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CVTD: A Robust Car-Mounted Video Text Detector

computers, Materials & Continua 2024年第2期78卷 1821-1842页

作者： Di Zhou Jianxun Zhang Chao Li Yifan Guo Bowen Li Department of Computer Science and Engineering Chongqing University of TechnologyChongqingChina College of Information and Engineering Jingdezhen Ceramic UniversityJingdezhenChina

Text perception is crucial for understanding the semantics of outdoor scenes,making it a key requirement for building intelligent systems for driver assistance or autonomous *** information in car-mounted videos can assist drivers in making ***,Car-mounted video text images pose challenges such as complex backgrounds,small fonts,and the need for real-time *** proposed a robust Car-mounted Video Text Detector(CVTD).It is a lightweight text detection model based on ResNet18 for feature extraction,capable of detecting text in arbitrary *** model efficiently extracted global text positions through the Coordinate Attention Threshold Activation(CATA)and enhanced the representation capability through stacking two Feature Pyramid Enhancement Fusion Modules(FPEFM),strengthening feature representation,and integrating text local features and global position information,reinforcing the representation capability of the CVTD *** enhanced feature maps,when acted upon by Text Activation Maps(TAM),effectively distinguished text foreground from non-text ***,we collected and annotated a dataset containing 2200 images of Car-mounted Video Text(CVT)under various road conditions for training and evaluating our model’s *** further tested our model on four other challenging public natural scene text detection benchmark datasets,demonstrating its strong generalization ability and real-time detection *** model holds potential for practical applications in real-world scenarios.

关键词： Deep learning text detection Car-mounted video text detector intelligent driving assistance arbitrary shape text detector

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Quadratic and Lagrange interpolation-based butterfly optimization algorithm for numerical optimization and engineering design problem

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Soft Computing 2025年第1期29卷 157-194页

作者： Sharma, Sushmita Saha, Apu Kumar Chakraborty, Sanjoy Deb, Suman Sahoo, Saroj Kumar Department of Computer Science and Engineering Indian Institute of Information Technology Tripura Agartala India Department of Computer Science and Engineering National Institute of Technology Tripura Agartala India Department of Mathematics National Institute of Technology Agartala Tripura Agartala India Department of Information Technology Bir Bikram Memorial College Tripura Agartala India

This work proposes a novel and improved Butterfly Optimization Algorithm (BOA), known as LQBOA, to solve BOA’s inherent limitations. The LQBOA uses Lagrange interpolation and simple quadratic interpolation techniques with adapted parameter settings to enhance the search strategy of BOA and to achieve a better balance of diversification and intensification. LQBOA’s performance was examined on 45 traditional benchmark issues as well as the IEEE CEC 2017 benchmark suites with 10, 30 and 50 dimensions, and the results were compared against popular state-of-the-art and modified algorithms. It was discovered that in more than 85% of cases, the proposed LQBOA outperformed the compared algorithms. The Friedman rank test and Wilcoxon rank test were used to validate the suggested LQBOA’s rank and significance. Additionally, convergence and diversity analyses were performed to investigate its convergence speed and searching behavior, respectively. Furthermore, LQBOA has been successfully deployed to solve twelve real-world issues including several engineering design problems and two multiple gravity assist spacecraft trajectory problems. The results of these problems were compared to those of a wide range of algorithms, demonstrating the superior performance of the proposed LQBOA. In conclusion, LQBOA makes a significant addition to the optimization area, and its application could greatly improve the performance of numerous optimization jobs. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Lagrange multipliers

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Powder spreading process monitoring of selective laser melting manufacturing by using a convolutional Takagi–Sugeno–Kang fuzzy neural network

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International Journal of Advanced Manufacturing technology 2024年第9-10期132卷 4989-5004页

作者： Lin, Chun-Hui Lin, Cheng-Jian Wang, Shyh-Hau Department of Computer Science and Information Engineering National Cheng Kung University Tainan701 Taiwan Department of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung411 Taiwan

