2022-2023

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MFEEDS
(NHCE, 2023) REVATHI R 1NH21MC076
This application is designed for Meltwater to generate RSS feeds. The application operates by allowing users to input specific keywords related to their interests, and then retrieves relevant fields from a various sources that mention about plots, sales announcement, jobs, patent etc. across the internet. The application is a custom solution for information tracking that generates a customized RSS feed based on the user's chosen keywords. Users can easily subscribe to their RSS feeds, which are updated in real-time, and receive a constant stream of relevant information. This application is ideal for businesses and individuals who want to stay up-to-date with the latest news and trends related to their interests.
Convention Center Booking
(2023) Sumanth KM 1NH21MC098
The Convention Center Booking System is a web-based application developed using the .NET framework, specifically designed to streamline the booking procedure and managing events at a convention center. This project aims to provide a comprehensive solution for convention center administrators, event organizers, and attendees to facilitate efficient event planning and coordination. The system's primary objective is to simplify the booking process by offering a user-friendly interface for event organizers to search for available dates, view venue layouts, and make reservations. The .NET API acts as the backbone of this system, providing the necessary endpoints and services for seamless data exchange and manipulation. 1. Venue Management: The system allows administrators to manage various aspects of the convention center, such as defining venue capacities, layouts, and availability schedules. 2. Event Booking: Event organizers can search for available dates, view venue layouts, and request bookings for specific time slots. The system automatically checks for conflicts and ensures accurate booking information. 3. Attendee Registration: The system provides a platform for attendees to register for events, providing necessary personal information and preferences. Attendee data can be securely stored and accessed as needed. 4. Payment Processing: The system integrates secure payment gateways to enable online transactions for event bookings. Several payment methods are accepted, ensuring convenience for both organizers and attendees. 5. Notification and Communication: The system facilitates real-time communication between organizers, attendees, and administrators through notifications, reminders, and updates regarding event details and changes. 6. Reporting and Analytics: Administrators can generate comprehensive reports and analytics to gain insights into event bookings, attendance, revenue, and other relevant metrics.
Scope of big data in healthcare analysis
(NHCE, 2023) MANOJ S M 1NH21MC048
Health care analysis using big data has revolutionized the way information is collected, analyzed, and utilized in the health care industry. The vast amount of data generated from electronic health records, wearable devices, and other sources has opened new possibilities for improving patient outcomes and optimizing health care processes. Big data analytics techniques, such as data mining, machine learning, and natural language processing, are employed to explore patterns, trends, and correlations within massive datasets. These insights enable health care providers and researchers to make informed decisions, enhance clinical decision-making, and personalize patient care. Moreover, big data analytics plays a crucial role in population health management. By analyzing large-scale datasets, health care organizations can identify public health trends, predict disease outbreaks, and allocate resources effectively. This facilitates the implementation of targeted interventions, health promotion campaigns, and preventive measures to improve population health outcomes. Additionally, big data analytics contributes to the optimization of health care operations and cost management. By analyzing data related to resource utilization, patient flow, and billing, providers can identify inefficiencies, streamline processes, and reduce costs. Fraud detection algorithms can also be employed to ensure accurate billing and reimbursement.
Sofie bot – rapid awareness
(NHCE, 2023) VIKASH KUMAR 1NH21MC106
The Sofie BOT - Rapid Awareness project is aimed at developing a bot on top of the Microsoft Teams platform to enhance cybersecurity awareness among employees in corporate organizations. The project addresses the critical need for proactive measures to combat cyber threats and keep employees informed about emerging security risks. The existing systems for disseminating cybersecurity information often lack real-time alerts and personalized content delivery. Therefore, the objective of this project is to design and implement a bot that can effectively send alerts related to cybersecurity threats and provide up-to-date information about new threats in the market. The proposed system, the Sofie BOT - Rapid Awareness, utilizes Microsoft Teams as the communication platform and incorporates features such as real-time alerts, personalized content recommendations, and reporting and analytics capabilities.
