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Congruent Feature Selection Method to Improve the Efficacy of Machine Learning-Based Classification in Medical Image Processing

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 357-384页

作者： Mohd Anjum Naoufel Kraiem Hong Min Ashit Kumar Dutta Yousef Ibrahim Daradkeh department of Computer Engineering Aligarh Muslim UniversityAligarh202002India College of Computer Science King Khalid UniversityAbha61413Saudi Arabia School of Computing Gachon UniversitySeongnam13120Republic of Korea department of Computer Science and information systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Saudi Arabia department of Computer Engineering and information College of Engineering in Wadi AlddawasirPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

Machine learning(ML)is increasingly applied for medical image processing with appropriate learning *** applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for *** primary concern of ML applications is the precise selection of flexible image features for pattern detection and region *** of the extracted image features are irrelevant and lead to an increase in computation ***,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image *** process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel *** similarity between the pixels over the various distribution patterns with high indexes is recommended for disease ***,the correlation based on intensity and distribution is analyzed to improve the feature selection ***,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the ***,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of ***,the probability of feature selection,regardless of the textures and medical image patterns,is *** process enhances the performance of ML applications for different medical image *** proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected *** mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.

关键词： Computer vision feature selection machine learning region detection texture analysis image classification medical images

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An Enhanced Lung Cancer Detection Approach Using Dual-Model Deep Learning Technique

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 835-867页

作者： Sumaia Mohamed Elhassan Saad Mohamed Darwish Saleh Mesbah Elkaffas department of information Technology Institute of Graduate Studies and ResearchAlexandria UniversityAlexandria21526Egypt College of Computing and information Technology Arab Academy for ScienceTechnology and Maritime TransportAlexandriaP.O.Box 1029Egypt

Lung cancer continues to be a leading cause of cancer-related deaths worldwide,emphasizing the critical need for improved diagnostic *** detection of lung tumors significantly increases the chances of successful treatment and ***,current diagnostic methods often fail to detect tumors at an early stage or to accurately pinpoint their location within the lung ***-model deep learning technologies for lung cancer detection,while beneficial,cannot capture the full range of features present in medical imaging data,leading to incomplete or inaccurate ***,it may not be robust enough to handle the wide variability in medical images due to different imaging conditions,patient anatomy,and tumor *** overcome these disadvantages,dual-model or multi-model approaches can be *** research focuses on enhancing the detection of lung cancer by utilizing a combination of two learning models:a Convolutional Neural Network(CNN)for categorization and the You Only Look Once(YOLOv8)architecture for real-time identification and pinpointing of *** automatically learn to extract hierarchical features from raw image data,capturing patterns such as edges,textures,and complex structures that are crucial for identifying lung ***8 incorporates multiscale feature extraction,enabling the detection of tumors of varying sizes and scales within a single *** is particularly beneficial for identifying small or irregularly shaped tumors that may be challenging to ***,through the utilization of cutting-edge data augmentation methods,such as Deep Convolutional Generative Adversarial Networks(DCGAN),the suggested approach can handle the issue of limited data and boost the models’ability to learn from diverse and comprehensive *** combined method not only improved accuracy and localization but also ensured efficient real-time processing,which is crucial for practical clinical applicati

关键词： Lung cancer detection dual-model deep learning technique data augmentation CNN YOLOv8

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Fully Automated Density-Based Clustering Method

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Computers, Materials & Continua 2023年第8期76卷 1833-1851页

作者： Bilal Bataineh Ahmad A.Alzahrani information systems department College of Computers and Information SystemsMakkahSaudi Arabia

