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

Submission Deadline: 28 February 2021 (closed)
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
  • Dynamic Voting Classifier for Risk Identification in Supply Chain 4.0
  • Abstract Supply chain 4.0 refers to the fourth industrial revolution’s supply chain management systems, which integrate the supply chain’s manufacturing operations, information technology, and telecommunication processes. Although supply chain 4.0 aims to improve supply chains’ production systems and profitability, it is subject to different operational and disruptive risks. Operational risks are a big challenge in the cycle of supply chain 4.0 for controlling the demand and supply operations to produce and deliver products across IT systems. This paper proposes a voting classifier to identify the operational risks in the supply chain 4.0 based on a Sine Cosine Dynamic Group (SCDG) algorithm.… More
  •   Views:199       Downloads:149        Download PDF

  • An Intelligent Graph Edit Distance-Based Approach for Finding Business Process Similarities
  • Abstract There are numerous application areas of computing similarity between process models. It includes finding similar models from a repository, controlling redundancy of process models, and finding corresponding activities between a pair of process models. The similarity between two process models is computed based on their similarity between labels, structures, and execution behaviors. Several attempts have been made to develop similarity techniques between activity labels, as well as their execution behavior. However, a notable problem with the process model similarity is that two process models can also be similar if there is a structural variation between them. However, neither a benchmark… More
  •   Views:187       Downloads:125        Download PDF


  • Predicting the Need for ICU Admission in COVID-19 Patients Using XGBoost
  • Abstract It is important to determine early on which patients require ICU admissions in managing COVID-19 especially when medical resources are limited. Delay in ICU admissions is associated with negative outcomes such as mortality and cost. Therefore, early identification of patients with a high risk of respiratory failure can prevent complications, enhance risk stratification, and improve the outcomes of severely-ill hospitalized patients. In this paper, we develop a model that uses the characteristics and information collected at the time of patients’ admissions and during their early period of hospitalization to accurately predict whether they will need ICU admissions. We use the… More
  •   Views:424       Downloads:374        Download PDF

  • Brain Tumour Detection by Gamma DeNoised Wavelet Segmented Entropy Classifier
  • Abstract Magnetic resonance imaging (MRI) is an essential tool for detecting brain tumours. However, identification of brain tumours in the early stages is a very complex task since MRI images are susceptible to noise and other environmental obstructions. In order to overcome these problems, a Gamma MAP denoised Strömberg wavelet segmentation based on a maximum entropy classifier (GMDSWS-MEC) model is developed for efficient tumour detection with high accuracy and low time consumption. The GMDSWS-MEC model performs three steps, namely pre-processing, segmentation, and classification. Within the GMDSWS-MEC model, the Gamma MAP filter performs the pre-processing task and achieves a significant increase in… More
  •   Views:394       Downloads:358        Download PDF


  • An E-Business Event Stream Mechanism for Improving User Tracing Processes
  • Abstract With the rapid development in business transactions, especially in recent years, it has become necessary to develop different mechanisms to trace business user records in web server log in an efficient way. Online business transactions have increased, especially when the user or customer cannot obtain the required service. For example, with the spread of the epidemic Coronavirus (COVID-19) throughout the world, there is a dire need to rely more on online business processes. In order to improve the efficiency and performance of E-business structure, a web server log must be well utilized to have the ability to trace and record… More
  •   Views:522       Downloads:481        Download PDF




  • Context and Machine Learning Based Trust Management Framework for Internet of Vehicles
  • Abstract Trust is one of the core components of any ad hoc network security system. Trust management (TM) has always been a challenging issue in a vehicular network. One such developing network is the Internet of vehicles (IoV), which is expected to be an essential part of smart cities. IoV originated from the merger of Vehicular ad hoc networks (VANET) and the Internet of things (IoT). Security is one of the main barriers in the on-road IoV implementation. Existing security standards are insufficient to meet the extremely dynamic and rapidly changing IoV requirements. Trust plays a vital role in ensuring security,… More
  •   Views:713       Downloads:608        Download PDF

