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An Improved K-means Clustering Algorithm Towards an Efficient Data-Driven Modeling

Annals of Data science 2024年第5期11卷 1525-1544页

作者： Zubair, Md. Iqbal, MD. Asif Shil, Avijeet Chowdhury, M.J.M. Moni, Mohammad Ali Sarker, Iqbal H. Department of Computer Science and Engineering Chittagong University of Engineering & ampTechnology Chittagong4349 Bangladesh Department of Computer Science and Information Technology La Trobe University Victoria3086 Australia School of Health and Rehabilitation Sciences Faculty of Health and Behavioural Sciences The University of Queensland St Lucia QLD4072 Australia

K-means algorithm is one of the well-known unsupervised machine learning algorithms. The algorithm typically finds out distinct non-overlapping clusters in which each point is assigned to a group. The minimum squared distance technique distributes each point to the nearest clusters or subgroups. One of the K-means algorithm’s main concerns is to find out the initial optimal centroids of clusters. It is the most challenging task to determine the optimum position of the initial clusters’ centroids at the very first iteration. This paper proposes an approach to find the optimal initial centroids efficiently to reduce the number of iterations and execution time. To analyze the effectiveness of our proposed method, we have utilized different real-world datasets to conduct experiments. We have first analyzed COVID-19 and patient datasets to show our proposed method’s efficiency. A synthetic dataset of 10M instances with 8 dimensions is also used to estimate the performance of the proposed algorithm. Experimental results show that our proposed method outperforms traditional kmeans++ and random centroids initialization methods regarding the computation time and the number of iterations. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.

关键词： Principal component analysis

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Predicting Microbe-Disease Association Based on Heterogeneous Network and Global Graph Feature Learning

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Chinese Journal of Electronics 2022年第2期31卷 345-353页

作者： WANG Yueyue LEI Xiujuan PAN Yi School of Computer Science Shaanxi Normal University Department of Computer Science Georgia State University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences

Numerous microbes inhabit human body,making a vast difference in human health. Hence, discovering associations between microbes and diseases is beneficial to disease prevention and treatment. In this study,we develop a prediction method by learning global graph feature on the heterogeneous network(called HNGFL).Firstly, a heterogeneous network is integrated by known microbe-disease associations and multiple *** on microbe Gaussian interaction profile(GIP) kernel similarity, we consider different effects of these microbes on organs in the human body to further improve microbe similarity. For disease similarity network, we combine GIP kernel similarity, disease semantic similarity and disease-symptom similarity. And then, an embedding algorithm called GraRep is used to learn global structural information for this network. According to vector feature of every node, we utilize support vector machine classifier to calculate the score for each microbe-disease pair. HNGFL achieves a reliable performance in cross validation, outperforming the compared methods. In addition, we carry out case studies of three diseases. Results show that HNGFL can be considered as a reliable method for microbe-disease association prediction.

关键词： microorganisms global graph feature learning support vector machines patient diagnosis global structural information disease semantic similarity disease-symptom similarity HNGFL pattern classification graph theory microbe Gaussian interaction profile kernel similarity bioinformatics support vector machine classifier GraRep diseases learning (artificial intelligence) microbe-disease association prediction heterogeneous network Gaussian processes GIP kernel similarity

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Automated Brain Tumor Diagnosis Using Deep Residual U-Net Segmentation Model

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computers, Materials & Continua 2023年第1期74卷 2179-2194页

作者： R.Poonguzhali Sultan Ahmad P.Thiruvannamalai Sivasankar S.Anantha Babu Pranav Joshi Gyanendra Prasad Joshi Sung Won Kim Department of Computer Science and Engineering Periyar Maniammai Institute of Science and TechnologyThanjavur613403India Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharjSaudi Arabia Department of Computer Science and Engineering Jain Deemed to-be UniversityBangalore560069India Department of Neurology Annapurna Neuro HospitalKathmandu44600Nepal Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-siGyeongbuk-do38541Korea

