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International Conference on Signal Processing and Communication (ICSC)

作者： Ratan Lal Rikriti Koirala Sai Swathi Thota Khaja Nayab Rasool Shaik School of Computer Science and Information Systems Northwest Missouri State University Maryville MO USA Faculty in the school of computer science and information systems Northwest Missouri State University USA School of computer science and information systems at Northwest Missouri State University USA

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

Real-time exercise detection is a challenging problem. Hence, we present a real-time exercise detection system that addresses the challenge of accurately monitoring exercises without the need for an expensive, real-life coach. Our approach leverages machine learning techniques to detect errors in exercises and provide actionable feedback, making fitness training more accessible and affordable. First, we collect a dataset, capturing multiple video frames with joint landmarks represented as key points in each image. These images are labeled with exercise actions such as curls, presses, and squats. Then, we perform data cleaning and splitting. After that we have trained and evaluated two machine learning models: Long Short-Term Memory (LSTM) networks and Random Forest based on metrics, namely, accuracy, precision, recall, F1 score. From our experimental results, we have found that the LSTM model outperforms the Random Forest, demonstrating its superior effectiveness in real-time exercise detection. Our results highlight the potential of AI-driven systems to provide personalized fitness guidance, opening doors to innovative solutions in the health and fitness industry.

关键词： Training Measurement Industries Presses Streaming media Signal processing Real-time systems Long short term memory Random forests Monitoring

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BLOCK-SECURE: AI-Based Blockchain Enabled Secure Framework for IoMT Applications

BLOCK-SECURE: AI-Based Blockchain Enabled Secure Framework f...

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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Panchal, Barkha Bhatia, Jitendra Kumhar, Malaram Tanwar, Sudeep Dutta, Ashit Kumar Rodrigues, Joel J.P.C. Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad India Dept. of Comp. Science and Information Systems College of Applied Sciences AlMaarefa University Saudi Arabia Amazonas State University AM Manaus Brazil

ISBN: (纸本)9798350350548

The Internet of Medical Things (IoMT) revolutionizes healthcare by integrating medical devices and systems with the internet. However, the vast amounts of sensitive medical data in IoMT networks pose significant security and privacy concerns. Traditional security measures often fall short in identifying the malicious data attacks within the IoMT ecosystems. This paper introduces an AI-based non-malicious data classification scheme based on blockchain. We applied and evaluated the machine learning (ML) classifiers, such as support vector machine (SVM), random forest (RF), and K-Nearest neighbor (KNN). We evaluated the proposed framework based on various performance metrics that includes accuracy, precision, recall, and F1 score. The accuracy using SVM obtained 75.3%, RF is 75.9%, and KNN is 84.3%. The results shows that KNN performs better than other models by the factor of 9%. © 2024 IEEE.

关键词： Adversarial machine learning

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Optimizing Customer Response Prediction in Auto Insurance: A Comparative Study of Machine Learning Models 2

Optimizing Customer Response Prediction in Auto Insurance: A...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Aburass, Sanad Dorgham, Osama Al Shaqsi, Jamil Rumman, Maha Abu Al Zarouni, Ahmed Maharishi International University Department Of Computer Science FairfieldIA United States School Of Information Technology Skyline University College Sharjah United Arab Emirates Al-Balqa Applied University Prince Abdullah Bin Ghazi Faculty Of Ict Al-Salt Jordan Sultan Qaboos University Information Systems Department Muscat Oman

ISBN: (纸本)9798350394962

This study examines various machine learning models to predict customer responses in the auto insurance industry. We focus on metrics like accuracy, precision, recall, and F1-score, carefully selecting threshold values to balance model performance with practical business applications. Our analysis reveals the XGB Classifier's superiority, achieving 99% accuracy and a 98% F1-score. We provide a comparative analysis of models, highlighting the XGB Classifier's strengths in handling complex data and its efficiency compared to other tested models, like Gaussian NB and Logistic Regression, which showed similar accuracies but varied in precision and recall. This study underscores the importance of choosing the right model and fine-tuning it for specific industry needs. © 2024 IEEE.

关键词： Forecasting

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UWB at WASSA-2024 Shared Task 2: Cross-lingual Emotion Detection 14

UWB at WASSA-2024 Shared Task 2: Cross-lingual Emotion Detec...

