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

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

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International Conference on computing and Networking Technology (ICCNT)

作者： Ks Sreekar Datta G Narasimha Naidu Rahul Dasari T V Jayakumar 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: (数字)9798350370249

ISBN: (纸本)9798350370270

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.

关键词： Deep learning Training Analytical models Accuracy Refining Pose estimation Games Transforms Feature extraction Sports

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Combating VoIP Spam: A Real-Time Algorithmic Approach 7

Combating VoIP Spam: A Real-Time Algorithmic Approach

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7th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2023

作者： Kumar, Ankit Singh, Kamred Udham Kumar, Gaurav Choudhury, Tanupriya Kotecha, Ketan GLA University Department of Computer Engineering & Applications UP Mathura India Graphic Era Hill University School of Computing Dehradun India CSE Dept. Lavale Campus Pune India Pune India

ISBN: (纸本)9798350383065

Spam calls, formerly a relatively insignificant issue due to the restricted usage of VOIP (Voice over Internet Protocol) services, are now on the verge of becoming a serious problem because of the growing use of internet-based telephone systems. Because it provides phone service over an internet connection rather than conventional phone lines, VOIP (Voice over Internet Protocol) has cheaper prices and more features than traditional PSTN phones, which has led to its growing acceptance alongside PSTN phones. However, this development has generated new security and quality concerns, such as call eavesdropping, service theft, call manipulation, and notably spamming. Spamming is particularly problematic because of its widespread nature. Because each VoIP account is tied to an IP address, spam in VOIP poses a particularly serious threat. Spammers are able to flood voicemail systems with messages, which are often accompanied by viruses and malware. These may lower the quality of calls or even cause VoIP servers to collapse because they use an excessive amount of bandwidth. At this moment, there is no technology that is both perfect and real-time that can efficiently filter spam calls out of VoIP traffic. In this research, I provide a revolutionary real-time algorithm that utilises a mix of techniques in order to get rid of spam calls. These techniques include verifying fuzzy RTP media parameters, evaluating fuzzy MOS value, performing Turing tests, and analysing specific call information. The effectiveness of this system is proved by its capacity to monitor and evaluate the operation of VOIP systems in real time. It is able to reject calls that are thought to be spam at different stages of the verification process. The aforementioned algorithm, which has been developed for incorporation into VOIP systems, provides a potentially useful solution for the rejection of VOIP spam calls in real time. © 2023 IEEE.

关键词： MATLAB

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RDI-SD: An Efficient Rice Disease Identification based on Apache Spark and Deep Learning Technique

RDI-SD: An Efficient Rice Disease Identification based on Ap...

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2022 International Conference on Artificial Intelligence and Data Engineering, AIDE 2022

作者： Prasad, M S Guru Pratap, M.S. Jain, Prithviraj Gujjar, J Praveen Kumar, M Anand Kukreti, Anurag Dept. of Computer Science & Engg Dehradun India School of Computing and Information Technology Reva University Bengaluru India SDM Institute of Technology Dept. of Computer Science & Engg Ujire India Faculty of Management Studies Bengaluru India Dept. of Computer Applications Dehradun India

ISBN: (纸本)9781665493048

Early and accurate detection of diseases affecting rice plants may have devastating impacts on productivity, and early and accurate detection of these diseases is essential for mitigating their consequences. However, the current methods for diagnosing rice diseases are neither precise nor effective. When it comes to diagnosing plant diseases, convolutional neural networks (CNNs) are the gold standard. The difficulty of using CNN for large-scale data analysis using conventional methods remains a major barrier to its wider adoption. In our work for massive data processing, we have adapted the CNN technique for use with pySpark. We used a dataset of 3,472 images of rice diseases to train and evaluate our suggested system. The proposed system will provide a tailored answer to the challenge of assessing rice diseases by employing smartphone plant images as its major data source. With the proposed model for identifying different rice diseases, a total accuracy of 99% was reached, which is pretty impressive given that some of the diseases look similar. © 2022 IEEE.

