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
The rapid growth of Android applications has significantly increased the risk of malware attacks, making reliable malware detection an important cybersecurity challenge. Traditional detection methods often fail to identify newly emerging malware due to their dependence on predefined signatures and limited feature learning capabilities. This paper presents an intelligent Android malware detection framework that combines deep learning with the Equilibrium Optimizer to improve detection performance. Initially, Android application data are preprocessed to extract meaningful features for analysis. A Channel Attention Long ShortTerm Memory (CA-LSTM) model is employed to capture complex behavioral patterns and accurately distinguish malicious applications from benign ones. To further enhance the model's performance, the Equilibrium Optimizer is used to tune the hyperparameters, resulting in improved classification accuracy and faster convergence. Experimental evaluation on a benchmark Android malware dataset demonstrates that the proposed framework achieves superior detection performance compared with conventional machine learning methods, 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