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Transfer Learning Empowered Skin Diseases Detection in Children

computer Modeling in Engineering & sciences 2024年第12期141卷 2609-2623页

作者： Meena N.Alnuaimi Nourah S.Alqahtani Mohammed Gollapalli Atta Rahman Alaa Alahmadi Aghiad Bakry Mustafa Youldash Dania Alkhulaifi Rashad Ahmed Hesham Al-Musallam Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Computer Information Systems College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Computer Engineering College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia ICS Department King Fahd University of Petroleum and MineralsDhahran31261Saudi Arabia

Human beings are often affected by a wide range of skin diseases,which can be attributed to genetic factors and environmental influences,such as exposure to sunshine with ultraviolet(UV)*** left untreated,these diseases can have severe consequences and spread,especially among *** detection is crucial to prevent their spread and improve a patient’s chances of ***,the branch of medicine dealing with skin diseases,faces challenges in accurately diagnosing these conditions due to the difficulty in identifying and distinguishing between different diseases based on their appearance,type of skin,and *** study presents a method for detecting skin diseases using Deep Learning(DL),focusing on the most common diseases affecting children in Saudi Arabia due to the high UV value in most of the year,especially in the *** method utilizes various Convolutional Neural Network(CNN)architectures to classify skin conditions such as eczema,psoriasis,and *** proposed method demonstrates high accuracy rates of 99.99%and 97%using famous and effective transfer learning models MobileNet and DenseNet121,*** illustrates the potential of DL in automating the detection of skin diseases and offers a promising approach for early diagnosis and treatment.

关键词： Deep learning MobileNet DenseNet121 skin diseases detection transfer learning

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Automated multi-class skin cancer classification using white shark optimizer with ensemble learning classifier on dermoscopy images

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Multimedia Tools and Applications 2025年第8期84卷 4857-4879页

作者： Arumugam, R. Vijay Saravanan, S. Department of Computer Science Government Arts and Science College Tamilnadu Manalmedu India Department of Computer and Information Science Annamalai University Annamalai Nagar Tamilnadu Chidambaram India

The most prevalent cancer around the world is Skin cancer (SC). Clinical assessment of skin lesions is essential to evaluate the features of the disease;but it is limited by the variety of interpretations and long timelines. A precise and quick diagnosis may assist in increasing the survival rate of the patient. It necessitates the improvement of a computer-aided diagnosis (CAD) system. As accurate and early diagnoses of SC are vital to increasing the survival rate of the patient, deep-learning (DL) and machine learning (ML) methods were introduced to support dermatologists and resolve the abovementioned problems. This manuscript introduces an Automated Multi-class Skin Cancer Classification using White Shark Optimizer with Ensemble Learning (AMCSCC-WHOEL) Classifier model on Dermoscopy Images. The presented AMCSCC-WHOEL technique exploits majority voting ensemble DL classifier models with a hyperparameter tuning strategy for the classification of SC. To accomplish this, the AMCSCC-WHOEL technique comprises image preprocessing in two stages: contrast enhancement and high-frequency filtering (HFF) based noise removal. Besides, the ShuffleNet-v2 model is used for the generation of feature vectors. For SC classification, a majority voting ensemble classifier comprising three DL models is used, namely sparse autoencoder (SAE), deep convolutional autoencoder (DCAE), and attention-based bidirectional gated recurrent unit. WHO method is applied for optimal hyperparameter tuning process to enhance the detection performance of the ensemble models. The experimental analysis of the AMCSCC-WHOEL algorithm is tested on a benchmark dataset. Extensive result analysis pointed out the improved detection results of the AMCSCC-WHOEL method over other recent approaches. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Learning systems

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Modern Machine Learning Solution for Electricity Consumption Management in Smart Buildings

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IEEE Engineering Management Review 2025年第1期53卷 54-62页

作者： Gautam, Sandeep Kumar Shrivastava, Vinayak Udmale, Sandeep S. Singh, Amit Kumar Singh, Sanjay Kumar Department of Computer Science and Engineering Varanasi221005 India Department of Computer Engineering and Information Technology Mumbai400019 India National Institute of Technology Department of Computer Science and Engineering Patna800005 India

