Titlebook: Intelligent Technologies; Concepts, Applicatio Himansu Das,Arup Abhinna Acharya,Kuan-Ching Li Book 2024 The Editor(s) (if applicable) and T

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Book 2024carry out research work in the proper direction. The industry people will be also facilitated to know about the current advances in research work and materialize the research work into industrial applications..

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Santwana Sagnika,Bhabani Shankar Prasad Mishra,Saroj K. Meherds of the aging population, ethnicity and diversity also must be taken into account when we are meeting the needs of caregivers. The role of the caregiver in the US is becoming increasingly medicalized with caregivers performing tasks previously performed by professional medical staff including mana

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,A Unified Deep Learning Framework for Sentiment Analysis of Reviews,identified useful and relevant reviews are retained in the dataset, and the unhelpful reviews are discarded to reduce the dataset size. The reduced dataset is then analyzed for subjectivity sentence-wise, using the improved word embeddings for feature representation. The identified objective sentenc

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Analysis of Cardiovascular Disease Prediction Using Various Machine Learning and Deep Learning Algoh 99.23 and RMSE by DT with 5.234. The classification technique for BO-SVM model outperformed with 93.30%, 100%, and 80% for accuracy, precision, and sensitivity, respectively, whereas the PSOGD optimization algorithm achieved the highest accuracy of 99.02%, a sensitivity of 99.01%, and a specificit

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IoT-Assisted Heterogeneous Ensemble Learning Environment for Smart Farming,e, we also discuss the various sensors for creating a reliable IoT-assisted model for smart farming employing standalone machine learning algorithms, and meta classifiers in a Heterogeneous Ensemble Learning Environment (HELE), optimizing smart-farming predictions through enhanced preprocessing, fea

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Computational Linguistics for , Language, used Statistical Machine Translation (SMT) and Neural Machine Translation (NMT) to experiment, having an accuracy of around 81%. The accuracy was measured by using the BLEU and ChrF scores. We examined automatic text summarization of . language using machine learning algorithms to reduce long texts

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