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An automated Deep learning-Based VAR for Enhancing Referee Decision-Making in Football

An Automated Deep Learning-Based VAR for Enhancing Referee D...

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intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Hamza Abu Zaken A. Prasanth Mohamad Ali Jahjah Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

While football is the most popular sport the world has ever seen, the question of referee decision-making has always been a shadow that hampers the spirit and justice of the game. Video Assistant Referee (VAR) was introduced to solve officiating errors through video reviews. Nonetheless, this technology has drawn heavy criticism for causing huge delays and not completely eradicating referee bias. In this research, a Tube Convolutional Neural Network (T-CNN) model is incorporated into a new VAR system as an advanced solution. By allowing for the input of spatial and temporal features for football footage, the T-CNN model processes features which allows for rapid and accurate analysis of complex match events such as offsides, fouls, and goal-line incidents. The proposed system makes gameplay more real-time while ensuring fairness by minimizing human involvement and the consequent delays in the decision-making process. Additionally, its unbiased AI-driven decisions also eliminate the challenge of home team bias. Comparative analyses with traditional VAR methods established that the T-CNN system achieves greater accuracy rate, real-time responsiveness, and fairness, ultimately transforming football officiating.

关键词： Reactive power Accuracy Video reviews Decision making Real-time systems Delays Convolutional neural networks Reliability Internet of Things Sports

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AI- ML based approach in plough to enhance the productivity 3

AI- ML based approach in plough to enhance the productivity

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3rd international conference on intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2021

作者： Priyadharsini, K. Dinesh Kumar, J.R. Udaya S. Susmaa Rao, N. Yogarajalakshmi, S. Sri Krishna College of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore Tamil Nadu India

ISBN: (纸本)9780738111834

In contemporary technologies, the field that never gets a change is agriculture, which is the back bone of many nations. Most of the time, prediction of weather leads to choose the wrong Ploughing, which could reduce productivity and crop quality. This article describes the development of automated system, which senses the changes in soil quality, and other parameters associated with those. This co-simulated design work has hardware support and software support. To support the agriculture in smarter way, the mobile app is developed which is connected with the cloud and supports data between the sensor and information. The type of Ploughing predicted by the sensor values and the weather reports for a cycle is analysed. So this app based on ML - AI ANN-deep learning could analyze the type of seasonal agricultural product and the expected level of yield can be generated by processing them. © 2021 IEEE.

关键词： Tillage

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A Study of Deep learning Models in Diagnosis and Prediction of Chronic Diseases

A Study of Deep Learning Models in Diagnosis and Prediction ...

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international conference on Issues and Challenges in intelligent Computing Techniques (ICICT)

作者： Sayyada Hajera Begum P Vidyullatha Dept of Computer Science & Engineering Koneru Lakshmaiah Education Foundation India

With the introduction of big data and its incredible advancement in image procurement devices, transformation of medical data into valuable knowledge has become an important challenge in the area of bioinformatics. The medical images procured require huge analysis and diagnosis of images using AI techniques like Machine and Deep learning (ML, DL) that yields automated diagnosis solutions. Deep learning methods can provide optimized and precise solutions for medical image diagnosis and can be an important methodology for imminent health care applications. Deep learning in biomedical image classification, automated image classification can aid in effective and early treatment of various diseases thus reducing mortality and morbidity rate. This paper gives an introduction to Deep learning models and reviews some contemporary deep learning models and their application in detecting various diseases.

关键词： Deep learning Procurement Biological system modeling Computational modeling Medical services Predictive models Bioinformatics

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Artificial Intelligence in Mobile Computing for English Language Testing: Redefining automated Language Assessment

Artificial Intelligence in Mobile Computing for English Lang...

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international Symposium on Advanced Computing and Communication (ISACC)

作者： Muththamizh Selvi S.I Senthamarai T S. Karthik Kumar N. Sheik Hameed S. Vijayakumar I Infant raj B.S. Abdur Rahman Crescent Institute of Science and Technology Chennai India Vels University (VISTAS) Chennai India Annamalai University Chidambaram India B. S Abdur Rahman Crescent Institute of Science and Technology Chennai India Department of CSE K. Ramakrishnan College of Engineering Trichy Tamil Nadu India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Artificial intelligence's (AI) quick development has created new opportunities in a number of domains, including education. Language evaluation is one such field that has experienced a great deal of innovation. The utilization of auto encoders to redefine automated language assessment is the focus of this work, which investigates the integration of artificial intelligence (AI) in mobile computing for English language evaluation. Traditional language assessment approaches are being challenged by AI's capability to offer real-time, scalable, and personalized evaluations. By leveraging autoencoders deep learning models optimized for unsupervised learning and feature extraction—the proposed system evaluates diverse linguistic attributes such as grammar, spelling, pronunciation, fluency, and comprehension. Autoencoders transform high-dimensional input data into low-dimensional latent spaces, enabling efficient assessment of nuanced language patterns. The methodology incorporates the LibriSpeech ASR Corpus for data preprocessing, feature extraction, and training. Key innovations include robust handling of diverse input types (text and audio), accurate reconstruction of linguistic features, and scalable evaluation of proficiency. Performance metrics such as accuracy, reconstruction error, and fluency scores demonstrate the model's superior learning and assessment capabilities. Compared to other deep learning methods like LSTMs, CNNs, and Transformers, the autoencoder-based approach excels in accuracy and adaptability, offering impartial and precise assessments. The study "concludes" with a discussion of the benefits and challenges of AI-driven automated testing, providing insights into its potential to revolutionize educational assessment and reduce the burden on instructors by automating routine evaluations

