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The Plight of People below The Poverty Line in India

One of India's most pressing societal problems is poverty. A sizable portion of the Indian population is impacted. Due to the recession brought on by the COVID-19 pandemic, the number of impoverished people in India has more than doubled from 60 million to 134 million in only one year. As a result, India has once again returned to the category of "country of mass poverty" after 45 years. The negative effects of poverty on our country's children include subpar housing, homelessness, poor nutrition, and food security, inadequate child care, a lack of access to health care, hazardous neighbourhoods, and underfunded schools. It is a must to take prompt, appropriate action to address the issue of poverty. Farmers should be provided with enough amenities that they can make farming viable and avoid moving to cities in search of work. People who lack literacy should be provided with the necessary training so they may earn their living. Family planning should be practiced to stop the population from growing. Additionally, steps should be taken to eradicate corruption so that we can address the wealth disparity. The issue of poverty affects the entire country, not just one individual. For India's people, society, and economy to thrive sustainably and inclusively, poverty must be eradicated.

Published by: Aleya Masand, Samara Masand

Author: Aleya Masand

Paper ID: V9I5-1157

Paper Status: published

Published: October 4, 2023

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Research Paper

Smart Water Management

The shortage of water supplies has emerged as a pressing worldwide issue in a world that must contend with the twin problems of a growing population and climate change. The need for effective water management has grown, and it is all too easy to see the results of carelessness and human mistake in managing water resources. Artificial Intelligence (AI), however, is a promising solution in the realm of computer science. A developing area of computer science called artificial intelligence has the power to completely alter how we manage our water resources. Computers, as opposed to people, are known for their accuracy and dependability. Utilizing AI in water management could not only correct past mistakes but also save millions of liters of water each year, thereby helping the world's population, which is always expanding. At its foundation, smart water management comprises effectively managing water resources with the least amount of human involvement. Data-driven "intelligent" applications have already revolutionized many elements of our daily lives in the digital age. Water utilities that are forward-thinking can greatly improve their operational performance by using this digital technology revolution. For water utilities starting their journey toward digital transformation, this abstract offers an introduction to the core AI ideas. It puts a strong emphasis on streamlining water distribution processes and dealing with the urgent problem of unaccounted-for water. Water utilities may use a wealth of data and information to improve service delivery, lower operating costs, and make better decisions by utilizing the power of AI algorithms and big data analytics. This succinct review describes the wide-ranging uses of big data analytics and AI-related algorithms in the water supply industry. It also explores how water utilities might use AI to predict and reduce unaccounted-for water, a problem that persists in the industry. Finally, actionable suggestions for implementing AI are offered, along with first cost projections.

Published by: Pranav Pradhan

Author: Pranav Pradhan

Paper ID: V9I5-1149

Paper Status: published

Published: September 30, 2023

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Research Paper

Periodicity of the Probability Distribution of a Particle in a Box

We consider a particle in a two-dimensional infinite potential square well in states that are superpositions of either two or three energy eigenstates. These have probability distributions that are periodic in time. We compute the periods in both cases and simulate the time dependence of the probability distributions.

Published by: Jettae Schroff

Author: Jettae Schroff

Paper ID: V9I5-1143

Paper Status: published

Published: September 27, 2023

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Research Paper

Free-body modal analysis of a Baja SAE vehicle chassis

With the growing need to design increasingly efficient and complex systems, engineering studies are increasingly resorting to computer simulation techniques to analyze the performance and behavior of physical systems. These simulations help to reduce the time and cost of developing new projects. The aim of this article was to carry out a free-body modal simulation of the chassis of a Baja SAE off-road mini vehicle, using the finite element method. The study used Solidworks software to generate the 3D model of the chassis and Ansys software to carry out the simulations. At the end of the simulations, it was possible to see that the chassis structure has natural frequencies between 36 and 86 Hertz (Hz) when the structure is free, which are different from the frequencies of the main source of forced vibration in the structure. In this way, it can be concluded that the structure does not enter the resonance phenomenon, meeting the design assumptions.

Published by: Leandro de Paula Freire, Luiz Augusto Ferreira de Campos Viana

Author: Leandro de Paula Freire

Paper ID: V9I5-1147

Paper Status: published

Published: September 26, 2023

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Research Paper

Utilizing LSTM neural networks for sentiment analysis of tweets

Deep Neural Networks are considered as one of the most powerful machine learning methods of recent times. Recurrent neural networks, including LSTM variations, exhibit exceptional performance in sequence-oriented assignments, while also falling within the domain of autoregressive models, wherein forecasts are tied to the historical input context. In this paper, we experiment with LSTM for Twitter sentiment analysis. Leveraging advances in Natural Language Processing (NLP), we show the efficacy of our algorithm with extremely competitive results.

Published by: Manan Gangwani

Author: Manan Gangwani

Paper ID: V9I5-1148

Paper Status: published

Published: September 23, 2023

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Research Paper

Vehicle Diagnostics Systems and Intelligent Failure Prediction

The automotive industry's rapid growth has led to increased vehicle numbers and subsequently higher failure rates. Conventional diagnostics react to failures, lacking preventive capabilities. Current On-Board Diagnostics are no longer sufficient, necessitating upgrades. To address this, I propose implementing Internet of Things (IoT) devices and Deep Learning Models to predict failures in advance, saving costs and avoiding mishaps. These models utilize historical data and vehicle tests to establish threshold values, triggering warnings if the system detects potential failures. The system comprises an OBD device sending data to a remote server, which updates a dashboard with real-time failure alerts.

Published by: Suyash Pustake

Author: Suyash Pustake

Paper ID: V9I5-1142

Paper Status: published

Published: September 23, 2023

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