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IEEE Transactions on Sustainable Computing 2025年第3期10卷 439-450页

作者： Li, Xiaocan Wen, Jigang Xie, Kun Xie, Gaogang Liang, Wei Hunan University College of Computer Science Electronics Engineering Changsha410012 China Ministry of Education Key Laboratory of Supercomputing Artificial Intelligence Fusion Computing Changsha410012 China School of Computer Science Engineering Hunan University of Science Technology Xiangtan411199 China Chinese Academy of Sciences Computer Network Information Center Beijing411199 China

Accurately detecting traffic anomalies becomes increasingly crucial in network management. Algorithms that model the traffic data as a matrix suffers from low detection accuracy, while the work using the tensor model often assumes the tensor is regular without considering that network nodes may dynamically join in or leave, which will fail in a practical network with the change of node set as a result of mobility and churn behaviors. We propose a novel Tensor Recovery scheme in a Dynamic Network (TRDN) with traffic data modeled as a practical irregular tensor for accurate anomaly detection. To take advantage of correlations among small tensors, each formed with a short time duration to capture more hidden information in the data for higher detection accuracy, we propose several novel techniques: 1) a new joint tensor factorization model to capture the characteristic shared by the common nodes of small tensors, 2) a tensor partition algorithm to identify the data that can be applied to train the shared parameters efficiently, and 3) a bar-based algorithm that partitions nodes into the minimum number of no-overlapping subsets to form the shared tensor model. Extensive experiments on two Internet traffic data sets, Abilene and GÈANT, demonstrate the effectiveness of the proposed TRDN. © 2016 IEEE.

关键词： Data accuracy

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Youtube Comment Spam Detection

Youtube Comment Spam Detection

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Nagaraj, P. Sudar, K. Muthamil Thrived, P. Reddy, P Girish Kumar Baji Babu, S.K. Krishna, P Siva Rama Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Virudhnagar India Mepco Schlenk Engineering College Department of Computer Science and Engineering Virudhnagar India

ISBN: (纸本)9798350348217

YouTube is the most commonly used application these days. There are both good users and bad users who use youtube. Spammers have realized that these types of social media are easier to lure the users into malicious activities. In the current project, spam detection was performed on selected youtube comments and we predict the entered comment as 'SPAM' or 'NOT SPAM'. Google Safe Search and Youtube Bookmaker are two tools that can be used to block harmful links;however, they cannot immediately safeguard real-time users. Four Artificial Intelligence approaches have been employed by victimization to run the survey for the spam comment detection methodology: Call tree, Ada Boost, logistic regression, and Random Forest. We shall achieve a 91.65 percent associate degree of accuracy by using neural networks and also to beat the current action course by around 18%. The difficulty of spam and the efficiency of the most well-known AI approaches (SVMs, Bayesian representation, ANNs, k-NN). © 2023 IEEE.

关键词： Social networking (online)

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IoT in Smart Homes: Enhancing Lifestyle Through Intelligent Automation and Control 3

IoT in Smart Homes: Enhancing Lifestyle Through Intelligent ...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Sarada, W. Prashanth, K.V. Mary, A.Beneta Premkumar, K. Vijayan, Vinodh P. Radha, G. Rbvrr Women's College Department of Computer Science Narayanaguda Telangana Hyderabad India Koneru Lakshmaiah Education Foundation Department of Electronics & Communication Engineering Telangana Hyderabad India St.Joseph College of Engineering Department of Computer Science and Engineering Kancheepuram India St. Joseph College of Engineering Department of Cse Chennai India Mangalam College of Engineering Kerala Kottayam India Department of Computer Science and Engineering Rathinam Technical Campus Coimbatore India

