Depression is a clinical entity that might be difficult for a psychiatrist to diagnose it effectively on time. A depressed person usually suffers from distress and anxiety, leading to serious consequences if not diagnosed early. Social media platforms facilitate users to exchange ideas and dialogs, resulting in the collection of a huge volume of data. Analyzing user's online behavior to categorize depression is a challenging task for researchers. This motivated researchers to investigate machine learning, deep learning, and natural language processing techniques supporting research related to depression prediction. The dataset used in the study is a large-scale Twitter dataset. This article aims to investigate a hybrid CNN-LSTM deep learning model with the Word2Vec feature extraction technique for classifying depressive sentiments from Twitter data. By using TF-IDF, PCA, and Word2Vec approaches, this model utilizes significant linguistic features present within the text. The proposed model is evaluated on four benchmark datasets and its efficiency is compared with four traditional machine learning models. Moreover, the proposed model's performance is compared to three deep learning-based hybrid models. The proposed model showed comparable performance with the hybrid deep learning-based models and outperformed state-of-the-art machine learning techniques with an accuracy of 96.78% and an MSE score of 3.21.
COVID-19 is a novel virus that presents challenges due to a lack of consistent and in-depth research. The news of the COVID-19 spreads across the globe, resulting in a flood of posts on social media sites. Apart from health, social, and economic disturbances brought by the COVID-19 pandemic, another important consequence involves public mental health crises which is of greater concern. Data related to COVID-19 is a valuable asset for researchers in understanding people's feelings related to the pandemic. It is thus important to extract the early information evolving public sentiments on social platforms during the outbreak of COVID-19. The objective of this study is to look at people's perceptions of the COVID-19 pandemic who interact with each other and share tweets on the Twitter platform. COVIDSenti, a large-scale benchmark dataset comprising 90,000 COVID-19 tweets collected from February to March 2020, during the initial phases of the outbreak served as the foundation for our experiments. A pre-trained bidirectional encoder representations from transformers (BERT) model is finetuned and embeddings generated are combined with two long short-term memory networks to propose the residual encoder transformation network model. The proposed model is used for multiclass text classification on a large dataset labeled as positive, negative, and neutral. The experimental outcomes validate that: (1) the proposed model is the best performing model, with 98% accuracy and 96% F1-score; (2) It also outperforms conventional machine learning algorithms and different variants of BERT, and (3) the approach achieves better results as compared to state-of-the-art on different benchmark datasets.
KeywordsCOVID-19 • Transfer learning • Tweets • Sentiment analysis • LSTM • BERT Abbreviations BERT Bidirectional encoder representations from transformers LSTM Long short-term memory network RETN Residual encoder transformation network SARS-CoV-2 Severe acute respiratory syndrome -Coronavirus
Once applied to real world images, most machine
learning models for the automated identification of diseases have
limited efficiency. Plant diseases cause major agricultural
production and economic loss. These illnesses also show visible
signs, including lines, streaks and shift in color, on leaf surfaces.
Many researchers have recently researched the potential use of
image treatment and computer processing in plants and leaves to
diagnose disease. There is space for improved performance
though several methods and computer procedures have been
developed in this area of investigation. Several previous models
only deal with a few morphological features of the diseased
regions. A new method for detecting plant leave's disease using
the segmentation, and CNN approach based on GLCM and LPQ
features of the Basil and Guava leaves feedback imagery has been
established in the present paper. The findings revealed that the
suggested model is as effective as possible, for both basil and
guava leaves, to better distinguish healthy and unhealthy leaves.
The overall accuracy of the Guava dataset is 97.1% and the basil
dataset is 92.1%.
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