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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Pahadiya, Bharat Ranawat, Rekha Sage University Department of Computer Science & Engineering Indore India Institute of Advance Computing Sage University Indore India

ISBN: (纸本)9798350343274

This review encapsulates the transformative potential of integrating the Internet of Things (IoT) and Reinforcement Learning (RL) in devising a Smart Traffic Operation System for urban traffic control. The contemporary urban landscape, marked by burgeoning vehicular populations and increasing instances of congestion, necessitates a paradigm shift in traffic management. Traditional systems, often static and reactive, are rendered inadequate to address the dynamism and unpredictability of modern traffic patterns. Enter the synergistic blend of IoT and RL. Through the pervasive data collection capabilities of IoT devices, the system gains real-time insights into traffic conditions, while RL facilitates adaptive and proactive decision-making. This fusion not only promises enhanced efficiency and safety but also offers environmental dividends by reducing vehicular emissions through minimized congestion. However, the successful implementation of this system hinges on addressing challenges related to data security, storage, and privacy concerns. This study comprehensively examines the intricacies of the integration under consideration, thoroughly analyzing its benefits, possible drawbacks, and the future trajectory for the management of urban traffic. © 2023 IEEE.

关键词： Internet of things

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Optimizing Video Compression and Transmission for Real-Time Applications

Optimizing Video Compression and Transmission for Real-Time ...

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2023 IEEE Region 10 Symposium, TENSYMP 2023

作者： Sharma, Sugandha Jindal, Harsh Jain, Amit Singh, Shatvik Binner, Pritpal Singh Chandgarh University Department of Computer Science & Engineering Mohali India University Institute of Computing Chandgarh University Mohali India

ISBN: (纸本)9781665482585

Customers' expectations for content have significantly increased in recent times, leading to a perpetual sense of dissatisfaction. They now demand fast loading times and uninterrupted video playback when using on-demand video platforms like YouTube and Netflix. Despite various CNN-based methods proposed to improve compressed video quality, the existing VVC and transmission protocols are struggling to meet the demands of this expanding market of content consumers. To tackle this problem, we present the latest video coding standard, H.266/VVC, which has been developed by Team JVET. H.266 employs a quad-tree with a layered multitype tree structure, although this approach increases the computational requirements for future video coding advancements. However, it is essential for enhancing the acceptance and utilization of video coding technology. Furthermore, we introduce the 'HESP' protocol for video coding-streaming, which enables the delivery of streaming services with extremely low latency and significantly reduced streaming bandwidth on a large scale. © 2023 IEEE.

关键词： Efficiency

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Schema-Driven Information Extraction from Heterogeneous Tables

Schema-Driven Information Extraction from Heterogeneous Tabl...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Bai, Fan Kang, Junmo Stanovsky, Gabriel Freitag, Dayne Dredze, Mark Ritter, Alan College of Computing Georgia Institute of Technology United States School of Computer Science and Engineering The Hebrew University of Jerusalem Israel Artificial Intelligence Center SRI International Department of Computer Science Johns Hopkins University United States

ISBN: (纸本)9798891761681

In this paper, we explore the question of whether large language models can support cost-efficient information extraction from *** introduce schema-driven information extraction, a new task that transforms tabular data into structured records following a human-authored *** assess various LLM's capabilities on this task, we present a benchmark comprised of tables from four diverse domains: machine learning papers, chemistry literature, material science journals, and *** use this collection of annotated tables to evaluate the ability of open-source and API-based language models to extract information from tables covering diverse domains and data *** experiments demonstrate that surprisingly competitive performance can be achieved without requiring task-specific pipelines or labels, achieving F1 scores ranging from 74.2 to 96.1, while maintaining cost ***, through detailed ablation studies and analyses, we investigate the factors contributing to model success and validate the practicality of distilling compact models to reduce API reliance. © 2024 Association for Computational Linguistics.

