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

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 Federated Feature Selection through Synthetic Data and Zero Trust Integration

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 2126-2140页

作者： Madathil, Nisha Thorakkattu Alrabaee, Saed Belkacem, Abdelkader Nasreddine United Arab Emirates University College of IT Department of Information Systems and Security United Arab Emirates United Arab Emirates University College of IT Information Systems and Security department United Arab Emirates UAE University Computer and Network Engineering Department Al Ain United Arab Emirates

Federated Learning (FL) allows healthcare organizations to train models using diverse datasets while maintaining patient confidentiality collaboratively. While promising, FL faces challenges in optimizing model accuracy and communication efficiency. To address these, we propose an algorithm that combines feature selection with synthetic data generation, specifically targeting medical datasets. Our method eliminates irrelevant local features, identifies globally relevant ones, and uses synthetic data to initialize model parameters, improving convergence. It also employs a zero-trust model, ensuring that data remain on local devices and only learned weights are shared with the central server, enhancing security. The algorithm improves accuracy and computational efficiency, achieving communication efficiency gains of 4 to 14 through backward elimination and threshold variation techniques. Tested on a federated diabetic dataset, the approach demonstrates significant improvements in the performance and trustworthiness of FL systems for medical applications. © 1983-2012 IEEE.

关键词： Federated learning

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Machine learning's model-agnostic interpretability on the prediction of students' academic performance in video-conference-assisted online learning during the covid-19 pandemic

Machine learning's model-agnostic interpretability on the pr...

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作者： Miranda, Eka Aryuni, Mediana Rahmawati, Mia Ika Hiererra, Siti Elda Dian Sano, Albert Verasius Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Surabaya Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Background: COVID-19 prompted a global shift to online learning, including video conference-assisted online learning (VCAOL), which necessitated educators understanding students' perspectives. Objective: This study aims to develop machine learning (ML) model-agnostic interpretability that could predict students' academic performance in VCAOL. Material and methods: Synthetic Minority Over-sampling Technique (SMOTE) and data augmentation were used to handle imbalanced data from small-scale datasets. The prediction model was developed using Random Forest (RF), Support Vector Machine (SVM), and Gaussian Naive Bayes (GNB). SHAP model-agnostic interpretability was used to interpret and comprehend prediction findings. The data was gathered from September 2022 to January 2023, resulting in 361 records. The research variables included students' academic performance as the dependent variable, and the video conference application (VC), learning material (LM), internet connection (IC), students' ability to learn (SL), and student knowledge (SK) as independent variables, which were mapped into 28 attributes. Result: The SMOTE improved the performance of three algorithms, with RF outperforming SVM and GNB in almost all tests, achieving an accuracy of 79.45%, precision of 75.71%, and recall of 79.45%. SHAP bar plots ranked attributes by importance demonstrated that "Performance," "Frequency Constraint," and "Increase Value" had a significant impact on prediction results. When we mapped the three attributes to our study perspective, we determined that SK and SL were the most important views for students to perform well in VCAOL. SHAP's beeswarm revealed students' performance in VCAOL was positively correlated with "Performance", "Increase Value", "Completing Project", "Adequate Method", "User Interface", and "Feature". As we mapped the three attributes to our study perspective, we found that SK, LM, SL, and VC were positively related to students' performance in VCAOL. Conclusion: T

关键词： Adversarial machine learning

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Technology Acceptance Model and Learning Management systems: Systematic Literature Review

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2022年第23期16卷 109-124页

作者： Cavus, Nadire Omonayajo, Babatomiwa Mutizwa, Melissa Rutendo Department of Computer Information Systems Near East University Nicosia Cyprus Computer Information Systems Research and Technology Centre Near East University Nicosia Cyprus

As the Internet has evolved rapidly, Learning Management systems in recent years, particularly during the pandemic era, have become increasingly popular and can effectively override time and gives people new insights into the education field. A substantial amount of research was performed on a Technology Acceptance Model (TAM) framework and popularity in Learning Management systems, in general, was indicated. However, there are gaps in established awareness of representative academic literature that form the basis of research in LMS and TAM. The summary of the current research effort on TAM implementation in the area of LMS is the main objective of this systematic literature review. This systematic literature review found 21 related studies between 2010 and 2020 based on the aim of this research through the systematic search of the most popular scientific databases. We hope that the findings of the review will inspire institutional administrators and users to recognize the factors that influence the quality and effectiveness of the use of LMS by TAM. © 2022,International Journal of Interactive Mobile Technologies. All Rights Reserved.

