Deep Reinforcement Learning for Action Based Object Tracking in Video Sequences

In this paper, we propose a valuable route for visual object tracker which catches a bounding box to zone of premium physically in the video frames by recognizing the activity got the hang of utilizing the convolution neural systems. The proposed convolution neural network used to control tracking actions is done with various training video sequences and fine-tuned during the actual tracking of the object. Pretrain of the video is done using deep reinforcement learning (RL) along with the supervised learning. Mostly named information from the RL can be utilized for semi supervised learning and assessing through object tracking benchmark dataset, the proposed tracker is confirmed to accomplish a good performance. The proposed method, which operates in real time on without graphics processing unit, outperforms the state of real time trackers with proper accuracy with performance 10%

An Examination of the Smote and Other Smote-based Techniques That Use Synthetic Data to Oversample the Minority Class in the Context of Credit-Card Fraud Classification

This research project seeks to investigate some of the different sampling techniques that generate and use synthetic data to oversample the minority class as a means of handling the imbalanced distribution between non-fraudulent (majority class) and fraudulent (minority class) classes in a credit-card fraud dataset. The purpose of the research project is to assess the effectiveness of these techniques in the context of fraud detection which is a highly imbalanced and cost-sensitive dataset. Machine learning tasks that require learning from datasets that are highly unbalanced have difficulty learning since many of the traditional learning algorithms are not designed to cope with large differentials between classes. For that reason, various different methods have been developed to help tackle this problem. Oversampling and undersampling are examples of techniques that help deal with the class imbalance problem through sampling. This paper will evaluate oversampling techniques that use synthetic data to balance the minority class. The idea of using synthetic data to compensate for the minority class was first proposed by (Chawla et al., 2002). The technique is known as Synthetic Minority Over-Sampling Technique (SMOTE). Following the development of the technique, other techniques were developed from it. This paper will evaluate the SMOTE technique along with other also popular SMOTE-based extensions of the original technique

視覚と言語情報を理解し人間や環境と作用し合う機械知能

Tohoku University博士（情報科学）thesi