Process monitoring plays a pivotal role in elucidating the intricate interplay among process, structure, and property in additive manufacturing production. The control of powder spreading affects not only particle adhesion but also rearrangement, both of which are indispensable constituents in the selective laser melting (SLM) process. To develop a novel monitoring process, this study proposed a convolutional Takagi–Sugeno–Kang fuzzy neural network (C-TSKFNN) to assess images with uneven powder distribution. The performance of the model was compared with that of conventional image analysis and state-of-the-art deep learning models. With its fusion layer and TSKFNN design, the C-TSKFNN model retains the distinctive characteristics of input images and minimizes expenditures through trainable parameters. The experimental results demonstrated that the average F1 score of the C-TSKFNN model achieved 97.07% and that it had the lowest standard deviation compared to state-of-the-art deep learning models. The trainable parameters were only 103,506, which is lower than those of models. Defect information was also provided locally through a model interpretation technique, facilitating the monitoring of SLM manufacturing. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Process monitoring

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Transient Characteristics and Accommodative Current Limiting Strategy for Bidirectional Interlinking Converters in Hybrid AC/DC Microgrids

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CSEE Journal of Power and Energy Systems 2024年第4期10卷 1575-1588页

作者： Xia Shen Zhikang Shuai Wen Huang Chao Shen Yang Shen Z.John Shen College of Electrical and Information Engineering Hunan UniversityChangsha 410082China Department of Electrical and Computer Engineering Illinois Institute of TechnologyChicagoIL 60616 USA

Bidirectional interlinking converter(BIC)is the core equipment in a hybrid AC/DC microgrid connected between AC and DC ***,the variety of control modes and flexible bidirectional power flow complicate the influence of AC faults on BIC itself and on DC sub-grid,which potentially threaten both converter safety and system *** study first investigates AC fault influence on the BIC and DC bus voltage under different BIC control modes and different pre-fault operation states,by developing a mathematical model and equivalent sequence ***,based on the analysis results,a general accommodative current limiting strategy is proposed for BIC without limitations to specific mode or operation *** amplitude is predicted and constrained according to the critical requirements to protect the BIC and relieving the AC fault influence on the DC bus *** with conventional methods,potential current limit failure and distortions under asymmetric faults can also be ***,experiments verify feasibility of the proposed method.

关键词： Bidirectional interlinking converter current limiting fault characteristic hybrid AC/DC microgrid variation/oscillation of DC bus voltage

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HDEL: a hierarchical deep ensemble approach for text-based emotion detection

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Multimedia Tools and Applications 2025年第4期84卷 1799-1820页

作者： Vora, Shivani Mehta, Rupa G. Information Technology Department C.K.Pithawala College of Engineering and Technology Gujarat Surat India Department of Computer Science and Engineering SVNIT Gujarat Surat India

Emotion detection from social media data plays a crucial role in studying societal emotions concerning different events, aiding in predicting the reactions of specific social groups. However, it is complex to automatically extract implicit emotional information from noisy social media text data. This study introduces the Hierarchical Deep Ensemble Learning (HDEL) system to identify emotions in text data. The proposed HDEL model utilizes BiLSTM (Bidirectional Long Short-Term Memory), CNN (Convolutional Neural Network), BiGRU (Bidirectional Gated Recurrent Unit), and RCNN (Recurrent Convolutional Neural Network) in the first level of its hierarchy. The predicted probabilities of the four models are embedded with input data to prepare the intermediate hybrid data. This hybrid data is fed to the next layer of the proposed system, which utilizes a Random Forest (RF) algorithm to predict the emotion. The proposed approach is tested using three emotion datasets: the WASSA-2017 Emotion Intensity (EmoInt) dataset, the International Survey on Emotion Antecedents and Reactions (ISEAR) dataset, and the CrowdFlower (CF) dataset. EmoInt and ISEAR are clean and balanced, while CF is noisy and imbalanced. The results are compared with various state-of- the-art Machine Learning models. The outperforming results depict the superiority of the proposed approach. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Learning systems

