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IAENG International Journal of computer science 2022年第1期49卷 260-269页

作者： Li, M.Y. Zhao, J. Master’s Student of School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

Accurately detecting the keypoints of small persons in an image using bottom-up multi-person pose estimation algorithms is exceptionally difficult owing to scale variation challenges. HigherHRNet initially solved the challenge of multi-player scale change pose estimation. However, because it uses repeated cross-scale fusion, owing to inherent defects in channel reduction, semantic information is lost. Furthermore, the aliasing effects produced by the miscellaneous feature maps formed after cross-scale fusion have a significant impact on the detection accuracy of small persons. In this paper, we propose a novel bottom-up human pose estimation algorithm based on HigherHRNet, called Channel-Enhanced HigherHRNet (CE-HigherHRNet). CE-HigherHRNet comprises three main components: a multiscale sub_pixel skip fusion module, a lightweight attention mechanism (with channel attention enhanced and spatial attention modules), and a high-resolution feature pyramid with an added Dupsampling module. The lightweight attention mechanism optimizes the feature map after each fusion. Deconvolution is replaced with Dupsampling, which strengthens the network’s scale awareness and makes it more sensitive to robust scale changes. The average precision (AP) of CE-HigherHRNet on the COCO test-dev dataset was 71.9% (an improvement of 1.4% compared with HigherHRNet). Furthermore, the average detection accuracy of small persons was 68.1% AP (an improvement of 1.5% AP). These results verify that the proposed CE-HigherHRNet is more robust in processing scale changes and has a stronger ability to handle crowded environments. Thus, it is more accurate in positioning small persons in images and human bodies in crowded environments. © 2022. All Rights Reserved.

关键词： Semantics

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Yolov8-Based Deep Learning Model for Improved Drone Intrusion Detection

Yolov8-Based Deep Learning Model for Improved Drone Intrusio...

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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Mohini Darji Khushi Patel Bansari Patel Krishna Thakkar Mehul Minat Meet K. Patel Computer Science & Engineering Faculty of Technology and Engineering (FTE) Devang Patel Institute of Advance Technology and Research (DEPSTAR) Charotar University of Science and Technology (CHARUSAT) Changa Gujarat India Department of Computer Science & Engineering Student of Bachelor of Technology Faculty of Technology and Engineering (FTE) Devang Patel Institute of Advance Technology and Research (DEPSTAR) Charotar University of Science and Technology (CHARUSAT) Changa Gujarat India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

Real-time identification of unmanned aerial vehicle (drone) is a relatively growing nascent research area which leverages on deep learning and computer vision methods. However, the question arises as to possible dangers, and misuse of drones in different circumstances. These concerns are in respect to privacy, safety and security possible violations. Cameras and software are for example bundled in detection systems where visual information is used to facilitate the detection process. Thus, This Study was devoted to examining the object detection feature of the YOLO Only Look Once (YOLOv8) algorithm and its applicability for analyzing visual material captured by drones. One of the challenges in reviewing literature was to look for an online dataset of small drones and make it publicly available. Therefore, a real-world dataset was established accurately in this study and it includes small drones. The outcome presented in the document is expected to help understand the capability of the chosen models when one attempts to recognize drones in complex conditions. Further this shall serve as base on enhancing the development of even better and long-lasting anti-drone detection systems. The mentioned issues were addressed and solved with the help of the YOLOv8 architecture implementation, and the outstanding results were obtained: the mean average precision (mAP) of $\mathbf{9 3. 9 \%}$ , the precision of $\mathbf{9 2. 9 \%}$ , and the recall of $\mathbf{9 0. 3 \%}$ .

关键词： Deep learning YOLO Visualization Software algorithms Software Safety Security Drones Videos Testing

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Bi-Directional Conversion Algorithm of Database Based on Artificial Intelligence

Bi-Directional Conversion Algorithm of Database Based on Art...

