Applied Machine Learning

• Format: eBook
• Language: English
• Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2023
• Subjects: academic performance; adaptive camouflage; adaptive e-learning systems; advanced design methods; airflow signal; answer set programming; anticipatory classifier systems; ARIMA; artificial neural network; autoencoder; autoencoders; backpropagation neural network; backscatter data; BERT; big data; BiLSTM; Bitcoin; cascaded classifier; Chenab River Basin; classification; climate change impact; climate zone; cloud detection; code auto-completion; comparison of various techniques for topic classification; compressive strength; Computer science; computer vision; Computing and Information Technology; construction productivity; construction safety; construction site management; consumer classification; context-aware; convolutional neural network; convolutional neural network (CNN); convolutional neural networks; COREG algorithm; cost-benefit function; course grades; COVID; cross referencing topic; crossover; crowdfunding; cyber security; cyclical learning rate; data mining; decision tree; deep learning; deep multi-target prediction; deep neural network; deep neural networks; drifter trajectory; early prognosis; earning rate; Economics, Finance, Business and Management; educational data mining; evolutionary computation; extreme gradient boosting; feature analysis; Felder–Silverman learning style; fly ash; Gaussian Mixture Models (GMM); genetic algorithms; genetic programming; geopolymer concrete; GPT-2 model; grade point average; grammatical inference; GRNN; healthcare; heatwaves; higher order neural network; human resources; hybrid models; image completion; image representation; Industry and industrial studies; Information technology industries; instance selection; interpretation; investigation framework; Jhelum River Basin; k-means; KdV equation; KdV-Burgers equation; keyword extraction; large-scale dataset; LDA; learning classifier systems; lidar ceilometer; live prediction; local features; LSTM; machine learning; machine learning algorithms; Machine learning analysis; management; mass operator; Media, entertainment, information and communication industries; micro-blogs (Twitter); multiple linear regression; n/a; nature language processing; NCLS; neural network; NLP; non-deterministic automata induction; object detection; online taxi-hailing demand; OpenAI gym; optimization; over-the-top; parsing expression grammar; partial differential equations; performance measurement; personal teaching and learning; physics-informed neural network; prediction; predictive analytics; preprocessing; Principal Component Analysis; project recommendation system; Prophet; random forest regression model; real-time prediction; reinforcement learning; relative support distance; remote sensing; ResNet; robust matrix factorization; rumor refuter; sales; saliency detection; sampling; scene classification; semi-regression; sentence modeling; sentiment analysis; side information graph; sleep apnea; social media; social network services; stock performance; structural stress; student grade prediction; student performance prediction; superpixel segmentation; support vector candidates; support vector machine; support vector machines; SVM; synthetic data; text representation; time-aware classification; time-series predictions; topic analysis; towards recommending influencers based on topic classification; tracking; transfer learning; TripAdvisor; undergraduate; user generated content; vibration test; volatility; volatility forecasting; vulnerability detection; warehouse optimization; wave equation; weather detection; weather nowcasting; word embedding; XGBoost
• ISBN: 9783036579061; 3036579060; 3036579079; 9783036579078
• DOI: 10.3390/books978-3-0365-7907-8

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.