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Automatic License Plate Detection Using information Processing system Vision and Deep Learning 2

Automatic License Plate Detection Using Information Processi...

2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications systems, ICMACC 2024

作者： Sharma, Avinash Dwivedi, Ishita Dawar, Divya Saxena, Rini Barua, Tarkcshwar Goyel, Parul Chandigarh Engineering Chandigarh Group of Colleges Department of information processing system Science and Engineering Mohali140307 India Mohali140307 India Roorkee Institute of Technology Computer Science & Engineering Department Roorkee India M. M. Engineering College Maharishi Markandeshwar University CSE Department Mullana Haryana Ambala133207 India

ISBN: (纸本)9798350366570

The content of this probe is to overcome the current cognitive content with ALPR playacting, with a special accent on developing a dead and impelling playacting for distinguishing License plates from video film. The study attempts to ameliorate the presentation norms of ALPR technology by utilizing the powerful accumulation of optical character identification (OCR)[20] and deep learning based object detection algorithmic program. The core of this occupation is the use of the cutting-edge YOLOv8. object distinguishing hypothesis, which has been pains taking and disciplined on an unequaled dataset premeditated with License plate detection in mind. Focusing on quality and efficiency, the YOLOv8 model accomplish an amazing 99.8 quality rate when it comes to accurately localization and categorization License plates[18] inside video frames. This occupation aims to formalize the willingness and dependability of the advisable ALPR playacting through extensive scientific research. The project aims to intensify ALPR technology and determinant the forthcoming of transport and police investigation playactings[18] by breaking through current obstruction and establishing new presentation norms. © 2024 IEEE.

关键词： Deep learning

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Empowering Deaf Children with Sinhala Sign Language, Emotion Detection, and Sound Recognition 5

Empowering Deaf Children with Sinhala Sign Language, Emotion...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Fernando, W.H.K. Perera, P.L.D.N. Fernando, M.I.N. Gunasekara, P.T.E.W. Fernando, Harinda De Zoysa, Rivoni Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer System Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

This study describes a broad endeavor to use cutting-edge technologies to empower deaf primary school students in Sri Lanka. Three key elements make up the study: a sound recognition and classification system, an Android software for translating gestures in Sinhala Sign Language (SSL), and a mobile app for emotion recognition and text-tospeech in Sinhala. By promptly alerting caregivers to potential dangers, the sound recognition technology secures the security of deaf children at home, providing an invaluable layer of security. The Android software bridges the communication gap and improves family interactions by translating SSL gestures captured by the device's camera into textual representations. This promotes successful communication between deaf children and their parents. Convolutional Neural Networks (CNNs) are used in the mobile application to detect and understand emotions based on facial expressions. This enables non-verbal youngsters to communicate successfully through Sinhala textto-speech output with adaptive tone modulation. This research aims to build a caring and inclusive atmosphere, enabling increased independence and enhanced communication skills in deaf children by combining these advances. The suggested solutions could significantly improve the lives of deaf kids, providing them with opportunities for better learning, social interaction, and emotional health. © 2023 IEEE.

关键词： Emotion Recognition

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Hybrid Grey Wolf and Dipper Throated Optimization in Network Intrusion Detection systems

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computers, Materials & Continua 2023年第2期74卷 2695-2709页

作者： Reem Alkanhel Doaa Sami Khafaga El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Rashid Amin Mostafa Abotaleb B.M.El-den Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science University of Engineering and TechnologyTaxilaPakistan Department of System Programming South Ural State UniversityChelyabinsk454080Russia

The Internet of Things(IoT)is a modern approach that enables connection with a wide variety of devices *** to the resource constraints and open nature of IoT nodes,the routing protocol for low power and lossy(RPL)networks may be vulnerable to several routing ***’s why a network intrusion detection system(NIDS)is needed to guard against routing assaults on RPL-based IoT *** imbalance between the false and valid attacks in the training set degrades the performance of machine learning employed to detect network ***,we propose in this paper a novel approach to balance the dataset classes based on metaheuristic optimization applied to locality-sensitive hashing and synthetic minority oversampling technique(LSH-SMOTE).The proposed optimization approach is based on a new hybrid between the grey wolf and dipper throated optimization *** prove the effectiveness of the proposed approach,a set of experiments were conducted to evaluate the performance of NIDS for three cases,namely,detection without dataset balancing,detection with SMOTE balancing,and detection with the proposed optimized LSHSOMTE *** results showed that the proposed approach outperforms the other approaches and could boost the detection *** addition,a statistical analysis is performed to study the significance and stability of the proposed *** conducted experiments include seven different types of attack cases in the RPL-NIDS17 *** on the 2696 CMC,2023,vol.74,no.2 proposed approach,the achieved accuracy is(98.1%),sensitivity is(97.8%),and specificity is(98.8%).