Automated segmentation and classification of biomedical images act as a vital part of the diagnosis of brain tumors(BT).A primary tumor brain analysis suggests a quicker response from treatment that utilizes for improving patient survival *** location and classification of BTs from huge medicinal images database,obtained from routine medical tasks with manual processes are a higher cost together in effort and *** automatic recognition,place,and classifier process was desired and *** study introduces anAutomatedDeepResidualU-Net Segmentation with Classification model(ADRU-SCM)for Brain Tumor *** presentedADRUSCM model majorly focuses on the segmentation and classification of *** accomplish this,the presented ADRU-SCM model involves wiener filtering(WF)based preprocessing to eradicate the noise that exists in *** addition,the ADRU-SCM model follows deep residual U-Net segmentation model to determine the affected brain ***,VGG-19 model is exploited as a feature ***,tunicate swarm optimization(TSO)with gated recurrent unit(GRU)model is applied as a classification model and the TSO algorithm effectually tunes *** performance validation of the ADRU-SCM model was tested utilizing FigShare dataset and the outcomes pointed out the better performance of the ADRU-SCM approach on recent approaches.

关键词： Brain tumor diagnosis image classification biomedical images image segmentation deep learning

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ORIGINAL STUDY Data Envelopment Analysis using Stochastic Frontier Analysis and Bootstrap Confidence Intervals

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer science and Mathematics 2025年第2期6卷 150-166页

作者： Alqahtani, Fahad F. Department of computer science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj11942 Saudi Arabia

Evaluating company growth potential has moved away from traditional financial focused ratios and ratios analysis that has origins in the early twentieth-century economics. However, these conventional methods might not be accurate in measuring such efficient factors as this combined proposed framework of Data Envelopment Analysis (DEA) and improved mathematical models do. The present research focuses on the prospect of growth of companies through evaluating the performance of 40 DMUs in terms of efficiency DEA and MMTs. DEA is used to determine the efficient DMUs while SFA underline the factors such as investment on research and development, effective marketing strategies and qualified human resource as the determinants of efficiency. Bootstrap Confidence Intervals (BCIs) are employed for the purpose of improving the efficiency scores precision. The integration of DEA with MMTs presents useful information on the identification of the key drivers of the growth and the effective formulation of efficient and competitive strategies for the managers and policymaker. The study emphasizes the need to apply these combined methodologies as valid in the growth evaluation of companies with implications to strategic decisions. This proposed framework can be used in finance, marketing, healthcare, and operations management to compare the current organizational effectiveness and future development. © 2025 The Author(s).

关键词： Analytical models

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Review of dehazing techniques: challenges and future trends

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Multimedia Tools and Applications 2025年第3期84卷 1103-1131页

作者： Ayoub, Abeer El-Shafai, Walid El-Samie, Fathi E. Abd Hamad, Ehab K. I. EL-Rabaie, El-Sayed M. Department of Electronics and Electrical Communication Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh11671 Saudi Arabia Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt

The phenomenon of atmospheric haze arises due to the scattering of light by minute particles suspended in the atmosphere. This optical effect gives rise to visual degradation in images and videos. The degradation is primarily influenced by two key factors: atmospheric attenuation and scattered light. Scattered light causes an image to be veiled in a whitish veil, while attenuation diminishes the image inherent contrast. Efforts to enhance image and video quality necessitate the development of dehazing techniques capable of mitigating the adverse impact of haze. This scholarly endeavor presents a comprehensive survey of recent advancements in the domain of dehazing techniques, encompassing both conventional methodologies and those founded on machine learning principles. Traditional dehazing techniques leverage a haze model to deduce a dehazed rendition of an image or frame. In contrast, learning-based techniques employ sophisticated mechanisms such as Convolutional Neural Networks (CNNs) and different deep Generative Adversarial Networks (GANs) to create models that can discern dehazed representations by learning intricate parameters like transmission maps, atmospheric light conditions, or their combined effects. Furthermore, some learning-based approaches facilitate the direct generation of dehazed outputs from hazy inputs by assimilating the non-linear mapping between the two. This review study delves into a comprehensive examination of datasets utilized within learning-based dehazing methodologies, elucidating their characteristics and relevance. Furthermore, a systematic exposition of the merits and demerits inherent in distinct dehazing techniques is presented. The discourse culminates in the synthesis of the primary quandaries and challenges confronted by prevailing dehazing techniques. The assessment of dehazed image and frame quality is facilitated through the application of rigorous evaluation metrics, a discussion of which is incorporated. To provide empiri

