Anas AbuKaraki¹, Tawfi Alrawashdeh¹, Sumaya Abusaleh¹, Malek Zakarya Alksasbeh^1,*, Bilal Alqudah¹, Khalid Alemerien², Hamzah Alshamaseen³

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1055-1073, 2024, DOI:10.32604/cmc.2024.051420

Abstract This paper presents a novel multiclass system designed to detect pleural effusion and pulmonary edema on chest X-ray images, addressing the critical need for early detection in healthcare. A new comprehensive dataset was formed by combining 28,309 samples from the ChestX-ray14, PadChest, and CheXpert databases, with 10,287, 6022, and 12,000 samples representing Pleural Effusion, Pulmonary Edema, and Normal cases, respectively. Consequently, the preprocessing step involves applying the Contrast Limited Adaptive Histogram Equalization (CLAHE) method to boost the local contrast of the X-ray samples, then resizing the images to 380 × 380 dimensions, followed by using the data… More >

Nasser Sweilam¹, Seham Al-Mekhlafi^2,*, Aya Ahmed³, Ahoud Alsheri⁴, Emad Abo-Eldahab³

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1619-1645, 2024, DOI:10.32604/cmes.2024.047896

Abstract In this paper, two crossover hybrid variable-order derivatives of the cancer model are developed. Grünwald-Letnikov approximation is used to approximate the hybrid fractional and variable-order fractional operators. The existence, uniqueness, and stability of the proposed model are discussed. Adams Bashfourth’s fifth-step method with a hybrid variable-order fractional operator is developed to study the proposed models. Comparative studies with generalized fifth-order Runge-Kutta method are given. Numerical examples and comparative studies to verify the applicability of the used methods and to demonstrate the simplicity of these approximations are presented. We have showcased the efficiency of the proposed More >

P. Kuppusamy^1,*, M. Sanjay¹, P. V. Deepashree¹, C. Iwendi²

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 445-466, 2023, DOI:10.32604/cmc.2023.042675

Abstract The infrastructure and construction of roads are crucial for the economic and social development of a region, but traffic-related challenges like accidents and congestion persist. Artificial Intelligence (AI) and Machine Learning (ML) have been used in road infrastructure and construction, particularly with the Internet of Things (IoT) devices. Object detection in Computer Vision also plays a key role in improving road infrastructure and addressing traffic-related problems. This study aims to use You Only Look Once version 7 (YOLOv7), Convolutional Block Attention Module (CBAM), the most optimized object-detection algorithm, to detect and identify traffic signs, and… More >

T. M. Nithya^1,*, R. Umanesan², T. Kalavathidevi³, C. Selvarathi⁴, A. Kavitha⁵

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2537-2547, 2023, DOI:10.32604/iasc.2022.028804

Abstract Big data analytics is a popular research topic due to its applicability in various real time applications. The recent advent of machine learning and deep learning models can be applied to analyze big data with better performance. Since big data involves numerous features and necessitates high computational time, feature selection methodologies using metaheuristic optimization algorithms can be adopted to choose optimum set of features and thereby improves the overall classification performance. This study proposes a new sigmoid butterfly optimization method with an optimum gated recurrent unit (SBOA-OGRU) model for big data classification in Apache Spark. More >

Saeed Mohsen^1,2,*, Sherif S. M. Ghoneim³, Mohammed S. Alzaidi³, Abdullah Alzahrani³, Ashraf Mohamed Ali Hassan⁴

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5271-5286, 2023, DOI:10.32604/cmc.2023.038758

Abstract Classification of electroencephalogram (EEG) signals for humans can be achieved via artificial intelligence (AI) techniques. Especially, the EEG signals associated with seizure epilepsy can be detected to distinguish between epileptic and non-epileptic regions. From this perspective, an automated AI technique with a digital processing method can be used to improve these signals. This paper proposes two classifiers: long short-term memory (LSTM) and support vector machine (SVM) for the classification of seizure and non-seizure EEG signals. These classifiers are applied to a public dataset, namely the University of Bonn, which consists of 2 classes –seizure and… More >

