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A Machine Learning Edged Self-Adaptable Vehicle Slowdown Earliest Warning Information System Using IOT Based Traffic Prediction Model

A Machine Learning Edged Self-Adaptable Vehicle Slowdown Ear...

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Murali, N. David, D. Beulah Saveetha School of Engineering Simats Department of Computer Science and Engineering Tamilnadu Chennai India Institute of Information Technology Saveetha School of Engineering Simats Department of Data Analytics Tamilnadu Chennai India

ISBN: (纸本)9798350334357

Development of vehicle safety is the work's primary goal, according to its abstract vehicle in traffic congestion using the Internet of Things (IoT). The intense growth of the city's vehicle population needs intelligent traffic systems to be considered resourcefully and sustainably by enhancing the full advantage of recent technology. The unpredictable traffic flow is a substantial issue that carries a colossal traffic movement in real-time scenarios. The random traffic control systems during peak and non-peak hours with unproductive human possessions, which leads to increased traffic and road offenses due to ineffective traffic monitoring systems. To sort down this dynamic scenario, the self-adaptable machine learning approach has been adopted to sequence the intelligent traffic flow by using IOT based early warning system in vehicles. The proposed work focused on the traffic congestion prediction operationalized using the unsupervised algorithm to train the gathered data sets using a neural network. It also aims to deliver a clarification that will upturn the comfort level of travelers to make intelligent and better transference choices. A neural network is a reasonable approach to finding traffic circumstances in sequence with this, the machine learning algorithms and their accuracy, which practices an outline in the collected data sets and then produces crucial decisions in evidence about traffic flow and congestion levels. The most focused part of the research is to enhance the intelligence in the IoT docking system, which prevents the vehicle congestion and returns a flawless, innovative design. © 2023 IEEE.

关键词： Machine learning

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Text Classification for Social Governance: A Novel Strategy with Adaptive Reward Mechanisms 6

Text Classification for Social Governance: A Novel Strategy ...

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6th International Conference on Communications, Information System and computer engineering, CISCE 2024

作者： Kong, Xu Zhu, Xiaomin Wang, Maoli Wang, Xinming Zou, Min School of Computer Science Qufu Normal University Rizhao China Shandong Institute of Big Data Jinan China School of Cyber Science and Engineering Qufu Normal University Qufu China

ISBN: (纸本)9798350353174

Given the increasing need for societal governance systems to effectively address public grievances, classifying texts from public service hotlines and other governance communications has become challenging, especially for texts with complex hierarchical structures. This study explores how adaptive reward mechanisms can be applied to classify complaint texts in societal governance. We propose a novel hierarchical text classification model that overcomes the limitations of traditional methods in handling hierarchical data. We designed a hierarchical loss function that considers inter-label hierarchical relationships to enhance the model's learning of hierarchical information. Additionally, we used a simple label encoding method to reduce computational complexity and improve training efficiency. Experimental results show that our model significantly outperforms existing methods in classification accuracy, both flat and hierarchical. Furthermore, ablation studies confirmed the hierarchical loss function's crucial role in enhancing model performance. This research offers an effective strategy for classifying complex hierarchical data, significantly improving classification tasks in natural language processing. © 2024 IEEE.

关键词： Adversarial machine learning

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A Word Sense Disambiguation Method Based on Multiple Sense Graph

A Word Sense Disambiguation Method Based on Multiple Sense G...

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2024 International Conference on Machine Learning and Intelligent Computing, MLIC 2024

作者： Liu, Wenjun Wang, Hailan Wang, Xiping Hou, Mengshu Chengdu611731 China School of Computer and Software Engineering Xihua University Chengdu610039 China School of Big Data and Artificial Intelligence Chengdu Technological University Chengdu611730 China

Word Sense Disambiguation is a process to determine the best meaning of an ambiguous word according to its contextual semantic information. Many methods of Word Sense Disambiguation cannot deal with polysemous words well because they only consider the meaning of the adjacent words before and after ambiguous words, and cannot consider the meaning of all words in the sentence globally. In order to solve the above problems, this paper proposes a word sense disambiguation method based on Multiple Sense Graph. This method applies the BERT model to generate word sense vectors, and globally considers the feature relationship between the ambiguous word and all words in the context. In addition, this method applies the PageRank algorithm to score the importance of each sense vector of the word, and the scoring results are sorted to obtain the best sense of the ambiguous word. The experimental results indicate that the proposed BERT-PageRank method improves the evaluation index compared with the other two semantic disambiguation methods. In summary, the proposed method improves the accuracy of word sense disambiguation to obtain the best word sense. © 2024 W. Liu, H. Wang, X. Wang & M. Hou.

