Storytelling plays an important role in education, creativity, and entertainment, but writing stories manually requires time, imagination, and language proficiency. This project presents an AI-Powered Multilingual Story Generation and Speech Synthesis System that automatically generates meaningful stories from user-provided keywords. The system allows users to register, log in, select their preferred language, and generate stories in languages such as English, Hindi, Odia, and Telugu. It is developed using Python, Django, and MySQL, while the Gemini AI model is used to understand the input keywords and produce coherent, grammatically correct, and context-aware stories. In addition to text generation, the system converts the generated stories into speech, providing a more engaging and accessible experience for users. The proposed system reduces manual effort, supports multilingual communication, and enhances storytelling through artificial intelligence. It offers a simple, interactive, and efficient platform suitable for educational, learning, and entertainment applications.
J.Soumya, G.Rajini· International Journal of Dat...· 0 citations
The rapid growth of online news platforms and social media has made it easier for information to spread quickly, but it has also increased the circulation of content that may create fear, misinformation, or potential security concerns. Detecting threat-related news at an early stage is essential for supporting public safety and informed decision-making. This study presents a hybrid lexicon-driven news threat detection framework that combines the NRC Emotion Lexicon with advanced machine learning models, namely Random Forest and XGBoost. Initially, news articles undergo preprocessing steps such as text cleaning, tokenization, and stop-word removal to improve data quality. Emotional features are then extracted using the NRC Lexicon and integrated with textual features to create an informative dataset for classification. The processed data is used to train and evaluate both machine learning models using performance measures including accuracy, precision, recall, and F1-score. Experimental results indicate that the proposed hybrid approach effectively identifies threatrelated news, with the Random Forest model providing slightly better classification performance than XGBoost. The combination of emotion-based lexical analysis and ensemble learning enhances prediction accuracy, making the proposed framework a practical and reliable solution for intelligent news threat detection in real-world applications.
G Bhavani Prasad, G.Rajini· International Journal of Eng...· 0 citations
Maintaining a healthy diet requires accurate knowledge of the food consumed and its nutritional value. However, manually identifying food items and estimating their calorie content can be difficult and time-consuming. This project, "AI-Driven Smart Food Perception and Nutrition Assessment," presents an intelligent system that automatically recognizes food items from images and provides an estimate of their calorie content using deep learning techniques. The proposed framework employs a Convolutional Neural Network (CNN) to learn visual features from food images and classify different food categories with high accuracy. Before model training, the dataset undergoes preprocessing steps such as image resizing, normalization, and shuffling to improve learning performance. After recognizing the food item, the system retrieves its nutritional information from a calorie database and calculates the estimated calorie intake. It also keeps track of the total calories consumed and the remaining daily calorie allowance, helping users monitor their eating habits. The developed application provides a simple graphical interface for food image upload, model prediction, and nutritional assessment. Overall, the proposed system offers an efficient and practical solution for supporting healthier food choices and improving daily dietary management.
Mekala Harshavardhan, G.Rajini· International Journal of Eng...· 0 citations
Web applications have become an essential part of modern businesses, making them a common target for cyberattacks. Among various security threats, SQL injection remains one of the most dangerous because it allows attackers to manipulate database queries and gain unauthorized access to sensitive information. Traditional detection techniques often rely on predefined rules or signatures, which are less effective against newly emerging attack patterns. This work presents an intelligent approach for improving web application security through the prediction and detection of SQL injection attacks using machine learning techniques. The proposed framework processes SQL queries by performing data cleaning, feature extraction, and text preprocessing before training an ensemble classification model. The trained model distinguishes normal queries from malicious ones with high accuracy, enabling early identification of potential attacks. Performance is evaluated using standard metrics such as accuracy, precision, recall, and F1-score to verify the effectiveness of the system. The developed framework also provides a simple interface for analyzing new SQL queries and predicting their security status. This approach supports proactive protection of web applications by enabling faster, more reliable, and automated detection of SQL injection vulnerabilities.
D. Aravind Reddy, G.Rajini· International Journal of Eng...· 0 citations
Intelligent Facial Age and Gender Analytics Using Deep Learning presents an automated approach for estimating a person's age and identifying gender from facial images using deep learning techniques. The system employs a Convolutional Neural Network (CNN) to learn facial characteristics directly from images, eliminating the need for manual feature extraction. Before training, facial images undergo preprocessing steps such as face detection, resizing, and normalization to improve data quality and model performance. The trained CNN analyses facial patterns and predicts both age and gender, making the system suitable for real-time applications using a webcam or image input. The proposed framework is designed to handle images captured under different lighting conditions, poses, and facial expressions, allowing it to perform effectively in practical environments. Experimental evaluation demonstrates that the model produces reliable predictions while maintaining a simple and efficient architecture. The developed system can be applied in areas such as intelligent surveillance, human-computer interaction, demographic analysis, smart retail, and access control. Overall, the proposed framework provides an accurate, practical, and user-friendly solution for automated facial age and gender analytics using deep learning.
Raghu ram Kandula, G.Rajini· International Journal of Eng...· 0 citations
An intelligent Android malware detection framework that combines deep learning with the Equilibrium Optimizer to improve detection performance is presented, providing an effective and reliable solution for securing Android devices against evolving malware threats.
Aishwarya Eklar, G.Rajini· International Journal of Eng...· 0 citations
Malware has become one of the biggest threats to computer systems and digital networks, affecting individuals, businesses, and government organizations. Traditional malware detection methods mainly depend on signatures and predefined rules, making them less effective against newly developed and constantly evolving attacks. As cybercriminals continue to use advanced techniques such as code obfuscation and polymorphism, there is a growing need for smarter and faster detection methods. This project proposes a robust malware detection system using deep learning to identify both known and unknown malware with improved accuracy. The model automatically learns meaningful patterns from malware data without relying heavily on manual feature extraction. By analyzing the behavior and characteristics of malicious files, the proposed system can classify malware efficiently while reducing false alarms. The approach improves detection speed, enhances security, and supports real-time threat analysis. This makes it a reliable solution for protecting modern computer systems against rapidly changing cyber threats.
S.Srikar, G.Rajini· International Journal of Dat...· 0 citations