Wei Zhang¹, Mulian Zheng^1,*, Yifeng Li², Wuxi Zheng³

Journal of Renewable Materials, Vol.11, No.5, pp. 2513-2529, 2023, DOI:10.32604/jrm.2023.025275

Abstract Steel slag is characterized by high strength, good wear resistance and micro-expansion. This study aims at exploring the potential of steel slag in cement stabilized aggregates, mainly including mechanical properties, shrinkage and compensation mechanisms. For this purpose, the compressive strength and compressive resilient modulus of cement stabilized aggregates with different steel slag contents (CSMS) were initially investigated. Subsequently, the effects of steel slag and cement on dry shrinkage, temperature shrinkage, and total shrinkage were analyzed through a series of shrinkage test designs. Additionally, in combination with X-ray diffraction (XRD) and Scanning electron microscope (SEM), the… More >

Shimaa Ouf^*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1213-1233, 2023, DOI:10.32604/cmc.2023.034399

Abstract The aviation industry is one of the most competitive markets. The most common approach for airline service providers is to improve passenger satisfaction. Passenger satisfaction in the aviation industry occurs when passengers’ expectations are met during flights. Airline service quality is critical in attracting new passengers and retaining existing ones. It is crucial to identify passengers’ pain points and enhance their satisfaction with the services offered. The airlines used a variety of techniques to improve service quality. They used data analysis approaches to analyze the passenger point data. These solutions have focused simply on surveys;… More >

Sakda Noinang¹, Zulqurnain Sabir², Muhammad Asif Zahoor Raja³, Soheil Salahshour⁴, Wajaree Weera^5,*, Thongchai Botmart⁵

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2575-2588, 2023, DOI:10.32604/cmc.2023.029046

Abstract The current investigations are presented to solve the fractional order HBV differential infection system (FO-HBV-DIS) with the response of antibody immune using the optimization based stochastic schemes of the Levenberg-Marquardt backpropagation (LMB) neural networks (NNs), i.e., LMBNNs. The FO-HBV-DIS with the response of antibody immune is categorized into five dynamics, healthy hepatocytes (H), capsids (D), infected hepatocytes (I), free virus (V) and antibodies (W). The investigations for three different FO variants have been tested numerically to solve the nonlinear FO-HBV-DIS. The data magnitudes are implemented 75% for training, 10% for certification and 15% for testing More >

Abeer S. Alnahdi^1,*, Mdi B. Jeelani¹, Hanan A. Wahash², Mansour A. Abdulwasaa^3,4

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.2, pp. 1315-1343, 2023, DOI:10.32604/cmes.2022.023694

Abstract This study aims to structure and evaluate a new COVID-19 model which predicts vaccination effect in the Kingdom of Saudi Arabia (KSA) under Atangana-Baleanu-Caputo (ABC) fractional derivatives. On the statistical aspect, we analyze the collected statistical data of fully vaccinated people from June 01, 2021, to February 15, 2022. Then we apply the Eviews program to find the best model for predicting the vaccination against this pandemic, based on daily series data from February 16, 2022, to April 15, 2022. The results of data analysis show that the appropriate model is autoregressive integrated moving average… More >

Muneerah Al Nuwairan^1,*, Zulqurnain Sabir², Muhammad Asif Zahoor Raja³, Maryam Alnami¹, Hanan Almuslem¹

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4441-4454, 2022, DOI:10.32604/cmc.2022.029432

Abstract In this paper, a fractional order model based on the management of waste plastic in the ocean (FO-MWPO) is numerically investigated. The mathematical form of the FO-MWPO model is categorized into three components, waste plastic, Marine debris, and recycling. The stochastic numerical solvers using the Levenberg-Marquardt backpropagation neural networks (LMQBP-NNs) have been applied to present the numerical solutions of the FO-MWPO system. The competency of the method is tested by taking three variants of the FO-MWPO model based on the fractional order derivatives. The data ratio is provided for training, testing and authorization is 77%, More >