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Research Paper

Employing Machine Learning, A Multiclass Prediction Model For The Student Grading System.

In today's higher education institutions, predictive analytics applications have become a pressing need. In order to generate high-quality performance and valuable data for all educational levels, predictive analytics used sophisticated analytics that included the application of machine learning. the majority of people are aware that One of the main metrics that may be used by educators to track students' academic progress is their grades. In the last ten years, a wide range of machine learning algorithms has been proposed by researchers in the field of education. To improve the performance of predicting student grades, addressing imbalanced datasets presents serious difficulties. Therefore, this study gives a thorough review of machine learning algorithms to predict the final student grades in the first semester courses by enhancing the performance of prediction accuracy. In this study, we'll emphasize two modules. Using a dataset of 1282 genuine student course grades, we assess the accuracy performance of six well-known machine learning techniques: Decision Tree (J48), Support Vector Machine (SVM), Nave Bayes (NB), K-Nearest Neighbor (kNN), Logistic Regression (LR), and Random Forest (RF). In order to reduce overfitting and misclassification results brought on by imbalanced multi-classification based on oversampling Synthetic Minority Oversampling Technique (SMOTE) using two feature selection methods, we have suggested a multiclass prediction model. The outcomes demonstrate that the suggested model integrates with RF and gives a notable improvement with the greatest f-measure of 99.5%. This model's suggested findings are comparable and encouraging, and they have the potential to improve the model's performance predictions for imbalanced multi-classification for student grade prediction.

Published by: Jahnavi Sannidhi, Dumpala Pavan Kumar Reddy, Akkaladevi Lumbhini Madhuri, Donka Suresh, Nimmagallu Swetha, D. Sarika

Author: Jahnavi Sannidhi

Paper ID: V9I2-1161

Paper Status: published

Published: April 3, 2023

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Research Paper

Diagnosis of transformer faults using multi-class AdaBoost algorithm

Low fault diagnosis accuracy is caused by the ineffectiveness of traditional shallow machine learning methods un exploring the connection between the oil-immersed transformer fault data. In response, this study suggests a method for diagnosing transformer faults based on multi-class adaBoost algorithms solves this issue. First, the SVM and the adaBoost algorithm are linked. The SVM is improved by the adaBoost approach, and the transformer defect data is thoroughly investigated. The IPSO is then used to optimize the SVM's parameters when the dynamic weight is added to the PSO algorithm. This is accomplished by updating the particle inertia weight in real-time. Lastly, by examining the relationship between the type of fault and the dissolved gas in the transformer oil, the uncoded ratio technique develops a novel gas set collaboration. The feature vector used as the input is produced using the enhanced ratio approach. The diagnosis method suggested in this paper has a significant increase in diagnostic accuracy when compared to conventional methods, according to simulations using 419 collection of transformer fault data and 117 groups of IECTC10 standard data that were gathered in China. Additionally, it has a fast confluence speed and a powerful search capability.

Published by: Chilla Kaveri, Chagam Reddy Bhargavi, Gandra Neeraja, Burandin Sayyad Dada Umar Hussain, Shaik Tabassum

Author: Chilla Kaveri

Paper ID: V9I2-1156

Paper Status: published

Published: April 3, 2023

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Research Paper

Detection and prediction of air pollution using Machine Learning

The regulation of air pollutant levels is rapidly increasing and it's one of the most important tasks for the governments of developing countries, especially India. It is important that people know what the level of pollution in their surroundings is and takes a step towards fighting against it. The meteorological and traffic factors. burning of fossil fuels, industrial parameters such as powerplant emissions play significant roles in air pollution. Among all the particulate matter (PM) that determine the quality of the air. When its level is high in the air, it causes serious issues on people's health. Hence, controlling it by constantly keeping a check on its level in the air is important.

Published by: Patan Masthan Vali, D. P. Neeha Kousar, T. Sai Pranathi, K. Nandini, M. Saikanth, A. Ramesh Babu

Author: Patan Masthan Vali

Paper ID: V9I2-1158

Paper Status: published

Published: April 3, 2023

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Research Paper

Implementation of ai based protective mask detector

The global impact of the corona virus disease is significant. Firmly stop the corona virus from spreading. A single-shot detector (SSD)-based object identification technique that focuses on accurate, real-time face mask detection in densely populated settings such as communities and workplaces where there are a lot of people is described. On the basis of two methodologies, we suggest a system in this project. Single-shot multi-box recognition, often known as SSD, is a technique for identifying people wearing face masks in an image in a single attempt. By removing the area recommendation network, which causes an accuracy loss, SSD is employed to accelerate the cycle. Implementing our application in closed-circuit television (CCTV) surveillance systems. It will identify who is wearing the mask and who is not by using mobilenetV2 and machine learning techniques. With the aid of the single shot detection technique, it can filter photographs on the spot and distinguish between them. The data collected during this process, such as image capture, is kept in the cloud to ensure that the application functions properly.

Published by: D. Sarika, C. Amrutha Sai, M. Ganesh Kumar, M. Arun Kumar, A. Bhargavi, B. Jyoshna

Author: D. Sarika

Paper ID: V9I2-1159

Paper Status: published

Published: April 3, 2023

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Research Paper

Credit card fraud detection: An evaluation of Machine Learning methods performance using SMOTE and AdaBoost

Online card transactions have increased daily as a result of the development of technologies like e-commerce and financial technology (FinTech) apps. As a result, there has been an increase in credit card fraud that impacts banks, merchants, and card issuers. Thus, it is critical to creating systems that guarantee the confidentiality and accuracy of credit card transactions. In this study, we use imbalanced real-world datasets produced by European credit cardholders to create a machine learning (ML) based framework for detecting credit card fraud. In order to address the class imbalance problem, we resampled the dataset using the Synthetic Minority over-sampling Technique (SMOTE).

Published by: Kethe Meghana, Nidimamidi Thahseen, Duragadda Dhana Lakshmi, Vamsharajula Seenu, Vattam Veda Prakash, Pola Nikhila

Author: Kethe Meghana

Paper ID: V9I2-1155

Paper Status: published

Published: April 1, 2023

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Short Communication

Proposed survey questionnaire for the diagnosis of irritable bowel syndrome: Based on patient complaints

Functional bowel disorders (FBD) are extremely common all over the globe. Enhancing knowledge of FBD is essential because they have a detrimental effect on the healthcare system. Irritable bowel syndrome (IBS) is one such FBD where recurrent abdominal pain is associated with a change in bowel habits. Many conditions mimic IBS which include celiac disease, microscopic colitis, inflammatory bowel disease (IBD), lactose and fructose intolerance, etc. In order to correctly differentiate these disorders from IBS, limited testing may be necessary. Considering these overlapping conditions we thought of developing a questionnaire to help the physicians diagnose IBS based on a scoring system. A set of questions were drafted based on patient complaints and each question had a scoring system. Basis the patient's response to each question the total score would help the physicians understand if the patient is suffering from IBS. This is a brand-new grading system that has not yet undergone testing. The medical community is urged to evaluate the scoring system's usefulness and provide input so that it can be improved.

Published by: Dr. Saurabh Srivastava, Dr. Ashish Kumar, Dr. Ànuj Maheshwari, Dr. Gayatri Kapse

Author: Dr. Saurabh Srivastava

Paper ID: V9I2-1150

Paper Status: published

Published: March 29, 2023

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