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Parkinson’s Detection Using RNN-Graph-LSTM with Optimization Based on Speech Signals

computers, Materials & Continua 2022年第7期72卷 871-886页

作者： Ahmed S.Almasoud Taiseer Abdalla Elfadil Eisa Fahd N.Al-Wesabi Abubakar Elsafi Mesfer Al Duhayyim Ishfaq Yaseen Manar Ahmed Hamza Abdelwahed Motwakel Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversityMahayil62529Saudi Arabia Department of Information Systems-Girls Section King Khalid UniversityMahayil62529Saudi Arabia Department of Computer Science College of Science and Artsat MahayilKing Khalid UniversityMahayil62529Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahSaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversityMahayil62529Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj62529Saudi Arabia

Early detection of Parkinson’s Disease(PD)using the PD patients’voice changes would avoid the intervention before the identification of physical *** machine learning algorithms were developed to detect PD ***,these ML methods are lack in generalization and reduced classification performance due to subject *** overcome these issues,this proposed work apply graph long short term memory(GLSTM)model to classify the dynamic features of the PD patient speech *** proposed classification model has been further improved by implementing the recurrent neural network(RNN)in batch normalization layer of GLSTM and optimized with adaptive moment estimation(ADAM)on network hidden *** consider the importance of feature engineering,this proposed system use Linear Discriminant analysis(LDA)for dimensionality reduction and SparseAuto-Encoder(SAE)for extracting the dynamic speech *** on the computation of energy content transited from unvoiced to voice(onset)and voice to voiceless(offset),dynamic features are *** PD datasets is evaluated under 10 fold cross validation without sample *** proposed smart PD detection method called RNN-GLSTM-ADAM is numerically experimented with persistent phonations in terms of accuracy,sensitivity,and specificity andMatthew correlation *** evaluated result of RNN-GLSTM-ADAM extremely improves the PD detection accuracy than static feature based conventional ML and DL approaches.

关键词： Dimensionality reduction LDA feature extraction auto encoder classification recurrent neural network LSTM and optimization

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Real-Time Data-Driven Maintenance Logistics: A Public-Private Collaboration

Real-Time Data-Driven Maintenance Logistics: A Public-Privat...

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2024 Commit2Data, C2D 2024

作者： van Jaarsveld, Willem Bliek, Laurens de Weerdt, Mathijs Kapodistria, Stella Pronk, Verus Verleijsdonk, Peter Voorberg, Simon Akçay, Alp da Costa, Paulo Eshuis, Rik Kaymak, Uzay van Houtum, Geert-Jan Verwer, Sicco Zhang, Yingqian Department of Industrial Engineering and Innovation Sciences Eindhoven University of Technology Netherlands Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Netherlands Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands Philips Research Laboratories Eindhoven Netherlands Department of Information Systems Supply Chain Management and Decision Analysis NEOMA Business School Rouen France Etsy Inc. Dublin Ireland Jheronimus Academy of Data Science Eindhoven University of Technology Netherlands

ISBN: (纸本)9783959773515

The project "Real-time data-driven maintenance logistics" was initiated with the purpose of bringing innovations in data-driven decision making to maintenance logistics, by bringing problem owners in the form of three innovative companies together with researchers at two leading knowledge institutions. This paper reviews innovations in three related areas: How the innovations were inspired by practice, how they materialized, and how the results impact practice. © Willem van Jaarsveld, Alp Akçay, Laurens Bliek, Paulo da Costa, Mathijs de Weerdt, Rik Eshuis, Stella Kapodistria, Uzay Kaymak, Verus Pronk, Geert-Jan van Houtum, Peter Verleijsdonk, Sicco Verwer, Simon Voorberg, and Yingqian Zhang.

