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  • Open Access

    ARTICLE

    Deep Reinforcement Learning Enabled Smart City Recycling Waste Object Classification

    Mesfer Al Duhayyim1, Taiseer Abdalla Elfadil Eisa2, Fahd N. Al-Wesabi3,4, Abdelzahir Abdelmaboud5, Manar Ahmed Hamza6,*, Abu Sarwar Zamani6, Mohammed Rizwanullah6, Radwa Marzouk7,8

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5699-5715, 2022, DOI:10.32604/cmc.2022.024431

    Abstract The Smart City concept revolves around gathering real time data from citizen, personal vehicle, public transports, building, and other urban infrastructures like power grid and waste disposal system. The understandings obtained from the data can assist municipal authorities handle assets and services effectually. At the same time, the massive increase in environmental pollution and degradation leads to ecological imbalance is a hot research topic. Besides, the progressive development of smart cities over the globe requires the design of intelligent waste management systems to properly categorize the waste depending upon the nature of biodegradability. Few of the commonly available wastes are… More >

  • Open Access

    ARTICLE

    Fusion Based Tongue Color Image Analysis Model for Biomedical Applications

    Esam A. AlQaralleh1, Halah Nassif2, Bassam A. Y. Alqaralleh2,*

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5477-5490, 2022, DOI:10.32604/cmc.2022.024364

    Abstract Tongue diagnosis is a novel and non-invasive approach commonly employed to carry out the supplementary diagnosis over the globe. Recently, several deep learning (DL) based tongue color image analysis models have existed in the literature for the effective detection of diseases. This paper presents a fusion of handcrafted with deep features based tongue color image analysis (FHDF-TCIA) technique to biomedical applications. The proposed FDHF-TCIA technique aims to investigate the tongue images using fusion model, and thereby determines the existence of disease. Primarily, the FHDF-TCIA technique comprises Gaussian filtering based preprocessing to eradicate the noise. The proposed FHDF-TCIA model encompasses a… More >

  • Open Access

    ARTICLE

    An Efficient HW/SW Design for Text Extraction from Complex Color Image

    Mohamed Amin Ben Atitallah1,2,3,*, Rostom Kachouri2, Ahmed Ben Atitallah4, Hassene Mnif1

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5963-5977, 2022, DOI:10.32604/cmc.2022.024345

    Abstract In the context of constructing an embedded system to help visually impaired people to interpret text, in this paper, an efficient High-level synthesis (HLS) Hardware/Software (HW/SW) design for text extraction using the Gamma Correction Method (GCM) is proposed. Indeed, the GCM is a common method used to extract text from a complex color image and video. The purpose of this work is to study the complexity of the GCM method on Xilinx ZCU102 FPGA board and to propose a HW implementation as Intellectual Property (IP) block of the critical blocks in this method using HLS flow with taking account the… More >

  • Open Access

    ARTICLE

    Optimal Hybrid Feature Extraction with Deep Learning for COVID-19 Classifications

    Majdy M. Eltahir1, Ibrahim Abunadi2, Fahd N. Al-Wesabi3,4, Anwer Mustafa Hilal5, Adil Yousif6, Abdelwahed Motwakel5, Mesfer Al Duhayyim7, Manar Ahmed Hamza5,*

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 6257-6273, 2022, DOI:10.32604/cmc.2022.024312

    Abstract Novel coronavirus 2019 (COVID-19) has affected the people's health, their lifestyle and economical status across the globe. The application of advanced Artificial Intelligence (AI) methods in combination with radiological imaging is useful in accurate detection of the disease. It also assists the physicians to take care of remote villages too. The current research paper proposes a novel automated COVID-19 analysis method with the help of Optimal Hybrid Feature Extraction (OHFE) and Optimal Deep Neural Network (ODNN) called OHFE-ODNN from chest x-ray images. The objective of the presented technique is for performing binary and multi-class classification of COVID-19 analysis from chest… More >

  • Open Access

    ARTICLE

    Machine Learning Based Analysis of Real-Time Geographical of RS Spatio-Temporal Data

    Rami Sameer Ahmad Al Kloub*

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5151-5165, 2022, DOI:10.32604/cmc.2022.024309

