Nazia Afrin^a , Yuwen Zhang^b,*

Frontiers in Heat and Mass Transfer, Vol.12, pp. 1-6, 2019, DOI:10.5098/hmt.12.27

Abstract The surrogate-based analysis and optimization of thermal damage in living biological tissue by laser irradiation are discussed in this paper. Latin Hypercube Sampling (LHS) and Response Surface Model (RSM) are applied to study the surrogate-based optimization of thermal damage in tissue using a generalized dual-phase lag model. Response value of high temperature as a function of input variables and the relationship of maximum temperature and thermal damage as a function of input variables are investigated. Comparisons of SBO model and simulation results for different sample sizes are examined. The results show that every input variable individually has quadratic response to… More >

Mengxiao Wang^1,2, Jing Huang^1,2,*

Computer Systems Science and Engineering, Vol.47, No.1, pp. 1023-1042, 2023, DOI:10.32604/csse.2023.039569

Abstract Due to the anonymity of blockchain, frequent security incidents and attacks occur through it, among which the Ponzi scheme smart contract is a classic type of fraud resulting in huge economic losses. Machine learning-based methods are believed to be promising for detecting ethereum Ponzi schemes. However, there are still some flaws in current research, e.g., insufficient feature extraction of Ponzi scheme smart contracts, without considering class imbalance. In addition, there is room for improvement in detection precision. Aiming at the above problems, this paper proposes an ethereum Ponzi scheme detection scheme through opcode context analysis and adaptive boosting (AdaBoost) algorithm.… More >

Mamoona Humayun^1,*, Danish Javed², Nz Jhanjhi², Maram Fahaad Almufareh¹, Saleh Naif Almuayqil¹

Computer Systems Science and Engineering, Vol.47, No.1, pp. 575-591, 2023, DOI:10.32604/csse.2023.038765

Abstract Twitter has emerged as a platform that produces new data every day through its users which can be utilized for various purposes. People express their unique ideas and views on multiple topics thus providing vast knowledge. Sentiment analysis is critical from the corporate and political perspectives as it can impact decision-making. Since the proliferation of COVID-19, it has become an important challenge to detect the sentiment of COVID-19-related tweets so that people’s opinions can be tracked. The purpose of this research is to detect the sentiment of people regarding this problem with limited data as it can be challenging considering… More >

Mesfer Al Duhayyim^1,*, Sidra Abbas^2,*, Abdullah Al Hejaili³, Natalia Kryvinska⁴, Ahmad Almadhor⁵, Uzma Ghulam Mohammad⁶

Computer Systems Science and Engineering, Vol.47, No.1, pp. 413-429, 2023, DOI:10.32604/csse.2023.037127

Abstract Stroke is a life-threatening disease usually due to blockage of blood or insufficient blood flow to the brain. It has a tremendous impact on every aspect of life since it is the leading global factor of disability and morbidity. Strokes can range from minor to severe (extensive). Thus, early stroke assessment and treatment can enhance survival rates. Manual prediction is extremely time and resource intensive. Automated prediction methods such as Modern Information and Communication Technologies (ICTs), particularly those in Machine Learning (ML) area, are crucial for the early diagnosis and prognosis of stroke. Therefore, this research proposed an ensemble voting… More >

Zhe Yuan^*, Zhewei Wei, Ji-rong Wen

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5009-5026, 2023, DOI:10.32604/cmc.2023.038531

Abstract Motif-based graph local clustering (MGLC) algorithms are generally designed with the two-phase framework, which gets the motif weight for each edge beforehand and then conducts the local clustering algorithm on the weighted graph to output the result. Despite correctness, this framework brings limitations on both practical and theoretical aspects and is less applicable in real interactive situations. This research develops a purely local and index-adaptive method, Index-adaptive Triangle-based Graph Local Clustering (TGLC+), to solve the MGLC problem w.r.t. triangle. TGLC+ combines the approximated Monte-Carlo method Triangle-based Random Walk (TRW) and deterministic Brute-Force method Triangle-based Forward Push (TFP) adaptively to estimate… More >