Effective management of electricity consumption (EC) in smart buildings (SBs) is crucial for optimizing operational efficiency, cost savings, and ensuring sustainable resource utilization. Accurate EC prediction enables proactive decision-making, ensuring that resources are allocated efficiently to meet actual demand levels while maintaining occupant comfort. Population growth, building expansion, and technology usage swiftly escalate electricity demand, thus necessitating economical EC management strategies and assist consumers to better understand and strategically plan their EC. To address these challenges, this article proposes a novel approach based on a hybrid prediction model combining temporal convolutional networks (TCNs) and gated recurrent units (GRU). This approach capitalizes on the strengths of both TCN and GRU. TCN is adept at efficiently identifying diverse patterns, particularly within the complex working environments of SBs, by effectively capturing high- and low-frequency information. Subsequently, GRU is leveraged to address the long-term dependencies within the data, enhancing the accuracy of EC prediction. In this article, the results demonstrate the effectiveness of the proposed hybrid model, outperforming competitive methods with an impressive mean absolute error score. This underscores the potential of this approach to improve energy management practices significantly within SB environments, ultimately enhancing both operational efficiency and occupant satisfaction. © 1973-2011 IEEE.

关键词： Forecasting

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Concept Drift Analysis and Malware Attack Detection System Using Secure Adaptive Windowing

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computers, Materials & Continua 2023年第5期75卷 3743-3759页

作者： Emad Alsuwat Suhare Solaiman Hatim Alsuwat Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif26571Saudi Arabia Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah24382Saudi Arabia

Concept drift is a main security issue that has to be resolved since it presents a significant barrier to the deployment of machine learning(ML)*** to attackers’(and/or benign equivalents’)dynamic behavior changes,testing data distribution frequently diverges from original training data over time,resulting in substantial model *** to their dispersed and dynamic nature,distributed denial-of-service attacks pose a danger to cybersecurity,resulting in attacks with serious consequences for users and *** paper proposes a novel design for concept drift analysis and detection of malware attacks like Distributed Denial of Service(DDOS)in the *** goal of this architecture combination is to accurately represent data and create an effective cyber security prediction *** intrusion detection system and concept drift of the network has been analyzed using secure adaptive windowing with website data authentication protocol(SAW_WDA).The network has been analyzed by authentication protocol to avoid malware *** data of network users will be collected and classified using multilayer perceptron gradient decision tree(MLPGDT)*** on the classification output,the decision for the detection of attackers and authorized users will be *** experimental results show output based on intrusion detection and concept drift analysis systems in terms of throughput,end-end delay,network security,network concept drift,and results based on classification with regard to accuracy,memory,and precision and F-1 score.

关键词： Concept drift machine learning DDOS cyber security SAW_WDA MLPGDT

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MLRT-UNet:An Efficient Multi-Level Relation Transformer Based U-Net for Thyroid Nodule Segmentation

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computer Modeling in Engineering & sciences 2025年第4期143卷 413-448页

作者： Kaku Haribabu Prasath R Praveen Joe IR Department of Computer Science and Engineering RMK College of Engineering and TechnologyTiruvallur601206India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India

Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and ***,achieving precise segmentation remains a challenge due to various factors,including scattering noise,low contrast,and limited resolution in ultrasound *** existing segmentation models have made progress,they still suffer from several limitations,such as high error rates,low generalizability,overfitting,limited feature learning capability,*** address these challenges,this paper proposes a Multi-level Relation Transformer-based U-Net(MLRT-UNet)to improve thyroid nodule *** MLRTUNet leverages a novel Relation Transformer,which processes images at multiple scales,overcoming the limitations of traditional encoding *** transformer integrates both local and global features effectively through selfattention and cross-attention units,capturing intricate relationships within the *** approach also introduces a Co-operative Transformer Fusion(CTF)module to combine multi-scale features from different encoding layers,enhancing the model’s ability to capture complex patterns in the ***,the Relation Transformer block enhances long-distance dependencies during the decoding process,improving segmentation *** results showthat the MLRT-UNet achieves high segmentation accuracy,reaching 98.2% on the Digital Database Thyroid Image(DDT)dataset,97.8% on the Thyroid Nodule 3493(TG3K)dataset,and 98.2% on the Thyroid Nodule3K(TN3K)*** findings demonstrate that the proposed method significantly enhances the accuracy of thyroid nodule segmentation,addressing the limitations of existing models.

