, but it can be accomplished through Machine Translation (MT). The primary goal of this paper is to translate Indian languages using neural networks translating Hindi into English and vice versa, with the help of a Neural Machine Translation (NMT) and sequence to sequence model with an encoder-decoder attention mechanism is put forth. © 2024 IEEE.

关键词： Neural machine translation

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