Volume of International Discoveries and Youth Academic Works, Achievements, and Novelties - VIDYAWAN

Urban traffic congestion has become a major challenge in rapidly growing cities, resulting in increased travel time, fuel consumption, and environmental pollution. This study presents an Artificial Intelligence-Based Smart Traffic Management System that utilizes machine learning algorithms, computer vision techniques, and real-time sensor data to monitor and regulate traffic flow. The proposed system collects data from surveillance cameras, IoT sensors, and GPS-enabled devices to predict traffic density and dynamically adjust traffic signal timings. Experimental simulations demonstrate that the system can significantly reduce average vehicle waiting time and improve road utilization efficiency. The research highlights the potential of AI technologies in developing intelligent transportation infrastructures capable of supporting sustainable urban development and enhancing commuter experience.

Agriculture plays a fundamental role in sustaining human civilization; however, crop diseases continue to pose a major threat to global food security. Early and accurate detection of plant diseases is essential for reducing crop losses and enhancing agricultural productivity, yet traditional disease identification methods rely heavily on manual inspection by agricultural experts, making the process time-consuming, expensive, and impractical for large-scale farming. Recent advances in deep learning, particularly Convolutional Neural Networks (CNNs), have shown significant potential in automating visual recognition tasks, including plant disease classification from leaf images. This paper presents a CNN-based approach for the automated detection of diseases across multiple crop varieties, including rice, wheat, tomato, and potato. The proposed model is trained and evaluated using the publicly available PlantVillage dataset, which contains thousands of labeled images of healthy and diseased plant leaves. To improve model generalization and minimize overfitting, data augmentation techniques such as rotation, flipping, and zooming are employed. Furthermore, the proposed CNN architecture is benchmarked against well-established models, including VGG16, ResNet50, and MobileNetV2. Experimental results indicate that the proposed model achieves a classification accuracy of 97.4%, outperforming several baseline architectures while maintaining computational efficiency. These findings highlight the strong potential of CNN-based automated disease detection systems for real-world applications in precision agriculture and smart farming environments.

Network Intrusion Detection Systems (NIDS) play a critical role in safeguarding modern computer networks against an ever-growing landscape of cyber threats. Traditional signature-based detection methods often fail to identify novel or zero-day attacks, while purely statistical machine learning models struggle to capture the temporal dependencies present in network traffic. This paper proposes a hybrid deep learning architecture that combines Convolutional Neural Networks (CNN) for spatial feature extraction with Long Short-Term Memory (LSTM) networks for temporal sequence modeling. The proposed model is evaluated on the NSL-KDD benchmark dataset and achieves an accuracy of 98.4%, outperforming standalone CNN, LSTM, and traditional machine learning classifiers such as Random Forest and Support Vector Machines. Experimental results demonstrate that the hybrid CNN-LSTM approach significantly improves detection accuracy while maintaining low false positive rates, making it suitable for real-time deployment in enterprise network environments.