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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Mandal, Gulabi Roy, Anik Palit, Basabdatta Indian Institute of Technology Department of Computer Science and Engineering Kharagpur India Indian Institute of Technology Advanced Technology Development Center Kharagpur India National Institute of Technology Department of Electronics and Communication Engineering Rourkela India

ISBN: (纸本)9798331531195

Vehicle platooning is a cooperative driving technology that can be supported by 5G enhanced Vehicle-to-Everything (eV2X) communication to improve road safety, traffic efficiency, and reduce fuel consumption. eV2X communication among the platoon vehicles involves the periodic transmission of Cooperative Awareness Messages (CAMs) containing vehicle information under strict latency and reliability requirements. These requirements can be maintained by administering the assignment of resources, in terms of time slots and frequency bands, for CAM transmission in a platoon, with the help of a resource allocation mechanism. State-of-the-art on control and communication design for vehicle platoons either consider a simplified platoon model with a detailed communication architecture or consider a simplified communication delay model with a detailed platoon control system. Departing from existing works, we have developed a comprehensive vehicle platoon communication and control framework using OMNET + +, the benchmarking network simulation tool. We have carried out an inclusive and comparative study of three different platoon information Flow Topologies (IFTs), namely Car-to-Server, Multi-Hop, and One-Hop over 5G using the Predecessor-Leader Following platoon control law to arrive at the best-suited IFT for platooning. Secondly, for the best-suited 5G eV2X IFT, we have analyzed the performance of three different resource allocation algorithms, namely Maximum of Carrier to Interference Ratio (MaxC/I), Proportional Fair (PF), and Deficit Round Robin (DRR). Exhaustive system-level simulations show that the One-Hop information flow strategy along with the MaxC/I resource allocation yields the best Quality of Service (QoS) performance, in terms of latency, reliability, Age of information (AoI), and throughput. © 2025 IEEE.

关键词： Vehicle to vehicle communications

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Enhancing Offensive Language Detection with Data Augmentation and Knowledge Distillation

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Research 2024年第2期6卷 29-40页

作者： Jiawen Deng Zhuang Chen Hao Sun Zhexin Zhang Jincenzi Wu Satoshi Nakagawa Fuji Ren Minlie Huang The CoAl group DCSTInstitute for Artificial InteligenceState Key Lab of Intelligent Technology and SystemsBeijing National Research Center for Information Science and TechnologyTsinghua UniversityBeijing 100084China Graduate School of Information Science&Technology The University of Tokyo.Tokyo 1138654Japan School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengduSichuan611731China

Offensive language detection has received important attention and plays a crucial role in promoting healthy communication on social platforms,as well as promoting the safe deployment of large language *** data is the basis for developing detectors;however,the available offense-related dataset in Chinese is severely limited in terms of data scale and coverage when compared to English *** significantly affects the accuracy of Chinese offensive language detectors in practical applications,especially when dealing with hard cases or out-of-domain *** alleviate the limitations posed by available datasets,we introduce AugCOLD(Augmented Chinese Offensive Language Dataset),a large-scale unsupervised dataset containing 1 million samples gathered by data crawling and model ***,we employ a multiteacher distillation framework to enhance detection performance with unsupervised *** is,we build multiple teachers with publicly accessible datasets and use them to assign soft labels to *** soft labels serve as a bridge for knowledge to be distilled from both AugCOLD and multiteacher to the student network,i.e.,the final offensive *** conduct experiments on multiple public test sets and our well-designed hard tests,demonstrating that our proposal can effectively improve the generalization and robustness of the offensive language detector.

关键词： COLD dealing limitations

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A Fruit and Vegetable Detection Based on Pytorch Machine Learning Frame Work Model 2

A Fruit and Vegetable Detection Based on Pytorch Machine Lea...

