Managing service requests can be challenging, particularly with the growing number of clients, customers, and associates associated with a business. Moreover, with the increasing reliance on electronic gadgets, maintaining them is becoming increasingly difficult due to frequent wear and tear. However, as these gadgets are an integral part of our daily lives, it is essential to find instant solutions to repair or service them regularly. To address this issue, we have developed an online service request management system that acts as a platform between the service center owner and the customer or mechanic. This system provides a simple and efficient way for users to request services or solutions from the service center. Additionally, the project makes it easier for service center owners to manage and address customer complaints and queries organizationally. Through this system, users can offer feedback, complaints, or compliments, which can help service centers to improve their operations. Furthermore, it provides trend, quality, and feedback information about the fulfillment of requests, contributing to overall improvement initiatives.

Keywords : service, clients, gadgets, customer

Author : Atharva Deshmukh, Arundhati Deshpande, Rutuja Jadhav, Dr. Rashmi Patil

Title : Request Management System for Electronic Gadgets and Appliances

Volume/Issue : 2023;05(04)

Page No : 1 - 10

Now that India is making a long-term policy for itself after the crisis of Covid-19, it also has an opportunity to shape its hospitality and tourism industry according to the coming times and future. India has entered a new phase by dealing with the outbreak of Covid-19 as a global pandemic and with this India has also managed to reopen its international hospitality and tourism industry. Now international airports have been reopened for those coming for tourism and business-related reasons. The pandemic has prompted people and stakeholders in the hospitality and tourism sector to rethink the hospitality and tourism industry as a more flexible and sustainable model. With measures in place to contain the possible spread of the pandemic, India is looking to market itself as one of the safest travel destinations in the world. This research paper studies the marketing strategies for the hospitality and tourism industry during the post Covid-19.

Keywords : Hospitality and Tourism Industry, Covid-19, Marketing Strategies, Safe Travel, Tourism Sector, Technology

Author : Mr. Ganesh Dagadu Shinde, Dr. K.M. Vasudevan

Title : THE STUDY OF THE MARKETING STRATEGIES FOR HOSPITALITY AND TOURISM INDUSTRY POST COVID-19

Volume/Issue : 2023;05(04)

Page No : 11 - 15

The outbreak of a fire is a dangerous act that has a number of consequences. Detecting fire at an early stage and extinguishing it can help in preventing various accidents. So far we rely on human resources. This often puts the person's life at risk. Therefore, fire protection becomes an important aspect for saving human lives. A firefighting robot was designed and designed to detect the fire location extinguish the fire using sprinklers when the pump is started. This robot uses three flame sensors for accurate fire detection. This proposed model of firefighting robot using NODEMCU is used to detect the presence of fire and extinguish it automatically without any human intervention. It contains gear motors and a motor controller to control the movement of the robot when it detects any occurrence of fire and automatically starts a water pump to extinguish that fire. This robot model has a water ejector that is capable of releasing water at the scene of a fire. The water ejector tube can be moved in the desired direction using a servo motor. The entire operation is controlled by the NODEMCU microcontroller.

Keywords : Arduino UNO, Firefighting, Robot, Temperature sensors, Water Pump

Author : Nivash C, Sanjay R, Kaviya R S, Mohammed Ansar M,Poorvasanthiya S, Monisha S

Title : SMART FIRE FIGHTING ROBOT

Volume/Issue : 2023;05(04)

Page No : 16 - 25

In this paper, we aim to provide a comprehensive analysis of a YouTube channel's performance, audience demographics, and video engagement levels. Using data from the channel and third- party tools, we will evaluate key metrics such as the number of subscribers, views, and comments, as well as factors that contribute to the success of videos, such as the type of content, length of the videos, and frequency of uploads. Furthermore, we will analyze the feedback received from viewers through comments and ratings to gain deeper insights into what the audience likes and dislikes. Our goal is to provide actionable recommendations that will improve the channel's performance and help it reach its full potential. The results of this analysis will be valuable for YouTube content creators and businesses who want to understand their channel's strengths and weaknesses and identify opportunities for growth and improvement. In addition to evaluating the performance of the YouTube channel, this paper will also explore the competitive landscape and identify key trends and best practices in the niche. This will involve comparing the channel's performance with that of other channels in the same niche and analyzing the strategies used by successful channels to attract and retain subscribers. The data collected from this analysis will be used to develop a content strategy that is tailored to the specific needs and preferences of the channel's audience. This will involve identifying the type of content that resonates best with the audience, the optimal length and frequency of videos, and the most effective marketing strategies for attracting new subscribers and increasing engagement levels. The results of this paper will provide a roadmap for growth and improvement for the YouTube channel and will help it reach its full potential.

Keywords : Youtube, Channel analysis , Machine Learning

Author : Dr.IMTIYAZ KHAN, MIR ABED ALI OSMANI, MOHD.ZAIN JAMAL

Title : YOUTUBE CHANNEL ANALYSIS

Volume/Issue : 2023;05(04)

Page No : 26 - 39

Alzheimer's disease is a neurological brain disorder that progresses and is incurable. Early diagnosis of Alzheimer's disease can aid in effective care and stop brain tissue destruction. Researchers have used a number of analytical and machine learning models to diagnose Alzheimer's disease. Clinical research routinely uses magnetic resonance imaging (MRI) analysis to identify Alzheimer's disease. Because Alzheimer's disease MRI data and typical MRI data of older persons are comparable, diagnosing Alzheimer's disease can be challenging. In many disciplines, including medical image processing, cutting-edge deep learning approaches have recently effectively proven performance at the level of a human. By analyzing brain MRI data, we suggest a deep convolutional neural network for diagnosing Alzheimer's disease.Our model performs better for early-stage diagnosis and can recognise different phases of Alzheimer's disease than most existing techniques, which mostly do binary classification

Keywords : Machine Learning, Classification, Analysis, Disease

Author : Dr. Sundharraj M 1 , Makila S 2 , Sakthi Keerthana M 3 , Vasundhara B 4

Title : ALZHEIMER DISEASE PREDICTION USING DEEP LEARNING

Volume/Issue : 2023;05(04)

Page No : 30 - 38