Hyun Yoo¹, Kyungyong Chung^2,*

Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 1-13, 2022, DOI:10.32604/iasc.2022.024890

Abstract Human pose estimation has been a major concern in the field of computer vision. The existing method for recognizing human motion based on two-dimensional (2D) images showed a low recognition rate owing to motion depth, interference between objects, and overlapping problems. A convolutional neural network (CNN) based algorithm recently showed improved results in the field of human skeleton detection. In this study, we have combined human skeleton detection and deep neural network (DNN) to classify the motion of the human body. We used the visual geometry group network (VGGNet) CNN for human skeleton detection, and the generated skeleton coordinates were… More >

Mahdi Souzanchi-K¹, Mohammad-R Akbarzadeh-T^1,*, Nadia Naghavi¹, Ali Sharifnezhad², Vahab Khoshdel³

Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 279-294, 2022, DOI:10.32604/iasc.2022.023717

Abstract Sliding mode control is often used for systems with parametric uncertainties due to its desirable robustness and stability, but this approach carries undesirable chattering. Similarly, joint elasticity is a common phenomenon induced by transmission systems in robots, but it presents additional complexity in robot dynamics that could lead to robot vibrations or even instability. Coupling these two phenomena presents further compounded challenges, particularly when faced with the human interface's added uncertainties. Here, a stable voltage-based adaptive fuzzy strategy to sliding mode control is proposed for an elastic joint robot arm that uses a human's upper limb electromyogram (EMG) signals to… More >

Abdullah Saad AL-Malaise AL-Ghamdi¹, Mahmoud Ragab^2,3,4,*, Maha Farouk S. Sabir¹, Ahmed Elhassanein^5,6, Ashraf A. Gouda⁴

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3385-3399, 2022, DOI:10.32604/cmc.2022.026477

Abstract Education acts as an important part of economic growth and improvement in human welfare. The educational sectors have transformed a lot in recent days, and Information and Communication Technology (ICT) is an effective part of the education field. Almost every action in university and college, right from the process from counselling to admissions and fee deposits has been automated. Attendance records, quiz, evaluation, mark, and grade submissions involved the utilization of the ICT. Therefore, security is essential to accomplish cybersecurity in higher security institutions (HEIs). In this view, this study develops an Automated Outlier Detection for CyberSecurity in Higher Education… More >

Yazeed Yasin Ghadi¹, Nida Khalid², Suliman A. Alsuhibany³, Tamara al Shloul⁴, Ahmad Jalal², Jeongmin Park^5,*

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2597-2615, 2022, DOI:10.32604/cmc.2022.024422

Abstract Human Activity Recognition (HAR) plays an important role in life care and health monitoring since it involves examining various activities of patients at homes, hospitals, or offices. Hence, the proposed system integrates Human-Human Interaction (HHI) and Human-Object Interaction (HOI) recognition to provide in-depth monitoring of the daily routine of patients. We propose a robust system comprising both RGB (red, green, blue) and depth information. In particular, humans in HHI datasets are segmented via connected components analysis and skin detection while the human and object in HOI datasets are segmented via saliency map. To track the movement of humans, we proposed… More >

Anil Kumar Yadav¹, R. K. Pateriya², Nirmal Kumar Gupta³, Punit Gupta^4,*, Dinesh Kumar Saini⁴, Mohammad Alahmadi⁵

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2697-2712, 2022, DOI:10.32604/cmc.2022.023052

Abstract Face recognition systems have enhanced human-computer interactions in the last ten years. However, the literature reveals that current techniques used for identifying or verifying faces are not immune to limitations. Principal Component Analysis-Support Vector Machine (PCA-SVM) and Principal Component Analysis-Artificial Neural Network (PCA-ANN) are among the relatively recent and powerful face analysis techniques. Compared to PCA-ANN, PCA-SVM has demonstrated generalization capabilities in many tasks, including the ability to recognize objects with small or large data samples. Apart from requiring a minimal number of parameters in face detection, PCA-SVM minimizes generalization errors and avoids overfitting problems better than PCA-ANN. PCA-SVM, however,… More >

