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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

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

Multi-Stage-Based Siamese Neural Network for Seal Image Recognition

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Computer Modeling in engineering & sciences 2025年第1期142卷 405-423页

作者： Jianfeng Lu Xiangye Huang Caijin Li Renlin Xin Shanqing Zhang Mahmoud Emam school of Computer science and Technology Hangzhou Dianzi UniversityHangzhou310018China Shangyu Institute of science and engineering Hangzhou Dianzi UniversityShaoxing312300China Faculty of Artificial Intelligence Menoufia UniversityShebin El-Koom32511Egypt

Seal authentication is an important task for verifying the authenticity of stamped seals used in various domains to protect legal documents from tampering and *** seal inspection is commonly audited manually to ensure document ***,manual assessment of seal images is tedious and laborintensive due to human errors,inconsistent placement,and completeness of the *** image recognition systems are inadequate enough to identify seal types accurately,necessitating a neural network-based method for seal image ***,neural network-based classification algorithms,such as Residual Networks(ResNet)andVisualGeometryGroup with 16 layers(VGG16)yield suboptimal recognition rates on stamp ***,the fixed training data categories make handling new categories to be a challenging *** paper proposes amulti-stage seal recognition algorithmbased on Siamese network to overcome these ***,the seal image is pre-processed by applying an image rotation correction module based on Histogram of Oriented Gradients(HOG).Secondly,the similarity between input seal image pairs is measured by utilizing a similarity comparison module based on the Siamese ***,we compare the results with the pre-stored standard seal template images in the database to obtain the seal *** evaluate the performance of the proposed method,we further create a new seal image dataset that contains two subsets with 210,000 valid labeled pairs in *** proposed work has a practical significance in industries where automatic seal authentication is essential as in legal,financial,and governmental sectors,where automatic seal recognition can enhance document security and streamline validation ***,the experimental results show that the proposed multi-stage method for seal image recognition outperforms state-of-the-art methods on the two established datasets.

关键词： Seal recognition seal authentication document tampering siamese network spatial transformer network similarity comparison network

Flexible Graphene Field‑Effect Transistors and Their Application in Flexible Biomedical Sensing

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Nano-Micro Letters 2025年第2期17卷 252-313页

作者： Mingyuan Sun Shuai Wang Yanbo Liang Chao Wang Yunhong Zhang Hong Liu Yu Zhang Lin Han Institute of Marine science and Technology Shandong UniversityQingdao 266237ShandongPeople’s Republic of China State Key Laboratory of Crystal Materials Shandong UniversityJinan 250100ShandongPeople’s Republic of China school of Integrated Circuits Shandong UniversityJinan 250100ShandongPeople’s Republic of China Shandong engineering Research Center of Biomarker and Artificial Intelligence Application Jinan250100ShandongPeople’s Republic of China

Flexible electronics are transforming our lives by making daily activities more *** to this innovation are field-effect transistors(FETs),valued for their efficient signal processing,nanoscale fabrication,low-power consumption,fast response times,and ***,known for its exceptional mechanical properties,high electron mobility,and biocompatibility,is an ideal material for FET channels and *** combination of graphene and FETs has given rise to flexible graphene field-effect transistors(FGFETs),driving significant advances in flexible electronics and sparked a strong interest in flexible biomedical ***,we first provide a brief overview of the basic structure,operating mechanism,and evaluation parameters of FGFETs,and delve into their material selection and patterning *** ability of FGFETs to sense strains and biomolecular charges opens up diverse application *** specifically analyze the latest strategies for integrating FGFETs into wearable and implantable flexible biomedical sensors,focusing on the key aspects of constructing high-quality flexible biomedical ***,we discuss the current challenges and prospects of FGFETs and their applications in biomedical *** review will provide valuable insights and inspiration for ongoing research to improve the quality of FGFETs and broaden their application prospects in flexible biomedical sensing.

关键词： Flexible Graphene Field-effect transistor Wearable Implantable Biosensor

Molecule‑Level Multiscale Design of Nonflammable Gel Polymer Electrolyte to Build Stable SEI/CEI for Lithium Metal Battery

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Nano-Micro Letters 2025年第1期17卷 404-423页

作者： Qiqi Sun Zelong Gong Tao Zhang Jiafeng Li Xianli Zhu Ruixiao Zhu Lingxu Wang Leyuan Ma Xuehui Li Miaofa Yuan Zhiwei Zhang Luyuan Zhang Zhao Qian Longwei Yin Rajeev Ahuja Chengxiang Wang Key Laboratory for Liquid‑Solid Structural Evolution and Processing of Materials(Ministry of Education) School of Materials Science and EngineeringShandong UniversityJinan 250061People’s Republic of China Condensed Matter Theory Department of Physics and AstronomyUppsala UniversityUppsala 75120Sweden

The risk of flammability is an unavoidable issue for gel polymer electrolytes(GPEs).Usually,flameretardant solvents are necessary to be used,but most of them would react with anode/cathode easily and cause serious interfacial instability,which is a big challenge for design and application of nonflammable ***,a nonflammable GPE(SGPE)is developed by in situ polymerizing trifluoroethyl methacrylate(TFMA)monomers with flame-retardant triethyl phosphate(TEP)solvents and LiTFSI–LiDFOB dual lithium *** is strongly anchored to PTFMA matrix via polarity interaction between-P=O and-CH_(2)CF_(3).It reduces free TEP molecules,which obviously mitigates interfacial reactions,and enhances flame-retardant performance of TEP *** TEP molecules are also inhibited in solvation of Li^(+),leading to anion-dominated solvation sheath,which creates inorganic-rich solid electrolyte interface/cathode electrolyte interface *** coordination structure changes Li^(+)transport from sluggish vehicular to fast structural transport,raising ionic conductivity to 1.03 mS cm^(-1) and transfer number to 0.41 at 30℃.The Li|SGPE|Li cell presents highly reversible Li stripping/plating performance for over 1000 h at 0.1 mA cm^(−2),and 4.2 V LiCoO_(2)|SGPE|Li battery delivers high average specific capacity>120 mAh g^(−1) over 200 *** study paves a new way to make nonflammable GPE that is compatible with Li metal anode.

