Breast cancer is one of the most common and life-threatening diseases, and early and accurate diagnosis is essential for the enhancement of survival rates. Histopathological image analysis is regarded as the gold standard of diagnosing breast cancer; nevertheless, manual practice carried out by the pathologists is time-consuming, subjective, and inter-observer variability may be present. The recent development in artificial intelligence and deep learning has made it possible to analyze medical images automatically, providing more diagnostic assistance and faster. The Automated Breast Cancer Detection Framework Using Hybrid CNN Models on Histopathological images that we propose in this paper combines various convolutional neural network (CNN) models to augment the feature detection and classification results. The hybrid model suggested is an integration of the merits of pre-trained deep learning models like the ResNet50, DenseNet121, and InceptionV3 to extract low-level and high-level features of histopathological images. The system consists of preprocessing, feature fusion, hybrid deep learning-founded classification, and decision support mechanisms. Experimental findings show that the proposed model yields an accuracy of 97.6%, precision of 96.9%, a recall of 96.4%, and an F1-score of 96.6%, and outperforms the traditional machine learning models as well as standalone CNN models. The results reveal that the hybrid CNN-based schemes can greatly enhance the accuracy and robustness of classification in using histopathological images.
P. Palsodkar, Naveen, Gagandeep Kaur et al.· International Conference Com...· 0 citations
Traditional signature-based intrusion detection systems (IDS) and rule-based security mechanisms frequently fail to detect these kinds of advanced and adaptive attacks because they rely on the patterns of attacks in the past. In addition, modern cyberattacks are very dynamic and polymorphic; traditional detection methods are not adequate for real-time cybersecurity protection. In this paper, an Adaptive Deep Learning Framework for Zero-Day Attack Detection Using Anomaly and Behavior Analysis is proposed to overcome the above limitations. The proposed scheme incorporates deep learning, anomaly detection, and behaviour analysis methods to detect novel cyber threats and malicious activities in real-time, intelligently. The framework constantly observes the traffic in the network, the pattern of user activity, system logs, and communication anomalies, and builds adaptive behavioral models that can differentiate between normal and malicious behavior. Advanced deep neural networks, Long Short-Term Memory (LSTM) architectures, and adaptive anomaly detection mechanisms are employed to analyze the temporal attack behavior and detect abnormal network activities with high accuracy. The findings support the adaptability, resilience, and efficiency of the proposed framework for intelligent zero-day attack detection and cybersecurity applications with deep learning.
Gagandeep Kaur, Anshu Vashisth, Naveen et al.· 2026 7th International Confe...· 0 citations
The adoption of a new communication paradigm is getting attention in the research world, where Flying Ad Hoc Networks (FANETs) have been deemed a viable approach for supporting coordinated operations of multiple Unmanned Aerial Vehicles (UAVs) in situations characterized by dynamic environments and the absence of infrastructure. Taking into consideration these drawbacks, in this paper, a novel and up-to-date AI-Based Mobility and Topology Management Framework for Flying Ad Hoc Networks via Hybrid Bio-Inspired Optimization is proposed. The proposed systems combine a Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) inspired model, introducing a novel hybrid model, with Artificial Intelligence techniques to provide a dynamic framework for optimizing UAV mobility patterns, topology formation, and communication paths within the proposed framework. Predictive mobility analysis using AI to make networks more adaptable and minimize topology changes. In addition, the hybrid optimization method will optimize the routing efficiency, reduce the communication overhead, and increase the packet delivery efficiency between nodes in the highly dynamic FANET environment. Results of experimental analysis prove that the proposed scheme has a better PDR of 96.4%, lower EED or end-to-end delay of 31%, and better topology stability that performs better than the traditional mobility management approaches with respect to reducing energy consumption.
Anshu Vashisth, Gagandeep Kaur, Ruhi Saxena et al.· 2026 7th International Confe...· 0 citations