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14th Workshop on Computational Approaches to Subjectivity, Sentiment, and Social Media Analysis, WASSA 2024

作者： Šmíd, Jakub Přibáň, Pavel Král, Pavel Department of Computer Science and Engineering University of West Bohemia Faculty of Applied Sciences Czech Republic NTIS – New Technologies for the Information Society Univerzitní 2732/8 Pilsen301 00 Czech Republic

ISBN: (纸本)9798891761568

This paper presents our system built for the WASSA-2024 Cross-lingual Emotion Detection Shared Task. The task consists of two subtasks: first, to assess an emotion label from six possible classes for a given tweet in one of five languages, and second, to predict words triggering the detected emotions in binary and numerical formats. Our proposed approach revolves around fine-tuning quantized large language models, specifically Orca 2, with low-rank adapters (LoRA) and multilingual Transformer-based models, such as XLM-R and mT5. We enhance performance through machine translation for both subtasks and trigger word switching for the second subtask. The system achieves excellent performance, ranking 1st in numerical trigger words detection, 3rd in binary trigger words detection, and 7th in emotion detection. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Enhancing Timely Graduations: An Explainable AI Approach to Predict Academic Risks in South African Students

Enhancing Timely Graduations: An Explainable AI Approach to ...

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2023 IEEE International Conference on Electrical, computer and Energy Technologies, ICECET 2023

作者： Bokgoshi, Lenny Sixhaxa, Khanyisile Jadhav, Ashwini Nyamane, Seipati Ajoodha, Ritesh School of Computer Science and Applied Mathematics University of Witwatersrand Johannesburg South Africa University of Witwatersrand Science Teaching and Learning Unit Faculty of Science Johannesburg South Africa Sol Plaatje University Department of Computer Science and Information Technology Kimberley South Africa

ISBN: (纸本)9798350327816

With rising dropout rates and extended degree completion times in South African institutions, there's a pressing need to better understand and address the hurdles faced by students during their academic journey. This research harnesses the power of explainable AI to predict and classify students based on their likelihood of not completing their degrees on time. Utilizing synthetic data generated via a Bayesian network, we used predictive models that categorize students into four distinct risk profiles. This clarity in prediction not only illuminates the underlying causes of academic delays but also empowers faculty, advisors, and student support services with actionable insights. The goal of this research is to facilitate timely interventions, tailored support, and seamless transitions for students transferring between universities, ensuring more students can realize their academic aspirations within expected time frames. © 2023 IEEE.

关键词： academic performance at-risk students classification models Machine learning student attrition

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Recognition of American sign language using modified deep residual CNN with modified canny edge segmentation

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Multimedia Tools and Applications 2025年 1-28页

作者： Hariharan, U. Devarajan, Devikanniga Kumar, P. Santhosh Rajkumar, K. Meena, M. Akilan, T. Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab Mohali140413 India Department of Computer Science and Engineering GITAM University Karnataka Bengaluru561203 India Department of Information Technology Faculty of Engineering and technology SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai600089 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu Tambaram602109 India Research Coordinator Dept. of IQAC Amity University Kolkata West Bengal 700135 India

American Sign Language (ASL) recognition aims to recognize hand gestures, and it is a crucial solution to communicating between the deaf community and hearing people. However, existing sign language recognition algorithms still have some drawbacks, such as difficulty recognizing hand movements and low recognition accuracy for most sign language recognition. A Modified Convolutional Neural Network (MCNN) deep residual 101 classifier-based American Sign Language identification system has been developed to address this problem. There are three main parts presents in the method. The first part is pre-processing the images to remove the noise, enhance the contrast of the picture, adjust the contrast level and smoothen the picture using various filters. The second part is the segmentation, and it’s used to partition the image using a modified canny edge detection method by removing all weak edges present in the image. Finally, classification will be done using the Modified CNN deep residual 101 classifiers. By classifying the image, the American Sign Language is accurately identified. This process is conducted through images. The outcome shows that the suggested approach has a 0.97% accuracy and a 0.05% False Positive Rate. Other CNN architectures such as resNet 50 and resNet 18 reached 0.90% and 0.80% accuracy, respectively, which is lower than our proposed method. In addition, the resNet 101 classifier effectively recognizes the difficult hand gestures through the image data. It obtains high recognition accuracy for 36 signs is 0 to 9 numbers and a to z alphabets from American Sign Language. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Multi-graph Networks with Graph Pooling for COVID-19 Diagnosis

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Journal of Bionic Engineering 2024年第6期21卷 3179-3200页

作者： Chaosheng Tang Wenle Xu Junding Sun Shuihua Wang Yudong Zhang Juan Manuel Górriz School of Computer Science and Technology Henan Polytechnic UniversityHenanJiaozuo454003People’s Republic of China Department of Biological Sciences School of ScienceXi’an Jiaotong Liverpool UniversitySuzhou215123JiangsuChina School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz University21589JeddahSaudi Arabia Department of Signal Theory Networking and CommunicationsUniversity of Granada52005GranadaSpain