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2nd International Conference on Emerging Trends in information technology and engineering, ic-ETITE 2024

作者： Priyadharshini, U. Vijayan, R. School of Advanced Science Vellore Institute of Information Technology Vellore India School of Computer Science Engineering and Information System Vellore Institute of Information Technology Vellore India

ISBN: (纸本)9798350328202

Today, Agricultures has become a significant element of people's life. Among them fruits and vegetables are a great thing for healthy lives. Classification of fruits and vegetable are difficult for normal person because of some same similarities like shape and color. Also, Fruits and Vegetables classification is an important process in industries applications as well as local market. Lots of similarities available between fruits like apples, pears and peaches, and that kinds of fruits are generally used in daily routine. As well as some vegetables are same in shape, so it will be difficult to differentiate in this type of situation for old persons. The previous research for solution of this type of problem for classification of fruits CNN is used but the problem in this research is when they use the self-made datasets then their accuracy decreases as compare to public datasets accuracy increases. To solve this type of problem, here produce a kind of algorithms in the paper to classify fruits and vegetable, using deep leaning technique such as VGG16 and DenseNet169 model have capability to extract the feature of images very deeply using various small filters so it provide a better result. On this premise, it was used to classify fruit kinds against a complex background and integrated with data improvement technology for future usage. It might be helpful for human for automatic differentiate the both categories. This project is implemented in the python programming language and the dataset used Fruits and Vegetables Image Recognition Dataset for the classification. Which will help to differentiate between Fruits and Vegetables with high accuracy rate. For the implementation of this project used Python programming and this implementation using the tool like Jupiter and Kaggle Notebook and used GPU for highspeed execution. © 2024 IEEE.

关键词： Fruits

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Influence of Nodes in Complex Networks 14

Influence of Nodes in Complex Networks

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Hansda, Raimoni Chakraborty, Susanta Indian Institute of Engineering Science and Technology Department of Computer Science and Technology Howrah India

ISBN: (纸本)9798350335095

Network measuring metric is a salient research topic in Complex networks. Each metric works effectively to explore certain characteristics based on the application requirements. Degree centrality and its various extensions are popular metrics for evaluating the nodes' influence in complex networks. The Weighted Degree Centrality (WDC) effectively determines the nodes' influence in weighted and undirected graphs. The WDC fails to distinguish the influence between the same weighted degree nodes. A new network measuring metric Node Contribution Factor (NCF) has been introduced to overcome this problem. The NCF effectively evaluates the nodes' influence by considering the neighbours' connectivity in weighted and undirected networks. This proposed metric has been appraised in varied structures of network. The metric has been examined on benchmark datasets to show the superiority of the suggested strategy. The proposed metric outperforms the state-of-the-art network measuring metrics to calculate the nodes' influence in the weighted and undirected networks. © 2023 IEEE.

关键词： Complex networks

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Transaction transmission model for blockchain channels based on non-cooperative games

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science China(information sciences) 2023年第1期66卷 97-114页

作者： Peiyun ZHANG Chenxi LI Mengchu ZHOU Wenjun HUANG Abdullah ABUSORRAH Omaimah O.BAMASAG Engineering Research Center of Digital Forensics of Ministry of Education School of Computer and SoftwareNanjing University of Information Science and Technology School of Computer and Information Anhui Normal University Department of Electrical and Computer Engineering New Jersey Institute of Technology Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering Faculty of Engineering and K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University Department of Computer Science Faculty of Computing and Information Technology Center of Excellence in Smart Environment Research King Abdulaziz University

In blockchain networks, transactions can be transmitted through channels. The existing transmission methods depend on their routing information. If a node randomly chooses a channel to transmit a transaction, the transmission may be aborted due to insufficient funds(also called balance) or a low transmission rate. To increase the success rate and reduce transmission delay across all transactions, this work proposes a transaction transmission model for blockchain channels based on non-cooperative game *** balance, channel states, and transmission probability are fully considered. This work then presents an optimized channel transaction transmission algorithm. First, channel balances are analyzed and suitable channels are selected if their balance is sufficient. Second, a Nash equilibrium point is found by using an iterative sub-gradient method and its related channels are then used to transmit transactions. The proposed method is compared with two state-of-the-art approaches: Silent Whispers and Speedy Murmurs. Experimental results show that the proposed method improves transmission success rate, reduces transmission delay,and effectively decreases transmission overhead in comparison with its two competitive peers.

关键词： Blockchain channel transaction transmission channel balance channel state channel transmission probability non-cooperative game

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A Short Survey of Elucidating the Emotion Recognition Methodologies Using Facial Images and EEG Signals 4th

A Short Survey of Elucidating the Emotion Recognition Method...