ISBN: (纸本)9798331542375

Internet of Things (IoT) functionality integrated into smart homes has transformed residents' ways of engaging with homes, from comfort, security, and energy saving aspects. This study aims at understanding the role played by IoT based smart automation and control in enhancing people's living standards within homes. Smart homes are designed utilizing applications that centralize the control of other smart devices like smart manage thermostat, smart lighting, home security cameras and smart voice assistant. These systems allow people to automate jobs, conserve and manage energy, and design environments to people's specific needs. Moreover, the study investigates threat mechanisms of IoT in smart home with respect to data privacy issues as well as the weaknesses of devices and the possibility of cyber threats. One of the major considerations of this work is focused on ways through which IoT systems can be designed to learn about users and their requirements as well as make decisions on their own using machine learning techniques. Thus, the study shows that through IoT technology not only by providing convenience and energy-saving opportunities, but also giving homeowners control to create and establish safer and more comfortable home environment. In this research, recommendations are made for the need to bring improvement on the reliability, security, and accessibility of IoT in home automation market it has made. © 2025 IEEE.

关键词： Energy efficiency

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Using Computational Models to Detect Autistic Tendencies for Children from their Story Book Narratives

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Multimedia Tools and Applications 2025年第6期84卷 3175-3196页

作者： Sun, Ruihan Wong, Jasin Chen, Eva E. Chen, Arbee L. P. Department of Computer Science National Tsing Hua University Hsinchu Taiwan Department of Special Education National Tsing Hua University Hsinchu Taiwan College of Education National Tsing Hua University Hsinchu Taiwan Department of Computer Science and Information Engineering Asia University Taichung Japan

Diagnosing autism spectrum disorder (ASD) conventionally demands significant time and resources. Language deficits are key markers of ASD, particularly in constructing narratives. This study leverages computational models to analyze story book narratives from seven children with ASD and 16 typically-developing (TD) peers. By transcribing and training models on limited data using augmentation techniques, our best model achieved over 90% accuracy, sensitivity, and specificity-outperforming previous models by 20% in ASD detection. This research showcases the efficacy of our approach in efficiently assessing language abilities and identifying ASD tendencies. The method holds promise for enhancing diagnostic efficiency and providing comprehensive language evaluations to support children with ASD and their caregivers. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Deep neural networks

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Medical Image Recognition for Diagnosis 15

Medical Image Recognition for Diagnosis

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Esukapalli, Vinesh Reddy Velmurugan, Sathya Narayanan Preethi, S. Kammara, Chaithanya Mungamuru, Divakar Settipalli, Harsha Vardhan Kalasalingam Academy of Research and Education Virudhunagar Department of Computer Science and Engineering India Kalasalingam Academy of Research and Education Virudhunagar Department of CSE India Kongu Engineering College Erode Department of Electronics and Communication Engineering India

ISBN: (纸本)9798350370249

Medical image recognition with Android Studio is a cutting-edge field that uses mobile technologies to better diagnosis and patient care. The objective of this research project is to develop an accurate, dependable, and userfriendly Android application for analyzing and interpreting medical images, such as CT, MRI, and X-ray scans. The system will employ a variety of deep learning algorithms, including Convolutional Neural Networks (CNNs), to detect irregularities and anomalies in the images. This will identify irregularities and abnormalities in the photos, which will assist medical experts in making critical judgments. A user-friendly interface will be part of the application, enabling fast diagnosis feedback, real-time processing, and picture input. By increasing diagnosis speed and accuracy, this state-of-The-Art Android-based medical photo recognition app has the potential to revolutionize healthcare delivery. Modern healthcare now relies heavily on the ability to identify medical images, as this enables more accurate diagnosis and treatment planning. The Proposed method CNN Shows accuracy of 98.3% for Medical Image Recognition for Diagnosis. © 2024 IEEE.

