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 >

Chunming Wu¹, Shupeng Zheng^2,*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4395-4411, 2024, DOI:10.32604/cmc.2024.049665 - 20 June 2024

Abstract Deep neural networks have been widely applied to bearing fault diagnosis systems and achieved impressive success recently. To address the problem that the insufficient fault feature extraction ability of traditional fault diagnosis methods results in poor diagnosis effect under variable load and noise interference scenarios, a rolling bearing fault diagnosis model combining Multi-Scale Convolutional Neural Network (MSCNN) and Long Short-Term Memory (LSTM) fused with attention mechanism is proposed. To adaptively extract the essential spatial feature information of various sizes, the model creates a multi-scale feature extraction module using the convolutional neural network (CNN) learning process.… More >

Ameni Ellouze¹, Nesrine Kadri², Alaa Alaerjan^3,*, Mohamed Ksantini¹

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 351-372, 2024, DOI:10.32604/cmc.2024.048061 - 25 April 2024

Abstract Recognizing human activity (HAR) from data in a smartphone sensor plays an important role in the field of health to prevent chronic diseases. Daily and weekly physical activities are recorded on the smartphone and tell the user whether he is moving well or not. Typically, smartphones and their associated sensing devices operate in distributed and unstable environments. Therefore, collecting their data and extracting useful information is a significant challenge. In this context, the aim of this paper is twofold: The first is to analyze human behavior based on the recognition of physical activities. Using the… More >

Ramiz Gorkem Birdal^*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4015-4028, 2024, DOI:10.32604/cmc.2024.048324 - 26 March 2024

Abstract Maintaining a steady power supply requires accurate forecasting of solar irradiance, since clean energy resources do not provide steady power. The existing forecasting studies have examined the limited effects of weather conditions on solar radiation such as temperature and precipitation utilizing convolutional neural network (CNN), but no comprehensive study has been conducted on concentrations of air pollutants along with weather conditions. This paper proposes a hybrid approach based on deep learning, expanding the feature set by adding new air pollution concentrations, and ranking these features to select and reduce their size to improve efficiency. In… More >

Jing-Doo Wang¹, Chayadi Oktomy Noto Susanto^1,2,*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3097-3112, 2023, DOI:10.32604/cmc.2023.040914 - 08 October 2023

Abstract Predicting traffic flow is a crucial component of an intelligent transportation system. Precisely monitoring and predicting traffic flow remains a challenging endeavor. However, existing methods for predicting traffic flow do not incorporate various external factors or consider the spatiotemporal correlation between spatially adjacent nodes, resulting in the loss of essential information and lower forecast performance. On the other hand, the availability of spatiotemporal data is limited. This research offers alternative spatiotemporal data with three specific features as input, vehicle type (5 types), holidays (3 types), and weather (10 conditions). In this study, the proposed model… More >

Alaa Omran Almagrabi^*

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2079-2093, 2023, DOI:10.32604/cmc.2023.039683 - 30 August 2023

Abstract A potential concept that could be effective for multiple applications is a “cyber-physical system” (CPS). The Internet of Things (IoT) has evolved as a research area, presenting new challenges in obtaining valuable data through environmental monitoring. The existing work solely focuses on classifying the audio system of CPS without utilizing feature extraction. This study employs a deep learning method, CNN-LSTM, and two-way feature extraction to classify audio systems within CPS. The primary objective of this system, which is built upon a convolutional neural network (CNN) with Long Short Term Memory (LSTM), is to analyze the… More >

R. D. Dhaniya¹, K. M. Umamaheswari^2,*

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 1129-1143, 2023, DOI:10.32604/iasc.2023.035905 - 29 April 2023

Abstract Current revelations in medical imaging have seen a slew of computer-aided diagnostic (CAD) tools for radiologists developed. Brain tumor classification is essential for radiologists to fully support and better interpret magnetic resonance imaging (MRI). In this work, we reported on new observations based on binary brain tumor categorization using HYBRID CNN-LSTM. Initially, the collected image is pre-processed and augmented using the following steps such as rotation, cropping, zooming, CLAHE (Contrast Limited Adaptive Histogram Equalization), and Random Rotation with panoramic stitching (RRPS). Then, a method called particle swarm optimization (PSO) is used to segment tumor regions More >

Zhao Yang¹, Yuan Zhang¹, Lei Zhu^2,*, Lei Huang¹, Fangyu Hu³, Yanping Du¹, Xiaowei Li¹

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2889-2904, 2023, DOI:10.32604/cmc.2023.037297 - 31 March 2023

Abstract High-frequency (HF) and ultrahigh-frequency (UHF) dual-band radio frequency identification (RFID) tags with both near-field and far-field communication can meet different application scenarios. However, it is time-consuming to calculate the return loss of a UHF antenna in a dual-band tag antenna using electromagnetic (EM) simulators. To overcome this, the present work proposes a model of a multi-scale convolutional neural network stacked with long and short-term memory (MSCNN-LSTM) for predicting the return loss of UHF antennas instead of EM simulators. In the proposed MSCNN-LSTM, the MSCNN has three branches, which include three convolution layers with different kernel… More >

Pavitra Vasudevan¹, R. Faerie Mattins¹, S. Srivarshan¹, Ashvath Narayanan¹, Gayatri Wadhwani¹, R. Parvathi¹, R. Maheswari^2,*

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6039-6063, 2023, DOI:10.32604/cmc.2023.032331 - 28 December 2022

Abstract Gait refers to a person’s particular movements and stance while moving around. Although each person’s gait is unique and made up of a variety of tiny limb orientations and body positions, they all have common characteristics that help to define normalcy. Swiftly identifying such characteristics that are difficult to spot by the naked eye, can help in monitoring the elderly who require constant care and support. Analyzing silhouettes is the easiest way to assess and make any necessary adjustments for a smooth gait. It also becomes an important aspect of decision-making while analyzing and monitoring… More >

Ernesto Lee¹, Furqan Rustam², Hina Fatima Shahzad², Patrick Bernard Washington³, Abid Ishaq³, Imran Ashraf^4,*

Computer Systems Science and Engineering, Vol.45, No.3, pp. 3053-3077, 2023, DOI:10.32604/csse.2023.029059 - 21 December 2022

Abstract With the increasing usage of drugs to remedy different diseases, drug safety has become crucial over the past few years. Often medicine from several companies is offered for a single disease that involves the same/similar substances with slightly different formulae. Such diversification is both helpful and dangerous as such medicine proves to be more effective or shows side effects to different patients. Despite clinical trials, side effects are reported when the medicine is used by the mass public, of which several such experiences are shared on social media platforms. A system capable of analyzing such… More >