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PneuX-Net: An Enhanced Feature Extraction and Transformation Approach for Pneumonia Detection in X-Ray Images

IEEE Access 2025年 13卷 84024-84037页

作者： Kashif Munir Muhammad Usama Tanveer Hasan J. Alyamani Amine Bermak Atiq Ur Rehman Institute of Information Technology Khwaja Fareed University of Engineering and Information Technology Rahim Yar Khan Pakistan Department of Information Systems Faculty of Computing and Information Technology in Rabigh King Abdulaziz University Jeddah Saudi Arabia Division of Information and Computing Technology College of Science and Engineering Hamad Bin Khalifa University Doha Qatar

Pneumonia is a critical respiratory condition characterized by inflammation in the alveoli, leading to impaired gas exchange and severe respiratory distress. It poses a significant threat to high-risk populations, including neonates, geriatric patients, and immunocompromised individuals. Early and precise detection is crucial to optimizing treatment strategies and reducing morbidity and mortality rates. To address this problem, we propose PneuX-Net, an ensemble-based feature extraction framework that integrates multiple machine learning (ML) models Random Forest (RF), Gaussian Naïve Bayes (GNB) and K-Nearest Centroid (KNC). Random Forest (RF) builds multiple decision trees to identify complex, non-linear patterns within the feature space. Gaussian Naïve Bayes (GNB) utilizes a probabilistic framework based on Bayes’ theorem to manage uncertainty in feature distributions. K-Nearest Centroid (KNC) improves class distinction by clustering feature vectors according to their proximity to centroids. The ensemble methodology harnesses the complementary strengths of these models, improving feature representation and mitigating overfitting, a prevalent issue in white-box models. To validate the effectiveness of PneuX-Net, we conduct extensive experimentation using a 10-fold cross-validation approach. Our results demonstrate that the PneuX-Net+KNC model achieves a remarkable 99.91% accuracy, highlighting its robustness and reliability in pneumonia classification tasks. This ensemble-driven methodology underscores the potential of machine learning in augmenting clinical decision-making by providing an accurate and automated pneumonia detection framework.

关键词： Pneumonia Accuracy Feature extraction X-ray imaging Deep learning Solid modeling Computational modeling Machine learning Lungs Bayes methods

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Federated learning with Blockchain on Denial-of-Service attacks detection and classification of edge IIoT networks using Deep Transfer Learning model

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Computers and Electrical Engineering 2025年 124卷

作者： Abdullah, Monir Mengash, Hanan Abdullah Maray, Mohammed Alrslani, Faheed A.F. Alkhudhayr, Hanadi Alghanmi, Nouf Atiahallah Subahi, Alanoud Majdoubi, Jihen Department of Computer Science and Artificial Intelligence College of Computing and Information Technology University of Bisha Bisha67714 Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Information Systems College of Computer Science King Khalid University Abha Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Rabigh25732 Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Rabigh25732 Saudi Arabia Engineering and Technical Programs Unit Applied College Majmaah University Al-Majmaah11952 Saudi Arabia

With the rapid evolution of Internet of Things (IoT) and artificial intelligence (AI), the industry 4.0 era has arisen. As per the IBM prediction, by the constant spread of 5G technology, the IoT intend to be more extensively utilized in industries. Recently, federated learning (FL) turned out to be significant focus among Industrial IoT (IIoT) scholars. Conversely, numerous devices in IIoT presently hold an issue of low computational power, so these devices can't able to function well while challenging the updating and training method tasks in FL. The latest development of machine learning (ML) and deep learning (DL) approaches will help to reinforce these IDSs. The secrecy behaviour of these databases and extensive advent of combative outbreaks makes it challenging for main institutions to transmit their fragile data. Therefore, the current study designs a Federated Learning on Denial-of-Service Attacks Detection and Classification using Deep Transfer Learning (FLDoSADC-DTL) model for BC-supported IIoT environment. The aim of the presented FLDoSADC-DTL approach is to recognize the presence of DoS attacks in the BC-based IIoT environment. To enable secure communication in the IIoT networks, BC technology is used. To accomplish this, the FLDoSADC-DTL technique performs sand cat swarm algorithm (SCSA) based feature subset selection. For the DoS attack detection process, a stacked auto-encoder (SAE) model is utilized in this study. Finally, the black widow optimization algorithm (BWOA) can be implemented for hyperparameter tuning of the SAE technique. A widespread of experiments were performed to emphasize the higher performance of FLDoSADC-DTL method. The complete experimentation outcomes indicated the enhanced performance of FLDoSADC-DTL approach over other recent methods. © 2025 Elsevier Ltd

