Tiantian Liang^*, Runze Wang, Xuxiu Zhang, Yingdong Wang, Jianxiong Yang

Structural Durability & Health Monitoring, Vol.17, No.5, pp. 433-455, 2023, DOI:10.32604/sdhm.2023.029331

Abstract In this study, an optimized long short-term memory (LSTM) network is proposed to predict the reliability and remaining useful life (RUL) of rolling bearings based on an improved whale-optimized algorithm (IWOA). The multi-domain features are extracted to construct the feature dataset because the single-domain features are difficult to characterize the performance degeneration of the rolling bearing. To provide covariates for reliability assessment, a kernel principal component analysis is used to reduce the dimensionality of the features. A Weibull distribution proportional hazard model (WPHM) is used for the reliability assessment of rolling bearing, and a beluga whale optimization (BWO) algorithm is… More > Graphic Abstract

Loc Vu-Quoc^1,*, Alexander Humer²

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1069-1343, 2023, DOI:10.32604/cmes.2023.028130

Abstract Three recent breakthroughs due to AI in arts and science serve as motivation: An award winning digital image, protein folding, fast matrix multiplication. Many recent developments in artificial neural networks, particularly deep learning (DL), applied and relevant to computational mechanics (solid, fluids, finite-element technology) are reviewed in detail. Both hybrid and pure machine learning (ML) methods are discussed. Hybrid methods combine traditional PDE discretizations with ML methods either (1) to help model complex nonlinear constitutive relations, (2) to nonlinearly reduce the model order for efficient simulation (turbulence), or (3) to accelerate the simulation by predicting certain components in the traditional… More >

Doaa Sami Khafaga¹, El-Sayed M. El-kenawy², Amel Ali Alhussan^1,*, Marwa M. Eid³

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2117-2132, 2023, DOI:10.32604/iasc.2023.038811

Abstract The accurate prediction of energy consumption has effective role in decision making and risk management for individuals and governments. Meanwhile, the accurate prediction can be realized using the recent advances in machine learning and predictive models. This research proposes a novel approach for energy consumption forecasting based on a new optimization algorithm and a new forecasting model consisting of a set of long short-term memory (LSTM) units. The proposed optimization algorithm is used to optimize the parameters of the LSTM-based model to boost its forecasting accuracy. This optimization algorithm is based on the recently emerged dipper-throated optimization (DTO) and stochastic… More >

Sarab AlMuhaideb^*, Yasmeen AlNegheimish, Taif AlOmar, Reem AlSabti, Maha AlKathery, Ghala AlOlyyan

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 195-220, 2023, DOI:10.32604/cmc.2023.038368

Abstract Healthcare organizations rely on patients’ feedback and experiences to evaluate their performance and services, thereby allowing such organizations to improve inadequate services and address any shortcomings. According to the literature, social networks and particularly Twitter are effective platforms for gathering public opinions. Moreover, recent studies have used natural language processing to measure sentiments in text segments collected from Twitter to capture public opinions about various sectors, including healthcare. The present study aimed to analyze Arabic Twitter-based patient experience sentiments and to introduce an Arabic patient experience corpus. The authors collected 12,400 tweets from Arabic patients discussing patient experiences related to… More >

Neelam Mughees^1,2, Mujtaba Hussain Jaffery¹, Abdullah Mughees³, Anam Mughees⁴, Krzysztof Ejsmont^5,*

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6375-6393, 2023, DOI:10.32604/cmc.2023.038564

Abstract Wind and solar energy are two popular forms of renewable energy used in microgrids and facilitating the transition towards net-zero carbon emissions by 2050. However, they are exceedingly unpredictable since they rely highly on weather and atmospheric conditions. In microgrids, smart energy management systems, such as integrated demand response programs, are permanently established on a step-ahead basis, which means that accurate forecasting of wind speed and solar irradiance intervals is becoming increasingly crucial to the optimal operation and planning of microgrids. With this in mind, a novel “bidirectional long short-term memory network” (Bi-LSTM)-based, deep stacked, sequence-to-sequence autoencoder (S2SAE) forecasting model… More >