关键词： BERT Model PageRank Algorithm Word Sense Disambiguation Word Sense Disambiguation Graph

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Secure RFID-Assisted Authentication Protocol for Vehicular Cloud Computing Environment

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IEEE Transactions on Intelligent Transportation Systems 2024年第9期25卷 12528-12537页

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Tariq, Tayyaba Alenazi, Mohammed J. F. Das, Ashok Kumar University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Institute for Cyber Security School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Sichuan University Key Laboratory of Data Protection and Intelligent Management Ministry of Education Chengdu610065 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518000 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Yunlin Douliu64002 Taiwan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500032 India

Vehicular network technology has made substantial advancements in recent years in the field of Intelligent Transportation Systems. Vehicular Cloud Computing (VCC) has emerged as a novel paradigm with a substantial increase in data exchange within Vehicular ad-hoc networks (VANETs). VCC integrates cloud computing, vehicular networking, and Internet of Things (IoT) technologies. It enables Infrastructure-to-Vehicle (I2V), Vehicle-to-Vehicle (V2V), and Vehicle-to-Device (V2D) communication. VCC optimizes vehicle, cloud infrastructure, and IoT resources while addressing significant communication security and vehicle-user privacy challenges. To address these issues, we developed an RFID-based authentication protocol for VCC based on a Henon map using a hash function. In addition, we also incorporated a Physical Unclonable Function (PUF) to resist physical tampering attacks. We validate the protocol's security formally and informally. The formal security analysis is conducted through a widely used RoR model. We use the Scyther simulation tool to verify the proposed protocol's security against various attacks. Moreover, we compare the performance of our protocol with similar existing protocols across important performance parameters such as communication and computation overheads and security attributes. The proposed protocol yields substantial improvements, demonstrating a 40.74% reduction in computation overhead and a 13.03% decrease in communication overhead as compared to related protocols, delivering both enhanced performance and resource efficiency. The analysis demonstrates its capacity to support secure communication in the VCC environment and satisfy desirable security attributes. © 2024 IEEE.

关键词： Authentication

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Piccolo: Large-Scale Graph Processing with Fine-Grained in-Memory Scatter-Gather 31

Piccolo: Large-Scale Graph Processing with Fine-Grained in-M...

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31st IEEE International Symposium on High Performance computer Architecture, HPCA 2025

作者： Shin, Changmin Song, Jaeyong Jang, Hongsun Kim, Dogeun Sung, Jun Kwon, Taehee Ju, Jae Hyung Liu, Frank Choi, Yeonkyu Lee, Jinho Seoul National University Department of Electrical and Computer Engineering Korea Republic of Old Dominion Univeristy School of Data Science United States Samsung Electronics Korea Republic of

ISBN: (纸本)9798331506476

Graph processing requires irregular, fine-grained random access patterns incompatible with contemporary off-chip memory architecture, leading to inefficient data access. This inefficiency makes graph processing an extremely memory-bound application. Because of this, existing graph processing accelerators typically employ a graph tiling-based or processing-in-memory (PIM) approach to relieve the memory bottleneck. In the tiling-based approach, a graph is split into chunks that fit within the on-chip cache to maximize data reuse. In the PIM approach, arithmetic units are placed within memory to perform operations such as reduction or atomic addition. However, both approaches have several limitations, especially when implemented on current memory standards (i.e., DDR). Because the access granularity provided by DDR is much larger than that of the graph vertex property data, much of the bandwidth and cache capacity are wasted. PIM is meant to alleviate such issues, but it is difficult to use in conjunction with the tiling-based approach, resulting in a significant disadvantage. Furthermore, placing arithmetic units inside a memory chip is expensive, thereby supporting multiple types of operation is thought to be impractical. To address the above limitations, we present Piccolo, an end-to-end efficient graph processing accelerator with fine-grained in-memory random scatter-gather. Instead of placing expensive arithmetic units in off-chip memory, Piccolo focuses on reducing the off-chip traffic with non-arithmetic function-in-memory of random scatter-gather. To fully benefit from in-memory scatter-gather, Piccolo redesigns the cache and miss-handling architecture (MHA) of the accelerator such that it can enjoy both the advantage of tiling and in-memory operations. Piccolo achieves a maximum speedup of 3.28 × and a geometric mean speedup of 1.62 ×, along with up to 59.7% reduction in energy consumption across various and extensive benchmarks. © 2025 IEEE.

