Computational Intelligence
N. Prasad; B. Rajpal; K. K. R. Mangalore; R. Shastri; N. Pradeep
Abstract
Face recognition has always been one of the most searched and popular applications of object detection, starting from the early seventies. Facial recognition is used for access control, authentication, fraud detection, surveillance, and by individuals to unlock their devices. The less intrusive and robustness ...
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Face recognition has always been one of the most searched and popular applications of object detection, starting from the early seventies. Facial recognition is used for access control, authentication, fraud detection, surveillance, and by individuals to unlock their devices. The less intrusive and robustness of the face detection systems, make it better than the fingerprint scanner and iris scanner. The frontal face can be easily detected, but multi-view face detection remains a difficult task, due to various factors like illumination, various poses, occlusions, and facial expressions. In this paper, we propose a Deep Neural Network (DNN) based approach to improve the accuracy of detection of the face. We show that Deep Neural Networks algorithms have better accuracy than traditional face detection algorithms for multi-view face detection. The Deep Neural Network (DNN) gives more precise and accurate results, as the DNN model is trained with large datasets and, the model learns the best features from the dataset.
Computational Intelligence
N. Pradeep; K. K. Rao Mangalore; B. Rajpal; N. Prasad; R. Shastri
Abstract
Recommendation based systems can be used for recommending different web page, books, restaurants, tv shows, movies etc. The aim of movie recommendation system is to recommend movies to different users based on their interests. This helps the user to save time browsing the internet looking for movies ...
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Recommendation based systems can be used for recommending different web page, books, restaurants, tv shows, movies etc. The aim of movie recommendation system is to recommend movies to different users based on their interests. This helps the user to save time browsing the internet looking for movies from the thousand already existing ones. Content-based recommendation system describes the items that may be recommended to the user. Based on a data set, it predicts what movies a user will like considering the attributes present in the previously liked movies. Recommendation systems can recommend movies based on one or a combination of two or more attributes. While designing a movie recommendation system various factors are considered such as the genre of the movie, the director or the actors present in it. In this paper, the recommendation system has been built on cast, keywords, crew, and genres. A single column is created which will be the sum of all the 4 attributes, and it acts as a dominant factor for this movie recommender system.
Computational Intelligence
R. Shastri; N. Pradeep; K. K. Rao Mangalore; B. Rajpal; N. Prasad
Abstract
Breast cancer has been the riskiest malignancy among ladies around the world. Nearly 2 million new cases were diagnosed in 2018. The main problem in the detection of breast cancer is to find how tumors turn into malignant or benign and we can do this with the help of machine learning techniques as they ...
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Breast cancer has been the riskiest malignancy among ladies around the world. Nearly 2 million new cases were diagnosed in 2018. The main problem in the detection of breast cancer is to find how tumors turn into malignant or benign and we can do this with the help of machine learning techniques as they provide an appropriate result. According to research, an experienced physician can diagnose cancer with 79% accuracy while using machine learning techniques provides an accuracy of 91%. In this work, machine learning techniques have been applied which include K-Nearest Neighbors algorithm (KNN), Support Vector Machine (SVM), and Decision Tree Classifier (DT). To predict whether the cause is benign or malignant we have used the breast cancer dataset. The SVM classifier gives more accurate and precise results as compared to others, and this classifier is trained with the larger datasets.