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Enhancing pediatric congenital heart disease detection using customized 1D CNN algorithm and phonocardiogram signals

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Heliyon 2025年第3期11卷 e42257页

作者： Haq, Ihtisham Ul Husnain, Ghassan Ghadi, Yazeed Yasin Innab, Nisreen alajmi, Masoud aljuaid, Hanan Department of Computer Science Modeling Electronics and Systems (DIMES) University of Calabria CS Rende 87036 Italy Department of Computer Science CECOS University of IT and Emerging Sciences Peshawar 25000 Pakistan Department of Computer Science Al Ain University United Arab Emirates Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Diriyah Riyadh 13713 Saudi Arabia Department of Computer Engineering College of Computers and Information Technology Taif University P.O. Box 11099 Taif 21944 Saudi Arabia Computer Sciences Department College of Computer and Information Sciences Princess Nourah bint Abdulrahman University (PNU) P.O. Box 84428 Riyadh 11671 Saudi Arabia

Congenital heart disease (CHD), impacting around 1 % of infants worldwide, constitutes a significant healthcare challenge. Early detection is crucial, however constrained by the intricacies of conventional diagnostic techniques such as auscultation and echocardiography. This research presents a tailored one-dimensional convolutional neural network (1D-CNN) for the classification of phonocardiogram (PCG) signals into normal or abnormal categories, providing an automated and efficient solution for congenital heart disease (CHD) diagnosis. The model was trained on a composite dataset consisting of local pediatric PCG signals and publicly accessible dataset. Preprocessing methods, such as low- and high-pass filtering (60–650 Hz), resampling, and noise reduction, were utilized to enhance signal quality. Data augmentation techniques, including chunking, padding, and pitch-shifting, were employed to rectify dataset imbalances and improve model efficacy. Experimental results indicate substantial enhancements, attaining an accuracy of 98.56 %, precision of 98.56 %, F1 score of 98.55 %, sensitivity of 0.98, and specificity of 0.99. The comparative analysis demonstrates the proposed approach's superiority over current methods regarding accuracy and reliability. The research highlights the promise of combining modern signal processing with deep learning for efficient CHD screening. The suggested model exhibits outstanding performance yet, issues like dataset variability and noise persist. Future endeavors involve extending to multiclass categorization and assessing performance across a wider range of medical problems. This study represents a significant advancement in accessible, automated CHD diagnoses, enhancing clinical competence to elevate pediatric treatment. © 2025 The Authors

关键词： Congenital heart disease Electrocardiogram Phonocardiogram Phonocardiography

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DSA: Deep Self-Attention Medical Transformer Neuro-Technology for Brain Tumor Segmentation

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International Journal of Imaging Systems and Technology 2025年第3期35卷

作者： Siddiqah, Mariyam Javed, Kashif Gilani, Syed Omer Khan, Muhammad Attique alsenan, Shrooq Damaševic̆ius, Robertas Zhang, Yudong Islamabad Pakistan Department of Electrical Computer and Biomedical Engineering Abu Dhabi University Abu Dhabi United Arab Emirates Department of AI College of Computer Engineering and Science Prince Mohammad Bin Fahd University Al Khobar Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia Department of Applied Informatics Vytautas Magnus University Kaunas Lithuania Department of Informatics University of Leicester Leicester United Kingdom

Transformer-based methods have shown remarkable outcomes in medical image segmentation tasks. Specifically, the Swin Transformer has proven to be an impressive approach for segmentation jobs, demonstrating its potential to further the discipline. Extensive research on integrating Swin Transformer architecture with U-Net models has shown significant progress toward improving segmentation accuracy. Currently, researchers are looking for innovative methods to improve the challenging segmentation accuracy of enhanced tumor regions due to their heterogeneous and indistinct boundaries. To improve its accuracy, we have proposed a modified version of Swin UNETR, DSA, which is deeper and more focused on extracting global features by an enhanced self-attention mechanism in the later stages of the encoder. It outperformed the enhancing tumor class with comparative performance for the other two classes. By fine-tuning some hyperparameters, we achieved SOTA performance for brain tumor segmentation. The proposed deep self-architecture obtained a mean dice score value of 0.889 and a mean Jaccard score of 0.806, respectively. A comparison was conducted with some recent state-of-the-art techniques, which showed improved accuracy and outperformed the recent best-performing UNet and transformer architectures. © 2025 Wiley Periodicals LLC.

