ARCHIVE

Enhancing Communication Accessibility: A Review Of Deep Learning Techniques in Hand Gesture Recognition for Sign Language Interpretation

Authors : Ashlesha Dhawanjewar DOI : 10.46335/IJIES.2024.9.6.1 Article Link Abstract— Hand gesture recognition, particularly in the context of sign language recognition for deaf-mute individuals, has garnered significant attention due to its potential to enhance communication accessibility. This paper reviews recent advancements in hand gesture recognition, focusing on deep learning methodologies. Specifically, we explore the application of […]

Breast Cancer Detection Using Ensemble Deep Learning

Authors : Pallavi Kapse, Rimzim Janwe , Dr. Yogesh Golhar DOI : 10.46335/IJIES.2024.9.9.9 Abstract – Breast Cancer is one of the highly increasing cancer diseases in women worldwide. Ensuring a precise diagnosis of this critical illness is pivotal for the patients’ survival. To attain outstanding results in breast cancer classification, we propose employing a sophisticated […]

Leaf Analytica Through AI

Authors : Rohan Raut , Anshul Suryawashi , Dhiray Tongse ,Shubhanshu singh, Prof.Pooja Wagh DOI : 10.46335/IJIES.2024.9.9.8 Abstract- The Proposed system aims to develop a robust and reliable system for automated plant disease detection using machine learning algorithms. The proposed system utilizes image processing techniques to extract relevant features from images of plant leaves exhibiting […]

Plant Disease Detection Using Image Segmentation Methods

Authors :Manesh Prakashrao Patil , Prof. Dr. Indrabhan S. Borse , Prof. Dr. Balveer Singh DOI : 10.46335/IJIES.2025.10.6.16 Abstract – Plant diseases pose a major threat to global farming and can greatly reduce crop production. To reduce their impact, it’s important to detect and manage these diseases effectively. One key step in detecting plant diseases […]

Skin Disease Detection Using Markov Decision Process

Authors :Nitish Kumar , Nishant Kumar, Rahul Kumar, Dr. R. Sudhakar DOI : 10.46335/IJIES.2025.10.8.9 Abstract – Diagnosing skin diseases can be challenging with traditional methods, as they rely on manual examination and a doctor’s expertise, which may lead to errors or delays. This project introduces an advanced approach by integrating Convolutional Neural Networks (CNNs) and […]