Ali M. Aseere^1,*, Ayodele Lasisi²

Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 683-699, 2021, DOI:10.32604/iasc.2021.014811

Abstract In the absence of fast and adequate measures to combat them, life-threatening diseases are catastrophic to human health. Computational intelligent algorithms characterized by their adaptability, robustness, diversity, and recognition abilities allow for the diagnosis of medical diseases. This enhances the decision-making process of physicians. The objective is to predict and classify diseases accurately. In this paper, we proposed a multi-agent stacked ensemble classifier based on a vaguely quantified rough set, simple logistic algorithm, sequential minimal optimization (SMO), and JRip. The vaguely quantified rough set (VQRS) is used for feature selection and eradicating noise in the More >

Haikel Alhichri^*

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3581-3599, 2021, DOI:10.32604/cmc.2021.015399

Abstract In January 2020, the World Health Organization declared a global health emergency concerning the spread of a new coronavirus disease, which was later named COVID-19. Early and fast diagnosis and isolation of COVID-19 patients have proven to be instrumental in limiting the spread of the disease. Computed tomography (CT) is a promising imaging method for fast diagnosis of COVID-19. In this study, we develop a unique preprocessing step to resize CT chest images to a fixed size (256 × 256 pixels) that preserves the aspect ratio and reduces image loss. Then, we present a deep… More >

Neelum Noreen^1,*, Sellapan Palaniappan¹, Abdul Qayyum², Iftikhar Ahmad³, Madini O. Alassafi³

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3967-3982, 2021, DOI:10.32604/cmc.2021.014158

Abstract Brain tumors are life-threatening for adults and children. However, accurate and timely detection can save lives. This study focuses on three different types of brain tumors: Glioma, meningioma, and pituitary tumors. Many studies describe the analysis and classification of brain tumors, but few have looked at the problem of feature engineering. Methods are needed to overcome the drawbacks of manual diagnosis and conventional feature-engineering techniques. An automatic diagnostic system is thus necessary to extract features and classify brain tumors accurately. While progress continues to be made, the automatic diagnoses of brain tumors still face challenges… More >

Jinping Zhang^1,2, Youlai Jin¹, Bin Sun^1,*, Yuping Han³, Yang Hong⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.126, No.2, pp. 755-770, 2021, DOI:10.32604/cmes.2021.012686

Abstract The complex nonlinear and non-stationary features exhibited in hydrologic sequences make hydrological analysis and forecasting difficult. Currently, some hydrologists employ the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method, a new time-frequency analysis method based on the empirical mode decomposition (EMD) algorithm, to decompose non-stationary raw data in order to obtain relatively stationary components for further study. However, the endpoint effect in CEEMDAN is often neglected, which can lead to decomposition errors that reduce the accuracy of the research results. In this study, we processed an original runoff sequence using the radial basis… More >

Yixuan Wang¹, Liping Yuan^2,3, Harish Garg⁴, Ali Bagherinia⁵, Parvïn Hamïd^6,7,8,*, Kim-Hung Pho⁹, Zulkefli Mansor¹⁰

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 369-392, 2021, DOI:10.32604/cmc.2021.012850

Abstract In order to improve performance and robustness of clustering, it is proposed to generate and aggregate a number of primary clusters via clustering ensemble technique. Fuzzy clustering ensemble approaches attempt to improve the performance of fuzzy clustering tasks. However, in these approaches, cluster (or clustering) reliability has not paid much attention to. Ignoring cluster (or clustering) reliability makes these approaches weak in dealing with low-quality base clustering methods. In this paper, we have utilized cluster unreliability estimation and local weighting strategy to propose a new fuzzy clustering ensemble method which has introduced Reliability Based weighted… More >

V. Priya¹, I. Sumaiya Thaseen¹, Thippa Reddy Gadekallu¹, Mohamed K. Aboudaif^2,*, Emad Abouel Nasr³

CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2457-2470, 2021, DOI:10.32604/cmc.2021.013852

Abstract Generally, the risks associated with malicious threats are increasing for the Internet of Things (IoT) and its related applications due to dependency on the Internet and the minimal resource availability of IoT devices. Thus, anomaly-based intrusion detection models for IoT networks are vital. Distinct detection methodologies need to be developed for the Industrial Internet of Things (IIoT) network as threat detection is a significant expectation of stakeholders. Machine learning approaches are considered to be evolving techniques that learn with experience, and such approaches have resulted in superior performance in various applications, such as pattern recognition,… More >

Rubina Aslam^1,*, Manzoor Illahi Tamimy¹, Waqar Aslam²

Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1233-1249, 2020, DOI:10.32604/iasc.2020.013086

Abstract Machine Learning (ML) has revolutionized intelligent systems that range from self-driving automobiles, search engines, business/market analysis, fraud detection, network intrusion investigation, and medical diagnosis. Classification lies at the core of Machine Learning and Multi-label Classification (MLC) is the closest to real-life problems related to heuristics. It is a type of classification problem where multiple labels or classes can be assigned to more than one instance simultaneously. The level of complexity in MLC is increased by factors such as data imbalance, high dimensionality, label correlations, and noise. Conventional MLC techniques such as ensembles-based approaches, Multi-label Stacking,… More >

Ji Qian¹, Fang Liu^2,*, Donghui Li³, Xin Jin⁴, Feng Li⁴

Journal of Cyber Security, Vol.2, No.4, pp. 157-166, 2020, DOI:10.32604/jcs.2020.011169

Abstract Anomaly detection using KPI (Key Performance Indicator) is critical for Internet-based services to maintain high service availability. However, given the velocity, volume, and diversified nature of monitoring data, it is difficult to obtain enough labelled data to build an accurate anomaly detection model for using supervised machine leaning methods. In this paper, we propose an automatic and generic transfer learning strategy: Detecting anomalies on a new KPI by using pretrained model on existing selected labelled KPI. Our approach, called KADT (KPI Anomaly Detection based on Transfer Learning), integrates KPI clustering and model pretrained techniques. KPI More >

Lewis Nkenyereye¹, Bayu Adhi Tama², Sunghoon Lim^3,*

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 2217-2227, 2021, DOI:10.32604/cmc.2020.012432

Abstract An anomaly-based intrusion detection system (A-IDS) provides a critical aspect in a modern computing infrastructure since new types of attacks can be discovered. It prevalently utilizes several machine learning algorithms (ML) for detecting and classifying network traffic. To date, lots of algorithms have been proposed to improve the detection performance of A-IDS, either using individual or ensemble learners. In particular, ensemble learners have shown remarkable performance over individual learners in many applications, including in cybersecurity domain. However, most existing works still suffer from unsatisfactory results due to improper ensemble design. The aim of this study More >

Yunfan Ye¹, Fang Liu^{1, *}, Shan Zhao², Wanting Hu³, Zhiyao Liang⁴

CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1361-1372, 2020, DOI:10.32604/cmc.2020.011632

Abstract By efficiently and accurately predicting the adoptability of pets, shelters and rescuers can be positively guided on improving attraction of pet profiles, reducing animal suffering and euthanization. Previous prediction methods usually only used a single type of content for training. However, many pets contain not only textual content, but also images. To make full use of textual and visual information, this paper proposed a novel method to process pets that contain multimodal information. We employed several CNN (Convolutional Neural Network) based models and other methods to extract features from images and texts to obtain the More >