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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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Modeling and Comprehensive Review of Signaling Storms in 3GPP-Based Mobile Broadband Networks:Causes,Solutions,and Countermeasures

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computer Modeling in engineering & Sciences 2025年第1期142卷 123-153页

作者： Muhammad Qasim Khan Fazal Malik Fahad Alturise Noor Rahman Department of computer Science Iqra National UniversityPeshawar25000Pakistan Department of Cybersecurity College of ComputerQassim UniversityBuraydah52555Saudi Arabia Department of computer Science and engineering Al-Fayha CollegeAl Jubayl31961Saudi Arabia

Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband ***,they consume important and scarce network resources such as bandwidth and processing *** have been several reports of these control signaling turning into signaling storms halting network operations and causing the respective Telecom companies big financial *** paper draws its motivation from such real network disaster incidents attributed to signaling *** this paper,we present a thorough survey of the causes,of the signaling storm problems in 3GPP-based mobile broadband networks and discuss in detail their possible solutions and *** provide relevant analytical models to help quantify the effect of the potential causes and benefits of their corresponding *** important contribution of this paper is the comparison of the possible causes and solutions/countermeasures,concerning their effect on several important network aspects such as architecture,additional signaling,fidelity,etc.,in the form of a *** paper presents an update and an extension of our earlier conference *** our knowledge,no similar survey study exists on the subject.

关键词： Signaling storm problems control signaling load analytical modeling 3GPP networks smart devices diameter signaling mobile broadband data access data traffic mobility management signaling network architecture 5G mobile communication

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Advancements in Liver Tumor Detection:A Comprehensive Review of Various Deep Learning Models

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computer Modeling in engineering & Sciences 2025年第1期142卷 91-122页

作者： Shanmugasundaram Hariharan D.Anandan Murugaperumal Krishnamoorthy Vinay Kukreja Nitin Goyal Shih-Yu Chen Deptartment of Artificial Intelligence and Data Science Vardhaman College of EngineeringHyderabad501218India Deptartment of CSE V.S.B College of EngineeringKarur639111India Deptartment of Electrical and Electronics engineering Vardhaman College of EngineeringHyderabad501218India Centre for Research Impact&Outcome Chitkara UniversityRajpura140401PunjabIndia Department of computer Science and engineering School of Engineering and TechnologyCentral University of HaryanaMahendergarh123031HaryanaIndia Department of computer Science and information engineering National Yunlin University of Science and TechnologyYunlin64002Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and TechnologyYunlin64002Taiwan

Liver cancer remains a leading cause of mortality worldwide,and precise diagnostic tools are essential for effective treatment *** Tumors(LTs)vary significantly in size,shape,and location,and can present with tissues of similar intensities,making automatically segmenting and classifying LTs from abdominal tomography images crucial and *** review examines recent advancements in Liver Segmentation(LS)and Tumor Segmentation(TS)algorithms,highlighting their strengths and limitations regarding precision,automation,and *** metrics are utilized to assess key detection algorithms and analytical methods,emphasizing their effectiveness and relevance in clinical *** review also addresses ongoing challenges in liver tumor segmentation and identification,such as managing high variability in patient data and ensuring robustness across different imaging *** suggests directions for future research,with insights into technological advancements that can enhance surgical planning and diagnostic accuracy by comparing popular *** paper contributes to a comprehensive understanding of current liver tumor detection techniques,provides a roadmap for future innovations,and improves diagnostic and therapeutic outcomes for liver cancer by integrating recent progress with remaining challenges.

关键词： Liver tumor detection liver tumor segmentation image processing liver tumor diagnosis feature extraction tumor classification deep learning machine learning

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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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A Case Study of computer Graphics Education in the Department of information engineering, college of Industrial Technology

A Case Study of Computer Graphics Education in the Departmen...

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第六届中日图学教育研讨会

作者： Kenichi HIROSE Naoki IIDA college of Industrial Technology Hyogo Japan college of Industrial Technology Hyogo Japan

＜正＞ Since the start of the Department of information engineering at the college of Industrial Technology, we have been positively implementing computer graphics education. Recently, in our department, the education is provided for the two main aims of "promotion of the person who makes software" and "promotion of the person who uses software". And computer graphics education is located as the main subject in the latter. Therefore it is necessary for the students not only to learn the basic theory of computer graphics, but also to acquire the operation ability of the application software for computer graphics. So we consider these educational situation, we tried to examine the educational methods for the student’s better understanding. This paper describes the present situation of the computer graphics education in our department, and also discusses the effectiveness of our educational methods.

关键词： computer graphics software operation education

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Throughput and BER of wireless powered DF relaying in Nakagami-m fading

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Science China(information Sciences) 2017年第10期60卷 204-216页

作者： Yan GAO Yunfei CHEN Aiqun HU School of Electronic information Nanjing College of Information Technology college of computer and information Hohai University college of information Science and engineering Southeast University

Energy harvesting provides a promising solution to the extra energy requirement at the relay due to relaying. In this paper, the throughput and bit error rate of a decode-and-forward relaying system are studied using power splitting wireless power. Three different transmission scenarios are considered: instantaneous transmission, delay-or error-constrained transmission and delay-or error-tolerant transmission. For each scenario,exact expressions for the throughput and bit error rate are derived. Numerical results show that, for instantaneous transmission, the optimum splitting factor is not sensitive to the channel gain of the source-to-relay *** delay-or error-constrained transmissions, the optimum splitting factor increases with the quality of the source-to-relay link and decreases with the quality of the relay-to-destination link. For delay-or error-tolerant transmissions, the optimum splitting factor is insensitive to the quality of the source-to-relay link.

