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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Deepak Sharma Amit Singhal Satvik Vats Bhavesh Kumar Sharma Department of Computer Applications SRM Institute of Science and Technology Ghaziabad UP India Computer Science & Engineering Raj Kumar Goel Institute of Technology Ghaziabad UP India Computer Science & Engineering Graphic Era Hill University Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Applications GL Bajaj Institute of Technology & Management Greater Noida UP India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

The cause of this summary is to offer a short assessment of the modern research on using synthetic Intelligence (AI) and device getting to know (ML) algorithms for advanced clustering and predictive modeling in smart healthcare. This study gives a complete evaluation of the literature at the utility of AI/ML algorithms in clever healthcare. The main targets of this paper include discovering the talents and limitations of the AI/ML algorithms utilized in smart healthcare, consisting of in medical decision help, disorder analytics, and patient risk stratification. Via an examination of several studies, the authors gift unique AI/ML effects and their implications for clever healthcare. They advise a workflow for predictive modeling in clever healthcare and talk extraordinary AI/ML algorithms used on this context. In the end, the paper focuses on the potential advantages and demanding situations associated with the deployment of AI/ML technologies in clever healthcare. The smart healthcare has the capacity to revolutionize the transport of care and will preserve to enjoy the insights derived from superior clustering and predictive modeling strategies.

关键词： clustering algorithms Medical services Predictive models Prediction algorithms Market research Data models Energy efficiency Telecommunication computing Artificial intelligence Predictive analytics

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Study of Explainability Analysis Methods for the LAMDA Family algorithms in Classification and clustering Tasks

Study of Explainability Analysis Methods for the LAMDA Famil...

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International Joint Conference on Neural Networks (IJCNN)

作者： Carlos Quintero Gull Jose Aguilar Rodrigo García Dpto de Cs Aplicadas y Humanísticas Facultad Ingeniería Universidad de Los Andes Mérida Venezuela IMDEA Networks Institute Madrid Spain CEMISID Universidad de Los Andes Mérida Venezuela GIDITIC Universidad EAFIT Medellín Colombia Universidad del Sinú Monteria Colombia

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Explainability analysis is a very relevant topic today, due to the interest of allowing the interpretability of machine learning models. In this work, we carry out an in-depth study of explainability analysis for the algorithms of the LAMDA (Learning Algorithm for Multivariate Data Analysis) family that have been used in the context of supervised and unsupervised learning. In particular, for the case of classification the LAMDA-HAD algorithm, and for the case of clustering the LAMDA-RD algorithm. For the explainability analysis, two classic methods from the explainability area were considered, LIME (Local Interpretable Model-Agnostic Explanation) and Feature Importance, and another one developed by us for the LAMDA family. In particular, our explainability method for LAMDA allows measuring the importance of each characteristic in a general way, and for each cluster. In general, the results obtained in both cases (classification and clustering) are satisfactory, especially because our explainability method for LAMDA gives an explainability similar to the traditional ones, but in addition, it can be given by cluster.

关键词： Analytical models Machine learning algorithms Neural networks clustering algorithms Machine learning Particle measurements Classification algorithms Viruses (medical) Unsupervised learning Cancer

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Development of clustering Methods Based on Interval Fuzzy Sets of the Second Type and Genetic algorithms

Development of Clustering Methods Based on Interval Fuzzy Se...

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Smart Information Systems and Technologies (SIST), IEEE International Conference on

作者： Oleksandr Terentiev Olha Serpinska Alex Zabarylo Yulia Korotkikh Pavlo Zabarylo Department of ITDAM Kyiv National University of Construction and Architecture Kiev Ukraine Department of Higher Mathematics Kyiv National University of Construction and Architecture Kiev Ukraine Department of Architectural Structures Kyiv National University of Construction and Architecture Kiev Ukraine

ISBN: (数字)9798350374865

ISBN: (纸本)9798350374872

Cluster analysis is often used to solve the problems of assessing the technical condition of construction objects. Among clustering methods, the cmeans method is the most popular. The application of the apparatus of the theory of fuzzy sets together with the method of c-means, namely, the algorithm of fuzzy cmeans, gives good results. The article considers the problem of solving the problem of object clustering using the algorithm of possible c-means together with interval fuzzy sets of the second type and the genetic algorithm. The results presented in the article demonstrate stable good results, which confirms the expediency of its application in solving clustering problems.

关键词： Fuzzy sets Uncertainty clustering methods clustering algorithms Genetic algorithms Information systems

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The Metagenomic Binning Problem: clustering Markov Sequences

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IEEE TRANSACTIONS ON MOLECULAR BIOLOGICAL AND MULTI-SCALE COMMUNICATIONS 2024年第1期10卷 32-42页

作者： Greenberg, Grant Shomorony, Ilan Univ Illinois Dept Elect & Comp Engn Urbana IL 61801 USA

The goal of metagenomics is to study the composition of microbial communities, typically using high -throughput shotgun sequencing. In the metagenomic binning problem, we observe random substrings (called contigs) from a mixture of genomes and aim to cluster them according to their genome of origin. Based on the empirical observation that genomes of different bacterial species can be distinguished based on their tetranucleotide frequencies, we model this task as the problem of clustering N sequences generated by M distinct Markov processes, where M << N. Utilizing the large -deviation principle for Markov processes, we establish the information -theoretic limit for perfect binning. Specifically, we show that the length of the contigs must scale with the inverse of the Chernoff divergence rate between the two most similar species. Furthermore, our result implies that contigs should be binned using the KL divergence rate as a measure of distance, as opposed to the Euclidean distance often used in practice.

