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  • Open Access


    An Artificial Intelligence Algorithm for the Real-Time Early Detection of Sticking Phenomena in Horizontal Shale Gas Wells

    Qing Wang*, Haige Wang, Hongchun Huang, Lubin Zhuo, Guodong Ji

    FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.10, pp. 2569-2578, 2023, DOI:10.32604/fdmp.2023.025349

    Abstract Sticking is the most serious cause of failure in complex drilling operations. In the present work a novel “early warning” method based on an artificial intelligence algorithm is proposed to overcome some of the known problems associated with existing sticking-identification technologies. The method is tested against a practical case study (Southern Sichuan shale gas drilling operations). It is shown that the twelve sets of sticking fault diagnostic results obtained from a simulation are all consistent with the actual downhole state; furthermore, the results from four groups of verification samples are also consistent with the actual downhole state. This shows that… More >

  • Open Access


    Performance Comparison of Deep and Machine Learning Approaches Toward COVID-19 Detection

    Amani Yahyaoui1, Jawad Rasheed2,*, Shtwai Alsubai3, Raed M. Shubair4, Abdullah Alqahtani5, Buket Isler6, Rana Zeeshan Haider7

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2247-2261, 2023, DOI:10.32604/iasc.2023.036840

    Abstract The coronavirus (COVID-19) is a disease declared a global pandemic that threatens the whole world. Since then, research has accelerated and varied to find practical solutions for the early detection and correct identification of this disease. Several researchers have focused on using the potential of Artificial Intelligence (AI) techniques in disease diagnosis to diagnose and detect the coronavirus. This paper developed deep learning (DL) and machine learning (ML) -based models using laboratory findings to diagnose COVID-19. Six different methods are used in this study: K-nearest neighbor (KNN), Decision Tree (DT) and Naive Bayes (NB) as a machine learning method, and… More >

  • Open Access


    XA-GANomaly: An Explainable Adaptive Semi-Supervised Learning Method for Intrusion Detection Using GANomaly

    Yuna Han1, Hangbae Chang2,*

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 221-237, 2023, DOI:10.32604/cmc.2023.039463

    Abstract Intrusion detection involves identifying unauthorized network activity and recognizing whether the data constitute an abnormal network transmission. Recent research has focused on using semi-supervised learning mechanisms to identify abnormal network traffic to deal with labeled and unlabeled data in the industry. However, real-time training and classifying network traffic pose challenges, as they can lead to the degradation of the overall dataset and difficulties preventing attacks. Additionally, existing semi-supervised learning research might need to analyze the experimental results comprehensively. This paper proposes XA-GANomaly, a novel technique for explainable adaptive semi-supervised learning using GANomaly, an image anomalous detection model that dynamically trains… More >

  • Open Access


    An Improved Fully Automated Breast Cancer Detection and Classification System

    Tawfeeq Shawly1, Ahmed A. Alsheikhy2,*

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 731-751, 2023, DOI:10.32604/cmc.2023.039433

    Abstract More than 500,000 patients are diagnosed with breast cancer annually. Authorities worldwide reported a death rate of 11.6% in 2018. Breast tumors are considered a fatal disease and primarily affect middle-aged women. Various approaches to identify and classify the disease using different technologies, such as deep learning and image segmentation, have been developed. Some of these methods reach 99% accuracy. However, boosting accuracy remains highly important as patients’ lives depend on early diagnosis and specified treatment plans. This paper presents a fully computerized method to detect and categorize tumor masses in the breast using two deep-learning models and a classifier… More >

  • Open Access


    Efficient Explanation and Evaluation Methodology Based on Hybrid Feature Dropout

    Jingang Kim, Suengbum Lim, Taejin Lee*

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 471-490, 2023, DOI:10.32604/csse.2023.038413

    Abstract AI-related research is conducted in various ways, but the reliability of AI prediction results is currently insufficient, so expert decisions are indispensable for tasks that require essential decision-making. XAI (eXplainable AI) is studied to improve the reliability of AI. However, each XAI methodology shows different results in the same data set and exact model. This means that XAI results must be given meaning, and a lot of noise value emerges. This paper proposes the HFD (Hybrid Feature Dropout)-based XAI and evaluation methodology. The proposed XAI methodology can mitigate shortcomings, such as incorrect feature weights and impractical feature selection. There are… More >

  • Open Access


    Artificial Intelligence Based Sentence Level Sentiment Analysis of COVID-19

    Sundas Rukhsar1, Mazhar Javed Awan1, Usman Naseem2, Dilovan Asaad Zebari3, Mazin Abed Mohammed4,*, Marwan Ali Albahar5, Mohammed Thanoon5, Amena Mahmoud6

