Table of Content

Deep Learning and Parallel Computing for Intelligent and Efficient IoT

Submission Deadline: 29 January 2021 (closed)

Guest Editors

Dr. Irfan Uddin, Kohat University of Science and Technology, Pakistan.
Dr. Jia-Bao Liu, Anhui Jianzhu University, China.
Dr. Furqan Aziz, University of Birmingham, UK.
Dr. Shamsul Huda, Deakin University, Australia.
Dr. Muhammad Asif Manzoor, University of Regina, Canada.

Summary

Artificial Intelligence (AI) is recently becoming very popular mainly because of advancements in Machine Learning (ML), more specifically in Deep Learning (DL) and Reinforcement Learning (RL). A wide range of applications are using these techniques. Internet of Things (IoT) is the future generation system. The complex, heterogeneous and distributed nature of IoT devices has inspired many researchers and practitioners to explore the usage of AI/ML/DL techniques to make intelligent IoT. Parallel computing techniques are used to make these devices more efficient and reliable. As a result of this massive adaption and growth, smart cities, smart grid, smart healthcare and smart industries are emerged.

A large number of distributed heterogeneous devices are interconnected in IoT and a huge amount of data is generated. This data is increasing in size and heterogeneity. The network of IoT devices is diverse and complex in nature. These devices contain limited computational power, memory and energy resources. Therefore, AI/ML/DL based devices are important to develop intelligent IoT systems and efficient management of resource and network. The objective is to improve the overall performance of IoT systems.

This special issue aims to bring together the academic and industrial researchers to explore the opportunities of DL and parallel computing for IOT, study its impact on the solution of the aforementioned challenges and propose viable solutions.

We solicit papers covering various topics of interest that include but not limited to the following topics:

• Architecture and technologies for intelligent IoT using Deep Learning and Parallel Computing

• Services for smart systems based on Deep Learning (smart building, smart cities, smart grids, smart transportation, smart healthcare)

• Big data mining and analytics for intelligent IOT based on Deep Learning

• Applications for intelligent IoT based on Deep Learning

• Transport protocols for intelligent IoT based on Deep Learning

• Data management for IoT based on Deep Learning

• Application for energy efficient IOT systems based on Deep Learning


Keywords

Deep Learning, Parallel Computing, GPUs, IoT and Performance improvements.

Published Papers


  • Open Access

    ARTICLE

    An Efficient Proxy Blind Signcryption Scheme for IoT

    Aamer Khan, Insaf Ullah, Fahad Algarni, Muhammad Naeem, M. Irfan Uddin, Muhammad Asghar Khan
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4293-4306, 2022, DOI:10.32604/cmc.2022.017318
    (This article belongs to this Special Issue: Deep Learning and Parallel Computing for Intelligent and Efficient IoT)
    Abstract Recent years have witnessed growing scientific research interest in the Internet of Things (IoT) technologies, which supports the development of a variety of applications such as health care, Industry 4.0, agriculture, ecological data management, and other various domains. IoT utilizes the Internet as a prime medium of communication for both single documents as well as multi-digital messages. However, due to the wide-open nature of the Internet, it is important to ensure the anonymity, untraceably, confidentiality, and unforgeability of communication with efficient computational complexity and low bandwidth. We designed a light weight and secure proxy blind signcryption for multi-digital messages based… More >

  • Open Access

    A Global Training Model for Beat Classification Using Basic Electrocardiogram Morphological Features

    Shubha Sumesh, John Yearwood, Shamsul Huda and Shafiq Ahmad
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4503-4521, 2022, DOI:10.32604/cmc.2022.015474
    (This article belongs to this Special Issue: Deep Learning and Parallel Computing for Intelligent and Efficient IoT)
    Abstract

    Clinical Study and automatic diagnosis of electrocardiogram (ECG) data always remain a challenge in diagnosing cardiovascular activities. The analysis of ECG data relies on various factors like morphological features, classification techniques, methods or models used to diagnose and its performance improvement. Another crucial factor in the methodology is how to train the model for each patient. Existing approaches use standard training model which faces challenges when training data has variation due to individual patient characteristics resulting in a lower detection accuracy. This paper proposes an adaptive approach to identify performance improvement in building a training model that analyze global training… More >

