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Computational intelligence approaches for classification of medical data: State-of-the-art, future challenges and research directions
Published 2018“…On the other hand, the hybridization of SVM with other methods such as SVM-Genetic Algorithm (SVM-GA), SVM-Artificial Immune System (SVM-AIS), SVM-AIRS and fuzzy support vector machine (FSVM) had great performances achieving better results in terms of accuracy, sensitivity and specificity.…”
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A case study of microarray breast cancer classification using machine learning algorithms with grid search cross validation
Published 2023“…Machine learning is a subfield of artificial intelligence (AI) and computer science that uses data and algorithms to mimic how humans learn, and gradually improving its accuracy. …”
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Artificial Intelligence (AI) to predict dental student academic performance based on pre-university results
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Hybrid harmony search-artificial intelligence models in credit scoring
Published 2019“…Statistical models are the main approaches but recently, Artificial Intelligence (AI) techniques have been popular due to their ability to account for flexible data patterns. …”
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Intelligent decision support systems: transforming smart cities management
Published 2024“…A comparison of the energy used by promised by these algorithms including LSTM, SVM, KNN, and the OPTIMUS, a system is developed that enables smart cities to significantly save energy hence highlighting its efficiency. …”
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Privacy in medical emergency system: cryptographic and security aspects
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Development of an accurate AI-based dermatology assistant for skin disease recognition using YOLOv8 models
Published 2024“…The research compares various algorithms, such as SVM, YOLOv3, YOLOv4, and Dual-Architecture CNN, through a comprehensive review of existing AI applications in dermatology. …”
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Real-time human activity recognition using external and internal spatial features
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Secure Image Steganography Using Encryption Algorithm
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A review of classification techniques for electromyography signals
Published 2023“…Machine Learning (ML) is an area of Artificial Intelligent (AI) with a concept that a computer program can learn and familiarize to new data without human intervention. …”
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Acoustic Emission and Artificial Intelligent Methods in Condition Monitoring of Rotating Machine – A Review
Published 2016“…Furthermore, the paper attempts to summarize and evaluate the recent condition monitoring research that utilizing AI includes fuzzy logic, artificial neural network (ANN), support vector machine (SVM), and genetic algorithms (GA) in fault diagnosis, fault classification, fault localization and fault size estimation in gear and bearing based on features extraction from AE signal. …”
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Geospatial AI-based approach to assess the spatiotemporal suitability of onshore wind-solar farms in Iraq
Published 2023“…In this context, global geospatial data for 13 conditioning factors were collected, and 55,619 inventory samples of wind and solar stations worldwide were prepared to train three machine learning (ML) algorithms, namely Random Forest (RF), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). …”
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Effectiveness of Artificial Intelligence Models for Cardiovascular Disease Prediction: Network Meta-Analysis
Published 2022“…The statistical evidence indicated that the DL algorithms performed well in the prediction of heart failure with AUC of 0.843 and CI 0.840-0.845, while in the ML algorithm, the gradient boosting machine (GBM) achieved an average accuracy of 91.10% in predicting heart failure. …”
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Effectiveness of Artificial Intelligence Models for Cardiovascular Disease Prediction: Network Meta-Analysis
Published 2022“…The statistical evidence indicated that the DL algorithms performed well in the prediction of heart failure with AUC of 0.843 and CI 0.840-0.845, while in the ML algorithm, the gradient boosting machine (GBM) achieved an average accuracy of 91.10% in predicting heart failure. …”
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Multi-label risk diabetes complication prediction model using deep neural network with multi-channel weighted dropout
Published 2025“…By employing Artificial Intelligence (AI), this study examines the interconnected nature of these complications. …”
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Crop yield prediction in agriculture: a comprehensive review of machine learning and deep learning approaches, with insights for future research and sustainability
Published 2024“…The research paper also examines the algorithms frequently utilized in the machine learning domain, including Random Forest (RF), Artificial Neural Networks (ANN), and Support Vector Machine (SVM). …”
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