Yahya Tashtoush^1,*, Areen Banysalim¹, Majdi Maabreh², Shorouq Al-Eidi³, Ola Karajeh⁴, Plamen Zahariev⁵

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3113-3134, 2025, DOI:10.32604/cmc.2025.062724 - 16 April 2025

Abstract Social media has emerged as one of the most transformative developments on the internet, revolutionizing the way people communicate and interact. However, alongside its benefits, social media has also given rise to significant challenges, one of the most pressing being cyberbullying. This issue has become a major concern in modern society, particularly due to its profound negative impacts on the mental health and well-being of its victims. In the Arab world, where social media usage is exceptionally high, cyberbullying has become increasingly prevalent, necessitating urgent attention. Early detection of harmful online behavior is critical to… More >

Aodi Liu, Xuehui Du^*, Na Wang, Xiangyu Wu

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1471-1489, 2025, DOI:10.32604/cmc.2025.059784 - 26 March 2025

Abstract Security attributes are the premise and foundation for implementing Attribute-Based Access Control (ABAC) mechanisms. However, when dealing with massive volumes of unstructured text big data resources, the current attribute management methods based on manual extraction face several issues, such as high costs for attribute extraction, long processing times, unstable accuracy, and poor scalability. To address these problems, this paper proposes an attribute mining technology for access control institutions based on hybrid capsule networks. This technology leverages transfer learning ideas, utilizing Bidirectional Encoder Representations from Transformers (BERT) pre-trained language models to achieve vectorization of unstructured text… More >

Shaoqi Hou^1,2,3,*, Zebang Qin², Chenyu Wu², Guangqiang Yin², Xinzhong Wang¹, Zhiguo Wang^2,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5019-5033, 2025, DOI:10.32604/cmc.2025.058233 - 06 March 2025

Abstract Visual Place Recognition (VPR) technology aims to use visual information to judge the location of agents, which plays an irreplaceable role in tasks such as loop closure detection and relocation. It is well known that previous VPR algorithms emphasize the extraction and integration of general image features, while ignoring the mining of salient features that play a key role in the discrimination of VPR tasks. To this end, this paper proposes a Domain-invariant Information Extraction and Optimization Network (DIEONet) for VPR. The core of the algorithm is a newly designed Domain-invariant Information Mining Module (DIMM)… More >

Efdal Kaya^*

Revue Internationale de Géomatique, Vol.34, pp. 103-123, 2025, DOI:10.32604/rig.2025.060420 - 24 February 2025

Abstract Global warming leads to climate change and hence effects tourism activities. Bioclimatic comfort indices are used to understand the changing climates of outdoor tourism. In this study, the models for the automatic calculation of the tourism climate index (TCI), heat index (HI), and new summer simmer index (NSSI) from bioclimatic comfort indices are used to determine the climatic conditions of outdoor tourism. The study compared the maps generated by the models with those manually created maps in ArcGIS. In order to statistically reveal how accurately the models produced maps, the relationship between the maps obtained… More >

Laith Abualigah^*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1539-1592, 2025, DOI:10.32604/cmc.2025.060765 - 17 February 2025

Abstract Particle Swarm Optimization (PSO) has been utilized as a useful tool for solving intricate optimization problems for various applications in different fields. This paper attempts to carry out an update on PSO and gives a review of its recent developments and applications, but also provides arguments for its efficacy in resolving optimization problems in comparison with other algorithms. Covering six strategic areas, which include Data Mining, Machine Learning, Engineering Design, Energy Systems, Healthcare, and Robotics, the study demonstrates the versatility and effectiveness of the PSO. Experimental results are, however, used to show the strong and More >

Santiago Bañales^1,2,*, Raquel Dormido¹, Natividad Duro¹

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 869-907, 2025, DOI:10.32604/cmes.2024.054946 - 17 December 2024

Abstract Customer segmentation according to load-shape profiles using smart meter data is an increasingly important application to vital the planning and operation of energy systems and to enable citizens’ participation in the energy transition. This study proposes an innovative multi-step clustering procedure to segment customers based on load-shape patterns at the daily and intra-daily time horizons. Smart meter data is split between daily and hourly normalized time series to assess monthly, weekly, daily, and hourly seasonality patterns separately. The dimensionality reduction implicit in the splitting allows a direct approach to clustering raw daily energy time series… More > Graphic Abstract

Bhanu Pratap Reddy Bhavanam, Prashanth Ragam^*

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 425-466, 2025, DOI:10.32604/cmes.2024.052355 - 17 December 2024

Abstract The Internet of Things (IoT) has orchestrated various domains in numerous applications, contributing significantly to the growth of the smart world, even in regions with low literacy rates, boosting socio-economic development. This study provides valuable insights into optimizing wireless communication, paving the way for a more connected and productive future in the mining industry. The IoT revolution is advancing across industries, but harsh geometric environments, including open-pit mines, pose unique challenges for reliable communication. The advent of IoT in the mining industry has significantly improved communication for critical operations through the use of Radio Frequency… More >

Xiayun Zhao^1,*, Haolin Zhang¹, Md Jahangir Alam¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.31, No.2, pp. 1-2, 2024, DOI:10.32604/icces.2024.011479

Abstract Laser powder bed fusion (LPBF) is one predominant additive manufacturing (AM) technology for producing metallic parts with sophisticated designs that can find numerous applications in critical industries such as aerospace. To achieve precise, resilient, and intelligent LPBF, a comprehensive understanding of the dynamic processes and material responses within the actual conditions of LPBF-based AM is essential. However, obtaining such insights is challenging due to the intricate interactions among the laser, powder, part layers, and gas flow, among other factors. Multimodal in-situ monitoring is desired to visualize diverse process signatures, allowing for the direct and thorough… More >

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 >