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2024 International Conference on Innovation and Intelligence for Informatics, computing, and technologies, 3ICT 2024

作者： Nossier, Soha A. Sharif, Mhd Saeed Biomedical Engineering Department Medical Research Institute Alexandria University Alexandria Egypt Intelligent Technologies Research Group School of Architecture Computing and Engineering UEL London United Kingdom

ISBN: (纸本)9798331533137

Deep learning techniques for speech enhancement rely on training a deep neural network to process noisy speech, regardless the gender of the speaker. However, research shows that the speech of male and female stimulates different parts in human brain, and that female speech requires more complex analysis. This implies that different processing is applied on the speech, based on the speaker gender. In this work, we argue that male and female speeches have different features that can help in the learning process of speech enhancement deep neural networks if the training is performed on male and female speech data, independently, and using two different deep neural networks, specifically implemented for enhancing the noisy speech signal of the target gender. This work presents a gender-specific speech enhancement architecture, which consists of a front-end binary classifier to detect the speaker gender. Based on the classifier decision, the noisy speech is enhanced using either a male or female speech enhancement model. One-stage and two-stage speech enhancement approaches are used to process male and female speeches, respectively. The results reveal that gender-specific speech enhancement has positive impact on the enhanced speech by deep neural networks. Additionally, the developed architecture achieved classifier accuracy 96.9% and 0.11 increase in Covl speech quality metric for the test data, in comparison to other best-performing networks. ©2024 IEEE.

关键词： Deep neural networks

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PREFACE: 3^rd International Conference on Robotics, Intelligent Automation and Control technologies (RIACT 2022)

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AIP Conference Proceedings 2023年第1期2946卷 1-8页

作者： Arockia Doss, Arockia Selvakumar Short, Michael Schilberg, Daniel Design and Automation Research Group School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Chennai India School of Computing Engineering and Digital Technologies Teesside University United Kingdom Department of Mechatronics and Mechanical Engineering University of Applied Sciences Bochum Germany

Preface: 3rd International Conference on Robotics, Intelligent Automation and Control technologies (RIACT 2022), AIP Conference Proceedings, Volume 2946, Issue

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Intelligent Systems-Enabled Decentralized E-Government Framework for Higher Education in Iraq: A Conceptual Data Fusion Approach

Intelligent Systems-Enabled Decentralized E-Government Frame...

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Emerging research in Computational Science (ICERCS), International Conference on

作者： Ali Fuad Burhanuddin Mohd Aboobaider Mohd Khanapi Abd Ghani Biomedical Computing and Engineering Technologies (BIOCORE) Applied Research Group Faculty of Information and Communication Technology Universiti Teknikal Malaysia Melaka Durian Tunggal Malaysia

The article presents an intelligent systems framework designed to facilitate the implementation of e-government in developing countries, with a specific focus on Iraq. This framework combines the principles of information system success and e-service quality to enhance the adoption of e-government. It encompasses ten influential factors that contribute to the successful implementation of e-government initiatives. By leveraging data from Iraq, the framework underwent thorough evaluation, demonstrating its efficacy in predicting e-government adoption. The versatility of this framework extends beyond Iraq, making it applicable to other developing nations seeking to enhance their e-government adoption rates. By integrating information system success and e-service quality, the framework provides a comprehensive approach to meet user needs and optimize e-government implementation.

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Key Factors Influencing a Decentralized IoT-Based Electronic Government Model for Higher Education in Iraq: An Intelligent Systems Perspective

Key Factors Influencing a Decentralized IoT-Based Electronic...

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Emerging research in Computational Science (ICERCS), International Conference on

The research conducted in this study aims to identify the factors that contribute to the successful development of an e-government framework for higher education. It specifically focuses on the integration of intelligent systems. Utilizing a quantitative research approach and employing the Structural Equation Modeling (SEM) technique, specifically Partial Least Squares (PLS), the study examines the impact of various predictors, such as IT skills, technical support, technological infrastructure, rules and regulations, awareness, cultural factors, trust in government, and quality of information on the intention to use e-government services. The findings highlight the significant influence of these predictors on the intention to use e-government services within the context of integrating intelligent systems. This study's results provide valuable insights for government decision-makers, offering them a strategic action plan for the future of electronic government in higher education. Higher education institutions may increase accessibility for students, teachers, and staff, expedite procedures, and improve service delivery by incorporating intelligent systems. The results of the study should help stakeholders and policymakers create strategies that will successfully integrate e-government in Iraqi higher education institutions, with a focus on integrating intelligent systems.

