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Research Paper

The effect of air pollution on APTI values of some plant species and their comparative study

Air pollution is one of the serious problems faced by people globally due to its transboundary dispersion of pollutants over the entire world. But, whatever mode, whether natural or artificial, is a major concern in to-days developing countries like India. The present research is aimed at assessing the air pollution tolerance index of plants at two different locations. The locations selected were Maheshwari Udayaan, Mumbai, India (located at the center of four signals routes (location 1), and Khalsa College garden, Mumbai, India (location 2). Plants available commonly in both locations were selected for the present research. Four physiological and biochemical parameters which are relative water content, leaf pH, Ascorbic acid, and total chlorophyll were used to compute the APTI. Plants' responses towards air pollution are assessed by the air pollution tolerance index (APTI) value. The plant species having higher APTI value can be given priority for plantation programs in urbanize and industrial areas; so as to reduce the effects of air pollution and to make the ambient atmosphere clean and healthy. The present study was conducted for evaluating the Air Pollution Tolerance Index (APTI) value of six plant species i.e., 1. Mangifera Indica 2.Polyathia longifolia 3. Ixora 4. Codiaeum variegatum 5.Canna Indica 6.Phyllanthus amarus.

Published by: Dr. Ranjeet Kaur

Author: Dr. Ranjeet Kaur

Paper ID: V7I3-1453

Paper Status: published

Published: May 24, 2021

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Research Paper

Prediction of plant leaf disease using image pre-processing and filter based optimal feature selection for KNN classifier

In plants, disease detection is a significant task related to the agricultural production of a country. The major economy of a country is linked with agricultural production which is very important for the development of a nation. Any kind of infection on the leaves of the plants leads to a loss in crop production and puts down the effort of farmers which in turn hits the economy and livelihood of the country. In this paper, we propose an image processing and filter-based feature selection method which distinguishes the disease of the plant leaf and classifies them using a KNN classifier. Using the feature selection process the unwanted redundancy and irrelevant data is filtered which helps the classifier to learn and classify the data more precisely.

Published by: Komala T., Ashwini S. S., Dr. M. Z. Kurian

Author: Komala T.

Paper ID: V7I3-1452

Paper Status: published

Published: May 24, 2021

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Case Study

Post-operative rehabilitation of fracture of the distal end of the humerus: A case study

Intra-articular fractures of the distal humerus constitute 0.5%–7% of all fractures and 30% of elbow fractures. Over 25% of such fractures develop significant complications during treatment and a few of them may need further surgery. Intraarticular fractures of the distal humerus can be treated by open reduction and internal fixation (ORIF). Methodology: This case study was conducted on Mr. Rangaswamy during the first week of March 2021 at ESIC hospital. The patient was treated with gentle range of motion mobilization for elbow flexion, extension to the patient’s pain tolerance level. He was also given wrist and hand ROM mobilization. Conclusion: During the 6 days of Physiotherapy sessions, we had with the patient, we both could see the improvement from day 1 until our last session together. He improved greatly during the course of treatment. The improvement came after joint mobilizations, functional exercises, and the patient’s positive attitude, and the willingness to get back to normalcy.

Published by: Sandesh Rao, Kangkana Goswami, Subarna Rabha, Diker Dev Joshi

Author: Sandesh Rao

Paper ID: V7I3-1450

Paper Status: published

Published: May 24, 2021

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Research Paper

Human activity recognition system

Understanding the activities of human from videos is demanding task in Computer Vision. Identifying the actions being accomplished by the human in the video sequence automatically and tagging their actions is the prime functionality of intelligent video systems. The goal of activity recognition is to identify the actions and objectives of one or more objects from a series of examination on the action of object and their environmental condition. The major applications of Human Activity Recognition vary from Content-based Video Analytics, Robotics, Human-Computer Interaction, Human fall detection, Ambient Intelligence, Visual Surveillance, Video Indexing etc. The Experimental Evaluation of various papers are observed efficiently with the various performance metrics like Precision, Recall, and Accuracy.

Published by: Akash Kumar, Varshini Shenoy, Puneet Tiwari

Author: Akash Kumar

Paper ID: V7I3-1445

Paper Status: published

Published: May 24, 2021

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Research Paper

Dual axis solar tracking system using Arduino UNO

Solar photovoltaic cells generates the energy by approximately following the intensity of light and using the light energy to create an electric current. There are many PV cells within solar panels and the current created by all of the cells together adds up to enough electricity. A dual axis solar energy system using “Arduino UNO” works as an automatic tracking system and controls the elevation and orientation angles of solar panels such that the panel always maintained perpendicular to sunlight. The measured variable of our automatic dual axis solar tracking system is low cost, reliable and efficient. As a result of experiment, by using monocrystalline solar panel and following the intensity of sunlight, the energy generated by dual axis solar tracking system increases consumption of energy up to 8%-25% more than fixed PV system.

Published by: Samiksha Sharad Ranpise, Aditya Bhagwaan Kadam, Tejesh Prabhakar Shinde, Viabhav Raghunath Mhatre, Manisha Bhendale

Author: Samiksha Sharad Ranpise

Paper ID: V7I3-1444

Paper Status: published

Published: May 24, 2021

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Technical Notes

Credit card fraud detection

Credit card Fraud has emerged as a bigger problem with an increase in online transactions and e-commerce. A credit card fraud happens when a fraudster uses your credit card information to make unauthorized purchases in your name. Credit card companies need to address these fraudulent transactions so that customers are not charged for the goods they did not buy. In this paper, we aim to tackle such fraudulent transactions by identifying them using various machine learning algorithms like Logistic Regression, Random Forest and, eXtreme Gradient Boosting. The results of the algorithms are based on accuracy, precision, recall, and F1-score. The algorithms are compared, and the algorithm that has the greatest accuracy, precision, recall, and F1-score is considered as the best algorithm that is used to detect fraudulent transactions.

Published by: Vadlamudi Tony Titus, Lakshmi Choudari, Shashank, Gunavardhan

Author: Vadlamudi Tony Titus

Paper ID: V7I3-1433

Paper Status: published

Published: May 24, 2021

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