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10th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2021

作者： Ojha, Divyank Jadon, Kuldeep Singh Dept. of Computer Science & Engineering Institute of technology and Management Gwalior India

ISBN: (纸本)9780738105239

Sentiment analysis and opinion mining have developed significantly throughout the last decades. This study aims to assess, amongst many other aspects, people's feelings, opinions, and emotions of someone or something. This review's principal objective is to automatically assess the direction of the review and contrast this with the evaluation made by the reviewers. This will allow scientists to categorize and evaluate reviews cross-sectionally and encourage a scientific article's overall evaluation more critically. An EM-VC ensemble approach has been proposed for analyzing the reviews. This approach uses POS to achieve a syntactic sentence structure. With dictionaries, this syntactic structure enables the semantic orientation of the analysis via the scoring algorithm to be determined. To test the proposed approach's efficiency and performance about the baseline method, some tests have been performed using typical performance metrics like precision, accuracy, F1-score, and recall. The findings reveal that improvements are achieved in binary classification, ternary classification, & five-point classification compared to the existing HS-SVM method. Still, it provides a slight improvement in accuracy in the context of multiclass classification compared to others. © 2021 IEEE.

关键词： Sentiment analysis

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Work in Progress: AI-Powered Engineering-Bridging Theory and Practice

arXiv

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arXiv 2025年

作者： Levy, Oz Dikman, Ilya Levy, Natan Winokur, Michael Faculty of Industrial Eng. and Technology Management - HIT Holon Israel dept. Computer Science - HUJI Jerusalem Israel

This paper explores how generative AI can help automate and improve key steps in systems engineering. It examines AI’s ability to analyze system requirements based on INCOSE’s "good requirement" criteria, identifying well-formed and poorly written requirements. The AI does not just classify requirements but also explains why some do not meet the standards. By comparing AI assessments with those of experienced engineers, the study evaluates the accuracy and reliability of AI in identifying quality issues. Additionally, it explores AI’s ability to classify functional and non-functional requirements and generate test specifications based on these classifications. Through both quantitative and qualitative analysis, the research aims to assess AI’s potential to streamline engineering processes and improve learning outcomes. It also highlights the challenges and limitations of AI, ensuring its safe and ethical use in professional and academic settings. Copyright © 2025, The Authors. All rights reserved.

关键词： Requirements engineering

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High-Performance File Searching with Rust: Parallelized Indexing for Enhanced Computational Efficiency

High-Performance File Searching with Rust: Parallelized Inde...

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Interdisciplinary Approaches in technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Snehil Sinha Prabir Kalwani Aayush Shah Jesleena Gonsalves Dept. of Computer Engineering Mukesh Patel School of Technology Management and Engineering SVKM’s NMIMS University Mumbai India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Conventional file explorer tools have many limitations, from sluggish performance to cluttered user interfaces. The Windows File Explorer is often criticized for its slow performance, taking an exorbitant amount of time to load search results, especially for extensive file collections. In contrast, the Rust-based file searcher employs parallelization to rapidly parse the file system and build a comprehensive index. This index is stored in memory using a HashMap data structure, providing constant-time lookups. This study presents a high-performance alternative using the Rust programming language to address these shortcomings. The study shows the advantages of parallelization of search in Rust and then compares file search using Rust and traditional Windows file explorer on multiple parameters, including Search time, CPU usage, and Memory usage. The Rust-Based File Searcher provides faster results at around 80 to 180 milliseconds compared to 4-16 seconds using Windows File Search.

关键词： Technological innovation computer languages File systems User interfaces Programming Data structures Computational efficiency Indexing

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SSGTA: A Novel Swap Sequence based Game Theory Algorithm for Traveling Salesman Problem 24

SSGTA: A Novel Swap Sequence based Game Theory Algorithm for...

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24th International Conference on computer and Information technology, ICCIT 2021

作者： Shahadat, Abu Saleh Bin Ayon, Safial Islam Khatun, Most. Rokeya Khulna University of Engineering Technology Dept. of Computer Science and Engineering Khulna Bangladesh Green University of Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781665494359

Many approaches have been developed to make intelligent moves imitating rational decision-makers. Game theory provides a theoretical framework that can be efficiently employed in solving complex optimization problems. The area of applied mathematics that investigates the strategic behavior of rational factors is known as game theory. In other terms, game theory is an analytical tool for making the optimal decision in interaction and decision-making situations. The Traveling Salesman Problem (TSP) is solved using this research's swap sequence-based game theory algorithm (SSGTA). TSP is a well-known combinatorial optimization real problem. TSP is also widely used to assess expertise in newly emerging optimization techniques. Furthermore, optimization techniques established for other tasks (such as numerical optimization) are tested for TSP competency. A player attempts to update its solution using another player. An expected payoff mechanism is proposed to choose the learning strategy. Based on the improvement of solution quality, a payoff is awarded to the winning player. When no improvement is noticed in the solution, the 2-opt algorithm is employed to get over the local optimal. SSGTA is tested for several benchmark TSP instances from TSPLIB and compared with some other recent methods. SSGTA performs better than different algorithms on accuracy and stability. © 2021 IEEE.