关键词： Learning systems

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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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Optimizing Pneumonia Classification on Pediatric Chest X-Ray Images Using ConvNeXt Network 9

Optimizing Pneumonia Classification on Pediatric Chest X-Ray...

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9th International Conference on information Management and Technology, ICIMTech 2024

作者： Prasetyo, Simeon Yuda Prabowo, Abram Setyo Santy Pepita, Patricia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

Pneumonia is a major worldwide health concern, particularly among children, and early and correct diagnosis is critical for successful treatment. This study addresses the challenges in classifying pediatric pneumonia on chest X-ray (CXR) images, where issues such as limited image data and non-standardized interpretation persist. Utilizing machine learning and deep learning techniques, notably Convolutional Neural Networks (CNNs), provides potential answers. The research explores the optimization of pneumonia classification in pediatric CXR images using the ConvNeXt network, an advanced CNN architecture. Through experiments utilizing five pretrained ConvNeXt models, the study evaluates transfer learning and fine-tuning methodologies. Results demonstrate the superior performance of ConvNeXt Large, achieving 95.97% accuracy with transfer learning, underscoring the robustness of deeper ConvNeXt architectures. Insights demonstrate that transfer learning is useful in improving classification accuracy. Additionally, the study emphasizes the practical significance of these findings in improving diagnostic accuracy and streamlining pediatric pneumonia detection in clinical settings. The inclusion of a confusion matrix provides a comprehensive understanding of model performance. Overall, ConvNeXt models present promising prospects for pediatric pneumonia detection, heralding a significant advancement in medical imaging and disease diagnosis. © 2024 IEEE.

关键词： Pediatrics

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An ellipse-guided routing algorithm in wireless sensor networks

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Digital Communications and Networks 2022年第5期8卷 770-777页

作者： Jianjun Yang Department of Computer Science and Information Systems University of North GeorgiaOakwoodGA30566USA

In wireless sensor networks,sensor nodes are deployed to collect data,perform calculations,and forward information to either other nodes or sink ***,geographic routing has become extremely popular because it only requires the locations of sensor nodes and is very ***,the local minimum phenomenon,which hinders greedy forwarding,is a major problem in geographic *** phenomenon is attributed to an area called a hole that lacks active sensors,which either prevents the packet from being forwarded to a destination node or produces a long detour *** order to solve the hole problem,mechanisms to detect holes and determine landmark nodes have been *** on the proposed mechanisms,landmark-based routing was developed in which the source node first sends a packet to the landmark node,and the landmark node then sends the packet to the ***,this approach often creates a constant node sequence,causing nodes that perform routing tasks to quickly run out of energy,thus producing larger *** this paper,a new approach is proposed in which two virtual ellipses are created with the source,landmark,and destination *** guide the forwarding along the virtual ***,a recursive algorithm is designed to ensure a shortcut even if there are multiple holes or a hole has multiple ***,the proposed approach improves both geographic routing and energy efficiency *** experiments show that the proposed approach increases the battery life of sensor nodes,lowers the end-to-end delay,and generates a short path.

关键词： Sensor networks Hole Landmark Ellipse Geographic routing Energy efficiency

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Diabetes Risk Prediction Exploration: Uncovering Patterns and Enhancing Predictive Accuracy through Ensemble Learning 4

Diabetes Risk Prediction Exploration: Uncovering Patterns an...

