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Machine Learning Techniques for Speech Recognition using the Magnitude
Krishnan, C. Gopala,Robinson, Y. Harold,Chilamkurti, Naveen Korea Multimedia Society 2020 The journal of multimedia information system Vol.7 No.1
Machine learning consists of supervised and unsupervised learning among which supervised learning is used for the speech recognition objectives. Supervised learning is the Data mining task of inferring a function from labeled training data. Speech recognition is the current trend that has gained focus over the decades. Most automation technologies use speech and speech recognition for various perspectives. This paper demonstrates an overview of major technological standpoint and gratitude of the elementary development of speech recognition and provides impression method has been developed in every stage of speech recognition using supervised learning. The project will use DNN to recognize speeches using magnitudes with large datasets.
Comparative Evaluation of Attribute-Enabled Supervised Classification in Predicting the Air Quality
P. Subbulakshmi,S. Vimal,Y. Harold Robinson,Amit Verma,Janmenjoy Nayak 대한공간정보학회 2023 Spatial Information Research Vol.31 No.4
Air pollution demonstrates the appearance of toxins into the air which is blocking human prosperity and the earth. It will portray as potentially the riskiest threats that humanity anytime faced. It makes hurt animals, harvests to thwart these issues in transportation territories need to expect air quality from pollutions utilizing AI systems and IoT. Along these lines, air quality evaluation and assumption has become a huge target for human health factors and also affect internal organs related to respiratory. The accuracy of Air Pollution prediction has been involved with the machine learning techniques and the best accuracy model is identified. The air quality prediction dataset is used for identifying the meteorology air pollution data while the predicted model is involved the decision tree computation for predicting the toxin contents in the region, the Air quality indicator is used to assess the pollution level and monitoring the air quality. The performance analysis shows that the decision tree technique has produced the better results in the performance metrics of Accuracy, precision, recall, and F1-score with the minimized error values while the comparative evaluation of Attribute-enabled classification has identified the best technique for predicting the air quality.
P. Subbulakshmi,S. Vimal,Y. Harold Robinson,Amit Verma,Janmenjoy Nayak 대한공간정보학회 2024 Spatial Information Research Vol.32 No.2
The Publisher has retracted this article in agreement with the Editor-in-Chief. The article was submitted to be part of a guest-edited issue. An investigation by the publisher found a number of articles, including this one, with a number of concerns, including but not limited to compromised editorial handling and peer review process, inappropriate or irrelevant references or not being in scope of the journal or guest-edited issue. Based on the investigation’s findings the publisher, in consultation with the Editor-in-Chief therefore no longer has confidence in the results and conclusions of this article. Author P. Subbulakshmi has stated that the authors disagree with this retraction