Special Issue "Emerging Trends in Artificial Intelligence and Machine Learning"

Submission Deadline: 30 April 2021 (closed)
Guest Editors
Dr. Mohammad Tabrez Quasim, University of Bisha, Saudi Arabia.
Dr. Kapal Dev, Trinity College Dublin, Ireland.
Dr. Surbhi Bhatia, King Faisal University, Saudi Arabia.
Dr. Rihem Farkh, King Saud university, Saudi Arabia.


Artificial Intelligence and Deep Learning are offering practical tools for many engineering applications. Computer learning, artificial intelligence and its learning, adaptation paradigms are able to improve engineering applications. This covers topics like logic, evolutionary algorithms, neural networks, and DNA computation. These methods can be very effective in dealing with uncertainties and contextual vagueness inherent in the decisions. The computer science study is able to lift the convergence on machine learning and artificial intelligence computing. This is possible to apply machine learning and artificial intelligence for data processing and engineering applications.

This special issue will focus on the problems that can be quickly addressed by using machine learning, deep learning, AI techniques and optimization algorithms.

• Artificial Intelligence for Engineering Application
• Machine Learning for Data Science
• Soft Computing for Emerging Applications
• Optimization Algorithms
• Genetic Algorithms
• Swarm Optimization
• Deep Learning
• Data Analytics

Published Papers
  • Using Link-Based Consensus Clustering for Mixed-Type Data Analysis
  • Abstract A mix between numerical and nominal data types commonly presents many modern-age data collections. Examples of these include banking data, sales history and healthcare records, where both continuous attributes like age and nominal ones like blood type are exploited to characterize account details, business transactions or individuals. However, only a few standard clustering techniques and consensus clustering methods are provided to examine such a data thus far. Given this insight, the paper introduces novel extensions of link-based cluster ensemble, and that are accurate for analyzing mixed-type data. They promote diversity within an ensemble through different initializations of the k-prototypes algorithm… More
  •   Views:75       Downloads:46        Download PDF

  • Droid-IoT: Detect Android IoT Malicious Applications Using ML and Blockchain
  • Abstract One of the most rapidly growing areas in the last few years is the Internet of Things (IoT), which has been used in widespread fields such as healthcare, smart homes, and industries. Android is one of the most popular operating systems (OS) used by IoT devices for communication and data exchange. Android OS captured more than 70 percent of the market share in 2021. Because of the popularity of the Android OS, it has been targeted by cybercriminals who have introduced a number of issues, such as stealing private information. As reported by one of the recent studies Android malware… More
  •   Views:107       Downloads:62        Download PDF

  • Blockchain Based Enhanced ERP Transaction Integrity Architecture and PoET Consensus
  • Abstract Enterprise Resource Planning (ERP) software is extensively used for the management of business processes. ERP offers a system of integrated applications with a shared central database. Storing all business-critical information in a central place raises various issues such as data integrity assurance and a single point of failure, which makes the database vulnerable. This paper investigates database and Blockchain integration, where the Blockchain network works in synchronization with the database system, and offers a mechanism to validate the transactions and ensure data integrity. Limited research exists on Blockchain-based solutions for the single point of failure in ERP. We established in… More
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  • An Ensemble Learning Based Approach for Detecting and Tracking COVID19 Rumors
  • Abstract Rumors regarding epidemic diseases such as COVID 19, medicines and treatments, diagnostic methods and public emergencies can have harmful impacts on health and political, social and other aspects of people’s lives, especially during emergency situations and health crises. With huge amounts of content being posted to social media every second during these situations, it becomes very difficult to detect fake news (rumors) that poses threats to the stability and sustainability of the healthcare sector. A rumor is defined as a statement for which truthfulness has not been verified. During COVID 19, people found difficulty in obtaining the most truthful news… More
  •   Views:86       Downloads:64        Download PDF

  • Recurrent Convolutional Neural Network MSER-Based Approach for Payable Document Processing
  • Abstract A tremendous amount of vendor invoices is generated in the corporate sector. To automate the manual data entry in payable documents, highly accurate Optical Character Recognition (OCR) is required. This paper proposes an end-to-end OCR system that does both localization and recognition and serves as a single unit to automate payable document processing such as cheques and cash disbursement. For text localization, the maximally stable extremal region is used, which extracts a word or digit chunk from an invoice. This chunk is later passed to the deep learning model, which performs text recognition. The deep learning model utilizes both convolution… More
  •   Views:179       Downloads:101        Download PDF

  • Automatic Detection of COVID-19 Using a Stacked Denoising Convolutional Autoencoder
  • Abstract The exponential increase in new coronavirus disease 2019 ({COVID-19}) cases and deaths has made COVID-19 the leading cause of death in many countries. Thus, in this study, we propose an efficient technique for the automatic detection of COVID-19 and pneumonia based on X-ray images. A stacked denoising convolutional autoencoder (SDCA) model was proposed to classify X-ray images into three classes: normal, pneumonia, and {COVID-19}. The SDCA model was used to obtain a good representation of the input data and extract the relevant features from noisy images. The proposed model’s architecture mainly composed of eight autoencoders, which were fed to two… More
  •   Views:154       Downloads:107        Download PDF

  • Adaptive Error Curve Learning Ensemble Model for Improving Energy Consumption Forecasting
  • Abstract Despite the advancement within the last decades in the field of smart grids, energy consumption forecasting utilizing the metrological features is still challenging. This paper proposes a genetic algorithm-based adaptive error curve learning ensemble (GA-ECLE) model. The proposed technique copes with the stochastic variations of improving energy consumption forecasting using a machine learning-based ensembled approach. A modified ensemble model based on a utilizing error of model as a feature is used to improve the forecast accuracy. This approach combines three models, namely CatBoost (CB), Gradient Boost (GB), and Multilayer Perceptron (MLP). The ensembled CB-GB-MLP model’s inner mechanism consists of generating… More
  •   Views:374       Downloads:327        Download PDF

  • Intelligent Multiclass Skin Cancer Detection Using Convolution Neural Networks
  • Abstract The worldwide mortality rate due to cancer is second only to cardiovascular diseases. The discovery of image processing, latest artificial intelligence techniques, and upcoming algorithms can be used to effectively diagnose and prognose cancer faster and reduce the mortality rate. Efficiently applying these latest techniques has increased the survival chances during recent years. The research community is making significant continuous progress in developing automated tools to assist dermatologists in decision making. The datasets used for the experimentation and analysis are ISBI 2016, ISBI 2017, and HAM 10000. In this work pertained models are used to extract the efficient feature. The… More
  •   Views:508       Downloads:529        Download PDF