关键词： Federated learning

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Blockchain based solid waste classification with AI powered tracking and IoT integration

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Scientific Reports 2025年第1期15卷 1-15页

作者： Alabdali, Aliaa M. Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University P. O. Box 344 Rabigh 21911 Saudi Arabia

Smart waste management is vital for reducing environmental impact and improving quality of life in smart cities. This study presents an AI-driven waste classification model that integrates IoT and Blockchain technologies. IoT-connected bins transmit data to a central server, which uses blockchain to ensure secure, transparent data storage. AI algorithms, including machine learning (ML) and deep learning (DL), classify waste in real-time, optimizing waste collection and recycling. Blockchain ensures data integrity, while ML and DL models enhance sorting efficiency. The system aims to improve waste management and sustainability through intelligent decision-making and secure data handling. Performance will be assessed using retrieval metrics and visualization tools to evaluate the impact of hybrid ML and DL models on waste detection and classification. © The Author(s) 2025.

关键词： Artificial intelligent Internet of things Smart applications Smart waste management

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A Content-Based Medical Image Retrieval Method Using Relative Difference-Based Similarity Measure

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Intelligent Automation & Soft computing 2023年第8期37卷 2355-2370页

作者： Ali Ahmed Alaa Omran Almagrabi Omar MBarukab Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz University–RabighRabigh21589Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz University-RabighRabigh21589Saudi Arabia

Content-based medical image retrieval(CBMIR)is a technique for retrieving medical images based on automatically derived image *** are many applications of CBMIR,such as teaching,research,diagnosis and electronic patient *** methods are applied to enhance the retrieval performance of CBMIR *** new and effective similarity measure and features fusion methods are two of the most powerful and effective strategies for improving these *** study proposes the relative difference-based similarity measure(RDBSM)for *** new measure was first used in the similarity calculation stage for the CBMIR using an unweighted fusion method of traditional color and texture ***,the study also proposes a weighted fusion method for medical image features extracted using pre-trained convolutional neural networks(CNNs)*** proposed RDBSM has outperformed the standard well-known similarity and distance measures using two popular medical image datasets,Kvasir and PH2,in terms of recall and precision retrieval *** effectiveness and quality of our proposed similarity measure are also proved using a significant test and statistical confidence bound.

关键词： Medical image retrieval feature extraction similarity measure fusion method

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A Hybrid Meta-Classifier of Fuzzy Clustering and Logistic Regression for Diabetes Prediction

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Computers, Materials & Continua 2022年第6期71卷 6089-6105页

作者： Altyeb Altaher Taha Sharaf Jameel Malebary Department of Information Technology Faculty of Computing and Information Technology in RabighKing Abdulaziz UniversityJeddahSaudi Arabia

Diabetes is a chronic health condition that impairs the body’s ability to convert food to energy,recognized by persistently high levels of blood *** diabetes can cause many complications,including retinopathy,nephropathy,neuropathy,and other vascular *** learning methods can be very useful for disease identification,prediction,and *** paper proposes a new ensemble learning approach for type 2 diabetes prediction based on a hybrid meta-classifier of fuzzy clustering and logistic *** proposed approach consists of two ***,a baselearner comprising six machine learning algorithms is utilized for predicting ***,a hybrid meta-learner that combines fuzzy clustering and logistic regression is employed to appropriately integrate predictions from the base-learners and provide an accurate prediction of *** hybrid metalearner employs the Fuzzy C-means Clustering(FCM)algorithm to generate highly significant clusters of predictions from *** predictions of base-learners and their fuzzy clusters are then employed as inputs to the Logistic Regression(LR)algorithm,which generates the final diabetes prediction *** were conducted using two publicly available datasets,the Pima Indians Diabetes Database(PIDD)and the Schorling Diabetes Dataset(SDD)to demonstrate the efficacy of the proposed method for predicting *** compared with other models,the proposed approach outperformed them and obtained the highest prediction accuracies of 99.00%and 95.20%using the PIDD and SDD datasets,respectively.

