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Advancements in Cricket Shot Detection: Integrating Human Pose Estimation and Deep Learning for Automated Analysis 15

Advancements in Cricket Shot Detection: Integrating Human Po...

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

作者： Datta, K.S. Sreekar Naidu, G. Narasimha Dasari, Rahul Jayakumar, T.V. Dept. Of Computer Science and Engineering Amrita School Of Computing Amrita Vishwa Vidyapeetham Amritapuri India Dept. Of Computer Science and Applications Amrita School Of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350370249

Cricket, a sport loved worldwide, often presents challenges for aspiring players due to limited access to infrastructure and coaching resources. Cricket’s automated learning solutions are still largely unexplored, despite its popularity. By concentrating on the application of cricket shot detection, a crucial component of the game, this research fills this gap. One essential component of computer vision that becomes apparent in deciphering the subtleties of cricket shots is human stance estimate. Pose estimate provides a detailed examination of batting skills and footwork by tracking important areas on the batsman’s body, such as joints and limbs. MoveNet is a significant framework in this domain that has garnered accolades for its real-time performance and lightweight architecture, which enable accurate identification of human poses, which is a crucial requirement in the dynamic world of cricket. This study explores the combination of deep learning techniques and human pose estimate for cricket shot recognition. More precise and reliable cricket shot identification and classification are made possible by the model’s ability to extract complex patterns and features from the input data by utilizing deep learning techniques. Our goal is to automatically identify and classify cricket shots based on players’ minor motions through theoretical investigation, empirical validation, and real-world applications. Through the integration of cutting-edge technologies, such as deep learning, with traditional cricket analysis, our goal is to gain a deeper understanding of the game and discover new avenues for sports analytics, coaching, and broadcasting. ©2024 IEEE.

关键词： Cricket (Sports)

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Medication Management Application for Directly Observed Treatment of TB Patients 6

Medication Management Application for Directly Observed Trea...

引用

6th International Conference on Electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Tania, Marzia Hoque Rahman, Md. Asib Ahmed, Kadir Uddin Centre for Big Data Research in Health Unsw Sydney Sydney Australia Buet Dept. Computer Science and Engineering Dhaka Bangladesh Independent Researcher Sydney Australia

ISBN: (纸本)9798350385779

Tuberculosis (TB) poses global health challenges, requiring efficient management and monitoring of patients undergoing Directly Observed Therapy (DOT). This paper introduces TB-meds, a proposed software application to automate essential aspects of TB patient management. TB-meds aims to optimize resource utilization, improve patient care, and enhance TB control efforts. The application streamlines medication distribution, facilitates medication adherence monitoring, sends timely clinical test requirement's notifications, and enables convenient treatment status updates. By automating these processes, TB-meds has the potential to enhance efficiency, improve treatment outcomes, and reduce transmission rates. Its user-friendly interface promotes seamless communication among healthcare providers and TB patients. With tasks automation, the workload of DOT providers can be reduced, and more time can be dedicated to patient education, nutrition counseling, and lifestyle guidance. Additionally, TB-meds proposes to revolutionize TB surveillance by enhancing data collection and analysis capabilities, enabling informed decision-making and targeted interventions. The implementation of TB-meds holds promise for transforming TB patient management, contributing to better health outcomes, and advancing TB control efforts. © 2024 IEEE.

关键词： Telemedicine

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An Improved Diagnostic Approach for Classifying Tomato Leaf Diseases using Ensemble Deep Learning based Technique 8

An Improved Diagnostic Approach for Classifying Tomato Leaf ...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Batra, Salil Chakraborty, Gouri Shankar Bortty, Joy Chakra Polok, Sourav Das Ahmed, Tamjid Taki, Tanvir Ahmed Lovely Professional University Dept. of Computer Science and Engineering Punjab Phagwara India Westcliff University Department of Computer Science IrvineCA United States

