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Deep learning Model for Predicting Basket-Ball Player Injuries Using VGG16

Deep Learning Model for Predicting Basket-Ball Player Injuri...

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2025 international conference on computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Avula, Sashikanth Reddy Haarika, Annapareddy Raghuram, Mahatee Rahul, S.K. Nitte Meenakshi Institute of Technology Department of Mca Bengaluru India Bms Institute of Technology and Management Department of Information Science and Engineering Bengaluru India

ISBN: (纸本)9798331538538

This growing need for player safety in sports has spurred the creation of newer models for predictive analysis. The project involves predicting the injury risk of basketball players using foot images and image classification techniques. Since VGG16 is a popular Convolutional Neural Network (CNN) whose architecture has been shown to perform well in image recognition, this model is used in this study. The target is to pinpoint and categorize movements more susceptible to inducing injuries and further recommend tactical alterations in training and gameplay. The scheme achieves high accuracy for predicting potential injury risks by finetuning the VGG16 model. The project presents how deep learning can be applied in sports analytics with the objective to reduce injury instances and augment player safety. The discussed methodology predicts the incidence of injury and can be a useful resource to coach- and medial staff to help formulate prevention plans. © 2025 IEEE.

关键词： Convolutional neural networks

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Evaluation of learning effect based on data analysis and BOPPPS teaching model 5

Evaluation of learning effect based on data analysis and BOP...

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5th international conference on E-Business, Information Management and Computer Science, EBIMCS 2022

作者： Li, Zhaomin Zhou, Shangheng College of Electronic Information Engineering Wuhan Business University Hubei Wuhan China

ISBN: (纸本)9781450397834

This paper takes the content of "Collaborative Filtering"in "Data Mining"as an example, discusses the method of course teaching design based on the BOPPPS model, puts forward a student-centered teaching design concept, encourages students to learn independently, and fully taps students' abilities. Interactive teaching practice that realizes the needs of learners' personalized learning and realizes the natural transition from the known to the unknown. The results show that the six-link teaching mode of BOPPPS can effectively stimulate students' interest in learning and strengthen their awareness of autonomous learning. Improve the practical ability, thereby greatly improving the teaching effect. CCS CONCEPTS •Applied computing ∼Education ∼Computer-assisted instruction © 2022 ACM.

关键词： Collaborative filtering

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Leveraging Internet of Things (IoT) Sensors and Deep learning Techniques for Precision Agriculture 1

Leveraging Internet of Things (IoT) Sensors and Deep Learnin...

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1st international conference on Innovative engineering Sciences and Technological Research, ICIESTR 2024

作者： Wilfred Blessing, N.R. Pyingkodi, M. Wasim Haidar, S.K. Palarimath, Suresh Paravathavarthini, S. Balakumar, C. Sheeja Kumari, V. Sutherlin Subitha, G. Seldev Christopher, S. University of Technology and Applied Sciences-Ibri College of Computing and Information Sciences Ibri Oman Kongu Engineering College Department of Computer Applications Erode India University of Technology and Applied Sciences-Salalah College of Computing and Information Sciences Salalah Oman Kongu Engineering College Department of Computer Technology Erode India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Saveetha Institute of Medical and Technical Sciences Department of Computational Intelligence Chennai India Stella Mary's College of Engineering Department of Computer Science and Engineering Aruthenganvilai India St. Xavier's Catholic College of Engineering Department of Computer Science and Engineering Nagercoil India

ISBN: (纸本)9798350348637

Precision agriculture, driven by advancements in technology, aims to optimize farming practices by utilizing data and technology to enhance efficiency, productivity, and sustainability This research introduces an innovative pilot investigation centered on the convergence of two revolutionary technologies are the Internet of Things (IoT) and Deep learning. With a specific focus on advancing precision agriculture, the primary objective of this study is to evaluate the combined effects of data collection facilitated by IoT and analytical capabilities of deep learning in enhancing and optimizing various agricultural processes. The integration of IoT in agriculture has revolutionized data acquisition, employing an array of sensors to monitor critical parameters such as soil moisture, temperature, and crop health. Concurrently, Deep learning, a subset of artificial intelligence, exhibits the potential to glean actionable insights from voluminous datasets, offering advanced analytics and predictive capabilities. This study investigates the practical implementation and efficacy of this integration in a controlled agricultural setting. Sensors strategically positioned in the pilot study capture real-time data, while deep learning algorithms process and analyze this information. The primary objectives include evaluating the effectiveness of this combined technology in optimizing irrigation schedules, predicting crop yields, and identifying anomalies in crop health. Preliminary findings underscore the transformative potential of IoT and Deep learning, empowering farmers with real-time data for informed decision-making. Key considerations encompassed in the study include IoT Sensors, Deep learning algorithms, and user adoption. The research not only sheds light on the technical intricacies of the integration but also delves into the challenges and opportunities inherent in merging these technologies within the agricultural landscape. As agriculture transitions towards the next

关键词： Smart agriculture

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Intrusion detection system using soft computing techniques in 5G communication systems

Intrusion detection system using soft computing techniques i...