Cluster analysis is a crucial technique in unsupervised machine learning,pattern recognition,and data ***,current clustering algorithms suffer from the need for manual determination of parameter values,low accuracy,and inconsistent performance concerning data size and *** address these challenges,a novel clustering algorithm called the fully automated density-based clustering method(FADBC)is *** FADBC method consists of two stages:parameter selection and cluster *** the first stage,a proposed method extracts optimal parameters for the dataset,including the epsilon size and a minimum number of points *** parameters are then used in a density-based technique to scan each point in the dataset and evaluate neighborhood densities to find *** proposed method was evaluated on different benchmark datasets andmetrics,and the experimental results demonstrate its competitive performance without requiring manual *** results show that the FADBC method outperforms well-known clustering methods such as the agglomerative hierarchical method,k-means,spectral clustering,DBSCAN,FCDCSD,Gaussian mixtures,and density-based spatial clustering *** can handle any kind of data set well and perform excellently.

关键词： Automated clustering data mining density-based clustering unsupervised machine learning

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A Robust Tuned Random Forest Classifier Using Randomized Grid Search to Predict Coronary Artery Diseases

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Computers, Materials & Continua 2023年第5期75卷 4633-4648页

作者： Sameh Abd El-Ghany A.A.Abd El-Aziz department of information systems College of Computer and Information SciencesJouf UniversityKSA department of information systems Faculty of Computers and InformationMansoura UniversityEgypt department of information systems and Technology Faculty of Graduate Studies for Statistical ResearchCairo UniversityEgypt

Coronary artery disease(CAD)is one of themost authentic cardiovascular afflictions because it is an uncommonly overwhelming heart *** breakdown of coronary cardiovascular disease is one of the principal sources of death all over *** deterioration is a challenge,especially in youthful and rural countries where there is an absence of humantrained *** heart diseases happen without apparent signs,high-level detection is *** paper proposed a robust and tuned random forest model using the randomized grid search technique to *** proposed framework increases the ability of CADpredictions by tracking down risk pointers and learning the confusing joint efforts between ***,the healthcare industry has a lot of data but needs to gain more *** proposed framework is used for extracting knowledge from data stores and using that knowledge to help doctors accurately and effectively diagnose heart disease(HD).We evaluated the proposed framework over two public databases,Cleveland and Framingham *** datasets were preprocessed by using a cleaning technique,a normalization technique,and an outlier detection ***,the principal component analysis(PCA)algorithm was utilized to lessen the feature dimensionality of the two ***,we used a hyperparameter tuning technique,randomized grid search,to tune a random forest(RF)machine learning(ML)*** randomized grid search selected the best parameters and got the ideal CAD *** proposed framework was evaluated and compared with traditional *** proposed framework’s accuracy,sensitivity,precision,specificity,and f1-score were 100%.The evaluation of the proposed framework showed that it is an unrivaled perceptive outcome with tuning as opposed to other ongoing existing frameworks.

关键词： Coronary artery disease tuned random forest randomized grid search classifier

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Molecular simulation study of the microstructures and properties of pyridinium ionic liquid[HPy][BF_(4)]mixed with acetonitrile

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原子与分子物理学报 2025年第4期42卷 27-32页

作者： XU Jian-Qiang MA Zhao-Peng CHENG Si LIU Zhi-Cong ZHU Guang-Lai department of Physics School of Physics and Electronic InformationAnhui Normal UniversityWuhu 241002China

The microstructures and thermodynamic properties of mixed systems comprising pyridinium ionic liquid[HPy][BF_(4)]and acetonitrile at different mole fractions were studied using molecular dynamics simulation in this *** following properties were determined:density,self-diffusion coefficient,excess molar volume,and radial distribution *** results show that with an increase in the mole fraction of[HPy][BF_(4)],the self-diffusion coefficient ***,the excess molar volume initially decreases,reaches a minimum,and then *** rules of radial distribution functions(RDFs)of characteristic atoms are *** increasing the mole fraction of[HPy][BF_(4)],the first peak of the RDFs of HA1-F decreases,while that of CT6-CT6 rises at first and then *** indicates that the solvent molecules affect the polar and non-polar regions of[HPy][BF_(4)]differently.

