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An Automated Deep Learning Based Muscular Dystrophy Detection and Classification Model

computers, Materials & Continua 2022年第4期71卷 305-320页

作者： T.Gopalakrishnan Periakaruppan Sudhakaran K.C.Ramya K.Sathesh Kumar Fahd N.Al-Wesabi Manal Abdullah Alohali Anwer Mustafa Hilal School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014India Department of Computer Science and Engineering SRM TRP Engineering CollegeTiruchirappalli621105India Department of Electrical and Electronics Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India School of Computing Kalasalingam Academy of Research and EducationKrishnankoil626128India Department of Computer Science King Khalid UniversityMuhayel AseerKSA Faculty of Computer and IT Sana’a UniversityYemen Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

Muscular Dystrophy (MD) is a group of inherited muscular diseases that are commonly diagnosed with the help of techniques such asmuscle biopsy, clinical presentation, and Muscle Magnetic Resonance Imaging(MRI). Among these techniques, Muscle MRI recommends the diagnosis ofmuscular dystrophy through identification of the patterns that exist in musclefatty replacement. But the patterns overlap among various diseases whereasthere is a lack of knowledge prevalent with regards to disease-specific ***, artificial intelligence techniques can be used in the diagnosis ofmuscular dystrophies, which enables us to analyze, learn, and predict forthe future. In this scenario, the current research article presents an automated muscular dystrophy detection and classification model using SynergicDeep Learning (SDL) method with extreme Gradient Boosting (XGBoost),called SDL-XGBoost. SDL-XGBoost model has been proposed to act as anautomated deep learning (DL) model that examines the muscle MRI dataand diagnose muscular dystrophies. SDL-XGBoost model employs Kapur’sentropy based Region of Interest (RoI) for detection purposes. Besides, SDLbased feature extraction process is applied to derive a useful set of featurevectors. Finally, XGBoost model is employed as a classification approach todetermine proper class labels for muscle MRI data. The researcher conductedextensive set of simulations to showcase the superior performance of SDLXGBoost model. The obtained experimental values highlighted the supremacyof SDL-XGBoost model over other methods in terms of high accuracy being96.18% and 94.25% classification performance upon DMD and BMD respectively. Therefore, SDL-XGBoost model can help physicians in the diagnosis of muscular dystrophies by identifying the patterns of muscle fatty replacementin muscle MRI.

关键词： Muscle magnetic resonance imaging XGBoost synergic deep learning roI detection kapur’s entropy muscular dystrophies

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Utilizing Genetic Programming to Enhance Polygenic Risk Score Calculation

Utilizing Genetic Programming to Enhance Polygenic Risk Scor...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Hurta, Martin Schwarzerova, Jana Naegele, Thomas Weckwerth, Wolfram Provaznik, Valentine Sekanina, Lukas Brno University of Technology Faculty of Information Technology Department of Computer Systems Brno Czech Republic Brno University of Technology Faculty of Electrical Engineering and Communication Department of Biomedical Engineering Brno Czech Republic Vienna Austria Lmu Munich Plant Evolutionary Cell Biology Faculty of Biology Munich Germany Vienna Austria

ISBN: (纸本)9798350337488

The polygenic risk score has proven to be a valuable tool for assessing an individual's genetic predisposition to phenotype (disease) within biomedicine in recent years. However, traditional regression-based methods for polygenic risk scores calculation have limitations that can impede their accuracy and predictive power. This study introduces an innovative approach to enhance polygenic risk scores calculation through the application of genetic programming. By harnessing the power of genetic programming, we aim to overcome the limitations of traditional regression techniques and improve the accuracy of polygenic risk scores predictions. Specifically, we showed that a polygenic risk score generated through Cartesian genetic programming yielded comparable or even more robust statistical distinctions between groups that we evaluated within three independent case studies. © 2023 IEEE.

关键词： Computational biology Genetic Programming Genetic Variations Polygenic risk score

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System for Predicting Heart Disease using Sophisticated Machine Learning Techniques

System for Predicting Heart Disease using Sophisticated Mach...

