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Workshop on Metrology for Industry 4.0 and IoT

作者： Marcantonio Catelani Lorenzo Ciani Cristian Garzón Alfonso Francesco Grasso Libero Paolucci Gabriele Patrizi Department of Information Engineering University of Florence Florence Italy

ISBN: (数字)9798350385823

ISBN: (纸本)9798350385830

Unsupervised machine learning algorithms, such as clustering and anomaly detection, work by identifying patterns and anomalies in data without the need for labeled training data. These algorithms are particularly useful in situations where relevant training data is limited or unavailable, making them well-suited for predicting and detecting abnormalities and Power Quality Disturbances (PQD) in electric systems. PQD can have significant impacts on electrical equipment and systems, leading to equipment malfunction, downtime, and potential safety hazards. By applying unsupervised machine learning algorithms to electric data it is possible to gain valuable insights into the underlying patterns and characteristics of different PQDs. For example, clustering algorithms can group similar disturbances together based on their features, helping to identify common causes or sources of disturbances. Anomaly detection algorithms, on the other hand, can automatically flag and alert users to unusual or unexpected disturbances that may indicate potential issues. In this manuscript, a study of the current trends in the field of clustering and anomaly detection unsupervised Machine Learning (ML) algorithm is presented, dealing with approaches available for PQD detection. Overall, the combination of unsupervised machine learning algorithms and power quality analysis can greatly enhance the ability to predict, monitor, and mitigate the impact of PQD in various industrial settings. By leveraging these advanced technologies, reliability and efficiency of electrical systems can be improved, ultimately leading to a more sustainable and optimized environment.

关键词： Electric potential Technological innovation Machine learning algorithms Power quality clustering algorithms Training data Machine learning

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Self-Weighted Multi-View Fuzzy clustering With Multiple Graph Learning

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IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 1585-1589页

作者： Liu, Chaodie Chang, Cheng Nie, Feiping Henan Normal Univ Sch Comp & Informat Engn Xinxiang 453007 Peoples R China Henan Normal Univ Key Lab Artificial Intelligence & Personalized Lea Xinxiang 453007 Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect IOPEN Xian 710072 Peoples R China Minist Ind & Informat Technol Key Lab Intelligent Interact & Applicat Xian 710072 Peoples R China

Graph-based multi-view clustering has garnered considerable attention owing to its effectiveness. Nevertheless, despite the promising performance achieved by previous studies, several limitations remain to be addressed. Most graph-based models employ a two-stage strategy involving relaxation and discretization to derive clustering results, which may lead to deviation from the original problem. Moreover, graph-based methods do not adequately address the challenges of overlapping clusters or ambiguous cluster membership. Additionally, assigning appropriate weights based on the importance of each view is crucial. To address these problems, we propose a self-weighted multi-view fuzzy clustering algorithm that incorporates multiple graph learning. Specifically, we automatically allocate weights corresponding to each view to construct a fused similarity graph matrix. Subsequently, we approximate it as the scaled product of fuzzy membership matrices to directly derive clustering assignments. An iterative optimization algorithm is designed for solving the proposed model. Experiment evaluations conducted on benchmark datasets illustrate that the proposed method outperforms several leading multi-view clustering approaches.

关键词： clustering algorithms Data mining Benchmark testing Linear programming Computational complexity Training Signal processing algorithms Optimization Flowering plants Vectors Fuzzy clustering graph learning multi-view clustering self-weighted

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Secure data communication framework based on fuzzy based secure clustering and hybridized metaheuristic algorithms

Secure data communication framework based on fuzzy based sec...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： C. Vennila Amritpal Sidhu Preetham K Rupali A. Mahajan M. Vigenesh Bhavuk Samrat Department of S&H Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Mangement UG Management JAIN (Deemed to be University) Bangalore Karnataka India COMP Vishwakarma Institute of Technology Pune India Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

