Data Mining Oriented Automatic Scientific Documents Summarization

The scientific research process usually begins with an examination of the advanced, which may include voluminous publications. Summarizing scientific articles can assist researchers in their research by speeding up the research process. The summary of scientific articles differs from the abstract text in general due to its specific structure and the inclusion of cited sentences. Most of the important information in scientific articles is presented in tables, statistics, and algorithm pseudocode. These features, however, rarely appear in the standard text. Therefore, a number of methods that consider the value of the structure of a scientific article have been suggested that improve the standard of the produced summary. This paper makes use of clustering algorithms to handle CL- SciSumm 2020 and longsumm 2020 tasks for summarization of scientific documents. There are three well-known clustering algorithms that are employed to tackle CL- SciSumm 2020 and LongSumm 2020 tasks, and several sentences recording functions, with textual deduction, are used to retrieved phrases from each cluster to generate summary

Smart Multi-Model Emotion Recognition System with Deep learning

Emotion recognition is added a new dimension to the sentiment analysis. This paper presents a multi-modal human emotion recognition web application by considering of three traits includes speech, text, facial expressions, to extract and analyze emotions of people who are giving interviews. Now a days there is a rapid development of Machine Learning, Artificial Intelligence and deep learning, this emotion recognition is getting more attention from researchers. These machines are said to be intelligent only if they are able to do human recognition or sentiment analysis. Emotion recognition helps in spam call detection, blackmailing calls, customer services, lie detectors, audience engagement, suspicious behavior. In this paper focus on facial expression analysis is carried out by using deep learning approaches with speech signals and input text

Association of ABO Blood Group Status in Patients with Breast Lesions and Emphasis on Invasive Breast Carcinoma

Introduction: The ABO blood group antigens are expressed on the erythrocyte membrane and on the surface of other normal and pathological cells. Recently, there has been an increasing research interest in the association between ABO blood group antigens and certain type of human cancers. Aim: To determine the association of ABO blood group and Rh blood type in patients with breast lesions. Materials and Methods: It was a retrospective observational study done at a rural tertiary care referral institute, PES Institute of Medical Sciences and Research (PESIMSR), Kuppam, Andhra Pradesh, India, from January 2015 to December 2018. Apparently healthy female voluntary blood donors constituted the control group (n=222). Patients with breast lesions constituted the study group (n=125). The association of the breast lesions with ABO blood group and Rh blood type was analysed. Frequencies, Chi‑square test and crosstabs were the statistical tools used for data analysis. All the statistical calculations were performed through Statistical Software for Data Science (STATA) version 14.1. Results: Total 125 cases of breast lesions were analysed. Neoplastic lesions 113 (90.4%) were more common than the non neoplastic lesions 12 (9.6%). Blood group “O” was the most common blood group in malignant neoplasms and was statistically significant (p=0.045). Blood group B was the most common blood group in grade II invasive breast carcinoma and was statistically just significant (p=0.05). Conclusion: A definite change in the pattern of distribution of ABO blood group was observed in grade II malignant neoplasms. It may be hypothesised that knowing the blood group of breast cancer patients may be beneficial in order to triage the patients for the purpose of efficient managemen