关键词： Anchoring effect Nonflammable gel electrolyte In situ cross-linked Electrode-electrolyte interface Li metal battery

Small-time scale network traffic prediction based on a local support vector machine regression model

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Chinese Physics B 2009年第6期18卷 2194-2199页

作者：孟庆芳陈月辉彭玉华 school of information science and engineering University of Jinan school of information science and engineering Shandong University

In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the reconstructed phase space, the local support vector machine prediction method is used to predict the traffic measurement data, and the BIC-based neighbouring point selection method is used to choose the number of the nearest neighbouring points for the local support vector machine regression model. The experimental results show that the local support vector machine prediction method whose neighbouring points are optimized can effectively predict the small-time scale traffic measurement data and can reproduce the statistical features of real traffic measurements.

关键词： network traffic small-time scale nonlinear time series analysis support vector machine regression model

Robust interface and excellent as-built mechanical properties of Ti–6Al–4V fabricated through laser-aided additive manufacturing with powder and wire

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International Journal of Minerals,Metallurgy and Materials 2025年第1期32卷 154-168页

作者： Fei Weng Guijun Bi Youxiang Chew Shang Sui Chaolin Tan Zhenglin Du Jinlong Su Fern Lan Ng Key Laboratory for Liquid-Solid Structural Evolution and Processing of Materials Ministry of EducationShandong Engineering&Technology Research Center for Superhard MaterialSchool of Materials Science and EngineeringShandong UniversityJinan 250061China Institute of Intelligent Manufacturing Guangdong Academy of SciencesGuangzhou 510095China Singapore Institute of Manufacturing Technology 5 Cleantech Loop636732Singapore

The feasibility of manufacturing Ti-6Al-4V samples through a combination of laser-aided additive manufacturing with powder(LAAM_(p))and wire(LAAM_(w))was explored.A process study was first conducted to successfully circumvent defects in Ti-6Al-4V deposits for LAAM_(p) and LAAM_(w),*** the optimized process parameters,robust interfaces were achieved between powder/wire deposits and the forged substrate,as well as between powder and wire *** characterization results revealed the epitaxial prior β grains in the deposited Ti-6Al-4V,wherein the powder deposit was dominated by a finerα′microstructure and the wire deposit was characterized by lamellar α *** mechanisms of microstructure formation and correlation with mechanical behavior were analyzed and *** mechanical properties of the interfacial samples can meet the requirements of the relevant Aerospace Material Specifications(AMS 6932)even without post heat *** fracture occurred within the interfacial area,further suggesting the robust *** findings of this study highlighted the feasibility of combining LAAM_(p) and LAAM_(w) in the direct manufacturing of Ti-6Al-4V parts in accordance with the required dimensional resolution and deposition rate,together with sound strength and ductility balance in the as-built condition.

关键词： laser-aided additive manufacturing powder deposition wire deposition interfacial characteristic mechanical behavior

Fast Two-Output Secure Computation with Optimal Error Probability

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Chinese Journal of Electronics 2017年第5期26卷 933-941页

作者： ZHAO Chuan JIANG Han XU Qiuliang WANG Yilei WEI Xiaochao CUI Shujie school of Computer science and Technology Shandong University school of information science and engineering University of Jinan school of information and Electrical engineering Ludong University

Cut-and-choose paradigm makes Yao’s protocol for two-party computation secure in malicious model with an error *** CRYPTO 2013,based on multi-phase cut-and-choose,Lindell reduced this probability to the optimal ***,this work can only compute single-output functions with optimal error *** transform multi-phase cut-and-choose for singleoutput case into one that can deal with two-output functions,meanwhile maintaining the optimal error *** on this new paradigm,we propose an efficient two-output secure computation ***,by utilizing the specific property of the output garbled keys,we solve the authenticity issue of the generator’s output with only symmetric cryptographic operations linear in the output length of the generator,which is the most efficient method so far in standard model without Random oracle(RO).

关键词： Secure two-party computation Yao’s protocol Malicious model Cut-and-choose Two-output function Optimal error probability

Comparative analysis of different methods for image enhancement

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Journal of Central South university 2014年第12期21卷 4563-4570页

作者：吴笑峰胡仕刚赵瑾李志明李劲唐志军席在芳 school of information and Electrical engineering Hunan University of Science and Technology school of information science and engineering University of Jinan

Image enhancement technology plays a very important role to improve image quality in image processing. By enhancing some information and restraining other information selectively, it can improve image visual effect. The objective of this work is to implement the image enhancement to gray scale images using different techniques. After the fundamental methods of image enhancement processing are demonstrated, image enhancement algorithms based on space and frequency domains are systematically investigated and compared. The advantage and defect of the above-mentioned algorithms are analyzed. The algorithms of wavelet based image enhancement are also deduced and generalized. Wavelet transform modulus maxima(WTMM) is a method for detecting the fractal dimension of a signal, it is well used for image enhancement. The image techniques are compared by using the mean(μ),standard deviation(?), mean square error(MSE) and PSNR(peak signal to noise ratio). A group of experimental results demonstrate that the image enhancement algorithm based on wavelet transform is effective for image de-noising and enhancement. Wavelet transform modulus maxima method is one of the best methods for image enhancement.

关键词： image enhancement wavelet transform histogram equalization unsharp masking(UM) modulus maxl mum threshold