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4th International Conference on Advanced Computational and Communication Paradigms, ICACCP 2023

作者： Kaur, Dilsheen Misra, Anuradha Vyas, O.P. Computer Science and Engineering Amity School of Engineering and Technology Amity University Lucknow Campus India Information Technology Indian Institute of Information Technology Prayagraj Allahabad India

ISBN: (纸本)9789819942831

Since the high demand for Human–computer Interaction (HCI), developing an automated model for recognizing facial gestures or emotions becomes challenging. Some experts have used facial images as a constructive part of recognizing different emotions of humans. Simultaneously, emotions can also be classified by using some electrical signals such as Electrocardiogram (ECG), Electroencephalogram (EEG), Electromyogram (EMG), and so on. Among all these signals, EEG captures the major information of brain activities. Recently, along with face images, EEG signal plays a pivotal role in emotion recognition. Though these methods have been estimated the promising results, the real-world implications and diverse feature identification of human states are still in place to gain more attention. Considering these factors, this survey aims to review the literature for analyzing emotion recognition performance using EEG signals and facial images. It also demonstrates the techniques that are utilized in machine learning and deep learning as well. The drawback of such implemented models is discussed, leading to future development. Subsequently, the survey part is followed by exploring the chronological review of the emotion recognition work, dataset utilization, methodologies employed, and experimental analysis with divergent metrics and features, and challenges. Finally, the research challenging factors provoke to raise the novel effective system for emotion recognition using facial images and EEG signals. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Electrocardiograms

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A Computational Model for Driver Risk Evaluation and Crash Prediction Using Contextual Data from On-board Telematics

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Recent Advances in computer science and Communications 2022年第4期15卷 620-635页

作者： Jain, Neetika Mittal, Sangeeta Department of Computer Science & Engineering and Information Technology Faculty of Engineering Jaypee Institute of Information Technology Noida India

Introduction: Vehicle crashes can be hazardous to public safety and may cause infrastructure damage. Risky driving significantly raises the possibility of the occurrence of a vehicle crash. As per statistics by the World Health Organization (WHO), approximately 1.35 million people are involved in road traffic crashes resulting in loss of life or physical disability. WHO attributes events like over-speeding, drunken driving, distracted driving, dilapidated road infrastructure and unsafe practices such as non-use of helmets and seatbelts to road traffic accidents. As these driving events negatively affect driving quality and enhance the risk of a vehicle crash, they are termed as negative driving attributes. Methods: A multi-level hierarchical fuzzy rules-based computational model has been designed to capture risky driving by a driver as a driving risk index. Data from the onboard telematics device and vehicle controller area network is used for capturing the required information in a naturalistic way during actual driving conditions. Fuzzy rules-based aggregation and inference mechanisms have been designed to alert about the possibility of a crash due to the onset of risky driving. Results: On-board telematics data of 3213 sub-trips of 19 drivers has been utilized to learn long term risky driving attributes. Furthermore, the current trip assessment of these drivers demonstrates the efficacy of the proposed model in correctly modeling the driving risk index of all of them, including 7 drivers who were involved in a crash after the monitored trip. Conclusion: In this work, risky driving behavior has been associated not just with rash driving but also other contextual data like driver’s long-term risk aptitude and environmental context such as type of roads, traffic volume and weather conditions. Trip-wise risky driving behavior of six out of seven drivers, who had met with a crash during that trip, was correctly predicted during evaluation. Similarly, for the other 12

关键词： Telematics

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Exploring the Impact of Different Similarity Measures on Query-by-Example Spoken Term Detection

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SN computer science 2025年第5期6卷 1-10页

作者： Gaonkar, Manisha Naik Thenkanidiyoor, Veena Dileep, A.D. Computer Science and Engineering Department National Institute of Technology Goa Kottamol Plateau Goa Cuncolim 403703 India Computer Science and Engineering Department Indian Institute of Technology Dharwad Karnataka Dharwad India Computer Engineering Department Goa College of Engineering Farmagudi Goa Ponda 403401 India

