Nesreen M. Alharbi^*, Ahmed Hamza Osman, Arwa A. Mashat, Hasan J. Alyamani

Computer Systems Science and Engineering, Vol.48, No.1, pp. 175-198, 2024, DOI:10.32604/csse.2023.041044

Abstract Recent years have witnessed significant advancements in the field of character recognition, thanks to the revolutionary introduction of machine learning techniques. Among various types of character recognition, offline Handwritten Character Recognition (HCR) is comparatively more challenging as it lacks temporal information, such as stroke count and direction, ink pressure, and unexpected handwriting variability. These issues contribute to a poor level of precision, which calls for the adoption of anomaly detection techniques to enhance Optical Character Recognition (OCR) schemes. Previous studies have not researched unsupervised anomaly detection using MLP for handwriting recognition. Therefore, this study proposes a novel approach for enhanced… More >

Abdelwahed Berguiga^1,2,*, Ahlem Harchay^1,2, Ayman Massaoudi^1,2, Mossaad Ben Ayed³, Hafedh Belmabrouk⁴

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 379-402, 2023, DOI:10.32604/cmc.2023.041667

Abstract Smart Agriculture, also known as Agricultural 5.0, is expected to be an integral part of our human lives to reduce the cost of agricultural inputs, increasing productivity and improving the quality of the final product. Indeed, the safety and ongoing maintenance of Smart Agriculture from cyber-attacks are vitally important. To provide more comprehensive protection against potential cyber-attacks, this paper proposes a new deep learning-based intrusion detection system for securing Smart Agriculture. The proposed Intrusion Detection System IDS, namely GMLP-IDS, combines the feedforward neural network Multilayer Perceptron (MLP) and the Gaussian Mixture Model (GMM) that can better protect the Smart Agriculture… More >

Emad Alsuwat^1,*, Suhare Solaiman¹, Hatim Alsuwat²

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3743-3759, 2023, DOI:10.32604/cmc.2023.035126

Abstract Concept drift is a main security issue that has to be resolved since it presents a significant barrier to the deployment of machine learning (ML) models. Due to attackers’ (and/or benign equivalents’) dynamic behavior changes, testing data distribution frequently diverges from original training data over time, resulting in substantial model failures. Due to their dispersed and dynamic nature, distributed denial-of-service attacks pose a danger to cybersecurity, resulting in attacks with serious consequences for users and businesses. This paper proposes a novel design for concept drift analysis and detection of malware attacks like Distributed Denial of Service (DDOS) in the network.… More >

Mohsen Sharifpur^1,2, Mohamed Salem³, Yonis M Buswig⁴, Habib Forootan Fard⁵, Jaroon Rungamornrat^6,*

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5679-5690, 2023, DOI:10.32604/cmc.2023.034726

Abstract Residential sector is one of the energy-consuming districts of countries that causes CO₂ emission in large extent. In this regard, this sector must be considered in energy policy making related to the reduction of emission of CO₂ and other greenhouse gases. In the present work, CO₂ emission related to the residential sector of three countries, including Indonesia, Thailand, and Vietnam in Southeast Asia, are discussed and modeled by employing Group Method of Data Handling (GMDH) and Multilayer Perceptron (MLP) neural networks as powerful intelligent methods. Prior to modeling, data related to the energy consumption of these countries are represented, discussed,… More >

Chao-Hsi Huang¹, Tsung-Shun Hsieh^2,3, Hsiao-Ting Chien⁴, Ehsan Eftekhari-Zadeh^5,*, Saba Amiri⁶

International Journal of Mental Health Promotion, Vol.24, No.6, pp. 825-841, 2022, DOI:10.32604/ijmhp.2022.021899

Abstract In addition to the effect that the COVID-19 pandemic has had on the physical and mental health of individuals, it has also led to a change in the mental and emotional state of many employees. Especially among businesses and private companies, which faced many restrictions due to the special conditions of the pandemic. Therefore, the present study aimed to design an artificial neural network with MLP technique to analyze the relationship between demographic variables, resilience, COVID-19 and burnout in start-ups in Iran. The research method was quantitative. Managers and employees of start-ups formed the statistical population of the study, based… More >

Jieren Cheng^1,3, Hua Li^2,*, Dengbo Li³, Shuai Hua², Victor S. Sheng⁴

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1941-1957, 2023, DOI:10.32604/cmc.2023.032757

Abstract Image semantic segmentation is an important branch of computer vision of a wide variety of practical applications such as medical image analysis, autonomous driving, virtual or augmented reality, etc. In recent years, due to the remarkable performance of transformer and multilayer perceptron (MLP) in computer vision, which is equivalent to convolutional neural network (CNN), there has been a substantial amount of image semantic segmentation works aimed at developing different types of deep learning architecture. This survey aims to provide a comprehensive overview of deep learning methods in the field of general image semantic segmentation. Firstly, the commonly used image segmentation… More >

Edgar Scavino^1,*, Mohd Amiruddin Abd Rahman¹, Zahid Farid²

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 379-397, 2023, DOI:10.32604/cmc.2023.023824

Abstract Accurate location or positioning of people and self-driven devices in large indoor environments has become an important necessity The application of increasingly automated self-operating moving transportation units, in large indoor spaces demands a precise knowledge of their positions. Technologies like WiFi and Bluetooth, despite their low-cost and availability, are sensitive to signal noise and fading effects. For these reasons, a hybrid approach, which uses two different signal sources, has proven to be more resilient and accurate for the positioning determination in indoor environments. Hence, this paper proposes an improved hybrid technique to implement a fingerprinting based indoor positioning, using Received… More >

Heba A. Elzeheiry^*, Sherief Barakat, Amira Rezk

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1283-1305, 2022, DOI:10.32604/cmc.2022.027345

Abstract Electronic Health Records (EHRs) are the digital form of patients’ medical reports or records. EHRs facilitate advanced analytics and aid in better decision-making for clinical data. Medical data are very complicated and using one classification algorithm to reach good results is difficult. For this reason, we use a combination of classification techniques to reach an efficient and accurate classification model. This model combination is called the Ensemble model. We need to predict new medical data with a high accuracy value in a small processing time. We propose a new ensemble model MDRL which is efficient with different datasets. The MDRL… More >

Fauzi A. Bala^1,2,*, Osman N. Ucan¹, Oguz Bayat¹

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 2187-2204, 2022, DOI:10.32604/cmc.2022.024205

Abstract Designing an authentication system for securing the power plants are important to allow only specific staffs of the power plant to access the certain blocks so that they can be restricted from using high risk-oriented equipment. This authentication is also vital to prevent any security threats or risks like compromises of business server, release of confidential data etc. Though conventional works attempted to accomplish better authentication, they lacked with respect to accuracy. Hence, the study aims to enhance the recognition rate by introducing a voice recognition system as a personal authentication based on Deep Learning (DL) due to its ability… More >

V. Rajalakshmi^1,*, S. Ganesh Vaidyanathan²

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1335-1348, 2022, DOI:10.32604/iasc.2022.024310

Abstract Traffic flow forecasting is the need of the hour requirement in Intelligent Transportation Systems (ITS). Various Artificial Intelligence Frameworks and Machine Learning Models are incorporated in today’s ITS to enhance forecasting. Tuning the model parameters play a vital role in designing an efficient model to improve the reliability of forecasting. Hence, the primary objective of this research is to propose a novel hybrid framework to tune the parameters of Multilayer Perceptron (MLP) using the Swarm Intelligence technique called Particle Swarm Optimization (PSO). The proposed MLP-PSO framework is designed to adjust the weights and bias parameters of MLP dynamically using PSO… More >