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Research Paper

Validity and reliability study of Menopausal Maladies Scale (MMS)

Menopause is a natural part of the aging process in women’s life during which the women juggle multiple caring responsibilities such as providing care and support to both parents and children or grandchildren. In fact, the women are “sandwiched” between the responsibilities related to two generations. Menopause has various phases as mentioned by STRAW + 10 classification among which perimenopausal and early postmenopausal periods are more significant because the women suffer from most significant health problems due to estrogen depletion. If the problems are not identified in the early stage and treated these problems become sicknesses (maladies) and bring the women’s life to an end. MENQOL, Menopausal Rating scale, and MODIFIED Kupperman’s Index are popularly used tools to assess menopausal problems. The tool Menopausal Maladies Scale (MMS) is developed from the above-mentioned tools. MMS includes a wide range of items relating to the Maladies of menopausal women which mainly ley in the physiological and psychological domains. MMS physiological domain and psychological domain are further classified under physiological Maladies Skin, Heart, Sleep, Joint and muscular problems, and urogenital problems and Psychological Maladies include Depression, Anxiety, Stress, and Memory & concentration. The MMS was a reliable and valid tool to assess menopausal maladies.

Published by: Malathi M., Dr .P. Padmavathi, B. Ashok

Author: Malathi M.

Paper ID: V8I5-1252

Paper Status: published

Published: December 19, 2022

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Review Paper

Diet and lifestyle -key factors in the manifestation and prevention of the disease

Nowadays prescribing medication as a first line of treatment is more common than modifying the dietary habits and lifestyle of people. Improper diet and a sedentary lifestyle are responsible for many diseases. A certain group of diseases popularly knowns as NCD or lifestyle disorders are directly related to these two factors. Ayurveda the most ancient science of medicine still aims at uprooting the cause of diseases rather than symptomatic relief. According to Ayurveda, to achieve and maintain Arogya (health) it is essential to practice healthy dietary and lifestyle interventions. A sufficient amount of safe and nutritious food is key to sustaining life and promoting health whereas improper and unsafe food items create a vicious cycle of disease. Similarly, lifestyle choice plays an important role in the manifestation, prevention, and treatment of the disease. Many lifestyle interventions mentioned in Ayurveda include avoiding the suppression of manifested urges and adapting Swasthavritta in the form of Dincharya, Nishachraya, Ritucharya, and Sadavritta have a huge impact on a person’s overall health. One must adopt these dietary and lifestyle practices to achieve and maintain healthy living. This paper is an attempt to emphasize the importance of dietary and lifestyle behaviors in the manifestation and prevention of diseases, it enlists the various dos and don’t that one must consider in day-to-day life for healthy living.

Published by: Dr. Laxmi Rathore, Dr. Mahesh Vyas

Author: Dr. Laxmi Rathore

Paper ID: V8I5-1251

Paper Status: published

Published: December 16, 2022

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Research Paper

Medical diagnosis using Machine Learning

Health is certainly the most important asset of human beings which directly reflects in their progress or development. In this paper, we propose a machine-based system that can be used for medical diagnosis. The user would be able to upload his medical data through the web application which would be further processed by a machine learning model for health disease detection. Here we focus on major diseases such as Diabetes, Heart, and Kidney. The sole aim is to highlight and prove the benefits of Machine learning in the prediction of diseases and their diagnosis.

Published by: Shubham Chorage, Manish Khodaskar

Author: Shubham Chorage

Paper ID: V8I5-1245

Paper Status: published

Published: December 8, 2022

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Research Paper

Song recommendation system using emotion detection

Human emotion detection is an immediate need so that modern AI systems can simulate and measure facial responses. It also has advantages in the identification of intent, promotion of products, and security verification. Real-time emotion recognition from images and video is a very simple task for the human eyes and brain, but it proves to be very difficult for machines and similar machine learning tools. Basically, Image Processing techniques are needed for feature extraction supported by a reliable database trained in a Machine Learning model for the system. Several machine learning algorithms and tools such as Convolutional Neural Networks, OpenCV, Deep Learning, Eigen values, and Eigen vectors are suitable for this job and I intend to use these methods in this project. For machines development of different modules and then training them using various images and real-time feed is essential. Various leading institutions and researchers have trained their own models for an accuracy of approximately 50% and above. This project explores the ML algorithms as well as emotion detection techniques which would help us in the correct identification of human emotion and furthermore, implementation of the system into a useful application of a music recommendation system.

Published by: Shubham Chorage

Author: Shubham Chorage

Paper ID: V8I5-1246

Paper Status: published

Published: December 8, 2022

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Review Paper

Sentiment analysis on COVID-19 Vaccine

In the current scenario of the COVID-19 pandemic, ensuring vaccination is a top priority. Our project aims to clarify people's attitudes toward vaccination against COVID-19. Various studies have already been conducted to measure mood associated with COVID-19 vaccines, but they all suffer from a major common flaw: poor mood classification accuracy. Our project aims to increase the accuracy of mood classification for COVID-19 vaccines compared to previous studies. In our project, we used ABSA (Aspect-Based Sentiment Analysis) and TF-IDF (Term Frequency-Inverse Document Frequency) models for sentiment classification. We also tested the classification accuracy using five traditional machine learning models: Random Forest, Naive Bayes, Support Vector Machines, Logistic Regression, and Ensemble Classification. For our project, we classify sentiment into three categories called positive, negative, and neutral. This in many ways distinguishes our project from the literature. First, according to the limited research we read, ABSA and TF-IDF were not used together. Second, most of the previous studies used the bag-of-words approach, an outdated model compared to ABSA. Finally, traditional machine learning models such as random forests and ensemble classification have never been used in ABSA and TF-IDF. This project provides higher accuracy than stated in our results as a mood classifier for COVID-19 vaccines, whereas previous studies have less than 67% accuracy.

Published by: Hiren Thakur, Divij Singh Chauhan, Aditya Dutta, Kaustubh Sharma

Author: Hiren Thakur

Paper ID: V8I5-1243

Paper Status: published

Published: December 7, 2022

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Research Paper

Supply and demand control system for farmer’s market

The farmers usually suffer and has to go through all the hassle just to get a poor deal for their hardwork. This is mainly due to the poor marketing strategies and farmers being unaware. Through our project this problem of the farmers will be resolved. Our application will help them to register their product to the market from anywhere. With the help of firebase the information can be stored and retrieved. The layout of the app is created using android studio.

Published by: Soham Pawar, Soham Phadke, Snehashish Mulgir, Sohan Kamble, Kuldip Solanke

Author: Soham Pawar

Paper ID: V8I5-1231

Paper Status: published

Published: December 3, 2022

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