Wael Said¹, Elsayed Mostafa^1,*, M. M. Hassan¹, Ayman Mohamed Mostafa²

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3193-3209, 2022, DOI:10.32604/cmc.2022.023554

Abstract User's data is considered as a vital asset of several organizations. Migrating data to the cloud computing is not an easy decision for any organization due to the privacy and security concerns. Service providers must ensure that both data and applications that will be stored on the cloud should be protected in a secure environment. The data stored on the public cloud will be vulnerable to outside and inside attacks. This paper provides interactive multi-layer authentication frameworks for securing user identities on the cloud. Different access control policies are applied for verifying users on the cloud. A security mechanism is… More >

Chao Mei^1,2, Qifu Wang^1,*, Chen Yu¹, Zhaohui Xia¹

CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.2, pp. 627-659, 2021, DOI:10.32604/cmes.2021.017704

Abstract This paper presents an effective fiber angle optimization method for two and multi-layered variable stiffness composites. A gradient-based fiber angle optimization method is developed based on isogeometric analysis (IGA). Firstly, the element densities and fiber angles for two and multi-layered composites are synchronously optimized using an extended Bi-layered continuous fiber angle optimization method (XBi-CFAO). The densities and fiber angles in the base layer are attached to the control points. The structure response and sensitivity analysis are accomplished using the non-uniform rational B-spline (NURBS) based IGA. By the benefit of the B-spline space, this method is free from checkerboards, and no… More >

Nebojsa Bacanin¹, Khaled Alhazmi^2,*, Miodrag Zivkovic¹, K. Venkatachalam³, Timea Bezdan¹, Jamel Nebhen⁴

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 4199-4215, 2022, DOI:10.32604/cmc.2022.020449

Abstract In the domain of artificial neural networks, the learning process represents one of the most challenging tasks. Since the classification accuracy highly depends on the weights and biases, it is crucial to find its optimal or suboptimal values for the problem at hand. However, to a very large search space, it is very difficult to find the proper values of connection weights and biases. Employing traditional optimization algorithms for this issue leads to slow convergence and it is prone to get stuck in the local optima. Most commonly, back-propagation is used for multi-layer-perceptron training and it can lead to vanishing… More >

Ibrahim Alsukayti¹, Aman Singh^2,*

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2489-2505, 2022, DOI:10.32604/cmc.2022.020264

Abstract A famous psychologist or researcher, Daniel Goleman, gave a theory on the importance of Emotional Intelligence for the success of an individual’s life. Daniel Goleman quoted in the research that “The contribution of an individual’s Intelligence Quotient (IQ) is only 20% for their success, the remaining 80% is due to Emotional Intelligence (EQ)”. However, in the absence of a reliable technique for EQ evaluation, this factor of overall intelligence is ignored in most of the intelligence evaluation mechanisms. This research presented an analysis based on basic statistical tools along with more sophisticated deep learning tools. The proposed cross intelligence evaluation… More >

Taesik Lee¹, Dongsan Jun^1,*, Sang-hyo Park², Byung-Gyu Kim³, Jungil Yun⁴, Kugjin Yun⁴, Won-Sik Cheong⁴

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 2013-2029, 2022, DOI:10.32604/cmc.2022.019604

Abstract As video compression is one of the core technologies required to enable seamless medical data streaming in mobile healthcare applications, there is a need to develop powerful media codecs that can achieve minimum bitrates while maintaining high perceptual quality. Versatile Video Coding (VVC) is the latest video coding standard that can provide powerful coding performance with a similar visual quality compared to the previously developed method that is High Efficiency Video Coding (HEVC). In order to achieve this improved coding performance, VVC adopted various advanced coding tools, such as flexible Multi-type Tree (MTT) block structure which uses Binary Tree (BT)… More >

Qazwan Abdullah^1,2,*, Ömer Aydoĝdu², Adeeb Salh³, Nabil Farah⁴, Md Hairul Nizam Talib⁴, Taha Sadeq⁵, Mohammed A. A. Al-Mekhalfi³, Abdu Saif⁶