关键词： Biological information theory metagenomics clustering algorithms

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Research on Penicillin Concentration Soft Sensing Method based on K-means++ clustering and Improved SVR algorithms

Research on Penicillin Concentration Soft Sensing Method bas...

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Computer, Big Data and Artificial Intelligence (ICCBD+AI), International Conference on

作者： Jian-Heng Li Yu-Ting Du Bo Liu Guang-Jie Li Ben-Rong Wang School of Mechanical and Electrical Engineering Central South University Changsha China School of Information Engineering Jingdezhen University Jingdezhen China

ISBN: (数字)9798331533991

ISBN: (纸本)9798331534004

For the problem that penicillin concentration cannot be measured by hardware sensors in the production process, a soft sensing method is proposed based on the combination of K-means++ clustering and improved SVR algorithm. Firstly, the production conditions are clustered by using the K-means algorithm to obtain the initial number of clusters. Then, by using K-means++ for improved clustering results, the initial cluster centers are optimized. Secondly, an improved SVR algorithm combining grid search and cross-validation is utilized to construct a predictive model for penicillin concentration. Finally, the data generated by the penicillin fermentation process simulation platform are utilized for simulation experiments. The simulation results are compared among three methods: K-means++ with improved SVR, K-means with improved SVR, and adaptive AP-GPR. The results indicate that the proposed soft sensing has better evaluation indicators and higher prediction accuracy.

关键词： Adaptation models Accuracy Soft sensors Computational modeling Simulation clustering algorithms Production Predictive models Prediction algorithms Classification algorithms

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Differentially Private Federated clustering Over Non-IID Data

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IEEE Internet of Things Journal 2024年第4期11卷 6705-6721页

作者： Li, Yiwei Wang, Shuai Chi, Chong-Yung Quek, Tony Q. S. Institute of Communications Engineering National Tsing Hua University Hsinchu30013 Taiwan Singapore University of Technology and Design Information Systems Technology and Design Tampines487372 Singapore Yonsei University Yonsei Frontier Laboratory Seoul03722 Korea Republic of

In this article, we investigate the federated clustering (FedC) problem, which aims to accurately partition unlabeled data samples distributed over massive clients into finite clusters under the orchestration of a parameter server (PS), meanwhile considering data privacy. Though it is an NP-hard optimization problem involving real variables denoting cluster centroids and binary variables denoting the cluster membership of each data sample, we judiciously reformulate the FedC problem into a nonconvex optimization problem with only one convex constraint, accordingly yielding a soft clustering solution. Then, a novel FedC algorithm using differential privacy (DP) technique, referred to as DP- FedC, is proposed in which partial clients participation (PCP) and multiple local model updating steps are also considered. Furthermore, various attributes of the proposed DP- FedC are obtained through theoretical analyses of privacy protection and convergence rate, especially for the case of nonidentically and independently distributed (non-i.i.d.) data, that ideally serve as the guidelines for the design of the proposed DP- FedC. Then, some experimental results on two real datasets are provided to demonstrate the efficacy of the proposed DP- FedC together with its much superior performance over some state-of-the-art FedC algorithms, and the consistency with all the presented analytical results. © 2014 IEEE.

关键词： clustering algorithms

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DAAG-SNP: Energy Efficient Distance and Angulation-Based Agglomerative clustering for Sink Node Placement

IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY

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IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY 2024年 5卷 5013-5026页

作者： Hanif, Maria Ahmad, Rizwan Ahmed, Waqas Drieberg, Micheal Alam, Muhammad Mahtab Natl Univ Sci & Technol NUST Sch Elect Engn & Comp Sci Islamabad 44000 Pakistan Pakistan Inst Engn & Appl Sci PIEAS Dept Elect Engn Islamabad 44000 Pakistan Univ Teknol PETRONAS Dept Elect & Elect Engn Seri Iskandar 32610 Malaysia Tallinn Univ Technol Thomas Johann Seebeck Dept Elect EE-12616 Tallinn Estonia

Wireless Body Area Networks (WBANs) have significantly enhanced various aspects of human life, particularly in healthcare, fitness, entertainment, sports, and etc. In WBANs, the sensor nodes are placed in and around the body along with the sink node, which collects the physiological data from these sensors and forwards it for further processing. The placement of the sink node is one of the critical aspects in the design of WABNs as it affects both the energy efficiency and connectivity. To this end, this paper introduces a hybrid method called Distance and Angulation based AGglomerative clustering (DAAG). DAAG, initially clusters the WBAN sensors using Distance and Angulation based k-Mean clustering. Afterward, Agglomerative clustering is applied to determine the optimal placement of the sink node. The results of DAAG are compared with various machine learning and optimization approaches, including D-RMS (Distance based Random mean shift clustering), Reinforcement Q-Learning Approach (QL), Humpback Whale optimization (HWOA), Multi-Angulation (MA) and Closeness Centrality (CC). Given an initial energy, the results show that the DAAG exhibits superior performance in terms of latency, packet error rate (PER), and energy consumption. DAAG shows an energy consumption of only 1.51% outperforming QL, HWOA, MA, CC, and D-RMS along with an improved localization accuracy of 0.36 m.

