Applied Machine Learning

doi:10.3390/books978-3-0365-7907-8

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https://mdpi.com/books/pdfview/book/7503

Contributor(s)

Dudek, Grzegorz (editor)

Language

English

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Abstract

This reprint focuses on applications of machine learning models in a diverse range of fields and problems. It reports substantive results on a wide range of learning methods; discusses the conceptualization of problems, data representation, feature engineering, machine learning models; undertakes critical comparisons with existing techniques; and presents an interpretation of the results. The topics within the chapters of the publication fall into six categories: computer vision, teaching and learning, social media, forecasting, basic problems of machine learning, and other topics.

URI

https://directory.doabooks.org/handle/20.500.12854/101406

Keywords

robust matrix factorization; student grade prediction; educational data mining; side information graph; personal teaching and learning; deep multi-target prediction; Felder–Silverman learning style; adaptive e-learning systems; artificial neural network; deep learning; transfer learning; student performance prediction; Machine learning analysis; sentence modeling; topic analysis; cross referencing topic; machine learning; classification; preprocessing; instance selection; data mining; predictive analytics; sales; performance measurement; human resources; rumor refuter; nature language processing; XGBoost; feature analysis; Bitcoin; higher order neural network; volatility forecasting; hybrid models; warehouse optimization; genetic algorithms; crossover; construction productivity; construction safety; synthetic data; tracking; academic performance; course grades; grade point average; prediction; undergraduate; cloud detection; superpixel segmentation; convolutional neural networks; support vector machines; machine learning algorithms; multiple linear regression; SVM; management; social network services; image representation; local features; autoencoder; convolutional neural network; user generated content; sentiment analysis; keyword extraction; text representation; sampling; TripAdvisor; adaptive camouflage; convolutional neural network (CNN); k-means; object detection; image completion; saliency detection; social media; micro-blogs (Twitter); towards recommending influencers based on topic classification; investigation framework; comparison of various techniques for topic classification; cost-benefit function; partial differential equations; physics-informed neural network; wave equation; KdV-Burgers equation; KdV equation; neural network; cyclical learning rate; remote sensing; scene classification; backscatter data; lidar ceilometer; weather detection; online taxi-hailing demand; backpropagation neural network; extreme gradient boosting; real-time prediction; climate zone; climate change impact; Jhelum River Basin; Chenab River Basin; support vector machine; decision tree; large-scale dataset; relative support distance; support vector candidates; answer set programming; non-deterministic automata induction; grammatical inference; geopolymer concrete; deep neural network; ResNet; compressive strength; fly ash; sleep apnea; airflow signal; Gaussian Mixture Models (GMM); cyber security; vulnerability detection; word embedding; drifter trajectory; evolutionary computation; NCLS; stock performance; earning rate; volatility; heatwaves; big data; random forest regression model; semi-regression; early prognosis; interpretation; COREG algorithm; cascaded classifier; computer vision; construction site management; consumer classification; over-the-top; time-aware classification; code auto-completion; GPT-2 model; advanced design methods; mass operator; structural stress; live prediction; vibration test; genetic programming; parsing expression grammar; BiLSTM; BERT; NLP; context-aware; LDA; LSTM; crowdfunding; project recommendation system; optimization; weather nowcasting; deep neural networks; autoencoders; Principal Component Analysis; learning classifier systems; anticipatory classifier systems; reinforcement learning; OpenAI gym; healthcare; COVID; time-series predictions; ARIMA; Prophet; GRNN; n/a