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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

Scalable Security Solution Against Wiretapping for Network Coding Based Priority Encoding Transmission

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Chinese Journal of Electronics 2018年第1期27卷 191-197页

作者： LIU Guangjun XIAO Song school of information engineering Xi'an University school of telecommunication engineering Xidian University

Network coding is promising to improve the throughput and robustness of video transmission over wireless networks. To address the security issue of network coding based Priority encoding transmission(PET), a special coding design against wiretapping is proposed with hierarchical security. The basic idea is first to randomize partial blocks of each layer and then to encode each layer with inter-layer overlapping coding. The method is characterized by quantifiable security for incremental decoding,and realizes scalability in confidentiality and video transmission, while incurring relatively low computational complexity and bandwidth overhead. A sufficient condition is also presented to design a stronger secure network coded PET system against the known-plaintext attacks. Analysis and evaluation show that the method can be efficient as a new paradigm to secure network coded PET system in realistic applications.

关键词： Network coding Hierarchical confidentiality Priority encoding transmission(PET) Video streaming

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

Sampling adaptive block compressed sensing reconstruction algorithm for images based on edge detection

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The Journal of China Universities of Posts and telecommunications 2013年第3期20卷 97-103页

作者： ZHENG Hai-bo ZHU Xiu-chang school of telecommunication and information engineering Nanjing University of Posts and Telecommunications

In this paper, a sampling adaptive for block compressed sensing with smooth projected Landweber based on edge detection （SA-BCS-SPL-ED） image reconstruction algorithm is presented. This algorithm takes full advantage of the characteristics of the block compressed sensing, which assigns a sampling rate depending on its texture complexity of each block. The block complexity is measured by the variance of its texture gradient, big variance with high sampling rates and small variance with low sampling rates. Meanwhile, in order to avoid over-sampling and sub-sampling, we set up the maximum sampling rate and the minimum sampling rate for each block. Through iterative algorithm, the actual sampling rate of the whole image approximately equals to the set up value. In aspects of the directional transforms, discrete cosine transform （DCT）, dual-tree discrete wavelet transform （DDWT）, discrete wavelet transform （DWT） and Contourlet （CT） are used in experiments. Experimental results show that compared to block compressed sensing with smooth projected Landweber （BCS-SPL）, the proposed algorithm is much better with simple texture images and even complicated texture images at the same sampling rate. Besides, SA-BCS-SPL-ED-DDWT is quite good for the most of images while the SA-BCS-SPL-ED-CT is likely better only for more-complicated texture images.

关键词： block compressed sensing edge detection sampling-adaptive variance directional transforms

A Unique Relative Entropy-Based Symmetrical Co-occurrence Matrix Thresholding with Statistical Spatial information

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Chinese Journal of Electronics 2015年第3期24卷 622-626页

作者： FAN Jiulun ZHANG Hong school of Electronic engineering Xidian University school of telecommunication and information engineering Xi’an University of Posts and Telecommunications

Thresholding based on gray-gray cooccurrence matrix is a local thresholding technique. Relative entropy is usually used to gauge the relative difference of uncertainties in two physical systems, and the relative entropy-based asymmetrical co-occurrence matrix thresholding has been applied successfully. We propose to construct symmetrical co-occurrence matrix with the statistical spatial information from the mean values in object and background regions of an image. In this way, a unique relative entropy-based symmetrical co-occurrence matrix thresholding method is derived. Computer-simulation results demonstrated the higher adaptability and efficiency of the proposed method, as compared with square distance based symmetrical co-ocurrence matrix thresholding, Otsu’s and relative entropy methods.

关键词： Image segmentation Symmetrical cooccurrence matrix Relative entropy thresholding Region mean value

A Robust Fuzzy Time Series Forecasting Method Based on Multi-partition and Outlier Detection

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Chinese Journal of Electronics 2019年第5期28卷 899-905页

作者： QU Hua ZHANG Yanpeng LIU Wei ZHAO Jihong school of Software engineering Xi'an Jiaotong University school of Electronics and information engineering Xi'an Jiaotong University school of Communication and information engineering Xi'an University of Posts and Telecommunication

We propose a robust fuzzy time series forecasting method based on multi-partition approach and outlier detection for forecasting market prices. The multipartition approach employs a specific partition criterion for each dimension of the time series. We use a Gaussian kernel version fuzzy C-means clustering to construct the fuzzy logic relationships and detect the outliers by calculating the grade of membership. We apply an additional model, which is trained on the set of outliers by Levenberg-Marquardt algorithm, for forecasting the outliers in testing set. The experiment results show that the proposed method improves the robustness and the average forecasting accuracy rate.

