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Robust Inverse Quantum Fourier Transform Inspired Algorithm for Unsupervised Image Segmentation

IEEE ACCESS 2024年 12卷 99029-99044页

作者： Akinola, Taoreed A. Li, Xiangfang Wilkins, Richard Obiomon, Pamela H. Qian, Lijun Prairie View A&M Univ Dept Elect & Comp Engn Prairie View TX 77446 USA Prairie View A&M Univ CREDIT Ctr Prairie View TX 77446 USA

Image segmentation is a crucial task in computer vision. In this study, we propose a method based on inverse quantum Fourier transform (IQFT) and develop a robust IQFT-inspired algorithm for unsupervised image segmentation. The proposed method leverages the underlying mathematical mechanism of the IQFT to cluster the input image pixels automatically and efficiently into different segments. Specifically, by considering the correlation between the within-cluster mean sum of squared error (MSSE) and the probability of quantum measurements, the proposed robust algorithm significantly improves the segmentation performance. It is an unsupervised method with characteristics similar to k-means, i.e., the proposed method does not require training. Extensive evaluation of the proposed method has been carried out, showing that it outperforms the classical k-means on the PASCAL VOC 2012 segmentation benchmark by as much as 4.21%, the Flowers dataset by as much as 4.4%, and the xVIEW2 challenge dataset by as much as 11.1% in terms of Intersection-Over-Union (IoU). It is also demonstrated that the proposed method has comparable or mixed performance compared to recent more complex approaches. However, compared to approaches such as GrabCut, which require a measure of the user interaction, and deep learning-based methods, which require generative models and deep feature extraction algorithms, the proposed method does not require training or user involvement. This makes it a promising choice for applications that do not have access to data before deployment or have very limited training data.

关键词： Image segmentation clustering algorithms Training Robustness Quantum computing Image color analysis Grasslands Computer vision Fourier transforms image segmentation inverse quantum Fourier transform unsupervised learning k-means

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Analysis of Formal Concepts for Verification of Pests and Diseases of Crops Using Machine Learning Methods

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IEEE ACCESS 2024年 12卷 19902-19910页

作者： Tussupov, Jamalbek Yessenova, Moldir Abdikerimova, Gulzira Aimbetov, Aidyn Baktybekov, Kazbek Murzabekova, Gulden Aitimova, Ulzada LN Gumilyov Eurasian Natl Univ Astana 010000 Kazakhstan JSC Natl Co Kazakhstan Garysh Sapary Astana 010000 Kazakhstan Kazakh French Space Enterprise Galam LLP Astana 010000 Kazakhstan S Seifullin Kazakh Agro Tech Res Univ Astana 010000 Kazakhstan

This article is devoted to a set of important areas of research: the analysis of formal representations and verification of pests and pathogens affecting crops using spectral brightness coefficients (SBR) for the period from 2021 to 2023. The database contains about 10,000 records covering the growing season, types of diseases and pests, as well as their growth phases in a real coordinate system. The work uses machine learning techniques including logistic regression, extreme gradient boosting (XGBoost), and Vanilla convolutional neural network (CNN) to analyze spectral data and classify the presence of pests and diseases in satellite images. The main goal of the work is to optimize and improve the quality of agricultural productivity through early detection and accurate classification of pests and diseases in the agricultural sector. The results of the study can be applied in the development of innovative agricultural systems that will increase yields, reduce the cost of pest and disease control, and optimize production processes. The conclusions of this work can be used both as scientific and practical recommendations for agricultural enterprises and organizations and for the development of new technologies and programs for automating agricultural processes. The use of machine learning techniques and spectral data analysis promises significant breakthroughs in the agricultural sector, helping to improve the efficiency, sustainability, and quality of crop production.