关键词： Convolutional neural networks

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A self-driving solution for resource-constrained autonomous vehicles in parked areas

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High-Confidence Computing 2024年第1期4卷 32-42页

作者： Jin Qian Liang Zhang Qiwei Huang Xinyi Liu Xiaoshuang Xing Xuehan Li College of Information Engineering Taizhou UniversityTaizhou 225300China Engineering Research Center of Integration and Application of Digital Learning Technology Ministry of EducationBeijing 100039China School of Computer Science and Engineering Changshu Institute of TechnologyChangshu 215500China The School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing 100044China

Autonomous vehicles in industrial parks can provide intelligent,efficient,and environmentally friendly transportation services,making them crucial tools for solving internal transportation *** the characteristics of industrial park scenarios and limited resources,designing and implementing autonomous driving solutions for autonomous vehicles in these areas has become a research *** paper proposes an efficient autonomous driving solution based on path planning,target recognition,and driving decision-making as its core *** designs for path planning,lane positioning,driving decision-making,and anti-collision algorithms are *** analysis and experimental validation of the proposed solution demonstrate its effectiveness in meeting the autonomous driving needs within resource-constrained environments in industrial *** solution provides important references for enhancing the performance of autonomous vehicles in these areas.

关键词： Autonomous vehicles Path planning Target recognition Driving decision-making Self-driving

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A Resourceful Energy Management and Improved Security in Wireless Sensor Networks by Optimized Energy-Based Security Prototype

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SN computer Science 2025年第3期6卷 1-13页

作者： Arya, Nancy Deore, Shailesh Shivaji Sharma, Mahendra Verma, Amit Patibandla, R. S. M. Lakshmi Krishna, K. Murali Pareek, Piyush Kumar Department of Computer Science and Engineering School of Engineering & Sciences GD Goenka University Sohna Gurugram India Department of Computer Engineering SSVPS B S DEORE College of Engineering Maharashtra Dhule India DoIT IMT College of Engineering UP Greater Noida India Department of Computer Science University Centre for Research and Development Chandigarh University Gharuan Punjab Mohali India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Department of Electronics Communication Engineering Rajalakshmi Engineering College TN Chennai India Department of AIML and IPR Cell Nitte Meenakshi Institute of Technology Bengaluru India

One must admit that while using a number of remote sensing points with limited nodes, it is crucial to optimize resource usage as intensely as possible and have a secure network with the least compromises, even though WSNs are applied in increasing numbers of industries. When deploying WSN, power utilization and ability to withstand a number of threats and attacks are important factors to be considered. This work presents a technique for a routing protocol that determines the shortest path between two nodes in a network while at the same time trying to conserve energy. Lifetime and energy consumption of the networks are two important factors when designing new protocols for WSN. An important problem of finding a load-sharing routing method with less energy consumption is difficult. TBEBR routing protocol optimizes the parameters that lead to minimal energy dissipation to enhance the lifetime of the network by identifying the path with the least energy consumption possible. Throughput, overhead, packet loss and end to end delay were used as performance parameters to assess the routing protocol. The analysis of the overall efficiency of the routing concept as well as the method of its application was described. As far as the performance of network and nodes is concerned, it is clear that TBEBR outperforms both DSR and AODV. For WSNs, the proposed routing protocol aims at minimizing the energy issue, which is a disadvantage of the technology. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： DoS attack EWMA Unit energy requirement WSN

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OptiFog: A Framework for Acquiring State Information and Predicting Resource Availability for Task Offloading in Cooperative Fog-Networks

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IEEE Transactions on Services Computing 2024年 1-13页

作者： Alam, Mehbub Ahmed, Nurzaman Ghosh, Shyamal Matam, Rakesh Barbhuiya, Ferdous Ahmed Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati India Department of Computer Science Dartmouth College Hanover USA School of Data Science Indian Institute of Science Education and Research Thiruvananthapuram India

The primary objective of fog computing is to minimize the reliance of IoT devices on the cloud by leveraging the resources of fog network. Typically, IoT devices offload computation tasks to fog to meet different task requirements such as latency in task execution, computation costs, etc. So, selecting such a fog node that meets task requirements is a crucial challenge. To choose an optimal fog node, access to each node's resource availability information is essential. Existing approaches often assume state availability or depend on a subset of state information to design mechanisms tailored to different task requirements. In this paper, OptiFog: a cluster-based fog computing architecture for acquiring the state information followed by optimal fog node selection and task offloading mechanism is proposed. Additionally, a continuous time Markov chain based stochastic model for predicting the resource availability on fog nodes is proposed. This model prevents the need to frequently synchronize the resource availability status of fog nodes, and allows to maintain an updated state information. Extensive simulation results show that OptiFog lowers task execution latency considerably, and schedules almost all the tasks at the fog layer compared to the existing state-of-the-art. IEEE

