Higher education in India- The past glory of excellence, the downfall, and an attempt to rise again
The current study titled “Higher Education in India –The Past Glory of Excellence, the Downfall, and an Attempt to Rise Again” is a quantitative study to analyse the perceptions of people about the Higher Education in India. The investigator used random sampling method to collect the information through an online survey. The sample consisted of 40 individuals who are directly or indirectly involved in the higher education system. The study revealed that India had enjoyed a well renowned higher education system and world class universities. India contributed immensely towards Science, Mathematics, surgery and medicine. But the statics show that now India’s performance in higher education in the global level is alarming and shameful. The young generation wants to move to world- class universities abroad for their higher education. The findings of the study shows that most of the respondents are aware about the positive aspects, negative aspects, brain drain and the current scenario of Indian Higher Education System. The finding also shows the Indian Education System needs radical changes and more investment from the government towards its development. There was a strong opinion that Indian universities should respond to global changes and also work hard towards the skill-based training rather than the age old theory-based learning.
Published by: Nisha Chakyarkandiyil
Author: Nisha Chakyarkandiyil
Paper ID: V6I4-1147
Paper Status: published
Published: July 3, 2020
The systemic pattern of Evolution
The paper depicts about the relation of the evolution in contest of comparison of gene difference of an organism in present generation and older generation
Published by: Ajil Benny
Author: Ajil Benny
Paper ID: V6I4-1142
Paper Status: published
Published: July 3, 2020
Screening COVID-19 cases using Deep Neural Networks with X-ray images
The novel coronavirus 2019 (COVID-2019), which first appeared in Wuhan city of China in December 2019, spread rapidly around the world and became a pandemic. It has caused a devastating effect on both daily lives, public health, and the global economy. It is critical to detect the positive cases as early as possible so as to prevent the further spread of this epidemic and to quickly treat affected patients. The need for auxiliary diagnostic tools has increased as there are no accurate automated toolkits available. Recent findings obtained using radiology imaging techniques suggest that such images contain salient information about the COVID-19 virus. Application of advanced artificial intelligence (AI) techniques coupled with radiological imaging can be helpful for the accurate detection of this disease, and can also be assistive to overcome the problem of a lack of specialized physicians in remote villages. In this study, a new model for automatic COVID-19 detection using raw chest X-ray images is presented. The proposed model is developed to provide accurate diagnostics for binary classification (COVID vs. No-Findings) and multiclass classification (COVID vs. No-Findings vs. Pneumonia). My model produced a classification accuracy of 98.08% for binary classes and 87.02% for multi-class cases. The DarkNet model was used in my study as a classifier for the you only look once (YOLO) real time object detection system. I implemented 17 convolutional layers and introduced different filtering on each layer. My model can be employed to assist radiologists in validating their initial screening, and can also be employed via cloud to immediately screen patients. With the ever increasing demand for screening millions of prospective “novel coronavirus” or COVID-19 cases, and due to the emergence of high false negatives in the commonly used PCR tests, the necessity for probing an alternative simple screening mechanism of COVID-19 using radiological images (like chest X-Rays) assumes importance. In this scenario, machine learning (ML) and deep learning (DL) offer fast, automated, effective strategies to detect abnormalities and extract key features of the altered lung parenchyma, which may be related to specific signatures of the COVID-19 virus. However, the available COVID-19 datasets are inadequate to train deep neural networks. Therefore, I propose a new concept called domain extension transfer learning (DETL).
Published by: Tarit Sengupta
Author: Tarit Sengupta
Paper ID: V6I3-1626
Paper Status: published
Published: July 3, 2020
Survey of machine learning methods for spam e-mail classification
The humongous volume of unsolicited bulk e-mail (spam) which is further increasing, is the major cause for developing anti-spam protection filters. Machine learning provides a very optimized approach to automatically filter spams at a very successful rate. Here, in this paper, we survey some of the most popular machine learning algorithms (Naïve Bayes, k-NN, SVMs and ANN) and their applicability to the problem of spam e-mail classification. Descriptions of the algorithms are presented, and the comparison of their performance on the UCI spam base dataset is presented.