关键词： Vegetation mapping Diseases Crops Machine learning Remote sensing Earth Monitoring clustering algorithms Formal verification Data models Brightness Spectral analysis Accuracy metrics classification clustering data verification machine learning spectral brightness coefficient

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MCARS-CC: A Salable Multicontext-Aware Recommender System

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2024年第1期11卷 612-624页

作者： Nawara, Dina Kashef, Rasha Toronto Metropolitan Univ Dept Elect Comp & Biomed Engn Toronto ON M5B 2K3 Canada

Context-aware recommendation systems (CARSs) leverage contextual information, e.g., time, location, or mood, to generate more personalized recommendations with high accuracy;however, existing CARSs fall short in: 1) handling the high sparsity of data;2) designing scalable solutions in real time;and 3) providing more personalized solutions with the current limited static contexts. This article proposes a multi-CARS based on consensus clustering (MCARS-CC) to solve these challenges. The item-based contextual information is acquired using explicit static and inferred contexts by applying sentiment analysis to the users' reviews. The proposed model is experimented using contextual prefiltering and postfiltering techniques applied to two benchmark datasets, Yelp and TripAdvisor. The model is evaluated using mean absolute error (MAE), root-mean-squared error (RMSE), response time, precision, recall, and F-measure. The experimental results show that the proposed MCARS-CC model outperforms other baseline techniques using the accuracy and error-based metrics. Incorporating hypergraph partitioning algorithm (HGPA) could improve the MAE and RMSE by 25.96% and 8.94% (Yelp), respectively. Also, HGPA led to an 18.47% and 15.94% improvement ratio in terms of MAE and RMSE (TripAdvisor), respectively.

关键词： clustering algorithms Context modeling Scalability Partitioning algorithms Recommender systems Automobiles Collaboration clustering consensus clustering context-aware recommender postfiltering prefiltering sentiment analysis

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A Structural Anomaly-Trace Detection Approach and its Implications in Process Mining and Reengineering

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IEEE ACCESS 2024年 12卷 171099-171111页

作者： Kim, Kwanghoon Pio Kyonggi Univ Div AI Comp Sci & Engn Data & Proc Engn Res Lab Suwon 16227 Gyeonggi South Korea

In order to effectively do the reengineering of process models within managing their lifecycles, it is important to maintain the qualitative levels of control-flow gateway-activity formations of the discovered process models, as highly as possible, in the process mining time. However, almost all the conventional process mining systems are hardly assured the consistent functionality in discovering the higher level of qualitative formations;Setting aside the quality issue, some of them even suffer from discovering inaccurate formations. Therefore, this paper tries to devise an algorithmic approach of detecting a small group of traces in a process log dataset, which may exert strong effects on degrading the qualitative level of control-flow gateway-activity formations of the discovered process model, because these traces negatively affect to form the structural formations during the process mining time. In detail, this paper formulates the new type of exceptional traces, which is named as structural anomaly-trace type, within a process log dataset, and formally defines a series of related conceptual definitions. Next, an algorithmic approach to automatically detect the structural anomaly-traces is described, and its functional correctness is verified through its implemented process mining system operating with an exemplary dataset of the Customer Summary process logs available from the 4TU Center for Research Data. Finally, this paper validates and discusses the feasibility as well the effectiveness of the proposed approach on the process mining time as well as the process reengineering time of the process lifecycle management scheme in process-aware enterprises and organizations.

关键词： Process control Process mining Logic gates Noise Process monitoring Filtering Predictive models clustering algorithms Xenon XML Workflow and business process enactment event log process log dataset process trace information control net structural anomaly-trace anomaly detection process mining process reengineering process lifecycle management

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Construction of Dynamic Traffic Pattern Recognition and Prediction Model Based on Deep Learning in the Background of Intelligent Cities

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IEEE ACCESS 2024年 12卷 1418-1433页

作者： Cui, Jian Zhao, Jianyou Changan Univ Sch Automobile Xian 710064 Peoples R China

To reduce traffic congestion, it is particularly important to use advanced technology to predict urban traffic flow. Therefore, a dynamic traffic pattern prediction model is proposed, which includes convolutional neural network, long and short term memory network and attention mechanism. The validity of the prediction model is verified by the loss function and the average absolute percentage error. In addition, the study also constructs a model for user travel pattern and parking point recognition based on deep learning and mobile signaling data. The performance of the recognition model is verified by the accuracy and other indicators. The research outcomes demonstrated that the max average absolute percentage error of the dynamic traffic mode prediction model was 7.8%, and the mini value was 2.9%. The average accuracy of the user travel pattern recognition model was 83.34%, and that of the parking point recognition model was 88.56%.The dynamic traffic model recognition and prediction model designed by the research institute has better results, and has practical guiding significance in smart city traffic management.