  • Development of Social Media Analytics System for Emergency Event Detection and Crisis Management
  • Abstract Social media platforms have proven to be effective for information gathering during emergency events caused by natural or human-made disasters. Emergency response authorities, law enforcement agencies, and the public can use this information to gain situational awareness and improve disaster response. In case of emergencies, rapid responses are needed to address victims’ requests for help. The research community has developed many social media platforms and used them effectively for emergency response and coordination in the past. However, most of the present deployments of platforms in crisis management are not automated, and their operational success largely depends on experts who analyze… More
  •   Views:602       Downloads:607        Download PDF

  • Predicted Oil Recovery Scaling-Law Using Stochastic Gradient Boosting Regression Model
  • Abstract In the process of oil recovery, experiments are usually carried out on core samples to evaluate the recovery of oil, so the numerical data are fitted into a non-dimensional equation called scaling-law. This will be essential for determining the behavior of actual reservoirs. The global non-dimensional time-scale is a parameter for predicting a realistic behavior in the oil field from laboratory data. This non-dimensional universal time parameter depends on a set of primary parameters that inherit the properties of the reservoir fluids and rocks and the injection velocity, which dynamics of the process. One of the practical machine learning (ML)… More
  •   Views:494       Downloads:485        Download PDF

  • Cognitive Skill Enhancement System Using Neuro-Feedback for ADHD Patients
  • Abstract The National Health Interview Survey (NHIS) shows that there are 13.2% of children at the age of 11 to 17 who are suffering from Attention Deficit Hyperactivity Disorder (ADHD), globally. The treatment methods for ADHD are either psycho-stimulant medications or cognitive therapy. These traditional methods, namely therapy, need a large number of visits to hospitals and include medication. Neurogames could be used for the effective treatment of ADHD. It could be a helpful tool in improving children and ADHD patients’ cognitive skills by using Brain–Computer Interfaces (BCI). BCI enables the user to interact with the computer through brain activity using… More
  •   Views:705       Downloads:493        Download PDF

  • A Genetic Based Leader Election Algorithm for IoT Cloud Data Processing
  • Abstract In IoT networks, nodes communicate with each other for computational services, data processing, and resource sharing. Most of the time huge data is generated at the network edge due to extensive communication between IoT devices. So, this tidal data is transferred to the cloud data center (CDC) for efficient processing and effective data storage. In CDC, leader nodes are responsible for higher performance, reliability, deadlock handling, reduced latency, and to provide cost-effective computational services to the users. However, the optimal leader selection is a computationally hard problem as several factors like memory, CPU MIPS, and bandwidth, etc., are needed to… More
  •   Views:622       Downloads:584        Download PDF

  • A Machine Learning Based Algorithm to Process Partial Shading Effects in PV Arrays
  • Abstract Solar energy is a widely used type of renewable energy. Photovoltaic arrays are used to harvest solar energy. The major goal, in harvesting the maximum possible power, is to operate the system at its maximum power point (MPP). If the irradiation conditions are uniform, the P-V curve of the PV array has only one peak that is called its MPP. But when the irradiation conditions are non-uniform, the P-V curve has multiple peaks. Each peak represents an MPP for a specific irradiation condition. The highest of all the peaks is called Global Maximum Power Point (GMPP). Under uniform irradiation conditions,… More
  •   Views:941       Downloads:669        Download PDF

  • Deep Learning-Based Hybrid Intelligent Intrusion Detection System
  • Abstract Machine learning (ML) algorithms are often used to design effective intrusion detection (ID) systems for appropriate mitigation and effective detection of malicious cyber threats at the host and network levels. However, cybersecurity attacks are still increasing. An ID system can play a vital role in detecting such threats. Existing ID systems are unable to detect malicious threats, primarily because they adopt approaches that are based on traditional ML techniques, which are less concerned with the accurate classification and feature selection. Thus, developing an accurate and intelligent ID system is a priority. The main objective of this study was to develop… More
  •   Views:850       Downloads:668        Download PDF

