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Statistical Reasoning of High school Teachers in a computer Environment of Dynamic Data Exploration

Journal of Mathematics and System Science 2012年第2期2卷 133-138页

作者： Santiago Inzunsa Cazares Jose Alfredo Juarez Duarte Faculty of computer Science Autonomous University of Sinaloa Josefa Ortiz de Dominguez S/N. Poligono Sur Ciudad Universitaria CP. 80040 Culiacan Sinaloa Mexico

Results of a research about statistical reasoning that six high school teachers developed in a computer environment are presented in this article. A sequence of three activities with the support of software Fathom was presented to the teachers in a course to investigate about the reasoning that teachers develop about the data analysis, particularly about the distribution concept, that involves important concepts such as averages, variability and graphics representations. The design of the activities was planned so that the teachers analyzed quantitative variables separately first, and later made an analysis of a qualitative variable versus a quantitative variable with the objective of establishing comparisons between distributions and use concepts as averages, variability, shape and outliers. The instructions in each activity indicated to the teachers to use all the resources of the software that were necessary to make the complete analysis and respond to certain questions that pretended to capture the type of representations they used to answer. The results indicate that despite the abundance of representations provided by the software, teachers focu,; on the calculation of averages to describe and compare distributions, rather than on the important properties of data such as variability, ：shape and outliers. Many teachers were able to build interesting graphs reflecting important properties of the data, but cannot use them 1：o support data analysis. Hence, it is necessary to extend the teachers＇ understanding on data analysis so they can take advantage of the cognitive potential that computer tools to offer.

关键词： Statistical reasoning data analysis computer environment to teaching

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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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Inspires effective alternatives to backpropagation:predictive coding helps understand and build learning

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Neural Regeneration Research 2025年第11期20卷 3215-3216页

作者： Zhenghua Xu Miao Yu Yuhang Song school of Health Sciences and Biomedical Engineering Hebei University of TechnologyTianjinChina Department of computer Science University of OxfordOxfordUK

Artificial neural networks are capable of machine learning by simulating the hiera rchical structure of the human *** enable learning by brain and machine,it is essential to accurately identify and correct the prediction errors,referred to as credit assignment(Lillicrap et al.,2020).It is critical to develop artificial intelligence by understanding how the brain deals with credit assignment in neuroscience.

关键词： assignment learning enable

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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

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Blood diagnostic and prognostic biomarkers in amyotrophic lateral sclerosis

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Neural Regeneration Research 2025年第9期20卷 2556-2570页

作者： Yongting Lv Hongfu Li Department of Neurology Second Affiliated HospitalZhejiang University School of MedicineHangzhouZhejiang ProvinceChina Department of Medical Genetics and Center for Rare disease Second Affiliated HospitalZhejiang University School of MedicineHangzhouZhejiang ProvinceChina Nanhu Brain-computer Interface Institute HangzhouZhejiang Province

Amyotrophic lateral sclerosis is a devastating neurodegenerative disease for which the current treatment approaches remain severely *** principal pathological alterations of the disease include the selective degeneration of motor neurons in the brain,brainstem,and spinal cord,as well as abnormal protein deposition in the cytoplasm of neurons and glial *** biological markers under extensive scrutiny are predominantly located in the cerebrospinal fluid,blood,and even *** these biomarke rs,neurofilament proteins and glial fibrillary acidic protein most accurately reflect the pathologic changes in the central nervous system,while creatinine and creatine kinase mainly indicate pathological alterations in the peripheral nerves and *** light chain levels serve as an indicator of neuronal axonal injury that remain stable throughout disease progression and are a promising diagnostic and prognostic biomarker with high specificity and ***,there are challenges in using neurofilament light chain to diffe rentiate amyotrophic lateral sclerosis from other central nervous system diseases with axonal *** fibrillary acidic protein predominantly reflects the degree of neuronal demyelination and is linked to non-motor symptoms of amyotrophic lateral sclerosis such as cognitive impairment,oxygen saturation,and the glomerular filtration *** DNA-binding protein 43,a pathological protein associated with amyotrophic lateral sclerosis,is emerging as a promising biomarker,particularly with advancements in exosome-related *** is currently lacking for the value of creatinine and creatine kinase as diagnostic markers;however,they show potential in predicting disease *** the vigorous progress made in the identification of amyotrophic lateral sclerosis biomarkers in recent years,the quest for definitive diagnostic and prognostic biomarke rs remains a formidable *** review summarizes the lat

关键词： amyotrophic lateral sclerosis biomarker blood biomarkers diagnosis glial fibrillary acidic protein neurofilament light chain prognosis TAR DNA-binding protein 43

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Global Piecewise Analysis of HIV Model with Bi-Infectious Categories under Ordinary Derivative and Non-Singular Operator with Neural Network Approach

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computer Modeling in Engineering & Sciences 2025年第1期142卷 609-633页