Openshop performance testing using load runner
(NHCE, 2023) SHILPA S 1NH21MC087
LoadRunner is a performance testing tool used for evaluating the performance, scalability, and reliability of software applications. It allows users to simulate real-life user scenarios and measure the system's response under various loads. LoadRunner supports a wide range of applications and protocols, including web, mobile, and enterprise applications. The tool works by creating virtual users that emulate real users interacting with the application. These virtual users generate a load on the system by performing tasks such as logging in, searching, submitting forms, and browsing pages. LoadRunner captures performance metrics such as response times, throughput, and resource utilization, enabling testers to identify performance bottlenecks and analyze system behavior under different loads. JMeter is an open-source performance testing tool developed by the Apache Software Foundation. It is designed to measure the performance and load capabilities of web applications, databases, and other services. JMeter can simulate high user loads and generate performance reports to assess the performance and scalability of applications. JMeter allows testers to create test plans comprising of different elements such as threads, samplers, controllers, and listeners. Thread groups represent virtual users, and each thread group can have one or more samplers that simulate user actions like sending HTTP requests, interacting with databases, or making FTP requests. Controllers define the logic and flow of the test, while listeners collect and display performance data.. In summary, both Load Runner and JMeter are powerful performance testing tools, with LoadRunner being a commercial tool offering a wide range of features and protocol support, while JMeter is an open-source tool with a strong community backing and flexibility for customization.
Azure sql database integration for powerful power bi reports
(NHCE, 2023) VARMA NITIN SHIVANANDJI 1NH21MC103
In the modern business landscape, data-driven insights play a crucial role in decision-making processes. To harness the full potential of data, organizations rely on robust analytics and reporting tools. Microsoft Power BI has emerged as a leading business intelligence platform, enabling users to create interactive reports and dashboards. However, the effectiveness of Power BI reports heavily relies on the underlying data source. This abstract focuses on the integration of Azure SQL Database with Power BI to enable the creation of powerful reports. Azure SQL Database, a fully managed cloud database service by Microsoft Azure, offers a scalable and efficient solution for data storage and management. By integrating Azure SQL Database with Power BI, users gain numerous benefits for developing impactful reports. This integration facilitates seamless data connectivity, allowing Power BI to directly query and retrieve data from Azure SQL Database. Real-time data updates and dynamic connections ensure that reports always reflect the latest information. The integration of Azure SQL Database and Power BI unlocks advanced querying and modeling capabilities. Users can leverage the rich SQL querying capabilities of Azure SQL Database to perform complex calculations, aggregations, and transformations directly within the database. This enhances report performance and enables efficient analysis of large datasets. Another advantage is the scalability and performance offered by Azure SQL Database. With its ability to handle growing data volumes and adapt to changing demands, organizations can confidently scale their database resources based on analytical needs. This ensures that Power BI reports deliver optimal performance even with expanding datasets.
Detect andriod malware using multiple linear regression models
(NHCE, 2023) ABHISHEK S 1NH21MC117
The objective of this internship project is to analyze and predict a framework for Android malware detection based on permissions is presented. This framework uses multiple linear regression methods. Application permissions, which are one of the most critical building blocks in the security of the Android operating system, are extracted through static analysis, and security analyzes of applications are carried out with machine learning techniques. Based on the multiple linear regression techniques, two classifiers are proposed for permission-based Android malware detection. These classifiers are compared on four different datasets with basic machine learning techniques such as support vector machine, k-nearest neighbor, Naive Bayes, and decision trees. In addition, using the bagging method, which is one of the ensemble learning, different classifiers are created, and the classification performance is increased. As a result, remarkable performances are obtained with classification algorithms based on linear regression models without the need for very complex classification algorithms. This paper proposes and investigates a parallel machine learning based classification approach for early detection of Android malware. Using real malware samples and benign applications, a composite classification model is developed from parallel combination of heterogeneous classifiers. The empirical evaluation of the model under different combination schemes demonstrates its efficacy and potential to improve detection accuracy. More importantly, by utilizing several classifiers with diverse characteristics, their strengths can be harnessed not only for enhanced Android malware detection but also quicker white box analysis by means of the more interpretable constituent classifiers.
