Open Access
ARTICLE
R. H. Aswathy1,*, P. Suresh1, Mohamed Yacin Sikkandar2, S. Abdel-Khalek3, Hesham Alhumyani4, Rashid A. Saeed4, Romany F. Mansour5
CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2097-2111, 2022, DOI:10.32604/cmc.2022.019790
Abstract In recent times, Internet of Things (IoT) and Cloud Computing (CC) paradigms are commonly employed in different healthcare applications. IoT gadgets generate huge volumes of patient data in healthcare domain, which can be examined on cloud over the available storage and computation resources in mobile gadgets. Chronic Kidney Disease (CKD) is one of the deadliest diseases that has high mortality rate across the globe. The current research work presents a novel IoT and cloud-based CKD diagnosis model called Flower Pollination Algorithm (FPA)-based Deep Neural Network (DNN) model abbreviated as FPA-DNN. The steps involved in the presented FPA-DNN model are data… More >
Open Access
ARTICLE
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
（This article belongs to this Special Issue: Recent Advances in Deep Learning, Information Fusion, and Features Selection for Video Surveillance Application)
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
Juan Pablo Villa-Pulgarin1, Anderson Alberto Ruales-Torres1,2, Daniel Arias-Garzón1, Mario Alejandro Bravo-Ortiz1, Harold Brayan Arteaga-Arteaga1, Alejandro Mora-Rubio1, Jesus Alejandro Alzate-Grisales1, Esteban Mercado-Ruiz1, M. Hassaballah3, Simon Orozco-Arias4,5, Oscar Cardona-Morales1, Reinel Tabares-Soto1,*
CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2131-2148, 2022, DOI:10.32604/cmc.2022.019529
（This article belongs to this Special Issue: Recent Advances in Deep Learning, Information Fusion, and Features Selection for Video Surveillance Application)
Abstract Skin cancer is one of the most severe diseases, and medical imaging is among the main tools for cancer diagnosis. The images provide information on the evolutionary stage, size, and location of tumor lesions. This paper focuses on the classification of skin lesion images considering a framework of four experiments to analyze the classification performance of Convolutional Neural Networks (CNNs) in distinguishing different skin lesions. The CNNs are based on transfer learning, taking advantage of ImageNet weights. Accordingly, in each experiment, different workflow stages are tested, including data augmentation and fine-tuning optimization. Three CNN models based on DenseNet-201, Inception-ResNet-V2, and… More >
Open Access
ARTICLE
Abdullah Musaed Alkhiari1, Shailendra Mishra2,*, Mohammed AlShehri1
CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2149-2169, 2022, DOI:10.32604/cmc.2022.019562
Abstract Smart Grid is a power grid that improves flexibility, reliability, and efficiency through smart meters. Due to extensive data exchange over the Internet, the smart grid faces many security challenges that have led to data loss, data compromise, and high power consumption. Moreover, the lack of hardware protection and physical attacks reduce the overall performance of the smart grid network. We proposed the BLIDSE model (Blockchain-based secure quantum key distribution and Intrusion Detection System in Edge Enables Smart Grid Network) to address these issues. The proposed model includes five phases: The first phase is blockchain-based secure user authentication, where all… More >
Open Access
ARTICLE
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
（This article belongs to this Special Issue: Recent Advances in Deep Learning, Information Fusion, and Features Selection for Video Surveillance Application)
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
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
Nasir Mahmud Khokhar1, Muhammad Nadeem Majeed2, Syed Muslim Shah3,*
CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2209-2224, 2022, DOI:10.32604/cmc.2022.019120
Abstract In this study, it is proposed that the diffusion least mean square (LMS) algorithm can be improved by applying the fractional order signal processing methodologies. Application of Caputo’s fractional derivatives are considered in the optimization of cost function. It is suggested to derive a fractional order variant of the diffusion LMS algorithm. The applicability is tested for the estimation of channel parameters in a distributed environment consisting of randomly distributed sensors communicating through wireless medium. The topology of the network is selected such that a smaller number of nodes are informed. In the network, a random sleep strategy is followed… More >
Open Access
ARTICLE
M. Iyapparaja1, Naif Khalaf Alshammari2,*, M. Sathish Kumar1, S. Siva Rama Krishnan1, Chiranji Lal Chowdhary1
CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2225-2239, 2022, DOI:10.32604/cmc.2022.015707
（This article belongs to this Special Issue: Green IoT Networks using Machine Learning, Deep Learning Models)
Abstract Infrastructure of fog is a complex system due to the large number of heterogeneous resources that need to be shared. The embedded devices deployed with the Internet of Things (IoT) technology have increased since the past few years, and these devices generate huge amount of data. The devices in IoT can be remotely connected and might be placed in different locations which add to the network delay. Real time applications require high bandwidth with reduced latency to ensure Quality of Service (QoS). To achieve this, fog computing plays a vital role in processing the request locally with the nearest available… More >
Open Access
ARTICLE
Waqas Haider Bangyal1, Abdul Hameed1, Jamil Ahmad2, Kashif Nisar3,*, Muhammad Reazul Haque4, Ag. Asri Ag. Ibrahim3, Joel J. P. C. Rodrigues5,6, M. Adil Khan7, Danda B. Rawat8, Richard Etengu4
CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2241-2259, 2022, DOI:10.32604/cmc.2022.017789
Abstract Bat algorithm (BA) is an eminent meta-heuristic algorithm that has been widely used to solve diverse kinds of optimization problems. BA leverages the echolocation feature of bats produced by imitating the bats’ searching behavior. BA faces premature convergence due to its local search capability. Instead of using the standard uniform walk, the Torus walk is viewed as a promising alternative to improve the local search capability. In this work, we proposed an improved variation of BA by applying torus walk to improve diversity and convergence. The proposed. Modern Computerized Bat Algorithm (MCBA) approach has been examined for fifteen well-known benchmark… More >
Open Access
ARTICLE
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
（This article belongs to this Special Issue: Recent Advances in Deep Learning for Medical Image Analysis)
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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