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An improved defect classification algorithm for six printing defects and its implementation on real printed circuit board images
Published 2012“…The defect classification algorithm is improved by incorporating proper image registration and thresholding techniques to solve the alignment and uneven illumination problem. …”
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Waste management using machine learning and deep learning algorithms
Published 2020“…So, we are proposing an automated waste classification problem utilizing Machine Learning and Deep Learning algorithms. …”
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Static hand gesture recognition using artificial neural network / Haitham Sabah Hasan
Published 2014“…Artificial neural network is built for the purpose of classification by using the back- propagation learning algorithm. …”
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Gene Selection For Cancer Classification Based On Xgboost Classifier
Published 2022“…XGBoost Classifier is applied in this research, which it is an efficient open-source implementation of the gradient boosted trees algorithm. …”
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An efficient intrusion detection model based on hybridization of artificial bee colony and dragonfly algorithms for training multilayer perceptrons
Published 2020“…However, the problem of improving the accuracy and efficiency of classification models remains open and yet to be resolved. …”
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Integrated combined layer algorithm of jamming detection and classification in manet / Ahmad Yusri Dak
Published 2019“…The fourth stage is to design evaluation methodology of Max-Min Rule-Based Classification Algorithm using classifier model. Finally, both algorithms are validated against the findings in various literatures. …”
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Study and Implementation of Data Mining in Urban Gardening
Published 2019“…Using the J48 tree algorithm implemented through WEKA API on a Java Servlet, data provided is processed to derive a health index of the plant, with the possible outcomes set to “Good,” “Okay”, or “Bad”. …”
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HYBRID SELECTION FRAMEWORK FOR CLASS BALANCING APPROACHES BASED ON INTEGRATED CNN AND DECISION MAKING TECHNIQUES FOR LUNG CANCER DIAGNOSIS
Published 2023“…This study can be applied to any data that suffers from the class imbalance problem to find the best technique that gives the highest classification accuracy � 2022, Authors. …”
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Single classifer vs. ensemble machine learning approaches for mental health prediction
Published 2023“…The data set contains responses to a survey questionnaire that was conducted by Open Sourcing Mental Illness (OSMI). Machine learning algorithms investigated in this study include Logistic Regression, Gradient Boosting, Neural Networks, K-Nearest Neighbours, and Support Vector Machine, as well as an ensemble approach using these algorithms. …”
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A review of recent approaches for emotion classification using electrocardiography and electrodermography signals
Published 2020“…The main objective of this paper is to analyze the current trends in terms of how signals including heart rate and skin conductance can be used as training features for machine learning classifiers to perform the emotion classification task. Some critical observations and open problems will be presented, followed by a discussion of promising avenues for future research in the use of ECG and EDG for emotion classification.…”
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Machine learning: tasks, modern day applications and challenges
Published 2019“…Particularly in today’s scenarios, deep learning algorithms are breaking all records. These algorithms mimic the neural system of humans and successfully breaking several barriers in image classification, NLP, and robotics, etc. …”
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Development of Real-Time Eye Tracking Algorithm
Published 2021“…This project develops a real-time eye tracking algorithm to improve the accuracy of iris detection and gaze classification. …”
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Mining Sequential Patterns using I-PrefixSpan
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Amplitude independent muscle activity detection algorithm of soft robotic glove system for hemiparesis stroke patients using single sEMG channel
Published 2020“…High classification accuracies have been achieved for the seven healthy subjects (92%-100%) which are comparable to that obtained by applying sophisticated single channel classification algorithms in previous studies; moreover, good accuracies (70%-85%) have been obtained for the stroke patients. …”
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Voiceless Bangla vowel recognition using sEMG signal
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