Journal of Science Engineering Technology and Management Science

Online social networks represent one of the largest digital ecosystems, with more than 4 billion users worldwide. With this rapid expansion, the risk of maintaining data integrity and security has significantly increased. Social media platforms face numerous security challenges, as users often provide limited personal information, making identity verification difficult. This creates opportunities for scammers to generate fake accounts and misuse them to steal sensitive information, spread misinformation, or harm other users. As a result, detecting fake profiles in social networks has become a complex and important task. This project utilizes various machine learning and deep learning techniques applied to different datasets to identify the most effective model for detection. Additionally, a web-based system is developed to determine whether a given user profile is real or fake. The system is designed with four modules representing major social media platforms that are rapidly gaining users. Each module contains a trained model specifically developed for detecting fake profiles on that platform. Currently, the system has been implemented for Instagram, enabling accurate identification of fake profiles through the developed model with improved performance.

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Author : G.Sravanthi, V. VENKATA SAHITHI, V. YASWANTH, P. SINDHUJA, T. AKHILA

Title : Fake Profile Detection on social media Using Python

Volume/Issue : 2026;03(3(1))

Page No : 1-4

With the rapid growth of textual data generated daily from social media, emails, articles, and online platforms, extracting meaningful insights has become increasingly challenging. The Text Analyzer using Machine Learning is a system developed to automatically process, classify, and derive valuable information from large volumes of text data. By applying machine learning techniques, the system performs tasks such as sentiment analysis, topic classification, keyword extraction, and text summarization, enabling users to interpret content more effectively. The system preprocesses textual data using methods like tokenization, stop-word removal, and vectorization to transform unstructured text into structured numerical formats. Machine learning algorithms such as Support Vector Machines (SVM), Random Forest, Naïve Bayes, and Neural Networks are then utilized to analyze and classify the data based on specific objectives. This approach enables automatic detection of patterns, trends, and sentiments within large datasets. By incorporating predictive analytics and intelligent classification, the Text Analyzer supports better decisionmaking, improves content management, and delivers actionable insights across domains such as marketing, education, and social media analysis. The system demonstrates strong accuracy, scalability, and efficiency, making it an effective solution for real-time text analysis and management.

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Author : R.Kalpana, M. SAI KOUSHIK, N. SRAVANI, P. HARSHITHA, P. KAVERI

Title : Text Analyzer Using Machine Learning

Volume/Issue : 2026;03(3(1))

Page No : 5-8

In biometric authentication systems, distinguishing between genuine traits and fake or artificially generated samples is a critical challenge, requiring the development of effective security mechanisms. This paper presents a novel software-based fake detection approach that can be applied across multiple biometric systems to identify fraudulent access attempts. The primary goal of the proposed system is to improve the security of biometric recognition frameworks by incorporating liveness detection in a fast, user-friendly, and non-intrusive manner using image quality assessment techniques. The method is designed with low computational complexity, making it suitable for real-time applications. It utilizes 25 general image quality features extracted from a single captured image—the same image used for authentication—to differentiate between genuine and fake samples. Experimental results on publicly available datasets, including fingerprint, iris, and 2D face images, demonstrate that the proposed method performs competitively compared to existing state-of-the-art techniques. The study highlights that analyzing general image quality provides valuable information, enabling efficient and accurate discrimination between authentic biometric traits and spoofed samples.

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Author : Easari Parusha Ramu, M. RUTHIKA GOUD, M. DIVYA, V. RAVI KUMAR, D. KEERTHI

Title : Image Quality Assessment for Fake Biometric detection

Volume/Issue : 2026;03(3(1))

Page No : 9-16

This project focuses on developing a software application that enables farmers to sell their agricultural products through a website or mobile platform. The system includes essential features such as product listings, shopping carts, secure payment gateways, and order management functionalities. The primary benefit of an online e-farming platform is its ability to directly connect farmers with consumers. This direct interaction allows farmers to receive fair prices for their produce while enabling consumers to purchase fresh and high-quality products straight from the source. Such a system supports local agriculture and encourages sustainable practices. Additionally, the application can incorporate advanced features like personalized recommendations and order tracking. Personalized recommendations help users discover products based on their preferences, while order tracking provides real-time updates on the status of their purchases, enhancing the overall user experience.

