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Social and Sustainable Innovations in Technology and engineering (SASI-ITE), International Conference on

作者： N Devakirubai Anitha Velu D Sumathi A Prasanth Department of Artificial Intelligence and Data Science R P Sarathy Institute of Technology Salem Tamilnadu India Department of Electronics and Communication Engineering Sri Sairam College of Engineering Bengaluru India Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bengaluru India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798350360790

ISBN: (纸本)9798350360806

In the continually evolving field of vehicular communications, the efficient allocation of time slots for Vehicle-to-Vehicle (V2V) communication is of utmost importance. This work introduces a novel approach that employs a K-Nearest Neighbors (KNN) algorithm to allocate time slots in NR V2X sidelink communications. The allocation is specifically tailored to dynamically adjust to the current density of traffic in real-time. The efficacy of the KNN-based time slot allocation system is assessed through simulating the V2X environment, encompassing a fleet of vehicles. The efficacy of the suggested methodology is assessed in relation to a random allocation technique and it is observed that the suggested strategy notably enhances allocation efficiency, slot utilization, contention window, and communication time. The findings illustrate the capacity of a KNN-based dynamic allocation system to adapt to different levels of traffic congestion, leading to the efficient utilization of network resources and the mitigation of communication latency. Furthermore, the examination of the implementation of priority queues in the allocation process is explored, resulting in enhanced efficiency of the system in scenarios involving a substantial amount of traffic.

关键词： Technological innovation Vehicular ad hoc networks Sidelink Nearest neighbor methods Dynamic scheduling Real-time systems Resource management

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Periodic Sampled-data Consensus for Leader-Following Nonholonomic Mobile Robots

Periodic Sampled-Data Consensus for Leader-Following Nonholo...

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第35届中国控制与决策会议

作者： Shicong Yu Youfeng Su He Cai School of Mathematics and Statistics Fuzhou University College of Computer and Data Science Fuzhou University School of Automation Science and Engineering South China University of Technology

In this paper,a sampled-data controller is designed for the exact discrete-time model of nonholonomic mobile robots,which solves the leader-following consensus *** describe the uniform global asymptotic stability of closed-loop systems with time-varying and periodic characteristics through the generalized Krasovskii-LaSalle Theorem together with the stability theorem of cascaded system.A numerical simulation is used to illustrate our conclusions.

关键词： Nonholonomic mobile robots Sampled-data Leader-following consensus

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Disentangling Tabular data Towards Better One-Class Anomaly Detection 39

Disentangling Tabular Data Towards Better One-Class Anomaly ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Ye, Jianan Tan, Zhaorui Hu, Yijie Yang, Xi Cheng, Guangliang Huang, Kaizhu School of Advanced Technology Xi'an Jiaotong-Liverpool University China School of Electrical Engineering Electronics and Computer Science University of Liverpool United Kingdom Data Science Research Center Duke Kunshan University China

ISBN: (纸本)157735897X

Tabular anomaly detection under the one-class classification setting poses a significant challenge, as it involves accurately conceptualizing "normal" derived exclusively from a single category to discern anomalies from normal data variations. Capturing the intrinsic correlation among attributes within normal samples presents one promising method for learning the concept. To do so, the most recent effort relies on a learnable mask strategy with a reconstruction task. However, this wisdom may suffer from the risk of producing uniform masks, i.e., essentially nothing is masked, leading to less effective correlation learning. To address this issue, we presume that attributes related to others in normal samples can be divided into two non-overlapping and correlated subsets, defined as CorrSets, to capture the intrinsic correlation effectively. Accordingly, we introduce an innovative method that disentangles CorrSets from normal tabular data. To our knowledge, this is a pioneering effort to apply the concept of disentanglement for one-class anomaly detection on tabular data. Extensive experiments on 20 tabular datasets show that our method substantially outperforms the state-of-the-art methods and leads to an average performance improvement of 6.1% on AUC-PR and 2.1% on AUC-ROC. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Towards Seamless Connectivity: Implementing 6G Communication Technologies in Next-Generation Networks 3

Towards Seamless Connectivity: Implementing 6G Communication...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Kalaiselvi, K. Sankar, R. Supriya, S. Kaushik, G. Swamy, Hemanth Devika, M. Saveetha College of Liberal Arts and Sciences SIMATS Department of Data Analytics Tamilnadu Chennai India Chennai Institute of Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India MLR Institute of Technology Department of Electronics and Communication Engineering Hyderabad India Motorola Solutions United States Dhanalakshmi College of Engineering Department of AI& DS Tamil Nadu Chennai India

ISBN: (纸本)9798350354171

Advanced wireless networks are essential given the increase in demand for smart services enabled by the Internet of Everything (IoE). Despite the fact that 5G networks could enable Internet of Everything (IoE)-based services, they can't meet all of the requirements needed to create intelligent applications. In order to overcome the shortcomings of the current infrastructure, there is an increasing demand to investigate the potential of 6G wireless communication technologies. Complex network optimization problems can be resolved by integrating artificial intelligence into sixth generation networks. An increasing user base and support for large-scale data-driven applications are essential given the fast evolving 6G landscape. In addition to highlighting network needs, critical supporting technologies, and a comparison of 5G and 6G networks, this study looks at recent advancements and trends in 6G technology. The report also explores new approaches to 6G connectivity, such as edge computing, backscatter communications, AI-driven IoT networks, and holographic beamforming, all of which are meant to support intelligent communities. In order to achieve the goal of 6Gbased IoT networks, it concludes by outlining potential research avenues. © 2024 IEEE.

