Jiongzhi Yang, Srivatsa Harish, Candy Li, Hengduo Zhao, Brittney Antous, Pinar Acar^*

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1285-1302, 2021, DOI:10.32604/cmc.2021.016829

Abstract This paper presents a de-novo computational design method driven by deep reinforcement learning to achieve reliable predictions and optimum properties for periodic microstructures. With recent developments in 3-D printing, microstructures can have complex geometries and material phases fabricated to achieve targeted mechanical performance. These material property enhancements are promising in improving the mechanical, thermal, and dynamic performance in multiple engineering systems, ranging from energy harvesting applications to spacecraft components. The study investigates a novel and efficient computational framework that integrates deep reinforcement learning algorithms into finite element-based material simulations to quantitatively model and design 3-D printed periodic microstructures. These algorithms… More >

Shabib Aftab^1,2, Saad Alanazi³, Munir Ahmad¹, Muhammad Adnan Khan^4,*, Areej Fatima⁵, Nouh Sabri Elmitwally^3,6

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1341-1357, 2021, DOI:10.32604/cmc.2021.016814

Abstract Diabetes mellitus, generally known as diabetes, is one of the most common diseases worldwide. It is a metabolic disease characterized by insulin deficiency, or glucose (blood sugar) levels that exceed 200 mg/dL (11.1 ml/L) for prolonged periods, and may lead to death if left uncontrolled by medication or insulin injections. Diabetes is categorized into two main types—type 1 and type 2—both of which feature glucose levels above “normal,” defined as 140 mg/dL. Diabetes is triggered by malfunction of the pancreas, which releases insulin, a natural hormone responsible for controlling glucose levels in blood cells. Diagnosis and comprehensive analysis of this… More >

Bozhi Xu¹, Jiarui Liu¹, Jifan Liang¹, Wei Lu^1,*, Yue Zhang²

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1375-1388, 2021, DOI:10.32604/cmc.2021.016760

Abstract In recent years, with the rapid development of deep learning technologies, some neural network models have been applied to generate fake media. DeepFakes, a deep learning based forgery technology, can tamper with the face easily and generate fake videos that are difficult to be distinguished by human eyes. The spread of face manipulation videos is very easy to bring fake information. Therefore, it is important to develop effective detection methods to verify the authenticity of the videos. Due to that it is still challenging for current forgery technologies to generate all facial details and the blending operations are used in… More >

Mohammed H. Alsharif¹, Abu Jahid², Mahmoud A. Albreem³, Peerapong Uthansakul^4,*, Jamel Nebhen⁵, Khalid Yahya⁶

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1321-1339, 2021, DOI:10.32604/cmc.2021.016738

Abstract The design of green cellular networking according to the traffic arrivals has the capability to reduce the overall energy consumption to a cluster in a cost-effective way. The cell zooming approach has appealed much attention that adaptively offloads the BS load demands adjusting the transmit power based on the traffic intensity and green energy availability. Besides, the researchers are focused on implementing renewable energy resources, which are considered the most attractive practices in designing energy-efficient wireless networks over the long term in a cost-efficient way in the existing infrastructure. The utilization of available solar can be adapted to acquire cost-effective… More >

Yi-Ming Huang¹, Xiao-Yu Li^1,*, Yi-Xuan Zhu¹, Hang Lei¹, Qing-Sheng Zhu², Shan Yang³

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 789-803, 2021, DOI:10.32604/cmc.2021.016663

Abstract Quantum machine learning (QML) is a rapidly rising research field that incorporates ideas from quantum computing and machine learning to develop emerging tools for scientific research and improving data processing. How to efficiently control or manipulate the quantum system is a fundamental and vexing problem in quantum computing. It can be described as learning or approximating a unitary operator. Since the success of the hybrid-based quantum machine learning model proposed in recent years, we investigate to apply the techniques from QML to tackle this problem. Based on the Choi–Jamiołkowski isomorphism in quantum computing, we transfer the original problem of learning… More >

Yoosef B. Abushark¹, Asif Irshad Khan^1,*, Fawaz Jaber Alsolami¹, Abdulmohsen Almalawi¹, Md Mottahir Alam², Alka Agrawal³, Rajeev Kumar^3,4, Raees Ahmad Khan³

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1203-1218, 2021, DOI:10.32604/cmc.2021.016610

Abstract Most of the security strategies today are primarily designed to provide security protection, rather than to solve one of the basic security issues related to adequate software product architecture. Several models, frameworks and methodologies have been introduced by the researchers for a secure and sustainable software development life cycle. Therefore it is important to assess the usability of the popular security requirements engineering (SRE) approaches. A significant factor in the management and handling of successful security requirements is the assessment of security requirements engineering method performance. This assessment will allow changes to the engineering process of security requirements. The consistency… More >

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 >