B. Gomathi^1,*, R. Manimegalai¹, Srivatsan Santhanam², Atreya Biswas³

Computer Systems Science and Engineering, Vol.48, No.6, pp. 1749-1765, 2024, DOI:10.32604/csse.2024.056659 - 22 November 2024

Abstract In enterprise operations, maintaining manual rules for enterprise processes can be expensive, time-consuming, and dependent on specialized domain knowledge in that enterprise domain. Recently, rule-generation has been automated in enterprises, particularly through Machine Learning, to streamline routine tasks. Typically, these machine models are black boxes where the reasons for the decisions are not always transparent, and the end users need to verify the model proposals as a part of the user acceptance testing to trust it. In such scenarios, rules excel over Machine Learning models as the end-users can verify the rules and have more… More >

Alaa U. Khawaja¹, Ahmad Shaf^2,*, Faisal Al Thobiani³, Tariq Ali⁴, Muhammad Irfan⁵, Aqib Rehman Pirzada², Unza Shahkeel²

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2399-2420, 2024, DOI:10.32604/cmes.2024.054257 - 31 October 2024

Abstract Electric motor-driven systems are core components across industries, yet they’re susceptible to bearing faults. Manual fault diagnosis poses safety risks and economic instability, necessitating an automated approach. This study proposes FTCNNLSTM (Fine-Tuned TabNet Convolutional Neural Network Long Short-Term Memory), an algorithm combining Convolutional Neural Networks, Long Short-Term Memory Networks, and Attentive Interpretable Tabular Learning. The model preprocesses the CWRU (Case Western Reserve University) bearing dataset using segmentation, normalization, feature scaling, and label encoding. Its architecture comprises multiple 1D Convolutional layers, batch normalization, max-pooling, and LSTM blocks with dropout, followed by batch normalization, dense layers, and More >

Pawat Chunhachatrachai^1,*, Chyi-Yeu Lin^1,2

Intelligent Automation & Soft Computing, Vol.39, No.4, pp. 599-618, 2024, DOI:10.32604/iasc.2024.055385 - 06 September 2024

Abstract Angle detection is a crucial aspect of industrial automation, ensuring precise alignment and orientation of components in manufacturing processes. Despite the widespread application of computer vision in industrial settings, angle detection remains an underexplored domain, with limited integration into production lines. This paper addresses the need for automated angle detection in industrial environments by presenting a methodology that eliminates training time and higher computation cost on Graphics Processing Unit (GPU) from machine learning in computer vision (e.g., Convolutional Neural Networks (CNN)). Our approach leverages advanced image processing techniques and a strategic combination of algorithms, including More >

Mohamed Karray^1,*, Nesrine Triki^2,*, Mohamed Ksantini²

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 119-138, 2024, DOI:10.32604/cmc.2024.051562 - 18 July 2024

Abstract Advanced Driver Assistance Systems (ADAS) technologies can assist drivers or be part of automatic driving systems to support the driving process and improve the level of safety and comfort on the road. Traffic Sign Recognition System (TSRS) is one of the most important components of ADAS. Among the challenges with TSRS is being able to recognize road signs with the highest accuracy and the shortest processing time. Accordingly, this paper introduces a new real time methodology recognizing Speed Limit Signs based on a trio of developed modules. Firstly, the Speed Limit Detection (SLD) module uses… More >

Ming Yan¹, Caijiang Lu^2,*, Pan Shi^1,*, Meiling Zhang³, Jiawei Zhang¹, Liang Wang¹

Frontiers in Heat and Mass Transfer, Vol.22, No.2, pp. 615-631, 2024, DOI:10.32604/fhmt.2024.048333 - 20 May 2024

Abstract In response to escalating challenges in energy conservation and emission reduction, this study delves into the complexities of heat transfer in two-phase flows and adjustments to combustion processes within coal-fired boilers. Utilizing a fusion of hybrid modeling and automation technologies, we develop soft measurement models for key combustion parameters, such as the net calorific value of coal, flue gas oxygen content, and fly ash carbon content, within the Distributed Control System (DCS). Validated with performance test data, these models exhibit controlled root mean square error (RMSE) and maximum absolute error (MAXE) values, both within the… More > Graphic Abstract