关键词： Generative adversarial networks

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Optimizing abnormality detection in fundus images with Triplet-OS and orchard search optimization model

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Neural Computing and Applications 2024年第36期36卷 23181-23194页

作者： Venkatraman, K. Hemalatha, R. Radhika, S. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha Nagar Thandalam Chennai India Department of Computer Science and Engineering St. Joseph’s College of Engineering Chennai India Department of EEE Sathyabama Institute of Science and Technology Chennai India

The accurate and early detection of abnormalities in fundus images is crucial for the timely diagnosis and treatment of various eye diseases, such as glaucoma and diabetic retinopathy. The detection of abnormalities in fundus images using traditional methods is often challenging due to high computational demands, scalability issues, and the requirement of large labeled datasets for effective training. To address these limitations, a new method called triplet-based orchard search (Triplet-OS) has been proposed in this paper. In this study, a GoogleNet (Inception) is utilized for feature extraction of fundus images. Also, the residual network is employed to detect abnormalities in fundus images. The Triplet-OS utilizes the medical imaging technique fundus photography dataset to capture detailed images of the interior surface of the eye, known as the fundus and the fundus includes the retina, optic disk, macula, and blood vessels. To enhance the performance of the Triplet-OS method, the orchard optimization algorithm has been implemented with an initial search strategy for hyperparameter optimization. The performance of the Triplet-OS method has been evaluated based on different metrics such as F1-score, specificity, AUC-ROC, recall, precision, and accuracy. Additionally, the performance of the proposed method has been compared with existing methods. Few-shot learning refers to a process where models can learn from just a small number of examples. This method has been applied to reduce the dependency on deep learning [1]. The goal is for machines to become as intelligent as humans. Today, numerous computing devices, extensive datasets, and advanced methods such as CNN and LSTM have been developed. AI has achieved human-like performance and, in many fields, surpasses human abilities. AI has become part of our daily lives, but it generally relies on large-scale data. In contrast, humans can often apply past knowledge to quickly learn new tasks [2]. For example, if given

关键词： Medical imaging

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Resilient TCP Variant Enabling Smooth Network Updates for Software-Defined Data Center Networks

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Tsinghua science and Technology 2024年第5期29卷 1615-1632页

作者： Abdul Basit Dogar Sami Ullah Yiran Zhang Hisham Alasmary Muhammad Waqas Sheng Chen Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China and also with Department of Informatics and SystemsUniversity of Management and TechnologyLahore 54660Pakistan Department of Computer Science Shaheed Benazir Bhutto UniversitySheringalUpper Dir 18050Pakistan School of Computer Science Beijing University of Posts and TelecommunicationsBeijing 100876China Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha 61421Saudi Arabia Department of Computer Engineering Faculty of Information TechnologyUniversity of BahrainSakheer 32038Bahrainand also with School of EngineeringEdith Cowan UniversityPerth WA 6027Australia School of Electronics and Computer Science University of SouthamptonSouthamptonSO171BJUK

Network updates have become increasingly prevalent since the broad adoption of software-defined networks(SDNs)in data *** TCP designs,including cutting-edge TCP variants DCTCP,CUBIC,and BBR,however,are not resilient to network updates that provoke flow *** this paper,we first demonstrate that popular TCP implementations perform inadequately in the presence of frequent and inconsistent network updates,because inconsistent and frequent network updates result in out-of-order packets and packet drops induced via transitory congestion and lead to serious performance *** look into the causes and propose a network update-friendly TCP(NUFTCP),which is an extension of the DCTCP variant,as a *** are used to assess the proposed *** findings reveal that NUFTCP can more effectively manage the problems of out-of-order packets and packet drops triggered in network updates,and it outperforms DCTCP considerably.