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Gender-Specific Speech Enhancement Architecture for Improving Deep Neural Networks Learning

Gender-Specific Speech Enhancement Architecture for Improvin...

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International Conference on Innovation and Intelligence for Informatics, computing, and technologies (3ICT)

作者： Soha A. Nossier Mhd Saeed Sharif Biomedical Engineering Department Medical Research Institute Alexandria University Alexandria Egypt Intelligent Technologies Research Group School of Architecture Computing and Engineering UEL London United Kingdom

ISBN: (数字)9798331533137

ISBN: (纸本)9798331533144

关键词： Training Deep learning Technological innovation Computational modeling Noise reduction Computer architecture Speech recognition Speech enhancement Noise measurement Informatics

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Single-and Multiuser Virtual Reality Training for Forklift Driving 2

Single-and Multiuser Virtual Reality Training for Forklift D...

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2nd IEEE International Conference on Cognitive Aspects of Virtual Reality, CVR 2023

作者： Froland, Tord Hettervik Ravyse, Werner Luimula, Mika Heldal, Ilona Western Norway University of Applied Sciences Chemistry and Biomedical Laboratory Sciences Department of Safety Bergen Norway School of ICT: Futuristic Interactive Technologies Research Group Turku University of Applied Sciences Turku Finland Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering and Mathematical Sciences Bergen Norway

ISBN: (纸本)9798350338638

Hands-on skills training is a constant requirement in a multitude of practical work settings and virtual reality (VR) is often found to be a valuable training opportunity to safely prepare students for their future work environments. One of the main benefits of utilizing VR-based training is providing high grades of presence and involvement. Although the user experiences of students are a key factor that impacts the use of a VR simulation, it becomes more challenging to decide whether single-or multi-user experiences should be planned and developed. This paper presents the results from a between-subjects case study comparing the user experiences of single-and multi-user VR training for forklift driving. Findings highlight the importance of considering various aspects of VR technology to design VR training environments for high user experiences, including the impact of age, group dynamics, and freedom of movement in the environment. The benefits and challenges of single-and multi-user VR training for high experiences and implications for utilizing serious games in VR-based training are discussed. © 2023 IEEE.

关键词： Virtual reality

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Preface: 4^th International Conference on Robotics, Intelligent Automation and Control technologies (RIACT 2023)

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AIP Conference Proceedings 2024年第1期3216卷

作者： Doss, Arockia Selvakumar Arockia Short, Michael Schilberg, Daniel Design and Automation Research Group School of Mechanical Engineering Vellore Institute of Technology Department of Mechatronics and Mechanical Engineering University of Applied Sciences School of Computing Engineering and Digital Technologies Teesside University Department of Mechatronics and Mechanical Engineering University of Applied Sciences

Preface: 4th International Conference on Robotics, Intelligent Automation and Control technologies (RIACT 2023), AIP Conference Proceedings, Volume 3216, Issue

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Development of Segmentation and Classification Algorithms for Computed Tomography Images of Human Kidney Stone

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Journal of Electronic research and Application 2021年第5期5卷 1-10页

作者： Orobosa L.Joseph Waliu O.Apena Department of Electrical and Electronic Engineering The Federal University of Technology AkureNigeria Biomedical Computing and Engineering Technology Applied Research GroupCoventry UniversityCoventry United Kingdom

Computed tomography(CT)scan diagnostics procedures adopt the use of image infbnnation retrieval system with the help of radiographer's ***,this technique is prone to errors,Significant height of accuracy is required in healthcare decision support,as 20%of CT scans are associated with *** application of artificial intelligence(Al)can improve performance level,mitigate human error,and enhance clinical decision support in the context of time and *** study introduced machine learning algorithm to analyze stream of anonymous CT scans of *** research adopted deep learning approach for segmentation and classification of kidney stone(renal calculi)images in Python(with Keras and TensorFlow)environment.A control volume of data along with 336 kidney stone images were used to train the deep learning network with 10 testing *** training images were divided into two sets(folders)as follows;one was labeled as STONE(containing 167 images)and the other as NO-STONE(containing 169 images);10 iterations were performed for model *** network layers were structured as input layer in the following with 2-D convolutional neural network machine learning(CNN-ML),ReLU activation,Maxpooling,and fully connected(dense)layer including the sigmoid activation *** training adopted a batch size of 8 with 10%*** output result,upon testing the model,has an accuracy of 90%,sensitivity value of 80%and effectiveness of 89%.The segmentation and classification algorithm model could be embedded in future CT diagnostic procedure to enhance medical decision support and accuracy.