关键词： Scheduled maintenance

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Mining the Chatbot Brain to Improve COVID-19 Bot Response Accuracy

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computers, Materials & Continua 2022年第2期70卷 2619-2638页

作者： Mukhtar Ghaleb Yahya Almurtadha Fahad Algarni Monir Abdullah Emad Felemban Ali M.Alsharafi Mohamed Othman Khaled Ghilan Department of Information Systems College of Sciences and ArtsUniversity of BishaAl Namas67392Saudi Arabia Department of Computer Science College of Computing and Information TechnologyUniversity of TabukTabuk71491Saudi Arabia Department of Computer Science College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia Computer Engineering Department Umm Al-Qura UniversityMakkah21955Saudi Arabia Department of Communication Technology and Network Universiti Putra MalaysiaSelangor43400Malaysia Faculty of Public Health and Tropical Medicine Jazan UniversityJazan45142Saudi Arabia

People often communicate with auto-answering tools such as conversational agents due to their 24/7 availability and unbiased ***,chatbots are normally designed for specific purposes and areas of experience and cannot answer questions outside their *** employ Natural Language Understanding(NLU)to infer their *** is a need for a chatbot that can learn from inquiries and expand its area of experience with *** chatbot must be able to build profiles representing intended topics in a similar way to the human brain for fast *** study proposes a methodology to enhance a chatbot’s brain functionality by clustering available knowledge bases on sets of related themes and building representative *** used a COVID-19 information dataset to evaluate the proposed *** pandemic has been accompanied by an“infodemic”of fake *** chatbot was evaluated by a medical doctor and a public trial of 308 real *** obtained and statistically analyzed tomeasure effectiveness,efficiency,and satisfaction as described by the ISO9214 *** proposed COVID-19 chatbot system relieves doctors from answering *** provide an example of the use of technology to handle an infodemic.

关键词： Machine learning text classification e-health chatbot COVID-19 awareness natural language understanding

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Leveraging Retinal Fundus Images with Deep Learning for Diabetic Retinopathy Grading and Classification

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computer systems Science & engineering 2023年第8期46卷 1901-1916页

作者： Mohammad Yamin Sarah Basahel Saleh Bajaba Mona Abusurrah ELaxmi Lydia Department of Management Information Systems Faculty of Economics and Administration King Abdulaziz UniversityP.O.Box 80201Jeddah21589Saudi Arabia Department of Business Administration King Abdulaziz UniversityP.O.Box 80201Jeddah21589Saudi Arabia Department of Management Information Systems College of Business AdministrationTaibah UniversityAl-MadinahSaudi Arabia Department of Computer Science and Engineering GMR Institute of TechnologyAndhra PradeshRajam 532127India

Recently,there has been a considerable rise in the number of diabetic patients suffering from diabetic retinopathy(DR).DR is one of the most chronic diseases and makes the key cause of vision loss in middle-aged people in the developed *** detection of DR becomes necessary for decreasing the disease severity by making use of retinal fundus *** article introduces a Deep Learning Enabled Large Scale Healthcare Decision Making for Diabetic Retinopathy(DLLSHDM-DR)on Retinal Fundus *** proposed DLLSHDM-DR technique intends to assist physicians with the DR decision-making *** the DLLSHDM-DR technique,image preprocessing is initially performed to improve the quality of the fundus ***,the DLLSHDM-DR applies HybridNet for producing a collection of feature *** retinal image classification,the DLLSHDM-DR technique exploits the Emperor Penguin Optimizer(EPO)with a Deep Recurrent Neural Network(DRNN).The application of the EPO algorithm assists in the optimal adjustment of the hyperparameters related to the DRNN model for DR detection showing the novelty of our *** assuring the improved performance of the DLLSHDMDR model,a wide range of experiments was tested on the EyePACS *** comparison outcomes assured the better performance of the DLLSHDM-DR approach over other DL models.

关键词： Decision making healthcare sector deep learning diabetic retinopathy emperor penguin optimizer

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Edge-Cloud Computing systems for Unmanned Aerial Vehicles Capable of Optimal Work Offloading with Delay 2

Edge-Cloud Computing Systems for Unmanned Aerial Vehicles Ca...

