The proposed system uses SVC, RF and Various other classifier algorithms to build the classifier to detect the disease. To handle data and to ensure a good level of detection error and optimal training time, a pre-processing step and data analysis is used. Later this dataset is divided into...
K. Srivastava, "A statistical significance of differences in classification accuracy of crop types using different classifica- tion algorithms," Geocarto International, vol. 32, no. 2, pp. 206- 224, 2017.Kumar P, Prasad R, Choudhary A, Mishra VN, Gupta DK, Srivastava PK (2016a) A ...
Recall, though, that better data often beats better algorithms, and designing good features goes a long way. And if you have a huge dataset, your choice of classification algorithm might not really matter so much in terms of classification performance (so choose your algorithm based on speed or...
Some techniques and classifier combination algorithms are investigated. The classifier ensemble consisting of five member classifiers is constructed. The results of every member classifier are evaluated. The voting strategy is experimented to combine the classification results of the member classifier. The ...
where each data point has a corresponding answer or classification. This is known as supervised learning. (Unsupervised learning uses unlabeled data.) These models learn to identify patterns in the data and use them to make predictions on new data. Here are some popular types of machine learning...
where each data point has a corresponding answer or classification. This is known as supervised learning. (Unsupervised learning uses unlabeled data.) These models learn to identify patterns in the data and use them to make predictions on new data. Here are some popular types of machine learning...
Machine Learning (ML) is an artificial intelligence branch that involves training algorithms to make predictions or decisions based on data. The main ML types are supervised learning, unsupervised learning, and reinforcement learning. Each type uses different methods for processing and learning from data...
One of the most popular machine learning based classification algorithms is SVM, especially for those with small datasets. This tool has been used for engineering problems and other areas such as epileptic seizure diagnosis applications [37], [38], [39]. There are different explanations of SVM ...
To set realistic expectations for AI without missing opportunities, it's important to understand both the capabilities and limitations of different model types. Two categories of algorithms that have propelled the field of AI forward are convolutional neural networks (CNNs) and recurrent neural networks...
Here’s a breakdown of some types of AI models that fall under the umbrellas of machine learning and deep learning: Machine learning models Machine learning (ML) models are trained on labeled data, where each data point has a corresponding answer or classification. This is known as supervised ...