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Artificial Neural Networks for Prediction of COVID-19 in Saudi Arabia

computers, Materials & Continua 2021年第3期66卷 2787-2796页

作者： Nawaf N.Hamadneh Waqar A.Khan Waqar Ashraf Samer H.Atawneh Ilyas Khan Bandar N.Hamadneh Department of Basic Sciences College of Science and Theoretical StudiesSaudi Electronic UniversityRiyadh11673Saudi Arabia Department of Mechanical Engineering College of EngineeringPrince Mohammad Bin Fahd UniversityAl Khobar31952Saudi Arabia Department of Mathematics and Natural Sciences College of Sciences&Human StudiesPrince Mohammad Bin Fahd UniversityAl Khobar31952Saudi Arabia College of Computing and Informatics Saudi Electronic UniversityRiyadh11673Saudi Arabia Faculty of Mathematics and Statistics Ton Duc Thang UniversityHo Chi Minh City72915Vietnam King Abdullah University Hospital University of Science and TechnologyIrbid22110Jordan

In this study,we have proposed an artificial neural network(ANN)model to estimate and forecast the number of confirmed and recovered cases of COVID-19 in the upcoming days until September 17,*** proposed model is based on the existing data(training data)published in the Saudi Arabia Coronavirus disease(COVID-19)situation—*** Prey-Predator algorithm is employed for the *** perceptron neural network(MLPNN)is used in this *** improve the performance of MLPNN,we determined the parameters of MLPNN using the prey-predator algorithm(PPA).The proposed model is called the MLPNN–*** performance of the proposed model has been analyzed by the root mean squared error(RMSE)function,and correlation coefficient(R).Furthermore,we tested the proposed model using other existing data recorded in Saudi Arabia(testing data).It is demonstrated that the MLPNN-PPA model has the highest performance in predicting the number of infected and recovering in Saudi *** results reveal that the number of infected persons will increase in the coming days and become a minimum of *** number of recoveries will be 2000 to 4000 per day.

关键词： COVID-19 ANN modeling multilayer perceptron neural network prey-predator algorithm

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Investigating Retrieval-Augmented Generation in Quranic Studies: A Study of 13 Open-Source Large Language Models

arXiv

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arXiv 2025年

作者： Khalila, Zahra Nasution, Arbi Haza Monika, Winda Onan, Aytug Murakami, Yohei Radi, Yasir Bin Ismail Osmani, Noor Mohammad Department of Informatics Engineering Universitas Islam Riau Pekanbaru28284 Indonesia Department of Library Information Universitas Lancang Kuning Riau28266 Indonesia Department of Computer Engineering College of Engineering and Architecture Izmir Katip Celebi University Izmir35620 Turkey Faculty of Information Science and Engineering Ritsumeikan University Shiga Kusatsu525-8577 Japan Perlis Kuala Perlis02000 Malaysia Department Of Qur’an And Sunnah Studies Ahas Kirkhs International Islamic University Malaysia Malaysia

Accurate and contextually faithful responses are critical when applying large language models (LLMs) to sensitive and domain-specific tasks, such as answering queries related to quranic studies. General-purpose LLMs often struggle with hallucinations, where generated responses deviate from authoritative sources, raising concerns about their reliability in religious contexts. This challenge highlights the need for systems that can integrate domain-specific knowledge while maintaining response accuracy, relevance, and faithfulness. In this study, we investigate 13 open-source LLMs categorized into large (e.g., Llama3:70b, Gemma2:27b, QwQ:32b), medium (e.g., Gemma2:9b, Llama3:8b), and small (e.g., Llama3.2:3b, Phi3:3.8b). A Retrieval-Augmented Generation (RAG) is used to make up for the problems that come with using separate models. This research utilizes a descriptive dataset of Quranic surahs including the meanings, historical context, and qualities of the 114 surahs, allowing the model to gather relevant knowledge before responding. The models are evaluated using three key metrics set by human evaluators: context relevance, answer faithfulness, and answer relevance. The findings reveal that large models consistently outperform smaller models in capturing query semantics and producing accurate, contextually grounded responses. The Llama3.2:3b model, even though it is considered small, does very well on faithfulness (4.619) and relevance (4.857), showing the promise of smaller architectures that have been well optimized. This article examines the trade-offs between model size, computational efficiency, and response quality while using LLMs in domain-specific applications. © 2025, CC BY.

