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Leveraging Support Vector Machine for Sports Injury Classification

Leveraging Support Vector Machine for Sports Injury Classifi...

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International Conference on Information Technology and Electrical Engineering (ICITEE)

作者： Thattapon Surasak Phornnatee Praking Kotcharat Kitchat Dept. of Computer and Information Science Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand Dept. of Computer Science and Information Engineering National Central University Taoyuan Taiwan

This study explores the utility of Support Vector Machines (SVM) for the classification of injuries in Athletes including tennis, table tennis, and badminton. The primary objective of this research was to develop an automated method to detect between normal and injury pose, providing a poten-tially powerful tool for early detection. In other words, this research is the pioneer study to represent one another solution for sports injury classification using the image data apart from the current approaches that might be focused on the sensor-based. Therefore, we collect a dataset from various social media across the three sports, labeled into two categories: “Normal” and “Injury”. The SVM model was trained on this dataset, and tuned to optimize its performance in distinguishing the two classes. Achieving an accuracy of 96.63%, the result demonstrates that the SVM model can effectively identify injury patterns in the considered sports. The implications of this study are sig-nificant, presenting the initial steps towards applying computer vision tasks in sports topics. By demonstrating the feasibility of an SVM-based approach to injury detection in these racket sports.

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Predictive Control and Production Process Forecasting Under Deterministic Chaos 23

Predictive Control and Production Process Forecasting Under ...

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23rd IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2022

作者： Manusov, Vadim Z. Orlov, Dmitry V. Karmanov, Vitaly S. Khusnutdinov, Alexander O. Kovalenko, Dmitry I. Ivanov, Gennady V. Novosibirsk State Technical University Dept. of Industrial Power Supply Systems Novosibirsk Russia Novosibirsk State Technical University Dept. of Theoretical and Applied Computer Science Novosibirsk Russia Surgut State University Dept. of Radio Electronics and Radio Engineering Surgut Russia

ISBN: (纸本)9781665498043

Electricity consumption forecasting is an integral part of the workflow in most industries. At the same time, daily load schedules of the mining industry are characterized by deterministic chaotic fluctuations in the electricity consumption curve caused by the technology of the production process. The paper proposes two mathematical forecasting models, one of which is based on the recurrent method, and the second one is based on the vector method. At the same time, the structure of the models allows taking into account the individual features of the operating cycle of the production process, as well as identifying and smoothing the 'noise' components of the rank parametric surface of this production process. The Singular Spectrum Analysis method was chosen as the algorithm for preprocessing and smoothing time series of electricity consumption. The method was applied for the first time for a multidimensional array of electricity consumption data. As a result, rank parametric surfaces were plotted, one of which is the initial surface, and the second one is the final surface. © 2022 IEEE.

关键词： Energy conservation

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Robust and Reliable Liveness Detection Models for Facial Recognition Systems 20

Robust and Reliable Liveness Detection Models for Facial Rec...

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20th International Conference on Frontiers of Information Technology, FIT 2023

作者： Anjum, Haris Arshad, Usama Ali, Raja Hashim Ul Abideen, Zain Shah, Muhammad Huzaifa Khan, Talha Ali Ijaz, Ali Zeeshan Siddique, Abu Bakar Imad, Muhammad Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Spain

ISBN: (纸本)9798350395785

Facial Liveness Detection is instrumental in combating fraudulent practices and identity theft by differentiating genuine faces from forgeries. Given that facial recognition is now an integral part of many sectors like banking and law enforcement, liveness detection has become a vital aspect for maintaining the trustworthiness of these applications. Unfortunately, the utility of prevalent facial liveness detection models is constrained by their lack of generalizability to novel, unseen data, which presents a substantial impediment to practical applications. To mitigate this issue, our research endeavored to construct a reliable and precise facial liveness detection model that can effectively navigate this challenge and ensure consistent performance across diverse datasets. The devised model exhibited excellent accuracy, which attests to its robustness. This research underscores the advantages of employing deep learning methods such as Convolutional Neural Networks (CNNs) as they significantly enhance the model's accuracy. We intend for our findings to drive further research and progress in this critical field, by laying the groundwork for future work that integrates multiple liveness detection methods and collects more varied training data to develop an even more resilient and efficient system. © 2023 IEEE.

