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An End-to-End Transformer-Based Automatic Speech Recognition for Qur’an Reciters

computers, Materials & Continua 2023年第2期74卷 3471-3487页

作者： Mohammed Hadwan Hamzah A.Alsayadi Salah AL-Hagree Department of Information Technology College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Computer Science College of Applied SciencesTaiz UniversityTaiz6803Yemen Computer Science Department Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Computer Science Department Faculty of SciencesIbb UniversityYemen Department of Computer Sciences&Information Ibb UniversityYemen

The attention-based encoder-decoder technique,known as the trans-former,is used to enhance the performance of end-to-end automatic speech recognition(ASR).This research focuses on applying ASR end-toend transformer-based models for the Arabic language,as the researchers’community pays little attention to *** Muslims Holy Qur’an book is written using Arabic diacritized *** this paper,an end-to-end transformer model to building a robust Qur’an *** is *** acoustic model was built using the transformer-based model as deep learning by the PyTorch framework.A multi-head attention mechanism is utilized to represent the encoder and decoder in the acoustic *** filter bank is used for feature *** build a language model(LM),the Recurrent Neural Network(RNN)and Long short-term memory(LSTM)were used to train an n-gram word-based *** a part of this research,a new dataset of Qur’an verses and their associated transcripts were collected and processed for training and evaluating the proposed model,consisting of 10 h *** recitations performed by 60 *** experimental results showed that the proposed end-to-end transformer-based model achieved a significant low character error rate(CER)of 1.98%and a word error rate(WER)of 6.16%.We have achieved state-of-the-art end-to-end transformer-based recognition for Qur’an reciters.

关键词： Attention-based encoder-decoder recurrent neural network long short-term memory qur’an reciters recognition diacritized arabic text

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Evolutionary optimization of Yagi–Uda antenna design using grey wolf optimizer

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Neural Computing and Applications 2025年第6期37卷 4155-4183页

作者： Braik, Malik Sheta, Alaa Aljahdali, Sultan El-Hefnawi, Fatma Al-Hiary, Heba Elashmawi, Walaa H. Computer Science Department Al-Balqa Applied University Salt Jordan Department of Computer Science Southern Connecticut State University New HavenCT United States Computer Science Department Taif University Taif Saudi Arabia Microwave Department Electronics Research Institute Giza Egypt Department of Computer Information System Al-Balqa Applied University Salt Jordan Department of Computer Science Faculty of Computers & Informatics Suez Canal University Ismailia Egypt Department of Computer Science Faculty of Computer Science Misr International University Cairo Egypt

Meta-heuristic optimization algorithms have become widely used due to their outstanding features, such as gradient-free mechanisms, high flexibility, and great potential for avoiding local optimal solutions. This research explored the grey wolf optimizer (GWO) to find the ideal configuration for a six-element Yagi–Uda antenna. The GWO algorithm adjusted the lengths of the antenna wires and the spacings between them. The goal was to maximize the antenna’s ability to transmit signals (throughput gain). Optimal antenna selection relies on various parameters, including gain, bandwidth, impedance matching, frequency, side-lobe levels, etc. The optimization of a six-element Yagi–Uda antenna presents a challenging engineering design problem due to its multimodal and nonlinear nature. Achieving optimal performance hinges on the intricate interplay between the lengths of the constituent elements and the spacing configurations. To this end, a multiobjective function was adopted to design this antenna. The performance of several meta-heuristic algorithms, including genetic algorithms, biogeography-based optimization, simulated annealing, and grey wolf optimizer, was compared. The GWO-based approach has performed better than its competitors. This optimized antenna design based on GWO reported a gain of 14.21 decibel. Therefore, the GWO-based method optimizes antennas that can be further investigated for other antenna design problems. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Simulated annealing

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Hand Gesture Classification for the Deaf and Mute Using the DenseNet169 Model 9

Hand Gesture Classification for the Deaf and Mute Using the ...

