Review paper on scope of Atmanirbhar Bharat
“Necessity is the mother of invention”. Like all the other countries India is also fighting with the Covid-19 Pandemic. The pandemic creates huge inflation, economic depression all over the world. However, it also turns out an opportunity for India to be stand out as global manufacture and self-reliant under the AtmaNirbhar Bharat program. This Abhiyan is started to mitigate the negative effects of the COVID-19 pandemic. We have to take steps to ensure that products which we import from elsewhere are manufactured in India. Our whole paper highlights the practical implementation areas with detailed reviews. It will also showcase how the Indian Youth should participate and shape the AtmaNirbhar Module for the betterment of the motherland. It tries to analyze the limitations and bring out various suggestions to utilize the existing capacity for becoming a global supplier. Our paper delivers the extended arm towards the five significant pillars of the AtmaNirbhar Bharat mission named Economy, Infrastructure, System, Demography, and Demand. It also highlights the four prime sectors like Rural Tourism, Electronic, Information Technology, and Modern Agriculture which are competent to give a quantum scale growth in this initiative. Our paper's sole aim is to converge the differential building blocks of the AtmaNirbhar Bharat mission.
Published by: Shritish Shete, Siddhi Dave
Author: Shritish Shete
Paper ID: V7I1-1271
Paper Status: published
Published: February 22, 2021
Optimism at workplace
Optimism is a management strategy that gives tremendous energy to face any challenges. Optimism is a state of mind. Optimistic employees never succumb to any negativity, instead, they stay determined to reach their goals and enjoy the work. If you want to succeed in your career you have to overcome all the odds. This article focuses on workplace optimism, its challenges, and opportunities.
Published by: S. Ramesh, Annie Valsan
Author: S. Ramesh
Paper ID: V7I1-1273
Paper Status: published
Published: February 19, 2021
Profanity detection in social media text using a hybrid approach of NLP and machine learning
Profanity is socially offensive language, which may also be called cursing, cussing, swearing, or expletives. Nowadays where everything is digitally managed, there are lots of online platforms and forums which people use. If we take an example of any social media platform like Twitter, their privacy policy suggests that users cannot share or write any obscene/vulgar language on a public platform. Several corporate and research organizations discuss how such content is found and controlled, such as computer vision research has developed to detect illegal practices in public spaces, NLP has progressed to detect profanity in social media texts. However, existing profanity detection systems still remain flawed because of various factors. In this paper, we define and analyze the system which will use NLP and Machine learning approach to solve this. It is usually framed as a supervised learning problem. Generic features such as Bag-Of-Words or embeddings systematically deliver fair success in classification. Lexical resources in combination with models such as Linear Support Vector Machine (SVM); feature modeling specific linguistic constructs making it more effective in classification.
Published by: Raktim Chatterjee, Sukanya Bhattacharya, Soumyajeet Kabi
Author: Raktim Chatterjee
Paper ID: V7I1-1269
Paper Status: published
Published: February 17, 2021
The changing trends in corporate culture
The pandemic continues to dominate global economic sentiments as the coronavirus also known as COVID-19 continues to spread globally. The Business Insider Intelligence and several eMarketer’s are continuing to work in order to analyze the impact of the virus on businesses across the world. Through this article, readers can have a better understanding of the current situation of businesses and their recent operation trends like remote working, employee motivation, and consumer behavior. The article further proceeds by evaluating the case study of few companies with a high growth perspective and profitability index. And seeks the relevance through checking the feasibility of business models and planning policies for the next consecutive quarters to combat the situation.
Published by: Aryan Dogra
Author: Aryan Dogra
Paper ID: V7I1-1262
Paper Status: published
Published: February 16, 2021
Smart traffic manager: Computer vision and deep learning-based approach
In the last couple of decades, the number of vehicles has been on the road increased drastically. Hence it has become very difficult to keep track of every vehicle for traffic management and law enforcement. With the increasing number of vehicles on roads, it is getting difficult to manually enforce laws and traffic rules for smooth traffic flow. Traffic Management systems are installed on traffic signals to check for vehicles breaking the traffic rules. To automate all these processes a system is required to easily identify a vehicle. The main aim to design this system is to reduce the mishaps which occur due to reckless driving and violations of the traffic rules. The important question here is how to identify a particular vehicle, The obvious answer to this question is by using the vehicle’s registered license plate as every vehicle has a unique number through which it is easily differentiated from all the other vehicles. Vehicles in each country have a unique license number, which is written on their registered number plate. This number distinguishes one vehicle from the other, which is useful especially when both are of the same make and model. So, the basic idea will be identifying whether the two-wheeler rider is wearing a helmet or not, over speeding vehicles, zebra crossing violators, etc. Most of the tasks in this will require machine learning/deep learning models for image processing tasks. In the end, this system would be very effective to automate the hectic task of the traffic police and can be very efficient in terms to reduce the workload and manage the different tasks autonomously.
Published by: Sahil Shaikh, Swapnil Patil, Niranjan Patil, Purushottam Kulkarni, J. K. Kamble
Author: Sahil Shaikh
Paper ID: V7I1-1236
Paper Status: published
Published: February 15, 2021
Studies on the compatibility of Ordinary Portland Cement with Polycarboxylate Superplasticisers
The addition of superplasticisers along with mineral admixtures like fly ash and slag in concrete improves the strength and workability of concrete at lesser water cement ratios. To get this benefit, the compatibility between cement and admixtures must be studied. SP’s are normally adsorbed on the cement particles and sometimes the adsorption will not be even and slightly unreliable. This is due to the clinker composition of cement and the type of SP’s used. Various combinations of materials including mineral admixtures affect the behaviour of the cement–based system and become incompatible like slump loss, delayed setting of concrete etc. In the present work, four brands of Ordinary Portland Cements are selected and is checked for its compatibility with 4 types of PCE superplasticisers available in the market using Marsh cone test. The results of these tests conducted on the cement paste is analyzed to find out the optimum dosage of superplasticiser for all the four brands. Chemical analysis and XRD analysis is done for the 4 types of cements to study the behaviour. The work concludes that the presence of MgO in ordinary portland cement affects the cement superplasticiser compatibility to greater extent.
Published by: Lelin Das, Asis Kumar K., Dinesh Kumar T.
Author: Lelin Das
Paper ID: V7I1-1207
Paper Status: published
Published: February 15, 2021