关键词： Semantics

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Convolutional Neural Network Based Deep Neural Network Model for Electrocardiogram Records Classification 23

Convolutional Neural Network Based Deep Neural Network Model...

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Proceedings of the 7th International Conference on Future Networks and Distributed Systems

作者： Ahmed Mahmood Khudhur Mohammed Hasan Alwan Qayssar Al Omairi Omar Abdulkareem Mahmood Yousif I Hammadi Mohammed Saleh Ali Muthanna Ahmed Aziz Ammar Muthanna Information Technology Department Kirkuk University Iraq Department of Communications Engineering University of Diyala Iraq Department of Computer Engineering University of Diyala Iraq Dep. of Medical Instruments Engineering Techniques Bilad Alrafidain University College Iraq Institute of Computer Technologies and Information Security Southern Federal University Russia Department of computer science Benha university Egypt and International Business Management Department Tashkent State University of Economics Uzbekistan Department of Telecommunication Networks and Data Transmission The Bonch Bruevich Saint Petersburg State University of Telecommunications Russia and Department of Applied Probability and Informatics Peoples Friendship University of Russia Russia

ISBN: (纸本)9798400709036

Сardiovascular diseases, also known as CVDs, currently rank as the primary incidence of mortality. The present approach for identifying illnesses involves the analysis of the Electrocardiogram (ECG), an electronic diagnostic device utilized to capture the rhythm of the heart. Regrettably, the process of seeking out specialists to conduct analysis on a substantial volume of electrocardiogram (ECG) data results in a significant depletion of medical capabilities. Hence, the utilization of deep learning algorithms for the identification of ECG characteristics has progressively gained prominence. Nevertheless, there exist certain limitations associated with these conventional approaches, which necessitate the need for manual characteristic identification, intricate models, and extensive training duration. This study presents a novel and effective approach for categorizing the five different types of heartbeats in the MIT-BIH Arrhythmia database. The proposed method involves the utilization of a 16-layer deep one-dimensional convolutional neural network, which exhibits both robustness and efficiency in its classification performance. Thus, the designed model is consisting of five-groups, first to fourth groups are the main feature extraction and mapping blocks, while the fifth group is fully connected and classification layers. The findings indicate that the model presented in this study has superior performance in terms of accuracy, precision, and F1-score. The proper classification of medical cases has a significant impact on the conservation of medical resources, hence positively influencing clinical practice. Additionally, the model training phase is characterized by its efficiency, since it does not significantly deplete resources.

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Smart AmbuBag: A Cloud-Based Automated Respiratory Ventilator 13

Smart AmbuBag: A Cloud-Based Automated Respiratory Ventilato...

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13th International Conference on Information and Communication Technology Convergence, ICTC 2022

作者： Sampedro, Gabriel Avelino Cobar, Jose Mari Abaja, Keith Daniel Baluis, Ronnie Huyo-A, Shekinah Lor Abisado, Mideth University of the Philippines - Open University Faculty of Information and Communication Studies Los Baños Philippines Research and Development Center Philippine Coding Camp Manila Philippines Technological Institute of the Philippines Computer Engineering Department Quezon City Philippines College of Computing and Information Technologies National University Manila Philippines

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

With the Bag-Valve-Mask (commonly known as an AmbuBag or BVM) being widely used to provide emergency ventilation to patients, it is one of the most critical instruments used in the medical industry. As was emphasized in some studies, there is an urgency to implement a resolution to the human error-induced risks posed by the manual BVM. Hence, this study explores how an IoT-Based Automated Valve Mask can aid in resolving accidents born from the use of the manual counterpart. This device aims to propose a solution to the potential root causes of such adverse events in the field. It uses an GSM-based technology to gather the patient's vital signs and the quality of the air. After being tested on both Child and Adult groups, it was remarked that the accuracy rate of the device surpassed 95%. © 2022 IEEE.