  • Open Access

    ARTICLE

    Adaptive Power Control Aware Depth Routing in Underwater Sensor Networks

    Ghufran Ahmed, Saiful Islam, Ihsan Ali, Isra Adil Hayder, Abdelmuttlib Ibrahim Abdalla Ahmed, Muhammad Talha, Sultan S. Alshamrani, Ag Asri Ag Ibrahim
    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 1301-1322, 2021, DOI:10.32604/cmc.2021.017062
    (This article belongs to this Special Issue: Deep Learning and Parallel Computing for Intelligent and Efficient IoT)
    Abstract Underwater acoustic sensor network (UASN) refers to a procedure that promotes a broad spectrum of aquatic applications. UASNs can be practically applied in seismic checking, ocean mine identification, resource exploration, pollution checking, and disaster avoidance. UASN confronts many difficulties and issues, such as low bandwidth, node movements, propagation delay, 3D arrangement, energy limitation, and high-cost production and arrangement costs caused by antagonistic underwater situations. Underwater wireless sensor networks (UWSNs) are considered a major issue being encountered in energy management because of the limited battery power of their nodes. Moreover, the harsh underwater environment requires vendors to design and deploy energy-hungry… More >

  • Open Access

    ARTICLE

    Deep-Learning-Empowered 3D Reconstruction for Dehazed Images in IoT-Enhanced Smart Cities

    Jing Zhang, Xin Qi, San Hlaing Myint, Zheng Wen
    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2807-2824, 2021, DOI:10.32604/cmc.2021.017410
    (This article belongs to this Special Issue: Deep Learning and Parallel Computing for Intelligent and Efficient IoT)
    Abstract With increasingly more smart cameras deployed in infrastructure and commercial buildings, 3D reconstruction can quickly obtain cities’ information and improve the efficiency of government services. Images collected in outdoor hazy environments are prone to color distortion and low contrast; thus, the desired visual effect cannot be achieved and the difficulty of target detection is increased. Artificial intelligence (AI) solutions provide great help for dehazy images, which can automatically identify patterns or monitor the environment. Therefore, we propose a 3D reconstruction method of dehazed images for smart cities based on deep learning. First, we propose a fine transmission image deep convolutional… More >

  • Open Access

    ARTICLE

    Machine Learning Approach for COVID-19 Detection on Twitter

    Samina Amin, M. Irfan Uddin, Heyam H. Al-Baity, M. Ali Zeb, M. Abrar Khan
    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2231-2247, 2021, DOI:10.32604/cmc.2021.016896
    (This article belongs to this Special Issue: Deep Learning and Parallel Computing for Intelligent and Efficient IoT)
    Abstract Social networking services (SNSs) provide massive data that can be a very influential source of information during pandemic outbreaks. This study shows that social media analysis can be used as a crisis detector (e.g., understanding the sentiment of social media users regarding various pandemic outbreaks). The novel Coronavirus Disease-19 (COVID-19), commonly known as coronavirus, has affected everyone worldwide in 2020. Streaming Twitter data have revealed the status of the COVID-19 outbreak in the most affected regions. This study focuses on identifying COVID-19 patients using tweets without requiring medical records to find the COVID-19 pandemic in Twitter messages (tweets). For this… More >

  • Open Access

    ARTICLE

    Hybrid Deep Learning Architecture to Forecast Maximum Load Duration Using Time-of-Use Pricing Plans

    Jinseok Kim, Babar Shah, Ki-Il Kim
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 283-301, 2021, DOI:10.32604/cmc.2021.016042
    (This article belongs to this Special Issue: Deep Learning and Parallel Computing for Intelligent and Efficient IoT)
    Abstract Load forecasting has received crucial research attention to reduce peak load and contribute to the stability of power grid using machine learning or deep learning models. Especially, we need the adequate model to forecast the maximum load duration based on time-of-use, which is the electricity usage fare policy in order to achieve the goals such as peak load reduction in a power grid. However, the existing single machine learning or deep learning forecasting cannot easily avoid overfitting. Moreover, a majority of the ensemble or hybrid models do not achieve optimal results for forecasting the maximum load duration based on time-of-use.… More >