关键词： computer architecture

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AAV-Assisted Joint Mobile Edge Computing and Data Collection via Matching-Enabled Deep Reinforcement Learning

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IEEE Internet of Things Journal 2025年第12期12卷 19782-19800页

作者： Wang, Boxiong Kang, Hui Li, Jiahui Sun, Geng Sun, Zemin Wang, Jiacheng Niyato, Dusit Jilin University College of Computer Science and Technology Changchun130012 China Jilin University College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China Nanyang Technological University College of Computing and Data Science Jurong West 639798 Singapore

Autonomous aerial vehicle (AAV)-assisted mobile edge computing (MEC) and data collection (DC) have been popular research issues. Different from existing works that consider MEC and DC scenarios separately, this article investigates a multi-AAV-assisted joint MEC-DC system. Specifically, we formulate a joint optimization problem to minimize the MEC latency and maximize the collected data volume. This problem can be classified as a nonconvex mixed integer programming problem that exhibits long-term optimization and dynamics. Thus, we propose a deep reinforcement learning-based approach that jointly optimizes the AAV movement, user transmit power, and user association in real time to solve the problem efficiently. Specifically, we reformulate the optimization problem into an action space-reduced Markov decision process (MDP) and optimize the user association by using a two-phase matching-based association (TMA) strategy. Subsequently, we propose a soft actor-critic (SAC)-based approach that integrates the proposed TMA strategy (SAC-TMA) to solve the formulated joint optimization problem collaboratively. Simulation results demonstrate that the proposed SAC-TMA is able to coordinate the two subsystems and can effectively reduce the system latency and improve the DC volume compared with other benchmark algorithms. © 2014 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Optimization of IoT Devices in Smart Home to Minimize Cost Energy Conservation Based on Human Detector 1st

Optimization of IoT Devices in Smart Home to Minimize Cost E...

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1st International Conference on Data engineering and Machine Intelligence, ICDEMI 2023

作者： Muthuvel, P. Raja Sekar, R. Rajkumar, T. Dhiliphan Mari Selvan, R. Department of Electronics and Communication Engineering Saveetha Engineering College Saveetha Nagar Thandalam India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Virudhunagar Krishnankoil India

ISBN: (纸本)9789819776153

Smart home systems are becoming an exciting new paradigm among researchers and homeowners. In modern society, the use of electrical appliances is an essential element for a comfortable life. However, in many classrooms or dorms, students may waste energy by leaving lighting and air conditioning systems on even when not in use and an optimized distribution of the used sensors is required for the resource-efficient (indoor) location of mobile humans. Indoor positioning, or the location of beacons or sensors inside buildings, may be a challenging and time-consuming process, particularly when done by hand. This topic has already been attempted to be solved by a number of researchers under various use cases and using various approaches. Nevertheless, none of the approaches that are now available use Boolean variables alone to do constraint programming. To address this issue, we are creating a smart IoT system that saves energy by turning on lights and fans when motion is detected in a specific part of a room. To be more precise, we want to optimize the beacons for an indoor locating system that uses trilateration. According to this method, three beacons have to be within range of each potential location inside the structure. The initially determined beacon placements are then further optimized in a subsequent stage. In order to do this, fewer beacons are used. Next, we assess the beacon placements’ quality by comparing them to a manually optimized beacon placement and assessing the quality of many sample places in an existing building. This system uses a PIR sensor for motion detection which makes it cost efficient. The electrical switch is controlled by a5V-1 channel relay connected to an Arduino board in the switchboard. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Smart homes

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