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The article examined the deep learning models and Fastai text classification technique to predict the medical speech utterances, transcriptions, and intent to extract the 25 medicals problems. The experimental work was conducted using a large amount of data which contains 6661.wav files and one.csv file, including 13 distinct categorization fields of medical speech utterances. Each illness's exploratory data analysis demonstrated the phrase length classes and disease categorization based on the recorded speech sound of patients for each disease. The preprocessing of the task included the wordcloud consisting of all the vocabulary words having different sizes based on the number of speech utterances in each category, eliminating Nan values, verifying for duplicates, and computing the corpus and their term index. Further, features are extracted to determine the number of words in each category, the length of phrases, and the number of words in each phrase, followed by lemmatization and tokenization. Deep learning models such as GRU (Gated Recurrent Unit), LSTM (Long Short Term Memory), bidirectional gated recurrent unit, bidirectional long short-term memory, and Fastai classifier have been used to exact category of disease from the medical speech utterances and their textual phrases. After the assessment, it was discovered that Fastai earned the most incredible precision, recall, accuracy, and lowest loss rate by 96.89%, 95.8%, 93.32%, and 0.169, respectively. In comparison, bidirectional LSTM had achieved the highest F1 score by 95.69% to predict the medical speech utterances for each category. [ABSTRACT FROM AUTHOR]

Mangrove forests are considered to be the most productive ecosystem yet vanishing rapidly over the world. They are mostly found in the intertidal zone and sheltered by the seacoast. Mangroves have potential socio-economic benefits such as protecting the shoreline from storm and soil erosion, flood and flow control, acting as a carbon sink, provides a fertile breeding ground for marine species and fauna. It also acts as a source of income by providing various forest products. Restoration and conservation of mangrove forests remain a big challenge due to the large and inaccessible areas covered by mangroves forests which makes field assessment difficult and time-consuming. Remote sensing along with various digital image classification approaches seem to be promising in providing better and accurate results in mapping and monitoring the mangroves ecosystem. This review paper aims to provide a comprehensive summary of the work undertaken, and addresses various remote sensing techniques applied for mapping and monitoring of the mangrove ecosystem, and summarize their potential and limitation. For that various digital image classification techniques are analyzed and compared based on the type of image used with its spectral resolution, spatial resolution, and other related image features along with the accuracy of the classification to derive specific class information related to mangroves. The digital image classification techniques used for mangrove mapping and monitoring in various studies can be classified into pixel-based, object-based, and knowledge-based classifiers. The various satellite image data analyzed are ranged from light detection and ranging (LiDAR), hyperspectral and multispectral optical imagery, synthetic aperture radar (SAR), and aerial imagery. Supervised state of the art machine learning/deep machine learning algorithms which use both pixel-based and object-based approaches and can be combined with the knowledge-based approach are widely used for classification purpose, due to the recent development and evolution in these techniques. There is a huge future scope to study the performance of these classification techniques in combination with various high spatial and spectral resolution optical imageries, SAR and LiDAR, and also with multi-sensor, multiresolution, and temporal data. [ABSTRACT FROM AUTHOR]