Published by: Sanjana Reddy, Navya Priya N, Varsha R Jenni
Author: Sanjana Reddy
Paper ID: V6I3-1672
Paper Status: published
Published: July 3, 2020
A cross-sectional observational study on adverse drug reactions of anti-depressant drugs and compliance in the psychiatry outpatient department at Tertiary Care Hospital, Mumbai
Background: Adverse drug reaction is now seen as one of the major reasons for mortality and morbidity in the world. Moreover, these ADRs are under-reported and underestimated. Thus, the Pharmacovigilance program has been started to reduce the risk of ADR and the safety of drugs. Depression is the most common disorder affecting people of all ages, sex, socio-economic group and religion all over the world; it may range from a very mild condition, bordering on normality, to severe (psychotic) depression. This study may reveal the common drugs which may induce ADR’s so that preventive care can be taken. Therefore the present study is planned to monitor, detect and analyze the adverse drug reactions of anti-depressant drugs in the Psychiatric Department. Aim and Objectives: Aim-To detects and analyze adverse drug reactions in patients with Depression and study patient’s compliance in tertiary care hospitals. Objectives: Primary objective- To detect the types of ADRs induced by anti-depressant drugs and also estimate its incidence rate. Secondary objective- To assess the causality & probability of ADRs, To assess the severity of patients with respect to ADRs, To study the patient’s compliance towards anti-depressant drugs. Methodology: Approval of Institutional ethics committee was taken prior to the initiation of the study. Enrolment of the patient was done as per inclusion and exclusion criteria. Only follow up patients were considered for our study to observe ADRs with the help of prescription copy. The hospital medical case record form of the patient was studied for the demographics, clinical history, clinical findings, diagnostic results and undergoing treatment and compliance. After total data collection from all patients, the ADRs were analyzed. Result: A total of 200 patients were enrolled in our study. Males were 44% while females were 56%. 49 patients between 31 to 40 years, which was mostly found in this age group. Among all ADRs seen weight gain 4%, insomnia 19%, tremors 9.5%, fatiguability 0.5%, nausea 3.5%, sedation 2%, rash 3.5%, and other 4%. Further, the causality of ADRs was observed respectively by using the WHO-causality assessment scale. In which about 54.5% cases of ADR seen to be possible, while 31.8% were unlikely and 13% of ADR were conditional ADR. Naranjo’s probability scale showed 69.6% of probable ADR while 30.3% of doubtful ADR. 82% of patients adhering to medication was measured by medication adherence rating scale while 18% were not adhering medication properly. Conclusion: Depression was seen mostly among people ranging from 31-40 years of age. Females were most affected by depression than the males. Among all the patients, Insomnia was mostly observed ADR in the patients. Other ADRs seen in patients were tremors, fatiguability, rash, weight gain, etc. Only possible and unlikely ADRs were found. Most of the patients were adhering to medication.
Published by: Dr. Subhangi Parkar, Shamil Darbar, Saurabh Ahire, Bhagyashri Sonavane
Author: Dr. Subhangi Parkar
Paper ID: V6I3-1668
Paper Status: published
Published: July 3, 2020
Development and validation of UV-spectrophotometric methods for simultaneous estimation of chlorzoxazone and tramadol in laboratory mixture
The present manuscript describes a simple, sensitive, rapid, accurate, precise, and economical Simultaneous equation method and first-order derivative spectrophotometry method for the simultaneous determination of Chlorzoxazone and Tramadol in laboratory mixture. The absorbance values at 243.3 nm and 271 nm for the simultaneous equation method and 236.6nm and 213.3nm for the first derivative spectrum were used for the estimation of Chlorzoxazone and Tramadol. This method obeyed beer’s law in the concentration range of 2-10 μg/ml for Chlorzoxazone and 10-100 μg/ml for Tramadol. The solvents used for UV-Spectrophotometric methods was 0.1 N NaOH. The % RSD of accuracy was found to be 0.2086 for Chlorzoxazone and 0.4717 for Tramadol. The method was successfully applied to the laboratory prepared mixture because no interference from the mixture excipients was found. The suitability of this method for the quantitative determination of Chlorzoxazone and Tramadol was proved by validation. The results of analysis have been validated statistically and by recovery studies.
Published by: Rushika Jaiswal
Author: Rushika Jaiswal
Paper ID: V6I3-1678
Paper Status: published
Published: July 3, 2020