ISBN: (纸本)9798350367904

Tomato is one of the most common vegetables that has high commercial importance for abundant production to meet the huge demand world-wide. There are several challenges that put impact on the mass production of tomato, and which are required to be resolved timely. One of the major challenges is tomato leaf diseases where different types of diseases are there which need to be identified correctly to protect the plants, otherwise it can cause severe harm to the plants which ultimately effects the production of tomato. To have a solution, several scientists and researchers have been working on this to classify diseases in a very initial stage. Diseases of the plants can be classified from leaf image, but it's difficult to identify disease manually by observing through eyes. Deep learning techniques are being used to identify different types of disease using the leaf image. Several existing approaches have been discussed in this study where different detection processes have been proposed. But, in most of the cases, several limitations have been observed regarding the performance of those traditional frameworks. In this study, a novel disease detection approach has been proposed to detect multiple types of tomato leaf disease through analyzing the leaf images. Here, after combing the performances of four CNN models (MobileNetv2, ResNet50, VGG19, VGG16), an ensemble deep learning technique has been applied to identify different types of the tomato leaf disease as well as to classify the healthy and affected leaf. The performance achieved from the proposed ensemble framework ensures optimum accuracy of 96.7% with efficiency. A proper comparative analysis has been presented that supports the novelty and potentiality of the work. © 2024 IEEE.

关键词： Fruits

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Object Detection Framework Using OpenCV for Low Cost and High Performance 11th

Object Detection Framework Using OpenCV for Low Cost and Hig...

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11th International Conference on Intelligent Computing and Applications, ICRTC 2023

作者： Bhardwaj, Sarthak Soni, Pratyaksh Ul Haq, Furqan Masih, Alvin Precious Sharma, Puneet Dept. of Computer Science & Engineering Amity University Uttar Pradesh Noida India School of Computer Science & AI SR University Telangana Warangal India

ISBN: (纸本)9789819717231

In contemporary times, advancements in technology have led to a renewed interest in the development of autonomous vehicles, commonly referred to as self-driving cars. These vehicles are equipped with sensors and computer vision tools that enable them to detect and respond to their environment, reducing the need for human intervention in the navigation process. Given the transformative potential of this technology, there has been a growing emphasis on researching and developing autonomous vehicles as a way of revolutionizing the transportation industry. The goal of this research work is to code highly optimized plus parameterized Python algorithms that would drive the object detection systems like Canny edge, pedestrian, vehicle, etc., with the help of pre-existing object models with greater emphasis on reducing the cost-performance trade-off. Various object models like EfficientNet, YOLOv4, R-CNN, etc. were deployed and tested for creating comparison in terms of ease of deployment, efficiency, accuracy, computational, and hardware costs to filter out the most efficient model for the coded computer vision tools. These innovative systems will possess the capability to detect lane markings, accurately identify pedestrians and other vehicles, and continually monitor the vehicle's immediate surroundings, thereby providing real-time information to the autonomous driving system through highly customizable parameters that can be changed according to the needs. By default, the codes in the research work will be customized for Indian roads, which are known for their unique challenges, such as lack of lane markings, heavy traffic, and poor road conditions. However, challenges remain, including legal and regulatory issues, code robustness, hardware integration, public perception, acceptance, and ensuring the safety and security of the autonomous driving system. Despite these challenges, the potential of autonomous vehicles to revolutionize transportation is significant. © The A

关键词： Autonomous vehicles

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IoT Enabled Water Flow Management and Monitoring System for Hydroelectric Power Generation

IoT Enabled Water Flow Management and Monitoring System for ...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Shaikat, Abu Salman Tasnim, Rumana Hasan, Jahid Zonayed, Md Hussein, Molla Rashied Shithil, Shaekh Mohammad Al Amin, Abdullah Laurentian University of Sudbury Dept. of Bharti School of Engineering and Computer Science Ontario Canada World University of Bangladesh Dept. of Mechatronics Engineering Dhaka Bangladesh Dhaka University of Engineering & Technology Dept. of Mechanical Engineering Dhaka Bangladesh University of Asia Pacific Dept. of Computer Science & Engineering Dhaka Bangladesh University Technology Malaysia Dept. of Mechanical Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9798350369106

