Rui Wen^1,*, Jie Zhou², Zhongliang Shen¹, Xiaorui Zhang^2,3,4, Sunil Kumar Jha⁵

Computer Systems Science and Engineering, Vol.47, No.1, pp. 187-204, 2023, DOI:10.32604/csse.2023.037889

Abstract Convolution Neural Networks (CNN) can quickly diagnose COVID-19 patients by analyzing computed tomography (CT) images of the lung, thereby effectively preventing the spread of COVID-19. However, the existing CNN-based COVID-19 diagnosis models do consider the problem that the lung images of COVID-19 patients in the early stage and incubation period are extremely similar to those of the non-COVID-19 population. Which reduces the model’s classification sensitivity, resulting in a higher probability of the model misdiagnosing COVID-19 patients as non-COVID-19 people. To solve the problem, this paper first attempts to apply triplet loss and center loss to… More >

Vaishnavi Jayaraman¹, Arun Raj Lakshminarayanan^1,*, Saravanan Parthasarathy¹, A. Suganthy²

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 301-320, 2023, DOI:10.32604/iasc.2023.037823

Abstract Waste production rises in tandem with population growth and increased utilization. The indecorous disposal of waste paves the way for huge disaster named as climate change. The National Environment Agency (NEA) of Singapore oversees the sustainable management of waste across the country. The three main contributors to the solid waste of Singapore are paper and cardboard (P&C), plastic, and food scraps. Besides, they have a negligible rate of recycling. In this study, Machine Learning techniques were utilized to forecast the amount of garbage also known as waste audits. The waste audit would aid the authorities… More >

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… 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… More >

Ping-Huan Kuo^1,2, Yu-Tsun Chen¹, Her-Terng Yau^1,2,*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 2687-2709, 2023, DOI:10.32604/csse.2023.036293

Abstract The power factor is the ratio between the active and apparent power, and it is available to determine the operational capability of the intended circuit or the parts. The excitation current of the synchronous motor is an essential parameter required for adjusting the power factor because it determines whether the motor is under the optimal operating status. Although the excitation current should predict with the experimental devices, such a method is unsuitable for online real-time prediction. The artificial intelligence algorithm can compensate for the defect of conventional measurement methods requiring the measuring devices and the… More >

Walaa H. Elashmawi^1,3, Ahmad Akram², Mohammed Yasser², Menna Hisham², Manar Mohammed², Noha Ihab², Ahmed Ali^4,5,*

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3637-3656, 2023, DOI:10.32604/iasc.2023.035605

Abstract Parking space is usually very limited in major cities, especially Cairo, leading to traffic congestion, air pollution, and driver frustration. Existing car parking systems tend to tackle parking issues in a non-digitized manner. These systems require the drivers to search for an empty parking space with no guarantee of finding any wasting time, resources, and causing unnecessary congestion. To address these issues, this paper proposes a digitized parking system with a proof-of-concept implementation that combines multiple technological concepts into one solution with the advantages of using IoT for real-time tracking of parking availability. User authentication More >

Ehsan Momeni¹, Biao He², Yasin Abdi^3,*, Danial Jahed Armaghani⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2527-2550, 2023, DOI:10.32604/cmes.2023.026531

Abstract When building geotechnical constructions like retaining walls and dams is of interest, one of the most important factors to consider is the soil’s shear strength parameters. This study makes an effort to propose a novel predictive model of shear strength. The study implements an extreme gradient boosting (XGBoost) technique coupled with a powerful optimization algorithm, the salp swarm algorithm (SSA), to predict the shear strength of various soils. To do this, a database consisting of 152 sets of data is prepared where the shear strength (τ) of the soil is considered as the model output… More >

J. Pavithra^*, S. Selvakumarasamy

Journal of Cyber Security, Vol.4, No.3, pp. 135-151, 2022, DOI:10.32604/jcs.2022.031889

Abstract Machine learning (ML) is often used to solve the problem of malware detection and classification, and various machine learning approaches are adapted to the problem of malware classification; still acquiring poor performance by the way of feature selection, and classification. To address the problem, an efficient novel algorithm for adaptive feature-centered XG Boost Ensemble Learner Classifier “AFC-XG Boost” is presented in this paper. The proposed model has been designed to handle varying data sets of malware detection obtained from Kaggle data set. The model turns the XG Boost classifier in several stages to optimize performance.… More >

Yanwen Lu^1,#, Wenliang Ma^1,#, Xiang Dong^1,#, Mackenzie Brown², Tong Lu^3,*, Weidong Gan^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 347-362, 2023, DOI:10.32604/cmes.2023.024909

Abstract This study aims to apply ResNet-18 convolutional neural network (CNN) and XGBoost to preoperative computed tomography (CT) images and clinical data for distinguishing Xp11.2 translocation renal cell carcinoma (Xp11.2 tRCC) from common subtypes of renal cell carcinoma (RCC) in order to provide patients with individualized treatment plans. Data from 45 patients with Xp11.2 tRCC from January 2007 to December 2021 are collected. Clear cell RCC (ccRCC), papillary RCC (pRCC), or chromophobe RCC (chRCC) can be detected from each patient. CT images are acquired in the following three phases: unenhanced, corticomedullary, and nephrographic. A unified framework More >

S. Praveenkumar^*, T. Karthick

Computer Systems Science and Engineering, Vol.45, No.3, pp. 2417-2433, 2023, DOI:10.32604/csse.2023.032480

Abstract Because stress has such a powerful impact on human health, we must be able to identify it automatically in our everyday lives. The human activity recognition (HAR) system use data from several kinds of sensors to try to recognize and evaluate human actions automatically recognize and evaluate human actions. Using the multimodal dataset DEAP (Database for Emotion Analysis using Physiological Signals), this paper presents deep learning (DL) technique for effectively detecting human stress. The combination of vision-based and sensor-based approaches for recognizing human stress will help us achieve the increased efficiency of current stress recognition… More >