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2nd International Conference on Self Sustainable artificial intelligence Systems, ICSSAS 2024

作者： Praveen, R.V.S. Vinoth, B. Sowmiya, S. Tharageswari, K. Vamsilala, Purushothapatnapu Naga Venkata Sathya, R. Digital Engineering LTIMindtree Telangana Hyderabad India Department of Statistics Bengaluru India Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Coimbatore India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India Vignan's Lara Institute of Technology and Science India Kongunadu College of Engineering and Technology Department of It Tamil Nadu Trichy India

ISBN: (纸本)9798350368413

Monitoring the environment is a complex task that is crucial for all living beings. Urban and industrial areas are particularly vulnerable to pollution, which can lead to various health issues. This system utilizes data analysis and machine learning to monitor and classify regions where gas emissions exceed permissible levels. Unlike existing systems, which can only predict air quality in small areas with limited efficiency, the proposed system can accurately predict air quality across larger regions. By factoring in the temperature of a specific location - since air quality is heavily influenced by global warming - the system can predict air grade. The DBSCAN algorithm proves to be particularly effective in identifying highly polluted areas, outperforming other algorithms in terms of accuracy and efficiency. Air Quality Operational Centres (AQOCs) are crucial in identifying regions with severe pollution. These centres analyse data gathered by various units to make final assessments. The predicting air pollution relied on complex physiochemical processes and dispersion models, which were both time-consuming and required large datasets, making them less efficient for practical use. © 2024 IEEE.

关键词： Air quality

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Optimizing Medical Healthcare Services Using Big data Analytics 9

Optimizing Medical Healthcare Services Using Big Data Analyt...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Muthumanickam, K. Arthi, A. Umarani, B. Kumaravel, T. Department of Information Technology Tamilnadu Thottiyam India Rajalakshmi Institute of Technology Department of Artificial Intelligence and Data Science Tamilnadu Chennai India Department of Electronics and Communication Engineering Tamilnadu Thottiyam India Kongu Engineering College Department of Computer Science and Engineering Tamilnadu Perundurai India

ISBN: (纸本)9798350397376

Big data is a very large size of datasets which come from many different sources and are in a wide variety of forms. Due to its enormous potential, big data has gained popularity in recent years. Big data enables us to investigate and reinvent numerous fields, including the healthcare industry, education, and others. Big data specifically in the healthcare sector comes from a variety of sources, including patient medical information, hospital records, findings from physical exams, and the outcomes of medical devices. Covid19 recently, one of the most neglected areas to concentrate on has come under scrutiny due to the pandemic: healthcare management. Patient duration of stay in a hospital is one crucial statistic to monitor and forecast if one wishes to increase the effectiveness of healthcare management in a hospital, even if there are many use cases for data science in healthcare management. At the time of admission, this metric aids hospitals in identifying patients who are at high Length of Stay namely LS risk (patients who will stay longer). Once identified, patients at high risk for LS can have their treatment plans improved to reduce LS and reduce the risk of infection in staff or visitors. Additionally, prior awareness of LS might help with planning logistics like room and bed allotment. The aim of the suggested system is to precisely anticipate the length of stay for each patient on an individual basis so that hospitals can use this knowledge for better functioning and resource allocation using data analytics. This would contribute to improving treatments and services. © 2023 IEEE.

关键词： COVID-19

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YOLOv8 Enhanced: Pioneering Accuracy in Traffic Sign Detection and Classification 9

YOLOv8 Enhanced: Pioneering Accuracy in Traffic Sign Detecti...

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9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Thapliyal, Nitin Aeri, Manisha Namdev, Dheeraj Kukreja, Vinay Sharma, Rishabh Graphic Era Hill University Computer Science And Engineering Uttrakhand Dehradun248002 India Graphic Era Deemed To Be University Computer Science And Engineering Uttarakhand Dehradun India Iimt College of Engineering Artificial Intelligence & Data Science Uttar Pradesh Greater Noida201310 India Chitkara University Institute of Engineering And Technology Chitkara University Punjab India

