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  • Open Access

    ARTICLE

    Epilepsy Radiology Reports Classification Using Deep Learning Networks

    Sengul Bayrak1,2, Eylem Yucel2,*, Hidayet Takci3

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3589-3607, 2022, DOI:10.32604/cmc.2022.018742

    Abstract The automatic and accurate classification of Magnetic Resonance Imaging (MRI) radiology report is essential for the analysis and interpretation epilepsy and non-epilepsy. Since the majority of MRI radiology reports are unstructured, the manual information extraction is time-consuming and requires specific expertise. In this paper, a comprehensive method is proposed to classify epilepsy and non-epilepsy real brain MRI radiology text reports automatically. This method combines the Natural Language Processing technique and statistical Machine Learning methods. 122 real MRI radiology text reports (97 epilepsy, 25 non-epilepsy) are studied by our proposed method which consists of the following steps: (i) for a given… More >

  • Open Access

    ARTICLE

    Effectiveness Assessment of the Search-Based Statistical Structural Testing

    Yang Shi*, Xiaoyu Song, Marek Perkowski, Fu Li

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2191-2207, 2022, DOI:10.32604/cmc.2022.018718

    Abstract Search-based statistical structural testing (SBSST) is a promising technique that uses automated search to construct input distributions for statistical structural testing. It has been proved that a simple search algorithm, for example, the hill-climber is able to optimize an input distribution. However, due to the noisy fitness estimation of the minimum triggering probability among all cover elements (Tri-Low-Bound), the existing approach does not show a satisfactory efficiency. Constructing input distributions to satisfy the Tri-Low-Bound criterion requires an extensive computation time. Tri-Low-Bound is considered a strong criterion, and it is demonstrated to sustain a high fault-detecting ability. This article tries to… More >

  • Open Access

    ARTICLE

    A Hybrid Approach for COVID-19 Detection Using Biogeography-Based Optimization and Deep Learning

    K. Venkatachalam1, Siuly Siuly2, M. Vinoth Kumar3, Praveen Lalwani1, Manas Kumar Mishra1, Enamul Kabir4,*

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3717-3732, 2022, DOI:10.32604/cmc.2022.018487

    Abstract The COVID-19 pandemic has created a major challenge for countries all over the world and has placed tremendous pressure on their public health care services. An early diagnosis of COVID-19 may reduce the impact of the coronavirus. To achieve this objective, modern computation methods, such as deep learning, may be applied. In this study, a computational model involving deep learning and biogeography-based optimization (BBO) for early detection and management of COVID-19 is introduced. Specifically, BBO is used for the layer selection process in the proposed convolutional neural network (CNN). The computational model accepts images, such as CT scans, X-rays, positron… More >

  • Open Access

    ARTICLE

    Image Segmentation Based on Block Level and Hybrid Directional Local Extrema

    Ghanshyam Raghuwanshi1, Yogesh Gupta2, Deepak Sinwar1, Dilbag Singh3, Usman Tariq4, Muhammad Attique5, Kuntha Pin6, Yunyoung Nam7,*

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3939-3954, 2022, DOI:10.32604/cmc.2022.018423

    Abstract In the recent decade, the digitalization of various tasks has added great flexibility to human lifestyle and has changed daily routine activities of communities. Image segmentation is a key step in digitalization. Segmentation plays a key role in almost all areas of image processing, and various approaches have been proposed for image segmentation. In this paper, a novel approach is proposed for image segmentation using a nonuniform adaptive strategy. Region-based image segmentation along with a directional binary pattern generated a better segmented image. An adaptive mask of 8 × 8 was circulated over the pixels whose bit value was 1 in the… More >

  • Open Access

    ARTICLE

    Human Gait Recognition Using Deep Learning and Improved Ant Colony Optimization

    Awais Khan1, Muhammad Attique Khan1, Muhammad Younus Javed1, Majed Alhaisoni2, Usman Tariq3, Seifedine Kadry4, Jung-In Choi5, Yunyoung Nam6,*

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2113-2130, 2022, DOI:10.32604/cmc.2022.018270

    Abstract Human gait recognition (HGR) has received a lot of attention in the last decade as an alternative biometric technique. The main challenges in gait recognition are the change in in-person view angle and covariant factors. The major covariant factors are walking while carrying a bag and walking while wearing a coat. Deep learning is a new machine learning technique that is gaining popularity. Many techniques for HGR based on deep learning are presented in the literature. The requirement of an efficient framework is always required for correct and quick gait recognition. We proposed a fully automated deep learning and improved… More >