S. Benisha^1,*, J. Anitha Roseline²

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1363-1378, 2022, DOI:10.32604/iasc.2022.023012

Abstract The scope of this research is to manage the speed of Permanent Magnet Brushless DC Motor Drive (PMBLDCMD) for various less capacity applications. In the circuit, a 1ϕ AC power is given to Diode Rectifier and the converted DC supply is given to condenser, which leads to abnormal pulsating current. Because of this pulsating current, the power quality disturbances arise at the supply point. Hence, the PMBLDCMD requires Power Factor Correction (PFC) converter for many household and profitable applications. The rotors of PMBLDCMD are driven by 3ϕ voltage source inverter (VSI), which performs electronic commutation. The range of PFC converter… More >

Zulqurnain Sabir¹, Abeer S. Alnahdi^2,*, Mdi Begum Jeelani², Mohamed A. Abdelkawy^2,3,*, Muhammad Asif Zahoor Raja⁴, Dumitru Baleanu^5,6, Muhammad Mubashar Hussain⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.2, pp. 763-785, 2022, DOI:10.32604/cmes.2022.018496

Abstract The present investigations are associated with designing Morlet wavelet neural network (MWNN) for solving a class of susceptible, infected, treatment and recovered (SITR) fractal systems of COVID-19 propagation and control. The structure of an error function is accessible using the SITR differential form and its initial conditions. The optimization is performed using the MWNN together with the global as well as local search heuristics of genetic algorithm (GA) and active-set algorithm (ASA), i.e., MWNN-GA-ASA. The detail of each class of the SITR nonlinear COVID-19 system is also discussed. The obtained outcomes of the SITR system are compared with the Runge-Kutta… More >

Maissa A. Al Metrik^*, Dhiaa A. Musleh

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1427-1444, 2022, DOI:10.32604/cmc.2022.025722

Abstract Electricity, being the most efficient secondary energy, contributes for a larger proportion of overall energy usage. Due to a lack of storage for energy resources, over supply will result in energy dissipation and substantial investment waste. Accurate electricity consumption prediction is vital because it allows for the preparation of potential power generation systems to satisfy the growing demands for electrical energy as well as: smart distributed grids, assessing the degree of socioeconomic growth, distributed system design, tariff plans, demand-side management, power generation planning, and providing electricity supply stability by balancing the amount of electricity produced and consumed. This paper proposes… More >

Shuguang Wei, Jiaqi Li^*, Zixu Zhao, Dong Yuan

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1035-1052, 2022, DOI:10.32604/cmc.2022.024201

Abstract This paper proposes an artificial neural network for monitoring and detecting the eccentric error of synchronous reluctance motors. Firstly, a 15 kW synchronous reluctance motor is introduced and took as a case study to investigate the effects of eccentric rotor. Then, the equivalent magnetic circuits of the studied motor are analyzed and developed, in cases of dynamic eccentric rotor and static eccentric rotor condition, respectively. After that, the analytical equations of the studied motor are derived, in terms of its air-gap flux density, electromagnetic torque, and electromagnetic force, followed by the electromagnetic finite element analyses. Then, the modal analyses of the… More >

S. Sudha^*, A. Srinivasan, T. Gayathri Devi

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 597-609, 2022, DOI:10.32604/cmc.2022.024065

Abstract Diabetes is associated with many complications that could lead to death. Diabetic retinopathy, a complication of diabetes, is difficult to diagnose and may lead to vision loss. Visual identification of micro features in fundus images for the diagnosis of DR is a complex and challenging task for clinicians. Because clinical testing involves complex procedures and is time-consuming, an automated system would help ophthalmologists to detect DR and administer treatment in a timely manner so that blindness can be avoided. Previous research works have focused on image processing algorithms, or neural networks, or signal processing techniques alone to detect diabetic retinopathy.… More >