PhosopNet: An improved grain localization and classification by image augmentation

Rice is a staple food for around 3.5 billion people in eastern, southern and south-east Asia. Prior to being rice, the rice-grain (grain) is previously husked and/or milled by the milling machine. Relevantly, the grain quality depends on its pureness of particular grain specie (without the mixing between different grain species). For the demand of grain purity inspection by an image, many researchers have proposed the grain classification (sometimes with localization) methods based on convolutional neural network (CNN). However, those papers are necessary to have a large number of labeling that was too expensive to be manually collected. In this paper, the image augmentation (rotation, brightness adjustment and horizontal flipping) is appiled to generate more number of grain images from the less data. From the results, image augmentation improves the performance in CNN and bag-of-words model. For the future moving forward, the grain recognition can be easily done by less number of images

A text segmentation approach for automated annotation of online customer reviews, based on topic modeling

Online customer review classification and analysis have been recognized as an important problem in many domains, such as business intelligence, marketing, and e-governance. To solve this problem, a variety of machine learning methods was developed in the past decade. Existing methods, however, either rely on human labeling or have high computing cost, or both. This makes them a poor fit to deal with dynamic and ever-growing collections of short but semantically noisy texts of customer reviews. In the present study, the problem of multi-topic online review clustering is addressed by generating high quality bronze-standard labeled sets for training efficient classifier models. A novel unsupervised algorithm is developed to break reviews into sequential semantically homogeneous segments. Segment data is then used to fine-tune a Latent Dirichlet Allocation (LDA) model obtained for the reviews, and to classify them along categories detected through topic modeling. After testing the segmentation algorithm on a benchmark text collection, it was successfully applied in a case study of tourism review classification. In all experiments conducted, the proposed approach produced results similar to or better than baseline methods. The paper critically discusses the main findings and paves ways for future work

Review of the application of machine learning to the automatic semantic annotation of images

The massive amount of digital content generated daily in the modern world has created the need for an image retrieval system built on image analysis via image processing and machine learning, therefore this study explains the role of machine learning in bridging the semantic gap in content-based image retrieval, proposes an automatic image annotation framework, in which training images are obtained from social media, and semantic indexing is achieved using a combination of supervised and unsupervised machine learning. Furthermore, the study also highlights the need for continuous vocabulary improvement for optimum system performance and recommends hardware implementation of machine learning algorithms to ensure high overall speed of image retrieval systems