Phan Nguyen Ky Phuc¹, Nguyen Van Thanh^2,*, Duong Bao Tram¹

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5165-5178, 2022, DOI:10.32604/cmc.2022.027967

Abstract The cross-docking is a very important subject in logistics and supply chain managements. According to the definition, cross-docking is a process dealing with transhipping inventory, in which goods and products are unloaded from an inbound truck and process through a flow-center to be directly loaded onto an outbound truck. Cross-docking is favored due to its advantages in reducing the material handing cost, the needs to store the product in warehouse, as well decreasing the labor cost by eliminating packaging, storing, pick-location and order picking. In cross-docking, products can be consolidated and transported as a full load, reducing overall distribution costs.… More >

Jun Li¹, Xinyi Qiu¹, Yifei Wei^1,*, Mei Song¹, Xiaojun Wang²

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5955-5967, 2022, DOI:10.32604/cmc.2022.027947

Abstract Traveling by high-speed rail and railway transportation have become an important part of people’s life and social production. Track is the basic equipment of railway transportation, and its performance directly affects the service lifetime of railway lines and vehicles. The anomaly detection of rail fasteners is in a priority, while the traditional manual method is extremely inefficient and dangerous to workers. Therefore, this paper introduces efficient computer vision into the railway detection system not only to locate the normal fasteners, but also to recognize the fasteners states. To be more specific, this paper mainly studies the rail fastener detection based… More >

Seon Hong Lee¹, Haein Lee¹, Jang Hyun Kim^2,*

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4983-4997, 2022, DOI:10.32604/cmc.2022.027943

Abstract Metaverse is one of the main technologies in the daily lives of several people, such as education, tour systems, and mobile application services. Particularly, the number of users of mobile metaverse applications is increasing owing to the merit of accessibility everywhere. To provide an improved service, it is important to analyze online reviews that contain user satisfaction. Several previous studies have utilized traditional methods, such as the structural equation model (SEM) and technology acceptance method (TAM) for exploring user satisfaction, using limited survey data. These methods may not be appropriate for analyzing the users of mobile applications. To overcome this… More >

Souad Larabi-Marie-Sainte¹, Eatedal Alabdulkreem², Mohammad Alamgeer³, Mohamed K Nour⁴, Anwer Mustafa Hilal^5,*, Mesfer Al Duhayyim⁶, Abdelwahed Motwakel⁵, Ishfaq Yaseen⁵

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4589-4601, 2022, DOI:10.32604/cmc.2022.027922

Abstract Electroencephalography (EEG) eye state classification becomes an essential tool to identify the cognitive state of humans. It can be used in several fields such as motor imagery recognition, drug effect detection, emotion categorization, seizure detection, etc. With the latest advances in deep learning (DL) models, it is possible to design an accurate and prompt EEG EyeState classification problem. In this view, this study presents a novel compact bat algorithm with deep learning model for biomedical EEG EyeState classification (CBADL-BEESC) model. The major intention of the CBADL-BEESC technique aims to categorize the presence of EEG EyeState. The CBADL-BEESC model performs feature… More >

Bareera Zafar¹, Syed Abbas Zilqurnain Naqvi¹, Muhammad Ahsan¹, Allah Ditta^2,*, Ummul Baneen¹, Muhammad Adnan Khan^3,4

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5099-5116, 2022, DOI:10.32604/cmc.2022.027874

Abstract This research proposes a method called enhanced collaborative and geometric multi-kernel learning (E-CGMKL) that can enhance the CGMKL algorithm which deals with multi-class classification problems with non-linear data distributions. CGMKL combines multiple kernel learning with softmax function using the framework of multi empirical kernel learning (MEKL) in which empirical kernel mapping (EKM) provides explicit feature construction in the high dimensional kernel space. CGMKL ensures the consistent output of samples across kernel spaces and minimizes the within-class distance to highlight geometric features of multiple classes. However, the kernels constructed by CGMKL do not have any explicit relationship among them and try… More >