    Abstract Flood disasters can be reliably monitored using remote sensing photos with great spatiotemporal resolution. However, satellite revisit periods and extreme weather limit the use of high spatial resolution images. As a result, this research provides a method for combining Landsat and MODIS pictures to produce high spatiotemporal imagery for flood disaster monitoring. Using the spatial and temporal adaptive reflectance fusion model (STARFM), the spatial and temporal reflectance unmixing model (STRUM), and three prominent algorithms of flexible spatiotemporal data fusion (FSDAF), Landsat fusion images are created by fusing MODIS and Landsat images. Then, to extract flood information, utilize a support vector… More >

  • Open Access

    ARTICLE

    Elite Opposition Based Metaheuristic Framework for Load Balancing in LTE Network

    M. R. Sivagar1,*, N. Prabakaran2

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5765-5781, 2022, DOI:10.32604/cmc.2022.024273

    Abstract In present scenario of wireless communications, Long Term Evolution (LTE) based network technology is evolved and provides consistent data delivery with high speed and minimal delay through mobile devices. The traffic management and effective utilization of network resources are the key factors of LTE models. Moreover, there are some major issues in LTE that are to be considered are effective load scheduling and traffic management. Through LTE is a depraved technology, it is been suffering from these issues. On addressing that, this paper develops an Elite Opposition based Spider Monkey Optimization Framework for Efficient Load Balancing (SMO-ELB). In this model,… More >

  • Open Access

    ARTICLE

    Human Faces Detection and Tracking for Crowd Management in Hajj and Umrah

    Riad Alharbey1, Ameen Banjar1, Yahia Said2,3,*, Mohamed Atri4, Abdulrahman Alshdadi1, Mohamed Abid5

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 6275-6291, 2022, DOI:10.32604/cmc.2022.024272

    Abstract Hajj and Umrah are two main religious duties for Muslims. To help faithfuls to perform their religious duties comfortably in overcrowded areas, a crowd management system is a must to control the entering and exiting for each place. Since the number of people is very high, an intelligent crowd management system can be developed to reduce human effort and accelerate the management process. In this work, we propose a crowd management process based on detecting, tracking, and counting human faces using Artificial Intelligence techniques. Human detection and counting will be performed to calculate the number of existing visitors and face… More >

  • Open Access

    ARTICLE

    Accurate Location Estimation of Smart Dusts Using Machine Learning

    Shariq Bashir1,*, Owais Ahmed Malik2, Daphne Teck Ching Lai2

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 6165-6181, 2022, DOI:10.32604/cmc.2022.024269

    Abstract Traditional wireless sensor networks (WSNs) are not suitable for rough terrains that are difficult or impossible to access by humans. Smart dust is a technology that works with the combination of many tiny sensors which is highly useful for obtaining remote sensing information from rough terrains. The tiny sensors are sprinkled in large numbers on rough terrains using airborne distribution through drones or aircraft without manually setting their locations. Although it is clear that a number of remote sensing applications can benefit from this technology, but the small size of smart dust fundamentally restricts the integration of advanced hardware on… More >

  • Open Access

    ARTICLE

    Quantum Particle Swarm Optimization Based Convolutional Neural Network for Handwritten Script Recognition

    Reya Sharma1, Baijnath Kaushik1, Naveen Kumar Gondhi1, Muhammad Tahir2,*, Mohammad Khalid Imam Rahmani2

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5855-5873, 2022, DOI:10.32604/cmc.2022.024232

    Abstract Even though several advances have been made in recent years, handwritten script recognition is still a challenging task in the pattern recognition domain. This field has gained much interest lately due to its diverse application potentials. Nowadays, different methods are available for automatic script recognition. Among most of the reported script recognition techniques, deep neural networks have achieved impressive results and outperformed the classical machine learning algorithms. However, the process of designing such networks right from scratch intuitively appears to incur a significant amount of trial and error, which renders them unfeasible. This approach often requires manual intervention with domain… More >

  • Open Access

    ARTICLE

    A New Handover Management Model for Two-Tier 5G Mobile Networks

    Mohammad Arifin Rahman Khan1,*, Mohammed Golam Kaosar2, Mohammad Shorfuzzaman3, Kire Jakimoski4

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5491-5509, 2022, DOI:10.32604/cmc.2022.024212

    Abstract There has been an exponential rise in mobile data traffic in recent times due to the increasing popularity of portable devices like tablets, smartphones, and laptops. The rapid rise in the use of these portable devices has put extreme stress on the network service providers while forcing telecommunication engineers to look for innovative solutions to meet the increased demand. One solution to the problem is the emergence of fifth-generation (5G) wireless communication, which can address the challenges by offering very broad wireless area capacity and potential cut-power consumption. The application of small cells is the fundamental mechanism for the 5G… More >

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