Water energy sources play a crucial role, particularly in providing electrical energy through hydroelectric power. Both the water flow rate and the water head determine the potential energy available from water. The hydroelectric system's energy generation directly depends on the water flow rate. A lower flow rate means less water is available to generate energy, even if the head is high. It is essential to increase the potential of the water flow so that it can generate enough electricity. Traditionally, the flow rate is restricted due to the limited capacity of single penstock. This work addresses this issue by introducing multiple penstock-based water flow management systems. Also, conventional water flow monitoring systems often face limitations, like relying on manual measurement, as it only provides periodic data, making it difficult to respond quickly to changes in flow conditions. IoT-based flow sensors leverage IoT technology to monitor water flow and transmit flow data in real-time, enabling operators to detect fluctuating flow conditions and implement timely adjustments to processes, therefore enhancing the overall efficiency. This work aims to design and develop an IoT-enabled prototype hydroelectric power generation system using multiple water flows for enhancing power generation efficiency. The prototype indicates the use of multiple pipes as penstocks in a dam. Additionally, if generating a significant amount of electricity is not required, the water flow can be discontinued, thereby automatically closing each pipeline. This work uses flow sensors, Kitsware software, Arduino IoT Cloud, and an LCD display to generate flow measurement data every ten seconds from multiple pipelines. Moreover, the system automatically generates Excel data and graphically represents multiple water flows, enabling the user to compare the water flow data. The completed work resulted in a hydropower system demonstrating enhanced output and effective compatibility with IoT for

关键词： Penstocks

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Detection of Multiple Plant Diseases by Leaf Diagnosis Using Deep Learning

Detection of Multiple Plant Diseases by Leaf Diagnosis Using...

引用

2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Mohd, Noor Mehta, Saransh Mehta, Prajjuwal Ghazi, Mohd. Rehan Chaturvedi, Narayan Martha, Sheshikala Dept. of Cse Dehradun India Department of Computer Science and Engineering Dehradun India Sr University Department of Computer Science and Artificial Intelligence Warangal India

ISBN: (纸本)9798350379716

India is a land of agriculture, and it has a deep impact in the economic growth of our country. Around 10-20% crops in India, suffer from various diseases that can lead to those crops getting wasted therefore this is one of the major concerns for the farmers to protect their crops from such diseases. To prevent the plants from getting wasted the disease should be detected as soon as possible so that the farmer can take the necessary steps to save the crops from getting wasted. Farmers have been using their conventional methods for years to identify diseases, but it is hard for them to identify various diseases with high accuracy. Now with the advancement in Artificial Intelligence and computer vision, we can use various techniques to solve this problem of disease detection in different plants with more accurate results. This will lead to a fast and early diagnosis of the disease and will help to decrease crop wastage making it more beneficial for the farmer and for the whole country. In this research paper, we have made use of Deep Learning a very popular technology nowadays as it provides various models and functionalities to work with images. We have used the Convolutional Neural Network (CNN) model for this research paper as it is one of the best in image classification and is also good at extracting smaller details from the image which will work well for the detection of disease in a plant by diagnosing the images of its leaf. We have made use of Plant Village dataset for our model which contains more than 60,000 RGB images of both healthy and unhealthy leaves as well. © 2024 IEEE.

关键词： CNN Deep Learning Plant Disease Plant Village dataset

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Smart Attendance Management System Using Face Recognition 1

Smart Attendance Management System Using Face Recognition

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Rawat, Shivam Goyal, Shweta Rawat, Nikita Graphic Era Hill University Dept. Computer Science & Engineering Dehradun India Graphic Era deemed to be University Dept. of Electrical Engineering Dehradun India

ISBN: (纸本)9798331518578

Managing attendance in educational institutions is often a time-consuming and error-prone task, with traditional methods like roll calls or sign-in sheets being inefficient and susceptible to proxy attendance. This paper presents a Smart Attendance Management System that leverages facial recognition technology to automate the attendance process. By using cameras to capture images of students, the system recognizes their faces and marks their attendance in real time. Developed in Python, the system employs the K-Nearest Neighbours (KNN) algorithm for accurate face recognition. Once attendance is recorded, it is stored in a CSV file and can be visualized using the flask web application. The proposed system significantly reduces manual effort, ensures accuracy, and operates efficiently in real-time environments. © 2025 IEEE.

