Taimoor Hassan¹, Ibrar Hussain^1,*, Hafiz Mahfooz Ul Haque², Hamid Turab Mirza³, Muhammad Nadeem Ali⁴, Byung-Seo Kim^4,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 2071-2094, 2025, DOI:10.32604/cmc.2025.068533 - 29 August 2025

Abstract Smart learning environments have been considered as vital sources and essential needs in modern digital education systems. With the rapid proliferation of smart and assistive technologies, smart learning processes have become quite convenient, comfortable, and financially affordable. This shift has led to the emergence of pervasive computing environments, where user’s intelligent behavior is supported by smart gadgets; however, it is becoming more challenging due to inconsistent behavior of Artificial intelligence (AI) assistive technologies in terms of networking issues, slow user responses to technologies and limited computational resources. This paper presents a context-aware predictive reasoning based… More >

Ze Wang, Jin Qin, Chuhua Huang^*, Yongjun Zhang^*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5811-5829, 2025, DOI:10.32604/cmc.2025.064537 - 30 July 2025

Abstract Semantic segmentation has made significant breakthroughs in various application fields, but achieving both accurate and efficient segmentation with limited computational resources remains a major challenge. To this end, we propose CGMISeg, an efficient semantic segmentation architecture based on a context-guided multi-scale interaction strategy, aiming to significantly reduce computational overhead while maintaining segmentation accuracy. CGMISeg consists of three core components: context-aware attention modulation, feature reconstruction, and cross-information fusion. Context-aware attention modulation is carefully designed to capture key contextual information through channel and spatial attention mechanisms. The feature reconstruction module reconstructs contextual information from different scales, modeling… More >

Sondes Baccouri^1,2,#,*, Takoua Abdellatif ^3,#

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 1071-1093, 2025, DOI:10.32604/cmes.2025.062902 - 11 April 2025

Abstract Managing sensitive data in dynamic and high-stakes environments, such as healthcare, requires access control frameworks that offer real-time adaptability, scalability, and regulatory compliance. BIG-ABAC introduces a transformative approach to Attribute-Based Access Control (ABAC) by integrating real-time policy evaluation and contextual adaptation. Unlike traditional ABAC systems that rely on static policies, BIG-ABAC dynamically updates policies in response to evolving rules and real-time contextual attributes, ensuring precise and efficient access control. Leveraging decision trees evaluated in real-time, BIG-ABAC overcomes the limitations of conventional access control models, enabling seamless adaptation to complex, high-demand scenarios. The framework adheres to the… More >

Yahia Said^1,*, Yahya Alassaf², Taoufik Saidani³, Refka Ghodhbani³, Olfa Ben Rhaiem⁴, Ali Ahmad Alalawi¹

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4349-4370, 2024, DOI:10.32604/cmc.2024.058903 - 19 December 2024

Abstract The integration of Unmanned Aerial Vehicles (UAVs) into Intelligent Transportation Systems (ITS) holds transformative potential for real-time traffic monitoring, a critical component of emerging smart city infrastructure. UAVs offer unique advantages over stationary traffic cameras, including greater flexibility in monitoring large and dynamic urban areas. However, detecting small, densely packed vehicles in UAV imagery remains a significant challenge due to occlusion, variations in lighting, and the complexity of urban landscapes. Conventional models often struggle with these issues, leading to inaccurate detections and reduced performance in practical applications. To address these challenges, this paper introduces CFEMNet,… More >

Canghong Jin^1,*, Jiapeng Chen¹, Shuyu Wu¹, Hao Wu², Shuoping Wang¹, Jing Ying³

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 873-891, 2024, DOI:10.32604/cmes.2023.043230 - 30 December 2023

Abstract Time series data plays a crucial role in intelligent transportation systems. Traffic flow forecasting represents a precise estimation of future traffic flow within a specific region and time interval. Existing approaches, including sequence periodic, regression, and deep learning models, have shown promising results in short-term series forecasting. However, forecasting scenarios specifically focused on holiday traffic flow present unique challenges, such as distinct traffic patterns during vacations and the increased demand for long-term forecastings. Consequently, the effectiveness of existing methods diminishes in such scenarios. Therefore, we propose a novel long-term forecasting model based on scene matching More >

Eugene Tan Boon Hong¹, Yung-Wey Chong^1,*, Tat-Chee Wan¹, Kok-Lim Alvin Yau²

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2337-2354, 2023, DOI:10.32604/cmc.2023.038695 - 30 August 2023

Abstract The existing dataset for visual dialog comprises multiple rounds of questions and a diverse range of image contents. However, it faces challenges in overcoming visual semantic limitations, particularly in obtaining sufficient context from visual and textual aspects of images. This paper proposes a new visual dialog dataset called Diverse History-Dialog (DS-Dialog) to address the visual semantic limitations faced by the existing dataset. DS-Dialog groups relevant histories based on their respective Microsoft Common Objects in Context (MSCOCO) image categories and consolidates them for each image. Specifically, each MSCOCO image category consists of top relevant histories extracted… More >

R. Sujatha^1,*, T. Abirami²

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1775-1787, 2023, DOI:10.32604/csse.2023.031552 - 09 February 2023

Abstract The digital technologies that run based on users’ content provide a platform for users to help air their opinions on various aspects of a particular subject or product. The recommendation agents play a crucial role in personalizing the needs of individual users. Therefore, it is essential to improve the user experience. The recommender system focuses on recommending a set of items to a user to help the decision-making process and is prevalent across e-commerce and media websites. In Context-Aware Recommender Systems (CARS), several influential and contextual variables are identified to provide an effective recommendation. A… More >

P. N. Ramesh^1,*, S. Kannimuthu²

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3845-3860, 2023, DOI:10.32604/iasc.2023.031329 - 17 August 2022

Abstract The use of programming online judges (POJs) has risen dramatically in recent years, owing to the fact that the auto-evaluation of codes during practice motivates students to learn programming. Since POJs have greater number of programming problems in their repository, learners experience information overload. Recommender systems are a common solution to information overload. Current recommender systems used in e-learning platforms are inadequate for POJ since recommendations should consider learners’ current context, like learning goals and current skill level (topic knowledge and difficulty level). To overcome the issue, we propose a context-aware practice problem recommender system… More >

Son-Lam VU, Quang-Hung LE^*

Computer Systems Science and Engineering, Vol.44, No.1, pp. 471-483, 2023, DOI:10.32604/csse.2023.025897 - 01 June 2022

Abstract Recommender systems are similar to an information filtering system that helps identify items that best satisfy the users’ demands based on their preference profiles. Context-aware recommender systems (CARSs) and multi-criteria recommender systems (MCRSs) are extensions of traditional recommender systems. CARSs have integrated additional contextual information such as time, place, and so on for providing better recommendations. However, the majority of CARSs use ratings as a unique criterion for building communities. Meanwhile, MCRSs utilize user preferences in multiple criteria to better generate recommendations. Up to now, how to exploit context in MCRSs is still an open More >

Moeiz Miraoui^1,2,*

Computer Systems Science and Engineering, Vol.44, No.1, pp. 677-692, 2023, DOI:10.32604/csse.2023.025799 - 01 June 2022

Abstract A cerebral vascular accident, known as common language stroke, is one of the main causes of mortality and remains the primary cause of acquired disabilities in adults. Those disabled people spend most of their time at home in their living rooms. In most cases, appliances of a living room (TV, light, cooler/heater, window blinds, etc.) are generally controlled by direct manipulation of a set of remote controls. Handling many remote controls can be disturbing and inappropriate for these people. In addition, in many cases these people could be alone at home and must open the… More >