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Data-Driven Decision Support by Utilizing Machine Learning to Predict Passenger Flow for Route and Station Optimization

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Journal of The Institution of Engineers (India): Series B 2025年 1-20页

作者： Patel, Madhuri Patel, Samir B. Swain, Debabrata Department of Computer Engineering Pandit Deendayal Energy University Gandhinagar India Department of Information Technology L D College of Engineering Ahmedabad India

A smart public transportation system with reliable services addresses urban challenges like traffic congestion, infrastructure maintenance, travel costs, and pollution. As part of smart city initiatives, urban public transport systems have adopted advanced data collection methods, enabling data analytics and predictive model development. This study evaluates optimal machine learning models for predicting passenger flow across routes and stations within the Thane Municipal Transport (TMT) network using real historical data from March 2021 to May 2022. Key spatial and temporal features such as weekday patterns, route distance and direction, public holidays, COVID-19 impacts, and weather conditions were identified through exploratory data analysis and incorporated into the models. Several algorithms, including Decision Trees, XGBoost, Extra Tree Regressor, and Random Forest, were implemented to predict daily passenger commute on an hourly basis. Random Forest emerged as the most accurate model, achieving an MAE of 114.55, RMSE of 236.71, and an R2 of 0.9516, leveraging its ensemble approach to capture complex, non-linear patterns and ensure robustness to variability, including disruptions caused by the pandemic. These findings provide actionable insights for optimizing route design, bus allocation, maintenance scheduling, and overall transport system efficiency, enabling stakeholders to achieve sustainable and adaptive public transport management. © The Institution of Engineers (India) 2025.

关键词： Traffic congestion

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A fully flexible key delegation mechanism with efficient fine-grained access control in CP-ABE

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Journal of Ambient Intelligence and Humanized Computing 2023年第9期14卷 12837-12856页

作者： Porwal, Shardha Mittal, Sangeeta Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Uttar Pradesh Noida India

Ciphertext Policy-Attribute Based Encryption (CP-ABE) is a secure one-to-many asymmetric encryption schemes where access control of a shared resource is defined in terms of a set of attributes possessed by a user. Key delegation service, where a user is able to temporally transfer its full or partial access rights, is an important requirement to make CP-ABE based systems practically useful. In existing CP-ABE delegation schemes, a delegator can delegate its entire or partial set of attributes such that all content accessible using those attributes becomes available to the delegatee also. Flexibility to delegate some specific content, which is a practical requirement, is not feasible with these schemes. Further, for a delegated resource, flexibility of fine grained delegation like delegating only a specific subset of read, write and update permissions is not yet available. In this work, CP-ABE key delegation has been enhanced to support specific content and specific permission delegation. The proposed scheme is fully flexible as it additionally supports limits on the number of times delegation can be done, delegatee revocation and traceability of forbidden delegation. Theoretical and practical analysis show that the proposed scheme achieves its objectives and is computationally efficient as well as secure against chosen plaintext and key abuse attacks. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Access control

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Analysis of community question-answering issues via machine learning and deep learning:State-of-the-art review

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CAAI Transactions on Intelligence technology 2023年第1期8卷 95-117页

作者： Pradeep Kumar Roy Sunil Saumya Jyoti Prakash Singh Snehasish Banerjee Adnan Gutub Department of Computer Science and Engineering Indian Institute of Information TechnologySuratIndia Indian Institute of Information Technology DharwadIndia Department of Computer Science and Engineering National Institute of TechnologyPatnaIndia The York Management School University of YorkYorkUK Computer Engineering Department College of Computer&Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia

Over the last couple of decades,community question-answering sites(CQAs)have been a topic of much academic *** have often leveraged traditional machine learning(ML)and deep learning(DL)to explore the ever-growing volume of content that CQAs *** clarify the current state of the CQA literature that has used ML and DL,this paper reports a systematic literature *** goal is to summarise and synthesise the major themes of CQA research related to(i)questions,(ii)answers and(iii)*** final review included 133 *** research themes include question quality,answer quality,and expert *** terms of dataset,some of the most widely studied platforms include Yahoo!Answers,Stack Exchange and Stack *** scope of most articles was confined to just one platform with few cross-platform *** with ML outnumber those with ***,the use of DL in CQA research is on an upward trajectory.A number of research directions are proposed.

