RUNGTA INTERNATIONAL OF JOURNAL OF COMPUTER SCIENCE AND INFORMATION TECHNOLOGY

• Volume 2 (Jan 2025- Dec 2025)
Issue 1

1. AI-Driven Precision Agriculture: Revolutionizing Farming

Tripti Sharma-1, Huma Khan-2, Aradhana Sahu-3, Bhavana Janghel-4, Shaziya Islam-5, Shweta Bandhekar-6

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

ABSTRACT:
AI-driven precision agriculture revolutionizes farming by integrating data from sensors, drones, and satellites with AI to monitor crop health and optimize output. This approach enables farmers to access real-time information on crop conditions, soil health, and environmental factors. Through meticulous data analysis, AI systems detect early signs of pests, diseases, and nutrient deficiencies, allowing timely interventions. Predictive analytics optimize irrigation, fertilization, and harvesting schedules, enhancing productivity and resource efficiency. This technology addresses global food demands while improving agricultural sustainability and productivity. A Python-based simulation demonstrates the potential of AI and IoT integration in precision agriculture, providing valuable insights into enhanced decision-making and productivity. Key aspects include crop health monitoring, yield optimization, sensor data analysis, drone technology, and satellite imagery, transforming the agricultural sector and improving food security globally.

Keywords: AI-driven Precision Agriculture, Crop Health Monitoring, Yield Optimization ,Sensor Data Analysis, Drone Technology, Satellite Imagery

Corresponding author’s email address: tripti.sharma@rungta.ac.in

2. The impact of 5G on global connectivity

Tisha Si-1, Shaziya Islam-2 , Priyank Kumar Sahu-3 , P.Bhawna-4

Author Affiliations
1234 – Department of Computer Science & Engineering -Data Science ,Rungta College of Technology, Bhilai, India-4900240

ABSTRACT:
5G technology is revolutionizing global connectivity by delivering unprecedented speed, ultra-low latency, and the capacity to connect a massive number of devices simultaneously. This fifth generation of mobile networks significantly upgrades 4G by enabling superfast broadband, ultrareliable low-latency communication, and massive machine-type communications, which collectively support the growing demands of the Internet of Things (IoT), smart cities, autonomous vehicles, and advanced industrial automation. Industries such as manufacturing, healthcare, transportation, and agriculture will particularly benefit from 5G through increased automation, improved efficiency, and real-time data analytics. For example, 5G-enabled smart diagnostics in healthcare could prevent millions of medical interventions, while manufacturing will see significant productivity gains from automation and connected devices. Additionally, 5G facilitates better communication, supports innovative business models, and fosters technological advancements like virtual and augmented reality. Despite its transformative potential, 5G deployment requires overcoming challenges related to infrastructure investment, security, and regulatory compliance. Nevertheless, its ability to interconnect people, devices, and systems promises to redefine global connectivity and drive the next wave of digital transformation across multiple sectors.

Keywords: Revolutionizing global connectivity; 5G technology; Virtual and augmented reality; Internet of Things(IoT); Security

Corresponding author’s email address: 6604748@rungta.org

3. Everything You Know About Space

Abhijeet Keshri-1, Shaziya Islam-2, Faiz Mohammad-3, Himanshu Chaturvedi-4

Author Affiliations
1234 – Department of computer science & Engineering (Data science), Rungta college of Technology, Bhilai, India-4900240

ABSTRACT:
Space, the vast and seemingly infinite expanse beyond Earth’s atmosphere, has captivated human curiosity for centuries. It is a realm of extremes—unimaginable distances, colossal celestial bodies, and the delicate dance of cosmic forces. Despite significant advancements in astronomy and space exploration, much of space remains a profound mystery. We know it houses billions of galaxies, each containing countless stars and planets, yet the true scale and structure of the universe are still beyond complete comprehension. The universe operates under physical laws that govern motion, gravity, and energy, yet phenomena like dark matter and dark energy challenge our current understanding. Space is not just emptiness—it contains radiation, particles, magnetic fields, and even rogue planets drifting without a star. Time behaves differently near massive objects like black holes, illustrating the complex relationship between space, time, and gravity. Technological milestones, from telescopes to interplanetary probes, have revealed exoplanets, cosmic microwave background radiation, and even gravitational waves. Meanwhile, space travel has taken humans to the Moon and robotic explorers to the outer reaches of our solar system. Yet, we have only scratched the surface. This abstract is not a final word but a glimpse into a subject that continues to evolve. Space is not only a frontier of science but a mirror of human imagination and ambition, urging us to explore further, question deeper, and reach beyond.