关键词： Computed tomography scan Diagnostics Convolutional neural network Accuracy Kidney Stone

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Single- and Multiuser Virtual Reality Training for Forklift Driving

Single- and Multiuser Virtual Reality Training for Forklift ...

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Cognitive Aspects of Virtual Reality (CVR), IEEE International Conference on

作者： Tord Hettervik Froland Werner Ravyse Mika Luimula Ilona Heldal Department of Safety Chemistry and Biomedical laboratory sciences Western Norway University of Applied Sciences Bergen Norway School of ICT: Futuristic Interactive Technologies research group Turku University of Applied Sciences Turku Finland Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway

Hands-on skills training is a constant requirement in a multitude of practical work settings and virtual reality (VR) is often found to be a valuable training opportunity to safely prepare students for their future work environments. One of the main benefits of utilizing VR-based training is providing high grades of presence and involvement. Although the user experiences of students are a key factor that impacts the use of a VR simulation, it becomes more challenging to decide whether single- or multi-user experiences should be planned and developed. This paper presents the results from a between-subjects case study comparing the user experiences of single- and multi-user VR training for forklift driving. Findings highlight the importance of considering various aspects of VR technology to design VR training environments for high user experiences, including the impact of age, group dynamics, and freedom of movement in the environment. The benefits and challenges of single- and multi-user VR training for high experiences and implications for utilizing serious games in VR-based training are discussed.

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Evaluation of the Performance of Wearables’ Inertial Sensors for the Diagnosis of Resting Tremor in Parkinson’s Disease

Evaluation of the Performance of Wearables’ Inertial Sensor...

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17th International Joint Conference on biomedical engineering Systems and technologies, BIOSTEC 2024

作者： Polvorinos-Fernández, Carlos Sigcha, Luis de Pablo, Laura Pereira Borzí, Luigi Cardoso, Paulo Costa, Nelson Costa, Susana López, Juan Manuel de Arcas, Guillermo Pavón, Ignacio Mechanical Engineering Department ETSI Industriales Universidad Politécnica de Madrid Spain Group University of Limerick LimerickV94 T9PX Ireland ALGORITMI Research Center School of Engineering University of Minho Guimarães Portugal PolitoBIOMed Lab–Biomedical Engineering Lab Politecnico di Torino Turin10129 Italy ANTHEA Lab–Data Analytics and Technologies for Health Lab Department of Control and Computer Engineering Politecnico di Torino Turin10129 Italy Physical Electronics Electrical Engineering and Applied Physics Department ETS de Ingeniería y Sistemas de Telecomunicación Universidad Politécnica de Madrid Spain

Currently, objective monitoring of resting tremor in Parkinson’s disease (PD) involves wearable devices and machine learning. Smartwatches may present an affordable method for remote and unintrusive tremor monitoring. However, the development of optimized systems is necessary to perform accurate monitoring in free-living settings. In this study, the potential of inertial sensors to detect resting tremors is evaluated. A smartwatch was placed on the wrist of six subjects with PD during the execution of MDS-UPDRS motor tasks. Data were collected over eight weeks from triaxial accelerometer and gyroscope simultaneously and used to implement machine learning algorithms to detect resting tremor. The best performance (accuracy 97.0% in tremor detection) was achieved using accelerometer data analysed with a Random Forest classifier, while the gyroscope showed lower performance (93.0%). The results indicates that the use of the accelerometer in commercial smartwatches can offer effective results for detecting resting tremors, while reducing computational workload. These results show opportunities for the development of robust free-living tremor monitoring systems using commodity devices and algorithms using a single sensor. © 2024 by SCITEPRESS–Science and Technology Publications, Lda.

关键词： Gyroscopes

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