关键词： Feature extraction

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CapSight: A Social Aid System for the Visually Impaired Using Facial Recognition and Haptic Feedback 13

CapSight: A Social Aid System for the Visually Impaired Usin...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Chi, Chun-Nan Hsu, Jia-Lien Hung, Chi-Huang Huang, Shih-Yung Chen, Cheng-Hsuan Hsu, Chia-Hsun Fu Jen Catholic University Graduate Institute of Applied Science and Engineering New Taipei City Taiwan Fu Jen Catholic University Department of Computer Science and Information Engineering New Taipei City Taiwan National Central University Fu Jen Catholic University Department of Electrical Engineering Taoyuan City Taiwan Fu Jen Catholic University Lee-Ming Institute of Technology Graduate Institute of Applied Science and Engineering Dept. of Information Technology New Taipei City Taiwan Kang Chiao International School Dept. of International High School New Taipei City Taiwan

ISBN: (纸本)9798350355079

This study introduces "CapSight,"a system for enhancing social interaction in visually impaired individuals. It includes a single-board computer, camera, vibration motors, and Bluetooth bone conduction headphones on a baseball cap. The camera identifies people using facial recognition and announces names through the headphones, with vibration motors indicating face positions. Users can add new friends by taking a photo and recording their name. This system helps visually impaired individuals recognize and locate social partners, boosting confidence and independence. © 2024 IEEE.

关键词： Headphones

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Low Acceptance Agreement Tests via Bounded-Degree Symplectic HDXs

Low Acceptance Agreement Tests via Bounded-Degree Symplectic...

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Annual IEEE Symposium on Foundations of computer science

作者： Yotam Dikstein Irit Dinur Alexander Lubotzky Computer Science and Discrete Mathematics Institute for Advanced Study Princeton New Jersey USA Dept. of Applied Math and Computer Science The Weizmann Institute of Science Rehovot Israel Department of Mathematics The Weizmann Institute of Science Rehovot Israel

ISBN: (数字)9798331516741

ISBN: (纸本)9798331516758

We solve the derandomized direct product testing question in the low acceptance regime, by constructing new high dimensional expanders that have no small connected covers. We show that our complexes have swap cocycle expansion, which allows us to deduce the agreement theorem by relying on previous work. Derandomized direct product testing, also known as agreement testing, is the following problem. Let $X$ be a family of k-element subsets of $[N]$ and let $\{f_{s}:s\rightarrow\Sigma\vert s\in X\}$ be an ensemble of local functions, each defined over a subset $s\subset\lceil N$ . Suppose that we run the following so-called agreement test: choose a random pair of sets $s_{1}, s_{2}\in X$ that intersect on $\sqrt{k}$ elements, and accept if $f_{s_{1}}, f_{s_{2}}$ agree on the elements in $s_{1}\cap s_{2}$ . We denote the success probability of this test by Agree $\{f_{s}\})$ Given that Agree $(\{f_{s}\})=\varepsilon > 0$ is there a global function $G:[N]\rightarrow\Sigma$ such that $f_{s}=G\vert _{s}$ for a non-negligible fraction of $s\in X\ ?$ We construct a family $X$ of k-subsets of $[N]$ such that $\vert X\vert =O(N)$ , and such that it satisfies the low acceptance agreement theorem. Namely, $\text{Agree}\left(\left\{f_s\right\}\right)>\varepsilon \Longrightarrow \exists G:[N] \rightarrow \Sigma, \quad \underset{s}{\mathbb{P}}\left[\left.f_s \stackrel{0.99}{\approx} G\right\vert_s\right] \geqslant \text{poly}(\varepsilon)$ . A key idea is to replace the well-studied LSV complexes by symplectic high dimensional expanders (HDXs). The family $X$ is just the k-faces of the new symplectic HDXs. The latter serve our needs better since their fundamental group satisfies the congruence subgroup property, which implies that they lack small covers. We also give a polynomial-time algorithm to construct this family of sym-plectic HDXs.