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Author : P.Swetha, CH. VAMSHI, D. VENKATESH, K.PAVAN KUMAR, K. SRINIVAS

Title : Harvest Hub: AI Driven Platform for Crop Selection, Price Prediction & Farmer Assistance

Volume/Issue : 2026;03(3(1))

Page No : 17-22

In the modern landscape of rising cyber threats, real-time web traffic monitoring has become essential for identifying and preventing security risks. This study presents the design and evaluation of a Pythonbased real-time web traffic monitoring system implemented on Ubuntu 22.04 LTS. The system combines machine learning-based anomaly detection, real-time traffic analysis, and cryptographic techniques to strengthen cybersecurity monitoring. The system is capable of detecting multiple types of cyber threats, including Distributed Denial of Service (DDoS) attacks with an accuracy of 97.2%, brute-force login attempts with 90.8% accuracy, and unauthorized access attempts with 89.6% accuracy. To improve precision and reduce false positives, optimized anomaly detection methods such as the Isolation Forest algorithm and threshold-based mechanisms are utilized. The system continuously evaluates key network parameters like packet size, request frequency, and response time to identify unusual traffic patterns. An interactive dashboard developed using Flask, along with visualization tools like Plotly and Seaborn, provides real-time insights into traffic behavior, anomaly alerts, and system performance, enabling quick responses to potential threats. Performance testing shows that the system can process up to 10,000 requests per second with an average response time of 150 ms, while maintaining a false positive rate below 10%. Compared to traditional rule-based systems, this approach uses adaptive machine learning models to detect evolving threats more effectively, ensuring improved reliability and efficiency. However, the study also highlights opportunities for future improvements, such as integrating deep learning techniques, deploying the system on cloud platforms for scalability, and incorporating edge computing for faster threat detection. Overall, this work contributes to the advancement of realtime cybersecurity solutions by delivering a high-performance, machine learning-driven monitoring system that enhances security while maintaining operational efficiency.

Keywords : Web Traffic Monitoring, Cybersecurity, Anomaly Detection, Machine Learning, Real-Time Analysis, Ddos Detection, Flask Dashboard, Network Security.

Author : K.Sravani, B. SANDEEP, E. VIKRANTH GOUD, K. JAI ADITHYA REDDY, B. MANIKANTH REDDY

Title : Cloud Security Monitoring

Volume/Issue : 2026;03(3(1))

Page No : 23-32

Waste management has become a significant global concern, as improper disposal methods pose serious risks to both the environment and public health. Conventional waste sorting techniques are often labor-intensive and inefficient, highlighting the need for smarter and more effective solutions. This study presents an AI-based garbage classification system integrated with IoT, combining realtime IoT-enabled sorting with deep learning-driven image classification. The system utilizes a convolutional neural network (CNN) trained on diverse categories of waste to achieve high classification accuracy. Once the waste is identified, an IoT-enabled actuator mechanism automatically segregates it, reducing human effort and improving operational efficiency. To ensure real-time performance and seamless integration with smart city systems, the proposed solution is deployed on edge computing devices. Experimental results demonstrate high accuracy in classification along with efficient waste segregation, indicating the practicality of the system for large-scale implementation. This study highlights the transformative potential of combining AI and IoT in modern waste management, contributing to a more sustainable and circular economy.

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Author : N.Manjula, B. THARUN KUMAR, A. SUNNY BHARGAV, G. GANESH KARTHIKEYA, K.VAMSHI

Title : Smart Waste Classification Using Deep Learning

Volume/Issue : 2026;03(3(1))

Page No : 33-42