Nizar Faisal Alkayem¹, Maosen Cao^2,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.26, No.4, pp. 1-1, 2023, DOI:10.32604/icces.2023.09367

Abstract Structural damage identification is a key engineering technique that attempts to ensure structural reliability. In this regard, one of the major intelligent approaches is the inverse analysis of structural damage using metaheuristics. By considering the recent achievements, an efficient hybrid objective function that combines the modal kinetic energy and modal strain energy is developed. The objective function aims to extract maximum modal information from the structure and overcome noisy conditions. Moreover, the original methods are usually vulnerable to the associated high multimodality and uncertainty of the inverse problem. Therefore, the particle swarm algorithm (PSO) mechanism More >

Cho Mar Aye¹, Kittinan Wansaseub², Sumit Kumar³, Ghanshyam G. Tejani⁴, Sujin Bureerat¹, Ali R. Yildiz⁵, Nantiwat Pholdee^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2111-2128, 2023, DOI:10.32604/cmes.2023.028632 - 03 August 2023

Abstract This work presents multi-fidelity multi-objective infill-sampling surrogate-assisted optimization for airfoil shape optimization. The optimization problem is posed to maximize the lift and drag coefficient ratio subject to airfoil geometry constraints. Computational Fluid Dynamic (CFD) and XFoil tools are used for high and low-fidelity simulations of the airfoil to find the real objective function value. A special multi-objective sub-optimization problem is proposed for multiple points infill sampling exploration to improve the surrogate model constructed. To validate and further assess the proposed methods, a conventional surrogate-assisted optimization method and an infill sampling surrogate-assisted optimization criterion are applied More > Graphic Abstract

Yousif Sufyan Jghef¹, Mohammed Jasim Mohammed Jasim², Subhi R. M. Zeebaree^3,*, Rizgar R. Zebari⁴

Computer Systems Science and Engineering, Vol.47, No.2, pp. 1651-1664, 2023, DOI:10.32604/csse.2023.037449 - 28 July 2023

Abstract Wireless Sensor Networks (WSNs) gather data in physical environments, which is some type. These ubiquitous sensors face several challenges responsible for corrupting them (mostly sensor failure and intrusions in external agents). WSNs were disposed to error, and effectual fault detection techniques are utilized for detecting faults from WSNs in a timely approach. Machine learning (ML) was extremely utilized for detecting faults in WSNs. Therefore, this study proposes a billiards optimization algorithm with modified deep learning for fault detection (BIOMDL-FD) in WSN. The BIOMDLFD technique mainly concentrates on identifying sensor faults to enhance network efficiency. To… More >

Mahmoud Ragab^1,2,*, Sultanah M. Alshammari^2,3, Abdullah S. Al-Malaise Al-Ghamdi^2,4

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2497-2512, 2023, DOI:10.32604/csse.2023.041446 - 28 July 2023

Abstract The Internet of Things (IoT) system has confronted dramatic growth in high dimensionality and data traffic. The system named intrusion detection systems (IDS) is broadly utilized for the enhancement of security posture in an IT infrastructure. An IDS is a practical and suitable method for assuring network security and identifying attacks by protecting it from intrusive hackers. Nowadays, machine learning (ML)-related techniques were used for detecting intrusion in IoTs IDSs. But, the IoT IDS mechanism faces significant challenges because of physical and functional diversity. Such IoT features use every attribute and feature for IDS self-protection… More >

T. Satyanarayana Murthy¹, Kollati Vijaya Kumar², Fayadh Alenezi³, E. Laxmi Lydia⁴, Gi-Cheon Park⁵, Hyoung-Kyu Song⁶, Gyanendra Prasad Joshi⁷, Hyeonjoon Moon^7,*

Computer Systems Science and Engineering, Vol.47, No.2, pp. 1633-1650, 2023, DOI:10.32604/csse.2023.034769 - 28 July 2023

Abstract Fruit classification utilizing a deep convolutional neural network (CNN) is the most promising application in personal computer vision (CV). Profound learning-related characterization made it possible to recognize fruits from pictures. But, due to the similarity and complexity, fruit recognition becomes an issue for the stacked fruits on a weighing scale. Recently, Machine Learning (ML) methods have been used in fruit farming and agriculture and brought great convenience to human life. An automated system related to ML could perform the fruit classifier and sorting tasks previously managed by human experts. CNN’s (convolutional neural networks) have attained… More >

Hadeel Alsolai¹, Mohammed Rizwanullah^2,*, Mashael Maashi³, Mahmoud Othman⁴, Amani A. Alneil², Amgad Atta Abdelmageed²

