Oscar Peña-Cáceres^1,2,*, Elvis Garay-Silupu³, Darwin Aguilar-Chuquizuta⁴, Henry Silva-Marchan⁴

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2151-2188, 2025, DOI:10.32604/cmc.2025.065149 - 03 July 2025

Abstract Self-Explaining Autonomous Systems (SEAS) have emerged as a strategic frontier within Artificial Intelligence (AI), responding to growing demands for transparency and interpretability in autonomous decision-making. This study presents a comprehensive bibliometric analysis of SEAS research published between 2020 and February 2025, drawing upon 1380 documents indexed in Scopus. The analysis applies co-citation mapping, keyword co-occurrence, and author collaboration networks using VOSviewer, MASHA, and Python to examine scientific production, intellectual structure, and global collaboration patterns. The results indicate a sustained annual growth rate of 41.38%, with an h-index of 57 and an average of 21.97 citations… More >

Moein Tosan¹, Mohammad Reza Gharib^2,*, Nasrin Fathollahzadeh Attar³, Ali Maroosi⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 1109-1154, 2025, DOI:10.32604/cmes.2025.058595 - 27 January 2025

Abstract Accurate estimation of evapotranspiration (ET) is crucial for efficient water resource management, particularly in the face of climate change and increasing water scarcity. This study performs a bibliometric analysis of 352 articles and a systematic review of 35 peer-reviewed papers, selected according to PRISMA guidelines, to evaluate the performance of Hybrid Artificial Neural Networks (HANNs) in ET estimation. The findings demonstrate that HANNs, particularly those combining Multilayer Perceptrons (MLPs), Recurrent Neural Networks (RNNs), and Convolutional Neural Networks (CNNs), are highly effective in capturing the complex nonlinear relationships and temporal dependencies characteristic of hydrological processes. These… More >

Seongung Jo¹, Heung-Seon Oh^1,*, Sanghun Im¹, Gibaeg Kim¹, Seonho Kim²

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2967-2980, 2023, DOI:10.32604/cmc.2023.031983 - 31 October 2022

Abstract Analyzing Research and Development (R&D) trends is important because it can influence future decisions regarding R&D direction. In typical trend analysis, topic or technology taxonomies are employed to compute the popularities of the topics or codes over time. Although it is simple and effective, the taxonomies are difficult to manage because new technologies are introduced rapidly. Therefore, recent studies exploit deep learning to extract pre-defined targets such as problems and solutions. Based on the recent advances in question answering (QA) using deep learning, we adopt a multi-turn QA model to extract problems and solutions from… More >

Jaiteg Singh¹, Tanya Gera¹, Farman Ali², Deepak Thakur¹, Karamjeet Singh³, Kyung-sup Kwak^4,*

CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2655-2670, 2021, DOI:10.32604/cmc.2021.014504 - 28 December 2020

Abstract Android has been dominating the smartphone market for more than a decade and has managed to capture 87.8% of the market share. Such popularity of Android has drawn the attention of cybercriminals and malware developers. The malicious applications can steal sensitive information like contacts, read personal messages, record calls, send messages to premium-rate numbers, cause financial loss, gain access to the gallery and can access the user’s geographic location. Numerous surveys on Android security have primarily focused on types of malware attack, their propagation, and techniques to mitigate them. To the best of our knowledge,… More >