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2nd International Conference on Electronics and Renewable systems, ICEARS 2023

作者： Narayana Tinnaluri, V.S. Vyankatesh Ghamande, Manasi Singh, Shashank Kuchipudi, Ramu Dattatraya Bhosale, Minakshi Dharani, R. Koneru Lakshmaiah Education Foundation Department of Computer Science Andhra Pradesh Vijayawada India Vishwakarma Institute of Information Technology Engineering and Applied Sciences Pune411037 India Integral University Lucknow Department of Computer Science and Engineering Uttar Pradesh India Chaitanya Bharathi Institute of Technology Department of Information Technology Telangana Hyderabad India Faculty of Mca Yashoda Technical Campus Satara Maharashtra Wadhe India M. Kumarasamy College of Engineering Department of Computer Science and Business Systems Tamilnadu Karur India

ISBN: (纸本)9798350346640

Mobile terminals' limited processing power and memory make it challenging to meet the needs of increasingly complex applications like autonomous vehicles and augmented reality. That's why there's been a rise in the demand for edge cloud computing power from endpoint gadgets. Due to its adaptability and proximity to the user, an unmanned aerial vehicle (UAV) may be used to support mobile edge computing (MEC) via job offloading, potentially relieving strain on edge clouds. Due to limitations in processing power and battery life, Unmanned Aerial Vehicles (UAV) devices face a huge issue with the rise of Applications and services for mobile devices that can't afford to be delayed and that need a lot of processing power. Task offloading is one way that mobile cloud computing may help you get around these restrictions. The biggest difficulties with this paradigm, however, are the significant latency and security concerns. The edge-cloud computing model was then proposed and has since seen widespread adoption as a means of dealing with these complications. However, the present task offloading models allow UAVs to perform their heavy jobs at the linked edge server, leading to unnecessary loads owing to the huge amount of UAVs and, in turn, increasing the latency. For this reason, this study has presented a delay-optimal task offloading strategy for multi-tier edge-cloud computing with many users. In this research, a system model has been proposed to assist an intelligent and perceptive agent in determining the best computational offloading strategy, minimizing both the time it takes to complete a job and the amount of power it needs to do so. The efficacy of an agent was shown, and simulation results revealed that its adoption may greatly cut delay and energy usage. © 2023 IEEE.

关键词： Mobile edge computing

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Bayesian Network Structure Discovery Using Antlion Optimization Algorithm.

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2022年第1期7卷 46-65页

作者： Awla, Hoshang Qasim Kareem, Shahab Wahhab Mohammed, Amin Salih Computer science department Faculty of Science Soran University Erbil Iraq Dep. of Technical Information Systems Engineering Erbil Technical Engineering College Erbil Poly-technic University Erbil Iraq Department of Information Technology College of Engineering and Computer Science Lebanese French University Kurdistan Region Erbil Iraq

Bayesian networks have recently been used for discovering an optimal learning structure in machine learning. Bayes networks can describe possible dependencies of explanatory variables. As a novel approach to studying the structure of a Bayesian network, the authors present the Antlion Optimization Algorithm (ALO). In the algorithm;deletion, rewind, insertion, and change are utilized to produce ALO to reach the best hull solution. Essentially, the technique used in the ALO algorithm imitates the antlions’ behaviors while hunting. The suggested approach is contrasted with simulated Annealing, Simulated Annealing Hybrid Bee, Greedy Search Hybrid Bee, optimization inspired by Pigeon, and greedy search using the BDe Score function. The researchers also studied the representation of the confusion matrix of these techniques using different reference data sets. The findings of the assessments reveal that the proposed algorithm works better than the other algorithms and has better consistency and score values. As shown by the experimental evaluations, the proposed method has a more reliable performance than other algorithms (including the production of excellent scores and accuracy values). © 2022. All Rights Reserved.

