3.327 hasil untuk machine learning
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Artikel Jurnal Teks lengkap
Advancements and Challenges in Machine Learning: A Comprehensive Review of Models, Libraries, Applications, and Algorithms
In the current world of the Internet of Things, cyberspace, mobile devices, businesses, social media platforms, healthcare systems, etc., there is a lot of data online today. Machine learning (ML) is something we need to understand to do smart analyses of these data and make smart, automated…
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DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data
…We introduce DeepProg, a novel ensemble framework of deep-learning and machine-learning approaches that robustly predicts patient survival subtypes using multi-omics data. It identifies two optimal survival subtypes in most cancers and yields significantly better risk-stratification than other…
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Multimodal Brain Tumor Classification Using Deep Learning and Robust Feature Selection: A Machine Learning Application for Radiologists
…Here, we present an automated multimodal classification method using deep learning for brain tumor type classification. The proposed method consists of five core steps. In the first step, the linear contrast stretching is employed using edge-based histogram equalization and discrete cosine…
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A Machine Learning Approach to Diagnosing Lung and Colon Cancer Using a Deep Learning-Based Classification Framework
…to assess more cases in less time and cost. With the help of modern Deep Learning (DL) and Digital Image Processing (DIP) techniques, this paper inscribes a classification framework to differentiate among five types of lung and colon tissues (two benign and three malignant) by analyzing their…
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Machine Learning and Deep Learning Methods for Enhancing Building Energy Efficiency and Indoor Environmental Quality – A Review
…impacts is necessary. Artificial intelligence (AI) techniques such as machine and deep learning have been increasingly and successfully applied to develop solutions for the built environment. This review provided a critical summary of the existing literature on the machine and deep learning methods…
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Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review
…which provides both spatial and spectral information. Advancement in machine learning techniques for rapid analysis with higher classification accuracy have improved the potential of using this technique for food applications. This paper provides an overview of the application of different machine…
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Remaining Useful Life prediction and challenges: A literature review on the use of Machine Learning Methods
…a lot of prior system knowledge. Data-driven methods have primarily used Machine Learning (ML) approaches. They require little historical data, are less complex and expensive, and are more applicable, providing a trade-off between complexity, cost, precision, and applicability. However, despite the…
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A Comprehensive Survey on Machine Learning-Based Big Data Analytics for IoT-Enabled Smart Healthcare System
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The Validity of Sentiment Analysis: Comparing Manual Annotation, Crowd-Coding, Dictionary Approaches, and Machine Learning Algorithms
…performance of manual annotation, crowd coding, numerous dictionaries and machine learning using both traditional and deep learning algorithms. The three main conclusions of this article are that: (1) The best performance is still attained with trained human or crowd coding; (2) None of the used…
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Increasing the Performance of Machine Learning-Based IDSs on an Imbalanced and Up-to-Date Dataset
…from them, Intrusion Detection System (IDS), which is trained with some machine learning techniques by using a pre-collected dataset, is one of the most preferred protection mechanisms. The used datasets were collected during a limited period in some specific networks and generally don't contain…
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Machine learning predicts the functional composition of the protein corona and the cellular recognition of nanoparticles
…in the prediction of the protein corona was achieved by integrating a machine learning model and meta-analysis. NPs without modification and surface modification were identified as the two most important factors determining protein corona formation. According to experimental verification, the…
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A Machine Learning-Based Big EEG Data Artifact Detection and Wavelet-Based Removal: An Empirical Approach
…is detected from a single-channel EEG signal using support vector machine (SVM) and preceded with further artifacts’ suppression. The signal features’ abstraction and further detection are done through ensemble empirical mode decomposition (EEMD) algorithm. Moreover, canonical correlation analysis…
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Performance analysis of the water quality index model for predicting water state using machine learning techniques
…environmental protection agency (EPA). In the present study, four machine-learning classifier algorithms, including support vector machines (SVM), Naïve Bayes (NB), random forest (RF), k-nearest neighbour (KNN), and gradient boosting (XGBoost), were utilized to identify the best classifier for…
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A Review of ARIMA vs. Machine Learning Approaches for Time Series Forecasting in Data Driven Networks
…models and techniques, the view is rapidly changing, with a shift towards machine and deep learning approaches becoming apparent, even without a complete evaluation of the superiority of the new approach over the classic statistical algorithms. Our work constitutes an extensive review of the…
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Application of remote sensing and machine learning algorithms for forest fire mapping in a Mediterranean area
…severe conditions. In this study, we developed five new hybrid machine learning algorithms namely, Frequency Ratio-Multilayer Perceptron (FR-MLP), Frequency Ratio-Logistic Regression (FR-LR), Frequency Ratio-Classification and Regression Tree (FR-CART), Frequency Ratio-Support Vector Machine…
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Smart cities: Fusion-based intelligent traffic congestion control system for vehicular networks using machine learning techniques
…including vehicular communications, navigation, and traffic control. Machine Learning (ML) is an efficient approach to finding hidden insights into ITS without being programmed explicitly by learning from data. This research proposed a fusion-based intelligent traffic congestion control system for…
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An Explainable Machine Learning Model for Early Detection of Parkinson's Disease using LIME on DaTSCAN Imagery
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