  • COVID19: Forecasting Air Quality Index and Particulate Matter (PM2.5)
  • Abstract Urbanization affects the quality of the air, which has drastically degraded in the past decades. Air quality level is determined by measures of several air pollutant concentrations. To create awareness among people, an automation system that forecasts the quality is needed. The COVID-19 pandemic and the restrictions it has imposed on anthropogenic activities have resulted in a drop in air pollution in various cities in India. The overall air quality index (AQI) at any particular time is given as the maximum band for any pollutant. PM2.5 is a fine particulate matter of a size less than 2.5 micrometers, the inhalation… More
  •   Views:1209       Downloads:1128        Download PDF

  • Recognition and Detection of Diabetic Retinopathy Using Densenet-65 Based Faster-RCNN
  • Abstract Diabetes is a metabolic disorder that results in a retinal complication called diabetic retinopathy (DR) which is one of the four main reasons for sightlessness all over the globe. DR usually has no clear symptoms before the onset, thus making disease identification a challenging task. The healthcare industry may face unfavorable consequences if the gap in identifying DR is not filled with effective automation. Thus, our objective is to develop an automatic and cost-effective method for classifying DR samples. In this work, we present a custom Faster-RCNN technique for the recognition and classification of DR lesions from retinal images. After… More
  •   Views:1714       Downloads:1255        Download PDF

  • QI-BRiCE: Quality Index for Bleeding Regions in Capsule Endoscopy Videos
  • Abstract With the advent in services such as telemedicine and telesurgery, provision of continuous quality monitoring for these services has become a challenge for the network operators. Quality standards for provision of such services are application specific as medical imagery is quite different than general purpose images and videos. This paper presents a novel full reference objective video quality metric that focuses on estimating the quality of wireless capsule endoscopy (WCE) videos containing bleeding regions. Bleeding regions in gastrointestinal tract have been focused in this research, as bleeding is one of the major reasons behind several diseases within the tract. The… More
  •   Views:805       Downloads:545        Download PDF

  • Performance of Lung Cancer Prediction Methods Using Different Classification Algorithms
  • Abstract In 2018, 1.76 million people worldwide died of lung cancer. Most of these deaths are due to late diagnosis, and early-stage diagnosis significantly increases the likelihood of a successful treatment for lung cancer. Machine learning is a branch of artificial intelligence that allows computers to quickly identify patterns within complex and large datasets by learning from existing data. Machine-learning techniques have been improving rapidly and are increasingly used by medical professionals for the successful classification and diagnosis of early-stage disease. They are widely used in cancer diagnosis. In particular, machine learning has been used in the diagnosis of lung cancer… More
  •   Views:834       Downloads:776        Download PDF

  • Aspect-Based Sentiment Analysis for Polarity Estimation of Customer Reviews on Twitter
  • Abstract Most consumers read online reviews written by different users before making purchase decisions, where each opinion expresses some sentiment. Therefore, sentiment analysis is currently a hot topic of research. In particular, aspect-based sentiment analysis concerns the exploration of emotions, opinions and facts that are expressed by people, usually in the form of polarity. It is crucial to consider polarity calculations and not simply categorize reviews as positive, negative, or neutral. Currently, the available lexicon-based method accuracy is affected by limited coverage. Several of the available polarity estimation techniques are too general and may not reflect the aspect/topic in question if… More
  •   Views:1214       Downloads:789        Download PDF

  • 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:936       Downloads:627        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:1346       Downloads:873        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:923       Downloads:590        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:1217       Downloads:721        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:790       Downloads:612        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:1030       Downloads:680        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:1360       Downloads:764        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:1283       Downloads:868        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:2084       Downloads:1233        Download PDF