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  • Open Access

    ARTICLE

    Explainable AI and Interpretable Model for Insurance Premium Prediction

    Umar Abdulkadir Isa*, Anil Fernando*

    Journal on Artificial Intelligence, Vol.5, pp. 31-42, 2023, DOI:10.32604/jai.2023.040213

    Abstract Traditional machine learning metrics (TMLMs) are quite useful for the current research work precision, recall, accuracy, MSE and RMSE. Not enough for a practitioner to be confident about the performance and dependability of innovative interpretable model 85%–92%. We included in the prediction process, machine learning models (MLMs) with greater than 99% accuracy with a sensitivity of 95%–98% and specifically in the database. We need to explain the model to domain specialists through the MLMs. Human-understandable explanations in addition to ML professionals must establish trust in the prediction of our model. This is achieved by creating a model-independent, locally accurate explanation… More >

  • Open Access

    ARTICLE

    Ensemble-Based Approach for Efficient Intrusion Detection in Network Traffic

    Ammar Almomani1,2,*, Iman Akour3, Ahmed M. Manasrah4,5, Omar Almomani6, Mohammad Alauthman7, Esra’a Abdullah1, Amaal Al Shwait1, Razan Al Sharaa1

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2499-2517, 2023, DOI:10.32604/iasc.2023.039687

    Abstract The exponential growth of Internet and network usage has necessitated heightened security measures to protect against data and network breaches. Intrusions, executed through network packets, pose a significant challenge for firewalls to detect and prevent due to the similarity between legitimate and intrusion traffic. The vast network traffic volume also complicates most network monitoring systems and algorithms. Several intrusion detection methods have been proposed, with machine learning techniques regarded as promising for dealing with these incidents. This study presents an Intrusion Detection System Based on Stacking Ensemble Learning base (Random Forest, Decision Tree, and k-Nearest-Neighbors). The proposed system employs pre-processing… More >

  • Open Access

    ARTICLE

    Detection of Alzheimer’s Disease Progression Using Integrated Deep Learning Approaches

    Jayashree Shetty1, Nisha P. Shetty1,*, Hrushikesh Kothikar1, Saleh Mowla1, Aiswarya Anand1, Veeraj Hegde2

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1345-1362, 2023, DOI:10.32604/iasc.2023.039206

    Abstract Alzheimer’s disease (AD) is an intensifying disorder that causes brain cells to degenerate early and destruct. Mild cognitive impairment (MCI) is one of the early signs of AD that interferes with people’s regular functioning and daily activities. The proposed work includes a deep learning approach with a multimodal recurrent neural network (RNN) to predict whether MCI leads to Alzheimer’s or not. The gated recurrent unit (GRU) RNN classifier is trained using individual and correlated features. Feature vectors are concatenated based on their correlation strength to improve prediction results. The feature vectors generated are given as the input to multiple different… More >

  • Open Access

    ARTICLE

    Cartographie des surfaces pastorales à l’aide des données Sentinel 2 L3A et des données ouvertes

    Promesses et réalités

    Urcel Kalenga Tshingomba1,2, Magali Jouven2, Lucile Sautot2 , Imad Shaqura2, Maguelonne Teisseire1

    Revue Internationale de Géomatique, Vol.30, No.2, pp. 245-277, 2020, DOI:10.3166/rig.2022.00112

    Abstract In this paper we experiment an approach allowing to produce a coherent land use and cover of rangelands in the French Mediterranean areas represented by the Occitania and Provence-Alpes-Côte d’Azur regions. Four different data sources are used: the OSO land use and cover (OSO), the Registre Parcellaire Graphique (RPG), the BD-Forêt V.2.0 and Sentinel 2 L3A satellite data. The RPG of 2019 and the BD-Forêt updated in 2018 were used as the main source of reference data for training the models to classify the OSO 2019 low F-score class objects, after extraction of the reflectances, spectral and textural indices variables… More >

  • Open Access

    ARTICLE

    Power Transformer Fault Diagnosis Using Random Forest and Optimized Kernel Extreme Learning Machine

    Tusongjiang Kari1, Zhiyang He1, Aisikaer Rouzi2, Ziwei Zhang3, Xiaojing Ma1,*, Lin Du1

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 691-705, 2023, DOI:10.32604/iasc.2023.037617

    Abstract Power transformer is one of the most crucial devices in power grid. It is significant to determine incipient faults of power transformers fast and accurately. Input features play critical roles in fault diagnosis accuracy. In order to further improve the fault diagnosis performance of power transformers, a random forest feature selection method coupled with optimized kernel extreme learning machine is presented in this study. Firstly, the random forest feature selection approach is adopted to rank 42 related input features derived from gas concentration, gas ratio and energy-weighted dissolved gas analysis. Afterwards, a kernel extreme learning machine tuned by the Aquila… More >

