Junjie Zhao, Diyuan Li^*, Jingtai Jiang, Pingkuang Luo

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 275-304, 2024, DOI:10.32604/cmes.2024.046960

Abstract Traditional laboratory tests for measuring rock uniaxial compressive strength (UCS) are tedious and time-consuming. There is a pressing need for more effective methods to determine rock UCS, especially in deep mining environments under high in-situ stress. Thus, this study aims to develop an advanced model for predicting the UCS of rock material in deep mining environments by combining three boosting-based machine learning methods with four optimization algorithms. For this purpose, the Lead-Zinc mine in Southwest China is considered as the case study. Rock density, P-wave velocity, and point load strength index are used as input variables, and UCS is regarded… More > Graphic Abstract

ABHIJEET SURYAWANSHI¹, NIRANJANA BEHERA^2,*

Journal of Polymer Materials, Vol.40, No.3-4, pp. 305-316, 2023, DOI:10.32381/JPM.2023.40.3-4.11

Abstract Resin composites are commonly applied as the material for dental restoration. Wear of these materials is a major issue. In this study specimens made of dental composite materials were subjected to an in-vitro test in a pin-on-disc tribometer. Four different dental composite materials applied in the experiment were soaked in a solution of chewing tobacco for certain days before being removed and put through a wear test. Subsequently, four different machine learning (ML) algorithms (AdaBoost, CatBoost, Gradient Boosting, Random Forest) were implemented for developing models for the prediction of wear of dental materials. AdaBoost, CatBoost, Gradient Boosting and Random Forest… More >

Muhammad Armghan Latif¹, Zohaib Mushtaq², Saad Arif³, Sara Rehman⁴, Muhammad Farrukh Qureshi⁵, Nagwan Abdel Samee⁶, Maali Alabdulhafith^6,*, Yeong Hyeon Gu⁷, Mohammed A. Al-masni⁷

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4225-4241, 2024, DOI:10.32604/cmc.2024.047621

Abstract Thyroid disorders represent a significant global health challenge with hypothyroidism and hyperthyroidism as two common conditions arising from dysfunction in the thyroid gland. Accurate and timely diagnosis of these disorders is crucial for effective treatment and patient care. This research introduces a comprehensive approach to improve the accuracy of thyroid disorder diagnosis through the integration of ensemble stacking and advanced feature selection techniques. Sequential forward feature selection, sequential backward feature elimination, and bidirectional feature elimination are investigated in this study. In ensemble learning, random forest, adaptive boosting, and bagging classifiers are employed. The effectiveness of these techniques is evaluated using… More >

Kai Chen^1,2, Jinwei Wang³, James Msughter Adeke^1,2, Guangjie Liu^1,2,*, Yuewei Dai^1,4

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3247-3265, 2024, DOI:10.32604/cmc.2024.046082

Abstract In recent years, various adversarial defense methods have been proposed to improve the robustness of deep neural networks. Adversarial training is one of the most potent methods to defend against adversarial attacks. However, the difference in the feature space between natural and adversarial examples hinders the accuracy and robustness of the model in adversarial training. This paper proposes a learnable distribution adversarial training method, aiming to construct the same distribution for training data utilizing the Gaussian mixture model. The distribution centroid is built to classify samples and constrain the distribution of the sample features. The natural and adversarial examples are… More >

Asma A. Alhashmi¹, Abdulbasit A. Darem^1,*, Sultan M. Alanazi¹, Abdullah M. Alashjaee², Bader Aldughayfiq³, Fuad A. Ghaleb^4,5, Shouki A. Ebad¹, Majed A. Alanazi¹

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3483-3498, 2023, DOI:10.32604/cmc.2023.041038

Abstract In an era marked by escalating cybersecurity threats, our study addresses the challenge of malware variant detection, a significant concern for a multitude of sectors including petroleum and mining organizations. This paper presents an innovative Application Programmable Interface (API)-based hybrid model designed to enhance the detection performance of malware variants. This model integrates eXtreme Gradient Boosting (XGBoost) and an Artificial Neural Network (ANN) classifier, offering a potent response to the sophisticated evasion and obfuscation techniques frequently deployed by malware authors. The model’s design capitalizes on the benefits of both static and dynamic analysis to extract API-based features, providing a holistic… More >

