RUNGTA INTERNATIONAL OF JOURNAL OF COMPUTER SCIENCE AND INFORMATION TECHNOLOGY

• Volume 1 (Jan 2024- Dec 2024)
Issue 1

1. Image Based Color Palette Generator

Shireen Fatima-1, Tripti Sharma-2

Author Affiliations
1- M. Tech CSE 4th Semester, RCET
2 -Professor & HOD, CSE Department, RCET

ABSTRACT:
In a world increasingly driven by visual communication and design intelligence, color plays a crucial role in defining user experience and functionality. This project introduces a web-based application that generates customized color palettes from user-uploaded images using the K-Means clustering algorithm. Unlike traditional palette tools, this application integrates six distinct use cases—ranging from military and aerospace to interior design and social media—providing contextual insights tailored to each field. The backend is built using Python and Flask, utilizing libraries such as NumPy, PIL, and Report Lab to process images, extract dominant colors, and generate detailed downloadable reports in PDF format. The interface, styled with HTML and CSS, offers a clean and user-friendly experience, making advanced technology accessible to creative professionals and technical users alike. This project stands at the intersection of machine learning, visual design, and real-world application, demonstrating how AI can drive sustainable design decisions across industries.

Keywords: Color Palette Generation, Image-based Color Extraction, Flask Web Application, HEX and CMYK Color Models, PDF Report Generation, Use-Case Based Color Insights

Corresponding author’s email address: shireenfatima22466@gmail.com

2. Social Media Sentiment Analysis Using Natural Language Processing Techniques

Jyoti Singh Kanwar-1, Shraddha Rani Sonkalihari-2, Ishita Gupta-3, Teekeshwari Deshmukh-4, Richa Sharma-5, Rahul Das-6

Author Affiliations
123456- Department of Computer Science and Engineering, Rungta College of Engineering and Technology, Bhilai CG 490024

ABSTRACT:
Social media has become a vital platform for individuals to express opinions, share experiences, and discuss topics of interest. Sentiment analysis, a key application of Natural Language Processing (NLP), enables the extraction and interpretation of emotions and sentiments from social media content. This study focuses on leveraging NLP techniques to analyze and categorize sentiments expressed in user-generated posts on platforms like Twitter, Facebook, and Instagram. By employing machine learning algorithms and sentiment analysis models, the research explores trends, public opinions, and behavioral patterns. The findings aim to demonstrate how sentiment analysis can be applied in various fields, including marketing, politics, and societal impact studies, highlighting its potential to provide actionable insights from social media data.

Keywords: Sentiment Analysis, Natural Language Processing (NLP),Social Media Analytics, Machine Learning, Text Classification

Corresponding author’s email address: jyoti.singh.kanwar@rungta.ac.in

3. Unleashing of audit script for windows and linux OS to verify CIS Benchmark

Atharva Gehlot-1, Abhinandan Patidar-2, Himani Namdeo-3, Ankit Chouhan-4

Author Affiliations
1234 – Acropolis Institute of Technology & Research, Indore

ABSTRACT:
This problem addresses the creaƟ on of automated audiƟ ng scripts of Windows and some Linux DistribuƟ ons according to the CIS benchmarks. The objecƟ ve is to improve security adherence and management by verifying computer systems against these security configuraƟ ons that have been defined within the industry. With the rising need for cybersecurity and that is the reason compliance with benchmarks such as those from CIS is important. The variaƟ ons of environments especially with respect to the different operaƟ ng systems being used such as Windows and Linux requires a consistent yet efficient way of audiƟ ng and ensuring compliance to these security standards. Scripts were conceived and evaluated on the Windows and Linux operaƟ ng systems. The approach took the form of CIS benchmark requirements as tasks to be executed on and within these operaƟ ng systems in this case checking configuraƟ on seƫ ngs and permissions depth. The audit scripts made it possible to discover the deficiencies and areas of use, which were out of compliance, and remedial acƟ ons taken on these areas where possible. To a large extent, Ɵme and blue level complexity relaƟ ng to manual audiƟ ng were eliminated and operaƟ onal efficiency and security were improved on the two systems. Ensuring security compliance is one of the challenges that comes in as more and more organizaƟ ons move towards adopƟ ng mulƟ -OS environments. These scripts provide an effecƟ ve strategy to comply with the requirements of CIS in different environments.