关键词： Metaheuristics grey wolf optimization dipper throated optimization dataset balancing locality sensitive hashing SMOTE

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Analyzing Different Models for Driver Behavior Detection Using EEG Data 15

Analyzing Different Models for Driver Behavior Detection Usi...

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15th International Conference on Communications, COMM 2024

作者： Al-Gburi, Sama Hussein Al-Sammak, Kanar Alaa Marghescu, Ion Oprea, Claudia Cristina Alheeti, Khattab M. Ali Almosa, Nadia Ali Abbas POLITEHNICA Bucharest Doctoral School of Electronics Telecommunications and Information Technology Bucharest Romania POLITEHNICA Bucharest Telecommunications Department Bucharest Romania University of Anbar College of Computer Sciences and Information Technology Department of Computer Networking System Ramadi Iraq University of Babylon Iraq College of Information Technology Networking Department Babylon Iraq

ISBN: (纸本)9798350355956

Ensuring road safety is becoming more and more significant for researchers and for all the society. One of the main issues in this area considers driver drowsiness. Therefore, the focus of this paper is on the comprehensive analysis and comparison of three models of Machine Learning Algorithms Deep Neural Network (DNN), Logistic Regression, and Random Forest. In this case we target the classification of Alertness State of drivers into 'Alert' and 'Drowsy' type by using a designed data set. The ultimate goal is to gain insights into the intricate factors that shape driver behavior. This study used a benchmark EEG dataset. The data preprocessing methods included careful handling of missing values through imputation techniques and the transformation of categorical variables to improve their suitability for machine learning models. When assessing the effectiveness of each model, four key metrics are taken into account: precision, recall, F1 score, and accuracy. In this study, we used a EEG dataset. The study yielded several significant findings regarding the performance of the models employed. The DNN model reached an accuracy of 0.88, indicative of a strong performance nonetheless slightly lower than the Random Forest model, which achieved the highest accuracy at 0.94. The Logistic Regression model had the lowest overall accuracy at 0.83. These findings recommend that random forest models could be principally beneficial for developing reliable, real-time drowsiness detection systems, potentially improving road safety. © 2024 IEEE.

关键词： Logistic regression

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Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 18-36页

作者： Ding Wang Ning Gao Derong Liu Jinna Li Frank L.Lewis IEEE the Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijing Laboratory of Smart Environmental Protectionand Beijing Institute of Artificial IntelligenceBeijing University of TechnologyBeijing 100124China the School of System Design and Intelligent Manufacturing Southern University of Science and TechnologyShenzhen 518055China the Department of Electrical and Computer Engineering University of Illinois at ChicagoChicago IL 60607 USA the School of Information and Control Engineering Liaoning Petrochemical UniversityFushun 113001China the UTA Research Institute the University of Texas at ArlingtonArlington TX 76118 USA

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, ***, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation ***, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

关键词： Adaptive dynamic programming(ADP) advanced control complex environment data-driven control event-triggered design intelligent control neural networks nonlinear systems optimal control reinforcement learning(RL)

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A Holistic Anti-Counterfeiting Platform Using NFC and Blockchain Technologies

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computers, Materials and Continua 2025年第3期83卷 4257-4280页

作者： Rajendren Subramaniam Saaidal Razalli Azzuhri Teh Ying Wah Atif Mahmood Vimala Balakrishnan Department of Information System Faculty of Computer Science & Information Technology Universiti Malaya Kuala Lumpur 50603 Malaysia Department of Computer System & Technology Faculty of Computer Science & Information Technology Universiti Malaya Kuala Lumpur 50603 Malaysia Faculty of Data Science and Information Technology INTI International University Nilai 71800 Malaysia