关键词： Data mining

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Methodical Tamil Character Recognition Using Fabricated CNN Model

Methodical Tamil Character Recognition Using Fabricated CNN ...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Bhavani, M. Ravikumar, S. Prithi, S. Arockia Raj, Y. Rajendiran, Babu Rajalakshmi Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India School of Computing Department of Computational Intelligence SRM Institute of Science and Technology Chennai India PSNA College of Engineering and Technology Department of Computer Science and Engineering Dindigul India

ISBN: (纸本)9798350348217

Recognizing text is one of the most challenging problems in computer vision. To put it simply, handwritten character recognition is the process of recognising text in photographs, papers, and other media and converting it into a form that a computer can read. You can solve this issue by employing the image classification method. We will be able to recognise the user's typed Tamil characters in this paper. We designed a brand new model, MVGG16, to handle character recognition and classification. Its basic structure is analogous to that of VGG16, but we added a few more layers to improve accuracy. So far, 156 Tamil characters have been learnt, with 350 images each class. In experimental settings, this model achieves an accuracy of 91.8%. As an input method, this work makes use of a whiteboard where the user can scribble the Tamil character to be recognised. The character will be sent for verification after the user has written it and the image has been exported. Once the model identifies the character, it will show it to the user. © 2023 IEEE.

关键词： Character recognition

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Car Lock Control System Using Radio Frequency Identification(RFID)

Car Lock Control System Using Radio Frequency Identification...

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2024 IEEE SmartBlock4Africa, SmartBlock4Africa 2024

作者： Izang Aaron, A. Jonathan, Erihri O. Oyerinde Emmanuel, I. Ominike, Akpovi Chukwu Esther, C. Omodara Oluwademilade, E. Micheal Goldrich, C. School of Computing and Engineering Sciences Babcock University Department of Information Technology Ogun State Ilishan-Remo Nigeria School of Information and Communication Technology Delta State Polytechnic Department of computer Science Ogwashiuku Nigeria Petroleum Training Institute Computer Science and Information Technology Department Delta State Effurun Nigeria School of Computing and Engineering Sciences Babcock University Department of Computer Science Ogun State Ilishan-Remo Nigeria

ISBN: (纸本)9798350376838

Car Lock controlled system addresses the critical issue of vehicle security by utilizing RFID technology to create a more secure, passive, and wireless vehicle access system. This innovation surpasses traditional key-based systems, offering enhanced convenience and security. The project involves the development of a digital door locking mechanism integrated with RFID for secure vehicle identification. The system's efficacy was validated through rigorous testing, showcasing its potential to significantly deter vehicle theft. Despite challenges such as signal interference, the outcomes suggest a promising future for RFID in vehicle security. The study concludes with recommendations for extending RF communication range and exploring emerging technologies for further enhancement. This research marks a significant step towards redefining vehicle security, demonstrating the feasibility and effectiveness of a radio frequency-controlled car lock system. © 2024 IEEE.

关键词： Radio frequency identification (RFID)

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Modeling a fuzzy expert system for predicting customers’ behavioral intention for restaurant business through parallel prediction of perceived values and satisfactions

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International Journal of Information Technology (Singapore) 2025年第2期17卷 1007-1014页

作者： Akhtaruzzaman, M. Shahryer, Fahim Halder, Sachitra Tanvin, Jamal Uddin Department of Computer Science and Engineering Military Institute of Science and Technology (MIST) Mirpur Cantonment Dhaka 1216 Bangladesh Quantum Robotics and Automation Research Group (QRARG) Dhaka Bangladesh

This paper focuses on the modeling of a recommendation process for the restaurant industry. It is a challenge job for a restaurant owner to ascertain the intentions of customers to evaluate their services, further improve their profit margin. So, the objective of this study is to design a fuzzy-based prediction model of customers’ intentions for a fine dining experience. To facilitate a comparative analysis and to identify the optimal approach, various membership functions (MF) and de-fuzzification methods are employed along with the analysis of the correlational behavior among various input subsets. Results have shown that CoA method with triangular MF are well enough to implement the model. The fuzzy-correlation analysis presents various levels of membership strengths for a same recommendation score in some cases. Moreover, the evaluation of the proposed model based on a custom dataset shows prominent results with as 0.938. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Customer intension Customer satisfaction FIS Perceived value Recommendation