关键词： Predictive models Pattern recognition clustering algorithms Data models Traffic control Prediction algorithms Roads Traffic mode recognition prediction mobile signaling data attention mechanism

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In Search of Excellence: SHOA as a Competitive Shrike Optimization Algorithm for Multimodal Problems

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IEEE ACCESS 2024年 12卷 98407-98425页

作者： Abdulkarim, Hanan K. Rashid, Tarik A. Salahaddin Univ Erbil Engn Coll Software Engn Dept Erbil 44002 Iraq Univ Kurdistan Hewler Comp Sci & Engn Dept Erbil 00964 Iraq

This paper proposes the Shrike Optimization Algorithm (SHOA) as a swarm intelligence optimization algorithm. Many creatures, who live in groups and survive for the next generation, randomly search for food;they follow the best one in the swarm, a phenomenon known as swarm intelligence. While swarm-based algorithms mimic the behaviors of creatures, they struggle to find optimal solutions in multi-modal problem competitions. The swarming behaviors of shrike birds in nature serve as the main inspiration for the proposed algorithm. The shrike birds migrate from their territory in order to survive. However, the SHOA replicates the survival strategies of shrike birds to facilitate their living, adaptation, and breeding. Two parts of optimization exploration and exploitation are designed by modeling shrike breeding and searching for foods to feed nestlings until they get ready to fly and live independently. This paper is a mathematical model for the SHOA to perform optimization. The SHOA benchmarked 19 well-known mathematical test functions, 10 from CEC-2019 and 12 from CEC-2022's most recent test functions, for a total of 41 competitive mathematical test functions and four real-world engineering problems with different conditions, both constrained and unconstrained. The statistical results obtained from the Wilcoxon ranking sum and Fridman test show that SHOA has a significant statistical superiority in handling the test benchmarks compared to competitor algorithms in multi-modal problems. The results for engineering optimization problems show the SHOA outperforms other nature-inspired algorithms in many cases.

关键词： Optimization methods Classification algorithms Birds Statistics Social factors Search problems clustering algorithms Particle swarm optimization Shrike optimization constrained optimization swarm intelligence multi-modal meta-heuristic population-based optimization engineering problem

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Research on Label Propagation Algorithm of Community Structure Analysis in Complex Networks

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2024年第6期11卷 7634-7645页

作者： Liu, Shan Shao, Fengxuan Tao, Ruixing Wen, Hao Commun Univ China Dept Intelligent Sci Beijing 100024 Peoples R China Commun Univ China Sch Informat & Commun Engn Beijing 100024 Peoples R China

The analysis of nodes interaction is crucial to evaluate the dynamics of complex networks, as well as the community structure of social networks. Particularly, the corresponding results can be used to reveal the nature of information propagation in the field of film industry. In this article, an analytical framework of nodes relationship is proposed to go deeply into the network topology. Specifically, the proposed framework can be used to effectively characterize the nodes interaction and thus simplify the network. Moreover, an object interaction similarity label propagation algorithm (OISLPA) based on label propagation theory is then formulated, which is essential to analyze the topology of community structure. It is possible to use OISLPA as an analytical tool for optimal seed nodes selection depending on the community structure. The result is significant as it can help us to predict the information propagation effects through different types of networks.

关键词： Motion pictures Complex networks Heuristic algorithms Data models Analytical models clustering algorithms Vectors Community structure complex networks information propagation object interaction similarity label propagation algorithm (OISLPA)

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Attentive Representation Learning With Adversarial Training for Short Text clustering

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2022年第11期34卷 5196-5210页

作者： Zhang, Wei Dong, Chao Yin, Jianhua Wang, Jianyong East China Normal Univ Sch Comp Sci & Technol Shanghai 200062 Peoples R China Shandong Univ Dept Comp Sci & Technol Qingdao 266237 Shandong Peoples R China Tsinghua Univ Dept Comp Sci & Technol Beijing 100086 Peoples R China Jiangsu Normal Univ Jiangsu Collaborat Innovat Ctr Innguage Abil Xuzhou 221009 Jiangsu Peoples R China