Counterfeiting is still a pervasive global issue, affecting multiple industries and hindering industrial innovation, while causing substantial financial losses, reputational damage, and risks to consumer safety. From luxury goods and pharmaceuticals to electronics and automotive parts, counterfeit products infiltrate supply chains, leading to a loss of revenue for legitimate businesses and undermining consumer trust. Traditional anti-counterfeiting measures, such as holograms, serial numbers, and barcodes, have proven to be insufficient as counterfeiters continuously develop more sophisticated replication techniques. As a result, there is a growing need for more advanced, secure, and reliable methods to prevent counterfeiting. This paper presents a novel, holistic anti-counterfeiting platform that integrates Near Field Communication (NFC)-enabled mobile applications with blockchain technology to provide an innovative, secure, and consumer-friendly authentication mechanism. Our approach addresses key gaps in existing solutions by incorporating dynamic product identifiers, which make replication significantly more difficult. The system enables consumers to verify the authenticity of products instantly using their smartphones, enhancing transparency and trust in the supply chain. Blockchain technology plays a crucial role in our proposed solution by providing an immutable, decentralized ledger that records product authentication data. This ensures that product verification records cannot be tampered with or altered, adding a layer of security that is absent in conventional systems. Additionally, NFC technology enhances security by offering unique identification capabilities, enabling real-time product verification. To validate the effectiveness of the proposed system, real-world testing was conducted across different industries. The results demonstrated the platform’s ability to significantly reduce counterfeit products in the supply chain, offering businesses and cons

关键词： Blockchain near field communication (NFC) anti-counterfeiting

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Chain-of-Specificity: Enhancing Task-Specific Constraint Adherence in Large Language Models 31

Chain-of-Specificity: Enhancing Task-Specific Constraint Adh...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Wei, Kaiwen Zhong, Jiang Zhang, Hongzhi Zhang, Di Jin, Li Yu, Yue Zhang, Jingyuan College of Computer Science Chongqing University China Kuaishou Inc. China Key Laboratory of Network Information System Technology Aerospace Information Research Institute Chinese Academy of Sciences China

ISBN: (纸本)9798891761964

Large Language Models (LLMs) exhibit remarkable generative capabilities, enabling the generation of valuable information. Despite these advancements, previous research found that LLMs sometimes struggle with adhering to specific constraints (e.g., in specific place or at specific time), at times even overlooking them, which leads to responses that are either too generic or not fully satisfactory. Existing approaches attempted to address this issue by decomposing, rewriting input instructions, or reflecting on prior failings, yet they fall short in adequately emphasizing specific constraints and in unlocking the underlying knowledge (e.g., programming within the context of software development). In response, this paper proposes a simple yet effective method Chain-of-Specificity (CoS). Specifically, CoS iteratively emphasizes the specific constraints in the input instructions, unlocks knowledge within LLMs, and refines responses. Experiments conducted on publicly available and self-build complex datasets demonstrate that CoS outperforms existing methods in enhancing generated content especially for the specificity. Besides, as the number of specific constraints increase, other baselines falter, while CoS still performs well. Moreover, we show that distilling responses generated by CoS effectively enhances the ability of smaller models to follow the constrained instructions. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Driver Behavior Assessment with Different ML Models Using EEG and Physiological Data - A Comparative Study 16

Driver Behavior Assessment with Different ML Models Using EE...

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16th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2024

作者： Al-Gburi, Sama Hussein Al-Sammak, Kanar Alaa Alheeti, Khattab M. Ali Suciu, George Abdulqader, Abedulqader Gailan Faculty of Electronics Telecommunications and Information Technology Bucharest Romania University of Anbar College of Computer Sciences and Information Technology Faculty of Department of Computer Networking System Ramadi Iraq Beia Consult International Faculty of Electronics Telecommunications and Information Technology Bucharest Romania Ministry of Education Karkh Second Directorate of Education Baghdad Iraq

