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ADVANCED INTELLIGENT SYSTEMS 2025年第3期7卷

作者： Boonchieng, Ekkarat Nadda, Wanchaloem Chiang Mai Univ Fac Sci Dept Comp Sci Chiang Mai 50200 Thailand Khon Kaen Univ Coll Comp Khon Kaen 40002 Thailand

data overlapping and imbalanced data are significant challenges in data classification. Extreme learning machine auto-encoding (ELM-AE) is a feature reduction method that transforms original features into a new set of features capturing essential information in the data. However, ELM-AE may not effectively solve the overlapping data problem. In this research, a new method called class-separable extreme learning machine auto-encoding (CS-ELM-AE) is proposed, to improve ELM-AE's efficacy in addressing the overlapping data problem and thereby increasing classification efficiency. CS-ELM-AE encodes points in the same class of the dataset to be closer together. Oversampling is also applied to the encoded dataset to solve the imbalanced data problem. The experiments demonstrate that CS-ELM-AE could significantly improve classification model performance and achieve higher levels of accuracy, as well as greater f1-score and G-mean values than the original ELM-AE.

关键词： auto-encoding data classification data overlapping extreme learning machine auto-encoding imbalanced data

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Lightweight on-edge clustering for wireless AI-driven applications

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IET COMMUNICATIONS 2025年第1期19卷

作者： Kadhim, Mustafa Raad Lu, Guangxi Shi, Yinong Wang, Jianbo Kui, Wu Univ Elect Sci & Technol China Sch Comp Sci & Engn Sch Cyber Secur Chengdu Sichuan Peoples R China

Advanced wireless communication is important in distribution systems for sharing information among Internet of Things (IoT) edges. Artificial intelligence (AI) analyzed the generated IoT data to make these decisions, ensuring efficient and effective operations. These technologies face significant security challenges, such as eavesdropping and adversarial attacks. Recent studies addressed this issue by using clustering analysis (CA) to uncover hidden patterns to provide AI models with clear interpretations. The high volume of overlapped samples in IoT data affects partitioning, interpretation, and reliability of CAs. Recent CA models have integrated machine learning techniques to address these issues, but struggle in the limited resources of IoT environments. These challenges are addressed by proposing a novel unsupervised lightweight distance clustering (DC) model based on data separation (beta$\beta$). beta$\beta$ raises the tension between samples using cannot-link relations to separate the overlap, thus DC provides the interpretations. The optimal time and space complexity enables DC-beta$\beta$ to be implemented on on-edge computing, reducing data transmission overhead, and improving the robustness of the AI-IoT application. Extensive experiments were conducted across various datasets under different circumstances. The results show that the data separated by beta$\beta$ improved the efficiency of the proposed solution, with DC outperforming the baseline model.

关键词： artificial intelligence clustering analysis data overlapping Internet of Things on-edge computing wireless communication

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Enhanced CNN Models for Binary and Multiclass Student Classification on Temporal Educational data at the Program Level

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VIETNAM JOURNAL OF COMPUTER SCIENCE 2021年第2期8卷 311-335页

作者： Vo Thi Ngoc Chau Nguyen Hua Phung Vietnam Natl Univ HCMC Ho Chi Minh City Univ Technol Ho Chi Minh City Vietnam

In educational data mining, student classification is an important and popular task by predicting final study status of each student. In the existing works, this task has been considered in many various contexts at both course and program levels with different learning approaches. However, its real-world characteristics such as temporal aspects, data imbalance, data overlapping, and data shortage with sparseness have not yet been fully investigated. Making the most of deep learning, our work is the first one addressing those challenges for the program-level student classification task. In a simple but effective manner, convolutional neural networks (CNNs) are proposed to exploit their well-known advantages on images for temporal educational data. As a result, the task is resolved by our enhanced CNN models with more effectiveness and practicability on real datasets. Our CNN models outperform other traditional models and their various variants on a consistent basis for program-level student classification.

