Special lssues
Table of Content

Intelligent Management and Machine Learning for Big Data in IoT-Enabled Pervasive Computing

Submission Deadline: 09 April 2024 (closed)

Guest Editors

Dr. Mazin Abed Mohammed, University of Anbar, Iraq.
Prof. Seifedine Kadry, Norrof University College, Norway.
Prof. Oana Geman, Universitatea Stefan cel Mare din Suceava, Romania.


Pervasive computing enables systems to solve problems anytime and anywhere by utilizing devices, data, and communication channels. With the advancement of technology, the Internet of Things (IoT), Big data management, and Machine learning have become essential components of pervasive computing. These technologies enable efficient solutions in a distributed computing environment.


Today's large-scale data processing requires context-aware data processing and robust machine learning algorithms. IoT device architecture comprises three layers: cloud, edge, and device layers. The cloud layer manages data arrival and departure, providing the capability to compute and apply machine learning methods. The edge layer provides functional connectivity to the cloud and big data repository, enabling devices to store sensory data anywhere and anytime. The device layer comprises incoming and outgoing connection channels that carry data payloads to other IoT devices and big data repositories.


However, merely observing data within an IoT device does not facilitate users in solving technical issues. To transform an IoT device into an intelligent IoT device, algorithms and techniques must be used to analyze problems in the cloud, edge, and device layers.

This special issue seeks conceptual, empirical, or technological papers that will offer new insights into the following topics, but is not limited to them:


• Context-aware data algorithm solutions for IoT devices

• Big data management techniques for rectifying IoT devices issues

• Machine learning algorithms for addressing IoT devices problems

• Programmable Pervasive approaches for delivering IoT device solutions

• Predictive, prescriptive, descriptive analytics for IoT device issues

• Deep learning techniques for identifying micro issues in IoT devices

• Environmental issues for figuring out IoT device issues

• Embedded solutions for IoT device problems

• Security issues in the IoT devices

• Network processing problems in the IoT devices

• Multihoming data exchange issues in the IoT devices

• Reprogrammable approaches for solving IoT devices issues.

• Hardware Abstraction Layer logs analytics for solving IoT device problems.

• Intelligent systems for multi-homing network architectures

• AI methods for Healthcare and Medical issues

• AI  methods for Foreign Object Detection Techniques

• AI and big data analytics applied in medical domain;

• AI methodologies for medical data analysis;

• Intelligent medical efficient solutions for future applications;

• AI and blockchain assisted medical efficient product designs;

• Optimization of medical assets using machine learning and deep learning techniques;

• Smart IoT sensor design and optimal utilization in Healthcare Systems;

• Applications of artificial intelligence, block chain IoT for sustainable medical and service;

• AI based intelligent solutions for Healthcare Systems;

• AI solutions to intelligent transportation systems;

• Optimization Methods for Complex Problems

• Multi-agent systems for multi-homing big data framework

• Fog computing framework for multi-homing network architectures


Pervasive computing
Internet of Things (IoT)
Big data management
Machine learning
Context-aware data processing
Cloud computing
Edge computing
Distributed computing
Sensor networks
Intelligent devices
Smart homes/buildings/cities
Wearable technology
Cyber-physical systems
Security and privacy in IoT
Energy efficiency in IoT

Published Papers

  • Open Access


    Pervasive Attentive Neural Network for Intelligent Image Classification Based on N-CDE’s

    Anas W. Abulfaraj
    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1137-1156, 2024, DOI:10.32604/cmc.2024.047945
    (This article belongs to the Special Issue: Intelligent Management and Machine Learning for Big Data in IoT-Enabled Pervasive Computing)
    Abstract The utilization of visual attention enhances the performance of image classification tasks. Previous attention-based models have demonstrated notable performance, but many of these models exhibit reduced accuracy when confronted with inter-class and intra-class similarities and differences. Neural-Controlled Differential Equations (N-CDE’s) and Neural Ordinary Differential Equations (NODE’s) are extensively utilized within this context. N-CDE’s possesses the capacity to effectively illustrate both inter-class and intra-class similarities and differences with enhanced clarity. To this end, an attentive neural network has been proposed to generate attention maps, which uses two different types of N-CDE’s, one for adopting hidden layers and the other to generate… More >

  • Open Access


    Intelligent Machine Learning Based Brain Tumor Segmentation through Multi-Layer Hybrid U-Net with CNN Feature Integration

    Sharaf J. Malebary
    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1301-1317, 2024, DOI:10.32604/cmc.2024.047917
    (This article belongs to the Special Issue: Intelligent Management and Machine Learning for Big Data in IoT-Enabled Pervasive Computing)
    Abstract Brain tumors are a pressing public health concern, characterized by their high mortality and morbidity rates. Nevertheless, the manual segmentation of brain tumors remains a laborious and error-prone task, necessitating the development of more precise and efficient methodologies. To address this formidable challenge, we propose an advanced approach for segmenting brain tumor Magnetic Resonance Imaging (MRI) images that harnesses the formidable capabilities of deep learning and convolutional neural networks (CNNs). While CNN-based methods have displayed promise in the realm of brain tumor segmentation, the intricate nature of these tumors, marked by irregular shapes, varying sizes, uneven distribution, and limited available… More >

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