Alia Alshehri, Duaa AlSaeed^*

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 939-970, 2023, DOI:10.32604/iasc.2023.037096

Abstract One of the most prevalent cancers in women is breast cancer. Early and accurate detection can decrease the mortality rate associated with breast cancer. Governments and health organizations emphasize the significance of early breast cancer screening since it is associated to a greater variety of available treatments and a higher chance of survival. Patients have the best chance of obtaining effective treatment when they are diagnosed early. The detection and diagnosis of breast cancer have involved using various image types and imaging modalities. Breast “infrared thermal” imaging is one of the imaging modalities., a screening instrument used to measure the… More >

Ouail Mjahed^1,*, Salah El Hadaj¹, El Mahdi El Guarmah^1,2, Soukaina Mjahed¹

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 265-298, 2023, DOI:10.32604/cmes.2023.027581

Abstract Due to the increasing number of cyber-attacks, the necessity to develop efficient intrusion detection systems (IDS) is more imperative than ever. In IDS research, the most effectively used methodology is based on supervised Neural Networks (NN) and unsupervised clustering, but there are few works dedicated to their hybridization with metaheuristic algorithms. As intrusion detection data usually contains several features, it is essential to select the best ones appropriately. Linear Discriminant Analysis (LDA) and t-statistic are considered as efficient conventional techniques to select the best features, but they have been little exploited in IDS design. Thus, the research proposed in this… More >

Kusnandar¹, Win-Jet Luo^1,2, Indra Permana¹, Fu-Jen Wang^2,*, Gantulga Bayarkhuu²

Energy Engineering, Vol.120, No.6, pp. 1387-1399, 2023, DOI:10.32604/ee.2023.027459

Abstract The heating, ventilating, and air conditioning (HVAC) system consumes nearly 50% of the building’s energy, especially in Taiwan with a hot and humid climate. Due to the challenges in obtaining energy sources and the negative impacts of excessive energy use on the environment, it is essential to employ an energy-efficient HVAC system. This study conducted the machine tools building in a university. The field measurement was carried out, and the data were used to conduct energy modelling with EnergyPlus (EP) in order to discover some improvements in energy-efficient design. The validation between field measurement and energy modelling was performed, and… More >

Zixiong Wang, Qiuying Chen, Sang-Joon Lee^*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2443-2459, 2023, DOI:10.32604/cmc.2023.037553

Abstract The rapid expansion of the non-fungible token (NFT) market has attracted many investors. However, studies on the NFT price fluctuations have been relatively limited. To date, the machine learning approach has not been used to demonstrate a specific error in NFT sale price fluctuation prediction. The aim of this study was to develop a prediction model for NFT price fluctuations using the NFT trading information obtained from OpenSea, the world’s largest NFT marketplace. We used Python programs to collect data and summarized them as: NFT information, collection information, and related account information. AdaBoost and Random Forest (RF) algorithms were employed… More >

Abdullah Ahmed Al-Dulaimi^1,*, Muhammet Tahir Guneser¹, Alaa Ali Hameed², Mohammad Shukri Salman³

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2291-2319, 2023, DOI:10.32604/cmes.2023.026065

Abstract Recently, the demand for renewable energy has increased due to its environmental and economic needs. Solar panels are the mainstay for dealing with solar energy and converting it into another form of usable energy. Solar panels work under suitable climatic conditions that allow the light photons to access the solar cells, as any blocking of sunlight on these cells causes a halt in the panels work and restricts the carry of these photons. Thus, the panels are unable to work under these conditions. A layer of snow forms on the solar panels due to snowfall in areas with low temperatures.… More > Graphic Abstract

Francisco José García-Peñalvo^*, Andrea Vázquez-Ingelmo, Alicia García-Holgado

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 1023-1051, 2023, DOI:10.32604/cmes.2023.023897

Abstract The exponential use of artificial intelligence (AI) to solve and automated complex tasks has catapulted its popularity generating some challenges that need to be addressed. While AI is a powerful means to discover interesting patterns and obtain predictive models, the use of these algorithms comes with a great responsibility, as an incomplete or unbalanced set of training data or an unproper interpretation of the models’ outcomes could result in misleading conclusions that ultimately could become very dangerous. For these reasons, it is important to rely on expert knowledge when applying these methods. However, not every user can count on this… More > Graphic Abstract

Bao Rong Chang¹, Hsiu-Fen Tsai^2,*, Han-Lin Chou¹

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 107-134, 2023, DOI:10.32604/cmes.2023.024018

Abstract Our previous work has introduced the newly generated program using the code transformation model GPT-2, verifying the generated programming codes through simhash (SH) and longest common subsequence (LCS) algorithms. However, the entire code transformation process has encountered a time-consuming problem. Therefore, the objective of this study is to speed up the code transformation process significantly. This paper has proposed deep learning approaches for modifying SH using a variational simhash (VSH) algorithm and replacing LCS with a piecewise longest common subsequence (PLCS) algorithm to faster the verification process in the test phase. Besides the code transformation model GPT-2, this study has… More > Graphic Abstract

Sakorn Mekruksavanich¹, Anuchit Jitpattanakul^2,*

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2111-2128, 2023, DOI:10.32604/iasc.2023.033542

Abstract The automated evaluation and analysis of employee behavior in an Industry 4.0-compliant manufacturing firm are vital for the rapid and accurate diagnosis of work performance, particularly during the training of a new worker. Various techniques for identifying and detecting worker performance in industrial applications are based on computer vision techniques. Despite widespread computer vision-based approaches, it is challenging to develop technologies that assist the automated monitoring of worker actions at external working sites where camera deployment is problematic. Through the use of wearable inertial sensors, we propose a deep learning method for automatically recognizing the activities of construction workers. The… More >

Abeer Bashab¹, Ashraf Osman Ibrahim^2,*, Ibrahim Abakar Tarigo Hashem³, Karan Aggarwal⁴, Fadhil Mukhlif⁵, Fuad A. Ghaleb⁵, Abdelzahir Abdelmaboud⁶

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6461-6484, 2023, DOI:10.32604/cmc.2023.034051

Abstract University timetabling problems are a yearly challenging task and are faced repeatedly each semester. The problems are considered non-polynomial time (NP) and combinatorial optimization problems (COP), which means that they can be solved through optimization algorithms to produce the aspired optimal timetable. Several techniques have been used to solve university timetabling problems, and most of them use optimization techniques. This paper provides a comprehensive review of the most recent studies dealing with concepts, methodologies, optimization, benchmarks, and open issues of university timetabling problems. The comprehensive review starts by presenting the essence of university timetabling as NP-COP, defining and clarifying the… More >

Erkan Akkur¹, Fuat TURK^2,*, Osman Erogul¹

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1017-1031, 2023, DOI:10.32604/csse.2023.033003

Abstract Breast cancer seriously affects many women. If breast cancer is detected at an early stage, it may be cured. This paper proposes a novel classification model based improved machine learning algorithms for diagnosis of breast cancer at its initial stage. It has been used by combining feature selection and Bayesian optimization approaches to build improved machine learning models. Support Vector Machine, K-Nearest Neighbor, Naive Bayes, Ensemble Learning and Decision Tree approaches were used as machine learning algorithms. All experiments were tested on two different datasets, which are Wisconsin Breast Cancer Dataset (WBCD) and Mammographic Breast Cancer Dataset (MBCD). Experiments were… More >