Drug classification using state of the art ml algo
(NHCE, 2023) BASTTY PREM 1NH21MC110
Drug classification plays a crucial role in the field of pharmaceutical research, drug discovery, and healthcare management. Traditionally, drug classification has relied on manual inspection and expert knowledge, which can be time-consuming and prone to human error. With the advancements in machine learning algorithms and the availability of large-scale drug data, automated drug classification using state-of-the-art ML algorithms has emerged as a promising approach. This research aims to explore the application of state-of-the-art machine learning algorithms for drug classification. We begin by compiling a comprehensive dataset consisting of various drug properties, such as chemical structure, molecular descriptors, and biological activity. The dataset encompasses a wide range of drugs belonging to different therapeutic classes. We then apply and compare several state- of-the-art machine learning algorithms for drug classification. These algorithms include but are not limited to random forest, support vector machines (SVM), convolutional neural networks (CNN), and recurrent neural networks (RNN). We utilize various feature engineering techniques to extract relevant features from the dataset and optimize the performance of the models. To evaluate the effectiveness of the ML algorithms, we employ metrics such as accuracy, precision, recall, and F1 score. We also conduct cross-validation experiments to assess the models' robustness and generalizability. Furthermore, we compare the performance of the ML algorithms with traditional manual classification methods to showcase the advantages of automated drug classification.
Event photography
(NHCE, 2023) RAKSHITHA C 1NH21MC073
Event photography is essential for documenting and preserving special moments during various occasions, such as weddings, conferences, festivals, and corporate events. However, managing and organizing the vast amount of photographs taken during an event can be a time-consuming and challenging task. This abstract presents a web application which is specifically for event photographers to streamline their workflow and enhance their efficiency in managing event photography assignments. The web application aims to provide a comprehensive solution for event photographers by integrating many functionalities into a single platform. Key features of the system include: Event Scheduling and Management: The web application allows photographers to create and manage their event schedule, including details such as date, time, location, and client information. This feature enables photographers to have a clear overview of their upcoming assignments and plan their workflow accordingly. Photo Upload and Organization: The application facilitates the seamless upload and organization of event photos. Photographers can easily categorize and tag their photos based on event type, client name, or any other custom criteria. This ensures efficient retrieval and quick access to specific photographs during post-processing and client delivery. Photo Editing and Enhancement: The web application provides basic photo editing tools, allowing photographers to perform quick adjustments, such as cropping, resizing, and color correction. Additionally, advanced editing features like exposure correction, retouching, and filters can be incorporated to improve the overall standard of photographs.
Prediction of h1n1 vaccination
(NHCE, 2023) DEVA MAHESH 1NH21MC112
The objective of this internship project is to analyze and predict vaccination rates based on historical data and relevant factors. By examining past vaccination campaigns and understanding the impact of various factors, this project aims to provide insights into potential vaccination trends for outbreaks. This research will utilize historical data on vaccination campaigns, including vaccination counts, demographic information, and relevant epidemiological factors. The analysis will involve exploring the relationships between vaccination rates and factors such as the prevalence and severity of the virus, vaccine availability and accessibility, public awareness campaigns, and the effectiveness of vaccines. By examining the historical context and patterns, this project will develop predictive models to estimate future vaccination rates. Machine learning algorithms will be employed to identify significant predictors and forecast vaccination counts based on various scenarios. Furthermore, this research will delve into understanding vaccine hesitancy and its potential influence on vaccination rates. By exploring reasons behind vaccine hesitancy and examining successful strategies employed in previous campaigns, this project aims to provide recommendations for addressing hesitancy and improving vaccination uptake. The outcomes of this internship project will contribute to the field of public health and inform policymakers, health organizations, and community leaders about potential vaccination trends for outbreaks. The results will also provide insights into effective strategies for boosting vaccination rates and addressing vaccine hesitancy, thereby enhancing public health response efforts and preparedness for future outbreaks. Note: It is essential to consult with supervisors, experts in public health, or epidemiology to ensure the accuracy and relevance of the analysis and findings.
Reliable access under dual provenance for public storage cloud
(NHCE, 2023) HARISH KUMAR P 1NH21MC113
In public cloud storage systems, data access control is a difficult problem to solve. A promising method known as Ciphertext-Policy Attribute-Based Encryption (CP-ABE) has been developed to offer flexible, fine-grained, and secure data access control for cloud storage with honest but sceptic cloud servers. A CP-ABE method is used in a large-scale cloud storage system, however because the sole attribute authority in existing CP-ABE schemes is required to carry out the cumbersome user legitimacy verification and secret key distribution, this results in a single-point performance bottleneck. Users might spend a lengthy time waiting in queue to get their secret keys, which would reduce the system's efficiency. Despite the fact that multiauthority access control systems have been put forth, they are still limited by the single-point bottleneck and their low efficiency since each authority still handles a disjoint attribute set individually. In this research, we provide a brand-new heterogeneous framework to solve the single-point performance bottleneck issue and to offer a more effective access control system with an auditing mechanism. To distribute the workload of user authenticity verification, our architecture makes use of various attribute authorities. In the meanwhile, a CA (Central Authority) is added to our system to create secret keys for users whose validity has been established. In contrast to existing multiauthority access control techniques, our scheme gives each authority unique control over the whole collection of attributes. In order to increase security, we also provide a technique for identifying which AAs (Attribute Authorities) carried out the validity verification process deceptively or wrongly. Analysis reveals that our solution significantly improves key generation performance while simultaneously guaranteeing the security requirements.