关键词： Internet of things

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Task groupings regularization: data-free meta-learning with heterogeneous pre-trained models 24

Task groupings regularization: data-free meta-learning with ...

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Proceedings of the 41st International Conference on Machine Learning

作者： Yongxian Wei Zixuan Hu Li Shen Zhenyi Wang Yu Li Chun Yuan Dacheng Tao Tsinghua Shenzhen International Graduate School Tsinghua University China Tsinghua Shenzhen International Graduate School Tsinghua University China and College of Computing & Data Science Nanyang Technological University Singapore School of Cyber Science and Technology Sun Yatsen University China University of Maryland College Park Department of Computer Science and Engineering The Chinese University of Hong Kong China College of Computing & Data Science Nanyang Technological University Singapore

Data-Free Meta-Learning (DFML) aims to derive knowledge from a collection of pre-trained models without accessing their original data, enabling the rapid adaptation to new unseen tasks. Current methods often overlook the heterogeneity among pre-trained models, which leads to performance degradation due to task conflicts. In this paper, we empirically and theoretically identify and analyze the model heterogeneity in DFML. We find that model heterogeneity introduces a heterogeneity-homogeneity trade-off, where homogeneous models reduce task conflicts but also increase the overfitting risk. Balancing this trade-off is crucial for learning shared representations across tasks. Based on our findings, we propose Task Groupings Regularization, a novel approach that benefits from model heterogeneity by grouping and aligning conflicting tasks. Specifically, we embed pre-trained models into a task space to compute dissimilarity, and group heterogeneous models together based on this measure. Then, we introduce implicit gradient regularization within each group to mitigate potential conflicts. By encouraging a gradient direction suitable for all tasks, the meta-model captures shared representations that generalize across tasks. Comprehensive experiments showcase the superiority of our approach in multiple benchmarks, effectively tackling the model heterogeneity in challenging multidomain and multi-architecture scenarios.

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Conflict-Free Genetic Algorithm with Nash Equilibrium Seeking for Game-Based Battery Swapping Station Recommendation

Conflict-Free Genetic Algorithm with Nash Equilibrium Seekin...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Chang-Long Sun Xin-Xin Xu Chun-Hua Chen Jun Hong Zhenan He Dengxiu Yu Sam Kwong Zhi-Hui Zhan School of Computer Science and Engineering South China University of Technology Guangzhou China School of Computer Science and Technology Ocean University of China Qingdao China School of Software Engineering South China University of Technology Guangzhou China College of Computer Science Sichuan University Chengdu China School of Artificial Intelligence Northwestern Polytechnical University Xi'an China Department of Computing and Decision Science Lingnan University Hong Kong SAR College of Artificial Intelligence Nankai University Tianjin China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The rapid growth of electric vehicles (EVs) has led to significant challenges in providing efficient and sustainable charging solutions. This paper addresses the battery swapping station (BSS) recommendation problem by proposing a novel conflict-free genetic algorithm (CFGA) integrated with a Nash equilibrium seeking (NES) approach to identify optimal Nash equilibrium (ONE) solutions to such a non-cooperative optimization problem. The CFGA employs specialized crossover and mutation operators to generate offspring that satisfy the constraints of the problem, ensuring that each EV decides a unique battery swap strategy without conflict. Firstly, an order crossover operator is proposed to preserve the order of genes in the chromosomes. Secondly, a replacement and exchange mutation operator is proposed to enhance mutation diversity. The resulting optimal solution is then used as the initial strategy for the NES, which iteratively converges to the ONE. The proposed CFGA with NES algorithm is evaluated under both small-scale and large-scale cases, demonstrating its effectiveness in achieving a balance between costs for EVs and utilization for BSSs. The study's findings have practical implications for the smart grid and EV integration, offering a robust method for optimizing EV infrastructure and operations.