ISBN: (纸本)9798350394474

This research paper considers the You Only Look Once version 8 (YOLOv8) model as a tool for traffic sign detection and classification, which is an important feature of the autonomous driving system and the intelligent transportation system. Analyzing the latest version of the YOLO series, which seeks to maintain a suitable balance between object detection speed and accuracy, is the aim of this study since precise and timely traffic sign recognition is a critical factor for road safety and traffic management. The YOLOv8 model was trained and the accuracy of it was tested using a thorough dataset of traffic signs under different environmental conditions. The approach relied on exploiting YOLOv8's advanced deep learning architecture, transfer learning, and data augmentation to improve the model performance. Statistics revealed that YOLOv8 surpasses the previous versions with its mAP of 95.2% and processing speeds that match real-time application demands. The model had high precision and recall across multiple classes of traffic signs, especially those that are unique in shape or color. This research indicates that YOLOv8 can be an effective tool to enhance the performance of autonomous driving systems by its robust sign detection and classification. The model has opened a new frontier for research on improving the model for more accuracy and further application of the model in more elaborate areas of road safety and intelligent transportation systems. © 2024 IEEE.

关键词： Object detection

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Enhancing Organizational Performance: Synergy of Cyber-Physical Systems, Cloud Services, and Crowdsensing

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International Journal of Crowd science 2025年第1期9卷 44-55页

作者： Al-Qerem, Ahmad Ali, Ali Mohd Nabot, Ahmad Jebreen, Issam Alauthman, Mohammad Alangari, Someah Aburub, Faisal Aldweesh, Amjad Computer Science Department Faculty of Information Technology Zarqa University Zarqa13110 Jordan Communications and Computer Engineering Department Faculty of Engineering Al-Ahliyya Amman University Amman19328 Jordan Department of Information Security University of Petra Amman11196 Jordan Department of Computer Science College of Science and Humanities Dawadmi Shaqra University Shaqra11911 Saudi Arabia Department of Business Intelligence and Data Analytics University of Petra Amman11196 Jordan College of Computing and Information Technology Shaqra University Shaqra11911 Saudi Arabia

In the contemporary business landscape, software has evolved into a strategic asset crucial for organizations seeking sustainable competitive advantage. The imperative of ensuring software quality becomes evident as low-quality software systems pose formidable challenges to organizational performance. This study delves into the profound impact of three key dimensions of information system quality on organizational performance—information quality (IQ), quality of service (QoS), and software quality (SQ). Anchored in the DeLone and McLean information system (IS) success model, a quantitative questionnaire was administered to 360 industry experts and academics. Rigorous data analysis, employing exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modeling (SEM), revealed significant positive effects of all three quality dimensions on organizational performance. Among these dimensions, software quality emerged as the most influential, showcasing substantial total effects, closely followed by information and service qualities. The study underscores the tangible value derived from strategic investments in enhancing software, information, and service quality. Elevating these facets manifests as a catalyst for improved organizational performance, empowering decision-makers with accurate and timely information while enhancing user satisfaction with the system. This research contributes significantly to the IS success literature by empirically validating the synergistic relationship between information quality, service quality, software quality, and organizational outcomes. The systematic analysis offered in this study goes beyond theoretical validation, providing actionable insights for managers. The findings guide the prioritization of quality initiatives and resource allocation, enabling organizations to maximize competitive advantage. As a future research direction, investigating moderator influences and exploring alternate qualit

关键词： data quality

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Bridging the Gap with AI-Based Semantic Search Strategies 15

Bridging the Gap with AI-Based Semantic Search Strategies

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Vennila, C. Chaudhari, Prasad B. Kantak, Ganesh Kumar Naval, Preeti Pramod, R. Nagpal, Manish Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Technology Department of Artificial Intelligence & Data Science Pune India Vivekananda Global University Department of Mechanical Engineering Jaipur India Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Electronics and Communication Engineering Karnataka Ramnagar District562112 India Chitkara University Institute of Engineering and technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350370249

Bridging the space with synthetic intelligence (AI)-based semantic seek strategies gives you practical techniques for navigating the ever-growing virtual facts space. With AI-pushed semantic search, users can query for valuable expertise in several databases, raising statistics discovery to a new degree of speed and efficiency. AI-pushed semantic search can identify valuable data in complicated record sets, bringing practical insights into sharper awareness while brushing off redundant content material from densely populated facts collections. With AI-pushed semantic seek, fact discovery is faster, more profound, informed, and more valuable. Lay users can enjoy the more advantageous manipulation of personal facts while expert workers receive the essential records to make more knowledgeable selections. © 2024 IEEE.

