Afzan Adam^1,*, Abdul Hadi Abd Rahman¹, Nor Samsiah Sani¹, Zaid Abdi Alkareem Alyessari¹, Nur Jumaadzan Zaleha Mamat², Basela Hasan³

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 761-777, 2021, DOI:10.32604/cmc.2021.014599

Abstract Boundary effect in digital pathology is a phenomenon where the tissue shapes of biopsy samples get distorted during the sampling process. The morphological pattern of an epithelial layer is greatly affected. Theoretically, the shape deformation model can normalise the distortions, but it needs a 2D image. Curvatures theory, on the other hand, is not yet tested on digital pathology images. Therefore, this work proposed a curvature detection to reduce the boundary effects and estimates the epithelial layer. The boundary effect on the tissue surfaces is normalised using the frequency of a curve deviates from being a straight line. The epithelial… More >

Nancy Awadallah Awad^*

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 979-990, 2021, DOI:10.32604/cmc.2021.014307

Abstract After the digital revolution, large quantities of data have been generated with time through various networks. The networks have made the process of data analysis very difficult by detecting attacks using suitable techniques. While Intrusion Detection Systems (IDSs) secure resources against threats, they still face challenges in improving detection accuracy, reducing false alarm rates, and detecting the unknown ones. This paper presents a framework to integrate data mining classification algorithms and association rules to implement network intrusion detection. Several experiments have been performed and evaluated to assess various machine learning classifiers based on the KDD99 intrusion dataset. Our study focuses… More >

Sulaiman Khan¹, Shah Nazir¹, Habib Ullah Khan^2,*

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 895-915, 2021, DOI:10.32604/cmc.2021.013878

Abstract During the last decade the emergence of Internet of Things (IoT) based applications inspired the world by providing state of the art solutions to many common problems. From traffic management systems to urban cities planning and development, IoT based home monitoring systems, and many other smart applications. Regardless of these facilities, most of these IoT based solutions are data driven and results in small accuracy values for smaller datasets. In order to address this problem, this paper presents deep learning based hybrid approach for the development of an IoT-based intelligent home security and appliance control system in the smart cities.… More >

Mavra Mehmood¹, Ember Ayub¹, Fahad Ahmad^1,6,*, Madallah Alruwaili², Ziyad A. Alrowaili³, Saad Alanazi², Mamoona Humayun², Muhammad Rizwan¹, Shahid Naseem⁴, Tahir Alyas⁵

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 641-657, 2021, DOI:10.32604/cmc.2021.013774

Abstract Clinical image processing plays a significant role in healthcare systems and is currently a widely used methodology. In carcinogenic diseases, time is crucial; thus, an image’s accurate analysis can help treat disease at an early stage. Ductal carcinoma in situ (DCIS) and lobular carcinoma in situ (LCIS) are common types of malignancies that affect both women and men. The number of cases of DCIS and LCIS has increased every year since 2002, while it still takes a considerable amount of time to recommend a controlling technique. Image processing is a powerful technique to analyze preprocessed images to retrieve useful information… More >

Choo-Yee Ting^1,*, Helmi Zakariah², Fadzilah Kamaludin², Darryl Lin-Wei Cheng¹, Nicholas Yu-Zhe Tan¹, Hui-Jia Yee²

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 835-848, 2021, DOI:10.32604/cmc.2021.013327

Abstract Ever since the COVID-19 pandemic started in Wuhan, China, much research work has been focusing on the clinical aspect of SARS-CoV-2. Researchers have been leveraging on various Artificial Intelligence techniques as an alternative to medical approach in understanding the virus. Limited studies have, however, reported on COVID-19 transmission pattern analysis, and using geography features for prediction of potential outbreak sites. Predicting the next most probable outbreak site is crucial, particularly for optimizing the planning of medical personnel and supply resources. To tackle the challenge, this work proposed distance-based similarity measures to predict the next most probable outbreak site together with… More >

