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  • Open Access


    Research on Demand Response Potential of Adjustable Loads in Demand Response Scenarios

    Zhishuo Zhang, Xinhui Du*, Yaoke Shang, Jingshu Zhang, Wei Zhao, Jia Su

    Energy Engineering, Vol.121, No.6, pp. 1577-1605, 2024, DOI:10.32604/ee.2024.047706

    Abstract To address the issues of limited demand response data, low generalization of demand response potential evaluation, and poor demand response effect, the article proposes a demand response potential feature extraction and prediction model based on data mining and a demand response potential assessment model for adjustable loads in demand response scenarios based on subjective and objective weight analysis. Firstly, based on the demand response process and demand response behavior, obtain demand response characteristics that characterize the process and behavior. Secondly, establish a feature extraction and prediction model based on data mining, including similar day clustering,… More >

  • Open Access


    Forecasting the Academic Performance by Leveraging Educational Data Mining

    Mozamel M. Saeed*

    Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 213-231, 2024, DOI:10.32604/iasc.2024.043020

    Abstract The study aims to recognize how efficiently Educational Data Mining (EDM) integrates into Artificial Intelligence (AI) to develop skills for predicting students’ performance. The study used a survey questionnaire and collected data from 300 undergraduate students of Al Neelain University. The first step’s initial population placements were created using Particle Swarm Optimization (PSO). Then, using adaptive feature space search, Educational Grey Wolf Optimization (EGWO) was employed to choose the optimal attribute combination. The second stage uses the SVM classifier to forecast classification accuracy. Different classifiers were utilized to evaluate the performance of students. According to… More >

  • Open Access


    Multiple-Object Tracking Using Histogram Stamp Extraction in CCTV Environments

    Ye-Yeon Kang1, Geon Park1, Hyun Yoo2, Kyungyong Chung1,*

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3619-3635, 2023, DOI:10.32604/cmc.2023.043566

    Abstract Object tracking, an important technology in the field of image processing and computer vision, is used to continuously track a specific object or person in an image. This technology may be effective in identifying the same person within one image, but it has limitations in handling multiple images owing to the difficulty in identifying whether the object appearing in other images is the same. When tracking the same object using two or more images, there must be a way to determine that objects existing in different images are the same object. Therefore, this paper attempts… More >

  • Open Access


    Analysis of Tourism Demand Difference Based on Data Mining and Intelligent Analysis

    Peng Cheng1,2,*

    Journal on Big Data, Vol.5, pp. 69-84, 2023, DOI:10.32604/jbd.2023.046294

    Abstract To serve as a reference for future foreign tourism study, relevant tourist sectors have done in-depth investigations on foreign tourism both domestically and internationally. A study of outbound tourism activities from the viewpoint of tourists can examine its development law and create successful marketing tactics based on the rise in the number of foreign tourists. Based on this, this study suggests a data mining technique to examine the variations in travel needs and marketing tactics among various consumer groups. The combined example analysis demonstrates how logical and useful our data mining analysis is. Our data More >

  • Open Access


    Chimp Optimization Algorithm Based Feature Selection with Machine Learning for Medical Data Classification

    Firas Abedi1, Hayder M. A. Ghanimi2, Abeer D. Algarni3, Naglaa F. Soliman3,*, Walid El-Shafai4,5, Ali Hashim Abbas6, Zahraa H. Kareem7, Hussein Muhi Hariz8, Ahmed Alkhayyat9

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 2791-2814, 2023, DOI:10.32604/csse.2023.038762

    Abstract Data mining plays a crucial role in extracting meaningful knowledge from large-scale data repositories, such as data warehouses and databases. Association rule mining, a fundamental process in data mining, involves discovering correlations, patterns, and causal structures within datasets. In the healthcare domain, association rules offer valuable opportunities for building knowledge bases, enabling intelligent diagnoses, and extracting invaluable information rapidly. This paper presents a novel approach called the Machine Learning based Association Rule Mining and Classification for Healthcare Data Management System (MLARMC-HDMS). The MLARMC-HDMS technique integrates classification and association rule mining (ARM) processes. Initially, the chimp… More >

