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  • Open Access

    ARTICLE

    Effect of Direct Statistical Contrast Enhancement Technique on Document Image Binarization

    Wan Azani Mustafa1,2,*, Haniza Yazid3, Ahmed Alkhayyat4, Mohd Aminudin Jamlos3, Hasliza A. Rahim3

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3549-3564, 2022, DOI:10.32604/cmc.2022.019801

    Abstract Background: Contrast enhancement plays an important role in the image processing field. Contrast correction has performed an adjustment on the darkness or brightness of the input image and increases the quality of the image. Objective: This paper proposed a novel method based on statistical data from the local mean and local standard deviation. Method: The proposed method modifies the mean and standard deviation of a neighbourhood at each pixel and divides it into three categories: background, foreground, and problematic (contrast & luminosity) region. Experimental results from both visual and objective aspects show that the proposed method can normalize the contrast… More >

  • Open Access

    ARTICLE

    A Machine Learning Approach for Early COVID-19 Symptoms Identification

    Omer Ali1,2, Mohamad Khairi Ishak1,*, Muhammad Kamran Liaquat Bhatti2

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3803-3820, 2022, DOI:10.32604/cmc.2022.019797

    Abstract Symptom identification and early detection are the first steps towards a health condition diagnosis. The COVID-19 virus causes pneumonia-like symptoms such as fever, cough, and shortness of breath. Many COVID-19 contraction tests necessitate extensive clinical protocols in medical settings. Clinical studies help with the accurate analysis of COVID-19, where the virus has already spread to the lungs in most patients. The majority of existing supervised machine learning-based disease detection techniques are based on clinical data like x-rays and computerized tomography. This is heavily reliant on a larger clinical study and does not emphasize early symptom detection. The aim of this… More >

  • Open Access

    ARTICLE

    Optimized Tuned Deep Learning Model for Chronic Kidney Disease Classification

    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

    Efficient Energy Optimized Faithful Adder with Parallel Carry Generation

    K. N. Vijeyakumar1, S. Maragatharaj2,*

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2543-2561, 2022, DOI:10.32604/cmc.2022.019789

    Abstract Approximate computing has received significant attention in the design of portable CMOS hardware for error-tolerant applications. This work proposes an approximate adder that to optimize area delay and achieve energy efficiency using Parallel Carry (PC) generation logic. For ‘n’ bits in input, the proposed algorithm use approximate addition for least n/2 significant bits and exact addition for most n/2 significant bits. A simple OR logic with no carry propagation is used to implement the approximate part. In the exact part, addition is performed using 4-bit adder blocks that implement PC at block level to reduce node capacitance in the critical… More >

  • Open Access

    ARTICLE

    Semi/Fully-Automated Segmentation of Gastric-Polyp Using Aquila-Optimization-Algorithm Enhanced Images

    Venkatesan Rajinikanth1, Shabnam Mohamed Aslam2, Seifedine Kadry3, Orawit Thinnukool4,*

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 4087-4105, 2022, DOI:10.32604/cmc.2022.019786

    Abstract The incident rate of the Gastrointestinal-Disease (GD) in humans is gradually rising due to a variety of reasons and the Endoscopic/Colonoscopic-Image (EI/CI) supported evaluation of the GD is an approved practice. Extraction and evaluation of the suspicious section of the EI/CI is essential to diagnose the disease and its severity. The proposed research aims to implement a joint thresholding and segmentation framework to extract the Gastric-Polyp (GP) with better accuracy. The proposed GP detection system consist; (i) Enhancement of GP region using Aquila-Optimization-Algorithm supported tri-level thresholding with entropy (Fuzzy/Shannon/Kapur) and between-class-variance (Otsu) technique, (ii) Automated (Watershed/Markov-Random-Field) and semi-automated (Chan-Vese/Level-Set/Active-Contour) segmentation… More >

  • Open Access

    ARTICLE

    An Optimal Distribution of RSU for Improving Self-Driving Vehicle Connectivity

    Khattab Alheeti1, Abdulkareem Alaloosy1, Haitham Khalaf2, Abdulkareem Alzahrani3,*, Duaa Al_Dosary4

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3311-3319, 2022, DOI:10.32604/cmc.2022.019773