Understanding consumer behavior through adobe analytics
(2023) SERENE MATHEW 1NH21MC114
Adobe Analytics is a toolset that helps small or big businesses understand how people interact with their digital platforms, such as websites or mobile apps. It collects data about user actions, such as page views, clicks, or form submissions, and provides valuable insights into user behavior. By implementing Adobe Analytics, businesses can track and measure various metrics that are important for their success, such as conversion rates, engagement levels, or the effectiveness of marketing campaigns. This data can be used to make informed decisions, improve user experiences, and optimize business strategies. The implementation process involves installing a small code on the digital platform, defining what actions to track, and configuring variables and metrics that capture specific information about user interactions. Rules can be set up to ensure data accuracy and relevance. Once implemented, the tool collects and processes the data, allowing businesses to access reports and visualizations that provide valuable insights. Adobe Analytics implementation enables businesses to gather and analyze data about user behavior, which indicates them make to data-driven decisions to improve their digital platforms and achieve their goals.
Customer chatbot for insurance
(NHCE, 2023) SHYAMAL DAS 1NH21MC090
A chatbot is a piece of software that uses artificial intelligence to replicate conversations between humans through text or speech. It functions as an online assistant, engaging with people, responding to their questions, giving them information, and carrying out activities. Natural language processing, or NLP, techniques are used by chatbots to comprehend and interpret user input. They are adaptable tools for a variety of sectors and use cases since they are capable of comprehending and reacting to a wide variety of conversational inputs. These sophisticated bots have a number of advantages. In the beginning, they offer quick and continuous customer service, improving user happiness. Chatbots provide effective and rapid responses and can manage several requests at once, lowering wait times and raising service standards. Additionally, chatbots can automate repetitive and typical operations like booking appointments, following up on orders, and finding information. As a result, human agents are given more time to concentrate on more intricate and valuable tasks, increasing overall output. Chatbots are useful resources for data collection and analysis. They acquire important information and trends from user interactions that can help them refine their marketing approaches and make better business decisions. Chatbots are growing more complex as technology develops, adding machine learning as well as artificial intelligence capabilities. Through ongoing learning from user interactions, they become increasingly precise and individualized over time. While chatbots are effective tools, it's crucial to remember that they have some drawbacks. They may have difficulty comprehending queries that are complicated or ambiguous, and they depend on precise information and training to deliver accurate answers. In general, chatbots are transforming how businesses communicate with their customers and streamlining corporate processes. They are valuable assets in a variety of businesses due to their capacity to comprehend natural language, automate processes, and offer quick support, all of which improve operational effectiveness and improve consumer experiences.
Analyzing Online P2P Lending Decision Using Social Profiles
(NHCE, 2023) SHARATH KUMAR P 1NH21MC085
Peer-to-peer (P2P) lending platforms have emerged as a popular alternative to traditional financial institutions, enabling individuals to lend and borrow money directly from each other. As the popularity of P2P lending grows, there is a need to assess borrowers' creditworthiness accurately. This study aims to investigate the effectiveness of utilizing social profiles in analyzing online P2P lending decisions. The analysis focuses on the relationship between borrowers' social profiles and their creditworthiness, exploring how social network data and online behavior patterns can contribute to assessing lending risk. By leveraging the extensive information available on social media platforms, including user demographics, social connections, and user-generated content, it becomes possible to uncover valuable insights that traditional credit scoring models may overlook. To conduct this research, a comprehensive dataset comprising social profiles and loan performance data from a P2P lending platform is collected. Various data mining and machine learning techniques are employed to analyze the relationship between borrowers' social profiles and loan default rates. The study also investigates the impact of specific social profile features, such as number of connections, user activity, and sentiment analysis of user-generated content, on loan default probabilities. The findings of this research have significant implications for both P2P lending platforms and borrowers. By incorporating social profile analysis into the lending decision process, platforms can enhance their credit assessment models, leading to more accurate risk evaluation and potentially reducing default rates. Borrowers, on the other hand, can benefit from a more comprehensive evaluation of their creditworthiness, potentially increasing their chances of obtaining loans at favorable terms. Overall, this study contributes to the growing body of research on P2P lending and the use of alternative data sources for credit risk assessment. The results shed light on the value of social profiles in predicting loan default probabilities, paving the way for improved lending decision-making processes in the digital lending ecosystem.