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9th International Conference on Communication and Electronics systems, ICCES 2024

作者： Dabwan, Basel A. Gazzan, Mazen Ismil, Omar A. Farah, Enaam A. Almula, Soad M. Ali, Yahya A. AlBaydha University Department of Information System AlBaydha Yemen College of Computer Science and Information Systems Najran University Department of Information System Saudi Arabia Najran University Department of Information System Saudi Arabia Najran University Department of Computer Science Saudi Arabia College of Computer Science and Information Systems Najran University Department of Computer Science Saudi Arabia Collage of Computer Science and Information Systems Najran University Department of Information System Saudi Arabia

ISBN: (纸本)9798350377972

Sign language is a crucial means of communication for people who are deaf or hard of hearing, relying on specific signs, hand shapes, gestures, and facial expressions to express ideas and information. Since many deaf individuals face challenges with verbal communication, reading, and writing, developing a Sign Language Recognition (SLR) system is crucial for enhancing their ability to interact with hearing individuals. This paper presents a solution using the DenseNet169 model to recognize American Sign Language (ASL) gestures. We utilized a dataset representing the ASL alphabet, consisting of 24 classes (excluding the letters J and Z, which involve movement), with a The dataset contains 27,455 samples for training and 7,172 samples for testing. It was divided such that 80% of the data was used for training and 20% for testing. The proposed model has achieved impressive results, with a validation accuracy of 98%, highlighting its potential to significantly enhance communication for the hearing-impaired community. This work contributes to the advancement of human-computer interaction by providing an effective tool for recognizing sign language gestures and facilitating communication. © 2024 IEEE.

关键词： Human computer interaction

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Recognition techniques of medicinal plants: A review

Recognition techniques of medicinal plants: A review

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Tiwari, Nidhi Gupta, Bineet Kumar Sharma, Rajat Department of Computer Science and Information Systems SRMU-Barabanki India

ISBN: (纸本)9781032669663

Pharmaceutical companies are gaining interest in medicinal plants due to their lower costs and fewer side effects as compared to modern drugs. These facts have led to many researchers expressing an interest in the study of automatic medicinal plant recognition. As a result of the development of strong classifiers that can categorize medicinal plants reliably in real time, several avenues for progress are available. This study focuses on the effectiveness and reliability of various machine learning techniques used in recent years for classifying plants using leaf pictures. In the review, several machine learning classifiers are discussed for detecting leaves and extracting significant leaf features. In this study, we investigate how these classifiers perform when classifying leaf ismages according to standard features such as shape, veins, texture, and a combination of these factors. A discussion of some of the most important ongoing studies and potential for improvement in this area wraps up our discussion of the leaf databases that is publicly available for pre-programmed plant recognition. © 2024 The Author(s).

关键词： Textures

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A Novel Hybrid Architecture for Superior IoT Threat Detection through Real IoT Environments

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computers, Materials & Continua 2024年第11期81卷 2299-2316页

作者： Bassam Mohammad Elzaghmouri Yosef Hasan Fayez Jbara Said Elaiwat Nisreen Innab Ahmed Abdelgader Fadol Osman Mohammed Awad Mohammed Ataelfadiel Farah H.Zawaideh Mouiad Fadeil Alawneh Asef Al-Khateeb Marwan Abu-Zanona Department of Computer Science Faculty of Computer Science and Information TechnologyJerash UniversityJerash26150Jordan Computer Engineering Department College of Engineering&Information Technology Buraydah CollegesBuraydah51418Saudi Arabia Faculty of Architecture and Design Al-Zaytoonah University of JordanAmman11733Jordan Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityDiriyahRiyadh13713Saudi Arabia Applied College King Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Business Intelligence and Data Analysis Faculty of Financial Sciences and BusinessIrbid National UniversityIrbid21110Jordan Faculty of Information Technology Ajloun National UniversityAjlun26810Jordan Department of Management Information Systems College of Business AdministrationKing Faisal UniversityAl-Ahsa31982Saudi Arabia