关键词： Image segmentation

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Hybrid Approach for Cost Efficient Application Placement in Fog-Cloud Computing Environments

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computers, Materials & Continua 2024年第6期79卷 4127-4148页

作者： Abdulelah alwabel Chinmaya Kumar Swain Department of Computer Sciences College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Science and Engineering Institute of Management and Information TechnologyCuttackBPUTOdishaIndia

Fog computing has recently developed as a new paradigm with the aim of addressing time-sensitive applications better than with cloud computing by placing and processing tasks in close proximity to the data ***,the majority of the fog nodes in this environment are geographically scattered with resources that are limited in terms of capabilities compared to cloud nodes,thus making the application placement problem more complex than that in cloud *** approach for cost-efficient application placement in fog-cloud computing environments that combines the benefits of both fog and cloud computing to optimize the placement of applications and services while minimizing *** approach is particularly relevant in scenarios where latency,resource constraints,and cost considerations are crucial factors for the deployment of *** this study,we propose a hybrid approach that combines a genetic algorithm(GA)with the Flamingo Search algorithm(FSA)to place application modules while minimizing *** consider four cost-types for application deployment:Computation,communication,energy consumption,and *** proposed hybrid approach is called GA-FSA and is designed to place the application modules considering the deadline of the application and deploy them appropriately to fog or cloud nodes to curtail the overall cost of the *** extensive simulation is conducted to assess the performance of the proposed approach compared to other state-of-the-art *** results demonstrate that GA-FSA approach is superior to the other approaches with respect to task guarantee ratio(TGR)and total cost.

关键词： Placement mechanism application module placement fog computing cloud computing genetic algorithm flamingo search algorithm

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Synergistic Enhancement of Dye-Sensitized solar cells with PANi/CZTS nanocomposite counter electrodes

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Inorganic Chemistry Communications 2025年 178卷

作者： Hsu, Chou-Yi Elshafie, Hashim Diakina, Ekaterina Kareem, A.K. Kanjariya, Prakash Rajiv, Asha Singh, Ashish Jindal, Pardeep Kumar Thunderbird School of Global Management Arizona State University Tempe Campus Phoenix 85004 AZ United States Department of Computer Engineering College of Computer Science King Khalid University Main Campus Al farah Abha 61421 Saudi Arabia Department of Mechanical Engineering Bauman Moscow State Technical University Moscow Russian Federation Polytechnic Institute far eastern federal university Vladivostok Russian Federation Biomedical Engineering Department College of Engineering and Technologies Al-Mustaqbal University Babil Hillah 51001 Iraq Marwadi University Research Center Department of Physics Faculty of Science Marwadi University Gujarat Rajkot 360003 India Department of Physics & Electronics School of Sciences JAIN (Deemed to be University) Karnataka Bangalore India Department of Electrical Electronics & Electric Vehicle Engineering NIMS Institute of Engineering & Technology NIMS University Rajasthan Jaipur India Department of Electronics and Communication Engineering Chandigarh College of Engineering Chandigarh Group of Colleges-Jhanjeri Punjab Mohali 140307 India

This study investigates the fabrication and application of polyaniline (PANi)/copper zinc tin sulfide (CZTS) nanocomposites as counter electrodes in dye-sensitized solar cells (DSSCs). CZTS and PANi were synthesized using conventional methods, and PANi/CZTS composites were prepared through simultaneous synthesis reactions. The synthesized materials were drop-cast to fabricate CZTS, PANi, and PANi/CZTS counter electrodes, which were then integrated into DSSCs. Characterization techniques including XRD, FTIR, Raman, FESEM, TEM, and HRTEM were employed to analyze the synthesized samples. Electrochemical properties were studied using EIS, CV, and Tafel analysis. The photovoltaic parameters of the prepared cells revealed enhanced performance for the PANi/CZTS composite compared to CZTS and PANi alone, with a power conversion efficiency (PCE) of 8.82%, notably surpassing the PCE of the CZTS (5.82%) and PANi (7.18%) counter electrodes. These results underscore the potential of PANi/CZTS nanocomposites as efficient and cost-effective alternatives in DSSCs. © 2025 Elsevier B.V.

