Shuo Zhang^1,2, Guibin Zou^1,*

Energy Engineering, Vol.122, No.1, pp. 85-99, 2025, DOI:10.32604/ee.2024.057452 - 27 December 2024

Abstract The multi-terminal direct current (DC) grid has extinctive superiorities over the traditional alternating current system in integrating large-scale renewable energy. Both the DC circuit breaker (DCCB) and the current flow controller (CFC) are demanded to ensure the multiterminal DC grid to operates reliably and flexibly. However, since the CFC and the DCCB are all based on fully controlled semiconductor switches (e.g., insulated gate bipolar transistor, integrated gate commutated thyristor, etc.), their separation configuration in the multiterminal DC grid will lead to unaffordable implementation costs and conduction power losses. To solve these problems, integrated equipment with… More >

Pratyush Kumar Das¹, Satyabrata Nanda^2,*

Phyton-International Journal of Experimental Botany, Vol.93, No.12, pp. 3217-3235, 2024, DOI:10.32604/phyton.2024.057956 - 31 December 2024

Abstract Insect pests cause severe crop damage, resulting in substantial economic losses and threats to global food security. Conventional insecticides are low-cost chemical agents that kill the target insects and some non-specific beneficial organisms. Due to their toxic and non-biodegradable nature, these conventional insecticides persist in the environment, thus causing pollution and accumulating in the food chain. The development of novel insecticidal products based on double-stranded (dsRNA)-based RNA interference (RNAi) technology is a sustainable tool to effectively control insect pests. The dsRNA-based insecticides are known for their specificity, non-toxicity, and biodegradability. The current review introduces the… More >

Chioma Ifeyinwa Madueke^1,*, Okwunna Maryjane Ekechukwu², Funsho Olaitan Kolawole³

Journal of Renewable Materials, Vol.12, No.12, pp. 2017-2047, 2024, DOI:10.32604/jrm.2024.055207 - 20 December 2024

Abstract Coir fibre has generated much interest as an eco-friendly, sustainable fibre with low density. This review findings show that coir fibres are abundant, with an average global annual production of 1019.7 × 103 tonnes, with about 63% of this volume produced from India. Extraction of coir has been carried out through water retting. However, the retting period has been limited to 4–10 months. The lignin content of coir is more than 60% higher than that of other natural fibres; hence, coir could double as a source of lignin for other applications. The diameter of coir… More >

Ali Hassan¹, N. Nizam-Uddin², Asim Quddus³, Syed Rizwan Hassan⁴, Ateeq Ur Rehman^5,*, Salil Bharany⁶

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3499-3559, 2024, DOI:10.32604/cmc.2024.057877 - 19 December 2024

Abstract Enhancing the interconnection of devices and systems, the Internet of Things (IoT) is a paradigm-shifting technology. IoT security concerns are still a substantial concern despite its extraordinary advantages. This paper offers an extensive review of IoT security, emphasizing the technology’s architecture, important security elements, and common attacks. It highlights how important artificial intelligence (AI) is to bolstering IoT security, especially when it comes to addressing risks at different IoT architecture layers. We systematically examined current mitigation strategies and their effectiveness, highlighting contemporary challenges with practical solutions and case studies from a range of industries, such More >

Buchi Reddy Ramakantha Reddy, Ramasamy Lokesh Kumar^*

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4081-4107, 2024, DOI:10.32604/cmc.2024.057071 - 19 December 2024

Abstract Traditional e-commerce recommendation systems often struggle with dynamic user preferences and a vast array of products, leading to suboptimal user experiences. To address this, our study presents a Personalized Adaptive Multi-Product Recommendation System (PAMR) leveraging transfer learning and Bi-GRU (Bidirectional Gated Recurrent Units). Using a large dataset of user reviews from Amazon and Flipkart, we employ transfer learning with pre-trained models (AlexNet, GoogleNet, ResNet-50) to extract high-level attributes from product data, ensuring effective feature representation even with limited data. Bi-GRU captures both spatial and sequential dependencies in user-item interactions. The innovation of this study lies… More >