关键词： Thyroid nodules endocrine system multi-level relation transformer U-Net self-attention external attention co-operative transformer fusion thyroid nodules segmentation

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Utilizing Machine Learning with Unique Pentaplet Data Structure to Enhance Data Integrity

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computers, Materials & Continua 2023年第12期77卷 2995-3014页

作者： Abdulwahab Alazeb Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia

Data protection in databases is critical for any organization,as unauthorized access or manipulation can have severe negative *** detection systems are essential for keeping databases *** in technology will lead to significant changes in the medical field,improving healthcare services through real-time information ***,reliability and consistency still need to be *** against cyber-attacks are necessary due to the risk of unauthorized access to sensitive information and potential data ***-ruptions to data items can propagate throughout the database,making it crucial to reverse fraudulent transactions without delay,especially in the healthcare industry,where real-time data access is *** research presents a role-based access control architecture for an anomaly detection ***,the Structured Query Language(SQL)queries are stored in a new data structure called *** pentaplets allow us to maintain the correlation between SQL statements within the same transaction by employing the transaction-log entry information,thereby increasing detection accuracy,particularly for individuals within the company exhibiting unusual *** identify anomalous queries,this system employs a supervised machine learning technique called Support Vector Machine(SVM).According to experimental findings,the proposed model performed well in terms of detection accuracy,achieving 99.92%through SVM with One Hot Encoding and Principal Component Analysis(PCA).

关键词： Database intrusion detection system data integrity machine learning pentaplet data structure

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Salp Swarm Algorithm with Multilevel Thresholding Based Brain Tumor Segmentation Model

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computers, Materials & Continua 2023年第3期74卷 6775-6788页

作者： Hanan T.Halawani Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajran55461Saudi Arabia

Biomedical image processing acts as an essential part of severalmedical applications in supporting computer aided disease diagnosis. MagneticResonance Image (MRI) is a commonly utilized imaging tool used tosave glioma for clinical examination. Biomedical image segmentation plays avital role in healthcare decision making process which also helps to identifythe affected regions in the MRI. Though numerous segmentation models areavailable in the literature, it is still needed to develop effective segmentationmodels for BT. This study develops a salp swarm algorithm with multi-levelthresholding based brain tumor segmentation (SSAMLT-BTS) model. Thepresented SSAMLT-BTS model initially employs bilateral filtering based onnoise removal and skull stripping as a pre-processing phase. In addition,Otsu thresholding approach is applied to segment the biomedical imagesand the optimum threshold values are chosen by the use of SSA. Finally,active contour (AC) technique is used to identify the suspicious regions in themedical image. A comprehensive experimental analysis of the SSAMLT-BTSmodel is performed using benchmark dataset and the outcomes are inspectedin many aspects. The simulation outcomes reported the improved outcomesof the SSAMLT-BTS model over recent approaches with maximum accuracyof 95.95%.

关键词： Brain tumor segmentation noise removal multilevel thresholding healthcare pre-processing

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Fine-Tuning Cyber Security Defenses: Evaluating Supervised Machine Learning Classifiers for Windows Malware Detection

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computers, Materials & Continua 2024年第8期80卷 2917-2939页

