Driving Pattern Profiling and Classification Using Deep Learning

Internal News Classification Using Deep Learning

For the last few years, text mining has been gaining significant importance. Since Knowledge is now available to users through variety of sources i.e. electronic media, digital media, print media, and many more. Due to huge availability of text in numerous forms, a lot of unstructured data has been recorded by research experts and have found numerous ways in literature to convert this scattered...

Porosity classification from thin sections using image analysis and neural networks including shallow and deep learning in Jahrum formation

The porosity within a reservoir rock is a basic parameter for the reservoir characterization. The present paper introduces two intelligent models for identification of the porosity types using image analysis. For this aim, firstly, thirteen geometrical parameters of pores of each image were extracted using the image analysis techniques. The extracted features and their corresponding pore types ...

Object Detection for Autonomous Driving Using Deep Learning

the relationship between using language learning strategies, learners’ optimism, educational status, duration of learning and demotivation

with the growth of more humanistic approaches towards teaching foreign languages, more emphasis has been put on learners’ feelings, emotions and individual differences. one of the issues in teaching and learning english as a foreign language is demotivation. the purpose of this study was to investigate the relationship between the components of language learning strategies, optimism, duration o...

Characterizing Driving Styles with Deep Learning

Characterizing driving styles of human drivers using vehicle sensor data, e.g., GPS, is an interesting research problem and an important real-world requirement from automotive industries. A good representation of driving features can be highly valuable for autonomous driving, auto insurance, and many other application scenarios. However, traditional methods mainly rely on handcrafted features, ...