Po-Yuan Yang^1,*, Yu-Cheng Liao², Fu-I Chou²

Computer Systems Science and Engineering, Vol.46, No.3, pp. 2861-2880, 2023, DOI:10.32604/csse.2023.036810

Abstract Aquaculture has long been a critical economic sector in Taiwan. Since a key factor in aquaculture production efficiency is water quality, an effective means of monitoring the dissolved oxygen content (DOC) of aquaculture water is essential. This study developed an internet of things system for monitoring DOC by collecting essential data related to water quality. Artificial intelligence technology was used to construct a water quality prediction model for use in a complete system for managing water quality. Since aquaculture water quality depends on a continuous interaction among multiple factors, and the current state is correlated with the previous state, a… More >

Manar Ahmed Hamza^1,*, Hala J. Alshahrani², Khaled Tarmissi³, Ayman Yafoz⁴, Amal S. Mehanna⁵, Ishfaq Yaseen¹, Amgad Atta Abdelmageed¹, Mohamed I. Eldesouki⁶

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3303-3319, 2023, DOI:10.32604/csse.2023.034823

Abstract The term ‘corpus’ refers to a huge volume of structured datasets containing machine-readable texts. Such texts are generated in a natural communicative setting. The explosion of social media permitted individuals to spread data with minimal examination and filters freely. Due to this, the old problem of fake news has resurfaced. It has become an important concern due to its negative impact on the community. To manage the spread of fake news, automatic recognition approaches have been investigated earlier using Artificial Intelligence (AI) and Machine Learning (ML) techniques. To perform the medicinal text classification tasks, the ML approaches were applied, and… More >

Saman Riaz¹, Ali Arshad², Shahab S. Band^3,*, Amir Mosavi⁴

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 2301-2312, 2023, DOI:10.32604/cmc.2023.028333

Abstract Personality distinguishes individuals’ patterns of feeling, thinking, and behaving. Predicting personality from small video series is an exciting research area in computer vision. The majority of the existing research concludes preliminary results to get immense knowledge from visual and Audio (sound) modality. To overcome the deficiency, we proposed the Deep Bimodal Fusion (DBF) approach to predict five traits of personality-agreeableness, extraversion, openness, conscientiousness and neuroticism. In the proposed framework, regarding visual modality, the modified convolution neural networks (CNN), more specifically Descriptor Aggregator Model (DAN) are used to attain significant visual modality. The proposed model extracts audio representations for greater efficiency… More >

Mesfer Alduhayyem^1,*, Mrim M. Alnfiai^2,3, Nabil Almalki⁴, Fahd N. Al-Wesabi⁵, Anwer Mustafa Hilal⁶, Manar Ahmed Hamza⁶

Computer Systems Science and Engineering, Vol.46, No.1, pp. 475-489, 2023, DOI:10.32604/csse.2023.034067

Abstract Visual impairment is one of the major problems among people of all age groups across the globe. Visually Impaired Persons (VIPs) require help from others to carry out their day-to-day tasks. Since they experience several problems in their daily lives, technical intervention can help them resolve the challenges. In this background, an automatic object detection tool is the need of the hour to empower VIPs with safe navigation. The recent advances in the Internet of Things (IoT) and Deep Learning (DL) techniques make it possible. The current study proposes IoT-assisted Transient Search Optimization with a Lightweight RetinaNet-based object detection (TSOLWR-ODVIP)… More >

Nga Nguyen Thi Thanh, Quang H. Nguyen^*

Computer Systems Science and Engineering, Vol.46, No.1, pp. 491-504, 2023, DOI:10.32604/csse.2023.032107

Abstract Nowadays, web systems and servers are constantly at great risk from cyberattacks. This paper proposes a novel approach to detecting abnormal network traffic using a bidirectional long short-term memory (LSTM) network in combination with the ensemble learning technique. First, the binary classification module was used to detect the current abnormal flow. Then, the abnormal flows were fed into the multilayer classification module to identify the specific type of flow. In this research, a deep learning bidirectional LSTM model, in combination with the convolutional neural network and attention technique, was deployed to identify a specific attack. To solve the real-time intrusion-detecting… More >