  • Open Access

    ARTICLE

    Process Modelling and Experimental Analysis of Optimal Specimen Selection in Organic CMCs

    P. V. Rajesh1, Kanak Kalita2,*, Xiao-Zhi Gao3

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2415-2433, 2022, DOI:10.32604/cmc.2022.018247

    Abstract Bone grafting is a surgical restructuring procedure of replacing broken bones and reconstructing missing bone pieces so that complex bone fractures can be repaired to avoid any serious health risk as well as permanent bone disfiguration. Normally, human bones tend to regenerate and heal completely from fracture. But it needs a small scaffold to provide the necessary space to grow. Bone implants allow a broken bone to grow seamlessly. Traditionally, non-corrosive metal alloys are used for fixing broken bones. A metal plate is fastened between two ends of broken bones to join them. However, issues like high weight, high cost,… More >

  • Open Access

    ARTICLE

    Anomaly Based Camera Prioritization in Large Scale Surveillance Networks

    Altaf Hussain1,2, Khan Muhammad1, Hayat Ullah1, Amin Ullah1,4, Ali Shariq Imran3, Mi Young Lee1, Seungmin Rho1, Muhammad Sajjad2,3,*

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2171-2190, 2022, DOI:10.32604/cmc.2022.018181

    Abstract Digital surveillance systems are ubiquitous and continuously generate massive amounts of data, and manual monitoring is required in order to recognise human activities in public areas. Intelligent surveillance systems that can automatically ide.pngy normal and abnormal activities are highly desirable, as these would allow for efficient monitoring by selecting only those camera feeds in which abnormal activities are occurring. This paper proposes an energy-efficient camera prioritisation framework that intelligently adjusts the priority of cameras in a vast surveillance network using feedback from the activity recognition system. The proposed system addresses the limitations of existing manual monitoring surveillance systems using a… More >

  • Open Access

    ARTICLE

    Designing Bayesian New Group Chain Sampling Plan For Quality Regions

    Waqar Hafeez1, Nazrina Aziz1,2,*, Zakiyah Zain1,2, Nur Azulia Kamarudin1

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 4185-4198, 2022, DOI:10.32604/cmc.2022.018146

    Abstract Acceptance sampling is a well-established statistical technique in quality assurance. Acceptance sampling is used to decide, acceptance or rejection of a lot based on the inspection of its random sample. Experts concur that the Bayesian approach is the best approach to make a correct decision, when historical knowledge is available. This paper suggests a Bayesian new group chain sampling plan (BNGChSP) to estimate average probability of acceptance. Binomial distribution function is used to differentiate between defective and non-defective products. Beta distribution is considered as a suitable prior distribution. Derivation is completed for the estimation of the average proportion of defectives.… More >

  • Open Access

    ARTICLE

    COVID19 Outbreak: A Hierarchical Framework for User Sentiment Analysis

    Ahmed F. Ibrahim1, M. Hassaballah2, Abdelmgeid A. Ali3, Yunyoung Nam4,*, Ibrahim A. Ibrahim3

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2507-2524, 2022, DOI:10.32604/cmc.2022.018131

    Abstract Social networking sites in the most modernized world are flooded with large data volumes. Extracting the sentiment polarity of important aspects is necessary; as it helps to determine people’s opinions through what they write. The Coronavirus pandemic has invaded the world and been given a mention in the social media on a large scale. In a very short period of time, tweets indicate unpredicted increase of coronavirus. They reflect people’s opinions and thoughts with regard to coronavirus and its impact on society. The research community has been interested in discovering the hidden relationships from short texts such as Twitter and… More >

  • Open Access

    ARTICLE

    A Multilevel Deep Feature Selection Framework for Diabetic Retinopathy Image Classification

    Farrukh Zia1, Isma Irum1, Nadia Nawaz Qadri1, Yunyoung Nam2,*, Kiran Khurshid3, Muhammad Ali1, Imran Ashraf4, Muhammad Attique Khan4

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2261-2276, 2022, DOI:10.32604/cmc.2022.017820

    Abstract Diabetes or Diabetes Mellitus (DM) is the upset that happens due to high glucose level within the body. With the passage of time, this polygenic disease creates eye deficiency referred to as Diabetic Retinopathy (DR) which can cause a major loss of vision. The symptoms typically originate within the retinal space square in the form of enlarged veins, liquid dribble, exudates, haemorrhages and small scale aneurysms. In current therapeutic science, pictures are the key device for an exact finding of patients’ illness. Meanwhile, an assessment of new medicinal symbolisms stays complex. Recently, Computer Vision (CV) with deep neural networks can… More >

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