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2023 IEEE Global Conference on information Technologies and Communications, GCITC 2023

作者： Pandey, Trilok Nath Bhusan Dash, Bibhuti Barik, Lalbihari De, Utpal Chandra Tripathy, Abinash Patra, Sudhansu Shekhar Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Chennai India Kiit Deemed to Be University School of Computer Applications Bhubaneswar India King Abdul Aziz University Faculty of Computing and Information Technology in Rabigh Department of Information Systems Saudi Arabia Kiit Deemed to Be University School of Computer Applications Odisha Bhubaneswar India

ISBN: (纸本)9798350308167

Cardiovascular illness stands as a significant global health issue, and timely identification of this condition holds paramount importance to ensure effective intervention. In this paper, we introduce a fused model for anticipating heart ailments. This amalgamation incorporates four distinct algorithms: LightGBM, XGBoost, Multilayer Perceptron, and AdaBoost. Through an assessment on the Cleveland dataset, AdaBoost emerged as the most accurate with a precision of 0.985. Subsequently, XGBoost followed closely with an accuracy of 0.980, succeeded by LightGBM at 0.956, and Multilayer Perceptron at 0.936. To harness the collective power of these models, we employed a weighted bagging technique that accords weights to predictions based on their individual accuracy rates. The resultant composite model attained an accuracy of 0.96. This outcome is slightly lower than the model with the highest accuracy, as it involves a weighted mean calculation across model predictions. This study indicates the potential utility of the proposed methodology in cardiac disease prediction, leveraging the ensemble approach to potentially outperform standalone models. © 2023 IEEE.

关键词： AdaBoost Ensemble model Heart Disease LightGBM Machine Learning Multilayer Perceptron XGBoost

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VR REAL RUN: An immersive Oculus Quest 2-Based Virtual Reality Exergaming 4

VR REAL RUN: An immersive Oculus Quest 2-Based Virtual Reali...

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4th International Conference on Cybernetics and Intelligent System, ICORIS 2022

作者： Mambu, Joe Y. Rismayani Sihotang, Jay Idoan Rantung, Vivi Peggy Computer Science Faculty Universitas Klabat Airmadidi Indonesia Dipa Makassar University Dept. of Software Engineering Makassar Indonesia Faculty of Information Technology Universitas Advent Indonesia Bandung Indonesia Faculty of Information System Universitas Negeri Manado Tondano Indonesia

ISBN: (纸本)9781665453950

There are many different types of exergaming, but one of the most engaging one is the one with virtual reality. This type of gaming allows you to immerse yourself in a completely different world, and it can be a lot of fun. Due to Covid-19 pandemic many people resort to their usual exercise activities and opt to do it at home yet only to find a monotonous and boring fitness routine. VR Real Run is aimed to give a solution on how to have fun and to get fit at the same time. By using the latest and front runner in the VR headsets products, the Oculus Quest 2, we developed an exergame that require user to run in place and jump to play. It also features high score to motivate users in perform better in the next session. A black box test has been well performed and a usability test was came out with a 79 SUS score which translate the application as 'good' therefore its usability is acceptable. © 2022 IEEE.

关键词： Virtual reality

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Wind Driven Optimization-Based Medical Image Encryption for Blockchain-Enabled Internet of Things Environment

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computers, Materials & Continua 2022年第11期73卷 3219-3233页

作者： C.S.S.Anupama Raed Alsini N.Supriya E.Laxmi Lydia Seifedine Kadry Sang-Soo Yeo Yongsung Kim Department of Electronics&Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Engineering Malla Reddy Engineering College(A)Hyderabad500100India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University College4612KristiansandNorway Department of Computer Engineering Mokwon UniversityDaejeon35349Korea Department of Technology Education Chungnam National University 99Daehak-roYuseong-guDaejeon34134Korea

Internet of Things(IoT)and blockchain receive significant interest owing to their applicability in different application areas such as healthcare,finance,transportation,*** image security and privacy become a critical part of the healthcare sector where digital images and related patient details are communicated over the public *** paper presents a new wind driven optimization algorithm based medical image encryption(WDOA-MIE)technique for blockchain enabled IoT *** WDOA-MIE model involves three major processes namely data collection,image encryption,optimal key generation,and data ***,the medical images were captured from the patient using IoT ***,the captured images are encrypted using signcryption *** addition,for improving the performance of the signcryption technique,the optimal key generation procedure was applied by WDOA *** goal of the WDOA-MIE algorithm is to derive a fitness function dependent upon peak signal to noise ratio(PSNR).Upon successful encryption of images,the IoT devices transmit to the closest server for storing it in the blockchain *** performance of the presented method was analyzed utilizing the benchmark medical image *** security and the performance analysis determine that the presented technique offers better security with maximum PSNR of 60.7036 dB.