关键词： Wireless communication Body area networks Energy efficiency clustering algorithms Bit error rate Q-learning Energy consumption WBAN sink node placement energy efficiency optimization machine learning clustering multiangulation mean shift Humpback Whale packet error rate

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AKDC: Ambiguous Kernel Distance clustering Algorithm for COVID-19 CT Scans Analysis

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2024年第10期54卷 6218-6229页

作者： Singh, Pritpal Huang, Yo-Ping The Quantum Optimization Research Lab Department of Data Science and Analytics Central University of Rajasthan Ajmer305817 India National Taipei University of Technology Department of Electrical Engineering Taipei10608 Taiwan National Taipei University Department of Computer Science and Information Engineering New Taipei City23741 Taiwan Chaoyang University of Technology Department of Information and Communication Engineering Taichung41349 Taiwan

Conventional soft clustering algorithms perform well on linearly distributed features, but their performance degrades on nonlinearly distributed features in high-dimensional space. In this study, a novel soft clustering algorithm, the ambiguous kernel distance clustering (AKDC) algorithm, is presented. This algorithm is developed by applying ambiguous set theory and the Gaussian kernel function. The ambiguous set theory defines the ambiguities inherent in each feature with four membership values: 1) true;2) false;3) true-ambiguous;and 4) false-ambiguous. The degree of membership values here forms a low-dimensional feature space that is not linearly distributed. Therefore, these nonlinearly distributed membership values are mapped into a high-dimensional feature space using the Gaussian kernel function. This study focuses on performing cluster analysis of computerized tomography scans of COVID-19 (CTSC-19) cases using AKDC. COVID-19, recognized as one of the most life-threatening diseases of this century, is highly contagious, and early diagnosis may prevent one-to-one transmission. Extensive empirical studies have been conducted with different types of CTSC-19 to demonstrate its effectiveness against existing kernel-based clustering and nonkernel-based clustering algorithms, namely mercer kernel fuzzy c-mean (MKFCM), kernel generalized FCM (KGFCM), kernel intuitionistic fuzzy entropy c-means (KIFECMs), morphological reconstruction and membership filtering clustering (FRFCM), and intuitionistic FCM based on membership information transferring and similarity measurements (IFCM-MS). The effectiveness of the proposed algorithm compared to the existing algorithms is evaluated using standard statistical metrics, such as dice index (DI), Jaccard index (JI), structural similarity index (SI), and correlation coefficient (CC). The empirical results show that AKDC is more effective than existing algorithms based on DI, JI, SI, and CC. © 2013 IEEE.

关键词： clustering algorithms

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Journal of Engineering and Applied Science 2024年第1期71卷 5页

作者： Gupta, Rakhi Kumar, Satish Mullana Ambala133207 India

The extension of classical fuzzy sets are hesitant fuzzy sets (HFSs), in which each element has a possible value from [0,1]. Similarity and distance measures are useful implements for solving medical, clustering and pattern-recognition problems. Most of the researchers have suggested their ideas for HFSs using distance measures and extract the similarity measure from distance measure but most of them are getting inadequate results. Therefore, we proposed a new similarity measure to resolve these problems and also satisfied the properties of proposed measure for HFSs. Additionally, numerous examples are taken in consideration using HFS and compared the performance of existing measures with proposed measure for different cases. Furthermore, we have applied proposed measure for pattern recognition problems using three different examples and also calculate performance index (i.e., Degree of Confidence) to explore the behavior of different measures. Finally, we suggested MST based clustering algorithm using HF-environment and contrast the performance of proposed measure with existing ones. All these comparison illustrate that proposed measure is getting efficient and reasonable results and it also verified that proposed measure is not restricted to particular domain, it can be effectively applied for diverse field of application. © 2023, The Author(s).

关键词： clustering algorithms

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Scalable algorithms for Individual Preference Stable clustering

arXiv

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arXiv 2024年

作者： Mosenzon, Ron Vakilian, Ali United States

In this paper, we study the individual preference (IP) stability, which is an notion capturing individual fairness and stability in clustering. Within this setting, a clustering is α-IP stable when each data point's average distance to its cluster is no more than α times its average distance to any other cluster. In this paper, we study the natural local search algorithm for IP stable clustering. Our analysis confirms a O(log n)-IP stability guarantee for this algorithm, where n denotes the number of points in the input. Furthermore, by refining the local search approach, we show it runs in an almost linear time, O(nk). Copyright © 2024, The Authors. All rights reserved.

关键词： clustering algorithms

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