作者： Ghaliah Alhamzi Badr Saad TAlkahtani Ravi Shanker Dubey Mati ur Rahman Department of Mathematics and Statistics College of ScienceImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh13318Saudi Arabia Department of Mathematics College of ScienceKing Saud UniversityRiyadh11989Saudi Arabia Department of Mathematics Amity School of Applied SciencesAmity University RajasthanJaipur302002India school of Mathematical Sciences Jiangsu UniversityZhenjiang212013China Department of computer Science and Mathematics Lebanese American UniversityBeirut13-5053Lebanon

This study directs the discussion of HIV disease with a novel kind of complex dynamical generalized and piecewise operator in the sense of classical and Atangana Baleanu(AB)derivatives having arbitrary *** HIV infection model has a susceptible class,a recovered class,along with a case of infection divided into three sub-different levels or categories and the recovered *** total time interval is converted into two,which are further investigated for ordinary and fractional order operators of the AB derivative,*** proposed model is tested separately for unique solutions and existence on bi *** numerical solution of the proposed model is treated by the piece-wise numerical iterative scheme of Newtons *** proposed method is established for piece-wise derivatives under natural order and non-singular Mittag-Leffler *** cross-over or bending characteristics in the dynamical system of HIV are easily examined by the aspect of this research having a memory effect for controlling the said *** study uses the neural network(NN)technique to obtain a better set of weights with low residual errors,and the epochs number is considered *** obtained figures represent the approximate solution and absolute error which are tested with NN to train the data accurately.

关键词： HIV infection model qualitative scheme approximate solution piecewise global operator neural network

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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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ANALYSIS OF DISCRETE-TIME QUEUES WITH BATCH RENEWAL INPUT AND MULTIPLE VACATIONS

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Journal of Systems Science & Complexity 2012年第3期25卷 486-503页

作者： Veena GOSWAMI G.B.MUND school of computer Application KIIT University school of computer Engineering KIIT University

This paper analyzes a discrete-time multiple vacations finite-buffer queueing system with batch renewal input in which inter-arrival time of batches are arbitrarily distributed. Service and vacation times are mutually independent and geometrically distributed. The server takes vacations when the system does not have any waiting jobs at a service completion epoch or a vacation completion epoch. The system is analyzed under the assumptions of late arrival system with delayed access and early arrival system. Using the supplementary variable and the imbedded Markov chain techniques, the authors obtain the queue-length distributions at pre-arrival, arbitrary and outside observer＇s ob- servation epochs for partial-batch rejection policy. The blocking probability of the first, an arbitrary- and the last-job in a batch have been discussed. The analysis of actual waiting-time distributions measured in slots of the first, an arbitrary- and the last-job in an accepted batch, and other performance measures along with some numerical results have also been investigated.

关键词： Batch input discrete-time finite buffer multiple vacations partial-batch rejection.

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ANALYSIS OF A DISCRETE-TIME GI/GEO/1/N QUEUE WITH MULTIPLE WORKING VACATIONS

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Journal of Systems Science and Systems Engineering 2010年第3期19卷 367-384页

作者： Veena GOSWAMI G.B.MUND school of computer Application KIIT University school of computer Engineering KIIT University

This paper analyzes a finite-buffer renewal input single server discrete-time queueing system with multiple working vacations. The server works at a different rate rather than completely stopping working during the multiple working vacations. The service times during a service period, service time during a vacation period and vacation times are geometrically distributed. The queue is analyzed using the supplementary variable and the imbedded Markov-chain techniques. We obtain steady-state system length distributions at pre-arrival, arbitrary and outside observer＇s observation epochs. The analysis of actual waiting-time distribution and some performance measures are carried out. We present some numerical results and discuss special cases of the model.

关键词： Discrete-time finite-buffer working vacations supplementary variable waiting-time

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LKLR: A Local Tangent Space-Alignment Kernel Least-Squares Regression Algorithm

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Tsinghua Science and Technology 2019年第4期24卷 389-399页

作者： Chao Tan Genlin Ji the school of computer Science and Engineering Southeast University the school of computer Science and Technology Nanjing Normal University the school of computer Science and Technology Nanjing Normal University

In the fields of machine learning and data mining, label learning is a nascent area of research, and within this paradigm, there is much room for improving multi-label manifold learning algorithms for high-dimensional data. Thus far, researchers have experimented with mapping relationships from the feature space to the traditional logical label space(using neighbors in the label space, for example, to predict logical label vectors from the feature space's manifold structure). Here we combine the feature manifold's and label space's local topological structures to reconstruct the label manifold. To achieve this, we use a nonlinear manifold learning algorithm to transform the local topological structure from the feature space to the label space. Our algorithm adopts a regularized leastsquares kernel method to realize the reconstruction process, employing an optimization function to find the best solution. Extensive experiments show that our algorithm significantly improves multi-label manifold learning in terms of learning accuracy and time complexity.

关键词： multi-label manifold learning local topological structure regularized least squares kernel method

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