Joby Mackolil^1,2, Mahanthesh Basavarajappa^1,3, Giulio Lorenzini^4,*

Frontiers in Heat and Mass Transfer, Vol.23, No.2, pp. 529-551, 2025, DOI:10.32604/fhmt.2025.058702 - 25 April 2025

Abstract This article explores the optimization of heat transport in a magnetohydrodynamic nanofluid flow with mixed Marangoni convection by using the Response Surface Methodology. The convective flow is studied with external magnetism, radiative heat flux, and buoyancy. An internal heat absorption through the permeable surface is also taken into account. The governing system includes the continuity equation, Navier-Stokes momentum equation, and the conservation of energy equations, approximated by the Prandtl boundary layer theory. The entropy generation in the thermodynamic system is evaluated. Experimental data (Corcione models) is used to model the single-phase alumina-water nanofluid. The numerical… More >

Feng Guo, Chen Yang^*, Dezhong Xia, Jingxiang Xu

Energy Engineering, Vol.122, No.5, pp. 1975-1999, 2025, DOI:10.32604/ee.2025.062035 - 25 April 2025

Abstract Short-term photovoltaic (PV) power forecasting plays a crucial role in enhancing the stability and reliability of power grid scheduling. To address the challenges posed by complex environmental variables and difficulties in modeling temporal features in PV power prediction, a short-term PV power forecasting method based on an improved CNN-LSTM and cascade learning strategy is proposed. First, Pearson correlation coefficients and mutual information are used to select representative features, reducing the impact of redundant features on model performance. Then, the CNN-LSTM network is designed to extract local features using CNN and learn temporal dependencies through LSTM,… More > Graphic Abstract

Ameer H. Hamzah¹, Abdulrazzak Akroot¹, Hasanain A. Abdul Wahhab^2,*

Energy Engineering, Vol.122, No.5, pp. 2059-2075, 2025, DOI:10.32604/ee.2025.061592 - 25 April 2025

Abstract The current work includes a numerical investigation of the effect of biodiesel blends with different aluminum oxide nanoparticle concentrations on the combustion process in the cylinder of a diesel engine. IC Engine Fluent, a specialist computational tool in the ANSYS software, was used to simulate internal combustion engine dynamics and combustion processes. Numerical analysis was carried out using biodiesel blends with three Al₂O₃ nanoparticles in 50, 100, and 150 ppm concentrations. The tested samples are called D100, B20, B20A50, B20A100, and B20A150 accordingly. The modeling runs were carried out at various engine loads of 0, 100,… More >

Sheik Mohideen Shah¹, Meganathan Selvamani¹, Mahesh Thyluru Ramakrishna^2,*, Surbhi Bhatia Khan^3,4,5, Shakila Basheer⁶, Wajdan Al Malwi⁷, Mohammad Tabrez Quasim⁸

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2907-2926, 2025, DOI:10.32604/cmc.2025.063809 - 16 April 2025

Abstract Efficient energy management is a cornerstone of advancing cognitive cities, where AI, IoT, and cloud computing seamlessly integrate to meet escalating global energy demands. Within this context, the ability to forecast electricity consumption with precision is vital, particularly in residential settings where usage patterns are highly variable and complex. This study presents an innovative approach to energy consumption forecasting using a bidirectional Long Short-Term Memory (LSTM) network. Leveraging a dataset containing over two million multivariate, time-series observations collected from a single household over nearly four years, our model addresses the limitations of traditional time-series forecasting… More >

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 >

Shabana Ziyad Puthu Vedu^1,*, Wafaa A. Ghonaim², Naglaa M. Mostafa³, Pradeep Kumar Singh⁴

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2599-2617, 2025, DOI:10.32604/cmc.2025.062695 - 16 April 2025

Abstract Hearing and Speech impairment can be congenital or acquired. Hearing and speech-impaired students often hesitate to pursue higher education in reputable institutions due to their challenges. However, the development of automated assistive learning tools within the educational field has empowered disabled students to pursue higher education in any field of study. Assistive learning devices enable students to access institutional resources and facilities fully. The proposed assistive learning and communication tool allows hearing and speech-impaired students to interact productively with their teachers and classmates. This tool converts the audio signals into sign language videos for the… More >

Hamza Murad Khan¹, Anwar Khan^1,*, Santos Gracia Villar^2,3,4, Luis Alonso Dzul Lopez^2,5,6, Abdulaziz Almaleh⁷, Abdullah M. Al-Qahtani⁸

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3369-3388, 2025, DOI:10.32604/cmc.2025.060474 - 16 April 2025

Abstract Traffic forecasting with high precision aids Intelligent Transport Systems (ITS) in formulating and optimizing traffic management strategies. The algorithms used for tuning the hyperparameters of the deep learning models often have accurate results at the expense of high computational complexity. To address this problem, this paper uses the Tree-structured Parzen Estimator (TPE) to tune the hyperparameters of the Long Short-term Memory (LSTM) deep learning framework. The Tree-structured Parzen Estimator (TPE) uses a probabilistic approach with an adaptive searching mechanism by classifying the objective function values into good and bad samples. This ensures fast convergence in… More >

Phan-Anh-Huy Nguyen^*, Luu-Luyen Than

Intelligent Automation & Soft Computing, Vol.40, pp. 221-234, 2025, DOI:10.32604/iasc.2025.058976 - 14 April 2025

Abstract The burgeoning e-commerce industry has made online customer reviews a crucial source of feedback for businesses. Sentiment analysis, a technique used to extract subjective information from text, has become essential for understanding consumer sentiment and preferences. However, traditional sentiment analysis methods often struggle with the nuances and context of natural language. To address these issues, this study proposes a comparison of deep learning models that figure out the optimal method to accurately analyze consumer reviews on women's clothing. CNNs excel at capturing local features and semantic information, while LSTMs are adept at handling long-range dependencies… More >

Ishaani Priyadarshini^*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 635-659, 2025, DOI:10.32604/cmc.2025.061062 - 26 March 2025

Abstract Predictive maintenance plays a crucial role in preventing equipment failures and minimizing operational downtime in modern industries. However, traditional predictive maintenance methods often face challenges in adapting to diverse industrial environments and ensuring the transparency and fairness of their predictions. This paper presents a novel predictive maintenance framework that integrates deep learning and optimization techniques while addressing key ethical considerations, such as transparency, fairness, and explainability, in artificial intelligence driven decision-making. The framework employs an Autoencoder for feature reduction, a Convolutional Neural Network for pattern recognition, and a Long Short-Term Memory network for temporal analysis.… More >

Nataliya Shakhovska, Volodymyr Shymanskyi^*, Maksym Prymachenko

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4469-4484, 2025, DOI:10.32604/cmc.2025.062675 - 06 March 2025

Abstract Time series forecasting is important in the fields of finance, energy, and meteorology, but traditional methods often fail to cope with the complex nonlinear and nonstationary processes of real data. In this paper, we propose the FractalNet-LSTM model, which combines fractal convolutional units with recurrent long short-term memory (LSTM) layers to model time series efficiently. To test the effectiveness of the model, data with complex structures and patterns, in particular, with seasonal and cyclical effects, were used. To better demonstrate the obtained results and the formed conclusions, the model performance was shown on the datasets More >