关键词： Pyridinium ionic liquids Thermodynamic properties Molecular dynamics simulation Radial distribution functions

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Load Balancing Based on Multi-Agent Framework to Enhance Cloud Environment

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Computers, Materials & Continua 2023年第2期74卷 3015-3028页

作者： Shrouk H.Hessen Hatem M.Abdul-kader Ayman E.Khedr Rashed K.Salem information systems department Faculty of Computers and InformationMenoufia UniversityShebin ElkomEgypt information systems department Faculty of Computers and Artificial IntelligenceSouth Valley UniversityHurghadaEgypt information systems department Faculty of Computers and Information TechnologyFuture University in Egypt(FUE)CairoEgypt

According to the advances in users’service requirements,physical hardware accessibility,and speed of resource delivery,Cloud Computing(CC)is an essential technology to be used in many ***,the Internet of Things(IoT)is employed for more communication flexibility and richness that are required to obtain fruitful services.A multi-agent system might be a proper solution to control the load balancing of interaction and communication among *** paper proposes a multi-agent load balancing framework that consists of two phases to optimize the workload among different servers with large-scale CC power with various utilities and a significant number of IoT devices with low *** agents are integrated based on relevant features of behavioral interaction using classification techniques to balance the *** balancing algorithm is developed to serve users’requests to improve the solution of workload problems with an efficient *** activity task from IoT devices has been classified by feature selection methods in the preparatory phase to optimize the scalability ***,the server’s availability is checked and the classified task is assigned to its suitable server in the main phase to enhance the cloud environment ***-agent load balancing framework is succeeded to cope with the importance of using large-scale requirements of CC and(low resources and large number)of IoT.

关键词： Cloud computing IoT multi-agent system load balancing algorithm server utilities

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Hybrid immunizing solution for job recommender system

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Frontiers of Computer Science 2017年第3期11卷 511-527页

作者： Shaha AL-OTAIBI Mourad YKHLEF department of information systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh 11671 Saudi Arabia department of information systems College of Computer and Information Sciences King Saud University Riyadh 11543 Saudi Arabia

Two traditional recommendation techniques, content-based and collaborative filtering （CF）, have been widely used in a broad range of domain areas. Both meth- ods have their advantages and disadvantages, and some of the defects can be resolved by integrating both techniques in a hybrid model to improve the quality of the recommendation. In this article, we will present a problem-oriented approach to design a hybrid immunizing solution for job recommen- dation problem from applicant＇s perspective. The proposed approach aims to recommend the best chances of opening jobs to the applicant who searches for job. It combines the artificial immune system （AIS）, which has a powerful explo- ration capability in polynomial time, with the collaborative filtering, which can exploit the neighbors＇ interests. We will discuss the design issues, as well as the hybridization process that should be applied to the problem. Finally, experimental studies are conducted and the results show the importance of our approach for solving the job recommendation problem.

关键词： content-based collaborative filtering (CF) hy- bridization computational intelligence (CI) artificial im- mune system (AIS) clonal selection correlation-based simi- larity

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Characteristic and Mechanism of Pressure Fluctuation Caused by Self-Induced Oscillation of Supersonic Impinging Jet

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Journal of Thermal Science 2013年第2期22卷 123-127页

作者： Tsuyoshi Yasunobu Yumiko Otobe Hideo Kashimura department of Control & information systems Engineering Kitakyushu College of Technology

When the underexpanded supersonic jet impinges on the obstacle, it is well known that the self-induced flow os- cillation occurs. This oscillation depends on the pressure ratio in the flowfield, the position of an obstacle and is related with the noise problems of aeronautical and other industrial engineering. The characteristic and the mechanism of self-induced flow oscillation, have to be clarified to control various noise problems. But, it seems that the characteristics of the oscillated flowfield and the mechanism of an oscillation have to be more cleared to control the oscillation. This paper aims to clarify the effect of the pressure ratio and the obstacle position and the mechanism of self-induced flow oscillation by numerical analysis and experiment, when the underexpanded su- personic jet impinges on the cylindrical body. From the result of this study, it is clear that occurrence of the self-induced flow osciUation depends on the pressure balance in the flowfield.