关键词： software defined data center networks network updates DCTCP out-of-order packets packet drop SDN

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Machine Learning-Based Chronic Kidney Cancer Prediction Application: A Predictive Analytics Approach

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Biomedical Materials and Devices 2024年第2期2卷 1028-1048页

作者： Uddin, Khandaker Mohammad Mohi Nahid, Md. Nuzmul Hossain Ullah, Md. Mehedi Hasan Mazumder, Badhan Khan, Md. Saikat Islam Dey, Samrat Kumar Department of Computer Science and Engineering Dhaka International University Faculty of Computer Science and Engineering Patuakhali Science and Technology University School of Science and Technology Bangladesh Open University

Chronic Kidney Cancer (CKC) is a disease that hindrances the blood-filtering mechanism of the kidney and is increasing at an alarming rate in the recent few years. As CKC does not show any earlier symptoms, the earlier prediction will be very effective to elevate its effect. In this paper, a machine learning-based method for diagnosing CKC at an early stage is proposed. SVM, Decision Tree, Random Forest, KNN, Native Bayes, Logistic Regression, LGBM, CatBoost, and AdaBoost are employed for classification purposes, and among these algorithms, Random Forest, AdaBoost, and LGBM give 99% accuracy. Data pre-processing and feature selection help improve the accuracy of the proposed model. The outcome of the comparative analysis of our proposed work with eight existing approaches proves the robustness and supremacy of the work. Furthermore, the best model is also used to develop a user-friendly web application. © The Author(s), under exclusive licence to Springer science+Business Media, LLC 2023.

关键词： AdaBoost Chronic kidney cancer Machine learning Random forest and LGBM Web application

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LWTSEO-VT: Lightweight TwinScope Election Optimizer based-Vision Transformer for Autism Spectral Disorder Detection for Toddlers and Adults 2

LWTSEO-VT: Lightweight TwinScope Election Optimizer based-Vi...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Balasubramani, Jagadesh Surendran, R. Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering India Saveetha School of Engineering Department of Computer Science and Engineering India

ISBN: (纸本)9798331534950

autism spectrum disorder (ASD) is a neurological condition that disturbs an individual's capability to attach and communicate with others. It instigates in childhood and continues beyond adolescence and adulthood. Because of complicated procedures and limitations of existing methods, it has been difficult to diagnose. One unsolved problem is determining how to learn discriminative deep representations for building computational models. In this paper, a new Lightweight TwinScope Election Optimizer based-Vision Transformer (LWTSEO-VT) for ASD recognition is proposed for toddlers and adults. First, pre-processing is applied to improve the data quality and then extracted the features using Path-selective LASSO regression model. Lastly, a Lightweight TwinScope Vision Transformer (LWTS-VT) based on election optimizer is used for detecting autism. The LWTSEO-VT model implemented using python platform using autism screening data for toddlers and autism screening data for adults datasets. The efficacy and dependability of the LWTSEO-VT model have been demonstrated by quantitative comparisons © 2024 IEEE.

关键词： Diseases

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Dynamics analysis and cryptographic implementation of a fractional-order memristive cellular neural network model

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Chinese Physics B 2024年第4期33卷 418-433页

作者：周新卫蒋东华 Jean De Dieu Nkapkop Musheer Ahmad Jules Tagne Fossi Nestor Tsafack 吴建华 Department of Information Engineering Gongqing CollegeNanchang UniversityJiujiang 332020China School of Computer Science and Engineering Sun Yat-Sen UniversityGuangzhou 511400China Department of Electrical Engineering and Industrial Computing University Institute of TechnologyDoualaCameroon Department of Computer Engineering Jamia Millia IslamiaNew Delhi 110025India Department of Physics Faculty of ScienceUniversity of YaoundeCameroon Electrical Engineering Department and Industrial Computing of ISTAMA University of DoualaDoualaCameroon

Due to the fact that a memristor with memory properties is an ideal electronic component for implementation of the artificial neural synaptic function,a brand-new tristable locally active memristor model is first proposed in this ***,a novel four-dimensional fractional-order memristive cellular neural network(FO-MCNN)model with hidden attractors is constructed to enhance the engineering feasibility of the original CNN model and its ***,its hardware circuit implementation and complicated dynamic properties are investigated on multi-simulation ***,it is used toward secure communication application *** it as the pseudo-random number generator(PRNG),a new privacy image security scheme is designed based on the adaptive sampling rate compressive sensing(ASR-CS)***,the simulation analysis and comparative experiments manifest that the proposed data encryption scheme possesses strong immunity against various security attack models and satisfactory compression performance.

关键词： cellular neural network memristor hardware circuit compressive sensing privacy data protection

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