Special Issue "Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications"

Submission Deadline: 28 February 2021
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Guest Editors
Dr. MUHAMMAD ADNAN KHAN, Lahore Garrison University, Pakistan.
Dr. RIZWAN ALI NAQVI, Sejong University, Korea.
Dr. MOHAMMED A. ALGHAMDI, Umm Al-Qura University, Saudi Arabia.

Summary

Machine learning has been a subject of increasing concern to scholars, both from academia and business, over the past few years. Unlike conventional learning methods, machine learning methods suggest the potential to learn and develop very broad sets of data. Machine learning methods in computer vision, natural language analysis, robots, and other fields have gained considerable popularity in numerous activities. Recent years have seen a tremendous advancement of the principle of machine learning and numerous implementations in the general area of artificial intelligence, including neural network architecture, automation, statistical analysis and deep learning.

Though machine learning has been extensively explored in recent decades, the use of machine learning strategies in intelligent systems faces several complexities. Well first of all, machine learning methods need a vast and varied amount of data as input to frameworks and provide a wide range of training requirements. Secondly, the teaching of machine learning models is quick to slip into overfitting issues. Furthermore, because machine learning systems have uncertainty or backbox problems, it is challenging to consider how a given algorithm makes a judgment, which is essential in certain fields such as financial trading or medical diagnosis.

Suggested topics include, but are not limited to, the following:

• Agent and Multi-Agent Systems

• Artificial Intelligence Applications

• Artificial Neural Networks

• Autonomous and Ubiquitous Computing

• Biomedical systems

• Colour/Image Analysis

• Computational Intelligence

• Computer Vision

• Cybersecurity and AI

• Distributed AI Systems and Architectures

• eBusiness, eCommerce, eHealth, eLearning

• Finance and AI

• Extreme Machine Learning

• Forensic Science

• Grid-Based Computing

• Internet of Things (IoT), IoMT, AIoT & AIoMT

• Medical Informatics and Biomedical

• Natural Language Processing

• Object and Face Recognition

• Pattern Recognition

• Robotics and Virtual Reality

• Signal and Image Processing

• Signal Processing Techniques

• Knowledge Extraction

• Smart Grids

• Smart City

• Time Series and Forecasting


Keywords
• AIoT
• IoMT
• MUD
• Fuzzy
• Swarm Intelligence
• Evolutionary Algorithms
• Neural Networks
• Extreme machine learning
• Smart Health
• Smart Traffic
• Intelligent Bussiness
• Image Processing

Published Papers
  • Identifying Driver Genes Mutations with Clinical Significance in Thyroid Cancer
  • Abstract Advances in technology are enabling gene mutations in papillary thyroid carcinoma (PTC) to be analyzed and clinical outcomes, such as recurrence, to be predicted. To date, the most common genetic mutation in PTC is in BRAF kinase (BRAF). However, whether mutations in other genes coincide with those in BRAF remains to be clarified. The aim of this study was to find mutations in other genes that co-exist with mutated BRAF, and to analyze their frequency and clinical relevance in PTC. Clinical and genetic data were collected from 213 PTC patients with a total of 36,572 mutation sites in 735 genes.… More
  •   Views:155       Downloads:89        Download PDF

  • Collision Observation-Based Optimization of Low-Power and Lossy IoT Network Using Reinforcement Learning
  • Abstract The Internet of Things (IoT) has numerous applications in every domain, e.g., smart cities to provide intelligent services to sustainable cities. The next-generation of IoT networks is expected to be densely deployed in a resource-constrained and lossy environment. The densely deployed nodes producing radically heterogeneous traffic pattern causes congestion and collision in the network. At the medium access control (MAC) layer, mitigating channel collision is still one of the main challenges of future IoT networks. Similarly, the standardized network layer uses a ranking mechanism based on hop-counts and expected transmission counts (ETX), which often does not adapt to the dynamic… More
  •   Views:132       Downloads:78        Download PDF

  • Cardiac Arrhythmia Disease Classification Using LSTM Deep Learning Approach
  • Abstract Many approaches have been tried for the classification of arrhythmia. Due to the dynamic nature of electrocardiogram (ECG) signals, it is challenging to use traditional handcrafted techniques, making a machine learning (ML) implementation attractive. Competent monitoring of cardiac arrhythmia patients can save lives. Cardiac arrhythmia prediction and classification has improved significantly during the last few years. Arrhythmias are a group of conditions in which the electrical activity of the heart is abnormal, either faster or slower than normal. It is the most frequent cause of death for both men and women every year in the world. This paper presents a… More
  •   Views:121       Downloads:60        Download PDF