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international conference on Artificial Intelligence, Blockchain, computing and Security, ICABCS 2023

作者： Dhanya, D. Shankari Kathir, I. Kuchipudi, Ramu Thamarai, I. Kumar, E. Rajesh Department of Computer Science and Engineering Mar Ephraem College of Engineering and Technology Tamil Nadu Kanyakumari India Department of Computer Science and Engineering RMK College of Engineering and Technology Tamil Nadu Thiruvallur India Department of Electrical and Electronics Engineering V.S.B. Engineering College Tamil Nadu Karur India Department of Information Technology Chaitanya Bharathi Institute of Technology Telangana Hyderabad India Department of Computer Science and Engineering Panimalar Engineering College Chennai City Campus Tamil Nadu Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9781032669663

The research deals with a thorough survey and starts by reviewing the fundamental knowledge of fuzzy systems over 5G communication. Future directions and scope can be used to demonstrate the desire for 5G communication networks to provide access to new applications and widespread connection. Therefore, the difficulties with the underlying technologies and hardware supporting them should allow for a greater level of service quality. The goal of this study is to create an intrusion detection system that employs fuzzy logic. This study proposes a model for a real-time intrusion detection expert system that seeks to find a variety of security breaches, from outsiders trying to break in to insiders breaking in and abusing the system. Analysis has also been done on the impact of changing the learning algorithms and membership function. The structure can manage very sizeable amount of data with the aid of data collection, storage, retrieval, and analysis, and it integrates the big data stream inside the network to enhance performance of the network. © 2024 The Author(s).

关键词： Intrusion detection

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Precision Forecasting of Stock Prices: Leveraging XGBoost and Technical Indicator for Advanced Predictive Modeling 1

Precision Forecasting of Stock Prices: Leveraging XGBoost an...

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1st international conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Yuvaraj, S. Chenni Kumaran, J. Vinston Raja, R. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology School of Computing Department of Computational Intelligence Kattankulathur Chennai India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529246

The investigation aims to evaluate the performance of machine learning techniques, particularly the XGBoost regression method, for stock price prediction with the help of technical indicators. The research targets the adjusted closing price to reduce the autocorrelation problem in time series. The Five Days and Ten Days Exponential Moving Averages (EMA-5 and EMA-10) are involved in refining feature selection. The performance of the model was evaluated using different error metrics, namely, Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R2). The results indicate that the XGBoost model, combined with EMA-5 and EMA-10, achieves high predictive performance, obtaining an r2 value of 0.98. It implies that incorporating short-term EMAs into advanced machine learning techniques drastically improves stock price forecasting. © 2025 IEEE.

关键词： Time series

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Advancements in Language Processing Algorithms Transforming Linguistic computing

Advancements in Language Processing Algorithms Transforming ...

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2024 international conference on Advances in computing Research on Science engineering and Technology, ACROSET 2024

作者： Mishra, Poonam Pandey, Tushar Kumar Roy, Vandana Kashyap, Ramgopal Chhaya Ahluwalia, Ritu Amity University Raipur India Krishi Vigyan Kendra-Birauli Dr. Rajendra Prasad Central Agricultural University Pusa India DoEC Ggits Jabalpur India Mangalayatan University Jabalpur India Ggits Dep of Cse Jabalpur India

ISBN: (纸本)9798350388800

New approaches that alter linguistic computing have resulted in breakthroughs in natural language processing. New technologies are to blame for these advancements. In this paper, we look at five cutting-edge methods: attention mechanisms, transformer models (like BERT and GPT), transfer learning (like ULMFiT), neural machine translation (NMT), and BERT-based embedded systems for tasks further down the line. All of these methodologies are considered to be at the forefront of their respective fields. When it comes to creating language, understanding language, and comprehending language in context, the integrated methodology is a way that is successful. To study the minute benefits that each technique provides in terms of sentiment analysis, language growth, and interaction between languages, the objective of this in-depth research is to investigate the implications of these advantages. A solution that is superior to others that came before it is produced because of the integration of cutting-edge models. As the field of linguistic computing continues to expand, this guide provides professionals and academics with assistance in making the most of new advancements to improve language processing platforms. © 2024 IEEE.

关键词： Neural machine translation

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Detection and Classification of Type 2 Diabetes using Machine learning Techniques 2

Detection and Classification of Type 2 Diabetes using Machin...

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2nd international conference on Advancements in Electrical, Electronics, Communication, computing and Automation, ICAECA 2023

作者： Keerthana, N. Sri Ramakrishna Engineering College Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798350306811

The Type 2 Diabetes has several consequences, one of which is diabetic retinopathy (DR). It typically comes from excessive glucose content in the blood arteries that supply nutrition to the retina, and it can also have many additional negative effects. In this paper, machine learning algorithms like CNN (Convolutional Neural Network), GLCM (Gray Level Co-occurrence Matrix), and Random Forest( RF) are used to predict diabetic retinopathy. In terms of performance metrices like Accuracy, Precision, Recall, and F1-score, the Convolutional Neural Network (CNN) produced the best results. Results generated by the CNN algorithm had a higher accuracy rate of 92%. The Django framework was used to develop the website, which aims to let people submit photographs and classify them into No DR, Mild DR, Moderate DR, Severe DR, and Proliferative DR categories. © 2023 IEEE.

