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 analyze effective combinations of adaptive… 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. The SBOA-OGRU technique involves the… 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 non-seizure. In addition, a fast… 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 characteristic peaks and microscopic images… 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; consequently, deep-learning approaches have received… 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 to solve the FO-HBV-DIS with… 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 ARIMA (1, 1, 2), and… 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%, 12%, and 11% respectively. The… More >

Walid El-Shafai^1,2, Hayam A. Abd El-Hameed³, Ashraf A. M. Khalaf³, Naglaa F. Soliman⁴, Amel A. Alhussan^5,*, Fathi E. Abd El-Samie¹

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2713-2730, 2022, DOI:10.32604/cmc.2022.028739

Abstract Authentication of the digital image has much attention for the digital revolution. Digital image authentication can be verified with image watermarking and image encryption schemes. These schemes are widely used to protect images against forgery attacks, and they are useful for protecting copyright and rightful ownership. Depending on the desirable applications, several image encryption and watermarking schemes have been proposed to moderate this attention. This framework presents a new scheme that combines a Walsh Hadamard Transform (WHT)-based image watermarking scheme with an image encryption scheme based on Double Random Phase Encoding (DRPE). First, on the sender side, the secret medical… More >

C. Ajitha^1,*, T. Jaya²

Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1033-1048, 2022, DOI:10.32604/iasc.2022.021984

Abstract Multicarrier waveforms with enhanced spectral efficiency, low latency, and high throughput are required for 5G wireless networks. The Orthogonal Frequency Division Multiplexing (OFDM) method is well-known in research, but due to its limited spectral efficiency, various alternative waveforms are being considered for 5G systems. In the recent communication world, NOMA (non-orthogonal multiple access) plays a significant part due to its wider transmission of data with less bandwidth allocation. Even if a high data rate can be attained, the transmission problem will arise due to the spread of multiple paths. In order to reduce complexity and area utilization, a novel PL-based… More >