Road accidents are a leading cause of injury and death around the world due to the delayed emergency response and drunk
driving, without the availability of real-time monitoring systems. Traditional accident-reporting procedures rely on
manual communication and lead to longer rescue times and limited prospects of timely medical support. In order to solve
these problems, this research introduces an intelligent smart accident detection and instant alert generation system based
on an embedded system and wireless communication technologies. This proposed solution combines the use of Arduino
Uno, vibration sensors, alcohol sensors, GPS modules, GSM communication, Wi-Fi connectivity, LCD displays, and
buzzer units to enable real-time monitoring of vehicles and automated emergency responses.
The system is constantly assessing the state of the vehicle and driver's actions in real time. The vibration sensor senses
anomaly of the intensity of impact in case of collision and can immediately begin the mechanism for the detection of the
accident. The GPS module gets the exact geographical position of the vehicle and the GSM module automatically sends
out emergency call messages of the accident and the geographical coordinates of the accident to preprogrammed
emergency contacts. The framework is also designed to include an alcohol monitoring feature that detects alcohol
impaired driving conditions, and provides warnings to prevent alcohol impaired driving. Furthermore, Wi-Fi connectivity
allows for cloud-based monitoring and integration with IoT, which can facilitate real-time data analysis and intelligent
transportation solutions.
Daripally Karthik, M. Ramesh, Dr. L Jagadeesh Naik· International Journal of Dru...· 0 citations
Deep learning-based generative models have made a major leap forward in the world of image generation with the help
of Artificial Intelligence. One of the most notable of these developments is text-to-image synthesis, which can
automatically generate images based on natural language descriptions. In this work, an AI-based image generation system
is introduced that utilizes a Stable Diffusion model fine-tuned with Low-Rank Adaptation (LoRA) for domain-specific
image generation. The main idea of the proposed system is to combine the text encoding of CLIP, the latent compression
of Variational Autoencoder (VAE), and the denoising ability of diffusion to create images that are both semantically
relevant and visually coherent based on text prompts. The proposed approach was tested on a Pokemon image-caption
dataset for fine-tuning the pre-trained Stable Diffusion model and its effectiveness evaluated. The study shows that the
diffusion-based architectures outperform the traditional GAN based methods in terms of image quality, training stability,
semantic alignment, and output diversity. The main advantage of LoRA fine-tuning was the substantial decrease in
computational load, which involved updating just a small fraction of trainable parameters without compromising the
model's performance. Experimental results indicated that successful images of Pokemon could be generated, and that the
images were consistent with the text attributes such as color, type, and appearance. The results demonstrate that SD+LoRA
is an efficient and scalable domain-specific text-to-image generation system. The research underscores the rising
significance of diffusion-based generative AI in digital content creation, imaginative design, entertainment, and cleverness
in visual generation systems
Ankam Pavitra, R. Mallikharjun, Dr. L Jagadeesh Naik· International Journal of Dru...· 0 citations
The research proves that RoBERTa is a very powerful and reliable model for the current sentiment classification problems and can be a major step towards developing intelligent opinion mining and automated text analysis systems.
Vemula Vandana, K. Ushamahalaxmi, Dr. L Jagadeesh Naik· International Journal of Dru...· 0 citations