关键词： answer quality community question answering deep learning expert user machine learning question quality

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NeurDB: an AI-powered autonomous data system

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Science China(information Sciences) 2024年第10期67卷 129-150页

作者： Beng Chin OOI Shaofeng CAI Gang CHEN Yanyan SHEN Kian-Lee TAN Yuncheng WU Xiaokui XIAO Naili XING Cong YUE Lingze ZENG Meihui ZHANG Zhanhao ZHAO School of Computing National University of Singapore College of Computer Science and Technology Zhejiang University Department of Computer Science and Engineering Shanghai Jiao Tong University School of Information Renmin University of China School of Computer Science and Technology Beijing Institute of Technology

In the wake of rapid advancements in artificial intelligence(AI), we stand on the brink of a transformative leap in data systems. The imminent fusion of AI and DB(AI×DB) promises a new generation of data systems, which will relieve the burden on end-users across all industry sectors by featuring AI-enhanced functionalities, such as personalized and automated in-database AI-powered analytics, and selfdriving capabilities for improved system performance. In this paper, we explore the evolution of data systems with a focus on deepening the fusion of AI and DB. We present NeurDB, an AI-powered autonomous data system designed to fully embrace AI design in each major system component and provide in-database AI-powered analytics. We outline the conceptual and architectural overview of NeurDB, discuss its design choices and key components, and report its current development and future plan.

关键词： AI$\times$DB in-database AI intelligent data system

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Improved Dynamic Johnson Sequencing Algorithm(DJS)in Cloud Computing Environment for Efficient Resource Scheduling for Distributed Overloading

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Journal of Systems Science and Systems engineering 2024年第4期33卷 391-424页

作者： Anurag Sinha Pallab Banerjee Sharmistha Roy Nitasha Rathore Narendra Pratap Singh Mueen Uddin Maha Abdelhaq Raed Alsaqour Department of Computer Science Engineering ICFAI Tech SchoolICFAI UniversityJharkhandRanchi834004India Department of Computer Science Engineering Amity UniversityJharkhandRanchi835303India Department of Computing and Information Technology Usha Martin UniversityRanchi835103India Department of Information Technology Bharti Vidyapeeth’s College of EngineeringPaschim ViharNew DelhiIndia Department of CSE Delhi UniversityNew DelhiIndia College of Computing and Information Technology University of Doha for Science and TechnologyDoha24449Qatar Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdurrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Technology College of Computing and InformaticsSaudi Electronic University93499RiyadhSaudi Arabia

This study introduces an innovative approach to optimize cloud computing job distribution using the Improved Dynamic Johnson Sequencing Algorithm(DJS).Emphasizing on-demand resource sharing,typical to Cloud Service Providers(CSPs),the research focuses on minimizing job completion delays through efficient task *** Johnson’s rule from operations research,the study addresses the challenge of resource availability post-task *** advocates for queuing models with multiple servers and finite capacity to improve job scheduling models,subsequently reducing wait times and queue *** Dynamic Johnson Sequencing Algorithm and the M/M/c/K queuing model are applied to optimize task sequences,showcasing their efficacy through comparative *** research evaluates the impact of makespan calculation on data file transfer times and assesses vital performance indicators,ultimately positioning the proposed technique as superior to existing approaches,offering a robust framework for enhanced task scheduling and resource allocation in cloud computing.

关键词： First come first served(FCFS)scheduling round robin task scheduling queuing model Johnson scheduling algorithm

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