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data learning » deep learning (Expand Search)
data finding » data mining (Expand Search), data hiding (Expand Search)
element » elements (Expand Search)
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181
Automatic keyword extraction from a real estate classifieds data set
Published 2011“…We begin with designing data cleansing algorithms to verify different attributes of the real estate classified. …”
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182
BioNetApp: An interactive visual data analysis platform for molecular expressions
Published 2019“…BioNetApp also provides data clustering based on molecular concentrations using Self Organizing Maps (SOM), K-Means, K-Medoids, and Farthest First algorithms.…”
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186
Recursive least-squares backpropagation algorithm for stop-and-go decision-directed blind equalization
Published 2002“…To overcome this problem, in this work, a fast converging recursive least squares (RLS)-based complex-valued backpropagation learning algorithm is derived for S&G-DD blind equalization. …”
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187
A flexible genetic algorithm-fuzzy regression approach for forecasting: The case of bitumen consumption
Published 2019“…Findings Production, import, export, road construction and price are considered as the input data used in the present study. …”
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Using machine learning for disease detection. (c2013)
Published 2016“…Classification consists of predicting group membership for new data instances by learning from pre-classified data instances. …”
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masterThesis -
190
Adaptive Secure Pipeline for Attacks Detection in Networks with set of Distribution Hosts
Published 2022“…So far none addresses the use of Threat Intelligence (IT) data in Ensemble Learning algorithms to improve the detection process, nor does it work as a function of time, that is, taking into account what happens on the network in a limited time interval. …”
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191
Benchmarking Concept Drift Detectors for Online Machine Learning
Published 2022“…Concept drift detection is an essential step to maintain the accuracy of online machine learning. The main task is to detect changes in data distribution that might cause changes in the decision bound aries for a classification algorithm. …”
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192
Deep Reinforcement Learning for Resource Constrained HLS Scheduling
Published 2022“…The two main steps in HLS are: operations scheduling and data-path allocation. In this work, we present a resource constrained scheduling approach that minimizes latency and subject to resource constraints using a deep Q learning algorithm. …”
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masterThesis -
193
Machine Learning Model for a Sustainable Drilling Process
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doctoralThesis -
194
Positive Unlabelled Learning to Recognize Dishes as Named Entity
Published 2019“…I work with Yelp dataset, going through each text review, using each noun as a candidate, label the positive samples using the aforementioned lookup table, then using Positive Unlabelled learning techniques to recognise more entities within the unlabelled data, by predicting the probability for each candidate. …”
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195
Cyberbullying Detection in Arabic Text using Deep Learning
Published 2023“…If conducted automatically, rather than relying on human moderators, the process will be faster, enabling the early detection of cyberbullying before severe harm is caused. Data-driven approaches, such as machine learning (ML), particularly deep learning (DL), have shown promising results. …”
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196
LDSVM: Leukemia Cancer Classification Using Machine Learning
Published 2022“…This study proposes a novel method using machine learning algorithms based on microarrays of leukemia GSE9476 cells. …”
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197
Machine Learning-Based Approach for EV Charging Behavior
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doctoralThesis -
198
A Machine Learning Approach to Predicting Diabetes Complications
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doctoralThesis -
199
Using machine learning to support students’ academic decisions
Published 2019“…This research tests and compares the performance of Decision Trees, Random Forests, Gradient-Boosted trees, and Deep Learning machine learning regression algorithms to predict student GPA. …”
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200
Energy-Efficient VoI-Aware UAV-Assisted Data Collection in Wireless Sensor Networks
Published 2025“…To address these objectives, our proposed approach incorporates deep reinforcement learning (RL-DQN) techniques to optimize UAV deployment, minimizing the number of UAVs while maximizing the number of successfully collected SNs with non-redundant data. …”
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masterThesis