关键词： Deep learning Technological innovation Accuracy Computational modeling Autoencoders Linguistics Feature extraction Mobile computing Unsupervised learning Testing

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Early Intervention in Autism Spectrum Disorder: Hybridizing Convolutional Neural Networks with Ensemble Methods for Enhanced Detection Using Facial Imagery

Early Intervention in Autism Spectrum Disorder: Hybridizing ...

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international Symposium on Advanced Computing and Communication (ISACC)

作者： Anupam Das Prasant Kumar Pattnaik Anjan Bandyopadhyay School of Computer Engineering KIIT Deemed to be University Bhubaneswar India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Autism Spectrum Disorder is a neurodevelopmental condition affecting social communication and behavior. Early and accurate detection is essential for improving long-term outcomes through timely interventions. However, traditional diagnostic methods rely heavily on behavioral observations, which are time-consuming, subjective, and can vary between clinicians. The objective of this study is to propose an automated, objective method for ASD detection using facial image analysis. Specifically, we present a hybrid model that integrates Convolutional Neural Networks for feature extraction with ensemble learning methods—Random Forest and Extreme Gradient Boosting for classification. EfficientNet and MobileNet were used as feature extractors, chosen for their efficiency and accuracy in image classification tasks, while RF and XGBoost were utilized to ensure robust classification. To prevent overfitting and enhance generalization, extensive data augmentation techniques were applied, along with class balancing methods to handle imbalanced data. The proposed model was evaluated on the Autism Image data dataset and achieved an accuracy of 97.35%, with precision and recall values of 95.2% and 95.75%, respectively. These results demonstrate the model’s potential in contributing to early ASD detection, which can be an essential tool for clinicians in diagnostic decision-making.

关键词： Autism Analytical models Accuracy Feature extraction data augmentation data models Convolutional neural networks Ensemble learning Random forests Overfitting

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Research on Dynamic Fuzzy Testing in Securing Cloud Infrastructure based on Deep learning

Research on Dynamic Fuzzy Testing in Securing Cloud Infrastr...

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Cloud Computing, Big data Applications and Software engineering (CBASE), international conference on

作者： Ningyun Dan Lun Zhao Wenjin Pan Yan Cai College of Physics and Information Engineering Zhaotong University Zhaotong China Yunnan Electric Test & Research Institute Group Co. Ltd. China

ISBN: (数字)9798331506582

ISBN: (纸本)9798331506599

As cloud infrastructure assumes an increasingly pivotal role in contemporary computing environments, ensuring its security becomes a matter of paramount importance. As a prevalent vulnerability detection technology, traditional fuzz testing encounters the challenge of scalability in the context of the intricate and evolving nature of cloud computing systems. Our approach employs deep neural networks (DNNs) to analyze the behavior of diverse software components within cloud systems. It incorporates multi-dimensional data, including system logs, network traffic, and resource utilization in virtualized environments. The neural network is capable of identifying potential vulnerability points through an automated process. To generate effective test cases, we introduce a reinforcement learning framework based on a policy gradient algorithm. This framework enables an intelligent agent to interact with the system in order to obtain feedback and subsequently update the policy in order to progressively generate more aggressive test cases. The framework is designed with an adaptive reward mechanism that not only encourages triggering crashes but also focuses on edge use cases that reveal system bottlenecks, thereby improving vulnerability coverage. Furthermore, in consideration of the dynamic nature of the cloud environment, we have developed a distributed test architecture that enables the parallel deployment of tasks in virtual machines or containers, simulates multi-tenancy and dynamic load change scenarios, and introduces a vulnerability detection mechanism at the virtualization layer. The proposed model is capable of adaptively generating highly targeted test cases and optimizing the test strategy through continuous learning, thereby markedly enhancing the detection capability of complex vulnerabilities in cloud infrastructure.

关键词： Deep learning Cloud computing security Software algorithms Reinforcement learning Telecommunication traffic Fuzzing Virtual machines Security Testing Software engineering

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intelligent 3D Visual defect Detection for Industrial Applications 3

Intelligent 3D Visual defect Detection for Industrial Applic...