关键词： Disregarding Enhanced Information Populated Processing

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Arabic Pilgrim Services dataset: Creating and Analysis 1

Arabic Pilgrim Services Dataset: Creating and Analysis

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Al-Barhamtoshy, Hassanin M. Himdi, Hanen T. Alyahya, Mohamad King Abdulaziz University Faculty of Computer & Information Technology IT Department Jeddah Saudi Arabia University of Jeddah Faculty of Computer and Engineering Computer Science & Artificial Intelligence Department Jeddah Saudi Arabia

ISBN: (纸本)9781665472753

With Countless Arabic news articles published daily;users have become increasingly concerned about obtaining news from credible sources. Nonetheless, to individuals, credible news sources are associated with certain countries where users have faith. Therefore, detecting the source of a news article is imperative to fake news detection and enables users a better trust in their consuming news. This paper introduces to create, filter, analyze, and evaluate a domain services-specific Arabic dataset for pilgrims. The Arabic Pilgrim Services (ArPiS) dataset is a collection of approximately 30,000 news, collected across three different Arabic countries and regions. The paper presents a creation for pilgrims' opinions measurement services dataset for text mining, text classification, clustering, and text summarization. The default basic search methods start with 124 web sites of Arabic news. Then, many of filtering features have been done to limit the dataset by pilgrim subjected services. A lot of topics are addressed, and a lot of filter with a discussion group have been made with many opinions| and extra comments. The huge of the collected data need some kind of additional effort and more analysis to produce valuable dataset. Balanced dataset is one of this extra effort, we are going to create. Therefore, the collected and annotated dataset represents real news for pilgrims' services. So, we need to build additional quantity of these data to be fake news. Accordingly, a precondition procedure invoked as a methodology to create and then annotate such dataset. © 2023 IEEE.

关键词： Classification (of information)

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Vision transformer for detecting traffic congestion using image mapping

Advances in Transportation Studies

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Advances in Transportation Studies 2025年 66卷 19-38页

作者： Khalfi, A. Guerroumi, M. Haid, T. Laradji, N. Faculty of Computer Science Artificial Intelligence and Data Science Department University of Science and Technology Houari Boumediene Algiers Algeria Matter Sciences and Computer Science Faculty Department of Computer Science University of Djilali Bounaama Khemis Miliana Algeria

Leveraging advanced computer vision techniques, we present a novel method for analyzing and categorizing traffic videos by utilizing an image mapping technique combined with a UniVit (Unit Vision Transformer) model. First, video data is transformed into an image dataset, where each frame undergoes a mapping process based on vehicle detection using the YOLOv8s algorithm. These mapped images are then input into a vision transformer, which utilizes attention mechanisms to identify and analyze complex patterns across frames. The approach is evaluated on the UCSD (University of California San Diego) dataset, which features diverse weather conditions such as clear, overcast, and rainy scenarios, enabling a rigorous assessment under varied environments. Our method achieves a notable accuracy score of 99.89%, surpassing previous approaches on the UCSD benchmark. This improvement underscores the effectiveness of combining image mapping with advanced vision transformers, suggesting promising applications for intelligent traffic management systems aimed at mitigating traffic issues. © 2025, Aracne Editrice. All rights reserved.

关键词： Photomapping

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Assessment of Musculoskeletal Loads in Three Types of Body Posture According to Dolphens 21st

Assessment of Musculoskeletal Loads in Three Types of Body ...

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21st Scientific Conference on Medical and Sport Technologies, 2024

作者： Nowakowska-Lipiec, Katarzyna Sośniak, Paulina Zadoń, Hanna Jochymczyk-Woźniak, Katarzyna Bibrowicz, Karol Szurmik, Tomasz Mitas, Andrzej W. Michnik, Robert Department of Biomechatronics Faculty of Biomedical Engineering Silesian University of Technology Roosevelta 40 Zabrze41-800 Poland Faculty of Biomedical Engineering Students Scientific Circle "Biokreatywni" Silesian University of Technology Zabrze Poland Science and Research Center of Body Posture College of Education and Therapy in Poznań Poznań Poland Faculty of Arts and Educational Science University of Silesia Cieszyn Poland Department of Medical Informatics and Artificial Intelligence Faculty of Biomedical Engineering Silesian University of Technology Zabrze Poland