ISBN: (纸本)9798350371154

Ensuring road safety is becoming more and more significant for researchers and for all the society. One of the main issues in this area considers driver drowsiness. Therefore, the focus of this paper is the comprehensive analysis and comparison of four distinct machine learning models: Logistic Regression, Random Forest, Support Vector Machine (SVM), and Gradient Boosting. The ultimate goal is to gain insights into the intricate factors that shape driver behavior. When assessing the effectiveness of each model, four key metrics are taken into account: precision, recall, F1 score, and accuracy. In this study, we used a 'TRYOUT' dataset. The study yielded several significant findings regarding the performance of the machine learning models employed. Remarkably, the accuracy scores for all models were notably high, falling within a range of 0.8651 to 0.9999. Among all the models, the Random Forest model stood out as the most effective, displaying an impressive accuracy score of 0.9999 on the merged data. The SVM model, while not as proficient as the Random Forest, still managed a respectable performance, particularly on the MPM39 dataset where it achieved an accuracy of 7 5. 0 6 %. The Logistic Regression model demonstrated its strength on the CVD08 dataset, although its performance was comparatively weaker on the other datasets. Lastly, the Gradient Boosting model demonstrated consistent competency across all datasets, underscoring its reliability in a range of circumstances. © 2024 IEEE.

关键词： Support vector machines

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A Coarse to Fine Detection Method for Prohibited Object in X-ray Images Based on Progressive Transformer Decoder 24

A Coarse to Fine Detection Method for Prohibited Object in X...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Ma, Chunjie Du, Lina Gao, Zan Zhuo, Li Wang, Meng Shandong Jinan China School of Computer Science and Technology Shandong Jianzhu University Shandong Jinan China Faculty of Information Technology Beijing University Of Technology Beijing China School of Computer Science and Information Engineering Hefei University of Technology Anhui Hefei China Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China

ISBN: (纸本)9798400706868

Currently, Transformer-based prohibited object detection methods in X-ray images appear constantly, but there are still some shortcomings such as poor performance and high computational complexity for prohibited object detection with heavily occlusion. Therefore, a coarse to fine detection method for prohibited object in X-ray images based on progressive Transformer decoder is proposed in this paper. Firstly, a coarse to fine framework is proposed, which includes two stages: coarse detection and fine detection. Through adaptive inference in stages, the computational efficiency of the model is effectively improved. Then, a position and class object queries method is proposed, which improves the convergence speed and detection accuracy of the model by fusing the position and class information of prohibited object with object queries. Finally, a progressive Transformer decoder is proposed, which distinguishes high and low score queries by decreasing confidence thresholds, so that high-score queries are not affected by low-score queries in the decoding stage, and the model can focus more on decoding low-score queries, which usually correspond to prohibited object with severe occlusion. The experimental results on three public benchmark datasets (SIXray, OPIXray, HiXray) demonstrate that compared with the baseline DETR, the proposed method achieves the state-of-the-art detection accuracy with a 21.6% reduction in model computational complexity. Especially for prohibited objects with heavily occlusion, accurate detection can be carried out. © 2024 ACM.

关键词： Decoding

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CFinDEE: A Chinese Fine-Grained Financial Dataset for Document-Level Event Extraction 24

CFinDEE: A Chinese Fine-Grained Financial Dataset for Docume...

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33rd Companion of the ACM World Wide Web Conference, WWW 2023

作者： Zhang, Tian Liu, Maofu Zhou, Bingying School of Computer Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9798400701726

Document-level event extraction faces numerous challenges in accurately modeling real-world financial scenarios, particularly due to the inadequacies in existing datasets regarding data scale and fine-grained annotations. The development of datasets is a crucial factor in driving research progress;therefore, we present a high-quality Chinese document-level event extraction dataset, CFinDEE. This dataset, grounded in real-world financial news, defines 22 event types and 116 argument roles, annotating 26,483 events and 107,096 event arguments. CFinDEE aims to address these shortcomings by providing more comprehensive annotations and data augmentation, offering richer resources for document-level event extraction in the financial domain. CFinDEE extends data both horizontally and vertically, where horizontal expansion enriches the types of financial events, enhancing the diversity of the dataset;vertical expansion, by increasing the scale of the data, effectively boosts the practical value of the dataset. Experiments conducted on multiple advanced models have validated the high applicability and effectiveness of the CFinDEE dataset for document-level event extraction tasks in the financial field. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Expansion

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