关键词： Game theory

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Fake News Detection Review: Concepts, Tools, Research GAPS, and Possible solutions

Fake News Detection Review: Concepts, Tools, Research GAPS, ...

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International Conference on Network Applications Protocols and Services (NETAPPS)

作者： Sapana Yakkundi Rudragoud Patil Swetha Indudhar Goudar Dept. of Computer Science and Engineering KLS Gogte Institute of Technology Belagavi Karnataka India Department of Computer Science and Engineering KLS Gogte Institute of Technology Belagavi Karnataka India Department. of Master of Computer Applications KLS Gogte Institute of Technology Belagavi Karnataka India

ISBN: (数字)9798331519513

ISBN: (纸本)9798331519520

The increasing number of social platforms means that there is a rapid expansion of online data. However, not all of this data can be trusted, as some users intentionally manipulate information to spread false news for personal or political reasons. Fake news can significantly impact public perception and societal outcomes. Its widespread influence was especially noticeable during the 2016 United States election cycle, leading to heated discussions about its role and consequences. Given the changing nature of news, real-time data processing has become essential for effectively identifying and countering misinformation. This research provides a comprehensive examination of the datasets, tools, and techniques utilized for detecting and combating fake news. Many researchers have developed models based on specific datasets, limiting their scope to particular domains. A thorough review of advanced models for real-time fake news detection highlights existing research gaps. The analysis of the review reveals that numerous datasets used in the literature are outdated, and there is a methodological challenge associated with the use of traditional machine learning approaches. With the continuous evolution of news, there is an urgent demand for utilizing sophisticated deep learning models for the automated, real-time verification of misinformation. This research presents promising approaches to tackle the challenge of detecting fake news.

关键词： Accuracy Protocols Limiting Reviews Social networking (online) Voting Real-time systems Data models Internet Fake news

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Work in Progress: AI-Powered Engineering-Bridging Theory and Practice

Work in Progress: AI-Powered Engineering-Bridging Theory and...

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IEEE World Engineering Education Conference (EDUNINE)

作者： Oz Levy Ilya Dikman Natan Levy Michael Winokur Faculty of Industrial Eng. and Technology Management HIT Holon Israel Dept. Computer Science HUJI Jerusalem Israel

ISBN: (数字)9798331542788

ISBN: (纸本)9798331542795

This paper explores how generative AI can help automate and improve key steps in systems engineering. It examines AI's ability to analyze system requirements based on INCOSE’s "good requirement" criteria, identifying well-formed and poorly written requirements. The AI does not just classify requirements but also explains why some do not meet the standards. By comparing AI assessments with those of experienced engineers, the study evaluates the accuracy and reliability of AI in identifying quality issues. Additionally, it explores AI’s ability to classify functional and non-functional requirements and generate test specifications based on these classifications. Through both quantitative and qualitative analysis, the research aims to assess AI’s potential to streamline engineering processes and improve learning outcomes. It also highlights the challenges and limitations of AI, ensuring its safe and ethical use in professional and academic settings.

关键词： Ethics Educational programs Generative AI Reliability engineering Safety Test pattern generators Requirements engineering Artificial intelligence Engineering education Standards

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Automatic Vehicle Detection using DETR: A Transformer-Based Approach for Navigating Treacherous Roads

arXiv

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arXiv 2025年

作者： Fahad, Istiaq Ahmed Arean, Abdullah Ibne Hanif Ahmed, Nazmus Sakib Hasan, Mahmudul Institute of Information Technology University of Dhaka Dhaka Bangladesh Dept. of Computer Science and Engineering University of Dhaka Dhaka Bangladesh

Automatic Vehicle Detection (AVD) in diverse driving environments presents unique challenges due to varying lighting conditions, road types, and vehicle types. Traditional methods, such as YOLO and Faster R-CNN, often struggle to cope with these complexities. As computer vision evolves, combining Convolutional Neural Networks (CNNs) with Transformer-based approaches offers promising opportunities for improving detection accuracy and efficiency. This study is the first to experiment with Detection Transformer (DETR) for automatic vehicle detection in complex and varied settings. We employ a Collaborative Hybrid Assignments Training scheme, Co-DETR, to enhance feature learning and attention mechanisms in DETR. By leveraging versatile label assignment strategies and introducing multiple parallel auxiliary heads, we provide more effective supervision during training and extract positive coordinates to boost training efficiency. Through extensive experiments on DETR variants and YOLO models, conducted using the BadODD dataset, we demonstrate the advantages of our approach. Our method achieves superior results, and improved accuracy in diverse conditions, making it practical for real-world deployment. This work significantly advances autonomous navigation technology and opens new research avenues in object detection for autonomous vehicles. By integrating the strengths of CNNs and Transformers, we highlight the potential of DETR for robust and efficient vehicle detection in challenging driving environments. © 2025, CC BY.

