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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.

Summary

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.

Keywords

• 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

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  • Open Access

    ARTICLE

    Handling Class Imbalance in Online Transaction Fraud Detection

    Kanika, Jimmy Singla, Ali Kashif Bashir, Yunyoung Nam, Najam UI Hasan, Usman Tariq
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2861-2877, 2022, DOI:10.32604/cmc.2022.019990
    （This article belongs to this Special Issue: Emerging Trends in Artificial Intelligence and Machine Learning)
    Abstract With the rise of internet facilities, a greater number of people have started doing online transactions at an exponential rate in recent years as the online transaction system has eliminated the need of going to the bank physically for every transaction. However, the fraud cases have also increased causing the loss of money to the consumers. Hence, an effective fraud detection system is the need of the hour which can detect fraudulent transactions automatically in real-time. Generally, the genuine transactions are large in number than the fraudulent transactions which leads to the class imbalance problem. In this research work, an… More >

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    2050

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    1489

  • Open Access

    ARTICLE

    Deep Reinforcement Learning Model for Blood Bank Vehicle Routing Multi-Objective Optimization

    Meteb M. Altaf, Ahmed Samir Roshdy, Hatoon S. AlSagri
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3955-3967, 2022, DOI:10.32604/cmc.2022.019448
    （This article belongs to this Special Issue: Emerging Trends in Artificial Intelligence and Machine Learning)
    Abstract The overall healthcare system has been prioritized within development top lists worldwide. Since many national populations are aging, combined with the availability of sophisticated medical treatments, healthcare expenditures are rapidly growing. Blood banks are a major component of any healthcare system, which store and provide the blood products needed for organ transplants, emergency medical treatments, and routine surgeries. Timely delivery of blood products is vital, especially in emergency settings. Hence, blood delivery process parameters such as safety and speed have received attention in the literature, as well as other parameters such as delivery cost. In this paper, delivery time and… More >

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    1619

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    1179

  • Open Access

    ARTICLE

    Using Link-Based Consensus Clustering for Mixed-Type Data Analysis

    Tossapon Boongoen, Natthakan Iam-On
    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1993-2011, 2022, DOI:10.32604/cmc.2022.019776
    （This article belongs to this Special Issue: Emerging Trends in Artificial Intelligence and Machine Learning)
    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 >

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    1673

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    1090

  • Open Access

    ARTICLE

    Droid-IoT: Detect Android IoT Malicious Applications Using ML and Blockchain

    Hani Mohammed Alshahrani
    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 739-766, 2022, DOI:10.32604/cmc.2022.019623
    （This article belongs to this Special Issue: Emerging Trends in Artificial Intelligence and Machine Learning)
    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 >

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    2058

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    1428

  • Open Access

    ARTICLE

    Defect Detection in Printed Circuit Boards with Pre-Trained Feature Extraction Methodology with Convolution Neural Networks

    Mohammed A. Alghassab
    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 637-652, 2022, DOI:10.32604/cmc.2022.019527
    （This article belongs to this Special Issue: Emerging Trends in Artificial Intelligence and Machine Learning)
    Abstract Printed Circuit Boards (PCBs) are very important for proper functioning of any electronic device. PCBs are installed in almost all the electronic device and their functionality is dependent on the perfection of PCBs. If PCBs do not function properly then the whole electric machine might fail. So, keeping this in mind researchers are working in this field to develop error free PCBs. Initially these PCBs were examined by the human beings manually, but the human error did not give good results as sometime defected PCBs were categorized as non-defective. So, researchers and experts transformed this manual traditional examination to automated… More >

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    2753

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    2440

  • Open Access

    ARTICLE

    Blockchain Based Enhanced ERP Transaction Integrity Architecture and PoET Consensus

    Tehreem Aslam, Ayesha Maqbool, Maham Akhtar, Alina Mirza, Muhammad Anees Khan, Wazir Zada Khan, Shadab Alam
    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1089-1109, 2022, DOI:10.32604/cmc.2022.019416
    （This article belongs to this Special Issue: Emerging Trends in Artificial Intelligence and Machine Learning)
    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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    2723

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    1553

    Cited by

    2

  • Open Access

    ARTICLE

    An Ensemble Learning Based Approach for Detecting and Tracking COVID19 Rumors

    Sultan Noman Qasem, Mohammed Al-Sarem, Faisal Saeed
    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1721-1747, 2022, DOI:10.32604/cmc.2022.018972
    （This article belongs to this Special Issue: Emerging Trends in Artificial Intelligence and Machine Learning)
    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 >

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    3444

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    1505

    Cited by

    1

  • Open Access

    ARTICLE

    Recurrent Convolutional Neural Network MSER-Based Approach for Payable Document Processing

    Suliman Aladhadh, Hidayat Ur Rehman, Ali Mustafa Qamar, Rehan Ullah Khan
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3399-3411, 2021, DOI:10.32604/cmc.2021.018724
    （This article belongs to this Special Issue: Emerging Trends in Artificial Intelligence and Machine Learning)
    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 >

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    1717

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    989

  • Open Access

    ARTICLE

    Automatic Detection of COVID-19 Using a Stacked Denoising Convolutional Autoencoder

    Habib Dhahri, Besma Rabhi, Slaheddine Chelbi, Omar Almutiry, Awais Mahmood, Adel M. Alimi
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3259-3274, 2021, DOI:10.32604/cmc.2021.018449
    （This article belongs to this Special Issue: Emerging Trends in Artificial Intelligence and Machine Learning)
    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 >

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    1864

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    1034

    Cited by

    1

  • Open Access

    ARTICLE

    Adaptive Error Curve Learning Ensemble Model for Improving Energy Consumption Forecasting

    Prince Waqas Khan, Yung-Cheol Byun
    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1893-1913, 2021, DOI:10.32604/cmc.2021.018523
    （This article belongs to this Special Issue: Emerging Trends in Artificial Intelligence and Machine Learning)
    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 >

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    2007

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    1323

    Cited by

    2

  • Open Access

    ARTICLE

    Intelligent Multiclass Skin Cancer Detection Using Convolution Neural Networks

    Reham Alabduljabbar, Hala Alshamlan
    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 831-847, 2021, DOI:10.32604/cmc.2021.018402
    （This article belongs to this Special Issue: Emerging Trends in Artificial Intelligence and Machine Learning)
    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 >

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    2628

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    1621

  • Open Access

    ARTICLE

    LOA-RPL: Novel Energy-Efficient Routing Protocol for the Internet of Things Using Lion Optimization Algorithm to Maximize Network Lifetime

    Sankar Sennan, Somula Ramasubbareddy, Anand Nayyar, Yunyoung Nam, Mohamed Abouhawwash
    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 351-371, 2021, DOI:10.32604/cmc.2021.017360
    （This article belongs to this Special Issue: Emerging Trends in Artificial Intelligence and Machine Learning)
    Abstract Energy conservation is a significant task in the Internet of Things (IoT) because IoT involves highly resource-constrained devices. Clustering is an effective technique for saving energy by reducing duplicate data. In a clustering protocol, the selection of a cluster head (CH) plays a key role in prolonging the lifetime of a network. However, most cluster-based protocols, including routing protocols for low-power and lossy networks (RPLs), have used fuzzy logic and probabilistic approaches to select the CH node. Consequently, early battery depletion is produced near the sink. To overcome this issue, a lion optimization algorithm (LOA) for selecting CH in RPL… More >

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    2399

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    1685

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