Xintao Duan^1,*, Zhiqiang Shao¹, Wenxin Wang¹, En Zhang¹, Dongli Yue¹, Chuan Qin², Haewoon Nam³

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5363-5375, 2022, DOI:10.32604/cmc.2022.027807

Abstract At present, the image steganography method based on CNN has achieved good results. The trained model and its parameters are of great value. Once leaked, the secret image will be exposed. To protect the security of steganographic network model parameters in the transmission process, an idea based on network model parameter scrambling is proposed in this paper. Firstly, the sender trains the steganography network and extraction network, encrypts the extraction network parameters with the key shared by the sender and the receiver, then sends the extraction network and parameters to the receiver through the public channel, and the receiver recovers… More >

Zhenzhou Wang¹, Shao Cui¹, Xiang Wang^1,*, JiaFeng Tian²

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4709-4725, 2022, DOI:10.32604/cmc.2022.027767

Abstract To generate realistic three-dimensional animation of virtual character, capturing real facial expression is the primary task. Due to diverse facial expressions and complex background, facial landmarks recognized by existing strategies have the problem of deviations and low accuracy. Therefore, a method for facial expression capture based on two-stage neural network is proposed in this paper which takes advantage of improved multi-task cascaded convolutional networks (MTCNN) and high-resolution network. Firstly, the convolution operation of traditional MTCNN is improved. The face information in the input image is quickly filtered by feature fusion in the first stage and Octave Convolution instead of the… More >

Haijun Geng^1,2, Zikun Jin¹, Jiangyuan Yao^3,*, Han Zhang⁴, Zhiguo Hu⁶, Bo Yang⁵, Yingije Guo⁷, Wei Wang¹, Qidong Zhang¹, Guoao Duan⁸

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4525-4540, 2022, DOI:10.32604/cmc.2022.027725

Abstract Network failures are unavoidable and occur frequently. When the network fails, intra-domain routing protocols deploying on the Internet need to undergo a long convergence process. During this period, a large number of messages are discarded, which results in a decline in the user experience and severely affects the quality of service of Internet Service Providers (ISP). Therefore, improving the availability of intra-domain routing is a trending research question to be solved. Industry usually employs routing protection algorithms to improve intra-domain routing availability. However, existing routing protection schemes compute as many backup paths as possible to reduce message loss due to… More >

Ashit Kumar Dutta¹, S. Srinivasan², S. N. Kumar³, T. S. Balaji^4,5, Won Il Lee⁶, Gyanendra Prasad Joshi⁷, Sung Won Kim^8,*

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5563-5576, 2022, DOI:10.32604/cmc.2022.027707

Abstract Due to the advances of intelligent transportation system (ITSs), traffic forecasting has gained significant interest as robust traffic prediction acts as an important part in different ITSs namely traffic signal control, navigation, route mapping, etc. The traffic prediction model aims to predict the traffic conditions based on the past traffic data. For more accurate traffic prediction, this study proposes an optimal deep learning-enabled statistical analysis model. This study offers the design of optimal convolutional neural network with attention long short term memory (OCNN-ALSTM) model for traffic prediction. The proposed OCNN-ALSTM technique primarily pre-processes the traffic data by the use of… More >

Md Zia Ur Rahman^1,*, S. Rooban¹, P. Rohini², M. V. S. Ramprasad³, Pradeep Vinaik Kodavanti³

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5713-5726, 2022, DOI:10.32604/cmc.2022.027672

Abstract The Thoracic Electrical Bioimpedance (TEB) helps to determine the stroke volume during cardiac arrest. While measuring cardiac signal it is contaminated with artifacts. The commonly encountered artifacts are Baseline wander (BW) and Muscle artifact (MA), these are physiological and non-stationary. As the nature of these artifacts is random, adaptive filtering is needed than conventional fixed coefficient filtering techniques. To address this, a new block based adaptive learning scheme is proposed to remove artifacts from TEB signals in clinical scenario. The proposed block least mean square (BLMS) algorithm is mathematically normalized with reference to data and error. This normalization leads, block… More >