Convolutional Neural Networks(CNNs)have shown remarkable capabilities in extracting local features from images,yet they often overlook the underlying relationships between *** address this limitation,previous approaches have attempted to combine CNNs with Graph Convolutional Networks(GCNs)to capture global ***,these approaches typically neglect the topological structure information of the graph during the global feature extraction *** paper proposes a novel end-to-end hybrid architecture called the Multi-Graph Pooling Network(MGPN),which is designed explicitly for chest X-ray image *** approach sequentially combines CNNs and GCNs,enabling the learning of both local and global features from individual *** that different nodes contribute differently to the final graph representation,we introduce an NI-GTP module to enhance the extraction of ultimate global ***,we introduce a G-LFF module to fuse the local and global features effectively.

关键词： Convolutional neural networks Graph convolutional networks Graph pooling COVID-19

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The Improvement Process for The Software Development and Requirements Management to Achieve Capability Level 3 of CMMI 26

The Improvement Process for The Software Development and Req...

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26th International computer science and Engineering Conference, ICSEC 2022

作者： Joembunthanaphong, Pitiphat Sriharee, Gridaphat King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Computer and Information Science Bangkok Thailand

ISBN: (纸本)9781665491983

This research proposes the improvement process for software development and requirements management for a small and medium company to achieve capability level 3 of CMMI 2.0. The process was proposed to be executed by the waterfall process model and scrum-based agile. The authors developed the improvement process and support tool. The proposed process was applied to use in the home-loan service software development 2 projects. The evaluation of the proposed process was analyzed by comparing it to past project two projects. The result showed that the projects that applied the proposed improvement process had a better outcome 34.34%. The SCAMPI evaluation of the proposed process was taken by three experts and the result showed that the capability of the proposed process was 91.63% and the user satisfaction was at the good level with score of 4.37. From the experimentation that was conducted with two software companies, it was obvious that the proposed improvement process had significance to enhance the software development process to achieve capability level 3. © 2022 IEEE.

关键词： Software design

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Neural Cryptography with Fog Computing Network for Health Monitoring Using IoMT

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computer systems science & Engineering 2023年第1期44卷 945-959页

作者： G.Ravikumar K.Venkatachalam Mohammed A.AlZain Mehedi Masud Mohamed Abouhawwash Department of Computer Science and Engineering Coimbatore Institute of Engineering and TechnologyCoimbatore641109India Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec Králové50003Hradec KrálovéCzech Republic Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Sleep apnea syndrome(SAS)is a breathing disorder while a person is *** traditional method for examining SAS is Polysomnography(PSG).The standard procedure of PSG requires complete overnight observation in a *** typically provides accurate results,but it is expensive and time ***,for people with Sleep apnea(SA),available beds and laboratories are ***,it may produce inaccurate ***,this paper proposes the Internet of Medical Things(IoMT)framework with a machine learning concept of fully connected neural network(FCNN)with k-near-est neighbor(k-NN)*** paper describes smart monitoring of a patient’s sleeping habit and diagnosis of SA using FCNN-KNN+average square error(ASE).For diagnosing SA,the Oxygen saturation(SpO2)sensor device is popularly used for monitoring the heart rate and blood oxygen *** diagnosis information is securely stored in the IoMT fog computing *** can care-fully monitor the SA patient remotely on the basis of sensor values,which are efficiently stored in the fog computing *** proposed technique takes less than 0.2 s with an accuracy of 95%,which is higher than existing models.

关键词： Sleep apnea polysomnography IOMT fog node security neural network KNN signature encryption sensor

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Incorporating Data Analytics into Accounting Curricula: The Case of Jordanian Universities 2

Incorporating Data Analytics into Accounting Curricula: The ...

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Idris, Mohammed Al-Okdeh, Saleh Mahd, Osama Siam, Yousef Abu Applied Science Private University Faculty of Business Accounting Department Amman Jordan Qatar University College of Business and Economics Department of Accounting and Information Systems Doha Qatar

ISBN: (纸本)9798350335644

There is a growing recognition of the importance of analytics and big data in the accounting profession and a need for undergraduate accounting programs to better integrate these technologies into their curriculum. To find out degree to which undergraduate accounting programs in Jordanian universities have embraced analytics and big data into their curricula, this research looks at how and where data analytics and big data have affected undergraduate accounting education in accordance with the data analysis abilities that accountants need. The results show that accounting programs have not fully embraced analytics and big data. Additionally, only five accounting programs have developed a set of curricular components that offer the proper level of learning of data analytics and big data. Such a result emphasizes the need of more research to determine the best way to effectively implement these changes in the accounting education system of Jordanian Universities. © 2023 IEEE.

关键词： Curricula

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