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Data science and Information System (ICDSIS), IEEE International Conference on

作者： Ning Tao Lin Geng Chong Xu School of Computer Science and Engineering Shenyang Jianzhu University Shenyang Liaoning China Lin Geng Department of Student Affairs Shenyang Jianzhu University Shenyang Liaoning China

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

The traditional database conversion algorithm is difficult to implement, expensive and difficult to popularize. The lack of conversion algorithm directly affects the result of data integration. The core problem of the mutual transformation between XML(eXtensible Markup Language) and relational data is how to construct the mapping between relational database and XML document, that is, the mapping between objects and relationships. In this paper, the element tree is used to transform the probabilistic relational data model and the probabilistic XML data model in heterogeneous databases. First, the corresponding relationship between the element tree and the unit nodes and various data models is established. Then, on this basis, the bidirectional transformation of the data model is realized through the mapping relationship between the element tree and the data. When the transaction sending frequency is 400 transactions $/ \mathrm{s}$, the write throughput is 89TPS and the query throughput is 185TPS. The query and write performance of the database bidirectional conversion algorithm based on artificial intelligence is better.

关键词： Databases XML Information sharing Transforms Relational databases Probabilistic logic Throughput

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Artificial intelligence in the detection and classification of dental caries

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Journal of Prosthetic Dentistry 2025年第5期133卷 1326-1332页

作者： Ahmed, Walaa Magdy Azhari, Amr Ahmed Fawaz, Khaled Ahmed Ahmed, Hani M. Alsadah, Zainab M. Majumdar, Aritra Carvalho, Ricardo Marins Assistant Professor Department of Restorative Dentistry Faculty of Dentistry King Abdulaziz University Jeddah Saudi Arabia Associate Professor Department of Orthopedic Surgery Faculty of Medicine Cairo University Cairo Egypt Assistant Professor Department of Civil Engineering Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia Consultant in Restorative Dentistry Dental Department East Jeddah General Hospital Ministry of Health Jeddah Saudi Arabia Graduate student Department of Computer Science Computer Science and Applications Virginia Polytechnic Institute and State University Blacksburg VA United States Professor Department of Oral Biological and Medical Sciences Faculty of Dentistry University of British Columbia Vancouver BC Canada

Statement of problem: Automated detection of dental caries could enhance early detection, save clinician time, and enrich treatment decisions. However, a reliable system is lacking. Purpose: The purpose of this study was to train a deep learning model and to assess its ability to detect and classify dental caries. Material and methods: Bitewings radiographs with a 1876×1402-pixel resolution were collected, segmented, and anonymized with a radiographic image analysis software program and were identified and classified according to the modified King Abdulaziz University (KAU) classification for dental caries. The method was based on supervised learning algorithms trained on semantic segmentation tasks. Results: The mean score for the intersection-over-union of the model was 0.55 for proximal carious lesions on a 5-category segmentation assignment and a mean F1 score of 0.535 using 554 training samples. Conclusions: The study validated the high potential for developing an accurate caries detection model that will expedite caries identification, assess clinician decision-making, and improve the quality of patient care. © 2025 Editorial Council for The Journal of Prosthetic Dentistry

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Monitoring patients suffering from restless leg syndrome (RLS) using IoT

Monitoring patients suffering from restless leg syndrome (RL...

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International Conference on Control, Instrumentation, and Automation

作者： Ali Sioofi Maral Samimi Mehrshad Khosravi Armin Niakousari Ali Akbar Safavi Student research committee Shiraz University of Medical Science Shiraz Iran School of Electrical and Computer Engineering Shiraz University Shiraz Iran

Restless legs syndrome is a common neurological disorder that is often not diagnosed. It causes unusual symptoms that usually happen in the evening and at night. This can lead to problems with sleep and daily activities. It presents in different ways and can be more serious for certain groups of patients. It is important to accurately diagnose and treat this disorder. It can be difficult to figure out if someone has restless legs syndrome, especially kids and old people. Therefore, this study aims to present a tool for monitoring abnormal movements related to restless leg syndrome. An IOT-based platform with “ESP32” board and “MPU6050” module with the “Online Web server” method are used to monitor the movements. The designed platform accurately detects any vibrations and movements associated with this syndrome without any wired or complicated hardware connected to the person. This IOT based online monitoring system proved effective and can be used for long-term monitoring.