关键词： Internet of Everything

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Time-Slotted On-Demand Predictive Routing for UAV Networks

Time-Slotted On-Demand Predictive Routing for UAV Networks

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IEEE Conference on Wireless Communications and Networking

作者： Houze Feng Jingjing Wang Jianrui Chen Xiangwang Hou Jiacheng Wang Geng Sun Dusit Niyato School of Cyber Science and Technology Beihang University Beijing China Department of Electronic Engineering Tsinghua University Beijing China College of Computing and Data Science Nanyang Technological University Singapore College of Computer Science and Technology Jilin University Changchun China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Flying ad hoc networks (FANETs) composed of small unmanned aerial vehicles (UAVs) are flexible, inexpensive, and fast to deploy, which have been used in an increasing number of mission scenarios. However, unstable link quality and frequently changing network topology pose significant challenges for adopting existing routing protocols in mobile ad hoc networks (MANETs). In this paper, we propose a time-slotted on-demand predictive (TSDP) routing protocol designed specifically for UAV networks. The TSDP protocol introduces a novel approach to route selection by incorporating multiple criteria, including delivery ratio, adjacent degree, and mobility prediction factor, to ensure reliable and efficient data transmission. By addressing high latency in route discovery and excessive broadcast overhead, TSDP employs a time-slotted communication mechanism that reduces packet drop rates and enhances route stability. Simulation results demonstrate that TSDP consistently outperforms ad hoc on-demand distance vector (AODV) and dynamic source routing (DSR) protocols in terms of throughput, packet delivery ratio, end-to-end delay, and overhead, particularly in highly dynamic network environments.

关键词： Simulation Stability criteria Autonomous aerial vehicles Throughput Routing Routing protocols Vectors Delays Vehicle dynamics AODV

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A Multiple Branch Fusion Network for Feature Learning and Hyperspectral Image Classification

A Multiple Branch Fusion Network for Feature Learning and Hy...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Murali Kanthi K. Venkateshwara Rao Sudha Pavani K Nuthanakanti Bhaskar Shankar Nayak Bhukya T. Hitendra Sarma Dept. of Computer Science & Engineering (Data Science) CMR Technical Campus Hyderabad Telangana India Dept. of Computer Science & Engineering CMR College of Engineering & Technology Hyderabad Telangana India Dept. of Computer Science & Engineering CMR Technical Campus Hyderabad Telangana India Dept. of Information Technology Vasavi College of Engineering Hyderabad Telangana India

Hyperspectral image (HSI) classification has made extensive use of deep learning approaches, specifically Convolutional Neural Network (CNN). CNN's ability to fully capture local contextual aspects of HSI makes it an excellent HSI classifier. Although, a significant factor in the poor classification performance is the scarcity of training data. Conventional CNN-based methods don't use the integrate-band relationships of HSI as much as they could because they typically employ 2D-CNNs to extract features. On the other hand, 3D-CNNs use a similar procedure to get both spectral and spatial data. This gets around the problem with 2D-CNNs, but it could mean that not enough features are extracted. As a solution, we offer a multi-scale 3D-CNN with multi branch feature fusion, which we call multi branch fusion CNN (MBF-CNN), for HSI classification. We begin by applying 3D-CNN on multiple scales to extract HSI features. After that, the multiple distinct characteristics are streamlined and combined together. The fused features then go through several fully connected layers to get the classification outcomes. The experiments are executed on both the new data sets and the publicly accessible benchmark data sets to expose the efficiency and durability of the proposed model. Empirically, it has been demonstrated that the presented model succeeds over the remaining state-of-the-art approaches in terms of classification accuracy.

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Machine Learning Enabled Intrusion Detection for Edge Devices in the Internet of Things

Machine Learning Enabled Intrusion Detection for Edge Device...

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IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Maram Alsharif Danda B. Rawat Department of Electrical Engineering & Computer Science Data Science and Cybersecurity Center (DSC2) Howard University Washington D.C. USA

In this paper, we present recent approaches proposed to secure the Internet of Things (IoT) devices against malicious cyber attacks and malware. As IoT devices have limited computing, storage, processing and communication capabilities, protection those devices by themselves is very challenging. Fog computing has been proposed to support resource constrained IoT devices to reduce delay caused by cloud computing. We also presented adopted ML models and datasets vs. targeted cyber attacks in a tabular form.

