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Research Paper

Delineating the psychological impact of COVID-19 on small businesses in India

The covid-19 pandemic has caused large-scale loss of life and economy across all sectors. It has psychologically and socially impacted the general population ranging from their health and well-being to their education, career, and businesses. It has impacted the global economy drastically. India saw its worst recorded decline in economic growth since 1996 during the pandemic. In India, most of more than 100 million people engaged in small businesses were laid off and about one-third of the small businesses went beyond the scope of rescuing during the pandemic. In such a scenario, this paper intends to delineate the psychological impact of the pandemic on small businesses in India which faced a significant economic blow. At the same time, the resilience of some small businesses that managed to cope and thrive during these difficult times have been briefly discussed.

Published by: Neha Jain, Inayaa Gulati, Ananay Rajput

Author: Neha Jain

Paper ID: V7I2-1269

Paper Status: published

Published: March 30, 2021

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Research Paper

Improved plant leaf disease classification by optimizing weight with convolution neural network learning approach

Agricultural productivity is something on which the economy highly depends. This is one of the reasons that disease detection in plants plays a vital role in the agriculture field, as having the disease in plants is quite natural. If proper care is not taken in this area, then it causes severe effects on plants and due to which respective product quality, quantity, or productivity is affected. In synopsis proposed approach optimized segmentation to find an active area for features and reduce noise, then extract texture base features and learning by ensemble classifier approach. In the Proposed framework main emphasis on getting sufficient features from disease and learning a combination of Convolution and nonlinear classification function.

Published by: Nisha Sharma, Dr. Sukhvinder Kaur, Dr. Rahul Malhotra

Author: Nisha Sharma

Paper ID: V7I2-1265

Paper Status: published

Published: March 30, 2021

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Review Paper

Analysis of operations research applications in agricultural research: A literature review

Agriculture is a productive process requiring the transformation of a set of productive inputs into output with the aim of satisfying wants. These inputs are limited and as such place a constraint in the transformational process. It, therefore, implies that decision-making on the allocation of these limited agricultural resources is a major area of concern in attaining the objectives of agricultural production. Operations research, an analytical method used in problem-solving and decision-making in organizations, have been applied for over 70 decades in decision-making in agriculture. A review of applications of Operations research by some researchers in agriculture problems at farm level, regional sector level, environment protection, risks, and uncertainty analysis, formulating livestock rations and feedstuffs, forestry management, etc., shows that its application in agriculture is extensive and its potential for development is limitless. The application is constrained by complex interacting drivers existing in productivity, markets, the environment, and the people. These drivers include accuracy of data, quantifiability of data. natural disasters, instability of prices, demand for products, changes in government subsidies and policies, and dependence on an electronic computer. In conclusion, the decision to implement the results from Operations research lies with human beings and so this human element is still the most significant part of the decision-making process. The changes and adjustments in the natural and economic environment, and new improved information in the subject area, must be incorporated in the mathematical models and their parameters to account for the change.

Published by: Abasilim Chinwe Frances

Author: Abasilim Chinwe Frances

Paper ID: V7I2-1219

Paper Status: published

Published: March 25, 2021

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Review Paper

Plant leaf disease detection by Features Selection and Learning Approaches Review

Agricultural productivity is something on which the economy highly depends. This is one of the reasons that disease detection in plants plays a vital role in the agriculture field, as having disease in plants is quite natural. If proper care is not taken in this area, then it causes severe effects on plants and due to which respective product quality, quantity, or productivity is affected. In this proposed approach optimized segmentation to find active area for features and reduce noise, then extract texture base features and learning by ensemble classifier approach. In Proposed framework main emphasis on getting sufficient features from disease and learning combination of classifier use linear and nonlinear classification function.

Published by: Nisha Sharma, Dr. Sukhvinder Kaur, Dr. Rahul Malhotra

Author: Nisha Sharma

Paper ID: V7I2-1255

Paper Status: published

Published: March 25, 2021

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Research Paper

Detecting autism from facial image

Autism is a serious developmental spectrum disorder that puts constraints on the ability to communicate linguistic, cognitive, and social interaction skills. Autism spectrum disorder screening is the process of detecting potential autistic traits in an individual where the early diagnose shortens the process and has more accurate results. The methods used to predict the presence of autism by doctors involve physical identification of facial features and questioners, this conventional method of diagnosis needs more time, cost and in the case of pervasive developmental disorders, the parents feel inferior to come out in open. Therefore, a time-efficient and accessible ASD screening are imminent to help health professionals and inform individuals whether they should pursue formal clinical diagnosis or not. A screening tool that could identify ASD risk during infancy offers the opportunity for intervention before the full set of symptoms is present. The proposed model by using a convolution neural network classifier helps in predicting the early autistic traits in children through facial features in images, with the least cost, less time, and a greater amount of accuracy when compared to the traditional type of diagnosis.

Published by: Shaik Jahanara, Shobana Padmanabhan

Author: Shaik Jahanara

Paper ID: V7I2-1181

Paper Status: published

Published: March 25, 2021

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Research Paper

Emotional intelligence, personality and mental health among sportsperson

The present study was undertaken to investigate differences between emotional intelligence, personality, and mental health among sportspersons and non-sportspersons by Shivani Nishad under the supervision of Dr. Monika Gwalani. Sample of the study 100 sportspersons(50) and non-sportspersons(50). The hypothesis of the study is that there is a statistically significant difference in the measure of emotional intelligence, personality, and mental health among sportspersons and non-sportspersons. Pethe and Hyde’s emotional intelligence test, Neo five-factor inventory, and Jagdish and Srivastava’s mental health inventory used for the study. In order to assess the statistically significant difference between sportspersons and non-sportspersons on the measure of emotional intelligence, personality, and mental health by ‘t’ test. There is a statistically significant difference in the sub-dimensions of measure of emotional intelligence which are- self-awareness, self-motivation, emotional stability, managing relations, and altruistic behavior. There is a statistically significant difference in the dimensions of the measure of personality which are- Extraversion, Openness, and Conscientiousness. There is also a statistically significant difference in the sub-dimension of the measure of mental health and they are- Positive Self-Evaluation, perception of reality, integration of personality, autonomy, group-oriented attitudes, and environmental-mastery. The implications of the outcome are that indulgence in physical activity makes a person physiologically fit and also psychologically and mentally fit. Physical activities are an easy, inexpensive, and appropriate strategy and approach that should be emphasized to increase mental health in adolescence. The results indicated that there is a higher level of emotional intelligence and mental health among the sportsperson than non-sportsperson also a statistically significant difference in dimensions of personality i.e. extraversion, openness, and conscientiousness.

Published by: Shivani Nishad, Monika Gwalani

Author: Shivani Nishad

Paper ID: V7I2-1246

Paper Status: published

Published: March 24, 2021

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