关键词： Ensemble learning fuzzy clustering diabetes prediction machine learning

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Coronavirus Detection Using Two Step-AS Clustering and Ensemble Neural Network Model

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Computers, Materials & Continua 2022年第6期71卷 6307-6331页

作者： Ahmed Hamza Osman Department of Information System Faculty of Computing and Information Technology King Abdulaziz University RabighSaudi Arabia

This study presents a model of computer-aided intelligence capable of automatically detecting positive COVID-19 instances for use in regular medical *** proposed model is based on an Ensemble boosting Neural Network architecture and can automatically detect discriminatory features on chestX-ray images through Two Step-As clustering algorithm with rich filter families,abstraction and weight-sharing *** contrast to the generally used transformational learning approach,the proposed model was trained before and after *** compilation procedure divides the datasets samples and categories into numerous sub-samples and subcategories and then assigns new group labels to each new group,with each subject group displayed as a distinct *** retrieved characteristics discriminant cases were used to feed the Multiple Neural Network method,which was then utilised to classify the *** Two Step-AS clustering method has been modified by pre-aggregating the dataset before applying Multiple Neural Network algorithm to detect COVID-19 cases from chest X-ray *** forMultiple Neural Network and Two Step-As clustering algorithms were optimised by utilising Ensemble Bootstrap Aggregating algorithm to reduce the number of hyper parameters they *** testswere carried out using theCOVID-19 public radiology database,and a cross-validationmethod ensured *** proposed classifier with an accuracy of 98.02%percent was found to provide the most efficient outcomes *** result is a lowcost,quick and reliable intelligence tool for detecting COVID-19 infection.

关键词： Two step-AS clustering ensemble learning bootstrap aggregating multiple neural network covid-19 X-ray images

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Pervasive Attentive Neural Network for Intelligent Image Classification Based on N-CDE’s

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Computers, Materials & Continua 2024年第4期79卷 1137-1156页

作者： Anas W.Abulfaraj Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityRabigh21911Saudi Arabia

The utilization of visual attention enhances the performance of image classification *** attentionbased models have demonstrated notable performance,but many of these models exhibit reduced accuracy when confronted with inter-class and intra-class similarities and ***-Controlled Differential Equations(N-CDE’s)and Neural Ordinary Differential Equations(NODE’s)are extensively utilized within this ***’s possesses the capacity to effectively illustrate both inter-class and intra-class similarities and differences with enhanced *** this end,an attentive neural network has been proposed to generate attention maps,which uses two different types of N-CDE’s,one for adopting hidden layers and the other to generate attention *** distinct attention techniques are implemented including time-wise attention,also referred to as bottom N-CDE’s;and element-wise attention,called topN-CDE’***,a trainingmethodology is proposed to guarantee that the training problem is sufficiently *** classification tasks including fine-grained visual classification andmulti-label classification,are utilized to evaluate the *** proposedmethodology is employed on five publicly available datasets,including CUB-200-2011,ImageNet-1K,PASCAL VOC 2007,PASCAL VOC 2012,and MS *** obtained visualizations have demonstrated that N-CDE’s are better appropriate for attention-based activities in comparison to conventional NODE’s.

关键词： Differential equations neural-controlled DE image classification attention maps N-CDE’s

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An Energy-Efficient Wireless Power Transmission-Based Forest Fire Detection System

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Computers, Materials & Continua 2022年第7期72卷 441-459页

作者： Arwa A.Mashat Niayesh Gharaei Aliaa M.Alabdali Faculty of Computing&Information Technology King Abdulaziz UniversityRabigh21911Saudi Arabia