关键词： Costs Scalability Ecosystems Nash equilibrium Electric vehicles Batteries Smart grids Optimization Cybernetics Genetic algorithms

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The Initial Screening of Laryngeal Tumors via Voice Acoustic Analysis Based on Siamese Network Under Small Samples

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Journal of Voice 2025年

作者： You, Zhenzhen Sun, Delong Shi, Zhenghao Du, Shuangli Hei, Xinhong Kong, Demin Du, Xiaoying Yan, Jing Ren, Xiaoyong Hou, Jin Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Engineering Xi'an University of Technology Xi'an China Department of Otolaryngology Second Affiliated Hospital of Medical College Xi'an Jiaotong University Xi'an China

Objective: The initial screening of laryngeal tumors via voice acoustic analysis is based on the clinician's experience that is subjective. This article introduces a Siamese network with an auxiliary gender classifier for automated, accurate, and objective initial screening of laryngeal tumors based on voice signals. Methods: The study involved 71 tumor patients and 293 non-tumor subjects of Chinese Mandarin. This dataset was divided into a training set and a test set in a ratio of 4:1. We applied nine data augmentation techniques to enlarge the voice training set and extracted the corresponding mel-frequency cepstral coefficients (MFCC) maps. The MFCC maps were randomly paired and fed into the proposed Siamese network to achieve multitask classification for tumor and non-tumor, woman and man. The performance of the proposed model was compared with one machine learning method and six classical deep learning models with and without the auxiliary gender classifier. Results: Experiments demonstrate the superiority of the proposed network compared with the reference models. The proposed model achieved an overall accuracy of 0.9437, an F score of 0.8462, a precision of 0.9167, a sensitivity of 0.7857, and a specificity of 0.9825. Conclusion: The proposed network can assist in the initial screening of laryngeal tumors through voice acoustic analysis. The initial screening solely through voice acoustic analysis can help individuals seek medical assistance outside the hospitals and reduce the burden on doctors as well. © 2025

关键词： Initial screening of laryngeal tumors—Voice acoustic analysis—Mel-frequency cepstral coefficients—Siamese network

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Hierarchical Multi-Agent DRL-Based Framework for Joint Multi-RAT Assignment and Dynamic Resource Allocation in Next-Generation HetNets

arXiv

引用

arXiv 2022年

作者： Alwarafy, Abdulmalik Ciftler, Bekir Sait Abdallah, Mohamed Hamdi, Mounir Al-Dhahir, Naofal Division of Information and Computing Technology College of Science and Engineering Hamad Bin Khalifa University Doha Qatar Electrical and Computer Engineering Department Erik Jonsson School of Engineering and Computer Science The University of Texas Dallas United States

This paper considers the problem of cost-aware downlink sum-rate maximization via joint optimal radio access technologies (RATs) assignment and power allocation in next-generation heterogeneous wireless networks (HetNets). We consider a future HetNet comprised of multi-RATs and serving multi-connectivity edge devices (EDs), and we formulate the problem as a mixed-integer non-linear programming (MINP) problem. Due to the high complexity and combinatorial nature of this problem and the difficulty to solve it using conventional methods, we propose a hierarchical multi-agent deep reinforcement learning (DRL)-based framework, called DeepRAT, to solve it efficiently and learn system dynamics. In particular, the DeepRAT framework decomposes the problem into two main stages;the RATs-EDs assignment stage, which implements a single-agent Deep Q Network (DQN) algorithm, and the power allocation stage, which utilizes a multi-agent Deep Deterministic Policy Gradient (DDPG) algorithm. Using simulations, we demonstrate how the various DRL agents efficiently interact to learn system dynamics and derive the global optimal policy. Furthermore, our simulation results show that the proposed DeepRAT algorithm outperforms existing state-of-the-art heuristic approaches in terms of network utility. Finally, we quantitatively show the ability of the DeepRAT model to quickly and dynamically adapt to abrupt changes in network dynamics, such as EDs' mobility. © 2022, CC BY.