关键词： Convolutional neural networks

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Deep Learning Driven - Pest Classification and Sustainable Agriculture 3

Deep Learning Driven - Pest Classification and Sustainable A...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Kumar, M. Sirish Usha, R. Reddy, B. Rama Kantha Ramachandra, S. Prakash, P. Jaya Swetha, B. Mohan Babu University School of Computing Dept. of Data Science India Madanapalle Institute of Technology and Science Madanapalle India Sri Venkateswara College of Engineering Tirupati India Mother Theresa Institute of Engineering and Technology Palamaner India Sri Venkateswara College of Engineering Department of It Tirupati India Mohan Babu University Dept. of Master of Computer Applications India

ISBN: (纸本)9798331509675

This study introduces a system designed to identify pests in crops and classify them as either beneficial or harmful. The project begins by providing a comprehensive overview of various pest identification methods, analyzing their strengths and limitations. To address the problem, a categorization approach is proposed that incorporates convolutional neural networks (CNN) to build the pest identification and classification model. The model was trained using a dataset containing images of different pest species. Extensive testing was conducted on a large dataset, and the system's performance was validated against multiple traditional classification models. The proposed framework achieved a classification accuracy of 90%, outperforming conventional methods. This research highlights agriculture's vital role in the economy and underscores the potential of advanced technologies in supporting sustainable farming practices. © 2025 IEEE.

关键词： Agriculture Sustainability Classification Accuracy Classification Model Convolutional Neural Network (CNN) Pest Classification

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A Study of Landmark-Augmented and Landmark-Free Approaches: Yoga Pose Recognition

A Study of Landmark-Augmented and Landmark-Free Approaches: ...

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International Conference for Convergence of Technology (I2CT)

作者： Jayasurya Marasani Dhanvanth Reddy Yerramreddy Abburi Sai Karthik Maddala H S M K Karthik Jayakumar T V Prema Nedungadi 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 Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

In the realm of yoga pose recognition, this project delves into a comprehensive investigation of two distinct methodologies, namely landmark-augmented and landmark-free approaches, to enhance the accuracy and efficacy of pose recognition systems. The first approach leverages state-of-the-art pose estimation techniques employing MoveNet, MediaPipe, and YOLO V8 to precisely detect pivotal anatomical landmarks that define yoga postures. Subsequently, these detected landmarks are utilized as foundational cues for the classification process. On the other hand, the landmark-free approach directly employs cutting-edge deep learning architectures, specifically Vision Transformer, ResNet50, and VGG16, to classify yoga poses without the explicit utilization of detected landmarks. By juxtaposing these two methodologies, the project endeavors to uncover the comparative advantages, challenges, and limitations associated with each approach in the domain of yoga pose recognition. Beyond performance evaluation, this study aims to provide valuable insights into the most effective and robust techniques for accurately identifying and classifying yoga poses. The findings are poised to contribute to the development of sophisticated yoga pose recognition systems that hold promise in assisting yoga practitioners in achieving proper alignment and posture during their practice, ultimately promoting their overall well-being and practice effectiveness.

关键词： YOLO Performance evaluation Pose estimation Deep architecture Media Transformers Residual neural networks

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Analysis of Bones Abnormality Detection and Segmentation 11

Analysis of Bones Abnormality Detection and Segmentation

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11th IEEE International Conference on Intelligent computing and Information Systems, ICICIS 2023

作者： Ismail, Mennat-Allah Mohammed, Yasmine Gamal, Nadine Hatem, Maram Ibrahim, Sara Abbas, Esraa Hamdi Mohsen, Mayar Nasser, Maram Ebied, Hala M. Ain Shams University Faculty of Computer and Information Sciences Dept. of Bioinformatics Cairo Egypt Ain Shams University Faculty of Computer and Information Sciences Dept. Bioinformatics Cairo Egypt Ain Shams University Faculty of Computer and Information Sciences Information System Department Cairo Egypt Ain Shams University Faculty of Computer and Information Sciences Scientific Computing Department Cairo Egypt