The growth in Internet audio data has highlighted the need for accurate and efficient search methodologies. In this context, query-by-example spoken term detection (QbE-STD) plays a pivotal role, mainly when dealing with multilingual datasets lacking proper transcription information. Recent advancements in the field include integrating convolutional neural networks (CNN) as classifiers, emphasizing the computation of matching matrices for accurate term detection. This research conducts a comparative analysis of similarity measures for QbE-STD, aiming to highlight the performance differences across different metrics. The results reveal distinctive patterns in accuracy, with kernel-based measures leading the pack. Distance-based measures demonstrate comparable accuracies, albeit slightly lower than other metrics, while DTW yields the lowest accuracy. The implications of these findings extend to optimizing QbE-STD systems, guiding practitioners in selecting suitable similarity measures based on their specific requirements. The study contributes valuable insights to spoken term detection, paving the way for enhanced methodologies in audio search applications. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： CNN Kernel-based matching QbE-STD Similarity measures Spoken term detection

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IPDD: Intelligent Parkinson Disease Detection System 4th

IPDD: Intelligent Parkinson Disease Detection System

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4th International Conference on Smart IoT Systems: Innovations and Computing, SSIC 2023

作者： Singhal, Manish Tiwari, Vinita Sharma, Ashish Electronic and Communication Indian Institute of Information Technology Rajasthan Kota325003 India Computer Science and Engineering Manipal University Rajasthan Jaipur303007 India

ISBN: (纸本)9789819751990

Worldwide, millions of peoples are affected by complex neuro degenerative disorder, that is known as PD (Parkinson Disease). In recent years, it is very critical for accurate diagnosis of PD at early stage for implementing timely interventions and improving patient life. There have been growing interest in developing intelligent systems for Parkinson’s disease detection, harnessing the potential of cutting-edge technologies such as artificial intelligence, machine l earning, and sensor-based data analysis. This research paper presents an in-depth exploration of an Intelligent Parkinson Disease Detection System (IPDDS) designed for early diagnosis and quality level monitoring of PD using two most accurate machine learning techniques—SVM and Random Forest. The presented work relates the use of various performance metrices like Accuracy, Confusion Matrix, Precision, etc. Standard Dataset having voice samples of human’s having Parkinson Disease and not having PD is used to validate the various performance parameters and predict the PD or non-PD. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Neurodegenerative diseases

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A State-Migration Particle Swarm Optimizer for Adaptive Latent Factor Analysis of High-Dimensional and Incomplete Data

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IEEE/CAA Journal of Automatica Sinica 2024年第11期11卷 2220-2235页

作者： Jiufang Chen Kechen Liu Xin Luo Ye Yuan Khaled Sedraoui Yusuf Al-Turki MengChu Zhou IEEE the College of Computer Science and Technology Chongqing University of Posts and Telecommunications the Department of Computer Science Columbia University the College of Computer and Information Science Southwest University the Department of Electrical and Computer Engineering Faculty of Engineeringand also with K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University the Center of Research Excellence in Renewable Energy and Power Systems the Department of Electrical and Computer EngineeringFaculty of Engineeringand also with K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University the Department of Electrical and Computer Engineering New Jersey Institute of Technology

High-dimensional and incomplete(HDI) matrices are primarily generated in all kinds of big-data-related practical applications. A latent factor analysis(LFA) model is capable of conducting efficient representation learning to an HDI matrix,whose hyper-parameter adaptation can be implemented through a particle swarm optimizer(PSO) to meet scalable ***, conventional PSO is limited by its premature issues,which leads to the accuracy loss of a resultant LFA model. To address this thorny issue, this study merges the information of each particle's state migration into its evolution process following the principle of a generalized momentum method for improving its search ability, thereby building a state-migration particle swarm optimizer(SPSO), whose theoretical convergence is rigorously proved in this study. It is then incorporated into an LFA model for implementing efficient hyper-parameter adaptation without accuracy loss. Experiments on six HDI matrices indicate that an SPSO-incorporated LFA model outperforms state-of-the-art LFA models in terms of prediction accuracy for missing data of an HDI matrix with competitive computational ***, SPSO's use ensures efficient and reliable hyper-parameter adaptation in an LFA model, thus ensuring practicality and accurate representation learning for HDI matrices.

关键词： Data science generalized momentum high-dimensional and incomplete(HDI)data hyper-parameter adaptation latent factor analysis(LFA) particle swarm optimization(PSO)

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