Keywords: cosmology; space exploration; galaxies; dark energy

Corresponding author’s email address: abhijeetkeshri824@gmail.com

4.Integration of AI and Aviation

Hitesh Pandey-1,Shaziya Islam-2

Author Affiliations
12- Department of Computer Science & Engineering -Data Science ,Rungta College of Technology, Bhilai, India-4900240

ABSTRACT:
AI can be defined as the technology that gives power to the computers and machines to simulate human learning, problem solving, analysing and making predictions based on a given set of data. Integration of AI technologies in the field of aviation is enhancing efficiency, safety, and automation across the various domains of aviation. Before the introduction of AI, the aviation industry faced many challenges such as the use of traditional systems which include the manual processes and rule-based decision making, which led to inefficient and slower operations. The aviation sector generates large quantities of data which were very difficult to process and extract the valuable insights from the data using traditional system. The infusion of AI technologies in the various domains of aviation such as Air Traffic Management (ATM), safety, maintenance and unmanned autonomous aircraft has had a significant impact. The AI technologies, such as Explainable AI (XAI), are being used in various contexts ranging from predictive maintenance and anomaly detection to air traffic management to make their process transparent and understandable. Long-Short Term Memory (LSTM) networks are applied in the industry of multiple uses, including forecasting the paths of aircraft, air traffic movements and forecasting Runway Visual Range(RVR). All these advances in the technologies notwithstanding, the industry continues to experience challenges like data security, ethics, and explainability of AI models, which continue to be major areas of research. This paper presents the revolutionary capabilities of AI in aviation, focusing on emerging technologies, applications, and future research directions.

Keywords: artificial intelligence; aviation; machine learning; LSTM; Explainable AI

Corresponding author’s email address: hiteshpandey2114@gmail.com

5. Customer Segmentation Using K-Means Clustering

Jeevitesh Singh Thakur-1, Shaziya Islam-2, Sumit Rajak-3, Jai Verma-4

Author Affiliations
1234- Department of Computer Science & Engineering -Data Science ,Rungta College of Technology, Bhilai, India-4900240

ABSTRACT:
In the modern business world, understanding customer behavior is crucial for delivering personalized experiences, improving satisfaction, and increasing profitability. One effective approach to achieving this is customer segmentation—dividing a customer base into smaller groups with similar characteristics or purchasing behavior. This research focuses on implementing KMeans Clustering, a popular unsupervised machine learning algorithm, to perform customer segmentation based on attributes like age, annual income, and spending score. The dataset used in this study is a real-world customer dataset commonly used in retail analytics. The data was cleaned, normalized, and visualized using Python libraries such as Pandas, Matplotlib, and Seaborn. The Elbow Method was applied to determine the optimal number of clusters (K), ensuring meaningful and well-separated segments. After applying the K-Means algorithm, customers were grouped into distinct clusters, such as high-income high spenders, young budget-conscious buyers, and older moderate spenders. The results were visualized using scatter plots and cluster centroids to clearly represent the behavioral patterns of each group. These insights can help businesses develop targeted marketing strategies, offer personalized services, and allocate resources more effectively. Additionally, the project highlights the importance of data preprocessing, feature selection, and model evaluation in real-world data science tasks. This research demonstrates that clustering algorithms like K-Means can extract meaningful patterns from unlabeled data and play a vital role in decision-making across various industries. The project serves as a foundational learning experience in unsupervised machine learning for second-year students, offering practical exposure to key concepts like clustering, distance metrics, data visualization, and business intelligence.

Keywords: Customer segmentation; K-Means clustering; Machine learning; Unsupervised learning; Data science

Corresponding author’s email address: jeeviteshsinghthakur007@gmail.com

RUNGTA INTERNATIONAL OF JOURNAL OF COMPUTER SCIENCE AND INFORMATION TECHNOLOGY