关键词： Counter electrode CZTS DSSC Nanocomposite PANi

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State-of-the-art AI-based Learning Approaches for Deepfake Generation and Detection, Analyzing Opportunities, Threading through Pros, Cons, and Future Prospects

arXiv

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arXiv 2025年

作者： Goyal, Harshika Wajid, Mohammad Saif Wajid, Mohd Anas Khanday, Akib Mohi Ud Din Neshat, Mehdi Gandomi, Amir Indian Institute of Technology Kharagpur India Department of Computer Science and Intelligent Systems Tecnológico de Monterrey Monterrey Mexico Institute for the Future of Education TEC de Monterrey Monterrey Mexico Department of Computer Science and Software Engineering United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates Department of Computer Science Samarkand International University of Technology Samarkand Uzbekistan Faculty of Engineering and Information Technology University of Technology Sydney Ultimo SydneyNSW2007 Australia Obuda University Budapest1034 Hungary School of Computer and Mathematical Sciences University of Adelaide Adelaide5005 Australia

The rapid expansion and advancement of deepfake technology, which is specifically designed to create incredibly lifelike facial imagery and video content, has ignited a remarkable level of interest and curiosity across a multitude of fields, including but not limited to the realms of the entertainment industry, forensic analysis, cybersecurity and the innovative creation of digital characters. By harnessing the latest breakthroughs in deep learning methodologies, especially through the implementation of techniques such as Generative Adversarial Networks, Variational Autoencoders, Few-Shot Learning Strategies, and Transformer Neural Networks, the outcomes achieved in the generation of deepfakes have been nothing short of astounding and transformative. Furthermore, the recent advancements in diffusion models have served to invigorate and catalyze ongoing research efforts in this fascinating domain. At the same time, the ongoing evolution of detection technologies is being developed to counteract the potential for misuse associated with deepfakes, effectively addressing critical concerns that range from political manipulation to the dissemination of fake news and the ever-growing issue of cyberbullying. This comprehensive review paper meticulously investigates the most recent developments in both deepfake generation and detection, including around 400 publications, providing an in-depth analysis of the cutting-edge innovations that are shaping this rapidly evolving landscape. Starting with a thorough examination of systematic literature review methodologies, we embark on a journey that delves into the complex technical intricacies inherent in the various techniques used for deepfake generation, comprehensively addressing the challenges faced, potential solutions available, and the nuanced details surrounding manipulation formulations. Besides, task definitions are standardized to ensure clarity, while datasets and metrics are diligently introduced, paving the way for a

关键词： Generative adversarial networks

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Extracting and Classifying 3D Medical Image Texture Characteristics using GLCM and LBP-Based Techniques

Extracting and Classifying 3D Medical Image Texture Characte...

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Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： R. Srinath R. Sharmila N. Raghavendran M. C. Babu K. Ramanan R. Saravanakumar Department of Computer Applications Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Artificial Intelligence and Date Science RMK College of Engineering and Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Sciences Chennai Institute of Technology Chennai Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Recent advances in 3D sensor technology have advanced 3D imaging systems. This sparks interest in 3D picture feature extraction and categorization. According to the literature, textural content is essential to visuals. In this case, we extract texture characteristics from three-dimensional photos to improve feature differentiation. Category of volumetric data with texture is possible using this method. We employ feature vectors from Local Binary Patterns (LBP) and Gray-Level Co-occurrence Matrix methods to achieve this. The visual pattern and volumetric data contrast, homogeneity, and local anisotropy are described. The proposed approach was tested using a publicly accessible dataset of modified volumetric textured pictures. Generative Adversarial Networks (GAN) and recurrent Neural Networks (RNN) are classified. Our solution outperforms manually constructed techniques for extracting texture characteristics from 3D and 2D data and standard deep-learning networks. Even with few photographs per class, the recommended method improves class distinction and yields good results. Show exceptional accuracy. Integrating the Gray Level Co-occurrence Matrix (GLCM) with Local Binary Patterns (LBP) improved classification performance. The GLCM approach, which captures spatial relationships, and the LBP method, which depicts local texture, was advantageous. This hybrid method improved feature extraction and classification, notably for complex 3D medical pictures with subtle texture changes.