Israa Ibraheem Al Barazanchi^1,2,*, Wahidah Hashim¹, Reema Thabit¹, Mashary Nawwaf Alrasheedy^3,4, Abeer Aljohan⁵, Jongwoon Park⁶, Byoungchol Chang⁶

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4787-4832, 2024, DOI:10.32604/cmc.2024.055079 - 19 December 2024

Abstract This research aims to enhance Clinical Decision Support Systems (CDSS) within Wireless Body Area Networks (WBANs) by leveraging advanced machine learning techniques. Specifically, we target the challenges of accurate diagnosis in medical imaging and sequential data analysis using Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) layers and echo state cells. These models are tailored to improve diagnostic precision, particularly for conditions like rotator cuff tears in osteoporosis patients and gastrointestinal diseases. Traditional diagnostic methods and existing CDSS frameworks often fall short in managing complex, sequential medical data, struggling with long-term dependencies and data… More >

Tianyu Chen¹, Yifan Wang^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.31, No.4, pp. 1-1, 2024, DOI:10.32604/icces.2024.011272

Abstract Biological organisms often possess remarkable multifunctionality through intricate structures, such as the concurrent shape-morphing and stiffness-variation in octopus. Soft robots, which are inspired by natural creatures, usually require the integration of separate modules to achieve these various functions. As a result, the whole structure is cumbersome and the control system is complex, often involving multiple control loops to finish the required task. Here, inspired by the scaly creatures in nature such as pangolins and fish, we develop a robotic structure that can vary stiffness and change shape simultaneously in a highly-integrated compact body. The scale-inspired… More >

Farhad Mortezapour Shiri^*, Thinagaran Perumal, Norwati Mustapha, Raihani Mohamed

Journal on Artificial Intelligence, Vol.6, pp. 301-360, 2024, DOI:10.32604/jai.2024.054314 - 20 November 2024

Abstract Deep learning (DL) has emerged as a powerful subset of machine learning (ML) and artificial intelligence (AI), outperforming traditional ML methods, especially in handling unstructured and large datasets. Its impact spans across various domains, including speech recognition, healthcare, autonomous vehicles, cybersecurity, predictive analytics, and more. However, the complexity and dynamic nature of real-world problems present challenges in designing effective deep learning models. Consequently, several deep learning models have been developed to address different problems and applications. In this article, we conduct a comprehensive survey of various deep learning models, including Convolutional Neural Network (CNN), Recurrent… More >

Tao Yan¹, Javed Rashid^2,3, Muhammad Shoaib Saleem^3,4, Sajjad Ahmad⁴, Muhammad Faheem^5,*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2685-2708, 2024, DOI:10.32604/cmc.2024.058186 - 18 November 2024

Abstract Electricity is essential for keeping power networks balanced between supply and demand, especially since it costs a lot to store. The article talks about different deep learning methods that are used to guess how much green energy different Asian countries will produce. The main goal is to make reliable and accurate predictions that can help with the planning of new power plants to meet rising demand. There is a new deep learning model called the Green-electrical Production Ensemble (GP-Ensemble). It combines three types of neural networks: convolutional neural networks (CNNs), gated recurrent units (GRUs), and… More >

Sameema Tariq¹, Ata-Ur- Rehman^2,3, Maria Abubakar², Waseem Iqbal⁴, Hatoon S. Alsagri⁵, Yousef A. Alduraywish⁵, Haya Abdullah A. Alhakbani^5,*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2493-2515, 2024, DOI:10.32604/cmc.2024.055787 - 18 November 2024

Abstract In video surveillance, anomaly detection requires training machine learning models on spatio-temporal video sequences. However, sometimes the video-only data is not sufficient to accurately detect all the abnormal activities. Therefore, we propose a novel audio-visual spatiotemporal autoencoder specifically designed to detect anomalies for video surveillance by utilizing audio data along with video data. This paper presents a competitive approach to a multi-modal recurrent neural network for anomaly detection that combines separate spatial and temporal autoencoders to leverage both spatial and temporal features in audio-visual data. The proposed model is trained to produce low reconstruction error… More >