关键词： computer science Testing

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Detection of Cancer Cells Using Convolutional Neural Networks (CNNs)

Detection of Cancer Cells Using Convolutional Neural Network...

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Computing and Data Analytics (ICCDA), International Conference on

作者： Md. Ghouse Mohiuddin D. Laxmi Sreenivasa Reddy Aijaz Ahmed Dept. of Computer Science University PG College Palamamuru University Mahabubnagar Telangana State India Dept. of IT Chaitanya Bharthi Institute of Technology - CBIT Hyderabad Telangana State India Dept. of IT University of Technology & Applied Science Shinas Sultante of Oman

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542580

The fastest spreading dieses among the cancer dieses throughout the world is the Breast cancer. Identifying it at the prior stage will be helpful for effective treatment. Convolutional Neural Networks (CNNs) is an efficient technique which has demonstrated significant ability in classifying and recognizing patterns in medical images. In this research paper we have explored the application of deep learning models for detecting cancer cells from histopathological images, and focused on various architectures, training methods, and assessment measures. Here we specifically evaluated the functioning of four well-known models of CNN comprising AlexNet, VGG16, InceptionV3 and, ResNet50 by employing the dataset from BreakHis for binary classification of the histopathological images into Malignant and Benign classes. Each model is fine-tuned and trained through transfer learning. Here we used cross-entropy loss with Adam optimizer for binary classification, through which the learning rate is adaptively adjusted, and facilitating efficient handling of large datasets. In this research work for training our models we applied Stratified k-fold cross-validation approach for ensuring stability and generalizability. The effectiveness and performance of the models are assessed with the help of Accuracy, Precision, & Recall, and F1-Score. Comparative analysis indicates that ResNet50 outperforms the other models in detecting cancer cells. Finally, we discussed the potencies and weakness of the applied models for offering valuable perceptions into their efficacy for medical image classification tasks.

关键词： Training Adaptation models Analytical models Accuracy Transfer learning Breast cancer Convolutional neural networks Biological cells Medical diagnostic imaging Residual neural networks

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The Extended RoSA Conceptual Architecture for Managing Consumer-Grade Social Robot Surrogates

The Extended RoSA Conceptual Architecture for Managing Consu...

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IEEE Games Media Entertainment (GEM)

作者： Jens-Martin Loebel Curtis L. Gittens Dept. of Economics Magdeburg-Stendal University of Applied Sciences Magdeburg Germany Dept. of Computer Science Mathematics and Physics The University of the West Indies Cave Hill Campus Bridgetown Barbados

ISBN: (数字)9798350374537

ISBN: (纸本)9798350374544

This paper introduces the Extended Robot Surrogate Architecture (eRoSA), a novel framework tailored for managing consumer-grade social robots used in entertainment and companion scenarios, including elder care. By extending the Robot Surrogate Architecture, eRoSA enhances the operational effectiveness of robotic surrogates by integrating advanced modules for state and personality management, allowing for dynamic and context-sensitive interactions across both healthcare and leisure domains. The architecture features a Hardware Abstraction Layer and a ReST Communication Module, which facilitate scalable, modular interactions across diverse robotic platforms, ranging from caregiving to entertainment applications. Challenges addressed include the complexity of integrating diverse robotic systems, ensuring real-time performance, and maintaining interaction quality. By emphasizing psychosocial interactions and modular adaptability, eRoSA aims to significantly improve the utility and acceptance of social robots in varied real-world settings, outlining a path for future empirical research and practical refinement.

关键词： Social robots Entertainment industry Medical services Media Real-time systems Hardware Distance measurement

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A GAN-LSTM Architecture for ECG Anomaly Detection

A GAN-LSTM Architecture for ECG Anomaly Detection

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Electrical, computer, Telecommunication and Energy Technologies (ECTE-Tech), International Conference on

作者： Soumia OUBADI Naçima MELLAL Rachida BOUCHOUAREB Soumia ZERTAL dept. Computer science Research Laboratory on Computer Science's and Complex System University of Oum El Bouaghi Algeria dept. Networks and telecommunication Research Laboratory of Artificial Intelligence and Autonomous Objects Institute of Applied Science & Technology University of Oum El Bouaghi Algeria dept. Computer science Research Laboratory of Artificial Intelligence and Autonomous Objects University of Oum El Bouaghi Algeria