关键词： Nearest neighbor search

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Exploring emerging spreaders through GCN-based link prediction and a novel centrality method 17

Exploring emerging spreaders through GCN-based link predicti...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Nandi, Suman Maji, Giridhar Dutta, Animesh National Institute of Technology Dept. of Computer Science and Engineering West Bengal Durgapur India Asansol Polytechnic Dept. of Electrical Engineering West Bengal India

ISBN: (纸本)9798331531195

Exploring influential spreaders and predicting missing links in complex networks is essential for understanding and effectively controlling network dynamics. This paper presents a Graph Convolutional Network (GCN)-based link prediction method to estimate the probability of future link formation. We incorporate node features that capture local and global topological connectivity structures and feed these into the GCN model, where convolutional layers aggregate neighboring information and transform node features. This approach enables the model to capture structural patterns by integrating local and global information from neighboring nodes. In the final layer, the GCN model computes a prediction score representing the likelihood of an edge's existence, using insights gained during training. Finally, considering the predicted links, we update the network structure and introduce a novel centrality method called Emerging Spreader Centrality (ESC) to identify emerging spreaders within this augmented network. We conduct two separate experiments to evaluate the performance of the GCN-based link prediction and the ESC method, comparing their effectiveness with various state-of-the-art methods. Results demonstrate that our approach not only effectively predicts future links but also identifies emerging spreaders in the augmented networks. © 2025 IEEE.

关键词： Prediction models

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Deep Learning Driven Multilingual System for Recognition and Categorization of Medicinal Plants with Extensive Elucidation 9

Deep Learning Driven Multilingual System for Recognition and...

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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Azhaguramyaa, V.R. Janet, J. Hariharan, A. Kamaleshwaran, A. Albert Joseph, A. Computer Science And Engineering Sri Krishna College of Engineering And Technology Tamil Nadu Coimbatore India Sri Krishna College of Engineering And Technology Dept. of Computer Science And Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350365092

Medicinal plant use is widespread throughout many cultures and geographical areas, frequently entwined with customs and knowledge. The use of technology, especially deep learning, to improve the identification and classification of these plants has gained popularity in recent years. This research introduces a new multilingual system with deep learning capabilities that can identify and classify medicinal plants with a great deal of explanation. The term 'deep learning-driven multilingual system for recognition and categorization of medicinal plants with extensive elucidation' describes an advanced technology that carries out several tasks by utilizing deep learning, one of the most advanced machine learning techniques. Based on pictures, the system can recognize and classify a variety of therapeutic plants. Because users may communicate with the system, ask questions, and get information in their native tongue, it is usable by a wide variety of people across the globe. Apart from identification and classification, the method offers comprehensive clarifications or in-dept. accounts of the recognized therapeutic herbs. © 2024 IEEE.

关键词： Deep learning

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Heart Abnormality Detection Through Neural Network

Heart Abnormality Detection Through Neural Network

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Sarada, B. Smitha Chowdary, Ch. Babu, Jampani Satish Kavitha, K. Dept. of Data Science Secunderabad India Koneru Lakshmiah Education Foundation Dept. of Cse Guntur India P.B.Siddhartha College of Arts and Science Dept. of Computer Science Vijayawada India

ISBN: (纸本)9798350300857

This research study aims to develop an algorithm using phonocardiograms and Deep learning to differentiate between normal and abnormal heart sounds. The increase in cardiovascular diseases (CVD's), primarily caused by unhealthy habits, necessitates efficient diagnosis. The study utilized 1055 data samples, with 833 being normal and 182 abnormal, each lasting 5 seconds. Logistic regression and a three-layered neural network were initially applied, yielding accuracies of 42% and 63%, respectively. By extracting features using spectral roll-off and reapplying the models, accuracies improved to 79% and 86%. Up sampling the data further increased the accuracies to 86% for logistic regression and 89% for the three-layered neural network. The findings demonstrate the potential of audio-based algorithms in diagnosing cardiovascular diseases accurately. © 2023 IEEE.

关键词： Multilayer neural networks

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