As the Internet of Things(IoT)continues to expand,incorporating a vast array of devices into a digital ecosystem also increases the risk of cyber threats,necessitating robust defense *** paper presents an innovative hybrid deep learning architecture that excels at detecting IoT threats in real-world *** proposed model combines Convolutional Neural Networks(CNN),Bidirectional Long Short-Term Memory(BLSTM),Gated Recurrent Units(GRU),and Attention mechanisms into a cohesive *** integrated structure aims to enhance the detection and classification of complex cyber threats while accommodating the operational constraints of diverse IoT *** evaluated our model using the RT-IoT2022 dataset,which includes various devices,standard operations,and simulated *** research’s significance lies in the comprehensive evaluation metrics,including Cohen Kappa and Matthews Correlation Coefficient(MCC),which underscore the model’s reliability and predictive *** model surpassed traditional machine learning algorithms and the state-of-the-art,achieving over 99.6%precision,recall,F1-score,False Positive Rate(FPR),Detection Time,and accuracy,effectively identifying specific threats such as Message Queuing Telemetry Transport(MQTT)Publish,Denial of Service Synchronize network packet crafting tool(DOS SYN Hping),and Network Mapper Operating System Detection(NMAP OS DETECTION).The experimental analysis reveals a significant improvement over existing detection systems,significantly enhancing IoT security *** our experimental analysis,we have demonstrated a remarkable enhancement in comparison to existing detection systems,which significantly strength-ens the security standards of *** model effectively addresses the need for advanced,dependable,and adaptable security solutions,serving as a symbol of the power of deep learning in strengthening IoT ecosystems amidst the constantly evolving cyber threat *** achievemen

关键词： A hybrid deep learning model IoT threat detection real IoT environments cybersecurity attention mechanism

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Efficient Object Segmentation and Recognition Using Multi-Layer Perceptron Networks

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computers, Materials & Continua 2024年第1期78卷 1381-1398页

作者： Aysha Naseer Nouf Abdullah Almujally Saud S.Alotaibi Abdulwahab Alazeb Jeongmin Park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Information System Department Umm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Engineering Tech University of KoreaGyeonggi-do15073South Korea

Object segmentation and recognition is an imperative area of computer vision andmachine learning that identifies and separates individual objects within an image or video and determines classes or categories based on their *** proposed system presents a distinctive approach to object segmentation and recognition using Artificial Neural Networks(ANNs).The system takes RGB images as input and uses a k-means clustering-based segmentation technique to fragment the intended parts of the images into different regions and label thembased on their ***,two distinct kinds of features are obtained from the segmented images to help identify the objects of *** Artificial Neural Network(ANN)is then used to recognize the objects based on their *** were carried out with three standard datasets,MSRC,MS COCO,and Caltech 101 which are extensively used in object recognition research,to measure the productivity of the suggested *** findings from the experiment support the suggested system’s validity,as it achieved class recognition accuracies of 89%,83%,and 90.30% on the MSRC,MS COCO,and Caltech 101 datasets,respectively.

关键词： K-region fusion segmentation recognition feature extraction artificial neural network computer vision

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Revisiting recommender systems: an investigative survey

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Neural Computing and Applications 2025年第4期37卷 2145-2173页

作者： Ibrahim, Osman Ali Sadek Younis, Eman M. G. Mohamed, Ebtsam A. Ismail, Walaa N. Computer Science Department Minia University Minia Egypt Information Systems Department Minia University Minia Egypt Management Information system Department Al-Yamamah University Riyadh Saudi Arabia

This paper provides a thorough review of recommendation methods from academic literature, offering a taxonomy that classifies recommender systems (RSs) into categories like collaborative filtering, content-based systems, and hybrid systems. It examines the effectiveness and challenges of these systems, such as filter bubbles, the "cold start" issue, and the reliance on collaborative filtering and content-based approaches. We trace the development of RSs, emphasizing the role of machine learning and deep learning models in overcoming these challenges and delivering more accurate, personalized, and context-aware recommendations. We also highlight the increasing significance of ethical considerations, including fairness, transparency, and trust, in the design of RSs. The paper presents a structured literature review, discussing various aspects of RSs, such as collaborative filtering, personalized recommender systems, and strategies to improve system robustness. It also points out the limitations of the existing approaches and suggests promising research directions for the future. In summary, this paper offers a comprehensive analysis of RSs, focusing on their evolution, challenges, and potential future improvements, particularly in enhancing accuracy, diversity, and ethical practices in recommendations. © The Author(s) 2025.