ISBN: (数字)9798350388480

ISBN: (纸本)9798350388497

Cardiovascular diseases (CVDs) continue to be a primary cause of global mortality, requiring prompt detection and care to avert serious consequences. Electrocardiograms (ECGs) are vital diagnostic instruments for cardiovascular disorders, yet traditional diagnostic methods encounter considerable obstacles, such as class imbalance and substantial heterogeneity in the available data. This study introduces a novel ECG classification technique utilizing GAN and LSTM. Generative Adversarial Networks (GANs) are employed to produce additional synthetic ECG samples, addressing class imbalance in the training dataset. The suggested design integrates LSTM units into the GAN generator to collect and maintain temporal signal patterns, while anomaly detection is performed using a CNN-LSTM model. Experimental findings indicate a significant enhancement in classification performance, achieving an accuracy of up to 94.74%. The findings indicate that the proposed GAN-LSTM model significantly improves ECG anomaly detection and classification, offering substantial potential for the rapid and precise identification of arrhythmias.

关键词： Training Instruments Mortality computer architecture Electrocardiography Generative adversarial networks Real-time systems Convolutional neural networks Anomaly detection Long short term memory

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Detecting Ransomware Encryption with File Signatures and Machine Learning Models

Detecting Ransomware Encryption with File Signatures and Mac...

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Irish Signals and Systems Conference (ISSC)

作者： Michael Duignan Michael Schukat Enda Barrett Dept. of Computer Science & Applied Physics Atlantic Technological University Galway Ireland Dept. of Information Technology University of Galway Galway Ireland

This study presents an analysis of the use of machine learning models in the identification and classification of ransomware encrypted files, differentiating them from standard encrypted or compressed files, and non-encrypted files (referred to as goodware). The study utilized a robust dataset of approximately 159,897 files, categorized into goodware, Chaos, Conti, and Xorist strains, and applied five machine learning models: Logistic Regression, Linear Discriminant Analysis, K-Nearest Neighbor, Naive Bayes, and Classification and Regression Trees to this dataset. The models were trained using an array of data points, including file headers and footers, entropy, Chi Squared, and file extensions. The analysis revealed high accuracy rates of between 97% and 100% in distinguishing ransomware encrypted files from other file types, demonstrating the importance of file extensions as a key determinant in this process. The study also draws attention to the increasing prevalence and complexity of ransomware strains, specifically those which do not alter file extensions, thereby posing additional challenges to identification and classification efforts. The research suggests further investigation and study into a wider array of ransomware strains and a more extensive range of file types. Special emphasis is recommended on strains that do not modify file extensions, as understanding these could significantly enhance the efficiency and effectiveness of machine learning models in ransomware detection.

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IoT and Machine Learning based Valve Stiction Estimation in Process Industry

IoT and Machine Learning based Valve Stiction Estimation in ...

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Control Instrumentation System Conference (CISCON)

作者： Vimal Kumar V Ragesh G. K Aparna George Priya S Dept. of Computer Science Indian Institute of Information Technology Kottayam Pala Kerala India Dept. of Applied Electronics & Instrumentation Rajagiri School of Engineering & Technology Ernakulam Kerala India

ISBN: (数字)9798350375480

ISBN: (纸本)9798350375497

Stiction in control valves represents an undesirable nonlinear phenomenon. Product quality and process safety frequently suffer due to the occurrence of valve stiction within process control systems utilizing control valves as their final control elements. This paper introduces a straightforward and pragmatic Machine Learning technique, supported by Internet of Things (IoT) technology, for identifying and assessing the initiation of stiction in all pertinent control loops on a periodic basis. Furthermore, this paper suggests an IoT-based innovative approach to facilitate collaboration among industrial experts across multiple process plants, enabling them to explore new avenues for preventive maintenance concerning stiction in control valves.

关键词： Training Maximum likelihood estimation Neural networks Process control Machine learning Valves Control systems Safety Internet of Things Preventive maintenance

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