  • Open Access

    ARTICLE

    Automatic Surveillance of Pandemics Using Big Data and Text Mining

    Abdullah Alharbi, Wael Alosaimi, M. Irfan Uddin
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 303-317, 2021, DOI:10.32604/cmc.2021.016230
    (This article belongs to this Special Issue: Deep Learning and Parallel Computing for Intelligent and Efficient IoT)
    Abstract COVID-19 disease is spreading exponentially due to the rapid transmission of the virus between humans. Different countries have tried different solutions to control the spread of the disease, including lockdowns of countries or cities, quarantines, isolation, sanitization, and masks. Patients with symptoms of COVID-19 are tested using medical testing kits; these tests must be conducted by healthcare professionals. However, the testing process is expensive and time-consuming. There is no surveillance system that can be used as surveillance framework to identify regions of infected individuals and determine the rate of spread so that precautions can be taken. This paper introduces a… More >

  • Open Access

    ARTICLE

    COVID-19 Infected Lung Computed Tomography Segmentation and Supervised Classification Approach

    Aqib Ali, Wali Khan Mashwani, Samreen Naeem, Muhammad Irfan Uddin, Wiyada Kumam, Poom Kumam, Hussam Alrabaiah, Farrukh Jamal, Christophe Chesneau
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 391-407, 2021, DOI:10.32604/cmc.2021.016037
    (This article belongs to this Special Issue: Deep Learning and Parallel Computing for Intelligent and Efficient IoT)
    Abstract The purpose of this research is the segmentation of lungs computed tomography (CT) scan for the diagnosis of COVID-19 by using machine learning methods. Our dataset contains data from patients who are prone to the epidemic. It contains three types of lungs CT images (Normal, Pneumonia, and COVID-19) collected from two different sources; the first one is the Radiology Department of Nishtar Hospital Multan and Civil Hospital Bahawalpur, Pakistan, and the second one is a publicly free available medical imaging database known as Radiopaedia. For the preprocessing, a novel fuzzy c-mean automated region-growing segmentation approach is deployed to take an… More >

  • Open Access

    ARTICLE

    Systematic Analysis of Safety and Security Risks in Smart Homes

    Habib Ullah Khan, Mohammad Kamel Alomari, Sulaiman Khan, Shah Nazir, Asif Qumer Gill, Alanoud Ali Al-Maadid, Zaki Khalid Abu-Shawish, Mostafa Kamal Hassan
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1409-1428, 2021, DOI:10.32604/cmc.2021.016058
    (This article belongs to this Special Issue: Deep Learning and Parallel Computing for Intelligent and Efficient IoT)
    Abstract The revolution in Internet of Things (IoT)-based devices and applications has provided smart applications for humans. These applications range from healthcare to traffic-flow management, to communication devices, to smart security devices, and many others. In particular, government and private organizations are showing significant interest in IoT-enabled applications for smart homes. Despite the perceived benefits and interest, human safety is also a key concern. This research is aimed at systematically analyzing the available literature on smart homes and identifying areas of concern or risk with a view to supporting the design of safe and secure smart homes. For this systematic review… More >

  • Open Access

    ARTICLE

    Pashto Characters Recognition Using Multi-Class Enabled Support Vector Machine

    Sulaiman Khan, Shah Nazir, Habib Ullah Khan, Anwar Hussain
    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 2831-2844, 2021, DOI:10.32604/cmc.2021.015054
    (This article belongs to this Special Issue: Deep Learning and Parallel Computing for Intelligent and Efficient IoT)
    Abstract During the last two decades significant work has been reported in the field of cursive language’s recognition especially, in the Arabic, the Urdu and the Persian languages. The unavailability of such work in the Pashto language is because of: the absence of a standard database and of significant research work that ultimately acts as a big barrier for the research community. The slight change in the Pashto characters’ shape is an additional challenge for researchers. This paper presents an efficient OCR system for the handwritten Pashto characters based on multi-class enabled support vector machine using manifold feature extraction techniques. These… More >