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IPL Data Analysis and Visualization for Team Selection and Profit Strategy

IPL Data Analysis and Visualization for Team Selection and P...

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International Conference on Computing Methodologies and Communication (ICCMC)

作者： Saranya G Aravind Swaminathan Joel Benjamin J Surendran R Leema Nelson Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai campus Chennai India MSc student Data Science Middlesex University Dubai Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Chitkara University Institute of Engineering & technology Chitkara University Punjab India

Day by day, the role of data science and machine learning in cricket is increasing due to the large amount of data generated from a single player on a whole line. The field of data science is the intensive study of data to extract insights and knowledge from the data and apply the acquired knowledge and actionable insights. These available data and statistics are used to predict things like the team's first-innings score and the probability of winning the second team, etc. This study works on Indian Premier League (IPL) Data Analysis 2022 and Data Visualization for the duration (2008–2020) using Python. This application modules are followed by preprocessing, data analysis, and visualization, and finally create a model that predicts the team's overall score and the probability of winning. When building models, python algorithms such as Numpy is used for Scientific Computing, Pandas used to perform Data Analysis, and finally Matplotlib and Seaborn to enable Data Visualization. Finally, this paper helps the trainers and owners of the team during the auction to select an emerging player to win the matches in the entire season and to win the trophy and to get profit to the owners by the profit strategy done in this analysis report.

关键词： Analytical models Scientific computing Computational modeling Data visualization Machine learning Data science Probability

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Human Activity Recognition: Fight Detection using Long term Recurrent Convolutional Network (LRCN) 5

Human Activity Recognition: Fight Detection using Long term ...

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5th International Conference on Data Analytics for Business and Industry, ICDABI 2024

作者： Jhnvi, Ngrddy Bhrdwj, S.K. Sharma, Ajay Sharma, Shamneesh Sodhi, Ankur Gochhait, Saikat Lovely Professional University School of Computer Science and Engineering Punjab Phagwara India Delivery and Student Success upGrad Education Private Limited Karnataka Bangalore India Enterprise Businees and Career Services upGrad Education Private Limited Karnataka Bangalore India Constituent of Symbiosis International Deemed University Symbiosis Institute of Digital and Telecom Management Pune India

ISBN: (纸本)9798350368710

Human Activity Recognition for Fight Detection is an important research domain aimed at automatically identifying patterns indicative of physical altercations. Leveraging deep learning models like CNNs and RNNs, this approach extracts features from video frames to recognize fight-related behavioural patterns. The primary objective is to develop a machine learning model capable of autonomously detecting instances of fights in video footage, using techniques such as the Long-term Recurrent Convolutional Network (LRCN). Training involves a comprehensive dataset encompassing examples of both fights and non-fight activities, with model performance evaluated using standard metrics. Each frame undergoes individual analysis by the model to predict the presence of any indications of a fight. The model predicts the action with an accuracy of 98.03% and the movements given as input can be categorized as fights or no fights. © 2024 IEEE.

关键词： Deep learning

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AI Based Technique for Language Translation: Breaking the Communication Barriers