关键词： Deep learning

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Optimized BIGRU-Attention Network for OSA and Cardiovascular Disease Prediction with Risk Factor Classification

Optimized BIGRU-Attention Network for OSA and Cardiovascular...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Sameer Yadav J. Sharmila Joseph Mayank Nagar Vidyabharathi Dakshinamurthi Usha M Vedant Dubey Dept of CSE Gyan Ganga Institute of Technology & Sciences Jabalpur School of Computing Science and Engineering VIT Bhopal University Department of Computer Science & Engineering IES College of Technology Bhopal Madhya Pradesh India Dept. of CSE Sona College of Technology Salem TN India MCA MEASI Institute of Information Technology GGITS MP India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Cardiac illness is a rapidly rising worldwide killer. The development of an automated prediction framework for OSA-related cardiovascular problems is tough given that to the extensive information needed to effectively forecast heart illness. Several algorithms have been developed for use in IoT-based medical oversight systems, which utilize sensor data to identify ailments *** deploy new learning models to improve prediction results. The existing methods lack the ability to forecast accurately, making them unsuitable for successful diagnosis. This study proposes BGEAN, a revolutionary framework that combines deep learning with computing devices to identify cardiac issues accurately. Data are pre-processed utilizing Studentized Residual Methods to address missing values and outliers. Improved Gated Recurrent Neural Networks help classify pre-processed data as normal or abnormal. Thorough testing and comparison to top deep learning techniques are conducted. Performance indicators, such as accuracy (99.99%), recall (99.35%), specificity (90.12%), precision (99.5), F1-score (99.99%), and Model Building Time (MBT) (0.212sec), are computed. The findings show that the technology that was suggested surpassed current algorithms in accuracy, error, and reaction time. With its many user situations, BGEAN provides the most diverse environment for forecasting cardiac diseases.

关键词： Deep learning Heart Accuracy Computational modeling Medical services Logic gates Prediction algorithms Real-time systems Data models Medical diagnostic imaging

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Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI 41

Position: Bayesian Deep Learning is Needed in the Age of Lar...

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41st International Conference on Machine Learning, ICML 2024

作者： Papamarkou, Theodore Skoularidou, Maria Palla, Konstantina Aitchison, Laurence Arbel, Julyan Dunson, David Filippone, Maurizio Fortuin, Vincent Hennig, Philipp Hernández-Lobato, José Miguel Hubin, Aliaksandr Immer, Alexander Karaletsos, Theofanis Khan, Mohammad Emtiyaz Kristiadi, Agustinus Li, Yingzhen Mandt, Stephan Nemeth, Christopher Osborne, Michael A. Rudner, Tim G.J. Rügamer, David Teh, Yee Whye Welling, Max Wilson, Andrew Gordon Zhang, Ruqi Department of Mathematics The University of Manchester Manchester United Kingdom Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge United States Spotify London United Kingdom Computational Neuroscience Unit University of Bristol Bristol United Kingdom Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University United States Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany Department of Computer Science Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge United Kingdom Department of Mathematics University of Oslo Oslo Norway Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative CA United States Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London United Kingdom Department of Computer Science UC Irvine Irvine United States Department of Mathematics and Statistics Lancaster University Lancaster United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Center for Data Science New York University New York United States Department of Statistics LMU Munich Munich Germany DeepMind London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences Center for Data Science Computer Science Department New York University New York United States Department of Computer Science Purdue University West Lafayette United States

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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