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4025-4042, 2021, DOI:10.32604/cmc.2021.018798

Abstract The multilayer structure is a promising technique used to minimize the size of planar microstrip filters. In the flexible design and incorporation of other microwave components, multilayer band-pass filter results in better and enhanced dimensions. This paper introduces a microstrip fifth-generation (5G) low-frequency band of 2.52–2.65 GHz using a parallel-coupled line (PCL) Bandpass filter and multilayer (ML) hairpin Bandpass filter. The targeted four-pole resonator has a center frequency of 2.585 GHz with a bandwidth of 130 MHz. The filters are designed with a 0.1 dB passband ripple with a Chebyshev response. The hairpin-line offers compact filter design structures. Theoretically, they… More >

Chintakindi Balaram Murthy¹, Mohammad Farukh Hashmi¹, Ghulam Muhammad^2,3,*, Salman A. AlQahtani^2,3

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3015-3031, 2021, DOI:10.32604/cmc.2021.018781

Abstract Real-time pedestrian detection is an important task for unmanned driving systems and video surveillance. The existing pedestrian detection methods often work at low speed and also fail to detect smaller and densely distributed pedestrians by losing some of their detection accuracy in such cases. Therefore, the proposed algorithm YOLOv2 (“YOU ONLY LOOK ONCE Version 2”)-based pedestrian detection (referred to as YOLOv2PD) would be more suitable for detecting smaller and densely distributed pedestrians in real-time complex road scenes. The proposed YOLOv2PD algorithm adopts a Multi-layer Feature Fusion (MLFF) strategy, which helps to improve the model’s feature extraction ability. In addition, one… More >

Sadia Kiran¹, Muhammad Attique Khan¹, Muhammad Younus Javed¹, Majed Alhaisoni², Usman Tariq³, Yunyoung Nam^4,*, Robertas Damaševičius⁵, Muhammad Sharif⁶

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4061-4075, 2021, DOI:10.32604/cmc.2021.017800

Abstract Human Action Recognition (HAR) is an active research topic in machine learning for the last few decades. Visual surveillance, robotics, and pedestrian detection are the main applications for action recognition. Computer vision researchers have introduced many HAR techniques, but they still face challenges such as redundant features and the cost of computing. In this article, we proposed a new method for the use of deep learning for HAR. In the proposed method, video frames are initially pre-processed using a global contrast approach and later used to train a deep learning model using domain transfer learning. The Resnet-50 Pre-Trained Model is… More >

Ruikun Li^1,*, Yun Li², Wen He^1,3, Lirong Chen¹, Jianchao Luo¹

CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.1, pp. 381-398, 2021, DOI:10.32604/cmes.2021.016264

Abstract Anomaly detection is an important method for intrusion detection. In recent years, unsupervised methods have been widely researched because they do not require labeling. For example, a nonlinear autoencoder can use reconstruction errors to attain the discrimination threshold. This method is not effective when the model complexity is high or the data contains noise. The method for detecting the density of compressed features in a hidden layer can be used to reduce the influence of noise on the selection of the threshold because the density of abnormal data in hidden layers is smaller than normal data. However, compressed features may… More >

Gang Yu^1,4,5, Jian Tang^2,*, Jian Zhang³, Zhonghui Wang⁶

Intelligent Automation & Soft Computing, Vol.29, No.1, pp. 273-290, 2021, DOI:10.32604/iasc.2021.016981

Abstract Kernel learning based on structure risk minimum can be employed to build a soft measuring model for analyzing small samples. However, it is difficult to select learning parameters, such as kernel parameter (KP) and regularization parameter (RP). In this paper, a soft measuring method is investigated to select learning parameters, which is based on adaptive multi-layer selective ensemble (AMLSEN) and least-square support vector machine (LSSVM). First, candidate kernels and RPs with K and R numbers are preset based on prior knowledge, and candidate sub-sub-models with K*R numbers are constructed through utilizing LSSVM. Second, the candidate sub-sub-models with same KPs and… More >