关键词： decode-and-forward energy harvesting power-splitting relaying

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Ru/NiMnB spherical cluster pillar for highly proficient green hydrogen electrocatalyst at high current density

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Journal of Energy Chemistry 2025年第01期 397-408页

作者： Md Ahasan Habib Shusen Lin Mehedi Hasan Joni Sumiya Akter Dristy Rutuja Mandavkar Jae-Hun Jeong Jihoon Lee Department of Electronic engineering College of Electronics and Information Kwangwoon University

Advanced OER/HER electrocatalytic alternatives are crucial for the wide adaptation of green hydrogen ***,Ru/NiMnB spherical cluster pillar （SCP）,denoted as Ru/NiMnB,is synthesized using a combination of electro-deposition and hydrothermal *** investigation of Ru doping in the NiMnB matrix revealed significant improvements in electrocatalytic *** Ru/NiMnB SCPs demonstrate superior OER/HER activity with low overpotentials of 150 and 103 mV at 50mA/cm2in 1 M KOH,making them highly competitive with state-of-the-art ***,the Ru/NiMnB SCPs exhibit a low 2-E cell voltage of 2.80 V at ultra-high current density of 2,000 m A/cm2in 1 M KOH,outperforming the standard benchmark electrodes of RuO2||Pt/C,thereby positioning Ru/NiMnB as one of the best bifunctional *** SCPs exhibit exceptional high-current characteristics,stability and corrosion resistance,as evidenced by continuous operation at 1,000 mA/cm2high-current density for over 150 h in 6 M KOH at elevated temperatures under harsh industrial *** a small amount of Ru incorporation significantly enhances the electrocatalytic performances of NiMnB,attributed to increased active sites and improved intrinsic properties such as conductivity,adsorption/desorption capability and reaction ***,Ru/NiMnB SCPs present a promising bi-functional electrode concept for efficient green H2production.

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Automatic Narrow-Deep Feature Recognition for Mould Manufacturing

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Journal of computer Science & Technology 2011年第3期26卷 528-537页

作者：陈正鸣何坤金刘景 college of computer Science and information engineering Hohai University

There usually exist narrow-long-deep areas in mould needed to be machined in special machining. To identify the narrow-deep areas automatically, an automatic narrow-deep feature （NF） recognition method is put forward accordingly. First, the narrow-deep feature is defined innovatively in this field and then feature hint is extracted from the mould by the characteristics of narrow-deep feature. Second, the elementary constituent faces （ECF） of a feature are found on the basis of the feature hint. By means of extending and clipping the ECF, the feature faces are obtained incrementally by geometric reasoning. As a result, basic narrow-deep features （BNF） related are combined heuristically. The proposed NF recognition method provides an intelligent connection between CAD and CAPP for machining narrow-deep areas in mould.

关键词： narrow-deep feature feature recognition face-pair mould

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Lattice Boltzmann simulation of fluid flows in two-dimensional channel with complex geometries

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Chinese Physics B 2009年第10期18卷 4353-4359页

作者：闻炳海刘海燕张超英王强 college of computer Science and information engineering Guangxi Normal University

Boundary conditions （BCs） play an essential role in lattice Boltzmann （LB） simulations. This paper investigates several most commonly applied BCs by evaluating the relative L2-norm errors of the LB simulations for two-dimensional （2-D） Poiseuille flow. It is found that the relative L2-norm error resulting from FHML＇s BC is smaller than that from other BCs as a whole. Then, based on the FHML＇s BC, it formulates an LB model for simulating fluid flows in 2-D channel with complex geometries. Afterwards, the flows between two inclined plates, in a pulmonary blood vessel and in a blood vessel with local expansion region, are simulated. The numerical results are in good agreement with the analytical predictions and clearly show that the model is effective. It is expected that the model can be extended to simulate some real biologic flows, such as blood flows in arteries, vessels with stenosises, aneurysms and bifurcations,

关键词： lattice Boltzmann method complex boundary fluid flow numerical simulation

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Dynamic Multi-Task Allocation Method for Passenger Diffusion in Mobile Crowd Sensing

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Chinese Journal of Electronics 2021年第5期30卷 940-946页

作者： JIANG Weijin LYU Sijian college of computer and information engineering Hunan University of Technology and Business

This paper aims to solve the problem of low efficiency, high cost and instability in opportunistic network transmission in the process of mobile group intelligence perception task allocation. Two multi-task dynamic distribution methods based on Lowest cost Participant selection algorithm(LC-PSA) based on user incentive cost and Least number Participant selection algorithm(LN-PSA) based on number of users are proposed respectively. Through these two algorithms,the goal of minimizing the number of people and moving distance required for the task and reducing the system's incentive cost is achieved. Simulation experiments show that compared with similar algorithms, the number of participants in the task distribution scheme selected by the LN-PSA algorithm is reduced by 24.0%, and the system resource consumption is lower, which can provide stable services for the system when users are insufficient in emergencies. Compared with the traditional greedy heuristics algorithm, the LC-PSA algorithm reduces the total system cost by 37.74% and has better overall performance in the comparison experiment.

关键词： mobile group intelligence perception task allocation system resource consumption Lowest cost Participant selection algorithm multitask dynamic distribution methods dynamic multitask allocation method optimisation LN-PSA algorithm passenger diffusion number Participant selection algorithm user incentive cost LC-PSA algorithm instability greedy algorithms opportunistic network transmission traditional greedy heuristics algorithm mobile computing moving distance task distribution scheme mobile crowd sensing resource allocation total system cost

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