Online voting system
(NHCE, 2023) VARSHITHA A 1NH21MC104
The word “vote” means to choose from list ,elect or to determine.The main goal of voting in a scenario involving the citizens of a given country is to come up with leaders of people’s choice.Most countries have problems when it comes to voting.Some of the problems involved include rigging votes during election,insecure or inaccessible polling stations,inadequate polling materials and also inexperienced personnel. This online voting/polling system seeks to solve the above issue.It should be noted that with this system in place,the users,citizens in this case shall be given ample time during voting period.This type of voting system will increase voter turnout because of convenience.
Opinion mining
(NHCE, 2023) ROHIT P SAIL 1NH21MC078
Opinion mining, also known as sentiment analysis, is an intriguing field within natural language processing that focuses on uncovering subjective information and sentiment expressed in online text. In today's digital age, where a vast amount of information is generated daily through social media platforms, blogs, and consumer evaluations, the ability to analyze and understand opinions has become crucial for businesses, organizations, and individuals alike. The objective of this project is to develop a machine learning-based system that can extract sentiment from textual input. By combining natural language processing techniques with sentiment analysis methods, the system aims to categorize text into positive, negative, or neutral sentiments based on the expressed thoughts. To achieve accurate sentiment classification, the project utilizes a large dataset of labeled text to train and fine-tune a machine learning model. The key objectives of this project encompass data preprocessing, feature extraction, and model training. In the data preprocessing phase, noise such as punctuation and stop words is removed, and text normalization techniques like stemming or lemmatization are applied. Feature extraction techniques like bag-of-words or word embeddings are employed to convert textual data into numerical representations. Subsequently, machine learning models such as recurrent neural networks (RNNs) or support vector machines (SVMs) are trained using these extracted features. Performance evaluation metrics such as precision, recall, and F1-score are utilized to assess the developed opinion mining system. The study also addresses challenges posed by sarcasm, irony, and context-dependent sentiments, which can significantly impact the accuracy of sentiment categorization. The findings of this study shed light on the effectiveness of different feature extraction methods and machine learning algorithms for sentiment analysis tasks. The resulting system holds potential for application in various real-world scenarios, including brand monitoring, customer review analysis, and evaluation of voter sentiment during elections or social movements. Ultimately, this project contributes to the growing field of opinion mining, expanding our understanding of sentiment analysis techniques.
PL service chatbot
(NHCE, 2023) SUMAN KUMAR SINGH 1NH21MC097
A chatbot or chatterbot is a software application used to conduct an on-line chat conversation via text or text-to-speech, in lieu of providing direct contact with a live human agent. A chatbot is a type of software that can help customers by automating conversations and interact with them through messaging platforms. The chatbot utilizes advanced natural language processing techniques to comprehend user inputs, extract relevant information, and identify the underlying intent. It employs techniques such as named entity recognition, sentiment analysis, and part-of-speech tagging to gain a comprehensive understanding of the user's message. These prompt-based chatbots let customers choose from a list of prompts then take them through a series of multiple-choice questions. The application will take them to the most helpful destination based on the answers. This type of chatbot is good for simple queries with a defined scope, as it limits customers to a certain number of inputs. This approach leverages symbolic AI to provide a more conversational approach to customer service. It uses natural language technology to understand the intent of a customer query. It provides full visibility into the rules that machines use to gain knowledge, with human oversight to adjust the learning models. This approach uses a machine learning engine to train itself to deliver an optimal response to a customer query. It learns based on past inquiries and evolves as inputs are analyzed. A large amount of data is needed to train the system, and machine learning of the chatbot application is done in a black box with no insight into what is learned
Ecobid
(NHCE, 2023) SAHANA S 1NH21MC081
Ecobid is a comprehensive application designed to promote the "Re-use" aspect of the "Reduce, Re-use, Recycle" principle within an organization. The goal is to encourage employees to list items they no longer need and offer them to their colleagues, similar to the concept of Freecycle. The application provides a user-friendly portal and mobile experience, enabling employees to list new items and view available items within their area or building. In addition to facilitating item listings, Ecobid incorporates features that promote charitable donations and support local communities. It offers details on local charities where donations can be made, encouraging users to contribute money to these organizations when trading an item. The application also provides the ability to auction items for charity events, expanding the potential impact of donations. To enhance the user experience and provide localized information, Ecobid integrates with charity databases, offering access to local information and mapping functionalities to display item locations. The application also supports internationalization by incorporating multi-language capabilities, ensuring that users from different regions can utilize the app in their preferred language. Ecobid aims to raise awareness about environmental issues, specifically the impact of fast fashion and its contribution to landfills. By integrating climate change and global warming APIs, the application provides insights and data on the environmental consequences of such practices, educating users about the urgency of sustainable actions. Ecobid strives to foster a culture of reusing items, promoting charitable donations, and raising environmental awareness within the organization. By providing a user-friendly platform that encourages collaboration, the application facilitates a sustainable and socially responsible approach to waste reduction and resource conservation.