The Internet of Things (IoT) has made real-time applications more flexible and deployable. IoT has security and energy efficiency issues despite its benefits. This study addresses these issues with a secure data transmission architecture using fuzzy-based secure clustering and hybridised metaheuristic algorithms. The suggested framework contains three phases. The first phase presents an energy-efficient and secure IoT data transfer infrastructure. Secure route selection is done using fuzzy-based secure clustering and the Whale Optimization-based Harris Hawks Optimisation (W-HHO) method. Parallel cypher block chaining and advanced encryption strengthen data security. Secure cluster-based routing is needed for the Internet of Medical Things (IoMT) in the second phase. An adaptive Gaussian mutation-based Sine Cosine Optimisation (SCO) approach clusters nodes, and mutual authentication assures security. Final phase of research delivers optimal and secure fuzzy inference route selection policy. The Levy Flight Distributed optimization-based Henry Gas Solubility Optimisation Algorithm (LFD-HGSO) clusters data, while concurrent encryption and cypher block chaining secure it. Fuzzy inference selects safest paths. Performance tests show that the proposed solutions improve packet delivery rate, throughput, energy efficiency, and network lifetime over conventional methods. These results demonstrate the framework's safe and energy-efficient data transfer for IoT and IoMT applications. Experimental findings show that the suggested strategy outperforms various QoS metrics like packet delivery ratio, throughput, network longevity, and energy usage. Time efficiency is also improved by the PAES-CBC key generation algorithm.

关键词： Measurement Fuzzy logic Data security Metaheuristics clustering algorithms Quality of service Throughput Data transfer Energy efficiency Encryption

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Understanding the Potential of Grouping algorithms for Genetics clustering

Understanding the Potential of Grouping Algorithms for Genet...

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Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA), International Conference on

作者： Zahid Ahmed Subbulakshmi Ganesan Rashmi Tirkey Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Chemistry and Biochemistry School of Sciences JAIN (Deemed to be University) Bangalore Karnataka India Department of Pharmacy ARKA JAIN University Jamshedpur Jharkhand India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349238

Genetics clustering is the manner of figuring out groupings of genes from large datasets that can be used to recognize the genetic basis of a trait or phenotype. Grouping algorithms are becoming increasingly critical equipment for genetics clustering as they allow the fast agency and efficient analysis of huge datasets. By using grouping algorithms, researchers are capable of deciding sets of genes from complex datasets which can be probable to be related to precise characteristics of the studied trait or phenotype. Furthermore, grouping algorithms can be used to perceive relationships between genes that won't have been previously located. The potential of grouping algorithms for genetics clustering is widespread, and by way of leveraging this generation, researchers can benefit perception of the tricky nature of genetic interactions.

关键词： Machine learning algorithms clustering algorithms Medical services Machine learning Internet of Things Monitoring

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Density peaks clustering algorithm based on multi-cluster merge and its application in the extraction of typical load patterns of users

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Journal of Ambient Intelligence and Humanized Computing 2024年第8期15卷 3193-3209页

作者： Zhao, Jia Yao, Zhanfeng Qiu, Liujun Fan, Tanghuai Lee, Ivan School of Information Engineering Nanchang Institute of Technology Nanchang330099 China Nanchang Key Laboratory of IoT Perception and Collaborative Computing for Smart City Nanchang Institute of Technology Nanchang330099 China UniSA STEM University of South Australia AdelaideSA5000 Australia

The density peaks clustering (DPC) algorithm is simple in principle, efficient in operation, and has good clustering effects on various types of datasets. However, this algorithm still has some defects: (1) due to the definition limitations of local density and relative distance of samples, it is difficult for the algorithm to find correct density peaks;(2) the allocation strategy of the algorithm has poor robustness and is prone to cause other problems. In response to solve the above shortcomings, we proposed a density peaks clustering algorithm based on multi-cluster merge (DPC-MM). In view of the difficulty in selecting density peaks of the DPC algorithm, a new method of calculating relative distance of samples was defined to make the density peaks found more accurate. The allocation strategy of multi-cluster merge was proposed to alleviate or avoid problems caused by allocation errors. Experimental results revealed that the DPC-MM algorithm can efficiently perform clustering on datasets of any shape and scale. The DPC-MM algorithm was applied in extraction of typical load patterns of users, and can more accurately perform clustering on user loads. The extraction results can better reflect electricity consumption habits of users. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： clustering algorithms

