Nahed Alsaleh^1,2, Reem Alnanih^1,*, Nahed Alowidi¹

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 949-976, 2025, DOI:10.32604/cmc.2024.059351 - 03 January 2025

Abstract App reviews are crucial in influencing user decisions and providing essential feedback for developers to improve their products. Automating the analysis of these reviews is vital for efficient review management. While traditional machine learning (ML) models rely on basic word-based feature extraction, deep learning (DL) methods, enhanced with advanced word embeddings, have shown superior performance. This research introduces a novel aspect-based sentiment analysis (ABSA) framework to classify app reviews based on key non-functional requirements, focusing on usability factors: effectiveness, efficiency, and satisfaction. We propose a hybrid DL model, combining BERT (Bidirectional Encoder Representations from Transformers) More >

Peng Li, Yanrui Wei, Lili Yin^*

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 609-625, 2025, DOI:10.32604/cmc.2024.056651 - 03 January 2025

Abstract Stock price prediction is a typical complex time series prediction problem characterized by dynamics, nonlinearity, and complexity. This paper introduces a generative adversarial network model that incorporates an attention mechanism (GAN-LSTM-Attention) to improve the accuracy of stock price prediction. Firstly, the generator of this model combines the Long and Short-Term Memory Network (LSTM), the Attention Mechanism and, the Fully-Connected Layer, focusing on generating the predicted stock price. The discriminator combines the Convolutional Neural Network (CNN) and the Fully-Connected Layer to discriminate between real stock prices and generated stock prices. Secondly, to evaluate the practical application… More >

Ammar Odeh^*, Anas Abu Taleb

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4149-4169, 2024, DOI:10.32604/cmc.2024.058052 - 19 December 2024

Abstract The proliferation of Internet of Things (IoT) technology has exponentially increased the number of devices interconnected over networks, thereby escalating the potential vectors for cybersecurity threats. In response, this study rigorously applies and evaluates deep learning models—namely Convolutional Neural Networks (CNN), Autoencoders, and Long Short-Term Memory (LSTM) networks—to engineer an advanced Intrusion Detection System (IDS) specifically designed for IoT environments. Utilizing the comprehensive UNSW-NB15 dataset, which encompasses 49 distinct features representing varied network traffic characteristics, our methodology focused on meticulous data preprocessing including cleaning, normalization, and strategic feature selection to enhance model performance. A robust… 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 >

Peng Xia¹, Wu Zeng^2,*, Yin Ni¹, Ye Jin³

Journal on Artificial Intelligence, Vol.6, pp. 361-377, 2024, DOI:10.32604/jai.2024.059114 - 13 December 2024

Abstract Stress is defined as a subjective reflection of an internal psychological state of tension or arousal, manifesting as an interpretive, emotional, and defensive coping process within the body. Prolonged and sustained stress can significantly increase the risk of psychological and physiological disorders. Heart rate variability (HRV) is a key biomarker for assessing autonomic cardiac function, typically increasing during relaxation and decreasing under stress. Although measuring stress through physiological parameters like HRV is a common approach, achieving ultra-high accuracy based on HRV measurements remains a challenging task. In this study, the role of HRV features as… More >

Yi-Chun Cheng¹, Mu-En Wu¹, Ju-Fang Yen², Sheng-Chi Luo¹, Jimmy Ming-Tai Wu^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.31, No.2, pp. 1-1, 2024, DOI:10.32604/icces.2024.011239

Abstract Quantitative trading is a strategy that relies on mathematical and statistical models to identify market trading opportunities. Trading strategies can be categorized into trend following and contrarian trading. Way of the Turtle is one of the famous trend following strategies. This study proposes a customized trend following trading mechanism based on Way of the Turtle. The focus of the strategy is to capture major trends over a few significant market moves, so it can be seen that the importance of the entry signals to the trend-following strategy. Therefore, this study applies Long Short-Term Memory (LSTM)… 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 >

Shaoxiong Wu¹, Ruoxin Li¹, Xiaofeng Tao¹, Hailong Wu^1,*, Ping Miao¹, Yang Lu¹, Yanyan Lu¹, Qi Liu², Li Pan²

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3063-3077, 2024, DOI:10.32604/cmc.2024.055381 - 18 November 2024

Abstract Time series prediction has always been an important problem in the field of machine learning. Among them, power load forecasting plays a crucial role in identifying the behavior of photovoltaic power plants and regulating their control strategies. Traditional power load forecasting often has poor feature extraction performance for long time series. In this paper, a new deep learning framework Residual Stacked Temporal Long Short-Term Memory (RST-LSTM) is proposed, which combines wavelet decomposition and time convolutional memory network to solve the problem of feature extraction for long sequences. The network framework of RST-LSTM consists of two More >

Alaa U. Khawaja¹, Ahmad Shaf^2,*, Faisal Al Thobiani³, Tariq Ali⁴, Muhammad Irfan⁵, Aqib Rehman Pirzada², Unza Shahkeel²

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2399-2420, 2024, DOI:10.32604/cmes.2024.054257 - 31 October 2024

Abstract Electric motor-driven systems are core components across industries, yet they’re susceptible to bearing faults. Manual fault diagnosis poses safety risks and economic instability, necessitating an automated approach. This study proposes FTCNNLSTM (Fine-Tuned TabNet Convolutional Neural Network Long Short-Term Memory), an algorithm combining Convolutional Neural Networks, Long Short-Term Memory Networks, and Attentive Interpretable Tabular Learning. The model preprocesses the CWRU (Case Western Reserve University) bearing dataset using segmentation, normalization, feature scaling, and label encoding. Its architecture comprises multiple 1D Convolutional layers, batch normalization, max-pooling, and LSTM blocks with dropout, followed by batch normalization, dense layers, and More >

Rasha Almarshdi^1,2,*, Etimad Fadel¹, Nahed Alowidi¹, Laila Nassef¹

Intelligent Automation & Soft Computing, Vol.39, No.5, pp. 949-983, 2024, DOI:10.32604/iasc.2024.056792 - 31 October 2024

Abstract The Internet of Medical Things (IoMT) is one of the critical emerging applications of the Internet of Things (IoT). The huge increases in data generation and transmission across distributed networks make security one of the most important challenges facing IoMT networks. Distributed Denial of Service (DDoS) attacks impact the availability of services of legitimate users. Intrusion Detection Systems (IDSs) that are based on Centralized Learning (CL) suffer from high training time and communication overhead. IDS that are based on distributed learning, such as Federated Learning (FL) or Split Learning (SL), are recently used for intrusion… More >