关键词： Supersonic Jet Mach Disk Barrel Shock Flow Oscillation Flow Visualization Numerical Analysis

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Small cell lung cancer with peripheral neuropathy as the first symptom:Two case reports

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World Journal of Clinical Cases 2025年第9期13卷 30-40页

作者： Man Luo Xiao-Xi Lu Dan-Yang Meng Jin Hu department of Neurology Affiliated Hospital of Jiaxing UniversityJiaxing 314000Zhejiang ProvinceChina department of information Technology Affiliated Hospital of Jiaxing UniversityJiaxing 314000Zhejiang ProvinceChina

BACKGROUND Small cell lung cancer(SCLC)is the most malignant type of lung *** in the latent period and early stage of the tumor,SCLC is prone to produce distant metastases with complex and diverse clinical *** is most closely related to paraneoplastic syndrome,and some cases present as paraneoplastic peripheral neuropathy(PPN).PPN in SCLC appears early,lacks specificity,and often occurs before diagnosis of the primary *** is easy to be misdiagnosed as a primary disease of the nervous system,leading to missed diagnosis and delayed diagnosis and *** SUMMARY This paper reports two cases of SCLC with limb weakness as the first *** first symptoms of one patient were rash,limb weakness,and abnormal *** patient was repeatedly referred to the hospital for limb weakness and rash for>1 year,during which time,treatment with hormones and immunosuppressants did not lead to significant improvement,and the condition gradually *** patient was later diagnosed with SCLC,and the dyskinesia did not worsen as the dermatomyositis improved after antineoplastic and hormone *** second case presented with limb numbness and weakness as the first symptom,but the patient did not pay attention to ***,the patient was diagnosed with SCLC after facial edema caused by tumor thrombus invading the ***,he was diagnosed with extensive SCLC and died 1 year after *** The two cases had PPN and abnormal electromyography,highlighting its correlation with early clinical indicators of SCLC.

关键词： Paraneoplastic peripheral neuropathy Small cell lung cancer Peripheral neuropathy Electromyography Dermatomyositis Case report

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On Density-Based Data Streams Clustering Algorithms: A Survey

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Journal of Computer Science & Technology 2014年第1期29卷 116-141页

作者： Amineh Amini Teh Ying Wah Hadi Saboohi IEEE department of information systems Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur ACM

Clustering data streams has drawn lots of attention in the last few years due to their ever-growing presence. Data streams put additional challenges on clustering such as limited time and memory and one pass clustering. Furthermore, discovering clusters with arbitrary shapes is very important in data stream applications. Data streams are infinite and evolving over time, and we do not have any knowledge about the number of clusters. In a data stream environment due to various factors, some noise appears occasionally. Density-based method is a remarkable class in clustering data streams, which has the ability to discover arbitrary shape clusters and to detect noise. Furthermore, it does not need the nmnber of clusters in advance. Due to data stream characteristics, the traditional density-based clustering is not applicable. Recently, a lot of density-based clustering algorithms are extended for data streams. The main idea in these algorithms is using density- based methods in the clustering process and at the same time overcoming the constraints, which are put out by data streanFs nature. The purpose of this paper is to shed light on some algorithms in the literature on density-based clustering over data streams. We not only summarize the main density-based clustering algorithms on data streams, discuss their uniqueness and limitations, but also explain how they address the challenges in clustering data streams. Moreover, we investigate the evaluation metrics used in validating cluster quality and measuring algorithms＇ performance. It is hoped that this survey will serve as a steppingstone for researchers studying data streams clustering, particularly density-based algorithms.

关键词： data stream density-based clustering grid-based clustering micro-clustering

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