The widespread use of social networking platforms has significantly increased the risk of malicious activities, including the spread of spam, misinformation, and the creation of fake user profiles. This paper presents an analytical study on the detection of spammers and fake users on social networks using data mining and machine learning techniques. The proposed system analyzes user behavior patterns, content characteristics, and network interactions to distinguish between genuine and suspicious accounts. Key features such as posting frequency, friend-to-follower ratio, message similarity, and account activity are extracted and evaluated. Machine learning algorithms, including Decision Tree, Random Forest, and Support Vector Machine (SVM), are applied to classify users based on these behavioral attributes. The study highlights the importance of feature selection and model optimization to enhance detection accuracy. Experimental results using real-world social network datasets show that the proposed approach effectively identifies fake and spam accounts, improving platform security and user trust.

Keywords : Social Networks, Spam Detection, Fake User Identification, Machine Learning, Data Mining, Behavioral Analysis

Authors : MOLLI SRINIVASA RAO

Title : Analysis on Detection of Spammers and Fake Users on Social Networks

Volume/Issue : 2020;2(4 (July - August))

Page No : 21 - 28

The advancement of the Internet of Things (IoT) has revolutionized healthcare by enabling continuous, real-time patient monitoring and data-driven medical decision-making. This paper proposes an IoT-based Health Care Monitoring System that collects and analyzes vital health parameters such as body temperature, heart rate, blood pressure, and oxygen levels using interconnected smart sensors. The data is transmitted to a cloud platform via wireless communication modules, where it can be accessed by healthcare professionals and caregivers remotely. The system generates alerts when abnormal readings are detected, ensuring timely medical intervention and reducing the risk of critical health incidents. Integration with mobile and web applications allows patients and doctors to monitor health data anytime, improving convenience and personalized care. The proposed system enhances healthcare efficiency, reduces hospital visits, and supports remote monitoring for elderly and chronically ill patients. This IoT-driven approach represents a step toward intelligent, accessible, and preventive healthcare solutions in smart medical systems.

Keywords : Internet of Things (IoT), Health Care Monitoring, Smart Sensors, Remote Patient Monitoring, Cloud Computing, Real-Time Health Data

Authors : MOLLI SRINIVASA RAO

Title : IoT based Health Care Monitoring System

Volume/Issue : 2020;2(4 (July - August))

Page No : 13 - 20

The gaming industry has witnessed rapid advancements with the integration of artificial intelligence (AI) and 3D graphics, creating more immersive and intelligent gaming experiences. This paper presents the design and development of “Warrior Queen,” a complete 3D AI-based action-adventure game that combines dynamic gameplay, realistic graphics, and adaptive artificial intelligence. The game features a central female protagonist — the Warrior Queen — who embarks on strategic missions, battles enemies, and explores interactive environments. AI techniques are implemented to control non-player characters (NPCs), enabling them to learn and react to the player’s actions in real time. The 3D environment is developed using advanced game engines that support realistic rendering, physics-based interactions, and animation systems. The project demonstrates the effective use of pathfinding algorithms, behavior trees, and reinforcement learning to enhance enemy intelligence and gameplay complexity. The integration of AI with 3D game design provides players with a challenging, engaging, and adaptive gaming experience.

Keywords : Artificial Intelligence (AI), 3D Game Development, Warrior Queen, Game Design, Non-Player Characters (NPCs), Interactive Gameplay

Authors : N P PATNAIK M

Title : Warrier Queen: A complete 3D AI Game

Volume/Issue : 2020;2(4 (July - August))

Page No : 12 - 19

With the rapid increase in the number of vehicles, traffic congestion and road safety have become major challenges in urban areas. To address these issues, this paper proposes a Traffic Information System (TIS) based on the Internet of Things (IoT) for efficient traffic monitoring and management. The system uses smart sensors, GPS modules, and wireless communication technologies to collect real-time data on vehicle movement, traffic density, and road conditions. The collected information is transmitted to a centralized cloud platform, where it is analyzed to detect congestion patterns, accidents, or road blockages. The processed data is then shared with drivers and traffic authorities through mobile or web-based applications to support dynamic route guidance and traffic control decisions. By integrating IoT with data analytics, the proposed system enhances traffic flow, reduces travel time, and improves urban mobility. This intelligent framework can be effectively implemented in smart cities to create safer and more efficient transportation networks.

Keywords : Internet of Things (IoT), Traffic Information System, Smart Cities, Real-Time Monitoring, Traffic Management, Intelligent Transportation

Authors : MOLLI SRINIVASA RAO

Title : Traffic Information System using Internet of Things

Volume/Issue : 2020;2(4 (July - August))

Page No : 5 - 11

With the exponential growth of digital data in recent years, data mining has become an essential process for extracting meaningful patterns, trends, and knowledge from large datasets. This paper presents an analytical study of various data mining techniques and their applications across multiple domains. Key methods such as classification, clustering, regression, association rule mining, and anomaly detection are examined for their effectiveness and practical implementation. The study highlights how these techniques are applied in diverse fields including healthcare, finance, education, marketing, and cybersecurity to support data-driven decision-making. Furthermore, the paper discusses challenges such as data quality, scalability, privacy, and model interpretability that affect mining performance. Comparative analysis reveals that hybrid and ensemble-based approaches often enhance accuracy and efficiency in complex datasets. Overall, this study emphasizes the growing significance of data mining as a core component of modern analytics and its role in transforming raw data into valuable insights for intelligent decision support systems.

Keywords : Data Mining, Classification, Clustering, Association Rule Mining, Predictive Analytics, Knowledge Discovery

Authors : SANKARA RAO LAMBURU

Title : Analysis of Data Mining Techniques and Applications

Volume/Issue : 2020;2(4 (July - August))

Page No : 1 - 4