关键词： Collaborative filtering

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Read for me: developing a mobile based application for both visually impaired and illiterate users to tackle reading challenge

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i-com 2023年第3期22卷 225-240页

作者： Alfayez, Zainab Hameed Alfayez, Batool Hameed Abdul-Samad, Nahla Hamad Department of Computer Information Systems College of Computer Science and Information Technology University of Basrah Basrah Iraq Department of Library and Information College of Art University of Basrah Basrah Iraq

In recent years, there have been several attempts to help visually impaired and illiterate people to overcome their reading limitations through developing different applications. However, most of these applications are based on physical button interaction and avoid the use of touchscreen devices. This research mainly aims to find a solution that helps both visually impaired and illiterate people to read texts present in their surroundings through a touchscreen-based application. The study also attempts to discover the possibility of building one application that could be used by both type of users and find out whether they would use it in the same efficiency. Therefore, a requirements elicitation study was conducted to identify the users’ requirements and their preferences and so build an interactive interface for both visually impaired and illiterate users. The study resulted in several design considerations, such as using voice instructions, focusing on verbal feedback, and eliminating buttons. Then, the reader mobile application was designed and built based on these design preferences. Finally, an evaluation study was conducted to measure the usability of the developed application. The results revealed that both sight impaired and illiterate users could benefit from the same mobile application, as they were satisfied with using it and found it efficient and effective. However, the measures from the evaluation sessions also reported that illiterate users had used the develop app more efficiently and effectively. Moreover, they were more satisfied, especially with the application’s ease of use. © 2023 the author(s), published by De Gruyter.

关键词： Human computer interaction

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Transparent and Accurate COVID-19 Diagnosis:Integrating Explainable AI with Advanced Deep Learning in CT Imaging

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computer Modeling in Engineering & sciences 2024年第6期139卷 3101-3123页

作者： Mohammad Mehedi Hassan Salman A.AlQahtani Mabrook S.AlRakhami Ahmed Zohier Elhendi Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Science Technology and Innovation Department King Saud UniversityRiyadh11543Saudi Arabia

In the current landscape of the COVID-19 pandemic,the utilization of deep learning in medical imaging,especially in chest computed tomography(CT)scan analysis for virus detection,has become increasingly *** its potential,deep learning’s“black box”nature has been a major impediment to its broader acceptance in clinical environments,where transparency in decision-making is *** bridge this gap,our research integrates Explainable AI(XAI)techniques,specifically the Local Interpretable Model-Agnostic Explanations(LIME)method,with advanced deep learning *** integration forms a sophisticated and transparent framework for COVID-19 identification,enhancing the capability of standard Convolutional Neural Network(CNN)models through transfer learning and data *** approach leverages the refined DenseNet201 architecture for superior feature extraction and employs data augmentation strategies to foster robust model *** pivotal element of our methodology is the use of LIME,which demystifies the AI decision-making process,providing clinicians with clear,interpretable insights into the AI’s *** unique combination of an optimized Deep Neural Network(DNN)with LIME not only elevates the precision in detecting COVID-19 cases but also equips healthcare professionals with a deeper understanding of the diagnostic *** method,validated on the SARS-COV-2 CT-Scan dataset,demonstrates exceptional diagnostic accuracy,with performance metrics that reinforce its potential for seamless integration into modern healthcare *** innovative approach marks a significant advancement in creating explainable and trustworthy AI tools for medical decisionmaking in the ongoing battle against COVID-19.

关键词： Explainable AI COVID-19 CT images deep learning

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Enhancing Indoor BLE Distance Estimation Through Machine and Deep Learning Utilizing Anchor information 10

Enhancing Indoor BLE Distance Estimation Through Machine and...

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10th International Conference on Smart Computing and Communication, ICSCC 2024

作者： Al Qathrady, Mimonah College of Computer Science and Information Systems Najran University Information Systems Department Najran Saudi Arabia

ISBN: (纸本)9798350363104

This study introduces a comprehensive framework to enhance distance estimation models in indoor environments by incorporating Bluetooth Low Energy (BLE) Anchor information along with Received Signal Strength Indicator (RSSI) and channel data. Utilizing a publicly available dataset, our research systematically implements and evaluates various machine and deep learning algorithms. These include ensemble models, linear models, neural and deep network models, as well as other machine learning models, analyzing their performance with and without the integration of Anchor data. Our results demonstrate that the inclusion of Anchor data significantly improves the accuracy of distance estimations, with error reductions observed on more than 80% of the models, achieving enhancements up to 67% in terms of error reduction. This finding has profound implications for indoor distance estimation and navigation systems, potentially revolutionizing IoT applications that rely on precise distance measurements, such as infection tracing, crowd monitoring, and various contact tracing applications. © 2024 IEEE.

关键词： Adversarial machine learning

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