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2022 3rd international conference on Internet of Things, Artificial Intelligence and Mechanical Automation, IoTAIMA 2022

作者： Yang, Rui Ke, Hualin Gan, Lin Li, Yuhong Hu, Ruihan Guangdong Institute of Intelligent Manufacturing Guangdong Key Laboratory of Modern Control Technology Guangzhou510070 China Cloud Computing Center Chinese Academy of Sciences Dongguan523808 China Guangdong Key Laboratory of Modern Control Technology Guangdong Institute of Intelligent Manufacturing Guangzhou China Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education Beijing China Xinjiang Production and Construction Corps Key Laboratory of Modern Agricultural Machinery Shihezi University Shihezi China

Currently, new energy industries such as electric vehicles and energy storage batteries are experiencing rapid growth throughout the world. As a recognized ideal energy storage element, lithium batteries have also attracted considerable interest. To improve image contrast, the industry employs image preprocessing algorithms. Due to the high requirement of image consistency in the template matching method, which is difficult to meet in practice, and the large apparent difference between defects, it is challenging to design features. Therefore, the framework for defect detection is based on a method of deep learning with strong feature expression capability. In light of the severe imbalance in the number of samples in defect detection, data augmentation and generation methods are used to simulate real samples in order to improve the training effect of deep neural networks and alleviate the burden of data annotation to some extent. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Lithium batteries

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RoadAtlas: intelligent Platform for automated Road Defect Detection and Asset Management 3

RoadAtlas: Intelligent Platform for Automated Road Defect De...

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3rd ACM international conference on Multimedia in Asia, MMAsia 2021

作者： Chen, Zhuoxiao Zhang, Yiyun Luo, Yadan Wang, Zijian Zhong, Jinjiang Southon, Anthony School of Information Technology and Electrical Engineering The University of Queensland Australia Logan City Council Australia

ISBN: (纸本)9781450386074

With the rapid development of intelligent detection algorithms based on deep learning, much progress has been made in automatic road defect recognition and road marking parsing. This can effectively address the issue of an expensive and time-consuming process for professional inspectors to review the street manually. Towards this goal, we present RoadAtlas, a novel end-to-end integrated system that can support 1) road defect detection, 2) road marking parsing, 3) a web-based dashboard for presenting and inputting data by users, and 4) a backend containing a well-structured database and developed APIs. © 2021 Owner/Author.

关键词： Defects

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Smart GPS Based Vehicle Speed Limit Controller on zone identification using Geo-Fencing Algorithm

Smart GPS Based Vehicle Speed Limit Controller on zone ident...

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Innovation in Technology (INOCON), IEEE international conference for

作者： Modem Upendra Varun Reddy Safa M Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai

In light of growing concerns about road safety and the surge in traffic accidents, this project aspires to an Internet of Things smart GPS-based vehicle speed limit controller system. The primary objective is to elevate road safety by harnessing real-time data and automated control mechanisms to enforce speed limits, thereby diminishing incidents of speeding-related accidents. This project adopts a comprehensive approach, integrating both hardware and software components. Vehicle-mounted sensors, encompassing speed sensors and GPS receivers, continually gather real-time data on vehicle speed and location. Furthermore, road condition data is sourced from multiple outlets, such as weather stations and traffic cameras. This data is wirelessly transmitted to a central control unit, which processes and analyzes it. The control unit deploys machine learning algorithms to evaluate vehicle speed limits against predefined speed limits for specific road segments. Depending on this analysis, it transmits control signals back to the vehicle when necessary. These signals can encompass driver warnings, power limitations, or autonomous speed control. These methodologies collectively enabled the creation of an intelligent and adaptable GPS-based vehicle speed control system, offering the potential to significantly enhance road safety by dynamically enhancing road safety regulating vehicle speeds in different zones, ensuring the safety of people and drivers alike. The Smart GPS-based vehicle speed limit controller system has undergone successful development and testing, both in controlled environments and on public roads. The outcomes manifest a remarkable reduction in incidents of speeding and an enhanced adherence to speed limits, exhibiting an accuracy level exceeding 96%. In real-world scenarios, the system effectively mitigates speeding-related risks, leading to a substantial decline in traffic accidents. Its ability to adapt to changing road conditions and. Its scalability ren

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Research on Course Quality Evaluation System Based on Artificial Intelligence 3

Research on Course Quality Evaluation System Based on Artifi...

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3rd IEEE international conference on Computer Science and Educational Informatization, CSEI 2021

作者： Li, Yongquan Wang, Yanqing Song, Xiankun Cheng, Xinyu Nanjing Xiaozhuang University College of Information Engineering Nanjing China

ISBN: (纸本)9781728184739

In view of the complexity and inefficiency of the course quality assessment process, this paper conducts an in depth study of behavior analysis technology and designs an automated and intelligent curriculum quality assessment system. The system uses various artificial intelligence algorithms to analyze the collected images and text data related to classroom quality and produce objective visual results. The course quality assessment system is divided into four functional modules: curriculum quality index management, data acquisition management, data analysis management and system of rights management, which can quickly generate visual course quality assessment results based on data analysis results. By comparison, the results of intelligent evaluation and existing artificial evaluation have high consistency. The system is fully functional and intelligent at the touch of a button, which can greatly improve the efficiency of curriculum quality assessment and reduce the complexity of work. © 2021 IEEE.

关键词： Curricula

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