ISBN: (纸本)9798350322101

Numerous musculoskeletal disorders can be caused by bone diseases, which are widespread. Musculoskeletal conditions are estimated to affect 1.71 billion people worldwide. In addition to musculoskeletal fractures, knee osteoarthritis, fractures, and femoral neck injuries are all relatively prevalent bone illnesses. Missed fractures, on the other hand, are a frequent prognostic failure in accidents and emergencies. As a result, patients' treatment and care are complicated and delayed. Deep Learning (DL) and Artificial Intelligence (AI) are currently garnering a lot of interest for their potential to help physicians identify bone fractures. Studies in traumatology and orthopedics have demonstrated the utility and potential of DL in the radiograph-based diagnosis of fractures and illnesses. In this paper, different classification techniques have been compared to classify the bone. The methods used for detection and segmentation of wrist bones are Inception-v3, VGG16, Densenet-121, and Resnet-50while for elbow bones, Resnet-50, Inception-v3, Densenet-121, and Densenet-169 are used. Two datasets are used in the experiments.: Kaggle dataset and MURA dataset. The experimental results show that the Densenet-169 and Inception-v3 performed better than the other models using MURA dataset and Kaggle dataset respectively. © 2023 IEEE.

关键词： Medical imaging

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Analytical Study of the Features and Parameters of Off-Angle Iris Image vs. Deep Learning Model

Analytical Study of the Features and Parameters of Off-Angle...

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2024 International Conference on Decision Aid Sciences and applications, DASA 2024

作者： Suleiman, Aghiad Khedr Shafek, Darin Mahfuri, Mahmoud Khishe, Mohammad Saeed, Ali Ghwanmeh, Sameh Alma'Moon University College Cybersecurity and Cloud Computing Technologies Engineering Dept. Baghdad Iraq Alma'Moon University College Computer Engineering Techniques Dept. Baghdad Iraq International University for Science and Technology Computer and Information Technology Department Syria Innovation Center for Artificial Intelligence Applications Yuan Ze University Taiwan Jadara Research Center Jadara University Jordan Jordan Computer Center Northern Technical University Ninevah Iraq College of Computer Information Technology Computer Science Dept American University in the Emirates United Arab Emirates

ISBN: (纸本)9798350369106

This paper presents an analytical study and a technique for extracting the features of a common case of images of the iris called off-angle iris which was taken for persons identification systems. The main problem when using biological iris measurements to identify the persons is the difficulty of identifying and extracting features of the iris. This problem is increasing when dealing with off-angle iris and it leads to decreased system accuracy and increased system rate error. In return, all the transfer learning techniques face difficulties in the case of heavily degraded data and partially occluded, and off-angle. A new method has a whole new methodology to deal with the image as it is without transformation processes. A new algorithm has been included for extracting features of the iris through the pupil switching points. The most discriminating points of the iris depend on the biological human eye statistics and analytical study. It has been trained and tested on the common images of the off-angle iris database so-called: 'CASIA Iris 1.0'. It has been implemented in the MATLAB environment. The results showed the efficiency of this technique, high precision and more importantly low failed acceptance rate. It emphasized that it is adaptive as well as efficiency improvement of the system. © 2024 IEEE.

关键词： Contrastive Learning

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Code Smell Detection using Hybrid Machine Learning Algorithms 5

Code Smell Detection using Hybrid Machine Learning Algorithm...