Dahyeon Kim¹, Namgi Kim², Junho Ahn^2,*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3867-3889, 2024, DOI:10.32604/cmc.2024.046871 - 26 March 2024

Abstract This research aims to propose a practical framework designed for the automatic analysis of a product’s comprehensive functionality and security vulnerabilities, generating applicable guidelines based on real-world software. The existing analysis of software security vulnerabilities often focuses on specific features or modules. This partial and arbitrary analysis of the security vulnerabilities makes it challenging to comprehend the overall security vulnerabilities of the software. The key novelty lies in overcoming the constraints of partial approaches. The proposed framework utilizes data from various sources to create a comprehensive functionality profile, facilitating the derivation of real-world security guidelines.… More >

Nazik Alturki¹, Raed Alharthi², Muhammad Umer^3,*, Oumaima Saidani¹, Amal Alshardan¹, Reemah M. Alhebshi⁴, Shtwai Alsubai⁵, Ali Kashif Bashir^6,7,8,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3387-3415, 2024, DOI:10.32604/cmes.2023.044700 - 11 March 2024

Abstract The concept of smart houses has grown in prominence in recent years. Major challenges linked to smart homes are identification theft, data safety, automated decision-making for IoT-based devices, and the security of the device itself. Current home automation systems try to address these issues but there is still an urgent need for a dependable and secure smart home solution that includes automatic decision-making systems and methodical features. This paper proposes a smart home system based on ensemble learning of random forest (RF) and convolutional neural networks (CNN) for programmed decision-making tasks, such as categorizing gadgets… More >

Wenlu Ji¹, Yingqi Liao^1,*, Liudong Zhang²

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2825-2848, 2023, DOI:10.32604/iasc.2023.039618 - 11 September 2023

Abstract Existing power anomaly detection is mainly based on a pattern matching algorithm. However, this method requires a lot of manual work, is time-consuming, and cannot detect unknown anomalies. Moreover, a large amount of labeled anomaly data is required in machine learning-based anomaly detection. Therefore, this paper proposes the application of a generative adversarial network (GAN) to massive data stream anomaly identification, diagnosis, and prediction in power dispatching automation systems. Firstly, to address the problem of the small amount of anomaly data, a GAN is used to obtain reliable labeled datasets for fault diagnosis model training… More >

M. Atikur Rahman^*, Amranur Rahman, Arman Hossain, Arifur Rahman Bhuiyan

Journal of New Media, Vol.5, No.1, pp. 33-43, 2023, DOI:10.32604/jnm.2023.038676 - 14 June 2023

Abstract As a result of the introduction of new infectious illnesses, key infection prevention measures were implemented. Now, a new coronavirus (SARS-CoV-2) epidemic has expanded swiftly, causing the coronavirus illness 2019 (COVID-19). Many microorganisms spread illness via hospital surfaces due to environmental pollution. This virus has been associated to close contact between persons in tight situations such as houses, hospitals, assisted living, and residential institutions. Aside from health care settings, public buildings, faith-based community centers, marketplaces, transportation, and corporate environments are prone to COVID-19 transmission. Physical contact to the sanitizer device may cause for spread Covid More >

Hammad Rustam¹, Muhammad Muneeb¹, Suliman A. Alsuhibany², Yazeed Yasin Ghadi³, Tamara Al Shloul⁴, Ahmad Jalal¹, Jeongmin Park^5,*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 2331-2346, 2023, DOI:10.32604/cmc.2023.028712 - 06 February 2023

Abstract Hand gesture recognition (HGR) is used in a numerous applications, including medical health-care, industrial purpose and sports detection. We have developed a real-time hand gesture recognition system using inertial sensors for the smart home application. Developing such a model facilitates the medical health field (elders or disabled ones). Home automation has also been proven to be a tremendous benefit for the elderly and disabled. Residents are admitted to smart homes for comfort, luxury, improved quality of life, and protection against intrusion and burglars. This paper proposes a novel system that uses principal component analysis, linear More >