关键词： Multiclass classification deep learning convolutional neural network data imbalance data overlapping data sparseness

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Swarm Plot: data Redistribution in Non-Random Technique 6

Swarm Plot: Data Redistribution in Non-Random Technique

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6th International Conference and Workshops on Recent Advances and Innovations in Engineering (ICRAIE)

作者： Idrus, Zainura Rusli, Fatin S. Idrus, Zanariah Nazri, Muhammad Aqil Mohd Al-zebari, Adel Talib, Noor Hasnita Abdul Univ Teknol MARA Fac Comp & Math Sci Shah Alam Selangor Malaysia Instantestore Com George Town Malaysia Univ Teknol MARA Fac Comp & Math Sci Merbok Kedah Malaysia Intec Int Educ Coll Shah Alam Selangor Malaysia Duhok Polytech Univ Dept Informat Technol Duhok Iraq

ISBN: (纸本)9781665434027

data visualization is a process of converting data into visual forms with the purpose to extract hidden knowledge behind the data. One of the commonly used visualization is swarm plot which is suitable for small dataset. This is due to the fact that as data getting bigger, the data points will tend to overlap and be congested on the plot. Thus, it might lead to misrepresentation of data patterns and could result in false knowledge extraction. Thus, there is a need to have an effective swarm plot technique that can manage bigger data with no overlapping of data points. To achieve the objective, this research has formulated and enhances the process of identifying the right coordinate for each point on the plots. Overall, the system has successfully improved the redistribution of data points as such it can manage bigger data and at the same time avoid points from overlapping.

关键词： Swarm Plot Point Redistribution Big data data overlapping data Visualization

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data GROUPING TECHNIQUES' PERFORMANCE ANALYSIS IN GM(1,1)'s PREDICTION ACCURACY IMPROVEMENT FOR FORECASTING TRAFFIC PARAMETERS

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MECHATRONIC SYSTEMS AND CONTROL 2020年第1期48卷 25-34页

作者： Getanda, Vincent B. Oya, Hidetoshi Kubo, Tomohiro Sato, Yosuke Jomo Kenyatta Univ Agr & Technol JKUAT Sch Elect Elect & Informat Engn Dept Elect & Elect Engn Nairobi Kenya Tokushima Univ Grad Sch Adv Technol & Sci Dept Elect & Elect Engn Tokushima Japan Tokyo City Univ Dept Comp Sci Tokyo Japan Univ Tokushima Grad Sch Technol Ind & Social Sci Dept Elect & Elect Engn Tokushima Japan Univ Electrocommun Grad Sch Electrocommun Dept Syst Engn Chofu Tokyo Japan

This paper is an extended version of our earlier work in which we improved the prediction accuracy of the conventional GM(1,1) by adopting a data grouping technique. We proposed various data grouping techniques and in this paper these data grouping techniques are graphically analysed, and their performance is evaluated in detail. In particular, the strong and weak grouping techniques are discussed, and we show that prediction accuracy continues to improve as the number of data groups increases. In addition, to show that traffic flow characteristics vary as hourly pattern, we forecast vehicle volume and CO2 emissions up to 25 data points unlike in our earlier work in which case we forecasted only 17 data points. Consequently, we unveil more information (e.g. morning peak hour of the day) to discern the happenings of a traffic system. Henceforth we show that 7:20 am is the morning peak time of traffic congestion. Moreover, capturing a longer period of traffic flow scenario validates the concept of data grouping technique.

关键词： Performance analysis number of groups group data points data overlapping strong grouping technique weak grouping technique

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A Hybrid Model for Predicting the End-Point Phosphorus Content of Electric Arc Furnace

A Hybrid Model for Predicting the End-Point Phosphorus Conte...

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Symposium on Materials Processing Fundamentals at Annual Meeting of The-Minerals-Metals-Materials-Society (TMS)

作者： Chen, Chao Wang, Nan Chen, Min Northeastern Univ Sch Met Shenyang 110819 Liaoning Peoples R China

ISBN: (纸本)9783030652531;9783030652524

A hybrid model based on the combination of k-means method, BP neural network and decision tree algorithm is proposed for predicting the end-point phosphorus content of electric arc furnace. The industrial data from electric arc furnace is filtered firstly by the box-plot method, and the processed data of end-point phosphorus content is classified into three clusters by k-means analysis method. Then, three BP neural networks with different parameters for each cluster are established to deal with the data overlapping problem and increase the model accuracy. In order to obtain the optimum prediction result, a new method combined with the posterior knowledge of dephosphorization ratio and the decision tree algorithm is employed. With this method, the results predicted, respectively, by the three different BP neural networks are merged according to the merging rule set established by decision tree algorithm and the identified result is taken as the final end-point phosphorus content. In comparison with the traditional BP neural network and deep layer neural network, the hybrid model increases the prediction accuracy of end-point P content to 97.8% with +/- 0.006% error range, and meanwhile for the error ranges of +/- 0.005% and +/- 0.004%, the prediction accuracy is 94.2% and 83.0%, respectively.

