Bushra Kanwal¹, Rana Saud Shoukat², Saif Ur Rehman^2,*, Mahwish Kundi³, Tahani AlSaedi⁴, Abdulrahman Alahmadi⁴

Intelligent Automation & Soft Computing, Vol.39, No.5, pp. 829-854, 2024, DOI:10.32604/iasc.2024.054645 - 31 October 2024

Abstract Education is the base of the survival and growth of any state, but due to resource scarcity, students, particularly at the university level, are forced into a difficult situation. Scholarships are the most significant financial aid mechanisms developed to overcome such obstacles and assist the students in continuing with their higher studies. In this study, the convoluted situation of scholarship eligibility criteria, including parental income, responsibilities, and academic achievements, is addressed. In an attempt to maximize the scholarship selection process, numerous machine learning algorithms, including Support Vector Machines, Neural Networks, K-Nearest Neighbors, and the C4.5… More >

Fahim Nasir¹, Abdulghani Ali Ahmed^1,*, Mehmet Sabir Kiraz¹, Iryna Yevseyeva¹, Mubarak Saif²

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1703-1728, 2024, DOI:10.32604/cmc.2024.055192 - 15 October 2024

Abstract Integrating machine learning and data mining is crucial for processing big data and extracting valuable insights to enhance decision-making. However, imbalanced target variables within big data present technical challenges that hinder the performance of supervised learning classifiers on key evaluation metrics, limiting their overall effectiveness. This study presents a comprehensive review of both common and recently developed Supervised Learning Classifiers (SLCs) and evaluates their performance in data-driven decision-making. The evaluation uses various metrics, with a particular focus on the Harmonic Mean Score (F-1 score) on an imbalanced real-world bank target marketing dataset. The findings indicate… More >

Cuiwei Peng¹, Jiahui Chen^2,*, Shicheng Wan³, Guotao Xu⁴

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4341-4360, 2024, DOI:10.32604/cmc.2024.054109 - 12 September 2024

Abstract In today’s highly competitive retail industry, offline stores face increasing pressure on profitability. They hope to improve their ability in shelf management with the help of big data technology. For this, on-shelf availability is an essential indicator of shelf data management and closely relates to customer purchase behavior. RFM (recency, frequency, and monetary) pattern mining is a powerful tool to evaluate the value of customer behavior. However, the existing RFM pattern mining algorithms do not consider the quarterly nature of goods, resulting in unreasonable shelf availability and difficulty in profit-making. To solve this problem, we… More >

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 - 21 May 2024

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 >

Mozamel M. Saeed^*

Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 213-231, 2024, DOI:10.32604/iasc.2024.043020 - 21 May 2024

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 >

Ye-Yeon Kang¹, Geon Park¹, Hyun Yoo², Kyungyong Chung^1,*

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3619-3635, 2023, DOI:10.32604/cmc.2023.043566 - 26 December 2023

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 >

Peng Cheng^1,2,*

Journal on Big Data, Vol.5, pp. 69-84, 2023, DOI:10.32604/jbd.2023.046294 - 24 November 2023

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 >

Firas Abedi¹, Hayder M. A. Ghanimi², Abeer D. Algarni³, Naglaa F. Soliman^3,*, Walid El-Shafai^4,5, 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 - 09 November 2023

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 >

Bohuai Xiao^1,2, Xiaolan Xie^1,2,*, Chengyong Yang³

Computer Systems Science and Engineering, Vol.47, No.3, pp. 3067-3082, 2023, DOI:10.32604/csse.2023.034712 - 09 November 2023

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 >

Fuad A. M. Al-Yarimi^*

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 633-645, 2023, DOI:10.32604/cmc.2023.038858 - 31 October 2023

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 >