  • Open Access

    ARTICLE

    Feasibility-Guided Constraint-Handling Techniques for Engineering Optimization Problems

    Muhammad Asif Jan, Yasir Mahmood, Hidayat Ullah Khan, Wali Khan Mashwani, Muhammad Irfan Uddin, Marwan Mahmoud, Rashida Adeeb Khanum, Ikramullah, Noor Mast
    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 2845-2862, 2021, DOI:10.32604/cmc.2021.015294
    (This article belongs to this Special Issue: Deep Learning and Parallel Computing for Intelligent and Efficient IoT)
    Abstract The particle swarm optimization (PSO) algorithm is an established nature-inspired population-based meta-heuristic that replicates the synchronizing movements of birds and fish. PSO is essentially an unconstrained algorithm and requires constraint handling techniques (CHTs) to solve constrained optimization problems (COPs). For this purpose, we integrate two CHTs, the superiority of feasibility (SF) and the violation constraint-handling (VCH), with a PSO. These CHTs distinguish feasible solutions from infeasible ones. Moreover, in SF, the selection of infeasible solutions is based on their degree of constraint violations, whereas in VCH, the number of constraint violations by an infeasible solution is of more importance. Therefore,… More >

  • Open Access

    ARTICLE

    Machine Learning-based USD/PKR Exchange Rate Forecasting Using Sentiment Analysis of Twitter Data

    Samreen Naeem, Wali Khan Mashwani, Aqib Ali, M. Irfan Uddin, Marwan Mahmoud, Farrukh Jamal, Christophe Chesneau
    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3451-3461, 2021, DOI:10.32604/cmc.2021.015872
    (This article belongs to this Special Issue: Deep Learning and Parallel Computing for Intelligent and Efficient IoT)
    Abstract This study proposes an approach based on machine learning to forecast currency exchange rates by applying sentiment analysis to messages on Twitter (called tweets). A dataset of the exchange rates between the United States Dollar (USD) and the Pakistani Rupee (PKR) was formed by collecting information from a forex website as well as a collection of tweets from the business community in Pakistan containing finance-related words. The dataset was collected in raw form, and was subjected to natural language processing by way of data preprocessing. Response variable labeling was then applied to the standardized dataset, where the response variables were… More >

  • Open Access

    ARTICLE

    Quality of Service Aware Cluster Routing in Vehicular Ad Hoc Networks

    Ishtiaq Wahid, Fasee Ullah, Masood Ahmad, Atif Khan, M. Irfan Uddin, Abdullah Alharbi, Wael Alosaimi
    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3949-3965, 2021, DOI:10.32604/cmc.2021.014190
    (This article belongs to this Special Issue: Deep Learning and Parallel Computing for Intelligent and Efficient IoT)
    Abstract In vehicular ad hoc networks (VANETs), the topology information (TI) is updated frequently due to vehicle mobility. These frequent changes in topology increase the topology maintenance overhead. To reduce the control message overhead, cluster-based routing schemes are proposed. In cluster-based routing schemes, the nodes are divided into different virtual groups, and each group (logical node) is considered a cluster. The topology changes are accommodated within each cluster, and broadcasting TI to the whole VANET is not required. The cluster head (CH) is responsible for managing the communication of a node with other nodes outside the cluster. However, transmitting real-time data… More >

  • Open Access

    ARTICLE

    Adaptation of Vehicular Ad hoc Network Clustering Protocol for Smart Transportation

    Masood Ahmad, Abdul Hameed, Fasee Ullah, Ishtiaq Wahid, Atif Khan, M. Irfan Uddin, Shafiq Ahmad, Ahmed M. El-Sherbeeny
    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1353-1368, 2021, DOI:10.32604/cmc.2021.014237
    (This article belongs to this Special Issue: Deep Learning and Parallel Computing for Intelligent and Efficient IoT)
    Abstract Clustering algorithms optimization can minimize topology maintenance overhead in large scale vehicular Ad hoc networks (VANETs) for smart transportation that results from dynamic topology, limited resources and non-centralized architecture. The performance of a clustering algorithm varies with the underlying mobility model to address the topology maintenance overhead issue in VANETs for smart transportation. To design a robust clustering algorithm, careful attention must be paid to components like mobility models and performance objectives. A clustering algorithm may not perform well with every mobility pattern. Therefore, we propose a supervisory protocol (SP) that observes the mobility pattern of vehicles and identifies the… More >