关键词： Memory architecture

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Advanced VANET Technology for Emergency Vehicle Safety in Time-Critical Scenario 8

Advanced VANET Technology for Emergency Vehicle Safety in Ti...

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8th IEEE International Conference on Science, Technology, engineering and Mathematics, ICONSTEM 2023

作者： Murali, N. Beulah David, D. Saveetha School of Engineering Simats Department of Computer Science and Engineering Tamilnadu Chennai India Institute of Information Technology Saveetha School of Engineering Simats Department of Data Analytics Tamilnadu Chennai India

ISBN: (纸本)9798350347791

The most pressing issue with ITS is minimal latency in communication between vehicles, especially given that unstable traffic flow and road safety are two of their main concerns. Because to these problems, I have concentrated my study on employing a vehicular ad hoc network to track speedy object detection and provide the quickest warning to drivers. These systems require ongoing monitoring to function properly, making it possible to use machine learning techniques on vast amounts of data produced by various VANET applications. Machine learning is a technique used to prevent accidents for emergency vehicles by automatically learning and improving the system's rapid object detection stage in situations with high traffic. Our methods effectively implement the VANET aim by providing efficient unsupervised learning of the collected data. In this research, we suggested a unique real-time (DT-YOLO) algorithm-based automatic discovering vehicle gap approach for detecting and identifying objects in congested metropolitan regions. By using VANET technologies, we were able to address traffic, communication, and safety concerns in metropolitan areas. We investigated, using DT-YOLO technology, how these problems may be resolved in a transparent object-tracking system via machine learning techniques. Fast-moving EVs have a high probability of finding a gap to let the driver to make a decision. Moreover, a case study demonstrating a VANET-based scenario of nearest-object and gap-finding to flee from congested traffic with the ideal vehicle moment is shown. The paper examined current challenges and future directions. © 2023 IEEE.

关键词： Object detection

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Generative Adversarial Networks for Spatio-Spectral Compression of Hyperspectral Images 14

Generative Adversarial Networks for Spatio-Spectral Compress...

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14th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2024

作者： Fuchs, Martin Hermann Paul Byju, Akshara Preethy Walda, Alisa Rasti, Behnood Demir, Begum Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India Technische Universität Berlin Faculty of Electrical Engineering and Computer Science Germany BIFOLD - Berlin Institute for the Foundations of Learning and Data Germany

ISBN: (纸本)9798331513139

Deep learning-based hyperspectral image (HSI) compression has recently attracted great attention in remote sensing due to the growth of hyperspectral data archives. Most of the existing models achieve either spectral or spatial compression and do not jointly consider the spatio-spectral redundancies present in HSIs. To address this problem, in this paper, we propose High Fidelity Compression (HiFiC)-based models for spatio-spectral compression of HSIs. In detail, we introduce two new models: i) HiFiC using Squeeze and Excitation (SE) blocks (denoted as HiFiCsE);and ii) HiFiC with 3D convolutions (denoted as HiFiC3D) in the framework of compression of HSIs. We analyze the effectiveness of HiFiCsE and HiFiC3D in compressing the spatio-spectral redundancies with channel attention and inter-dependency analysis. Experimental results show the efficacy of the proposed models in performing spatio-spectral compression, while reconstructing images at reduced bitrates with higher reconstruction quality. The code of the proposed models is publicly available at https://***/rsim/HSI-SSC. © 2024 IEEE.