Short text clustering has far-reaching effects on semantic analysis, showing its importance for multiple applications such as corpus summarization and information retrieval. However, it inevitably encounters the severe sparsity of short text representations, making the previous clustering approaches still far from satisfactory. In this paper, we present a novel attentive representation learning model for shot text clustering, wherein cluster-level attention is proposed to capture the correlations between text representations and cluster representations. Relying on this, the representation learning and clustering for short texts are seamlessly integrated into a unified model. To further ensure robust model training for short texts, we apply adversarial training to the unsupervised clustering setting, by injecting perturbations into the cluster representations. The model parameters and perturbations are optimized alternately through a minimax game. Extensive experiments on four real-world short text datasets demonstrate the superiority of the proposed model over several strong competitors, verifying that robust adversarial training yields substantial performance gains.

关键词： Training Task analysis Computational modeling Perturbation methods Adaptation models Gallium nitride clustering algorithms Short text clustering representation learning attention mechanisms robust adversarial training

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Comprehensive Data-Driven Framework for Detecting and Classifying Non-Technical Distribution Losses

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IEEE ACCESS 2024年 12卷 96537-96551页

作者： Jene-Vinuesa, Marc Aragues-Penalba, Monica Sumper, Andreas Univ Politecn Cataluna Ctr Innovacio Tecnol Convertidors Estat Accionamen Dept Engn Elactr ETS Engn Ind Barcelona Barcelona 08028 Spain

Non-technical losses (NTL) pose multiple challenges across distribution grids. This paper introduces a comprehensive framework combining data-driven methods to detect and categorize fraud due to meter tampering or direct connections while identifying potential culprits. The hybrid methodology utilizes grid and consumer-related data to obtain NTL curves through an energy balance approach, yielding indicators such as magnitude, duration, and other features. A Random Forest classifier trained with real historical cases of NTL achieves a weighted F1 score of 0.859, effectively labelling fraud types. Additionally, an unsupervised detection model, integrating clustering and correlation methods, enables accurate identification of tampered meters. The paper introduces two adjustable parameters enabling utilities to fine-tune meter tampering detection strategies based on economic considerations. The results demonstrate that true positives can be increased at the expense of increasing false positives. Accurate fraud identification is achieved using the Fuzzy C-Means algorithm, with an F1 score of 0.9. The algorithm is tested on grids with distributed generation, with a decrease of 10% on the predictive performance when half of the users are prosumers, demonstrating the methodology's promising performance in real-world scenarios.

关键词： Fraud Classification algorithms Meters Voltage control Support vector machines Hardware clustering algorithms Unsupervised learning Classification direct connection false data injection (FDI) fraud detection hybrid algorithm inspection strategies meter tampering non-technical losses (NTL) unsupervised learning

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clustering Demographics and Sequences of Diagnosis Codes

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2022年第5期26卷 2351-2359页

作者： Zhong, Haodi Loukides, Grigorios Pissis, Solon P. Kings Coll London London WC2B 4BG England CWI NL-1081 HV Amsterdam Netherlands Vrije Univ NL-1081 HV Amsterdam Netherlands

A Relational-Sequential dataset (or RS-dataset for short) contains records comprised of a patient's values in demographic attributes and their sequence of diagnosis codes. The task of clustering an RS-dataset is helpful for analyses ranging from pattern mining to classification. However, existing methods are not appropriate to perform this task. Thus, we initiate a study of how an RS-dataset can be clustered effectively and efficiently. We formalize the task of clustering an RS-dataset as an optimization problem. At the heart of the problem is a distance measure we design to quantify the pairwise similarity between records of an RS-dataset. Our measure uses a tree structure that encodes hierarchical relationships between records, based on their demographics, as well as an edit-distance-like measure that captures both the sequentiality and the semantic similarity of diagnosis codes. We also develop an algorithm which first identifies k representative records (centers), for a given k, and then constructs k clusters, each containing one center and the records that are closer to the center compared to other centers. Experiments using two Electronic Health Record datasets demonstrate that our algorithm constructs compact and well-separated clusters, which preserve meaningful relationships between demographics and sequences of diagnosis codes, while being efficient and scalable.

关键词： Codes clustering algorithms Task analysis Data mining Semantics Atomic measurements Market research clustering demographics diagnosis codes

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