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Deep Learning-Based ECG Classification for Arterial Fibrillation Detection

computers, Materials & Continua 2024年第6期79卷 4805-4824页

作者： Muhammad Sohail Irshad Tehreem Masood Arfan Jaffar Muhammad Rashid Sheeraz Akram Abeer Aljohani Faculty of Computer Science&Information Technology The Superior UniversityLahore54000Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54000Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Department of Computer Science Applied CollegeTaibah UniversityMedina42353Saudi Arabia

The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant *** and timely diagnosis increases the patient’s chances of ***,issues like overfitting and inconsistent accuracy across datasets remain *** a quest to address these challenges,a study presents two prominent deep learning architectures,ResNet-50 and DenseNet-121,to evaluate their effectiveness in AFib *** aim was to create a robust detection mechanism that consistently performs *** such as loss,accuracy,precision,sensitivity,and Area Under the Curve(AUC)were utilized for *** findings revealed that ResNet-50 surpassed DenseNet-121 in all evaluated *** demonstrated lower loss rate 0.0315 and 0.0305 superior accuracy of 98.77%and 98.88%,precision of 98.78%and 98.89%and sensitivity of 98.76%and 98.86%for training and validation,hinting at its advanced capability for AFib *** insights offer a substantial contribution to the existing literature on deep learning applications for AFib detection from ECG *** comparative performance data assists future researchers in selecting suitable deep-learning architectures for AFib ***,the outcomes of this study are anticipated to stimulate the development of more advanced and efficient ECG-based AFib detection methodologies,for more accurate and early detection of AFib,thereby fostering improved patient care and outcomes.

关键词： Convolution neural network atrial fibrillation area under curve ECG false positive rate deep learning classification

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A DES Homomorphic Scheme for Blackbox Data Analytics 4

A DES Homomorphic Scheme for Blackbox Data Analytics

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4th International Multidisciplinary information Technology and Engineering Conference, IMITEC 2024

作者： Mletshe, Luthando Modiba, Nthabiseng Makhoere, Lucky Ntlatywa, Phumezo Nogwina, Mnoneleli Chibaya, Colin Computer Science and Information Technology Faculty of Natural and Applied Science Kimberley South Africa

ISBN: (纸本)9798350387988

Data confidentiality is a critical concern in corporate settings, particularly as data analysts seek to uncover trends and insights from sensitive datasets. Traditional approaches often require plaintext data, posing a significant risk to data privacy during analytics processes. In response to this challenge, homomorphic encryption schemes have emerged as a promising solution to enable secure data analytics while preserving confidentiality. In this work-in-progress paper, we present an ongoing implementation of a homomorphic scheme based on the Data Encryption Standard (DES) as a proof of concept. Our goal is to showcase how DES homomorphic encryption can facilitate secure data analytics by allowing computations on encrypted data without compromising confidentiality. While our work is still in the early stages, we aim to empower data custodians by enabling them to share complete cipher datasets with confidence, knowing that data analytics operations remain blackbox and preserve data availability. Overall, this work represents an ongoing effort to advance data security practices and contribute to the development of practical solutions for secure data analytics. © 2024 IEEE.

关键词： Data Analytics

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A Depth-Based DES Model for Enhanced Confidentiality in Datasets 4

A Depth-Based DES Model for Enhanced Confidentiality in Data...

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4th International Multidisciplinary information Technology and Engineering Conference, IMITEC 2024

作者： Sebithing, Oratile Makhoere, Lucky Modiba, Nthabiseng Nogwina, Mnoneleli Ntlatywa, Phumezo Chibaya, Colin Computer Science & Information Tech Faculty of Natural and Applied Science Kimberly South Africa

ISBN: (纸本)9798350387988

Data encryption is a fundamental aspect of ensuring the confidentiality and security of sensitive information in various applications. In this paper, we present an implementation of the Data Encryption Standard (DES) model for encrypting datasets, aiming to enhance data security through multilevel depth encryption with dynamic key transformation. The DES algorithm, known for its robustness and efficiency, forms the basis of our encryption framework. We explore novel encryption techniques by applying the DES algorithm at multiple levels of depth, leveraging dynamic key transformation strategies to enhance security. Specifically, we introduce multiple levels of depth encryptions through the use of random numbers that determine the number of key transformations. The key transformation process dynamically modifies the encryption key to provide additional layers of security. Our initial experimental results in particular CPU time/speed utilization and RAM demands demonstrate the effectiveness of our approach in enhancing data security. By combining the robustness of the DES algorithm with innovative encryption techniques, we achieve a higher level of protection for sensitive information, ensuring its confidentiality. © 2024 IEEE.