Mesfer Al Duhayyim^1,*, Sidra Abbas², Abdullah Al Hejaili³, Natalia Kryvinska^4,*, Ahmad Almadhor⁵, Huma Mughal⁶

Computer Systems Science and Engineering, Vol.47, No.1, pp. 823-842, 2023, DOI:10.32604/csse.2023.037488

Abstract : Cardiotocography (CTG) represents the fetus’s health inside the womb during labor. However, assessment of its readings can be a highly subjective process depending on the expertise of the obstetrician. Digital signals from fetal monitors acquire parameters (i.e., fetal heart rate, contractions, acceleration). Objective:: This paper aims to classify the CTG readings containing imbalanced healthy, suspected, and pathological fetus readings. Method:: We perform two sets of experiments. Firstly, we employ five classifiers: Random Forest (RF), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LGBM) without over-sampling to classify CTG readings into three categories:… More >

Qifen Li, Xiaoxiao Yan^*, Yongwen Yang, Liting Zhang, Yuanbo Hou

Journal of Renewable Materials, Vol.11, No.7, pp. 3177-3191, 2023, DOI:10.32604/jrm.2023.027749

Abstract Microbial Electrolytic Cell (MEC) is an electrochemical reaction device that uses electrical energy as an energy input and microorganisms as catalysts to produce fuels and chemicals. The regenerative electrochemical system is a MEC improvement system for methane gas produced by biological carbon sequestration technology using renewable energy sources to provide a voltage environment. In response to the influence of fluctuating disturbances of renewable electricity and the long system start-up time, this paper analyzes the characteristics of two strategies, regulating voltage parameter changes and activated sludge pretreatment, on the methane production efficiency of the renewable gas electrochemical system. In this system,… More >

Mustafa Sami Abdullatef^*, Faten N. Alzubaidi, Anees Al-Tamimi, Yasser Ahmed Mahmood

FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.8, pp. 2083-2107, 2023, DOI:10.32604/fdmp.2023.027266

Abstract The ongoing effort to create methods for detecting and quantifying fatigue damage is motivated by the high levels of uncertainty in present fatigue-life prediction approaches and the frequently catastrophic nature of fatigue failure. The fatigue life of high strength aluminum alloy 2090-T83 is predicted in this study using a variety of artificial intelligence and machine learning techniques for constant amplitude and negative stress ratios (). Artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), support-vector machines (SVM), a random forest model (RF), and an extreme-gradient tree-boosting model (XGB) are trained using numerical and experimental input data obtained from fatigue tests… More > Graphic Abstract

Shahid Mohammad Ganie¹, Pijush Kanti Dutta Pramanik², Majid Bashir Malik³, Anand Nayyar⁴, Kyung Sup Kwak^5,*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3993-4006, 2023, DOI:10.32604/csse.2023.035244

Abstract Cardiovascular disease is among the top five fatal diseases that affect lives worldwide. Therefore, its early prediction and detection are crucial, allowing one to take proper and necessary measures at earlier stages. Machine learning (ML) techniques are used to assist healthcare providers in better diagnosing heart disease. This study employed three boosting algorithms, namely, gradient boost, XGBoost, and AdaBoost, to predict heart disease. The dataset contained heart disease-related clinical features and was sourced from the publicly available UCI ML repository. Exploratory data analysis is performed to find the characteristics of data samples about descriptive and inferential statistics. Specifically, it was… More >

Hala Ahmed¹, Hassan Soliman¹, Shaker El-Sappagh^2,3,4, Tamer Abuhmed^4,*, Mohammed Elmogy¹

Computer Systems Science and Engineering, Vol.46, No.3, pp. 2773-2795, 2023, DOI:10.32604/csse.2023.036371

Abstract The precise diagnosis of Alzheimer’s disease is critical for patient treatment, especially at the early stage, because awareness of the severity and progression risks lets patients take preventative actions before irreversible brain damage occurs. It is possible to gain a holistic view of Alzheimer’s disease staging by combining multiple data modalities, known as image fusion. In this paper, the study proposes the early detection of Alzheimer’s disease using different modalities of Alzheimer’s disease brain images. First, the preprocessing was performed on the data. Then, the data augmentation techniques are used to handle overfitting. Also, the skull is removed to lead… More >