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Deep Transfer Learning Driven Automated Fall Detection for Quality of Living of Disabled Persons

Computers, Materials & Continua 2023年第3期74卷 6719-6736页

作者： nabil almalki Mrim M.Alnfiai Fahd N.Al-Wesabi Mesfer Alduhayyem Anwer Mustafa Hilal Manar Ahmed Hamza King Salman Center for Disability Research RiyadhSaudi Arabia Department of Special Education College of EducationKing Saud UniversityRiyadh12372Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer Science College of Science&ArtsKing Khaled UniversitySaudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Mobile communication and the Internet of Things(IoT)technologies have recently been established to collect data from human beings and the *** data collected can be leveraged to provide intelligent services through different *** is an extreme challenge to monitor disabled people from remote *** is because day-to-day events like falls heavily result in *** a person with disabilities,a fall event is an important cause of mortality and post-traumatic ***,detecting the fall events of disabled persons in smart homes at early stages is essential to provide the necessary support and increase their survival *** current study introduces a Whale Optimization Algorithm Deep Transfer Learning-DrivenAutomated Fall Detection(WOADTL-AFD)technique to improve the Quality of Life for persons with *** primary aim of the presented WOADTL-AFD technique is to identify and classify the fall events to help disabled *** attain this,the proposed WOADTL-AFDmodel initially uses amodified SqueezeNet feature extractor which proficiently extracts the feature *** addition,the WOADTLAFD technique classifies the fall events using an extreme Gradient Boosting(XGBoost)*** the presented WOADTL-AFD technique,the WOA approach is used to fine-tune the hyperparameters involved in the modified SqueezeNet *** proposedWOADTL-AFD technique was experimentally validated using the benchmark datasets,and the results confirmed the superior performance of the proposedWOADTL-AFD method compared to other recent approaches.

关键词： Quality of living disabled persons intelligent models deep learning fall detection whale optimization algorithm

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Deep Learning Driven Arabic Text to Speech Synthesizer for Visually Challenged People

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Intelligent Automation & Soft Computing 2023年第6期36卷 2639-2652页

作者： Mrim M.Alnfiai nabil almalki Fahd N.Al-Wesabi Mesfer Alduhayyem Anwer Mustafa Hilal Manar Ahmed Hamza King Salman Center for Disability Research Riyadh13369Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Special Education College of EducationKing Saud UniversityRiyadh12372Saudi Arabia Department of Computer Science College of Science&Arts at MuhayelKing Khaled UniversityAbha62217Saudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAl-Aflaj16733Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16242Saudi Arabia

Text-To-Speech(TTS)is a speech processing tool that is highly helpful for visually-challenged *** TTS tool is applied to transform the texts into human-like ***,it is highly challenging to accomplish the TTS out-comes for the non-diacritized text of the Arabic language since it has multiple unique features and *** special characters like gemination and diacritic signs that correspondingly indicate consonant doubling and short vowels greatly impact the precise pronunciation of the Arabic ***,such signs are not frequently used in the texts written in the Arabic language since its speakers and readers can guess them from the context *** this background,the current research article introduces an Optimal Deep Learning-driven Arab Text-to-Speech Synthesizer(ODLD-ATSS)model to help the visually-challenged people in the Kingdom of Saudi *** prime aim of the presented ODLD-ATSS model is to convert the text into speech signals for visually-challenged *** attain this,the presented ODLD-ATSS model initially designs a Gated Recurrent Unit(GRU)-based prediction model for diacritic and gemination ***,the Buckwalter code is utilized to capture,store and display the Arabic *** improve the TSS performance of the GRU method,the Aquila Optimization Algorithm(AOA)is used,which shows the novelty of the *** illustrate the enhanced performance of the proposed ODLD-ATSS model,further experi-mental analyses were *** proposed model achieved a maximum accu-racy of 96.35%,and the experimental outcomes infer the improved performance of the proposed ODLD-ATSS model over other DL-based TSS models.

关键词： Saudi Arabia visually challenged people deep learning Aquila optimizer gated recurrent unit

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IoT-Driven Optimal Lightweight RetinaNet-Based Object Detection for Visually Impaired People

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Computer Systems Science & Engineering 2023年第7期46卷 475-489页

作者： Mesfer Alduhayyem Mrim M.Alnfiai nabil almalki Fahd N.Al-Wesabi Anwer Mustafa Hilal Manar Ahmed Hamza Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia King Salman Center for Disability Research RiyadhAl-Hayātim 16273Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Special Education College of EducationKing Saud UniversityRiyadh12372Saudi Arabia Department of Computer Science College of Science&ArtsKing Khaled UniversityAr-Riyad 12372Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Visual impairment is one of the major problems among people of all age groups across the *** Impaired Persons(VIPs)require help from others to carry out their day-to-day *** they experience several problems in their daily lives,technical intervention can help them resolve the *** this background,an automatic object detection tool is the need of the hour to empower VIPs with safe *** recent advances in the Internet of Things(IoT)and Deep Learning(DL)techniques make it *** current study proposes IoT-assisted Transient Search Optimization with a Lightweight RetinaNetbased object detection(TSOLWR-ODVIP)model to help *** primary aim of the presented TSOLWR-ODVIP technique is to identify different objects surrounding VIPs and to convey the information via audio message to *** data acquisition,IoT devices are used in this ***,the Lightweight RetinaNet(LWR)model is applied to detect objects ***,the TSO algorithm is employed for fine-tuning the hyperparameters involved in the LWR ***,the Long Short-Term Memory(LSTM)model is exploited for classifying *** performance of the proposed TSOLWR-ODVIP technique was evaluated using a set of objects,and the results were examined under distinct *** comparison study outcomes confirmed that the TSOLWR-ODVIP model could effectually detect and classify the objects,enhancing the quality of life of VIPs.

关键词： Visually impaired people deep learning object detection computer vision long short-term memory transient search optimization

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