关键词： Confidentiality Dataset DES Encryption

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Data Augmentation Impact on Deep learning Performance for Stress Detection 11

Data Augmentation Impact on Deep learning Performance for St...

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11th IEEE International Conference on Intelligent Computing and information systems, ICICIS 2023

作者： Hamdi, Eman Mabrouk, Mohamed Rady, Sherine Aref, Mostafa Faculty of Computer and Information Science Computer Science Department Cairo Egypt Faculty of Computer and Information Science Information Systems Department Cairo Egypt

ISBN: (纸本)9798350322101

The mental health of individuals has a major influence on society. Mental disorders such as depression and anxiety are related to issues and distress to function in work, social, or family gatherings. Motivated by helping people to early detect stress, we propose a deep learning model for stress detection that is trained and tested on Stress Annotated Dataset (SAD). Also, different techniques of textual data augmentation are applied to generate more data samples to study the impact on the stress detection task. The model achieves competitive results in the task of stress detection and proves that data augmentation has a great effect on the performance. As the accuracy is raised by 6.1% to 10.4 % by applying various techniques of data augmentation. © 2023 IEEE.

关键词： Stresses

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Enhancing Cultural Tourism with VR: A Virtual Tour of Wat Pho 10

Enhancing Cultural Tourism with VR: A Virtual Tour of Wat Ph...

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10th International Conference on Digital Arts, Media and Technology and 8th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications Engineering, ECTI DAMT and NCON 2025

作者： Seripanichkarn, Pitcha Piyasoranart, Chananart Ratanasanya, San Visutsak, Porawat KMUTNB Faculty of Applied Science Department of Computer and Information Science Bangkok Thailand

ISBN: (纸本)9798331543273

This paper presents the development of an interactive virtual reality (VR) web application for exploring Wat Pho, a UNESCO Memory of the World heritage site in Bangkok, Thailand. Aimed at enhancing cultural tourism and accessibility, the application provides a 360° virtual tour of key locations within the temple grounds. Developed using Unity and Firebase, the application integrates interactive elements such as navigational arrows, Thai audio narration, textual information, and a chatbot for user queries. This immersive VR experience offers a novel way to engage with Wat Pho's rich history and cultural significance for both local and remote audiences, overcoming barriers of physical access. © 2025 IEEE.

关键词： Virtual reality

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Modern Standard Arabic speech disorders corpus for digital speech processing applications

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International Journal of Speech Technology 2024年第1期27卷 157-170页

作者： Alqudah, Assal A. M. Alshraideh, Mohammad A. M. Abushariah, Mohammad A. M. Sharieh, Ahmad A. S. Department of Computer Science King Abdullah II School of Information Technology The University of Jordan Amman Jordan Department of Computer Information Systems King Abdullah II School of Information Technology The University of Jordan Amman Jordan Department of Computer Science Faculty of Science and Information Technology Al-Zaytoonah University of Jordan Amman Jordan