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5th International Conference on Inventive Research in computing applications, ICIRCA 2023

作者： Mahalakshmi, D. Kasinathan, Prabakaran Elangovan, D. Rohith Bhat, C. Balamurugan, M. Sivakumar, S. Panimalar Engineering College Dept. of Information Technology India School of Computing Veltech Rangarajan Dr. Sagunthula R & D Institute of Science and Technology Dept. of Cse India Panimalar Engineering College Dept. of Cse India Chennai India Sri Sairam Engineering College Affiliated to Anna University Dept. of Cse India Lakshmi Bangaru Arts and Science College Dept. of Computer Applications melmaruvathur India

ISBN: (纸本)9798350321425

Code smells frequently leads to the discovery of decreased code quality, drains on application resources, or even critical security vulnerabilities embedded within the application's code. While code smells may not always indicate a particularly serious problem, it do often lead to the discovery of these issues. Software's structural characteristics lead to a design issue that makes it challenging to manage and maintain code refactoring. The goal of the current research is to create methods for identifying code smells. The machine learning algorithm is a reliable method for individualized smell detection, but there aren't many studies on how well it works for different developers. In this proposed work used two different deep learning algorithms and five different machine learning ensembles to detect suspicious code. Investigation of the Data class, God class, Feature-envy, and Long-method datasets revealed that each contained various levels of code smells. Although there is room for improvement, the outcomes of prior publications' applications of machine learning and stacking ensemble learning methods to this dataset were satisfactory. A class balancing method (SMOTE) was implemented to address the problem of class imbalance within the datasets. While the Feature- envy dataset with the selected dozen metrics produced the lowest accuracy (91.45%) for the Max voting method, the Long-method dataset with the various chosen metrics produced the highest accuracy (100%) for all five methods. © 2023 IEEE.

关键词： Learning systems

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Customer Lifetime Value Prediction: Using Machine Learning to Forecast CLV and Enhance Customer Relationship Management 7

Customer Lifetime Value Prediction: Using Machine Learning t...

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7th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2023

作者： Kumar, Ankit Singh, Kamred Udham Kumar, Gaurav Choudhury, Tanupriya Kotecha, Ketan Gla University Department of Computer Engineering & Applications UP Mathura India School of Computing Graphic Era Hill University Dehradun India Cse Dept. Pune India Symbiosis Centre for Applied Artificial Intelligence Pune411045 India

ISBN: (纸本)9798350342154

In today's cutthroat corporate realm, consistent innovation and market adaptability don't cut it;mastering client relationships is key. A pivotal metric here is the Customer Lifetime Value (CLV), shedding light on long-term profit potentials per customer, an understanding of which is critical for business growth. A study delves into enhancing Customer Relationship Management (CRM) using machine learning to refine CLV predictions. Conducted collaboratively, it explores CLV's role in marketing, client segmentation, and resource optimization before delving into its measurement. Using advanced machine learning tools like regression, clustering, and neural networks on a rich dataset of customer transactions and behaviors, it's found that machine learning significantly outperforms traditional CLV estimation methods. This leads to better CLV accuracy and paves the way for enhanced resource allocation and targeted marketing, further emphasizing CLV's importance in crafting personalized customer journeys and refining CRM strategies amidst a constantly evolving market landscape. © 2023 IEEE.

关键词： Probability distributions

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Analysis of Image Restoration Techniques on Lunar Surface Images

Analysis of Image Restoration Techniques on Lunar Surface Im...

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Innovations in Power and Advanced computing Technologies (i-PACT)

作者： Dhanvanth Reddy Yerramreddy Jayasurya Marasani Ponnuru Sathwik Venkata Gowtham Don S 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

Lunar surface images captured by Chandrayaan-II, leveraging advanced image restoration techniques. The images obtained from the space craft can subject to various noise influence depending on the data acquisition system. Our goal is to investigate the effect of various types of noise in the image, such as Gaussian, Salt and Pepper, and Speckle noise, necessitating advanced restoration approaches for optimal data extraction. Our primary objective is to employ three restoration techniques such as Wiener, Median and Gaussian filters to restore the lunar images. Through the implementation of these restoration techniques, we aim to mitigate the adverse effect of sensor noise, thermal noise, cosmic ray noise, transmission noise and compression artifacts, all of which impede the precise interpretation of lunar surface features. These restoration methods are then compared in various noise levels and has been evaluated in terms of SNR, PSNR, image fidelity and MSE. The outcomes are encouraging and the suggested technique provides more precise restoration for images of the lunar surface.

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