  • Open Access

    ARTICLE

    Optimized Decision Tree and Black Box Learners for Revealing Genetic Causes of Bladder Cancer

    Sait Can Yucebas*

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 49-71, 2023, DOI:10.32604/iasc.2023.036871

    Abstract The number of studies in the literature that diagnose cancer with machine learning using genome data is quite limited. These studies focus on the prediction performance, and the extraction of genomic factors that cause disease is often overlooked. However, finding underlying genetic causes is very important in terms of early diagnosis, development of diagnostic kits, preventive medicine, etc. The motivation of our study was to diagnose bladder cancer (BCa) based on genetic data and to reveal underlying genetic factors by using machine-learning models. In addition, conducting hyper-parameter optimization to get the best performance from different models, which is overlooked in… More >

  • Open Access

    ARTICLE

    Code Reviewer Intelligent Prediction in Open Source Industrial Software Project

    Zhifang Liao1, Bolin Zhang1, Xuechun Huang1, Song Yu1,*, Yan Zhang2

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 687-704, 2023, DOI:10.32604/cmes.2023.027466

    Abstract Currently, open-source software is gradually being integrated into industrial software, while industry protocols in industrial software are also gradually transferred to open-source community development. Industrial protocol standardization organizations are confronted with fragmented and numerous code PR (Pull Request) and informal proposals, and different workflows will lead to increased operating costs. The open-source community maintenance team needs software that is more intelligent to guide the identification and classification of these issues. To solve the above problems, this paper proposes a PR review prediction model based on multi-dimensional features. We extract 43 features of PR and divide them into five dimensions: contributor,… More >

  • Open Access

    ARTICLE

    Fatigue Life Estimation of High Strength 2090-T83 Aluminum Alloy under Pure Torsion Loading Using Various Machine Learning Techniques

    Mustafa Sami Abdullatef*, Faten N. Alzubaidi, Anees Al-Tamimi, Yasser Ahmed Mahmood

    FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.8, pp. 2083-2107, 2023, DOI:10.32604/fdmp.2023.027266

    Abstract The ongoing effort to create methods for detecting and quantifying fatigue damage is motivated by the high levels of uncertainty in present fatigue-life prediction approaches and the frequently catastrophic nature of fatigue failure. The fatigue life of high strength aluminum alloy 2090-T83 is predicted in this study using a variety of artificial intelligence and machine learning techniques for constant amplitude and negative stress ratios (). Artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), support-vector machines (SVM), a random forest model (RF), and an extreme-gradient tree-boosting model (XGB) are trained using numerical and experimental input data obtained from fatigue tests… More > Graphic Abstract

    Fatigue Life Estimation of High Strength 2090-T83 Aluminum Alloy under Pure Torsion Loading Using Various Machine Learning Techniques

  • Open Access

    ARTICLE

    Intelligent Sound-Based Early Fault Detection System for Vehicles

    Fawad Nasim1,2,*, Sohail Masood1,2, Arfan Jaffar1,2, Usman Ahmad1, Muhammad Rashid3

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3175-3190, 2023, DOI:10.32604/csse.2023.034550

    Abstract An intelligent sound-based early fault detection system has been proposed for vehicles using machine learning. The system is designed to detect faults in vehicles at an early stage by analyzing the sound emitted by the car. Early detection and correction of defects can improve the efficiency and life of the engine and other mechanical parts. The system uses a microphone to capture the sound emitted by the vehicle and a machine-learning algorithm to analyze the sound and detect faults. A possible fault is determined in the vehicle based on this processed sound. Binary classification is done at the first stage… More >

  • Open Access

    ARTICLE

    Prediction of NFT Sale Price Fluctuations on OpenSea Using Machine Learning Approaches

    Zixiong Wang, Qiuying Chen, Sang-Joon Lee*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2443-2459, 2023, DOI:10.32604/cmc.2023.037553

    Abstract The rapid expansion of the non-fungible token (NFT) market has attracted many investors. However, studies on the NFT price fluctuations have been relatively limited. To date, the machine learning approach has not been used to demonstrate a specific error in NFT sale price fluctuation prediction. The aim of this study was to develop a prediction model for NFT price fluctuations using the NFT trading information obtained from OpenSea, the world’s largest NFT marketplace. We used Python programs to collect data and summarized them as: NFT information, collection information, and related account information. AdaBoost and Random Forest (RF) algorithms were employed… More >

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