    Abstract Self-driving and semi-self-driving cars play an important role in our daily lives. The effectiveness of these cars is based heavily on the use of their surrounding areas to collect sensitive and vital information. However, external infrastructures also play significant roles in the transmission and reception of control data, cooperative awareness messages, and caution notifications. In this case, roadside units are considered one of the most important communication peripherals. Random distribution of these infrastructures will overburden the spread of self-driving vehicles in terms of cost, bandwidth, connectivity, and radio coverage area. In this paper, a new distributed roadside unit is proposed… More >

  • Open Access

    REVIEW

    Reconfigurable Pattern Patch Antenna for Mid-Band 5G: A Review

    Siti Rahena Isa1,2, Muzammil Jusoh2,*, Thennarasan Sabapathy2, Jamel Nebhen3, Muhammad Ramlee Kamarudin4, Mohamed Nasrun Osman2, Qammer Hussain Abbasi5, Hasliza A. Rahim2, Mohd Najib Mohd Yasin2, Ping Jack Soh2

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2699-2725, 2022, DOI:10.32604/cmc.2022.019769

    Abstract New requirements in communication technologies make it imperative to rehash conventional features such as reconfigurable antennas to adapt with the future adaptability advancements. This paper presents a comprehensive review of reconfigurable antennas, specifically in terms of radiation patterns for adaptation in the upcoming Fifth Generation (5G) New Radio frequency bands. They represent the key of antenna technology for materializing a high rate transmission, increased spectral and energy efficiency, reduced interference, and improved the beam steering and beam shaping, thereby land a great promise for planar antennas to boost the mid-band 5G. This review begins with an overview of the underlying… More >

  • Open Access

    ARTICLE

    Sales Prediction and Product Recommendation Model Through User Behavior Analytics

    Xian Zhao, Pantea Keikhosrokiani*

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3855-3874, 2022, DOI:10.32604/cmc.2022.019750

    Abstract The COVID-19 has brought us unprecedented difficulties and thousands of companies have closed down. The general public has responded to call of the government to stay at home. Offline retail stores have been severely affected. Therefore, in order to transform a traditional offline sales model to the B2C model and to improve the shopping experience, this study aims to utilize historical sales data for exploring, building sales prediction and recommendation models. A novel data science life-cycle and process model with Recency, Frequency, and Monetary (RFM) analysis method with the combination of various analytics algorithms are utilized in this study for… More >

  • Open Access

    ARTICLE

    Magneto-Thermoelasticity with Thermal Shock Considering Two Temperatures and LS Model

    F. S. Bayones1, S. M. Abo-Dahab2,3, N. S. Hussein4, A. M. Abd-Alla5,*, H. A. Alshehri1

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3365-3381, 2022, DOI:10.32604/cmc.2022.019711

    Abstract The present investigation is intended to demonstrate the magnetic field, relaxation time, hydrostatic initial stress, and two temperature on the thermal shock problem. The governing equations are formulated in the context of Lord-Shulman theory with the presence of bodily force, two temperatures, thermal shock, and hydrostatic initial stress. We obtained the exact solution using the normal mode technique with appropriate boundary conditions. The field quantities are calculated analytically and displayed graphically under thermal shock problem with effect of external parameters respect to space coordinates. The results obtained are agreeing with the previous results obtained by others when the new parameters… More >

  • Open Access

    ARTICLE

    Data-Driven Self-Learning Controller for Power-Aware Mobile Monitoring IoT Devices

    Michal Prauzek*, Tereza Paterova, Jaromir Konecny, Radek Martinek

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2601-2618, 2022, DOI:10.32604/cmc.2022.019705

    Abstract Nowadays, there is a significant need for maintenance free modern Internet of things (IoT) devices which can monitor an environment. IoT devices such as these are mobile embedded devices which provide data to the internet via Low Power Wide Area Network (LPWAN). LPWAN is a promising communications technology which allows machine to machine (M2M) communication and is suitable for small mobile embedded devices. The paper presents a novel data-driven self-learning (DDSL) controller algorithm which is dedicated to controlling small mobile maintenance-free embedded IoT devices. The DDSL algorithm is based on a modified Q-learning algorithm which allows energy efficient data-driven behavior… More >

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