关键词： Convolutional neural networks

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Automated Healthcare Medical Imaging through EOA Optimized Hyperparameter in CNN

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Procedia computer Science 2025年 259卷 1106-1114页

作者： Umang Kumar Agrawal Nibedan Panda Debashreet Das Asish Kumar Dalai B.V. Ramana Ayush Mishra School of Computer Engineering KIIT Deemed to be University Bhubaneswar Odisha India-751024 Dept. of Computer Science and Engineering Centre for UG and PG studies BPUT Rourkela Odisha School of Computer Science and Engineering VIT-AP University Amaravati AP India Department of IT Aditya Institute of Technology and Management Tekkali AP India-532201

Medical imaging (MI) is the prefatory field of healthcare that plays a vital role in the patient’s clinical and medical intervention. Recent developments in MI help healthcare professionals such as doctors and radiologists diagnose patients with improved quality of MI methods such as X-ray, CT and MRI scans. The upsurge of the Convolutional Neural Network (CNN) offers the latent for timely detection and classification of the disease with deeper insights. It adapts the spatial features and learns the input data hierarchically, which makes well-suited for this task. To improve the robustness of the CNN, the parameters are optimized through a nature-inspired dynamic mass balance-based metaheuristic approach named the Equilibrium Optimizer Algorithm (EOA). As a real-life implication, the suggested EOA-CNN approach has been verified by utilizing two benchmark datasets. The competence of EOA-CNN is assessed by present-day methods in terms of parameters namely accuracy, error rate, min, avg, std, accuracy, sensitivity and specificity. By streamlining the workflow, the said approach is a valuable asset conforming to the reliability to resolve healthcare imaging complications with advanced diagnostics support. This framework is being used to optimize the constraints meanwhile bridging the gaps in healthcare convenience.

关键词： EOA CNN Optimizing Parameters Healthcare Imaging Brain MRI Chest X-ray

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Data Generation in Healthcare Using Constraint Satisfaction Parameters (CSP) 2

Data Generation in Healthcare Using Constraint Satisfaction ...

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2nd International Conference on Advanced Innovations in Smart Cities, ICAISC 2025

作者： Al-Barhamtoshy, Hassanin M. Himdi, Tarik King Abdulaziz University Information Technology Dept. Faculty of Computing Jeddah Saudi Arabia Jeddah International College Computer Science Dept. Jeddah Saudi Arabia

ISBN: (纸本)9798331506995

In this research, we examine the synthetic creation, alteration, and enhancement of data through Generative Artificial Intelligence (GAI). This inaugural study focused on generating healthy data via GAI for analysis and detection of chronic diseases (Depression, Stress, and Anxiety). Following the generation process using this deep learning approach, we analyze and visualize synthetic data through various metrics. The outcomes of this methodology are expected to yield novel insights into data generation concerning pressure-related diseases (depression, stress, and anxiety). Integrating high-quality synthetic data is anticipated to improve the production of financial transaction data. Finally, the proposed model is evaluated, and the results of the initial GAI model are generated. In this paper, we introduce a specialized generative Transformer language model that has been pre-trained on an extensive collection of medical constraint satisfaction parameters. We assess generation across medical natural language processing tasks and show that it surpasses existing models in most evaluations. Additionally, we offer a comprehensive analysis of the model's limitations by detailing numerous errors and challenges it faces. The mean scores for the produced descriptive data and shape pair trends in continuous columns were around 0.99 and 0.92, respectively. Conversely, the average scores for the categorical text generated in the initial region were notably high. This highlights the GAI model utilized in implementing each method. Our evaluation revealed that a majority of studies employ synthetic data generators to (i) reduce the costs and duration linked to clinical trials for rare diseases, (ii) enhance the predictive performance of AI models in personalized medicine, and (iii) provide researchers with access to high-quality, representative multimodal datasets while protecting sensitive patient information, among other advantages. Synthetic data generators based on deep learn

关键词： Theranostics

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Optimization of Building Construction Control System Based on Nonlinear Image Enhancement

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computer-Aided Design and Applications 2023年 20卷 213-224页

作者： Liu, Congru Lin, Mingsen Ghafoor, Kayhan Zrar Al-Dabagh, Mustafa Zuhaer Nayef Karras, Dimitrios A. School of Urban Construction Wuhan University of Science and Technology Wuhan China Changjiang Polytechnic Wuhan China Department of Computer Science Knowledge University Erbil44001 Iraq Department of Software & Informatics Engineering Salahaddin University-Erbil Erbil44001 Iraq School of Science Dept. General Athens Greece

To reflect the nonlinear characteristics of the building structural adjustment system, an active vibration control strategy based on the nonlinear is proposed. In this method, the size of the structural control force is determined by comparing the allowable energy with the actual energy, and its direction is determined by taking the derivative of the energy. Because there is no need to solve the linear matrix inequality, the calculation is simple and the operation is convenient. Moreover, when multiple actuators with the same capacity limit are used, the control output force is consistent, thus avoiding the design of multiple control systems and simplifying the controller design. The simulation results show that the maximum controlled displacements of floor 6 to floor 8 are 1.16cm, 1.12cm, and 0.97cm, respectively. The proposed nonlinear energy control method has the advantages of simple control process and avoiding actuator saturation. © 2023 CAD Solutions, LLC.

关键词： Floors

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