Ling Tan¹, Yun Tan², Jiaohua Qin², Hao Tang^1,*, Xuyu Xiang², Dongshu Xie¹, Neal N. Xiong³

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2583-2598, 2021, DOI:10.32604/cmc.2021.017779

Abstract During the COVID-19 pandemic, the treatment of aortic dissection has faced additional challenges. The necessary medical resources are in serious shortage, and the preoperative waiting time has been significantly prolonged due to the requirement to test for COVID-19 infection. In this work, we focus on the risk prediction of aortic dissection surgery under the influence of the COVID-19 pandemic. A general scheme of medical data processing is proposed, which includes five modules, namely problem definition, data preprocessing, data mining, result analysis, and knowledge application. Based on effective data preprocessing, feature analysis and boosting trees, our proposed fusion decision model can… More >

Omar Almutiry¹, Muhammad Ayaz², Tariq Sadad³, Ikram Ullah Lali⁴, Awais Mahmood^1,*, Najam Ul Hassan⁵, Habib Dhahri¹

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1915-1926, 2021, DOI:10.32604/cmc.2021.017702

Abstract Guava is one of the most important fruits in Pakistan, and is gradually boosting the economy of Pakistan. Guava production can be interrupted due to different diseases, such as anthracnose, algal spot, fruit fly, styler end rot and canker. These diseases are usually detected and identified by visual observation, thus automatic detection is required to assist formers. In this research, a new technique was created to detect guava plant diseases using image processing techniques and computer vision. An automated system is developed to support farmers to identify major diseases in guava. We collected healthy and unhealthy images of different guava… More >

K. K. Thyagharajan, I. Kiruba Raji^*

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2061-2076, 2021, DOI:10.32604/cmc.2021.017591

Abstract This paper proposes an approach to detecting diseases in neem leaf that uses a Fuzzy-Higher Order Biologically Inspired Neuron Model (F-HOBINM) and adaptive neuro classifier (ANFIS). India exports USD 0.28-million worth of neem leaf to the UK, USA, UAE, and Europe in the form of dried leaves and powder, both of which help reduce diabetes-related issues, cardiovascular problems, and eye disorders. Diagnosing neem leaf disease is difficult through visual interpretation, owing to similarity in their color and texture patterns. The most common diseases include bacterial blight, Colletotrichum and Alternaria leaf spot, blight, damping-off, powdery mildew, Pseudocercospora leaf spot, leaf web… More >

Hemant Petwal^*, Rinkle Rani

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2563-2582, 2021, DOI:10.32604/cmc.2021.017548

Abstract In the healthcare system, a surgical team is a unit of experienced personnel who provide medical care to surgical patients during surgery. Selecting a surgical team is challenging for a multispecialty hospital as the performance of its members affects the efficiency and reliability of the hospital’s patient care. The effectiveness of a surgical team depends not only on its individual members but also on the coordination among them. In this paper, we addressed the challenges of surgical team selection faced by a multispecialty hospital and proposed a decision-making framework for selecting the optimal list of surgical teams for a given… More >

Faisal Mehmood¹, Shabir Ahmad^2,3, Israr Ullah¹, Faisal Jamil¹, DoHyeun Kim^1,*

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2231-2244, 2021, DOI:10.32604/cmc.2021.017528

Abstract The data being generated by the Internet of Things needs to be stored, monitored, and analyzed for maximum IoT resource utilization. Software Defined Networking has been extensively utilized to address issues such as heterogeneity and scalability. However, for small-scale IoT application, sometimes it is considered an inefficient approach. This paper proposes an alternate lightweight mechanism to the design and implementation of a dynamic virtual network based on user requirements. The key idea is to provide users a virtual interface that enables them to reconfigure the communication flow between the sensors and actuators at runtime. The throughput of the communication flow… More >

Lina Wang^1,2,*, Qixiang Zhang¹, Xiling Niu¹, Yongjun Ren³, Jinyue Xia⁴

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1765-1781, 2021, DOI:10.32604/cmc.2021.017516

Abstract Outlier detection is a key research area in data mining technologies, as outlier detection can identify data inconsistent within a data set. Outlier detection aims to find an abnormal data size from a large data size and has been applied in many fields including fraud detection, network intrusion detection, disaster prediction, medical diagnosis, public security, and image processing. While outlier detection has been widely applied in real systems, its effectiveness is challenged by higher dimensions and redundant data attributes, leading to detection errors and complicated calculations. The prevalence of mixed data is a current issue for outlier detection algorithms. An… More >

Guimin Hou¹, Jiaohua Qin^1,*, Xuyu Xiang¹, Yun Tan¹, Neal N. Xiong²

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1877-1891, 2021, DOI:10.32604/cmc.2021.017481

Abstract Medical image segmentation is an important application field of computer vision in medical image processing. Due to the close location and high similarity of different organs in medical images, the current segmentation algorithms have problems with mis-segmentation and poor edge segmentation. To address these challenges, we propose a medical image segmentation network (AF-Net) based on attention mechanism and feature fusion, which can effectively capture global information while focusing the network on the object area. In this approach, we add dual attention blocks (DA-block) to the backbone network, which comprises parallel channels and spatial attention branches, to adaptively calibrate and weigh… More >

Jagmeet Kaur¹, Shakeel Ahmed², Yogesh Kumar³, A. Alaboudi⁴, N. Z. Jhanjhi⁵, Muhammad Fazal Ijaz^6,*

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2617-2633, 2021, DOI:10.32604/cmc.2021.017470

Abstract There has been an explosion of cloud services as organizations take advantage of their continuity, predictability, as well as quality of service and it raises the concern about latency, energy-efficiency, and security. This increase in demand requires new configurations of networks, products, and service operators. For this purpose, the software-defined network is an efficient technology that enables to support the future network functions along with the intelligent applications and packet optimization. This work analyzes the offline cloud scenario in which machines are efficiently deployed and scheduled for user processing requests. Performance is evaluated in terms of reducing bandwidth, task execution… More >

K. Muthumayil¹, S. Manikandan², S. Srinivasan³, José Escorcia-Gutierrez^4,*, Margarita Gamarra⁵, Romany F. Mansour⁶

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2031-2044, 2021, DOI:10.32604/cmc.2021.017116

Abstract White Blood Cell (WBC) cancer or leukemia is one of the serious cancers that threaten the existence of human beings. In spite of its prevalence and serious consequences, it is mostly diagnosed through manual practices. The risks of inappropriate, sub-standard and wrong or biased diagnosis are high in manual methods. So, there is a need exists for automatic diagnosis and classification method that can replace the manual process. Leukemia is mainly classified into acute and chronic types. The current research work proposed a computer-based application to classify the disease. In the feature extraction stage, we use excellent physical properties to… More >

Dengyong Zhang^1,2, Jiawei Hu^1,2, Feng Li^1,2,*, Xiangling Ding³, Arun Kumar Sangaiah⁴, Victor S. Sheng⁵

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1503-1517, 2021, DOI:10.32604/cmc.2021.017089

Abstract In the past several years, remarkable achievements have been made in the field of object detection. Although performance is generally improving, the accuracy of small object detection remains low compared with that of large object detection. In addition, localization misalignment issues are common for small objects, as seen in GoogLeNets and residual networks (ResNets). To address this problem, we propose an improved region-based fully convolutional network (R-FCN). The presented technique improves detection accuracy and eliminates localization misalignment by replacing position-sensitive region of interest (PS-RoI) pooling with position-sensitive precise region of interest (PS-Pr-RoI) pooling, which avoids coordinate quantization and directly calculates… More >