Keywords: CIS Benchmark, audit script, Windows , Linux, PowerShell, Shell scripting, cybersecurity, security compliance, automation, firewall, password policy, system pdates, security configuration, audit logging,

Corresponding author’s email address: atharvasingh220867@acropolis.in

4. Real-Time Lemon Classification Using Raspberry Pi for Smart Agriculture

Shraddha Rani Sonkalihari 1, Ishita Gupta-2, Teekeshwari Deshmukh-3, Richa Sharma-4, Rahul Das-5, Jyoti Singh Kanwar -6

Author Affiliations
123456 – Department of CSE, Rungta College of Engineering and Technology, Bhilai, India-490000

ABSTRACT:
This research investigates the deployment of a lemon classification system utilizing the Raspberry Pi 3 Model B as the core hardware platform. The system is designed to provide an affordable, scalable, and portable solution for real-time image classification in agricultural applications. The hardware configuration includes a Raspberry Pi Camera Module v2 for high-quality image capture, LED lighting for consistent illumination, and a custom-designed case for enhanced durability and protection. Emphasis is placed on optimizing the system’s hardware setup to ensure efficient operation under resource constraints. Key considerations such as camera calibration, lighting placement, and thermal management are addressed to enhance system reliability. Testing results demonstrate the practicality of leveraging the Raspberry Pi’s capabilities for robust image acquisition and processing in field conditions. This study highlights the potential of low-cost edge computing devices in enabling innovative solutions for agriculture and similar domains, emphasizing the importance of hardware-centric approaches in real-world deployments.
Keywords: Lemon Classification, Raspberry Pi, Camera Module

Corresponding author’s email address: shraddha.sonkalihari@rungta.ac.in

5. Preventing Heart Disease in India: A Holistic Approach to Early Detection and Lifestyle Modification

Mandeep Kumar-1, Dr Ajoy Kumar Behera-2, Irshad Alam-3, Pradeep Kumar-4, Abhijeet Keshri-5 , Dr Shaziya Islam-6

Author Affiliations
13456- Department of Computer Science & Engineering -Data Science, Rungta College of Technology, Bhilai, India-490024

2 – Professor in Department of Pulmonary Medicine- AIIMS RAIPUR, Raipur, India-492099

ABSTRACT:
Heart attacks have quietly become one of the leading causes of death in India. With growing urbanization and lifestyle shifts—like unhealthy diets, lack of physical activity, high stress, smoking, and alcohol consumption—heart disease is now a major public health concern. Even though modern treatments exist, prevention is still a big challenge, especially due to limited awareness and poor access to healthcare in many parts of India. Our study aims to explore how heart attacks can be prevented through simple yet impactful lifestyle changes. To ensure our approach is practical and medically sound, we developed a heart disease detection and prevention project combining technology and clinical insight. As part of our research, we visited AIIMS Raipur and consulted Ajoy Kumar Behera, an expert in cardiovascular and pulmonary health. His vast experience gave us a deeper understanding of how common lifestyle habits in India can be modified to reduce heart disease risk. With his guidance, we identified ten key preventive strategies: regular physical activity, heart-healthy diets, stress management, proper hydration, avoiding tobacco and alcohol, adequate sleep, regular health check-ups, maintaining healthy weight, and giving attention to mental and spiritual well-being. Behera emphasized that these simple, everyday choices can go a long way in protecting the heart. This paper highlights the importance of making preventive care accessible and relatable for Indian communities. By encouraging such habits through awareness and education, we can lower the number of heart attacks and create a healthier future. Our project not only spreads awareness but also offers a roadmap for individuals and policymakers to prioritize heart health in a culturally meaningful way.

Keywords: Heart Attack Prevention, Cardiovascular Disease, Lifestyle Modification, Preventive Healthcare, AIIMS Raipur, Ajoy Kumar Behera, Public Health Strategy in India.

Corresponding author’s email address: mk6207114453@gmail.com

RUNGTA INTERNATIONAL OF JOURNAL OF COMPUTER SCIENCE AND INFORMATION TECHNOLOGY