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Deep Learning in Remote Sensing for Climate-Induced Disaster Resilience: A Comprehensive Interdisciplinary Approach

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Remote Sensing in Earth Systems sciences 2024年第1期8卷 145-160页

作者： Padmaja, S.M. Naveenkumar, R. Kumari, N. P. Lavanya Pimo, Er. S. John Bindhu, M. Konduri, Bhagavan Jangir, Pradeep Department of Electrical and Electronics Engineering Shri Vishnu Engineering College for Women Bhimavaram India Department of Computer Science and Engineering Chandigarh Colleges of Engineering Chandigarh Group of Colleges Jhanjeri Punjab Mohali 140307 India Department of Computer Science and Systems Engineering AUCE Andhra University Visakhapatnam India Department of Computer Science and Engineering St. Xavier’s Catholic College of Engineering Nagercoil India Department of ECE Sri Venkateswara College of Engineering Sriperumbudur India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan

Climate-induced disasters pose significant threats to human lives, infrastructure, and ecosystems. Deep learning techniques, combined with remote sensing, offer powerful tools for predicting, detecting, and mitigating the impacts of such disasters. This study presents a comprehensive interdisciplinary approach using a hybrid cascaded CNN-RNN model to perform spatio-temporal analysis, enhanced image segmentation with U-Net and SegNet architectures, and a hybrid CNN-LSTM-XGBoost ensemble for multi-hazard prediction. The proposed CNN-RNN model achieves an accuracy of 96.78%, significantly outperforming traditional models in predicting flood-prone areas, wildfires, and droughts. Key metrics such as Intersection over Union (IoU), precision, recall, and F1-scores are used to evaluate model performance, highlighting the model’s ability to accurately identify disaster-prone areas and improve disaster resilience. This research not only demonstrates the efficacy of deep learning for climate-induced disaster prediction but also paves the way for future integration of socio-economic data to refine disaster risk assessment. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Climate-induced disasters CNN-RNN Deep learning Disaster resilience Hybrid ensemble Multi-hazard prediction Remote sensing SegNet Spatio-temporal analysis U-Net

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An Optimal Deep Learning for Cooperative Intelligent Transportation System

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computers, Materials & Continua 2022年第7期72卷 19-35页

作者： K.Lakshmi Srinivas Nagineni E.Laxmi Lydia A.Francis Saviour Devaraj Sachi Nandan Mohanty Irina V.Pustokhina Denis A.Pustokhin Department of Computer Science and Engineering Periyar Maniammai Institute of Science&TechnologyThanjavur613403India Department of Computer Science and Engineering KG Reddy College of Engineering and TechnologyTelangana500075India Department of Computer Science and Engineering Vignan’s Institute of Information Technology(Autonomous)Visakhapatnam530049India Department of Computer Science and Engineering Kalasalingam Academy of Research and EducationKrishnankoil626128India Department of Computer Engineering College of Engineering PunePuneMaharashtra411005India Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia

Cooperative Intelligent Transport System(C-ITS)plays a vital role in the future road traffic management system.A vital element of C-ITS comprises vehicles,road side units,and traffic command centers,which produce a massive quantity of data comprising both mobility and service-related *** the extraction of meaningful and related details out of the generated data,data science acts as an essential part of the upcoming C-ITS *** the same time,prediction of short-term traffic flow is highly essential to manage the traffic *** to the rapid increase in the amount of traffic data,deep learning(DL)models are widely employed,which uses a non-parametric approach for dealing with traffic flow *** paper focuses on the design of intelligent deep learning based short-termtraffic flow prediction(IDL-STFLP)model for C-ITS that assists the people in various ways,namely optimization of signal timing by traffic signal controllers,travelers being able to adapt and alter their routes,and so *** presented IDLSTFLP model operates on two main stages namely vehicle counting and traffic flow *** IDL-STFLP model employs the Fully Convolutional Redundant Counting(FCRC)based vehicle count *** addition,deep belief network(DBN)model is applied for the prediction of short-term traffic *** further improve the performance of the DBN in traffic flow prediction,it will be optimized by Quantum-behaved bat algorithm(QBA)which optimizes the tunable parameters of *** results based on benchmark dataset show that the presented method can count vehicles and predict traffic flowin real-time with amaximumperformance under dissimilar environmental situations.

关键词： Cooperative intelligent transportation systems traffic flow prediction deep belief network deep learning vehicle counting

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A Systematic Review of Data Augmentation Techniques in Natural Language Processing for Sentiment Analysis using Machine learning Techniques 4

A Systematic Review of Data Augmentation Techniques in Natur...

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4th International Conference on Soft computing for Security Applications, ICSCSA 2024

作者： Patil, Uday Dhanaji Sudhakaran, Pradeep SRM Institute of Science and Technology Department of Computer Science & Engineering Tamil Nadu Kattankulathur603203 India Kolhapur India SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798331515720

Due to the enormous growth of mobile technology, Social media has created a crucial platform to express the feelings and opinions of people. Based on these opinion considerations, it is very beneficial for making strategic decisions in organizations, businesses, trading, political entities, and so on. To achieve effective improvement, sentiment analysis is taken into account, and the public opinion polarity for further reference. In this perspective, the main contribution of the study is to explore the benefits and limitations of various learning techniques under the evaluation of sentiment analysis. Generally, Sentiment analysis is evaluated by Natural language processing tasks with various learning techniques, to achieve effective generalization ability and efficiency, data augmentation techniques are integrated, which reduces the overfitting problem effectively. To explore the limitations of the traditional methods, 25 literary papers were taken into account to analyze various approaches, and methods under the utilization of distinct datasets. Together with this, the achieved evaluation metrics along with the achievements are also discussed briefly in the study. Additionally, the study provides the shortcomings and future scope of the conventional methods with a detailed description. The main objective of the survey includes knowledgeable categorization, which aims to explore a new model with the illustration of recent trends and achieve effective data augmentation in future analysis. © 2024 IEEE.

关键词： Adversarial machine learning

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CoChain: High Concurrency Blockchain Sharding via Consensus on Consensus 42

CoChain: High Concurrency Blockchain Sharding via Consensus ...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Li, Mingzhe Lin, You Zhang, Jin Wang, Wei Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Research Institute of Trustworthy Autonomous Systems Computer Science and Engineering Department China Hong Kong University of Science and Technology Computer Science and Engineering Department Hong Kong Institute of High Performance Computing and Agency for Science Technology and Research Singapore

ISBN: (纸本)9798350334142

Sharding is an effective technique to improve the scalability of blockchain. It splits nodes into multiple groups so that they can process transactions in parallel. To achieve higher parallelism and concurrency at large scales, it is desirable to maintain a large number of small shards. However, simply configuring small shards easily results in a higher fraction of malicious nodes inside shards, causing shard corruption and compromising system security. Existing sharding techniques hence demand large shards, at the expense of limited concurrency. To address this limitation, we propose CoChain: a blockchain sharding system that can securely configure small shards for enhanced concurrency. CoChain allows some shards to be corrupted. For security, each shard is monitored by multiple other shards. The latter reach a cross-shard Consensus on the Consensus results of their monitored shard. Once a corrupted shard is found, its subsequent consensus will be taken over by another shard, hence recovering the system. Via Consensus on Consensus, CoChain allows the existence of shards with more fraction of malicious nodes ( © 2023 IEEE.

关键词： Blockchain

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