Pinned Repositories
Canadian-Medicinal-Plant-Detection-using-Convolutional-Neural-Network-with-Transfer-Learning
Nowadays, computerized plant species classification systems are used to help the people in the detection of the various species. However, the automated analysis of plant species is challenging as compared to human interpretation. This research as been provided in this field for the better classification of plant species. Even now, these methodologies lack an exact classification of the plant species. The challenge is due to the inappropriate classification algorithm. In Particular, when we consider the medicinal plant species recognition, the accuracy will be the main criteria. In this research, the suggested system implements the deep learning technique to obtain high accuracy in the classification process using computer prediction methods.The Convolutional Neural Network (CNN) is employed beside transfer learning for deep learning of medicinal plant images. This research work has been carried out on the flower images dataset of four Canadian medical plants; namely, Clubmoss, Dandelion, Lobelia, and Bloodroot, which is fed as the training dataset for the CNN and machine learning-based proposed system. Finally, an accuracy of 96% has been achieved in classification of the medicinal plant species.
Whasapp-Data-Analysis
The sudden outburst of the Internet has made it easier for people to communicate with people across the world. Social media provides humongous data to carry out various algorithms and predict the output. WhatApp is considered to be the most popular application due to its huge customer base. In this project, we have performed the text and opinion mining on WhatsApp chat data to understand the people in a better way. We chose WhatsApp data due to its number of users and way of retrieving data. Our project will deliver the graphical visualizations for Text analysis and sentimental analysis which include message count, emojis, frequency of the texts and emojis, lexical diversity, word cloud and sentiment score. We believe that the results of this project could help the Government, companies and Organizations to gain better insights about the people. This analysis might also help to avoid conflicts that might arise due to emotional mismatch or misunderstanding.
HRotatE
attributedataset
bert_basic
for telus interview
Data
HTransE
ieeesmc
KnowledgeGraphEmbedding
shoppify
programmingboy's Repositories
programmingboy/bert_basic
for telus interview
programmingboy/HRotatE
programmingboy/Canadian-Medicinal-Plant-Detection-using-Convolutional-Neural-Network-with-Transfer-Learning
Nowadays, computerized plant species classification systems are used to help the people in the detection of the various species. However, the automated analysis of plant species is challenging as compared to human interpretation. This research as been provided in this field for the better classification of plant species. Even now, these methodologies lack an exact classification of the plant species. The challenge is due to the inappropriate classification algorithm. In Particular, when we consider the medicinal plant species recognition, the accuracy will be the main criteria. In this research, the suggested system implements the deep learning technique to obtain high accuracy in the classification process using computer prediction methods.The Convolutional Neural Network (CNN) is employed beside transfer learning for deep learning of medicinal plant images. This research work has been carried out on the flower images dataset of four Canadian medical plants; namely, Clubmoss, Dandelion, Lobelia, and Bloodroot, which is fed as the training dataset for the CNN and machine learning-based proposed system. Finally, an accuracy of 96% has been achieved in classification of the medicinal plant species.
programmingboy/ieeesmc
programmingboy/shoppify
programmingboy/HTransE
programmingboy/Data
programmingboy/KnowledgeGraphEmbedding
programmingboy/attributedataset
programmingboy/Whasapp-Data-Analysis
The sudden outburst of the Internet has made it easier for people to communicate with people across the world. Social media provides humongous data to carry out various algorithms and predict the output. WhatApp is considered to be the most popular application due to its huge customer base. In this project, we have performed the text and opinion mining on WhatsApp chat data to understand the people in a better way. We chose WhatsApp data due to its number of users and way of retrieving data. Our project will deliver the graphical visualizations for Text analysis and sentimental analysis which include message count, emojis, frequency of the texts and emojis, lexical diversity, word cloud and sentiment score. We believe that the results of this project could help the Government, companies and Organizations to gain better insights about the people. This analysis might also help to avoid conflicts that might arise due to emotional mismatch or misunderstanding.