Farrukh Zia¹, Isma Irum¹, Nadia Nawaz Qadri¹, Yunyoung Nam^2,*, 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 >

Asif Mehmood¹, Muhammad Attique Khan², Usman Tariq³, Chang-Won Jeong⁴, Yunyoung Nam^5,*, Reham R. Mostafa⁶, Amira ElZeiny⁷

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 343-360, 2022, DOI:10.32604/cmc.2022.019250

Abstract Background—Human Gait Recognition (HGR) is an approach based on biometric and is being widely used for surveillance. HGR is adopted by researchers for the past several decades. Several factors are there that affect the system performance such as the walking variation due to clothes, a person carrying some luggage, variations in the view angle. Proposed—In this work, a new method is introduced to overcome different problems of HGR. A hybrid method is proposed or efficient HGR using deep learning and selection of best features. Four major steps are involved in this work-preprocessing of the video frames, manipulation of the pre-trained… More >

Jehyeok Rew, Hyungjoon Kim, Eenjun Hwang^*

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 801-817, 2021, DOI:10.32604/cmc.2021.017892

Abstract Objective and quantitative assessment of skin conditions is essential for cosmeceutical studies and research on skin aging and skin regeneration. Various handcraft-based image processing methods have been proposed to evaluate skin conditions objectively, but they have unavoidable disadvantages when used to analyze skin features accurately. This study proposes a hybrid segmentation scheme consisting of Deeplab v3+ with an Inception-ResNet-v2 backbone, LightGBM, and morphological processing (MP) to overcome the shortcomings of handcraft-based approaches. First, we apply Deeplab v3+ with an Inception-ResNet-v2 backbone for pixel segmentation of skin wrinkles and cells. Then, LightGBM and MP are used to enhance the pixel segmentation… More >

Yue Li¹, Guangquan Xu^2,3, Hequn Xian^1,*, Longlong Rao³, Jiangang Shi^4,*

Intelligent Automation & Soft Computing, Vol.25, No.3, pp. 637-647, 2019, DOI:10.31209/2019.100000118

Abstract In order to prevent the spread of Android malware and protect privacy information from being compromised, this study proposes a novel multidimensional hybrid features extraction and analysis method for Android malware detection. This method is based primarily on a multidimensional hybrid features vector by extracting the information of permission requests, API calls, and runtime behaviors. The innovation of this study is to extract greater amounts of static and dynamic features information and combine them, that renders the features vector for training completer and more comprehensive. In addition, the feature selection algorithm is used to further optimize the extracted information to… More >

Yang Yang^1,*, Jing Dong¹, Chao Fang², Ping Xie³, Na An³

CMES-Computer Modeling in Engineering & Sciences, Vol.123, No.3, pp. 1015-1031, 2020, DOI:10.32604/cmes.2020.09404

Abstract The development of cloud computing and virtualization technology has brought great challenges to the reliability of data center services. Data centers typically contain a large number of compute and storage nodes which may fail and affect the quality of service. Failure prediction is an important means of ensuring service availability. Predicting node failure in cloud-based data centers is challenging because the failure symptoms reflected have complex characteristics, and the distribution imbalance between the failure sample and the normal sample is widespread, resulting in inaccurate failure prediction. Targeting these challenges, this paper proposes a novel failure prediction method FP-STE (Failure Prediction… More >