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Document retrieval using clustering-based Aquila hash-Q optimization with query expansion based on pseudo relevance feedback

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International Journal of Computers and Applications 2024年第7期46卷 496-507页

作者： Inje, Bhushan Nagwanshi, Kapil Kumar Rambola, Radhakrishna Amity School of Engineering and Technology Amity University Jaipur India NMIMS University MPSTME Shirpur Shirpur Dist Dhule India Department of Computer Science and Engineering SoS Engineering and Technology Guru Ghasidas Vishwavidyalaya Chhattisgarh Bilaspur495009 India

A document retrieval system helps users to retrieve the relevant documents corresponding to their query quickly and easily. In the real world, document retrieval is a difficult task due to high volumes of data, unstructured data, and different formats of data. Even though many research techniques are introduced, major problems like vocabulary mismatch and non-linear matching still need to be solved. In this work, the Aquila hash-q optimizer is the proposed matching technique with the clustering technique to retrieve the document in a time-efficient manner for the user query without collision. First, preprocessing is done by eliminating the stop words from the document, stemming, and grouping documents in a cluster into a single document using Hierarchical Density-based Sampling Spatial Cluster of Applications with Noise (HDBSSCAN) clustering. This clustering algorithm is powerful, robust to noise, and scalable and identifies clusters of documents that are related to each other. Additionally, the sampling technique used in this clustering algorithm increases the clustering speed by reducing the size of the document which improves the performance of document retrieval systems. Secondly, the queries are searched using the Aquila hash-q optimizer matching technique by which the relevant documents are retrieved. The Aquila hash-q optimization works by pre-computing a hash table of the terms in a document collection and then using this hash table to quickly identify the relevant documents from the given query. This can significantly improve the speed of document retrieval, especially for large document collections. Aquila hash-q optimization can improve the accuracy, efficiency, and scalability of document retrieval systems. The effectiveness of the Hierarchical Density-Based clustering Aquila Optimization approach is determined by various analyses through NPL, LISA, and CACM data in terms of precision @ 5 (0.497), precision @ 10 (0.425), Mean Average Precision (MAP) (0.4

关键词： clustering algorithms

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A Nonparametric Split and Kernel-Merge clustering Algorithm

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4443-4457页

作者： Khan, Khurram ur Rehman, Atiq Khan, Adnan Naqvi, Syed Rameez Belhaouari, Samir Brahim Bermak, Amine Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology Haripur22620 Pakistan Hamad Bin Khalifa University Division of Information and Computing Technology Doha34110 Qatar Comsats University Islamabad Wah47040 Pakistan

This work proposes a novel split and kernel-merge clustering (S-KMC), a nonparametric clustering algorithm that combines the strengths of hierarchical clustering, partitional clustering, and density-based clustering. It consists of two main phases: splitting and merging. In the splitting phase, a ranking-based operator is used to divide the data into optimal subclusters. In the merging phase, a kernel function estimates the density of these subclusters after projecting them onto a straight line passing through their centers, facilitating the merging operation. S-KMC is fully nonparametric, eliminating the need for prior information about the data. It effectively handles 1) shape diversity, 2) density variability, 3) high dimensionality, 4) outliers, and 5) missing values. The algorithm offers easily tunable hyperparameters, enhancing its applicability to complex problems and robustness against data anomalies. Experimental analysis on 21 benchmark datasets demonstrates the improved performance of S-KMC in terms of cluster accuracy, handling high-dimensional data, and managing data anomalies and outliers. Comprehensive comparisons with state-of-the-art techniques further validate the superior or comparable performance of the proposed S-KMC algorithm. © 2020 IEEE.

关键词： clustering algorithms

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A Novel Integration of Hierarchical clustering and Ensemble Classification algorithms for News Classification

A Novel Integration of Hierarchical Clustering and Ensemble ...