Yap Bee Wah^1,5,*, Azlan Ismail^1,2, Nur Niswah Naslina Azid³, Jafreezal Jaafar⁴, Izzatdin Abdul Aziz⁴, Mohd Hilmi Hasan⁴, Jasni Mohamad Zain^1,2

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 4821-4841, 2023, DOI:10.32604/cmc.2023.034470

Abstract Prediction of machine failure is challenging as the dataset is often imbalanced with a low failure rate. The common approach to handle classification involving imbalanced data is to balance the data using a sampling approach such as random undersampling, random oversampling, or Synthetic Minority Oversampling Technique (SMOTE) algorithms. This paper compared the classification performance of three popular classifiers (Logistic Regression, Gaussian Naïve Bayes, and Support Vector Machine) in predicting machine failure in the Oil and Gas industry. The original machine failure dataset consists of 20,473 hourly data and is imbalanced with 19945 (97%) ‘non-failure’ and 528 (3%) ‘failure data’. The… More >

Chandana Gouri Tekkali, Karthika Natarajan^*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3171-3187, 2023, DOI:10.32604/cmc.2023.036865

Abstract Fraud Transactions are haunting the economy of many individuals with several factors across the globe. This research focuses on developing a mechanism by integrating various optimized machine-learning algorithms to ensure the security and integrity of digital transactions. This research proposes a novel methodology through three stages. Firstly, Synthetic Minority Oversampling Technique (SMOTE) is applied to get balanced data. Secondly, SMOTE is fed to the nature-inspired Meta Heuristic (MH) algorithm, namely Binary Harris Hawks Optimization (BinHHO), Binary Aquila Optimization (BAO), and Binary Grey Wolf Optimization (BGWO), for feature selection. BinHHO has performed well when compared with the other two. Thirdly, features… More >

Pramote Charongrattanasakul¹, Wimonmas Bamrungsetthapong^2,*, Poom Kumam³

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1631-1651, 2023, DOI:10.32604/csse.2023.036179

Abstract A novel adaptive multiple dependent state sampling plan (AMDSSP) was designed to inspect products from a continuous manufacturing process under the accelerated life test (ALT) using both double sampling plan (DSP) and multiple dependent state sampling plan (MDSSP) concepts. Under accelerated conditions, the lifetime of a product follows the Weibull distribution with a known shape parameter, while the scale parameter can be determined using the acceleration factor (AF). The Arrhenius model is used to estimate AF when the damaging process is temperature-sensitive. An economic design of the proposed sampling plan was also considered for the ALT. A genetic algorithm with… More >

Hussein Ibrahim Hussein¹, Said Amirul Anwar^2,*, Muhammad Imran Ahmad²

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 547-564, 2023, DOI:10.32604/cmc.2023.036025

Abstract Imbalanced data classification is one of the major problems in machine learning. This imbalanced dataset typically has significant differences in the number of data samples between its classes. In most cases, the performance of the machine learning algorithm such as Support Vector Machine (SVM) is affected when dealing with an imbalanced dataset. The classification accuracy is mostly skewed toward the majority class and poor results are exhibited in the prediction of minority-class samples. In this paper, a hybrid approach combining data pre-processing technique and SVM algorithm based on improved Simulated Annealing (SA) was proposed. Firstly, the data pre-processing technique which… More >

Young-Man Kwon, Sunghoon Bae, Dong-Keun Chung, Myung-Jae Lim^*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 683-696, 2023, DOI:10.32604/cmc.2023.033370

Abstract Recently, semantic segmentation has been widely applied to image processing, scene understanding, and many others. Especially, in deep learning-based semantic segmentation, the U-Net with convolutional encoder-decoder architecture is a representative model which is proposed for image segmentation in the biomedical field. It used max pooling operation for reducing the size of image and making noise robust. However, instead of reducing the complexity of the model, max pooling has the disadvantage of omitting some information about the image in reducing it. So, this paper used two diagonal elements of down-sampling operation instead of it. We think that the down-sampling feature maps… More >