关键词： End-point phosphorus content BP neural network model K-means method data overlapping Decision tree algorithm

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Cluster CV2: a Computer Vision Approach to Spatial Identification of data Clusters 22

Cluster CV2: a Computer Vision Approach to Spatial Identific...

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22nd Symposium on Image, Signal Processing and Artificial Vision (STSIVA)

作者： Contreras Contreras, Ghiordy Ferney Delgado, Byron Medina Ibarra, Dinael Guevara de Castro, Cristiano Leite Acevedo Jaimes, Brayan Rene Univ Francisco de Paula Santander Elect & Elect Cucuta Colombia Univ Fed Minas Gerais Elect Engn Belo Horizonte MG Brazil

ISBN: (纸本)9781728114910

This work shows a novel application based on techniques of Computer Vision and Machine Learning to identify k clusters into a data set with overlapping issue. Used in area of unsupervised data clustering, where separation between groups is tricky. Through pair-to-pair distance calculations upon original data, is gotten a Distances Matrix as representative information of data. This matrix contains visual information, then using morphological operators extract relevant features for individual identification of groups in data set. Next, matrix decomposition performed to covariance matrix, being calculation of data elements for each cluster in order to project data into a new linear space. So, overlapping and separation distances among clusters are corrected without loss information. Results present correct identification of k clusters, without loss information, and eliminating data overlap. Clustering validation metrics such as Silhouette and Precision was used to test the methodology.

关键词： Unsupervised clustering data overlapping computer vision machine learning

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Ecological Monitoring of Dangerous Objects on the Basis of Vegetation Indexing and Evolutionary Approach 5

Ecological Monitoring of Dangerous Objects on the Basis of V...

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5th Mediterranean Conference on Embedded Computing (MECO)

作者： Taganov, Aleksandr Kolesenkov, Aleksandr Babaev, Sergey Ryazan State Radio Engn Univ Ryazan Russia

ISBN: (纸本)9781509022212

The present paper has represented a technology of remote ecological monitoring of objects using evolutionary approach and vegetation indices. Developed software complex allows monitoring dynamics of ecological processes, providing information support to state and private enterprises interested in it. The paper has shown an algorithm of automated overlapping of aerospace images on the basis of correlation optimum analysis. Researches and choice of vegetation indexing methods for aerospace images have been carried out. Experimental researches based on data obtained from satellites Elektro-L and Landsat have been fulfilled.

关键词： ecological monitoring geographic information system GIS vegetation index Earth remote sensing ERS data overlapping evolutionary approach

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Broadcasting on-demand data with time constraints using multiple channels in wireless broadcast environments

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INFORMATION SCIENCES 2013年 242卷 76-91页

作者： Liu, Chuan-Ming Su, Ta-Chih Natl Taipei Univ Technol Dept Comp Sci & Informat Engn Taipei 106 Taiwan

data Broadcasting is an effective approach to provide information to a large group of clients in ubiquitous environments. How to generate the data broadcast schedule to make the clients' average waiting time as short as possible is an important issue. In particular, when the data access pattern is dynamic and data have time constraints, such as traffic and stock information, scheduling the broadcast for such data to fulfill the requests is challenging. Since the content of the broadcast is dynamic and the request deadlines should be met, such data broadcasting is referred to as on-demand data broadcasting with time constraints. Many papers have discussed this type of data broadcasting with a single broadcast channel. In this paper, we investigate how to schedule the on-demand broadcast for the data with time constraints using multiple broadcast channels and provide two heuristics to schedule the data broadcast. The objective of the proposed heuristics is to minimize the miss rate (i.e., ratio of the number of requests missing deadlines to the number of all requests) and latency (i.e., time between issuing and termination of the request). We show that the offline version of the considered problem is NP-hard and present a competitive analysis on the proposed heuristics. More discussion about the proposed heuristics is given through extensive simulation experiments. The experimental results validate that the proposed heuristics achieve the objectives. (C) 2013 Elsevier Inc. All rights reserved.

关键词： On-demand data broadcasting data overlapping NP-hard Competitive analysis Latency Miss rate

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