Qianqian Li¹, Meng Li², Lei Guo^3,*, Zhen Zhang⁴

Journal of Information Hiding and Privacy Protection, Vol.2, No.4, pp. 199-205, 2020, DOI:10.32604/jihpp.2020.016299

Abstract On-site programming big data refers to the massive data generated in the process of software development with the characteristics of real-time, complexity and high-difficulty for processing. Therefore, data cleaning is essential for on-site programming big data. Duplicate data detection is an important step in data cleaning, which can save storage resources and enhance data consistency. Due to the insufficiency in traditional Sorted Neighborhood Method (SNM) and the difficulty of high-dimensional data detection, an optimized algorithm based on random forests with the dynamic and adaptive window size is proposed. The efficiency of the algorithm can be elevated by improving the method… More >

Peng Mei¹, Gangyi Ding¹, Qiankun Jin¹, Fuquan Zhang^2,*, Yeh-Cheng Chen³

Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 159-171, 2021, DOI:10.32604/iasc.2021.012813

Abstract Community structure is a key component in complex network systems. This paper aims to improve the effectiveness of community detection and community discovery in complex network systems by providing directions for the reconstruction and optimization of community structures to expand the application of intelligent optimization algorithms in community structures. First, deep learning algorithms and ant colony algorithms are used to elaborate the community detection and community discovery in complex networks. Next, we introduce the technology of transfer learning and propose an algorithm of deep self-encoder modeling based on transfer learning (DSEM-TL). The DSEM-TL algorithm’s indicators include normalized mutual information and… More >

Shumaila Shahzadi¹, Fahad Ahmad^1,*, Asma Basharat¹, Madallah Alruwaili², Saad Alanazi², Mamoona Humayun², Muhammad Rizwan¹, Shahid Naseem³

CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2723-2749, 2021, DOI:10.32604/cmc.2021.014594

Abstract With the rising demand for data access, network service providers face the challenge of growing their capital and operating costs while at the same time enhancing network capacity and meeting the increased demand for access. To increase efficacy of Software Defined Network (SDN) and Network Function Virtualization (NFV) framework, we need to eradicate network security configuration errors that may create vulnerabilities to affect overall efficiency, reduce network performance, and increase maintenance cost. The existing frameworks lack in security, and computer systems face few abnormalities, which prompts the need for different recognition and mitigation methods to keep the system in the… More >

Khairol Amali bin Ahmad¹, Mohammad Nazmul Hasan², Md. Sharif Hossen^2,*, Khaleel Ahmad³

CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 3075-3087, 2021, DOI:10.32604/cmc.2021.014436

Abstract Pocket Switched Networks (PSN) represent a particular remittent network for direct communication between the handheld mobile devices. Compared to traditional networks, there is no stable topology structure for PSN where the nodes observe the mobility model of human society. It is a kind of Delay Tolerant Networks (DTNs) that gives a description to circulate information among the network nodes by the way of taking the benefit of transferring nodes from one area to another. Considering its inception, there are several schemes for message routing in the infrastructure-less environment in which human mobility is only the best manner to exchange information.… More >

Hyun Kyu Shin¹, Si Woon Lee², Goo Pyo Hong³, Lee Sael², Sang Hyo Lee⁴, Ha Young Kim^5,*

CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2493-2507, 2021, DOI:10.32604/cmc.2021.014170

Abstract The demand for defect diagnoses is gradually gaining ground owing to the growing necessity to implement safe inspection methods to ensure the durability and quality of structures. However, conventional manpower-based inspection methods not only incur considerable cost and time, but also cause frequent disputes regarding defects owing to poor inspections. Therefore, the demand for an effective and efficient defect-diagnosis model for concrete structures is imminent, as the reduction in maintenance costs is significant from a long-term perspective. Thus, this paper proposes a deep learning-based image object-identification method to detect the defects of paint peeling, leakage peeling, and leakage traces that… More >