关键词： Internet of things image security medical images encryption optimal key generation blockchain

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Parkinson’s Disease Detection from Resting State EEG using Multi-Head Graph Structure Learning with Gradient Weighted Graph Attention Explanations

arXiv

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arXiv 2024年

作者： Neves, Christopher Zeng, Yong Xiao, Yiming Department of Computer Science and Software Engineering Concordia University MontrealQC Canada Concordia Institute for Information Systems Engineering Concordia University MontrealQC Canada

Parkinson’s disease (PD) is a debilitating neurodegenerative disease that has severe impacts on an individual’s quality of life. Compared with structural and functional MRI-based biomarkers for the disease, electroencephalography (EEG) can provide more accessible alternatives for clinical insights. While deep learning (DL) techniques have provided excellent outcomes, many techniques fail to model spatial information and dynamic brain connectivity, and face challenges in robust feature learning, limited data sizes, and poor explainability. To address these issues, we proposed a novel graph neural network (GNN) technique for explainable PD detection using resting state EEG. Specifically, we employ structured global convolutions with contrastive learning to better model complex features with limited data, a novel multi-head graph structure learner to capture the non-Euclidean structure of EEG data, and a head-wise gradient-weighted graph attention explainer to offer neural connectivity insights. We developed and evaluated our method using the UC San Diego Parkinson’s disease EEG dataset, and achieved 69.40% detection accuracy in subject-wise leave-one-out cross-validation while generating intuitive explanations for the learnt graph topology. © 2024, CC BY-NC-SA.

关键词： Graph neural networks

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Air Particulate Matters Auto-Rule-Based Labeling to Support Long-Distance Run Environment Data Classification 13

Air Particulate Matters Auto-Rule-Based Labeling to Support ...

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13th IEEE Symposium on computer Applications and Industrial Electronics, ISCAIE 2023

作者： Wandy, Wandy Adi, Kusworo Ayu, Media Anugerah Diponegoro University Doctoral Program of Information Systems School of Postgraduate Studies Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Physics Semarang Indonesia Sampoerna University Faculty of Engineering and Technology Department of Computer Science South Jakarta Indonesia

ISBN: (纸本)9798350347319

Sports Science is an interdisciplinary and multidisciplinary science that strives to increase athletic performance and endurance. Sport Science recognizes and prevents injuries. Sensors and statistics formalize Sports Science. Runners need coaches and teams to support them before, during, and after the run race. Coaches generate running training plans to boost performance. Running race performances may be impacted by air pollution exposure while training, so coaches should consider limiting air pollution exposure when training. One of the external factors is Particulate Matter (PM2.5 and PM10). Sensors connecting to the Internet can record external factors and produce csv data. The foundation of supervised machine learning is the labeling process. Labeling a set of data is one of the laborious and time-consuming phases in every machine-learning application because it requires verifying the accuracy of the labels and making any necessary revisions. This research aimed to find a solution to automatically label numerous air particulate matter raw data using a rule based on parameters to reduce manual work, human errors and faster processes. This labeled data will later be used for supervised machine learning classification to support the coach in generating training programs for the runners in a Sports information System. Based on Indonesia Air Quality index rule-based approach, labeled text data in csv has been generated and tested with PM2.5 and PM10 parameters in three scenarios with a 100% success rate. It was possible to automate the labeling process, and it explained how automation results in fast and accurate results. © 2023 IEEE.

关键词： Classification (of information)

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B2A, A Straight-Through Approach from Basics to Advanced Level: Case Study for Python Course 11

B2A, A Straight-Through Approach from Basics to Advanced Lev...

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11th Workshop on software Quality Analysis, Monitoring, Improvement, and Applications, SQAMIA 2024

作者： Shazmeen, Abbasi Dakić, Pavle Zoltan, Alwahab Dhulfiqar Department of Programming Languages and Compilers Faculty of Informatics Eötvös Loránd University 1/C Pázmány Péter st. BudapestH-1117 Hungary Faculty of Informatics and Computing Singidunum University Danijelova 32 Belgrade Serbia Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Ilkovičova 2 Bratislava842 16 Slovakia

In this study, we present a straightforward teaching strategy for teaching the Python programming language. Our method emphasizes a clear progression from fundamental to sophisticated concepts. Beginning with a thorough explanation of core Python ideas, we show a beginner-level script that dissects each line of code and displays the results. One distinguishing feature of our system is that students use ChatGPT to optimize code, which encourages them to improve the core program with fewer lines. After getting the updated solutions, students carefully compare the original and optimized programs to discover improvements and potential flaws. This study underlines the effectiveness of the Straight-Through Approach, which focuses on improving Python understanding while also developing critical thinking and code optimization skills. Our findings show that this strategy is a simple and effective way for teachers to assist students in growing from basic to advanced Python abilities, resulting in a dynamic and successful learning environment. The proposed technique will teach students how to use AI-driven modules correctly, such as ChatGPT. © 2024 Copyright for this paper by its authors.