作者： Islam Zada Mohammed Naif Alatawi Syed Muhammad Saqlain Abdullah Alshahrani Adel Alshamran Kanwal Imran Hessa Alfraihi Department of Software Engineering International Islamic UniversityIslamabad25000Pakistan Information Technology Department Faculty of Computers and Information TechnologyUniversity of TabukTabuk71491Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Computer Science University of PeshawarPeshawar25121Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention *** machine learning classifiers have emerged as promising tools for malware ***,there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware *** this gap can provide valuable insights for enhancing cybersecurity *** numerous studies have explored malware detection using machine learning techniques,there is a lack of systematic comparison of supervised classifiers for Windows malware *** the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security *** study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows *** objectives include Investigating the performance of various classifiers,such as Gaussian Naïve Bayes,K Nearest Neighbors(KNN),Stochastic Gradient Descent Classifier(SGDC),and Decision Tree,in detecting Windows *** the accuracy,efficiency,and suitability of each classifier for real-world malware detection *** the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and *** recommendations for selecting the most effective classifier for Windows malware detection based on empirical *** study employs a structured methodology consisting of several phases:exploratory data analysis,data preprocessing,model training,and *** data analysis involves understanding the dataset’s characteristics and identifying preprocessing *** preprocessing includes cleaning,feature encoding,dimensionality reduction,and optimization to prepare the data for *** training utilizes various

关键词： Security and privacy challenges in the context of requirements engineering supervisedmachine learning malware detection windows systems comparative analysis Gaussian Naive Bayes K Nearest Neighbors Stochastic Gradient Descent Classifier Decision Tree

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Comparison of Machine Learning Algorithms for Predicting Thyroid Disorders in Diabetic Patients

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Informatica (Slovenia) 2025年第12期49卷 105-114页

作者： Sayyid, Hiba O. Mahmood, Salma A. Hamadi, Saad S. Department of Computer Science University of Basrah College of Computer Sciences and Information Technology Basrah Iraq Department of Intelligent Medical Systems University of Basrah College of Computer Sciences and Information Technology Basrah Iraq Department of Internal Medicine University of Basrah College of Medicine Basrah Iraq

Machine Learning (ML), a subfield of Artificial Intelligence (AI), has been used successfully in the healthcare domain for disease diagnosis. Thyroid disorders and diabetes are two of the most prevalent and interconnected chronic diseases, as both play critical roles in regulating various physiological processes in the body. This study aims to predict thyroid disorders in diabetes patients using six machine learning algorithms: Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), Logistic Regression (LR), Naïve Bayes (NB), and Support Vector Machine (SVM). A locally sourced dataset comprising 44,539 instances of diabetic patients was utilized, undergoing preprocessing steps including data cleaning, encoding, and balancing. Two balancing techniques were employed: manual balancing and RandomUnderSampler. The dataset was partitioned into training and testing sets using a Stratified K-Fold cross-validation approach with 10 folds to ensure robust evaluation. Each algorithm’s performance was assessed using metrics such as accuracy and F1-score. Among the models, the RF algorithm outperformed the others, achieving the highest accuracy of 95% on the manually balanced dataset and 84% when the RandomUnderSampler technique was employed. Additionally, the F1-scores for RF were 95% and 82%, respectively, indicating its robustness in handling imbalanced datasets. This study highlights the importance of selecting appropriate preprocessing techniques and machine learning methods for healthcare datasets. The findings can assist healthcare providers in making early diagnoses and interventions for thyroid disorders in diabetic patients, potentially improving their quality of life and overall healthcare outcomes. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Logistic regression

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Deep Learning Based Signal Detection for Quadrature Spatial Modulation System

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China Communications 2024年第10期21卷 78-85页

作者： Shu Dingyun Peng Yuyang Yue Ming Fawaz AL-Hazemi Mohammad Meraj Mirza School of Computer Science and Engineering Macao University of Science and TechnologyMacaoChina Department of Computer and Network Engineering University of JeddahSaudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia

With the development of communication systems, modulation methods are becoming more and more diverse. Among them, quadrature spatial modulation(QSM) is considered as one method with less capacity and high efficiency. In QSM, the traditional signal detection methods sometimes are unable to meet the actual requirement of low complexity of the system. Therefore, this paper proposes a signal detection scheme for QSM systems using deep learning to solve the complexity problem. Results from the simulations show that the bit error rate performance of the proposed deep learning-based detector is better than that of the zero-forcing(ZF) and minimum mean square error(MMSE) detectors, and similar to the maximum likelihood(ML) detector. Moreover, the proposed method requires less processing time than ZF, MMSE,and ML.

关键词： bit error rate complexity deep learning quadrature spatial modulation

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