关键词： Antlion Optimization Bayesian Network Hunting Search Local & Global Search Metaheuristics Search and Score Structure Learning

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Discrepancies of remote techno-tolerance due to COVID-19 pandemic within Arab middle-east countries

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Journal of Umm Al-Qura University for engineering and Architecture 2023年第3期14卷 151-165页

作者： Abu-Hashem, Muhannad A. Gutub, Adnan Salem, Osama Shambour, Mohd Khaled Shambour, Qusai Shehab, Mohammad Izzat, Ahmad Alrawashdeh, Mufda J. Department of Geomatics Architecture and Planning Faculty King Abdulaziz University Jeddah Saudi Arabia Computer Engineering Department College of Computer and Information Systems Umm Al-Qura University Makkah Saudi Arabia Educational Technology and E-Learning Department College of Education Umm Al-Qura University Makkah Saudi Arabia The Custodian of the Two Holy Mosques Institute for Hajj and Umrah Research Umm Al-Qura University Makkah Saudi Arabia Software Engineering Department Faculty of IT Al-Ahliyya Amman University Amman Jordan Artificial Intelligence & Software Engineering Departments Faculty of Information Technology Aqaba University of Technology Aqaba Jordan Faculty of Engineering and Information Technology Israa University Gaza Palestine Department of Mathematics College of Sciences and Arts Qassim University Al-Ras Saudi Arabia

The coronavirus disease (COVID-19) changed the world’s lifestyle switching many techno-services to be provided remotely instead of direct usual physical interactions between people. This study focused on university students’ perceptions of this virtual technology-engineering change as discrepancies to be analyzed. The research surveyed 777 different students from four Arab middle-east neighboring countries, with related demographics and specifications, expressing full remarkable experiences of e-technology in virtual as well as traditional performances helping reveal overall tolerance possibilities. The study adopted examination technology via ANOVA to test discrepancies within students' perceptions for overall multi-factors deep analysis classification. The exploration highlighted an interesting range of pros and cons remarks including feminism and economic effect as well as other cultural and engineering interaction issues, raising signals to essentially consider and mutually benefit from adequate future generation e-techno adaptation within the region. © The Author(s) 2023.

关键词： ANOVA COVID-19 E-techno Students’ perceptions Traditional and virtual classes

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Privacy-Preserving information Fusion Technique for Device to Server-Enabled Communication in the Internet of Things:A Hybrid Approach

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computers, Materials & Continua 2024年第7期80卷 1305-1323页

作者： Amal Al-Rasheed Rahim Khan Tahani Alsaed Mahwish Kundi Mohamad Hanif Md.Saad Mahidur R.Sarker Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science AbdulWali Khan University MardanMardan23200Pakistan Faculty of Computing and Informatics University Malaysia SabahKota KinabaluSabah88400Malaysia Applied College Taibah UniversityMadinah41477Saudi Arabia Maynooth International Engineering College Maynooth UniversityCo KildareW23 X021Irland Department of Mechanical Engineering Faculty of Engineering and Built EnvironmentUniversiti Kebangsaan MalaysiaBangiSelangor43600Malaysia Institute of Visual Informatics Universiti Kebangsaan MalaysiaBangiSelangor43600Malaysia University of Design Innovation and Technology(UDIT)Av.Alfonso XIII97Madrid28016Spain

Due to the overwhelming characteristics of the Internet of Things(IoT)and its adoption in approximately every aspect of our lives,the concept of individual devices’privacy has gained prominent attention from both customers,i.e.,people,and industries as wearable devices collect sensitive information about patients(both admitted and outdoor)in smart healthcare *** addition to privacy,outliers or noise are among the crucial issues,which are directly correlated with IoT infrastructures,as most member devices are resource-limited and could generate or transmit false data that is required to be refined before processing,i.e.,***,the development of privacy-preserving information fusion techniques is highly encouraged,especially those designed for smart IoT-enabled *** this paper,we are going to present an effective hybrid approach that can refine raw data values captured by the respectivemember device before transmission while preserving its privacy through the utilization of the differential privacy technique in IoT *** window,i.e.,δi based dynamic programming methodology,is implemented at the device level to ensure precise and accurate detection of outliers or noisy data,and refine it prior to activation of the respective transmission ***,an appropriate privacy budget has been selected,which is enough to ensure the privacy of every individualmodule,i.e.,a wearable device such as a smartwatch attached to the patient’s *** contrast,the end module,i.e.,the server in this case,can extract important information with approximately the maximum level of ***,refined data has been processed by adding an appropriate nose through the Laplace mechanism to make it useless or meaningless for the adversary modules in the *** proposed hybrid approach is trusted from both the device’s privacy and the integrity of the transmitted information *** and analytical results ha