AI Based Technique for Language Translation: Breaking the Co...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Hyndavi Gundapuneni Aditya Bukkaraya Samudram Naga Padma Maanasa Shamneesh Sharma Department of Computer Science and Engineering Lovely Professional University Phagwara Punjab India Delivery and Student Success upGrad Campus upGrad Education Private Limited Bangalore Karnataka India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Recent years have seen significant developments in language translation technology, which has made it possible to communicate with people all over the world in a seamless manner. In this research paper, a comprehensive investigation into the impact that a variety of cutting-edge technologies, such as Transformers, the Google Translate API, Automatic Speech Recognition (ASR), Optical Character Recognition (OCR), Website Translation, Document Translation, Audio Translation, and Voice Assistance, have had on language translation systems is carried out. Based on a comprehensive assessment of the relevant literature, this article provides an explanation of various technologies that have the potential to improve the capacities of translators. In addition, the approaches that can be utilized to incorporate these technologies into the translation systems that are already in place are outlined. By investigating the ways in which these technologies complement one another, the purpose of this study is to offer insights into the changing landscape of language translation and the implications that this landscape has for communication across cultural boundaries.

关键词： Translation Optical character recognition Transformers Internet Character recognition Global communication Cultural differences Informatics Artificial intelligence Automatic speech recognition

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Recognition of E-Commerce through Big Data Classification and Data Mining Techniques Involving Artificial Intelligence

Recognition of E-Commerce through Big Data Classification an...

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International Conference on Communication and Electronics Systems (ICCES)

作者： Vishwanadham Mandala R.Rajavarman C.Jamunadevi R.Janani Dr.T.Avudaiappan Student in Indiana University Bloomington USA Department of Computer Science and Engineering K. Ramakrishnan College of Technology Trichy Tamilnadu Department of Computer Technology PG Kongu Engineering College Sri Krishna College of Engineering and Technology Coimbatore Department of of Computer Science and Engineering K. Ramakrishnan College of Technology Trichy Tamilnadu

The e-commerce or electronic commerce is defined as the process of interchange the goods and services on online platform. This is a commercial exchange of goods based on internet. Several advantages of the online trade include time saving through quicker buying of products, enhances marketing and advertising in easier way and helps in the faster comparison of the price and product overall performance. This electronic commerce includes various data that are needed to be processed and stored to function properly over the internet. Hence this needs the big data and its classification. This helps to provide the data that are necessary in trading through online platform. This is done through the Big Data Analytics which helps to enhance the decision making process through analysing the data. To increase the number of customers and vendors in the online platform, the big data analytics in involved. Further this is enhanced and implemented through the data mining techniques and artificial intelligence.

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NUC-Net: Non-uniform Cylindrical Partition Network for Efficient LiDAR Semantic Segmentation

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Wang, Xuzhi Feng, Wei Kong, Lingdong Wan, Liang Tianjin Normal University College of Computer and Information Engineering Tianjin300380 China Tianjin Key Laboratory of Student Mental Health and Intelligence Assessment China Tianjin University College of Intelligence and Computing Tianjin300350 China National University of Singapore Department of Computer Science 117597 Singapore

LiDAR semantic segmentation plays a vital role in autonomous driving. Existing voxel-based methods for LiDAR semantic segmentation apply uniform partition to the 3D Li- DAR point cloud to form a structured representation based on cartesian/cylindrical coordinates. Although these methods show impressive performance, the drawback of existing voxel-based methods remains in two aspects: (1) it requires a large enough input voxel resolution, which brings a large amount of computation cost and memory consumption. (2) it does not well handle the unbalanced point distribution of LiDAR point cloud. In this paper, we propose a non-uniform cylindrical partition network named NUC-Net to tackle the above challenges. Specifically, we propose the Arithmetic Progression of Interval (API) method to non-uniformly partition the radial axis and generate the voxel representation which is representative and efficient. Moreover, we propose a non-uniform multi-scale aggregation method to improve contextual information. Our method achieves state-of-the-art performance on SemanticKITTI and nuScenes datasets with much faster speed and much less training time. And our method can be a general component for LiDAR semantic segmentation, which significantly improves both the accuracy and efficiency of the uniform counterpart by 4× training faster and 2× GPU memory reduction and 3× inference speedup. We further provide theoretical analysis towards understanding why NUC is effective and how point distribution affects performance. Code is available at https://***/alanWXZ/NUC-Net. © 1991-2012 IEEE.

关键词： Semantic Segmentation

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