  • Open Access


    A Graph Neural Network Recommendation Based on Long- and Short-Term Preference

    Bohuai Xiao1,2, Xiaolan Xie1,2,*, Chengyong Yang3

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 3067-3082, 2023, DOI:10.32604/csse.2023.034712

    Abstract The recommendation system (RS) on the strength of Graph Neural Networks (GNN) perceives a user-item interaction graph after collecting all items the user has interacted with. Afterward the RS performs neighborhood aggregation on the graph to generate long-term preference representations for the user in quick succession. However, user preferences are dynamic. With the passage of time and some trend guidance, users may generate some short-term preferences, which are more likely to lead to user-item interactions. A GNN recommendation based on long- and short-term preference (LSGNN) is proposed to address the above problems. LSGNN consists of More >

  • Open Access


    Comparative Evaluation of Data Mining Algorithms in Breast Cancer

    Fuad A. M. Al-Yarimi*

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 633-645, 2023, DOI:10.32604/cmc.2023.038858

    Abstract Unchecked breast cell growth is one of the leading causes of death in women globally and is the cause of breast cancer. The only method to avoid breast cancer-related deaths is through early detection and treatment. The proper classification of malignancies is one of the most significant challenges in the medical industry. Due to their high precision and accuracy, machine learning techniques are extensively employed for identifying and classifying various forms of cancer. Several data mining algorithms were studied and implemented by the author of this review and compared them to the present parameters and… More >

  • Open Access


    Fully Automated Density-Based Clustering Method

    Bilal Bataineh*, Ahmad A. Alzahrani

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1833-1851, 2023, DOI:10.32604/cmc.2023.039923

    Abstract Cluster analysis is a crucial technique in unsupervised machine learning, pattern recognition, and data analysis. However, current clustering algorithms suffer from the need for manual determination of parameter values, low accuracy, and inconsistent performance concerning data size and structure. To address these challenges, a novel clustering algorithm called the fully automated density-based clustering method (FADBC) is proposed. The FADBC method consists of two stages: parameter selection and cluster extraction. In the first stage, a proposed method extracts optimal parameters for the dataset, including the epsilon size and a minimum number of points thresholds. These parameters More >

  • Open Access


    Exploration of the oxidative-inflammatory potential targets of Coicis Semen in osteoarthritis: Data mining and systematic pharmacology


    BIOCELL, Vol.47, No.7, pp. 1623-1643, 2023, DOI:10.32604/biocell.2023.028331

    Abstract Objective: On the basis of data mining, systematic pharmacology, molecular docking, and experiment validation, the oxidative-inflammatory molecular targets of Coicis Semen in the therapy of osteoarthritis (OA) were explored. Methods: The association rule analysis was effectively applied to highlight the correlation between Coicis Semen and oxidative inflammation indices. The random walk model was subsequently used to evaluate the clinical efficacy of Coicis Semen. Network pharmacology was used to predict network targets. The binding affinity of the active ingredient in Coicis Semen to the key target of OA was also successfully predicted. Results: Coicis Semen showed a significant reduction in oxidative-inflammatory indicators… More >

  • Open Access


    Statistical Data Mining with Slime Mould Optimization for Intelligent Rainfall Classification

    Ramya Nemani1, G. Jose Moses2, Fayadh Alenezi3, K. Vijaya Kumar4, Seifedine Kadry5,6,7,*, Jungeun Kim8, Keejun Han9

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 919-935, 2023, DOI:10.32604/csse.2023.034213

    Abstract Statistics are most crucial than ever due to the accessibility of huge counts of data from several domains such as finance, medicine, science, engineering, and so on. Statistical data mining (SDM) is an interdisciplinary domain that examines huge existing databases to discover patterns and connections from the data. It varies in classical statistics on the size of datasets and on the detail that the data could not primarily be gathered based on some experimental strategy but conversely for other resolves. Thus, this paper introduces an effective statistical Data Mining for Intelligent Rainfall Prediction using Slime… More >

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