Digital speech processing applications including automatic speech recognition (ASR), speaker recognition, speech translation, and others, essentially require large volumes of speech data for training and testing purposes. Although there are available speech corpora, speech data for speakers suffering speech disorders are hardly available for many languages including Arabic language. Consequently, developing digital speech processing applications that target the entire society becomes hard due to the unavailability of speech corpora that contain sufficient speakers’ variations including healthy and disordered speech. This research presents our work towards developing a Modern Standard Arabic (MSA) speech corpus for speakers suffering distortion and substitution articulation disorders. The speech corpus was recorded by 40 (20 male and 20 female) Jordanian speakers who suffer either distortion or/and substitution articulation disorders. This speech corpus can be used for various applications including ASR, speech and hearing, and others. Part of this speech corpus is used for developing and evaluating an ASR for MSA using the Carnegie Mellon University (CMU) Pocketsphinx tools based on Mel-Frequency Cepstral Coefficients (MFCC) and Hidden Markov Model (HMM) techniques. Furthermore, Linear Discriminant Analysis (LDA) and Maximum Likelihood Linear Transform (MLLT) optimization techniques were applied. Using three different testing data sets, this work obtained 98.38% and 1.76% average word recognition correctness rate (WRCR) and average Word Error Rate (WER), respectively, for speaker-dependent and text-independent. For speaker-independent and text-dependent, this work obtained 99.37% and 0.68% average WRCR and average WER, respectively, whereas for speaker-independent and text-independent this work obtained 96.53% and 4.00% average WRCR and average WER, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Natur

关键词： Hidden Markov models

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Fuzzy clustering for electric field characterization and its application to thunderstorm interpretability

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Digital Communications and Networks 2025年第2期11卷 299-307页

作者： Xu Yang Hongyan Xing Xinyuan Ji Wei Xu Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and Technology Department of Electrical and Computer Engineering University of Alberta Systems Research Institute Polish Academy of Sciences Department of Computer Engineering Faculty of Engineering and Natural Sciences Istinye University

Changes in the Atmospheric Electric Field Signal(AEFS) are highly correlated with weather changes, especially with thunderstorm activities. However, little attention has been paid to the ambiguous weather information implicit in AEFS changes. In this paper, a Fuzzy C-Means(FCM) clustering method is used for the first time to develop an innovative approach to characterize the weather attributes carried by AEFS. First, a time series dataset is created in the time domain using AEFS attributes. The AEFS-based weather is evaluated according to the time-series Membership Degree(MD) changes obtained by inputting this dataset into the FCM. Second, thunderstorm intensities are reflected by the change in distance from a thunderstorm cloud point charge to an AEF apparatus. Thus, a matching relationship is established between the normalized distance and the thunderstorm dominant MD in the space domain. Finally, the rationality and reliability of the proposed method are verified by combining radar charts and expert experience. The results confirm that this method accurately characterizes the weather attributes and changes in the AEFS, and a negative distance-MD correlation is obtained for the first time. The detection of thunderstorm activity by AEF from the perspective of fuzzy set technology provides a meaningful guidance for interpretable thunderstorms.

关键词： Atmospheric electric field (AEF) Thunderstorm Fuzzy C-means (FCM) Attribute

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A Novel Framework for Gauging information Extracted from Smartphones Using Neutrosophic Logic

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Neutrosophic Sets and systems 2025年 76卷 154-171页

作者： Abou Alzahab, R.M. Ismail, Amr Abd Elkhalik, S.H. Shams, M.Y. El-Bakry, H.M. Salama, A.A. Department of Mathematics and Computer Science Faculty of Science Port Said University Egypt Department of Cybersecurity College of Engineering and Information Technology Buraydah Private Colleges Buraydah51418 Saudi Arabia Department. of Communication and Computer Engineering Air Defense University Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt Department of Information Systems. Faculty of Computer and Information Science Mansoura University Egypt

Smartphones contain a vast amount of information about their users, which can be used as evidence in criminal cases. However, the sheer volume of data can make it challenging for forensic investigators to identify and use the most relevant information. Neutrosophic logic is a generalization of fuzzy logic that allows for uncertainty and vagueness, making it a more potent tool for dealing with the ambiguity of real-world data. The proposed framework aims to identify and utilize the most relevant information for forensic investigators, making it easier to solve criminal cases using Neutrosophic logic. This novel approach provides a more powerful tool for dealing with the ambiguity of smartphone data, ultimately improving the accuracy and effectiveness of forensic investigations. Our research has utilized Neutrosophic logic to evaluate the degree of truth, falsity, or Indeterminacy of this information. Additionally, this study analyzes conversations between individuals using Excel’s fuzzy lookup add-in to determine the percentage of truth and false in each conversation. The results were compiled into a dataset and utilized a Neutrosophic Python code to evaluate the information. The results indicate the percentage of truth, false, and Indeterminacy in each conversation and which can be used to determine its admissibility as evidence and which not. © 2025, University of New Mexico. All rights reserved.

