Titlebook: Machine Learning for Predictive Analysis; Proceedings of ICTIS Amit Joshi,Mahdi Khosravy,Neeraj Gupta Conference proceedings 2021 The Edito

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https://doi.org/10.1007/978-981-15-7106-0ICTIS 2020; ICTIS Proceedings; Machine Learning; Predictive Analysis; Engineering Applications; Big Data

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978-981-15-7108-4The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor

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Machine Learning for Predictive Analysis978-981-15-7106-0Series ISSN 2367-3370 Series E-ISSN 2367-3389

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A Hybrid Deep Learning Approach for Stock Price Prediction,es associated with a company. This work presents a hybrid approach for the prediction of intra-day stock prices by considering both time-series and sentiment analysis. Furthermore, it focuses on long short-term memory (LSTM) architecture for the time-series analysis of stock prices and Valence Aware

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Detection of Alphanumeric Characters by Connectionist Temporal Classification with Vanilla Beam Sea. A new method, called, heuristic search algorithm, usually used in database management applications and natural language preprocessing is introduced for extracting the features of the alphanumeric text imprinted on a rough and noisy surface. Fifty epochs, each containing 115 batches written by vari

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Multilabel Toxic Comment Classification Using Supervised Machine Learning Algorithms,ng spaces generate a plethora of expressive content in the public domain, which contributes to a healthy environment for humans. However, it also has threats and dangers of cyberbullying, personal attacks, and the use of abusive language. This motivates industry researchers to model an automated pro

Misgiving

Model of Speed Spheroidization of Metals and Alloys Based on Multiprocessor Computing Complexes,ion and spheroidizing annealing of calibrated steel. The proposed model is based on the heat treatment method of a billet from low- and medium-carbon steels intended for the fasteners’ manufacture. The achieved technical result provided a process for monitoring parameters in the heat treatment of me

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Prediction of Sales Using Stacking Classifier,dered to enhance the performance of the sales forecasting predictive model. A stacking methodology was studied to build a single model regression ensemble. The findings indicate that we can improve the performance of predictive sales forecasting models using stacking techniques. The concept is that

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