关键词： Internet of things information fusion differential privacy dynamic programming Laplace function

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Predictive Analysis of Jordan Road Accidents Severity Using Machine Learning

Predictive Analysis of Jordan Road Accidents Severity Using ...

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International Symposium on Networks, computers and Communications (ISNCC)

作者： Dheya Mustafa Layan Sawaged Safaa M. Khabour Dept. of Computer Engineering Faculty of Engineering The Hashemite University Zarqa Jordan Dept. of Information Systems Faculty of Information Technology and Computer Sciences Yarmouk University Irbid Jordan

Jordan has a high and growing level of traffic accidents reaching 160,600) accidents in 2021, (11,241) of them had human injuries, (589) deaths, and (320) million JOD losses. Road traffic accidents are currently the 8th leading cause of death worldwide, accounting for about 1.35 million fatalities annually. Understanding the primary causes of these accidents and the circumstances in which they occur is essential if governments throughout the world are to put policies in place to limit the number of fatalities brought on by traffic accidents. This project has two objectives: (i) to learn more about Jordan's present road accident situation by performing an exploratory study on several road accident datasets, and (ii) to investigate the effectiveness of various machine-learning techniques in predicting the severity of road accidents in Jordan. The dataset entities were collected, collated, explored, and prepared for use in the model. To assess their predictive performance, five different classification algorithms were trained and tested. The findings of the present study demonstrate that the best algorithm was Logistic Regression, which had an accuracy of 98.1%.

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A Novel Transfer Learning Framework for Diagnosing Covid-19 from Chest X-Ray Images with Minimal Computational Resources

SSRN

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SSRN 2023年

作者： Abdelwahed, Ahmed Helmi, Ahmed M. Issa, Mohamed Computer and Systems Department Faculty of Engineering Zagazig University Egypt Computer Engineering Dept. Engineering and Information Technology College Buraydah Private Colleges Saudi Arabia Faculty of Computer Science and Information Technology Egypt−Japan University for Science and Technology Egypt

Deep learning proved effective in detecting COVID-19 from chest X-ray images. Although computing is the current bottleneck in training and deploying models, few studies focus on developing efficient frameworks while many aim only at performance. We propose a novel framework for detecting COVID-19 cases from chest X-ray images efficiently. Our study utilizes four high-performing architectures, including MobileNetV3, RegNetX, NasNetMobile, and EfficientNetV2, which use minimal resources, to extract features from X-ray images and classify COVID-19 cases using various statistical models and machine learning algorithms. We evaluated our proposed framework using metrics such as accuracy, precision, recall, specificity, f1-score, and AUC scores. The false negative rate for each feature extractor and classifier pair is reported, and the average inference time, to measure resource efficiency, is calculated. The results show that EfficientNetV2 was the best-performing feature extractor, while MobileNetV3 had the best average inference time and the lowest false negative rate. The Neural Network classifier performed the best, with state-of-the-art accuracy reaching 99.28%, followed by both RBF-SVM and XGBoost. The proposed framework is more robust, reliable, and resource-efficient for COVID-19 detection compared to existing literature. Our findings indicate the prospects of using the proposed framework in efficient clinical diagnosis. © 2023, The Authors. All rights reserved.

关键词： COVID-19

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