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1
Classification of Immunosignature Using Random Forests for Cancer Diagnosis
Published 2015“…To attain this essential research purpose, a minimum set of genes that can assure higher performance in classification using data mining algorithms need to be detected. …”
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Proceeding Paper -
2
Multi-Objective Hybrid Algorithm For The Classification Of Imbalanced Datasets
Published 2019“…The proposed algorithm is grounded on the two famous metaheuristic algorithms: cuckoo search (CS) and covariance matrix adaptation evolution strategy (CMA-es). …”
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3
Classification with degree of importance of attributes for stock market data mining
Published 2004“…The SVM is a training algorithm for learning classification and regression rules from data [7]. …”
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Article -
4
Y-type Random 2-satisfiability In Discrete Hopfield Neural Network
Published 2024“…Additionally, the proposed Hybrid Differential Evolution Algorithm was implemented into the training phase to ensure that the cost function of the Discrete Hopfield Neural Network is minimized. …”
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5
Assessment of cognitive load using multimedia learning and resting states with deep learning perspective
Published 2019“…It is a well-understood fact that the brain activity increases with the increased demand of cognition. The deep learning algorithm based on Pre-trained convolutional neural network (CNN) networks have been used as a transfer learning for the classification of rest and cognitive states and also assessed the cognitive load using brain waves particularly alpha wave. …”
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Conference or Workshop Item -
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Detection and classification of conflict flows in SDN using machine learning algorithms
Published 2021“…Moreover, applying machine learning algorithms in the identification and classification of conflicting flows has limitations. …”
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Article -
7
An optimized variant of machine learning algorithm for datadriven electrical energy efficiency management (D2EEM)
Published 2024“…The scope of this study is tri folded, First, an exhaustive and parametric comparative study on a wide variety of machine learning algorithms is presented to evaluate the performance of machine learning algorithms in energy load prediction. …”
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8
Daily maximum load forecasting of consecutive national holidays using OSELM-based multi-agents system with weighted average strategy
Published 2023Subjects:Article -
9
Fault classification in smart distribution network using support vector machine
Published 2023“…In this paper, a machine-learning algorithm known as Support Vector Machine (SVM) for fault type classification in distribution system has been developed. …”
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10
Class binarization with self-adaptive algorithm to improve human activity recognition
Published 2018“…Therefore, feature selection using Relief-f with self-adaptive Differential Evolution (rsaDE) algorithm is proposed to select the most significant features. …”
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11
Development of electromyography-controlled 3D printed robot hand and supervised machine learning for signal classification
Published 2019“…Furthermore, the Support vector machine (SVM) and Linear discriminant analysis (LDA) machine learning for the hand posture classification based on the EMG signal pattern were investigated and compared in term of classification performance. …”
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12
Cyberbullying detection: a machine learning approach
Published 2022“…Those algorithms are used in the classification or regression model to predict an input. …”
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Final Year Project / Dissertation / Thesis -
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Ganoderma boninense classification based on near-infrared spectral data using machine learning techniques
Published 2022“…However, a few overlapped NIRs’ spectral data between healthy and infected samples require for further validation which chemometric and machine learning (ML) classification technique are chosen. …”
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Application of data mining techniques for economic evaluation of air pollution impact and control
Published 2007“…For that purpose, we use data mining techniques. Data mining techniques applied in this thesis were: 1) Group method of data handling (GMDH), originally from engineering, introducing principles of evolution - inheritance, mutation and selection - for generating a network structure systematically to develop the automatic model, synthesis, and its validation; 2) The weighted least square (WLS) and step wise regression were also applied for some cases; 3) The classification-based association rules were applied. …”
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15
Gender Classification: A Convolutional Neural Network Approach
Published 2016“…Unlike in conventional CNNs, we replace the convolution operation with cross-correlation, hence reducing the computational load. The network is trained using a second-order backpropagation learning algorithm with annealed global learning rates. …”
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16
Driver behaviour classification: a research using OBD-II data and machine learning
Published 2024“…The relationship between all features and engine speed is analysed to select the optimal features, which include engine speed, vehicle speed, throttle position, and calculated engine load. Then, the proposed model makes use of the K-Means algorithm to create driving behaviour labels whether belong to safe or aggressive - validated by the safety score criteria. …”
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Development of vehicle detection and counting system for traffic analysis using computer vision
Published 2024“…Once the trained weights are ready a deep learning algorithm detection model is applied to detect and draw a bounding box on the vehicles. …”
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Final Year Project / Dissertation / Thesis -
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Data mining for structural damage identification using hybrid artificial neural network based algorithm for beam and slab girder / Meisam Gordan
Published 2020“…In the modeling phase, amongst all DM algorithms, the applicability of machine learning, artificial intelligence and statistical data mining techniques were examined using Support Vector Machine (SVM), Artificial Neural Network (ANN) and Classification and Regression Tree (CART) to detect the hidden patterns in vibration data. …”
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20
A systematic review on emperor penguin optimizer
Published 2021“…To-date, EPO has slowly gaining considerable momentum owing to its successful adoption in many broad range of optimization problems, that is, from medical data classification, economic load dispatch problem, engineering design problems, face recognition, multilevel thresholding for color image segmentation, high-dimensional biomedical data analysis for microarray cancer classification, automatic feature selection, event recognition and summarization, smart grid system, and traffic management system to name a few. …”
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