  • A Bio-Inspired Routing Optimization in UAV-enabled Internet of Everything
  • Abstract Internet of Everything (IoE) indicates a fantastic vision of the future, where everything is connected to the internet, providing intelligent services and facilitating decision making. IoE is the collection of static and moving objects able to coordinate and communicate with each other. The moving objects may consist of ground segments and flying segments. The speed of flying segment e.g., Unmanned Ariel Vehicles (UAVs) may high as compared to ground segment objects. The topology changes occur very frequently due to high speed nature of objects in UAV-enabled IoE (Ue-IoE). The routing maintenance overhead may increase when scaling the Ue-IoE (number of… More
  •   Views:112       Downloads:58        Download PDF

  • Authenblue: A New Authentication Protocol for the Industrial Internet of Things
  • Abstract The Internet of Things (IoT) is where almost anything can be controlled and managed remotely by means of sensors. Although the IoT evolution led to quality of life enhancement, many of its devices are insecure. The lack of robust key management systems, efficient identity authentication, low fault tolerance, and many other issues lead to IoT devices being easily targeted by attackers. In this paper we propose a new authentication protocol called Authenblue that improve the authentication process of IoT devices and Coordinators of Personal Area Network (CPANs) in an Industrial IoT (IIoT) environment. This study proposed Authenblue protocol as a… More
  •   Views:92       Downloads:55        Download PDF

  • Intelligent Cloud Based Load Balancing System Empowered with Fuzzy Logic
  • Abstract Cloud computing is seeking attention as a new computing paradigm to handle operations more efficiently and cost-effectively. Cloud computing uses dynamic resource provisioning and de-provisioning in a virtualized environment. The load on the cloud data centers is growing day by day due to the rapid growth in cloud computing demand. Elasticity in cloud computing is one of the fundamental properties, and elastic load balancing automatically distributes incoming load to multiple virtual machines. This work is aimed to introduce efficient resource provisioning and de-provisioning for better load balancing. In this article, a model is proposed in which the fuzzy logic approach… More
  •   Views:93       Downloads:57        Download PDF

  • Machine Learning Enabled Early Detection of Breast Cancer by Structural Analysis of Mammograms
  • Abstract Clinical image processing plays a significant role in healthcare systems and is currently a widely used methodology. In carcinogenic diseases, time is crucial; thus, an image’s accurate analysis can help treat disease at an early stage. Ductal carcinoma in situ (DCIS) and lobular carcinoma in situ (LCIS) are common types of malignancies that affect both women and men. The number of cases of DCIS and LCIS has increased every year since 2002, while it still takes a considerable amount of time to recommend a controlling technique. Image processing is a powerful technique to analyze preprocessed images to retrieve useful information… More
  •   Views:114       Downloads:69        Download PDF

  • Understanding Research Trends in Android Malware Research Using Information Modelling Techniques
  • Abstract Android has been dominating the smartphone market for more than a decade and has managed to capture 87.8% of the market share. Such popularity of Android has drawn the attention of cybercriminals and malware developers. The malicious applications can steal sensitive information like contacts, read personal messages, record calls, send messages to premium-rate numbers, cause financial loss, gain access to the gallery and can access the user’s geographic location. Numerous surveys on Android security have primarily focused on types of malware attack, their propagation, and techniques to mitigate them. To the best of our knowledge, Android malware literature has never… More
  •   Views:271       Downloads:200        Download PDF

  • A Real-Time Sequential Deep Extreme Learning Machine Cybersecurity Intrusion Detection System
  • Abstract In recent years, cybersecurity has attracted significant interest due to the rapid growth of the Internet of Things (IoT) and the widespread development of computer infrastructure and systems. It is thus becoming particularly necessary to identify cyber-attacks or irregularities in the system and develop an efficient intrusion detection framework that is integral to security. Researchers have worked on developing intrusion detection models that depend on machine learning (ML) methods to address these security problems. An intelligent intrusion detection device powered by data can exploit artificial intelligence (AI), and especially ML, techniques. Accordingly, we propose in this article an intrusion detection… More
  •   Views:356       Downloads:202        Download PDF

  • Hajj Crowd Management Using CNN-Based Approach
  • Abstract Hajj as the Muslim holy pilgrimage, attracts millions of humans to Mecca every year. According to statists, the pilgrimage has attracted close to 2.5 million pilgrims in 2019, and at its peak, it has attracted over 3 million pilgrims in 2012. It is considered as the world’s largest human gathering. Safety makes one of the main concerns with regards to managing the large crowds and ensuring that stampedes and other similar overcrowding accidents are avoided. This paper presents a crowd management system using image classification and an alarm system for managing the millions of crowds during Hajj. The image classification… More
  •   Views:587       Downloads:332        Download PDF