关键词： Deep learning Visualization Three-dimensional displays Machine learning algorithms Recurrent neural networks Liver Feature extraction Generative adversarial networks Classification algorithms Tumors

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Time efficiency and precision of splinted complete arch implant scanning

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Journal of Prosthetic Dentistry 2025年

作者： Abdelrehim, aly bin Sulaiman, Eshamsul Sofian, Hazrina Salleh, Nosizana Mohd Graduate PhD student Department of Restorative Dentistry Faculty of Dentistry Universiti Malaya Kuala Lumpur Malaysia Department of Restorative Dentistry Faculty of Dentistry Universiti Malaya Kuala Lumpur Malaysia Department of Software Engineering College of Computer and Cyber Sciences University of Prince Mugrin Madinah Saudi Arabia

Statement of problem: The precision and accuracy of intraoral scanners for complete arch implant scanning is questionable as it is affected by the absence of anatomic features, the presence of large homogenous areas, and the lack of clear reference points, all of which create inherent errors in the image stitching process by the scanner software program. Purpose: The purpose of this in vitro study was to test a newly designed scan-aid apparatus for evaluating the effect of geometric heterogeneity and additional reference points on the time efficiency and scanning precision of complete arch implant scanning. Material and methods: A total of 60 scans (20 per group) were performed using an intraoral scanner in 3 different clinical simulations: 2 parallel implants, 4 parallel implants, and 4 implants;2 anterior implants in parallel axial orientation;and 2 distal implants with a 30-degree posterior inclination. The scanning procedure was alternated between scanning with the scan-aid apparatus (test groups;4IP+, 4IA+, 2IP+) and without (control groups;4IP-, 4IA-, 2IP-). A 3-dimensional (3D) inspection software program was used to assess scan precision. Test group scans were compared with a reference scan for repeatability, with and without the scan-aid apparatus. One scan per group was the reference;the others were superimposed by using a best-fit algorithm. The 3D standard deviations were recorded for repeatability analysis. To assess time efficiency, the total scanning time, actual scanning time, and time lost were measured. A paired t test was used to compare means between the groups (α=.05). Results: Precision was significantly higher in the test groups using the scan-aid apparatus than in the control groups (P<.05). Furthermore, scanning with the scan-aid apparatus was significantly more time efficient (P<.05). Conclusions: Additional reference points and geometric heterogeneity using a scan-aid apparatus significantly improved the precision and time efficiency of co

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PCA: Semi-Supervised Segmentation with Patch Confidence Adversarial Training

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IEEE Transactions on Network Science and engineering 2025年

作者： Xu, Zhenghua Yang, Runhe Xu, Zihang Zhang, Shuo Yang, Yuchen Liu, Weipeng Xu, Weichao Chen, Junyang Lukasiewicz, Thomas Leung, Victor C. M. Hebei University of Technology State Key Laboratory of Reliability and Intelligence of Electrical Equipment School of Health Sciences and Biomedical Engineering Tianjin China The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong The Johns Hopkins University Department of Applied Mathematics and Statistic Baltimore United States Hebei University of Technology School of Artificial Intelligence Tianjin China Hebei Provincial Hospital of Traditional Chinese Medicine Digestive Disease Center Shijiazhuang China Shenzhen University College of Computer Science and Software Engineering Shenzhen China University of Oxford Department of Computer Science University of Oxford United Kingdom Department of Computer Science United Kingdom Vienna University of Technology institute of Logic and Computation nstitute of Logic and Computation Vienna Austria