关键词： Fuzzy time series Multi-partition approach Outlier detection

Defects‑Rich Heterostructures Trigger Strong Polarization Coupling in Sulfides/Carbon Composites with Robust Electromagnetic Wave Absorption

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Nano-Micro Letters 2025年第1期17卷 528-547页

作者： Jiaolong Liu Siyu Zhang Dan Qu Xuejiao Zhou Moxuan Yin Chenxuan Wang Xuelin Zhang Sichen Li Peijun Zhang Yuqi Zhou Kai Tao Mengyang Li Bing Wei Hongjing Wu Mengyang Li Bing Wei Hongjing Wu school of Physics Xidian UniversityXi’an 710071People’s Republic of China school of Advanced Materials and Nanotechnology State Key Discipline Laboratory of Wide Band Gap Semiconductor TechnologyXidian UniversityXi’an 710071People’s Republic of China school of Microelectronics Xidian UniversityXi’an 710071People’s Republic of China school of telecommunication engineering Xidian UniversityXi’an 710071People’s Republic of China MOE Key Laboratory of Material Physics and Chemistry Under Extraordinary School of Physical Science and TechnologyNorthwestern Polytechnical UniversityXi’an 710072People’s Republic of China The Ministry of Education Key Laboratory of Micro and Nano Systems forAerospace School of Mechanical EngineeringNorthwestern PolytechnicalUniversityXi’an 710072People’s Republic of China

Defects-rich heterointerfaces integrated with adjustable crystalline phases and atom vacancies,as well as veiled dielectric-responsive character,are instrumental in electromagnetic *** methods,however,constrain their delicate ***,an innovative alternative is proposed:carrageenan-assistant cations-regulated(CACR)strategy,which induces a series of sulfides nanoparticles rooted in situ on the surface of carbon *** unique configuration originates from strategic vacancy formation energy of sulfides and strong sulfides-carbon support interaction,benefiting the delicate construction of defects-rich heterostructures in M_(x)S_(y)/carbon composites(M-CAs).Impressively,these generated sulfur vacancies are firstly found to strengthen electron accumulation/consumption ability at heterointerfaces and,simultaneously,induct local asymmetry of electronic structure to evoke large dipole moment,ultimately leading to polarization coupling,i.e.,defect-type interfacial ***“Janus effect”(Janus effect means versatility,as in the Greek two-headed Janus)of interfacial sulfur vacancies is intuitively confirmed by both theoretical and experimental investigations for the first ***,the sulfur vacancies-rich heterostructured Co/Ni-CAs displays broad absorption bandwidth of 6.76 GHz at only 1.8 mm,compared to sulfur vacancies-free CAs without any dielectric *** defects-rich heterostructures,this one-pot CACR strategy may steer the design and development of advanced nanomaterials,boosting functionality across diverse application domains beyond electromagnetic response.

关键词： Defects-rich heterointerfaces Sulfides Polarization coupling Electromagnetic wave absorption

A Distributed User Association and Resource Allocation Method in Cache-Enabled Small Cell Networks

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China Communications 2017年第10期14卷 95-107页

作者： Gongye Ren Hua Qu Jihong Zhao Shuyuan Zhao Zhirong Luan school of Electronic and information engineering Xi'an Jiaotong University school of telecommunication and information engineering Xi'an University of Posts and Telecommunications

Cache-enabled small cell networks have been regarded as a promising approach for network operators to cope with the explosive data traffic growth in future 5 G networks. However, the user association and resource allocation mechanism has not been thoroughly studied under given content placement situation. In this paper, we formulate the joint optimization problem of user association and resource allocation as a mixed integer nonlinear programming(MINLP) problem aiming at deriving a balance between the total utility of data rates and the total data rates retrieved from caches. To solve this problem, we propose a distributed relaxing-rounding method. Simulation results demonstrate that the distributed relaxing-rounding method outperforms traditional max-SINR method and range-expansion method in terms of both total utility of data rates and total data rates retrieved from caches in practical scenarios. In addition, effects of storage and backhaul capacities on the performance are also studied.

关键词： proactive caching user association resource allocation distributed algorithm dual decomposition small cell networks

Robust Digital Non-Linear Self-Interference Cancellation in Full Duplex Radios with Maximum Correntropy Criterion

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China Communications 2016年第9期13卷 53-59页

作者： Zhirong Luan Hua Qu Jihong Zhao Badong Chen school of Electronic and information engineering Xi'an Jiaotong University school of telecommunication and information engineering Xi'an University of Posts and Telecommunications

Full duplex radio increases the frequency efficiency but its performance is limited by the self-interference （SI）. We first analyze the multiple noises in the full duplex radio system and model such noises as an α - stable distribution. Then we formulate a novel non-Gaussian SI problem. Under the maximum correntropy criterion （MCC）, a robust digital non-linear self-interference cancellation algorithm is proposed for the SI channel estimation. A gradient descent based algorithm is derived to search the optimal solution. Simulation results show that the proposed algorithm can achieve a smaller estimation error and a higher pseudo signal to interference plus noise ratio （PSINR） than the well-known least mean square （LMS） algorithm and least square （LS） algorithm.

关键词： full duplex radio robust estimation digital non-linear self-interference can- cellation a-stable noise correntropy