Rihem Farkh^1,5,*, Mohammad Tabrez Quasim², Khaled Al jaloud¹, Saad Alhuwaimel³, Shams Tabrez Siddiqui⁴

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1065-1079, 2021, DOI:10.32604/cmc.2021.016600

Abstract With the development of artificial intelligence technology, various sectors of industry have developed. Among them, the autonomous vehicle industry has developed considerably, and research on self-driving control systems using artificial intelligence has been extensively conducted. Studies on the use of image-based deep learning to monitor autonomous driving systems have recently been performed. In this paper, we propose an advanced control for a serving robot. A serving robot acts as an autonomous line-follower vehicle that can detect and follow the line drawn on the floor and move in specified directions. The robot should be able to follow the trajectory with speed… More >

Ying Fu^1,2,*, Minxue Gong¹, Guang Yang¹, Hong Wei³, Jiliu Zhou^1,2

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1359-1374, 2021, DOI:10.32604/cmc.2021.016536

Abstract Generative adversarial networks (GANs) have considerable potential to alleviate challenges linked to data scarcity. Recent research has demonstrated the good performance of this method for data augmentation because GANs synthesize semantically meaningful data from standard signal distribution. The goal of this study was to solve the overfitting problem that is caused by the training process of convolution networks with a small dataset. In this context, we propose a data augmentation method based on an evolutionary generative adversarial network for cardiac magnetic resonance images to extend the training data. In our structure of the evolutionary GAN, the most optimal generator is… More >

Pengfei Gong^1,2, Qun Wang^2,*, Junjun Zhu³

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1389-1408, 2021, DOI:10.32604/cmc.2021.016483

Abstract Fatal traffic accidents in urban areas can adversely affect the urban road traffic system and pose many challenges for urban traffic management. Therefore, it is necessary to first classify emergency responses to such accidents and then handle them quickly and correctly. The aim of this paper is to develop an evaluation index system and to use appropriate methods to investigate emergency-response classifications to fatal traffic accidents in Chinese urban areas. This study used a multilevel hierarchical structural model to determine emergency-response classification. In the model, accident attributes, urban road network vulnerability, and institutional resilience were used as classification criteria. Each… More >

Yao Zhao¹, Jian Dong^1,*, Hongwei Liu¹, Jin Wu², Yanxin Liu¹

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 727-741, 2021, DOI:10.32604/cmc.2021.016462

Abstract Efficient cache management plays a vital role in in-memory data-parallel systems, such as Spark, Tez, Storm and HANA. Recent research, notably research on the Least Reference Count (LRC) and Most Reference Distance (MRD) policies, has shown that dependency-aware caching management practices that consider the application’s directed acyclic graph (DAG) perform well in Spark. However, these practices ignore the further relationship between RDDs and cached some redundant RDDs with the same child RDDs, which degrades the memory performance. Hence, in memory-constrained situations, systems may encounter a performance bottleneck due to frequent data block replacement. In addition, the prefetch mechanisms in some… More >

Xiuyu Yang^1,*, Qi Guo¹, Jianzhong Gui², Renyong Chai³, Xueyuan Liu¹

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1081-1097, 2021, DOI:10.32604/cmc.2021.016375

Abstract Centralized delivery has become the main operation mode under the scaled development of wind power. Transmission channels are usually the guarantee of out-delivered wind power for large-scale wind base. The configuration of transmission capacity, which has the features of low utilization and poor economy, is hardly matching correctly due to the volatility and low energy density of wind. The usage of energy storage can mitigate wind power fluctuations and reduce the requirement of out-delivery transmission capacity, but facing the issue of energy storage cost recovery. Therefore, it is necessary to optimize the allocation of energy storage while considering the problem… More >

Miaocao Wang¹, Yuhua Huang¹, Jinming Li¹, Ling Xu², Fulong Zhu^1,*

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 743-759, 2021, DOI:10.32604/cmc.2021.016372

Abstract From an ingot to a wafer then to a die, wafer thinning plays an important role in the semiconductor industry. To reveal the material removal mechanism of semiconductor at nanoscale, molecular dynamics has been widely used to investigate the grinding process. However, most simulation analyses were conducted with a single phase space trajectory, which is stochastic and subjective. In this paper, the stress field in wafer thinning simulations of 4H-SiC was obtained from 50 trajectories with spatial averaging and phase space averaging. The spatial averaging was conducted on a uniform spatial grid for each trajectory. A variable named mask was… More >

Yingya Guo^1,2,3,7, Kai Huang¹, Cheng Hu^4,*, Jiangyuan Yao⁵, Siyu Zhou⁶

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 655-670, 2021, DOI:10.32604/cmc.2021.016364

Abstract The emergence of Segment Routing (SR) provides a novel routing paradigm that uses a routing technique called source packet routing. In SR architecture, the paths that the packets choose to route on are indicated at the ingress router. Compared with shortest-path-based routing in traditional distributed routing protocols, SR can realize a flexible routing by implementing an arbitrary flow splitting at the ingress router. Despite the advantages of SR, it may be difficult to update the existing IP network to a full SR deployed network, for economical and technical reasons. Updating partial of the traditional IP network to the SR network,… More >

Muhammad Attique Khan¹, Tallha Akram², Muhammad Sharif¹, Seifedine Kadry³, Yunyoung Nam^4,*

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1041-1064, 2021, DOI:10.32604/cmc.2021.016307

Abstract In this work, we propose a new, fully automated system for multiclass skin lesion localization and classification using deep learning. The main challenge is to address the problem of imbalanced data classes, found in HAM10000, ISBI2018, and ISBI2019 datasets. Initially, we consider a pre-trained deep neural network model, DarkeNet19, and fine-tune the parameters of third convolutional layer to generate the image gradients. All the visualized images are fused using a High-Frequency approach along with Multilayered Feed-Forward Neural Network (HFaFFNN). The resultant image is further enhanced by employing a log-opening based activation function to generate a localized binary image. Later, two… More >

Kun Ding¹, Shanshan Liu¹, Yuhao Zhang², Hui Zhang¹, Xiaoxiong Zhang^1,*, Tongtong Wu^2,3, Xiaolei Zhou¹

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 377-389, 2021, DOI:10.32604/cmc.2021.016301

Abstract The joint extraction of entities and their relations from certain texts plays a significant role in most natural language processes. For entity and relation extraction in a specific domain, we propose a hybrid neural framework consisting of two parts: a span-based model and a graph-based model. The span-based model can tackle overlapping problems compared with BILOU methods, whereas the graph-based model treats relation prediction as graph classification. Our main contribution is to incorporate external lexical and syntactic knowledge of a specific domain, such as domain dictionaries and dependency structures from texts, into end-to-end neural models. We conducted extensive experiments on… More >

Fawaz Waselallah Alsaade¹, Theyazn H. H. Aldhyani^2,*, Mosleh Hmoud Al-Adhaileh³

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 805-819, 2021, DOI:10.32604/cmc.2021.016264

Abstract The COVID-19 pandemic poses an additional serious public health threat due to little or no pre-existing human immunity, and developing a system to identify COVID-19 in its early stages will save millions of lives. This study applied support vector machine (SVM), k-nearest neighbor (K-NN) and deep learning convolutional neural network (CNN) algorithms to classify and detect COVID-19 using chest X-ray radiographs. To test the proposed system, chest X-ray radiographs and CT images were collected from different standard databases, which contained 95 normal images, 140 COVID-19 images and 10 SARS images. Two scenarios were considered to develop a system for predicting… More >