ISBN: (纸本)9783031821424

This study aimed to evaluate the effect of body posture type according to the Dolphens on musculoskeletal loads. There are three types of posture: neutral, swayback and lordotic according to Dolphens classification. The study involved a group of 29 subjects. Assessment of body posture was carried out using the Posture Photographic Assessment System. The following parameters were measured for each subject: pelvic tilt angle, lumbosacral and thoracolumbar tilt, upper kyphosis tilt angle. The position of the joints and individual body segments in relation to the vertical axis, the position of the centre of mass of the body and the values of the ground reaction forces were determined. The results obtained provided the input for the modelling of the posture of each person in the AnyBody Modelling System. A full-body Standing Model was used. The model was scaled to the weight and height of each subject. The highest values of lumbar loads were observed in subjects with a kyphotic posture. For those with a kyphotic posture, the average resultant force at the L5-S1 level was 0.97 ± 0.31 BW (BW - body weight). For those with a lordotic posture it was 0.87 ± 0.27 BW and for those with a neutral posture it was 0.86 ± 0.15 BW. Subjects with a kyphotic posture had the highest values for the compressive component of the force at the L5-S1 joint (0.96 ± 0.31 BW). The highest shear force values were obtained for the lordotic posture (0.1 ± 0.04 BW). The study also assessed the force values of the abdominal and dorsal muscles. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Physiological models

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The Quest for Fairness: A Comparative Study of Accuracy in AI Hiring Systems 3

The Quest for Fairness: A Comparative Study of Accuracy in A...

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3rd IEEE Asian Conference on Innovation in technology, ASIANCON 2023

作者： D'Souza, Jason Kadam, Vedant Shinde, Pranali Saxena, Kumkum Thadomal Shahani Engineering College Information Technology Department Mumbai India Thadomal Shahani Engineering College Artificial Intelligence and Data Science Department Mumbai India Thadomal Shahani Engineering College Computer Engineering Department Mumbai India

ISBN: (纸本)9798350302288

artificial intelligence and Machine Learning based models are increasingly being used in developing models for automating recruitment systems in various Multinational Companies as well as small-scale and developing companies and industries. With the advent of Generative artificial intelligence models, many companies are turning towards automatic and AI-based hiring software. J.P. Morgan uses HireVue, Deloitte incorporates Hiretual for talent sourcing and engagement, IBM incorporates *** for automated interview scheduling and many more. But in all these systems a major factor to consider is the biasness in the models and how to detect them and efficiently mitigate them. Some models unwillingly sometimes amplify the biases in the models and results in faulty and biased predictions which can lead to the inaccurate matching of profiles to the job description and result in wayward hiring processes. The biasness can be based on the parameters such as gender, address, etc., or biasness transferred from the programmer into the model while creating the model which is called unconscious bias. The models implemented in the research are Random Forest, Decision Tree, KNN Tree, AdaBoost, and XGBoost. The accuracies for the models Random Forest, Decision Tree, KNN Tree, AdaBoost, and XGBoost were 0.80, 0.81, 0.81, 0.78, and 0.84 respectively. © 2023 IEEE.

关键词： Decision trees

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Dynamic Key Generationfor Secure File Sharing System Using Time Stamp

Dynamic Key Generationfor Secure File Sharing System Using T...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Annie Silviya, S.H. Hilda Jerlin, C.M. Mohana Suganthi, N. Helen Sathiya, V. Jayashree, R. Oviya, N. Rajalakshmi Institute of Technology Chennai India Artificial Intelligence and Data Science Panimalar Engineering College Chennai India School of Computing Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Chennai India Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala Engineering College Chennai India

ISBN: (纸本)9798331543617

Dynamic key generation for secure file sharing system using timestamp is a novel approach to military-based secure file sharing, which adds an additional layer of security to AES-2S6 which is prominently considered as the military-grade encryption. It is designed to enhance the confidentiality and integrity of transmitted files. When a Sender uploads a file, the system prompts for a character, converts it to its ASCII value, adds the current system time value to it and utilizes this aggregated value as a unique code to secure the uploaded file. To retrieve a file, the user must possess either the file key or initiate the retrieval process during the exact time of the original upload or in the next precise clock cycle. The combination of these factors provides an extra layer of security, making unauthorized access significantly more challenging. © 2024 IEEE.

关键词： Cryptography Encryption File Sharing System Timestamp

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