关键词： Convolutional neural networks

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Enhanced Node Localization in IoT Sensor Networks via Deep learning and Virtual Node Modeling 7

Enhanced Node Localization in IoT Sensor Networks via Deep L...

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7th international conference on Contemporary computing and Informatics, IC3I 2024

作者： Balasubramaniam, Vanitha Sivasankaran Kumar, P.M. Ashok Gopalakrishna, Pandi Kirupa Agrawal, Shashwat Goel, Om Mahadik, Siddhey Pt Rajan Salai Kk Nagar Chennai600078 India Koneru Lakshmaiah Education Foundation Department of Computer Science Engineering Vaddeswaram A.P Guntur522502 India Karnataka Bengaluru India Mehrauli Uttar Pradesh Ghaziabad201002 India Abes Engineering College U.P Ghaziabad India Vashi Maharashtra Navi Mumbai400703 India

ISBN: (纸本)9798350350067

With the rapid increase in the deployment of Internet of Things (IoT) devices, numerous innovative and practical applications are being developed. IoT enables these applications through the use of resource-constrained fixed and mobile nodes. Using the whale optimization algorithm (WOA), we develop a deep learning technique that overcomes traditional DV-Hop localization challenges. In our approach, deep neural networks (DNNs) are used to estimate distances between nodes that have not yet been determined (non-coordinated) and anchor nodes that have been determined (coordinated) at low costs for IoT infrastructure. In contrast to DL methods that typically require substantial training data to achieve high accuracy, this problem can be solved by implementing a data augmentation strategy (DAS). DNNs are improved by creating virtual anchors around actual anchors to increase the dataset size and enhance their performance. Simulation results indicate that our deep learning-based DV-Hop localization method significantly surpasses other localization techniques, especially in terms of positional accuracy. © 2024 IEEE.

关键词： Contrastive learning

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Research on Fuzzy Variable Step Size LMS Adaptive Filtering Algorithm Based on Convex Combination 2

Research on Fuzzy Variable Step Size LMS Adaptive Filtering ...

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2nd international conference on Signal Processing and Intelligent computing, SPIC 2024

作者： Li, Wenjun Wu, Caiyun Li, Xinze Li, Na Shenyang Ligong University School of Equipment Engineering Shenyang China Beijing University of Technology Machinery and Energy Engineering Institute Beijing China

ISBN: (纸本)9798350368888

This study investigates the convergence speed and mean square error capability of the Least Mean Square (LMS) adaptive filtering algorithm, presenting a novel approach termed the Fuzzy Variable learning Rate LMS algorithm based on convex combination. Firstly, in order to enhancing convergence speed, an enhanced LMS algorithm employing variable learning rate based on sine functions, denoted as the Improved Variable Step Size LMS Algorithm based on Sine Function (γ-SLMS), is introduced. Building upon this, to mitigate the steady-state error, the Fuzzy Variable learning Rate LMS Algorithm based on Mamdani Model (FMS-LMS) is proposed. Finally, utilizing a convex combination framework, the Fuzzy Variable learning Rate LMS Adaptive Filtering Algorithm Based on Convex Combination (cFMS-LMS) is formulated. Simulation results indicate that the cFMS-LMS algorithm surpasses traditional LMS algorithms in both convergence speed and mean square error. Furthermore, it shows lower computational complexity compared to LMS algorithms utilizing Sugeno fuzzy inference. © 2024 IEEE.

关键词： Wiener filtering

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Enhanced Detection of Down Syndrome through Convolutional Neural Networks: A Deep learning Approach 6

Enhanced Detection of Down Syndrome through Convolutional Ne...

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Saini, Archana Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331522667

Down syndrome is a chromosomal anomaly caused by having an extra copy of chromosome 21 in millions of people worldwide. Early identification of Down syndrome is critical for delivering prompt therapies and assistance that improve the quality of life for affected persons and their families. In this article, a unique technique for detecting Down syndrome using convolutional neural networks (CNNs) is presented. The solution uses deep learning algorithms to analyze children's facial photos and automatically detect probable signs of Down syndrome. The experimental findings illustrate the efficiency of the proposed CNN-based technique in reliably identifying Down syndrome, with high accuracy, sensitivity, and specificity. In this article, three different batch sizes are applied with epoch 100, and the performance of the model is evaluated. At batch size 8 the loss is 0.3560 and the accuracy is 0.8419. Similarly, at batch size 16, the loss is 0.3420 and the accuracy is 0.8474 and lastly, at epoch 32 the minimum loss is measured as 0.3388, and the maximum accuracy of 0.8501 is achieved. The highest AUC, precision, and recall are also calculated at batch size 32 which is 0.9297, 0.8380, and 0.8670 respectively. © 2025 IEEE.

关键词： Convolutional neural networks

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