Search Results - (( using classification modeling algorithm ) OR ( data visualization learning algorithm ))
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A hybrid spiking neural network model for multivariate data classification and visualization.
Published 2011“…Therefore, this hybrid learning model is proposed to harness the advantages of both SOM-AC and SNN to produce intuitive multivariate data classification and visualization. …”
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Proceeding -
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An improved algorithm for iris classification by using support vector machine and binary random machine learning
Published 2018“…In machine learning, there are three type of learning branch that can used in classification procedures for data mining. …”
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The formulation of a transfer learning pipeline for the classification of the wafer defects
Published 2023“…However, limitations such as robustness and difficulty in setting up the parameters required for image processing algorithm encourages the investigation in using Deep learning classification in detecting the wafer defects. …”
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4
A novel framework for potato leaf disease detection using an efficient deep learning model
Published 2022“…Therefore, this article proposes a technique based on an improved deep learning algorithm that uses the potato leaf visual features to classify them into five classes i.e., Potato Late Blight (PLB), Potato Early Blight (PEB), Potato Leaf Roll (PLR), Potato Verticilliumwilt (PVw) and Potato Healthy (PH) class. …”
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A novel framework for potato leaf disease detection using an efficient deep learning model
Published 2022“…Therefore, this article proposes a technique based on an improved deep learning algorithm that uses the potato leaf visual features to classify them into five classes i.e., Potato Late Blight (PLB), Potato Early Blight (PEB), Potato Leaf Roll (PLR), Potato Verticilliumwilt (PVw) and Potato Healthy (PH) class. …”
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6
Predicting 30-day mortality after an acute coronary syndrome (ACS) using machine learning methods for feature selection, classification and visualization
Published 2021“…The performance of ML models using the area under the curve (AUC) ranged from 0.48 to 0.80. …”
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Classification of labour pain using electroencephalogram signal based on wavelet method / Sai Chong Yeh
Published 2020“…These methods present a robust system that enables fully automated identification and removal of artifacts from EEG signals, without the need of visual inspection or arbitrary thresholding. The training and parameters selection of the machine learning algorithms are conducted using EEG data collected from ten subjects in the laboratory. …”
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8
Unsupervised classification of multi-class chart images: A comparison of customized CNNs and transfer learning techniques
Published 2025“…This study investigates the unsupervised classification of chart images using a combination of deep learning and clustering techniques. …”
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Machine learning application for concrete surface defects automatic damage classification
Published 2024“…This model is trained using 80% of the image data and tested using another 20% of the image data. …”
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Analysis of hyperspectral reflectance for disease classification of soybean frogeye leaf spot using Knime analytics
Published 2023“…In terms of reproducibility, data flow control, data exploration, analysis and visualization, KNIME Analytics Platform provided great convenience in connecting tools graphically and ensuring the same results on different operating systems. …”
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Development Of Machine Learning User Interface For Pump Diagnostics
Published 2022“…The data collected for this machine learning model is using the statistically significant features from vibration and acoustic analysis. …”
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Monograph -
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Converged Classification Network For Matching Cost Computation
Published 2020“…Stereoscopic vision lets us identify the world around us in 3D by incorporating data from depth signals into a clear visual model of the world. …”
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Edge assisted crime prediction and evaluation framework for machine learning algorithms
Published 2022“…Criminal risk is predicted using classification models for a particular time interval and place. …”
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Conference or Workshop Item -
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Enhanced emotion recognition in videos: a convolutional neural network strategy for human facial expression detection and classification
Published 2023“…Despite extensive research employing machine learning algorithms like convolutional neural networks (CNN), challenges remain concerning input data processing, emotion classification scope, data size, optimal CNN configurations, and performance evaluation. …”
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Automatic detection and indication of pallet-level tagging from rfid readings using machine learning algorithms
Published 2020“…The ensemble learning technique, changes of activation function in Neural Network as well as the unsupervised learning (k-means clustering algorithm and Friis Transmission Equation) was also applied to classify the multiclass classification in pallet-level. …”
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16
Identifying the mechanism to forecast the progression of Alzheimer’s disease from mild cognitive impairment using deep learning
Published 2022“…This study also implemented a CNN algorithm based on 3D ResNet-18 model using weights from ImageNet for the classification task of CN vs AD and sMCI vs pMCI. …”
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Final Year Project / Dissertation / Thesis -
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Decoding of visual activity patterns from fMRI responses using multivariate pattern analyses and convolutional neural network
Published 2017“…General linear model (GLM) is used to find the unknown parameters of every individual voxel and the classification is done using multi-class support vector machine (SVM). …”
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Poverty Classification in Indonesia Using BiGRU, BPNN, and Stacking AdaBoost Frameworks
Published 2024“…The primary objective is to enhance the precision and reliability of poverty classification using advanced machine learning technologies. …”
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Predicting the onset of acute coronary syndrome events and in-hospital mortality using machine learning approaches / Song Cheen
Published 2023“…The ML models for regression and classification were developed and optimized; the regression models aimed to predict ACS patients’ hospitalization and mortality rates, while the classification models were designed to predict the mortality risk of ACS patients under the influence of air pollution. …”
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