  • Open Access

    ARTICLE

    PeachNet: Peach Diseases Detection for Automatic Harvesting

    Wael Alosaimi, Hashem Alyami, M. Irfan Uddin
    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1665-1677, 2021, DOI:10.32604/cmc.2021.014950
    (This article belongs to this Special Issue: Deep Learning and Parallel Computing for Intelligent and Efficient IoT)
    Abstract To meet the food requirements of the seven billion people on Earth, multiple advancements in agriculture and industry have been made. The main threat to food items is from diseases and pests which affect the quality and quantity of food. Different scientific mechanisms have been developed to protect plants and fruits from pests and diseases and to increase the quantity and quality of food. Still these mechanisms require manual efforts and human expertise to diagnose diseases. In the current decade Artificial Intelligence is used to automate different processes, including agricultural processes, such as automatic harvesting. Machine Learning techniques are becoming… More >

  • Open Access

    ARTICLE

    Liver-Tumor Detection Using CNN ResUNet

    Muhammad Sohaib Aslam, Muhammad Younas, Muhammad Umar Sarwar, Muhammad Arif Shah, Atif Khan, M. Irfan Uddin, Shafiq Ahmad, Muhammad Firdausi, Mazen Zaindin
    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1899-1914, 2021, DOI:10.32604/cmc.2021.015151
    (This article belongs to this Special Issue: Deep Learning and Parallel Computing for Intelligent and Efficient IoT)
    Abstract Liver tumor is the fifth most occurring type of tumor in men and the ninth most occurring type of tumor in women according to recent reports of Global cancer statistics 2018. There are several imaging tests like Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and ultrasound that can diagnose the liver tumor after taking the sample from the tissue of the liver. These tests are costly and time-consuming. This paper proposed that image processing through deep learning Convolutional Neural Network (CNNs) ResUNet model that can be helpful for the early diagnose of tumor instead of conventional methods. The existing studies… More >

  • Open Access

    ARTICLE

    Detecting Information on the Spread of Dengue on Twitter Using Artificial Neural Networks

    Samina Amin, M. Irfan Uddin, M. Ali Zeb, Ala Abdulsalam Alarood, Marwan Mahmoud, Monagi H. Alkinani
    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 1317-1332, 2021, DOI:10.32604/cmc.2021.014733
    (This article belongs to this Special Issue: Deep Learning and Parallel Computing for Intelligent and Efficient IoT)
    Abstract Social media platforms have lately emerged as a promising tool for predicting the outbreak of epidemics by analyzing information on them with the help of machine learning techniques. Many analytical and statistical models are available to infer a variety of user sentiments in posts on social media. The amount of data generated by social media platforms, such as Twitter, that can be used to track diseases is increasing rapidly. This paper proposes a method for the classification of tweets related to the outbreak of dengue using machine learning algorithms. An artificial neural network (ANN)-based method is developed using Global Vector… More >

  • Open Access

    ARTICLE

    Smart Object Detection and Home Appliances Control System in Smart Cities

    Sulaiman Khan, Shah Nazir, Habib Ullah Khan
    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 895-915, 2021, DOI:10.32604/cmc.2021.013878
    (This article belongs to this Special Issue: Deep Learning and Parallel Computing for Intelligent and Efficient IoT)
    Abstract During the last decade the emergence of Internet of Things (IoT) based applications inspired the world by providing state of the art solutions to many common problems. From traffic management systems to urban cities planning and development, IoT based home monitoring systems, and many other smart applications. Regardless of these facilities, most of these IoT based solutions are data driven and results in small accuracy values for smaller datasets. In order to address this problem, this paper presents deep learning based hybrid approach for the development of an IoT-based intelligent home security and appliance control system in the smart cities.… More >

Share Link

WeChat scan