关键词： Python

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Investigating Online Searching Behavior Based on Google Trends in MENA Region Before and After COVID-19 53

Investigating Online Searching Behavior Based on Google Tren...

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Belkacem, Abdelkader Nasreddine Jamil, Nuraini Alrabaee, Saed College of It Uae University Dept. of Computer and Network Engineering Al Ain United Arab Emirates College of It Uae University Dept. of Computer Science and Software Engineering Al Ain United Arab Emirates College of It Uae University Dept. of Information Systems and Security Al Ain United Arab Emirates

ISBN: (纸本)9798350336429

The outbreak of the coronavirus disease (COVID-19) has had a profound impact on education worldwide. The rise of remote learning is one of the most significant changes in this regard, as many schools and universities were forced to close down by regional health authorities. This has also caused people to become more conservative in trade-offs between healthcare and education. Google Trends is the most common tool for analyzing online search behaviors. It is a free resource that provides information on the trends and changes in users' online interests over time based on certain terms and subjects. The online search queries on Google can be used to assess users' behaviors concerning online learning to forecast their choices regarding online education. This paper examines the frequency of users' web searches for online communication tools, courses, and learning terms. We statistically compared users in the Middle East and North Africa regions by using the volumes of searches recorded on Google Trends from January 2016 to August 2022. Moreover, we used machine learning techniques to identify differences among the keywords used. The findings statistically show that COVID-19 has led to an increase in the extent of students' attention to and interest in online learning. © 2023 IEEE.

关键词： COVID-19 Google Trends MENA Online learning Remote learning Web Behavior

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Text Analysis on Early Reactions to ChatGPT as a Tool for Academic Progress or Exploitation

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SN computer Science 2024年第4期5卷 366页

作者： Bukar, Umar Ali Sayeed, Md Shohel Razak, Siti Fatimah Abdul Yogarayan, Sumendra Amodu, Oluwatosin Ahmed Raja Mahmood, Raja Azlina Centre for Intelligent Cloud Computing (CICC) Faculty of Information Science & amp Technology Multimedia University Jalan Ayer Keroh Lama Bukit Beruang Melaka 75450 Malaysia Department of Electrical Electronics & amp Systems Engineering Universiti Kebangsaan Malaysia (UKM) Selangor Bangi 43600 Malaysia Information and Communication Engineering Department Elizade University Ondo State Ilara-Mokin Nigeria Department of Communication Technology and Network Faculty of Computer Science and Information Technology Universiti Putra Malaysia Selangor Darul Ehsan Serdang 43400 Malaysia

Large language models (LLMs) have garnered significant attention lately, and one particular implementation that has captivated users is ChatGPT, a first-of-its kind innovation that sparks intense debates among professionals regarding its ethical boundaries, especially after it was released for public use. Some of the debates are ongoing, with several people getting more used to this tool and some losing touch with several ethical concerns. Thus, it is crucial to explore the early professional’s perspectives surrounding ChatGPT to comprehend its potential impact on the educational sector and, in particular, society in general. This research aims to address this knowledge gap by employing data mining techniques on LinkedIn to analyze ChatGPT-related discussions and provide a comprehensive textual examination. The dataset for this investigation consists of 166 records written in English by LinkedIn users discussing ChatGPT in its early stage. The study utilized VOSviewer software to clean, analyze, mine, and visualize the data. Among the sample, it was observed that 49% of the users are PhD holders, predominantly with experience in scientific research and data science fields. Hence, the findings reveal that issues such as plagiarism, references, citations, manuscripts, papers, and literature reviews stand out as the major concerns. These results suggest that while ChatGPT can contribute to academic progress by fostering new knowledge, thoughts, and ideas, it can also facilitate academic misconduct, including plagiarism and inaccurate information dissemination. Therefore, this paper also provides discussions on the implications and ethical issues of ChatGPT. In particular, it is imperative to establish clear ethical boundaries and guidelines for the appropriate use of LLMs in academic settings. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Academic ChatGPT Ethics LinkedIn LLMs Text analysis Visualization VOSviewer

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