关键词： Generative adversarial networks

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Advancing Early Detection: CNN for Automated Blood Cancer Diagnosis and Anomaly Detection 2

Advancing Early Detection: CNN for Automated Blood Cancer Di...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Raji, V. Seetharaman, Anburaman Kasi, Baskar Govindaraju, Sakthi Paarkavy, B. S K P Engineering College Department of Computer Science and Engineering Tiruvannamalai India Sri Venkateswaraa College of Technology Department of Artificial Intelligence and Data Science Chennai India Galgotias University School of Computer Science and Engineering Greater Noida India Galgotias University School of Computer Science and Engineering Greater Noida India Saveetha Engineering College Department of AI&DS Chennai India

ISBN: (纸本)9798350368109

The evaluation and diagnosis of diseases related to blood cancer can be intricate and time-consuming. This complexity is compounded by the reliance on manual analyses employing techniques that consume a significant amount of time. Numerous methods have been created over the past ten years for the finding, analysis, and categorization of human blood cancer. The lack of methods or models that can automatically analyze human blood cells to detect the presence of cancer, however, is still a significant issue. The development of such a model holds the potential to enhance the early detection and prevention of these diseases, thereby facilitating more prompt medical diagnoses. In our ongoing research, we present our current endeavors in the creation of a Deeper with Convolutions Neural Network (DCNN) Learning Model capable of recognizing anomalies in blood cells. We employ a Hybrid Ensemble DCNN Learning technique for the identification of blood cancer. With accuracy rates surpassing 99%, this method is one of the most sophisticated deep learning models. © 2024 IEEE.

关键词： Contrastive Learning

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Designing Smart Grid and Irrigation Management Systems for Information Sharing for Enhanced Efficiency and Sustainability

Designing Smart Grid and Irrigation Management Systems for I...

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2023 IEEE International Conference on Modelling, Simulation and Intelligent Computing, MoSICom 2023

作者： Singh, Jagendra Neeraj Garg, Neha Saini, Mayank Singh, Ishaan Mall, Shachi Bennett University School of Computer Science Engineering and Technology Greater Noida India Publicis Sapient Manager Data Science Greater Noida India Galgotias University School of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350393415

In this paper, the Secure Entry Gateway (SEG) was devised and tested, to optimize Smart Grid and Smart Irrigation systems, aiming to assess its efficiency and security in managing data within a home irrigation network. It showed promising results with a data throughput of 98 Mbps and a 97% cyber-Attack detection rate, exceeding expectations. However, a 20% increase in latency and minor command response delays were noted. The user-friendly SEG, with its seamless smartphone interface, represents a shift towards intuitively accessible advanced systems. It serves as a pivotal gatekeeper in an envisioned future of interconnected smart systems, emphasizing balanced functionality and robust security, reaffirming the importance of innovative, secure, and user-friendly systems. © 2023 IEEE.

关键词： Irrigation

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A IoT Edge Advanced VANET Technique for Vehicle Communication and Improve Safety in Hill Station Critical Scenario 8

A IoT Edge Advanced VANET Technique for Vehicle Communicatio...

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8th IEEE International Conference on Science, Technology, engineering and Mathematics, ICONSTEM 2023

作者： Murali, N. Beulah David, D. Saveetha School of Engineering Simats Department of Computer Science and Engineering Tamilnadu Chennai India Saveetha School of Engineering Simats Institute of Information Technology Department of Data Analytics Tamilnadu Chennai India

ISBN: (纸本)9798350347791

The safety of the vehicles is given great importance by advanced intelligent transportation systems. We are here to make sure that swift transit vehicles and infrastructure communicate with one other quickly. The majority of the vehicles need regular monitoring for appropriate warnings and road maps to achieve their ultimate objective in a timely manner in the hill station bad environment, and travel in hairpin bends and risky turning in particular is a duty that is vital for ITS. Here, my study will provide a strategy for preventing accidents by applying a sophisticated early warning system before notifying the car in a certain circumstance. In order to prevent accidents, machine learning is a technique where the system automatically learns and enhances the rapid object detection stage in bobby pin road turning scenarios. Here, my study offers effective unsupervised learning of these characteristics based on the acquired data, which in turn serves as the foundation for the clustering I performed, which successfully builds a mobile ad hoc network. In this work, we suggested an innovative automated discovering vehicle prier that uses the UNetXST technique in real-time to notify warring for vehicle in three-way shining LED light with blinking mode, slowing warring message in display unit, and beep alarm sound. By combining V2I and V2V technologies and sending messages to the central traffic light management system, we highlighted safety and communication. In worst-case scenarios, we employ a v2v technology sensor to detect the vehicle that will cross the junction road, and we investigate two-way merging technology's potential to implement and resolve these problems in transparent object tracking in the turning system. We looked at future direction and hurdles at this time in addition to a case study illustrating a VANET-based scenario of a critical urban road turning, receiving the earliest notice to flee from a traffic collision, and experiencing the perfect EV momen

关键词： Vehicles

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