关键词： Smartphones

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Abnormal event detection in surveillance videos through LSTM auto-encoding and local minima assistance

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Discover Internet of Things 2025年第1期5卷 1-25页

作者： Sengonul, Erkan Samet, Refik Abu Al-Haija, Qasem Alqahtani, Ali Alsemmeari, Rayan A. Alghamdi, Bandar Alturki, Badraddin Alsulami, Abdulaziz A. Department of Computer Engineering Ankara University Ankara06100 Turkey Department of Cybersecurity Faculty of Computer & Information Technology Jordan University of Science and Technology PO Box 3030 Irbid22110 Jordan Department of Networks & Communications Engineering College of Computer Science & Information Systems Najran University Najran61441 Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

Abnormal event detection in video surveillance is critical for security, traffic management, and industrial monitoring applications. This paper introduces an innovative methodology for anomaly detection in video data, encompassing three primary stages: preprocessing, feature learning, and anomaly detection. We employ background subtraction and noise reduction during preprocessing to refine the data. The feature-learning stage involves training an LSTM autoencoder to capture the essential features of normal video sequences. For anomaly detection, we map video data to a lower-dimensional space (latent code) and compare it against the distribution of codes from normal sequences. We determine regularity scores and identify local minima points exceeding a specified threshold while scrutinizing shadows between adjacent maxima to confirm and pinpoint anomalies. When tested on the CUHK Avenue and UMN datasets, our methodology demonstrated performance with AUCs of 93.8% and 94.1%, respectively, outperforming several baseline models. Our results show the high precision of our method that can detect anomalies, highlighting its potential advantages that it can achieve for enhancing systems of surveillance. © The Author(s) 2025.

关键词： Noise abatement

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An Optimization Approach for Convolutional Neural Network Using Non-Dominated Sorted Genetic Algorithm-Ⅱ

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computers, Materials & Continua 2023年第3期74卷 5641-5661页

作者： Afia Zafar Muhammad Aamir Nazri Mohd Nawi Ali Arshad Saman Riaz Abdulrahman Alruban Ashit Kumar Dutta Badr Almutairi Sultan Almotairi Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn MalaysiaMalaysia School of Electronics Computing and MathematicsUniversity of DerbyUK Department of Computer Science National University of TechnologyIslamabadPakistan Department of Information Technology College of Computer and Information SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Kingdom of Saudi Arabia Department of Information Technology College of Computer Sciences and Information Technology CollegeMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Natural and Applied Sciences Faculty of Community CollegeMajmaah UniversityMajmaah11952Saudi Arabia Department of Information Systems Faculty of Computer and Information SciencesIslamic University of MadinahMadinah42351Saudi Arabia

In computer vision,convolutional neural networks have a wide range of *** representmost of today’s data,so it’s important to know how to handle these large amounts of data *** neural networks have been shown to solve image processing problems ***,when designing the network structure for a particular problem,you need to adjust the hyperparameters for higher *** technique is time consuming and requires a lot of work and domain *** a convolutional neural network architecture is a classic NP-hard optimization *** the other hand,different datasets require different combinations of models or hyperparameters,which can be time consuming and *** approaches have been proposed to overcome this problem,such as grid search limited to low-dimensional space and queuing by random *** address this issue,we propose an evolutionary algorithm-based approach that dynamically enhances the structure of Convolution Neural Networks(CNNs)using optimized *** study proposes a method using Non-dominated sorted genetic algorithms(NSGA)to improve the hyperparameters of the CNN *** addition,different types and parameter ranges of existing genetic algorithms are *** study was conducted with various state-of-the-art methodologies and *** have shown that our proposed approach is superior to previous methods in terms of classification accuracy,and the results are published in modern computing literature.

关键词： Non-dominated sorted genetic algorithm convolutional neural network hyper-parameter optimization

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