Q&A hub-usingmern stack
(NHCE, 2023) Vinayak D 1Nh21MC107
The "Q&A Hub-Using MERN Stack" project aims to develop a comprehensive question and answer platform using the MERN (MongoDB, Expressis, React is. Node is) stack. The system provides a user-friendly interface for users to ask questions, receive answers, and engage i discussions on various topics, The project involves the integration of several technologies and framewodes to build a robust and scalable web application. The MERN stack is chosen for im flexibuitry, efficiency, and of use in developing modern web applications. MongoDB is used as the database to share and mmage user data, questions, and answers Express is handles the server-side logic and API development, while Nodejs provides the runtime environment The frontend of the application is built using React is, a popular JavaScript library fir bolding user interfaces. React.js allows for the creation of reusable components, ensuring a modular and efficient development process. The frontend interacts with the backend through RESTfal APIs, enabling seamless data exchange between the client and the server The key features of the Q&A Hub include user registration and authentication, creating and editing questions, providing answers, upvoting and dowuvating answers, and comment functionalities. The platform also includes a search functionality, allowing users to easily find relevant questions and answers based on keywords or tags. Throughout the development process, best practices in software engineering, such as version control with Git and collaborative development, were followed. The project emphasizes the use of clem code, efficient algorithms, and responsive design principles to provide an optimal user experience across vadous devices In conclusion, the Q&A Hub-Using MERN Stack project showcases the implementation of a feature-rich question and answer platform using the MERN stack. The project demonstrates the effective utilization of the MERN technologies, along with best practices in software development, to create a robust and scalable web application
Go-FIT
(NHCE, 2023) SAGAR M 1NH21MC079
This abstract presents a fitness tracker application developed using the ServiceNow platform, specifically designed for tracking fitness activities, setting challenges, creating workout schedules, and providing route mapping functionality. The application is implemented within the Service Portal framework, offering an intuitive and user-friendly interface for users to access its features. The fitness tracker application offers a Challenge module that allows users to participate in various fitness challenges, such as step count challenges, distance challenges, or calorie burn challenges. Users can create their own challenges or join existing ones, fostering a sense of competition and motivation among participants. The Route Map feature enables users to track their outdoor activities, such as running, cycling, or hiking, by providing real-time GPS-based route mapping. Users can view their routes on interactive maps, track their distance covered, and analyze their performance metrics, including speed, elevation, and duration. The Activity module allows users to log and monitor their daily fitness activities, including steps taken, calories burned, and active minutes. Users can set personal fitness goals and track their progress over time, helping them stay motivated and accountable. Additionally, the Workout Schedule feature enables users to create personalized workout plans, specifying exercises, sets, and repetitions. The application provides reminders and notifications to ensure users adhere to their workout routines, assisting in maintaining consistency and achieving fitness goals. Developed using the ServiceNow platform, the fitness tracker application leverages its robust capabilities, including workflow management, database integration, and user authentication, to deliver a secure and scalable solution. The Service Portal framework enhances the user experience by providing a modern and customizable interface, allowing users to access the application from various devices and platforms. Overall, this fitness tracker application developed using ServiceNow in the service portal aims to empower users in their fitness journeys by offering challenges, route mapping, activity tracking, and workout scheduling features. It promotes a healthy and active lifestyle, enabling users to monitor their progress, stay motivated, and achieve their fitness goals effectively.

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