Deep-learning-based semi-supervised learning (SSL) methods have achieved a strong performance in medical image segmentation, which can alleviate doctors' expensive annotation by utilizing a large amount of unlabeled data. Unlike most existing semi-supervised learning methods, adversarial training methods distinguish samples from different sources by learning the data distribution of the segmentation map, leading the segmenter to generate more accurate predictions. We argue that the current performance restrictions for such approaches are the problems of feature extraction and learning preferences. In this paper, we propose a new semi-supervised adversarial method called Patch Confidence Adversarial Training (PCA) for medical image segmentation. The PCA method's discriminator penalizes patch-level structures, guiding the generator to optimize different patch areas, by leveraging pixel context, the generator is driven to focus on high-frequency features, making it harder to deceive the discriminator and easy to converge to an ideal state, which more effectively guides the segmenter to generate high-quality pseudo-labels. Furthermore, at the discriminator's input, we supplement image information constraints, making it simpler to fit the expected data distribution. Extensive experiments on the Automated Cardiac Diagnosis Challenge (ACDC) 2017 dataset and the Brain Tumor Segmentation (BraTS) 2019 challenge dataset show that our method outperforms the state-of-the-art semi-supervised methods, which demonstrates its effectiveness for medical image segmentation. © 2025 IEEE. all rights reserved.

关键词： Adversarial machine learning

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Fiber optic SERS sensors for rapid detection and identification of Salmonella in turkey products 25

Fiber optic SERS sensors for rapid detection and identificat...

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Optical Fibers and Sensors for Medical Diagnostics, Treatment, and Environmental Applications XXV 2025

作者： Abuhelwa, Mai Chenggeer, Fnu Bandara, Adheesha Liu, Jiayu Willoughby, Paige Carlson, Anna Trout, Kate E. Morey, Amit Kinzel, Ed Mustapha, Azlin almasri, Mahmoud Department of Electrical Engineering and Computer Science University of Missouri ColumbiaMO65211 United States Food Science Program University of Missouri ColumbiaMO65211 United States Cargill Inc WichitaKS67202 United States College of Health Sciences University of Missouri ColumbiaMO65211 United States Department of Poultry Science Auburn University AuburnAL36849 United States University of Notre Dame IN46556 United States

ISBN: (纸本)9781510683686

This study validates a fiber optics-based Surface Enhanced Raman Spectroscopy (SERS) sensor for detecting Salmonella in raw turkey samples. The sensor uses nanoantenna arrays on a side-polished optical fiber core with a fixed periodicity of 0.77 μm to maximize the SERS signal intensity. A 3D-printed chamber and microstructure optimize light reflection, improving sensitivity. The sensor detects Salmonella at 0.4-0.5 CFU/ml in 10 minutes, offering cost-effective, portable pathogen detection. © 2025 SPIE.

关键词： Multimode fibers

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Profiling Casualty Severity Levels of Road Accident Using Weighted Majority Voting

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computers, Materials & Continua 2022年第6期71卷 4609-4626页

作者： Saba Awan Zahid Mehmood Hassan Nazeer Chaudhry Usman Tariq Amjad Rehman Tanzila Saba Muhammad Rashid Department of Software Engineering University of Engineering and TechnologyTaxila47050Pakistan Department of Computer Engineering University of Engineering and TechnologyTaxila47050Pakistan Department of Electronics Information and BioengineeringPolitecnico di MilanoMilano20122Italy College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia College of Computer and Information Sciences Prince Sultan UniversityRiyadhSaudi Arabia Department of Computer Engineering Umm Al-Qura UniversityMakkah21421Saudi Arabia

To determine the individual circumstances that account for a road traffic accident,it is crucial to consider the unplanned connections amongst various factors related to a crash that results in high casualty *** of the road accident data concentrated mainly on categorizing accidents into different types using individually built classification methods which limit the prediction accuracy and fitness of the *** this article,we proposed a multi-model hybrid framework of the weighted majority voting(WMV)scheme with parallel structure,which is designed by integrating individually implemented multinomial logistic regression(MLR)and multilayer perceptron(MLP)classifiers using three different accident datasets i.e.,IRTAD,NCDB,and *** proposed WMV hybrid scheme overtook individual classifiers in terms of modern evaluation measures like ROC,RMSE,Kappa rate,classification accuracy,and performs better than state-of-theart approaches for the prediction of casualty severity ***,the proposed WMV hybrid scheme adds up to accident severity analysis through knowledge representation by revealing the role of different accident-related factors which expand the risk of casualty in a road *** aspects related to casualty severity recognized by the proposed WMV hybrid approach can surely support the traffic enforcement agencies to develop better road safety plans and ultimately save lives.

关键词： Prediction hybrid framework severity class casualty

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