Pavan Krishna and S Fahad Kamraan, guided by Dr Priyanka, Department of Computer Science and Engineering, SRM University-AP, Andhra Pradesh, successfully published a research paper titled “Stock market prediction using sentimental analysis” in the Scopus indexed “International Journal of Advanced Research Engineering and Technology (IJARET)” on March 2021. Being the students of the Department of Computer Science and Engineering, both researchers have apt knowledge on the sentimental analysis commonly known as opinion mining or emotion AI led to the successful execution of the research project.

Sentimental analysis is used to help find patterns in textual data, to understand people’s emotions and sentiments in a much deeper and holistic way. It is used to find the polarity of the information and to classify it under different emotion/ sentiment radar all the way from sad to happy or too excited.

The key title takes a dig into a study involving a new method of predicting stocks by performing sentimental analysis on the financial data. A sentiment is analysed on the subjectivity and polarity index by classifying it into positive and negative news. Here, to predict the real-time stock price, sentiment analysis measures the polarity or subjectivity score, respectively.

“While pursuing my minor in Business Management, I gained Financial Literacy and eventually found an interest in the Stock Market & its behaviour. At the same time, we noticed how social media plays a crucial role in deciding stock prices. Fahad and I were so intrigued that we decided to take up a Project on Predicting Stock Market Prices based on Sentimental Analysis of statement – News & Tweets”, informed Pavan.

Fahad further acknowledged that the project was a big step for undergraduate students, and it could not have been possible without the guidance and motivation of Dr Priyanka, their mentor. “Dr Priyanka was the constant support and path-bearer for us as she showed immense faith until the goal is achieved”, said Fahad.

The duo was excited after receiving high praises and acclaims for their research project. They look forward to coming up with more research ideas that can benefit society.

Dr Jatindra Kumar Dash, Associate Professor, Computer Science and Engineering, has recently published a paper, “Content-based image retrieval system for HRCT lung images: Assisting radiologists in self-learning and diagnosis of Interstitial Lung Diseases” in the reputed Springer Journal- Multimedia Tools and Applications. The research has been carried out in collaboration with Prof. Sudipta Mukhopadhyay, IIT Kharagpur and Professor & Head, Department of RADIO DIAGNOSIS & IMAGING, Post Graduate Institute of Medical Education and Research, Chandigarh.

Content-based Image Retrieval (CBIR) is a technique that can exploit the wealth of the data stored in the repository and help radiologists in decision making by providing references to the image in hand. A CBIR system for High-Resolution Computed Tomography (HRCT) lung images depicting the sign of Interstitial Lung Diseases (ILDs) is built, and the system can be used as a self-learning tool by budding radiologists. The system is built by addressing several challenges using advanced machine learning techniques. The objective of this work is to develop a CBIR system for ILDs that is reliable and needs minimal human intervention for ling disease diagnosis.

The system developed will act as a helping tool for radiologist by providing a second opinion for the diagnosis of a diverse group of lung diseases called Interstitial Lung Disease. It will help the budding radiologist for self-learning. When used in daily medical practice, the system may reduce the workload of radiologists in countries, having a low number of physicians per inhabitants.

Dr Dash is associated with SRM University-AP for almost three years. His research interests include Content-Based Image Retrieval, Medical Image Analysis and Texture Analysis. He has currently employed his time into the design and development of a Computer-Aided Diagnosis System for Lung Cancer Screening.

Read More: https://link.springer.com/article/10.1007/s11042-020-10173-4