Ali M. Aseere^1,*, Ayodele Lasisi²

Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 683-699, 2021, DOI:10.32604/iasc.2021.014811 - 01 March 2021

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 >

Heyam H. Al-Baity^*, Nourah Al-Mutlaq

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3089-3106, 2021, DOI:10.32604/cmc.2021.015291 - 01 March 2021

Abstract Machine-learning algorithms have been widely used in breast cancer diagnosis to help pathologists and physicians in the decision-making process. However, the high dimensionality of genetic data makes the classification process a challenging task. In this paper, we propose a new optimized wrapper gene selection method that is based on a nature-inspired algorithm (simulated annealing (SA)), which will help select the most informative genes for breast cancer prediction. These optimal genes will then be used to train the classifier to improve its accuracy and efficiency. Three supervised machine-learning algorithms, namely, the support vector machine, the decision… More >

Muhammad Wajeeh Uz Zaman¹, Yaser Hafeez¹, Shariq Hussain², Haris Anwaar³, Shunkun Yang^4,*, Sadia Ali¹, Aaqif Afzaal Abbasi², Oh-Young Song⁵

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2373-2391, 2021, DOI:10.32604/cmc.2021.015112 - 05 February 2021

Abstract The rapid growth in software demand incentivizes software development organizations to develop exclusive software for their customers worldwide. This problem is addressed by the software development industry by software product line (SPL) practices that employ feature models. However, optimal feature selection based on user requirements is a challenging task. Thus, there is a requirement to resolve the challenges of software development, to increase satisfaction and maintain high product quality, for massive customer needs within limited resources. In this work, we propose a recommender system for the development team and clients to increase productivity and quality… More >

Sally M. Elghamrawy¹, Aboul Ella Hassnien^2,*, Vaclav Snasel³

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2353-2371, 2021, DOI:10.32604/cmc.2021.014767 - 05 February 2021

Abstract Detecting COVID-19 cases as early as possible became a critical issue that must be addressed to avoid the pandemic’s additional spread and early provide the appropriate treatment to the affected patients. This study aimed to develop a COVID-19 diagnosis and prediction (AIMDP) model that could identify patients with COVID-19 and distinguish it from other viral pneumonia signs detected in chest computed tomography (CT) scans. The proposed system uses convolutional neural networks (CNNs) as a deep learning technology to process hundreds of CT chest scan images and speeds up COVID-19 case prediction to facilitate its containment.… More >

Yasemin Gültepe^*

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2015-2028, 2021, DOI:10.32604/cmc.2021.014631 - 05 February 2021

Abstract In 2018, 1.76 million people worldwide died of lung cancer. Most of these deaths are due to late diagnosis, and early-stage diagnosis significantly increases the likelihood of a successful treatment for lung cancer. Machine learning is a branch of artificial intelligence that allows computers to quickly identify patterns within complex and large datasets by learning from existing data. Machine-learning techniques have been improving rapidly and are increasingly used by medical professionals for the successful classification and diagnosis of early-stage disease. They are widely used in cancer diagnosis. In particular, machine learning has been used in… More >

Rami Sihwail^*, Khairuddin Omar, Khairul Akram Zainol Ariffin

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2301-2320, 2021, DOI:10.32604/cmc.2021.014510 - 05 February 2021

Abstract The study of malware behaviors, over the last years, has received tremendous attention from researchers for the purpose of reducing malware risks. Most of the investigating experiments are performed using either static analysis or behavior analysis. However, recent studies have shown that both analyses are vulnerable to modern malware files that use several techniques to avoid analysis and detection. Therefore, extracted features could be meaningless and a distraction for malware analysts. However, the volatile memory can expose useful information about malware behaviors and characteristics. In addition, memory analysis is capable of detecting unconventional malware, such… More >

Shichao Wang, Yu Xue^*, Weiwei Jia

Journal on Big Data, Vol.3, No.1, pp. 1-9, 2021, DOI:10.32604/jbd.2021.010364 - 25 January 2021

Abstract In many fields such as signal processing, machine learning, pattern recognition and data mining, it is common practice to process datasets containing huge numbers of features. In such cases, Feature Selection (FS) is often involved. Meanwhile, owing to their excellent global search ability, evolutionary computation techniques have been widely employed to the FS. So, as a powerful global search method and calculation fast than other EC algorithms, PSO can solve features selection problems well. However, when facing a large number of feature selection, the efficiency of PSO drops significantly. Therefore, plenty of works have been… More >

Jalal Sadoon Hameed Al-bayati^1,*, Burak Berk Üstündağ²

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1479-1496, 2021, DOI:10.32604/cmc.2020.013329 - 26 November 2020

Abstract The Fused Modified Grasshopper Optimization Algorithm has been proposed, which selects the most specific feature sets from images of the disease of plant leaves. The Proposed algorithm ensures the detection of diseases during the early stages of the diagnosis of leaf disease by farmers and, finally, the crop needed to be controlled by farmers to ensure the survival and protection of plants. In this study, a novel approach has been suggested based on the standard optimization algorithm for grasshopper and the selection of features. Leaf conditions in plants are a major factor in reducing crop… More >

Inzamam Mashood Nasir¹, Asima Bibi², Jamal Hussain Shah², Muhammad Attique Khan¹, Muhammad Sharif², Khalid Iqbal³, Yunyoung Nam⁴, Seifedine Kadry^5,*

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1949-1962, 2021, DOI:10.32604/cmc.2020.012945 - 26 November 2020

Abstract Agriculture is essential for the economy and plant disease must be minimized. Early recognition of problems is important, but the manual inspection is slow, error-prone, and has high manpower and time requirements. Artificial intelligence can be used to extract fruit color, shape, or texture data, thus aiding the detection of infections. Recently, the convolutional neural network (CNN) techniques show a massive success for image classification tasks. CNN extracts more detailed features and can work efficiently with large datasets. In this work, we used a combined deep neural network and contour feature-based approach to classify fruits… More >

Xingxing Cao¹, Liming Jiang^1,*, Xiaoliang Wang¹, Frank Jiang²

Journal of Information Hiding and Privacy Protection, Vol.2, No.4, pp. 187-197, 2020, DOI:10.32604/jihpp.2020.016243 - 07 January 2021

Abstract Due to the lack of consideration of movement behavior information other than time and location perception in current location prediction methods, the movement characteristics of trajectory data cannot be well expressed, which in turn affects the accuracy of the prediction results. First, a new trajectory data expression method by associating the movement behavior information is given. The pre-association method is used to model the movement behavior information according to the individual movement behavior features and the group movement behavior features extracted from the trajectory sequence and the region. The movement behavior features based on pre-association More >