关键词： Cloud computing Computational modeling Intrusion detection Optimized production technology Delays Internet of Things Security

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Joint Optimization of Model Splitting and Device Task Assignment for Private Multi-hop Split Learning

Joint Optimization of Model Splitting and Device Task Assign...

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Annual Conference on Information sciences and Systems (CISS)

作者： Dongyu Wei Qianqian Zhang Yuchen Liu Mingzhe Chen Department of Electrical and Computer Engineering University of Miami Coral Gables FL USA Department of Electrical and Computer Engineering Rowan University Glassboro NJ USA Department of Computer Science NC State University Raleigh NC USA Frost Institute for Data Science and Computing University of Miami Coral Gables FL USA

ISBN: (数字)9798331513269

ISBN: (纸本)9798331513276

In this paper, a deceptive signal-assisted private split learning is investigated. In our model, several edge devices jointly perform collaborative training, and some eavesdroppers aim to collect the model and data information from devices. To prevent the eavesdropper from collecting model and data information, a subset of devices can transmit deceptive signals. Therefore, it is necessary to determine the subset of devices used for deceptive signal transmission, the subset of model training devices, and the models assigned to each model training device. This problem is formulated as an optimization problem whose goal is to minimize the eavesdropped information while meeting the model training energy consumption and delay constraints. To solve this problem, we propose an actor-critic deep reinforcement learning (AC-DRL) framework that enables a centralized agent to dynamically determine the model training devices, the deceptive signal transmission devices, the transmit power, and sub-models assigned to each model training device while considering network topology and device properties (e.g., energy and privacy constraints). The proposed AC-DRL framework integrates an actor network to generate optimal actions and a critic network to stabilize the learning process by evaluating the cumulative rewards. Simulation results demonstrate that the proposed method improves the convergence rate by 3× and the accumulated reward by up to 40% compared with the proximal policy optimization (PPO) algorithm.

关键词： Training Performance evaluation Privacy Network topology Simulation Heuristic algorithms data models Delays Optimization Convergence

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Utilizing Web Scraping and Machine Learning for Improving Accuracy of Smartphone Price Prediction

Utilizing Web Scraping and Machine Learning for Improving Ac...

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Advances in data engineering and Intelligent Computing Systems (ADICS), International Conference on

作者： V. Vasanth Kumar C. TamizhSelvan L. Manoharan R. Vishnu Sekhar V. Vinoth Kumar Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology India Department of Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India Department of Biotechnology Integrated Bioprocessing Laboratory School of Bioengineering SRM Institute of Science and Technology India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

Mobile prices play a pivotal role in determining their popularity amongst consumers and their competitive standing within the market. As customers consider their budget while evaluating a mobile phone's specifications and design, estimating the price beforehand becomes essential to cater to their needs and expectations. Hence, accurately forecasting mobile phone prices is a vital step in the product launch process to remain competitive and assess the market dynamics and competitors. The primary aim of this research is to determine the true market value of mobile phones. In this research, the dataset was extracted by means of utilizing a web scraping process from the flipkart website. The main objective of this research is to fetch the most recent dataset based on the most recent features. The extracted dataset is fitted into regression models such as linear regression, decision tree regressor, random forest regressor, KNN regressor, and gradient boosting regressor and performs the comparative analysis. The models are evaluated based on the mean absolute error metric.

关键词： Measurement Computational modeling Linear regression Predictive models Feature extraction Boosting data models

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Congestion aware clustered WSN based on an improved ant colony algorithm

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Measurement: Sensors 2024年 34卷

作者： Anto Pravin, R. Asha Shiny, X.S. Baby Vennila, V. Selvaraju, P. Uma Mageswari, R. Satish kumar, S. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu India Department of Information Technology CMR Engineering College Hyderabad India Department of Information Technology SSM College of Engineering Tamil Nadu India Department of Artificial Intelligence and Data Science Excel Engineering College Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu India Department of Electrical and Electronics Engineering AMET University Tamil Nadu India

Conventional works carried out in Wireless Sensor Networks (WSN) mostly focussed on energy oriented services and very less significant measures given to delay oriented and congestion aware services. Hence the proposed mechanism specially focuses on network structural design by placing rendezvous location for each cluster as well as route segmentation for controlling the congestion occurrence and unwanted delay. Here Congestion Aware Clustering with Improved Ant Colony Algorithm (CAC_IACA) is proposed. This mechanism involves two steps (i) identifying the best route by following the Ant Colony Optimization (ACO) algorithm and (ii) data segmentation using rendezvous mobile nodes. The Rendezvous nodes are present in each cluster to reduce the congestion rate on receiver side during data transmission. This proposed methodology mainly concentrates on reducing coverage cost for 3D environmental monitoring. Simulation results are analysed and the efficiency of the proposed scheme proves 26.54 % better than the conventional method. © 2024 The Authors

关键词： Ant colony optimization

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