Our Recent Publications
A look at the latest peer-reviewed, open-access research papers published on PaperNova by students and scholars from over 60 countries. Every paper is citable, downloadable as PDF, and issued with a certificate of publication.
Looking to publish your own work? Submit your research paper and go live in minutes.
Snakebite Detection and Treatment Using AI
Snakebite envenomation remains a critical public health challenge, particularly in rural and resource-limited regions where timely diagnosis and treatment are often unavailable. Traditional diagnostic methods rely heavily on clinical symptoms and patient history, which can lead to delays and misclassification. This research introduces an AI-driven snakebite detection and treatment system that leverages machine learning and computer vision to classify snakebite images into specific snake types and recommend appropriate treatment protocols. The model is trained using convolutional neural networks (CNNs) and deployed via the Django framework, ensuring accessibility through a user-friendly interface.
By KOMAL SINGH MARKAM · Parul Institute of Engineering and Technology, Parul University, Vadodara, India
Farmatics
Plant diseases are one of the leading causes of agricultural losses worldwide, annually destroying an estimated 20–40% of global crop production and threatening the food security of billions. Early and accurate identification of plant diseases is critical to enabling timely intervention and minimizing crop losses. Farmatics is a deep learning-based plant disease detection system specifically designed to assist farmers in identifying diseases at an early stage across multiple crop varieties directly from leaf photographs captured via a mobile or web interface. The system leverages a custom Convolutional Neural Network (CNN) architecture trained on the PlantVillage benchmark dataset comprising 54,306 images across 38 disease classes and 14 crop varieties. Unlike existing solutions that are limited to single-crop detection, require constant internet connectivity, or lack early-stage detection capability, Farmatics supports multi-crop inference and is optimized for practical agricultural deployment. Experimental evaluation demonstrates that the proposed system achieves 96.4% classification accuracy, outperforming established baselines including VGG16 (91.2%), ResNet50 (92.5%), MobileNetV2 (89.7%), and InceptionV3 (93.1%). The system further provides confidence-scored disease identification, crop-specific treatment recommendations, and a confidence thresholding mechanism for quality assurance. Farmatics represents a significant step toward democratizing precision agricultural technology for smallholder farmers in India and comparable developing economies. Index Terms — Plant Disease Detection, Convolutional Neural Network, Deep Learning, Precision Agriculture, Image Classification, Early Detection, Multi-Crop Analysis, PlantVillage, Transfer Learning, Computer Vision.
By Ayush Santosh Naik · Vishwaniketan Institute ,khalapur
About PaperNova's research archive
PaperNova is an international platform where students and researchers can publish research papers online instantly and receive a verifiable certificate of publication. Our open-access archive covers engineering, computer science, artificial intelligence, biotechnology, management, finance, and more.
Every paper on PaperNova is peer-reviewed, citable, and indexed on the open web so it can be discovered by future researchers. If you are looking for the fastest way to publish a journal paper internationally, start by browsing our AI research papers, machine learning papers, or computer science archive.