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 791-807, 2023, DOI:10.32604/csse.2023.038384

    Abstract Web-blogging sites such as Twitter and Facebook are heavily influenced by emotions, sentiments, and data in the modern era. Twitter, a widely used microblogging site where individuals share their thoughts in the form of tweets, has become a major source for sentiment analysis. In recent years, there has been a significant increase in demand for sentiment analysis to identify and classify opinions or expressions in text or tweets. Opinions or expressions of people about a particular topic, situation, person, or product can be identified from sentences and divided into three categories: positive for good, negative for bad, and neutral for… More >

  • Open Access


    Machine Learning Based Cybersecurity Threat Detection for Secure IoT Assisted Cloud Environment

    Z. Faizal Khan1, Saeed M. Alshahrani2,*, Abdulrahman Alghamdi2, Someah Alangari3, Nouf Ibrahim Altamami4, Khalid A. Alissa5, Sana Alazwari6, Mesfer Al Duhayyim7, Fahd N. Al-Wesabi8

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 855-871, 2023, DOI:10.32604/csse.2023.036735

    Abstract The Internet of Things (IoT) is determine enormous economic openings for industries and allow stimulating innovation which obtain between domains in childcare for eldercare, in health service to energy, and in developed to transport. Cybersecurity develops a difficult problem in IoT platform whereas the presence of cyber-attack requires that solved. The progress of automatic devices for cyber-attack classifier and detection employing Artificial Intelligence (AI) and Machine Learning (ML) devices are crucial fact to realize security in IoT platform. It can be required for minimizing the issues of security based on IoT devices efficiently. Thus, this research proposal establishes novel mayfly… More >

  • Open Access


    Deep Belief Network for Lung Nodule Segmentation and Cancer Detection

    Sindhuja Manickavasagam*, Poonkuzhali Sugumaran

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 135-151, 2023, DOI:10.32604/csse.2023.030344

    Abstract Cancer disease is a deadliest disease cause more dangerous one. By identifying the disease through Artificial intelligence to getting the mage features directly from patients. This paper presents the lung knob division and disease characterization by proposing an enhancement calculation. Most of the machine learning techniques failed to observe the feature dimensions leads inaccuracy in feature selection and classification. This cause inaccuracy in sensitivity and specificity rate to reduce the identification accuracy. To resolve this problem, to propose a Chicken Sine Cosine Algorithm based Deep Belief Network to identify the disease factor. The general technique of the created approach includes… More >

  • Open Access


    Design and Development of an Intelligent Energy Management System for a Smart Grid to Enhance the Power Quality

    Nisha Vasudevan1,*, Vasudevan Venkatraman2, A. Ramkumar1, T. Muthukumar3, A. Sheela4, M. Vetrivel5, R. J. Vijaya Saraswathi6, F. T. Josh7

    Energy Engineering, Vol.120, No.8, pp. 1747-1761, 2023, DOI:10.32604/ee.2023.027821

    Abstract MigroGrid (MG) has emerged to resolve the growing demand for energy. But because of its inconsistent output, it can result in various power quality (PQ) issues. PQ is a problem that is becoming more and more important for the reliability of power systems that use renewable energy sources. Similarly, the employment of nonlinear loads will introduce harmonics into the system and, as a result, cause distortions in the current and voltage waveforms as well as low power quality issues in the supply system. Thus, this research focuses on power quality enhancement in the MG using hybrid shunt filters. However, the… More >

  • Open Access


    Implementation of Strangely Behaving Intelligent Agents to Determine Human Intervention During Reinforcement Learning

    Christopher C. Rosser, Wilbur L. Walters, Abdulghani M. Abdulghani, Mokhles M. Abdulghani, Khalid H. Abed*

    Journal on Artificial Intelligence, Vol.4, No.4, pp. 261-277, 2022, DOI:10.32604/jai.2022.039703

    Abstract Intrinsic motivation helps autonomous exploring agents traverse a larger portion of their environments. However, simulations of different learning environments in previous research show that after millions of timesteps of successful training, an intrinsically motivated agent may learn to act in ways unintended by the designer. This potential for unintended actions of autonomous exploring agents poses threats to the environment and humans if operated in the real world. We investigated this topic by using Unity’s Machine Learning Agent Toolkit (ML-Agents) implementation of the Proximal Policy Optimization (PPO) algorithm with the Intrinsic Curiosity Module (ICM) to train autonomous exploring agents in three… More >

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