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Cognitive Robotics and Intelligent Systems (ICC - ROBINS), International Conference on

作者： Keshetti Sreekala Modugula Sivajyothi Dukuru Chiranjevi Gadipe Sunitha Mucha Swetha Department of CSE Mahatma Gandhi Institute of Technology (A) Hyderabad India Department of IT CMR Technical Campus Medchal Hyderabad Telangana India Department of IT Aditya Engineering College Surampalem India Department of CSE Sumathi Reddy Institute of Technology for Women Warangal India

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372755

This research work proposes a novel approach for news classification by integrating hierarchical clustering and ensemble algorithms. The proposed methodology began with the acquisition and preprocessing of the BBC News dataset taken from Kaggle. Text data underwent preprocessing by including tokenization, removal of stop words, and TF-IDF vectorization. Hierarchical clustering was then applied to group similar documents based on their content by using the Agglomerative clustering algorithm. The number of clusters was adjustable for varying granularity. Subsequently, cluster labels obtained were assigned to corresponding documents, and additional features were extracted from the clustered data. These features, alongside the TF-IDF matrix, served as an input for training Gradient Boosting, Bagging Classifier and Random Forest classifiers. These classifiers were trained to predict document categories based on both text features and cluster assignments. The trained models were then tested on a separate test set to evaluate their generalization ability. clustering and classification results were carefully evaluated to assess method efficacy, elucidating the relationship between document clustering and classification performance.

关键词： Training Text analysis clustering algorithms Boosting Feature extraction Tokenization Classification algorithms

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Robust subspace clustering via two-way manifold representation

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Multimedia Tools and Applications 2024年第16期84卷 1-18页

作者： Ezeora, Nnamdi Johnson Anichebe, Gregory Emeka Nzeh, Royransom Chiemela Uzo, Izuchukwu Uchenna Department of Computer Science University of Nigeria Nsukka Nigeria School of Computer Science and Communication Engineering JiangSu University JiangSu212013 China

Subspace clustering has shown great potential in discovering the hidden low-dimensional subspace structures in high-dimensional data. However, most existing methods still face the problem of noise distortion and overlapping subspaces. To tackle this problem and ensure that each sample is only assigned to a single subspace, a new method is proposed in this paper. Specifically, a two-way learning technique is introduced by inducing data manifold via two representative structures. The first is a low-rank structure learned directly from original data. The second structure is an affinity matrix obtained via a k symmetric nearest neighbor graph. By further introducing a dual regularization term, both structures are allowed to guild themselves adaptively to find robust clustering directly without spectral post-processing. In order to evaluate the effectiveness of the proposed method, several experiments are conducted on multiple benchmark datasets. The experimental results, evaluated using six standard metrics, clearly demonstrate that the proposed method outperforms state-of-the-art methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： clustering algorithms

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A clustering-based differential privacy protection algorithm for weighted social networks

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Mathematical Biosciences and Engineering 2024年第3期21卷 3755-3773页

作者： Zhang, Lei Ge, Lina School of Artificial Intelligence Guangxi Minzu University Nanning530006 China Key Laboratory of Network Communication Engineering Guangxi Minzu University Nanning530006 China Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis Nanning530006 China

Weighted social networks play a crucial role in various fields such as social media analysis, healthcare, and recommendation systems. However, with their widespread application and privacy issues have become increasingly prominent, including concerns related to sensitive information leakage, individual behavior analysis, and privacy attacks. Despite traditional differential privacy protection algorithms being able to protect privacy for edges with sensitive information, directly adding noise to edge weights may result in excessive noise, thereby reducing data utility. To address these challenges, we proposed a privacy protection algorithm for weighted social networks called DCDP. The algorithm combines the density clustering algorithm OPTICS to partition the weighted social network into multiple sub-clusters and adds noise to different sub-clusters at random sampling frequencies. To enhance the balance of privacy protection, we designed a novel privacy parameter calculation method. Through theoretical derivation and experimentation, the DCDP algorithm demonstrated its capability to achieve differential privacy protection for weighted social networks while effectively maintaining data accuracy. Compared to traditional privacy protection algorithms, the DCDP algorithm reduced the average relative error by approximately 20% and increases the proportion of unchanged shortest paths by about 10%. In summary, we aimed to address privacy issues in weighted social networks, providing an effective method to protect user-sensitive information while ensuring the accuracy and utility of data analysis. © 2024 the Author(s).

关键词： clustering algorithms

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