Jinsu Kim¹, Sungwook Ryu², Namje Park^1,3,*

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 4169-4184, 2022, DOI:10.32604/cmc.2022.019277

Abstract A significant number of cloud storage environments are already implementing deduplication technology. Due to the nature of the cloud environment, a storage server capable of accommodating large-capacity storage is required. As storage capacity increases, additional storage solutions are required. By leveraging deduplication, you can fundamentally solve the cost problem. However, deduplication poses privacy concerns due to the structure itself. In this paper, we point out the privacy infringement problem and propose a new deduplication technique to solve it. In the proposed technique, since the user’s map structure and files are not stored on the server, the file uploader list cannot… More >

Batyrkhan Omarov^1,2,*, Nurbek Saparkhojayev², Shyrynkyz Shekerbekova³, Oxana Akhmetova¹, Meruert Sakypbekova¹, Guldina Kamalova³, Zhanna Alimzhanova¹, Lyailya Tukenova³, Zhadyra Akanova⁴

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2815-2833, 2022, DOI:10.32604/cmc.2022.019246

Abstract Diseases of the cardiovascular system are one of the major causes of death worldwide. These diseases could be quickly detected by changes in the sound created by the action of the heart. This dynamic auscultations need extensive professional knowledge and emphasis on listening skills. There is also an unmet requirement for a compact cardiac condition early warning device. In this paper, we propose a prototype of a digital stethoscopic system for the diagnosis of cardiac abnormalities in real time using machine learning methods. This system consists of three subsystems that interact with each other (1) a portable digital subsystem of… More >

Shahab Ul Islam¹, Saifullah Jan², Abdul Waheed^3,4,*, Gulzar Mehmood⁵, Mahdi Zareei⁶, Faisal Alanazi⁷

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 4123-4145, 2022, DOI:10.32604/cmc.2022.019226

Abstract Spatial and temporal information on urban infrastructure is essential and requires various land-cover/land-use planning and management applications. Besides, a change in infrastructure has a direct impact on other land-cover and climatic conditions. This study assessed changes in the rate and spatial distribution of Peshawar district’s infrastructure and its effects on Land Surface Temperature (LST) during the years 1996 and 2019. For this purpose, firstly, satellite images of bands7 and 8 ETM+(Enhanced Thematic Mapper) plus and OLI (Operational Land Imager) of 30 m resolution were taken. Secondly, for classification and image processing, remote sensing (RS) applications ENVI (Environment for Visualising Images)… More >

Shoroog Khenkar^1,*, Salma Kammoun Jarraya^1,2

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2655-2677, 2022, DOI:10.32604/cmc.2022.019152

Abstract This paper proposes a novel, efficient and affordable approach to detect the students’ engagement levels in an e-learning environment by using webcams. Our method analyzes spatiotemporal features of e-learners’ micro body gestures, which will be mapped to emotions and appropriate engagement states. The proposed engagement detection model uses a three-dimensional convolutional neural network to analyze both temporal and spatial information across video frames. We follow a transfer learning approach by using the C3D model that was trained on the Sports-1M dataset. The adopted C3D model was used based on two different approaches; as a feature extractor with linear classifiers and… More >

Oday A. Hassen^1,*, Nur Azman Abu¹, Zaheera Zainal Abidin¹, Saad M. Darwish²

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2453-2469, 2022, DOI:10.32604/cmc.2022.019125

Abstract A robust smile recognition system could be widely used for many real-world applications. Classification of a facial smile in an unconstrained setting is difficult due to the invertible and wide variety in face images. In this paper, an adaptive model for smile expression classification is suggested that integrates a fast features extraction algorithm and cascade classifiers. Our model takes advantage of the intrinsic association between face detection, smile, and other face features to alleviate the over-fitting issue on the limited training set and increase classification results. The features are extracted taking into account to exclude any unnecessary coefficients in the… More >

Nasir Mahmud Khokhar¹, Muhammad Nadeem Majeed², Syed Muslim Shah^3,*

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 >

Nazar Hussain¹, Muhammad Attique Khan¹, Usman Tariq², Seifedine Kadry^3,*, MuhammadAsfand E. Yar⁴, Almetwally M. Mostafa⁵, Abeer Ali Alnuaim⁶, Shafiq Ahmad⁷

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3281-3294, 2022, DOI:10.32604/cmc.2022.019036

Abstract Agriculture is an important research area in the field of visual recognition by computers. Plant diseases affect the quality and yields of agriculture. Early-stage identification of crop disease decreases financial losses and positively impacts crop quality. The manual identification of crop diseases, which are mostly visible on leaves, is a very time-consuming and costly process. In this work, we propose a new framework for the recognition of cucumber leaf diseases. The proposed framework is based on deep learning and involves the fusion and selection of the best features. In the feature extraction phase, VGG (Visual Geometry Group) and Inception V3… More >

Javaria Tahir¹, Syed Rameez Naqvi^2,*, Khursheed Aurangzeb³, Musaed Alhussein³

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3235-3250, 2022, DOI:10.32604/cmc.2022.018949

Abstract Melanoma, due to its higher mortality rate, is considered as one of the most pernicious types of skin cancers, mostly affecting the white populations. It has been reported a number of times and is now widely accepted, that early detection of melanoma increases the chances of the subject’s survival. Computer-aided diagnostic systems help the experts in diagnosing the skin lesion at earlier stages using machine learning techniques. In this work, we propose a framework that accurately segments, and later classifies, the lesion using improved image segmentation and fusion methods. The proposed technique takes an image and passes it through two… More >

Abdul Hanan Ashraf¹, Muhammad Imran¹, Abdulrahman M. Qahtani^2,*, Abdulmajeed Alsufyani², Omar Almutiry³, Awais Mahmood³, Muhammad Attique⁴, Mohamed Habib^5,6

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2761-2775, 2022, DOI:10.32604/cmc.2022.018785

Abstract In recent years, the number of Gun-related incidents has crossed over 250,000 per year and over 85% of the existing 1 billion firearms are in civilian hands, manual monitoring has not proven effective in detecting firearms. which is why an automated weapon detection system is needed. Various automated convolutional neural networks (CNN) weapon detection systems have been proposed in the past to generate good results. However, These techniques have high computation overhead and are slow to provide real-time detection which is essential for the weapon detection system. These models have a high rate of false negatives because they often fail… More >

Sengul Bayrak^1,2, Eylem Yucel^2,*, 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 >