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 975-992, 2023, DOI:10.32604/cmc.2023.037936 - 08 June 2023

Abstract Soil classification is one of the emanating topics and major concerns in many countries. As the population has been increasing at a rapid pace, the demand for food also increases dynamically. Common approaches used by agriculturalists are inadequate to satisfy the rising demand, and thus they have hindered soil cultivation. There comes a demand for computer-related soil classification methods to support agriculturalists. This study introduces a Gradient-Based Optimizer and Deep Learning (DL) for Automated Soil Classification (GBODL-ASC) technique. The presented GBODL-ASC technique identifies various kinds of soil using DL and computer vision approaches. In the… More >

Abdelwahed Motwakel^1,*, Marwa Obayya², Nadhem Nemri³, Khaled Tarmissi⁴, Heba Mohsen⁵, Mohammed Rizwanulla⁶, Ishfaq Yaseen⁶, Abu Sarwar Zamani⁶

Computer Systems Science and Engineering, Vol.47, No.1, pp. 1267-1281, 2023, DOI:10.32604/csse.2023.037735 - 26 May 2023

Abstract Recently, renewable energy (RE) has become popular due to its benefits, such as being inexpensive, low-carbon, ecologically friendly, steady, and reliable. The RE sources are gradually combined with non-renewable energy (NRE) sources into electric grids to satisfy energy demands. Since energy utilization is highly related to national energy policy, energy prediction using artificial intelligence (AI) and deep learning (DL) based models can be employed for energy prediction on RE and NRE power resources. Predicting energy consumption of RE and NRE sources using effective models becomes necessary. With this motivation, this study presents a new multimodal… More >

G. Anitha¹, N. Supriya², Fayadh Alenezi³, E. Laxmi Lydia⁴, Gyanendra Prasad Joshi⁵, Jinsang You^6,*

Computer Systems Science and Engineering, Vol.47, No.1, pp. 221-238, 2023, DOI:10.32604/csse.2023.036479 - 26 May 2023

Abstract Rooftop units (RTUs) were commonly employed in small commercial buildings that represent that can frequently do not take the higher level maintenance that chillers receive. Fault detection and diagnosis (FDD) tools can be employed for RTU methods to ensure essential faults are addressed promptly. In this aspect, this article presents an Optimal Deep Belief Network based Fault Detection and Classification on Packaged Rooftop Units (ODBNFDC-PRTU) model. The ODBNFDC-PRTU technique considers fault diagnosis as a multi-class classification problem and is handled using DL models. For fault diagnosis in RTUs, the ODBNFDC-PRTU model exploits the deep belief More >

Sultan Alahmari¹, Saud Yonbawi², Suneetha Racharla³, E. Laxmi Lydia⁴, Mohamad Khairi Ishak⁵, Hend Khalid Alkahtani^6,*, Ayman Aljarbouh⁷, Samih M. Mostafa⁸

Computer Systems Science and Engineering, Vol.47, No.1, pp. 375-391, 2023, DOI:10.32604/csse.2023.036362 - 26 May 2023

Abstract Hyperspectral imaging instruments could capture detailed spatial information and rich spectral signs of observed scenes. Much spatial information and spectral signatures of hyperspectral images (HSIs) present greater potential for detecting and classifying fine crops. The accurate classification of crop kinds utilizing hyperspectral remote sensing imaging (RSI) has become an indispensable application in the agricultural domain. It is significant for the prediction and growth monitoring of crop yields. Amongst the deep learning (DL) techniques, Convolution Neural Network (CNN) was the best method for classifying HSI for their incredible local contextual modeling ability, enabling spectral and spatial… More >

Abdelwahed Motwakel^1,*, Najm Alotaibi², Eatedal Alabdulkreem³, Hussain Alshahrani⁴, Mohamed Ahmed Elfaki⁴, Mohamed K Nour⁵, Radwa Marzouk⁶, Mahmoud Othman⁷

Computer Systems Science and Engineering, Vol.47, No.1, pp. 1249-1266, 2023, DOI:10.32604/csse.2023.034227 - 26 May 2023

Abstract Sentiment Analysis (SA) of natural language text is not only a challenging process but also gains significance in various Natural Language Processing (NLP) applications. The SA is utilized in various applications, namely, education, to improve the learning and teaching processes, marketing strategies, customer trend predictions, and the stock market. Various researchers have applied lexicon-related approaches, Machine Learning (ML) techniques and so on to conduct the SA for multiple languages, for instance, English and Chinese. Due to the increased popularity of the Deep Learning models, the current study used diverse configuration settings of the Convolution Neural… More >