Compared with the traditional techniques of forest fires detection,wireless sensor network(WSN)is a very promising green technology in detecting efficiently the ***,the power constraint of sensor nodes is one of the main design limitations of WSNs,which leads to limited operation time of nodes and late fire *** the past years,wireless power transfer(WPT)technology has been known as a proper solution to prolong the operation time of sensor *** WPT-based mechanisms,wireless mobile chargers(WMC)are utilized to recharge the batteries of sensor nodes ***,the energy of WMC is provided using energy-harvesting or energy-scavenging techniques with employing huge,and expensive ***,the high price of energy-harvesting devices hinders the use of this technology in large and dense networks,as such networks require multiple WMCs to improve the quality of service to the sensor *** solve this problem,multiple power banks can be employed instead of utilizing ***,the long waiting time of critical sensor nodes located outside the charging range of the energy transmitters is another limitation of the previous ***,the sensor nodes are equipped with radio frequency(RF)technology,which allows them to exchange energy ***,critical sensor nodes located outside the charging range of the WMC can easily receive energy from neighboring ***,in this paper,an energy-efficient and cost-effective wireless power transmission(ECWPT)scheme is presented to improve the network lifetime and performance in forest fire detection-based *** results exhibit that ECWPT scheme achieves improved network performance in terms of computational time(12.6%);network throughput(60.7%);data delivery ratio(20.9%);and network overhead(35%)as compared to previous related *** conclusion,the proposed scheme significantly improves network energy efficiency for WSN.

关键词： Forest fire detection rechargeable wireless sensor networks wireless mobile charger power constraint sustainable network lifetime

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Network Intrusion Detection Model Using Fused Machine Learning Technique

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Computers, Materials & Continua 2023年第5期75卷 2479-2490页

作者： Fahad Mazaed Alotaibi Faculty of Computing and Information Technology in Rabigh(FCITR) King Abdulaziz UniversityJeddahSaudi Arabia

With the progress of advanced technology in the industrial revolution encompassing the Internet of Things(IoT)and cloud computing,cyberattacks have been increasing rapidly on a large *** rapid expansion of IoT and networks in many forms generates massive volumes of data,which are vulnerable to security *** a result,cyberattacks have become a prevalent and danger to society,including its infrastructures,economy,and citizens’privacy,and pose a national security risk ***,cyber security has become an increasingly important issue across all levels and *** progress is being made in developing more sophisticated and efficient intrusion detection and defensive *** the scale of complexity of the cyber-universe is increasing,advanced machine learning methods are the most appropriate solutions for predicting cyber *** this study,a fused machine learning-based intelligent model is proposed to detect intrusion in the early stage and thus secure networks from harmful *** results confirm the effectiveness of the proposed intrusion detection model,with 0.909 accuracy and a miss rate of 0.091.

关键词： Cyberattack machine learning prediction solution intrusion detection

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Enhancing Ulcerative Colitis Diagnosis:A Multi-Level Classification Approach with Deep Learning

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Computer Modeling in Engineering & Sciences 2024年第7期140卷 1129-1142页

作者： Hasan J.Alyamani Department of Information Systems Faculty of Computing and Information Technology in RabighKing Abdulaziz UniversityJeddah21589Saudi Arabia

The evaluation of disease severity through endoscopy is pivotal in managing patients with ulcerative colitis,a condition with significant clinical ***,endoscopic assessment is susceptible to inherent variations,both within and between observers,compromising the reliability of individual *** study addresses this challenge by harnessing deep learning to develop a robust model capable of discerning discrete levels of endoscopic disease *** initiate this endeavor,a multi-faceted approach is embarked *** dataset is meticulously preprocessed,enhancing the quality and discriminative features of the images through contrast limited adaptive histogram equalization(CLAHE).A diverse array of data augmentation techniques,encompassing various geometric transformations,is leveraged to fortify the dataset’s diversity and facilitate effective feature extraction.A fundamental aspect of the approach involves the strategic incorporation of transfer learning principles,harnessing a modified ResNet-50 *** augmentation,informed by domain expertise,contributed significantly to enhancing the model’s classification *** outcome of this research endeavor yielded a highly promising model,demonstrating an accuracy rate of 86.85%,coupled with a recall rate of 82.11%and a precision rate of 89.23%.

关键词： Ulcerative colitis deep learning CLAHE transfer learning geometric transformations

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