Sentiment Analysis means determining the views of the user from the text regarding that topic i.e. how one feels about it. It can be used to classify the text content into positive or negative. Various researchers have used a wide range of methods to train the classifiers for the Twitter dataset with varying results. This paper introduces a hybrid approach of using Swarm Intelligence optimization algorithms with classifiers. For each tweet, pre-processing will be done by performing various processes i.e. tokenization; removal of stop-words and emoticons; stemming. Then their feature vectors are being made by the calculation of TF-IDF and optimized with Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) before performing the binary text categorization. Naïve Bayes and Support Vector Machine (SVM) is the machine learning techniques used for the binary classification of tweets. The results drawn using optimization with classifiers is much efficient than using classifier alone.
Plant identification is a significant undertaking however its care is considerably more significant. The identification of plants has a lot of benefits for wide range of people ranging from forestry services, pharmaceutical labs, government and common public. The research paper aims on developing a machine learning technology combined with mobile app development which is able to recognize plants and suggest good care for them like how much water, sunlight, fertilizer, etc is optimum for the plant. Plant identification with its care, based on leaf, is becoming an interesting task. Each plant leaf carries unique identity, which matches to no other plant, information that can be used in the identification and subsequently, determining measures for its care. A large number of features were taken from each leaf such as its length, area of hull, perimeter and color. All the work of research in this filed has further aggravated an interest in the development of automated systems for the recognition of different plant species. A full autonomous method of recognizing plants using Artificial Intelligence tools like Teachable Machine and TfLite. We gathered dataset from google images and several images with varying textures and lighting conditions clicked from my smartphone camera from the Kangra forest range. There are several applications that help in recognizing the plant species using features from their leaves but we are also adding an additional feature of its care.
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