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4th International Conference of Science and information Technology in Smart Administration, ICSINTESA 2024

作者： Prasetyo, Simeon Yuda Santy Yunanda, Rezki Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Information Systems Department School of Information Systems Jakarta11480 Indonesia

ISBN: (纸本)9798350376111

Diabetes, recognized as a chronic noncommunicable condition, has become increasingly prevalent and now affects an estimated 425 million individuals worldwide as of 2020. This escalating global prevalence underscores the urgent need for comprehensive research initiatives to address this significant health challenge. Undiagnosed diabetes poses grave risks, including serious complications such as cardiac stroke and diabetic nephropathy, emphasizing the critical importance of early detection and intervention. Leveraging ensemble learning methods like Gradient Boosting, Random Forest, XGBoost, and AdaBoost, this study aims to construct a robust machine learning framework for precise diabetes classification, utilizing pertinent health indicators from the BRFSS dataset. Through rigorous evaluation, Gradient Boosting emerged as the most effective model, achieving an accuracy of 84.85%. This finding highlights the potential of ensemble learning techniques in refining predictive accuracy for diabetes risk assessment and ultimately improving patient outcomes, contributing significantly to the advancement of diagnostic tools in combating this global health challenge. The results underscore the importance of continued research in leveraging machine learning for enhancing diabetes management and public health strategies. © 2024 IEEE.

关键词： BRFSS dataset Diabetes Ensemble learning Gradient boosting Machine learning

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Leveraging Geographic information systems and Remote Sensing for Enhanced Water Resource Management in Kazakhstan 4

Leveraging Geographic Information Systems and Remote Sensing...

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4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Hamada, Mohamed Ahmed Tolkyn, Orden Hamada, Gehad M. International It University Information Systems Department Almaty Kazakhstan International It University Computer Systems and Software Engineering Department Almaty Kazakhstan

ISBN: (纸本)9798350387490

Kazakhstan's deputy member is vast, and is one of sparse population, scattering and creating an overwhelming challenge to map and assess the water resources. Most of the waters are in remote and poorly equipped areas, making their monitoring by conventional means arduous. This includes costly and inefficient monitoring that results in a blurred condition of the water resources. Such a predicament stands in the way of effective management and decision-making regarding water resources. Therefore, we used Sentinel-3 satellite images to assess the spatiotemporal dynamics of water resources in Kazakhstan and obtained the results through GIS analysis. The main case of the region for case studies reflecting problems in the management of water resources in the country is the northern part of the Aral Sea. It presents a study characterized by in-depth data analysis and mapping that provided insight into the spatial trends of water resources, their variability, and potential areas of concern. The present research found that GIS and remote sensing technologies have emerged as a strong water resource observation platform. They show promise in the resource sector decision support. The development thus justifies the integration of these technology tools within the water resource management practices to ensure sustainable water use in Kazakhstan. The publication has equally availed a model for similarly water-stressed regions regarding technology-driven solutions in matters of environmental sustainability and resource management. © 2024 IEEE.

关键词： information management

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Robust Human Interaction Recognition Using Extended Kalman Filter

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computers, Materials & Continua 2024年第11期81卷 2987-3002页

作者： Tanvir Fatima Naik Bukht Abdulwahab Alazeb Naif Al Mudawi Bayan Alabdullah Khaled Alnowaiser Ahmad Jalal Hui Liu Faculty of Computing&Artificial Intelligence Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Engineering College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Cognitive Systems Lab University of BremenBremen28359Germany

In the field of computer vision and pattern recognition,knowledge based on images of human activity has gained popularity as a research *** recognition is the process of determining human behavior based on an *** implemented an Extended Kalman filter to create an activity recognition system *** proposed method applies an HSI color transformation in its initial stages to improve the clarity of the frame of the *** minimize noise,we use Gaussian *** of silhouette using the statistical *** use Binary Robust Invariant Scalable Keypoints(BRISK)and SIFT for feature *** next step is to perform feature discrimination using Gray *** that,the features are input into the Extended Kalman filter and classified into relevant human activities according to their definitive *** experimental procedure uses the SUB-Interaction and HMDB51 datasets to a 0.88%and 0.86%recognition rate.

关键词： Pattern recognition geometric features activity recognition full-body texture

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