Publications

This research work dives into an in-depth evaluation of the YOLOv8 (You Only Look Once) algorithm's efficiency in object detection, specially focusing on Barcode and QR code recognition. Utilizing the real-time detection abilities of YOLOv8, we performed a study aimed at enhancing its talent in swiftly and correctly figuring out objects. Through large training and high-quality-tuning on Kaggle datasets tailored for Barcode and QR code detection, our goal became to optimize YOLOv8's overall performance throughout numerous situations and environments. The look encompasses the assessment of YOLOv8 throughout special version iterations:- Nano, Small, and Medium, with a meticulous attention on precision, recall, and F1 assessment metrics. The consequences exhibit large improvements in object detection accuracy with every subsequent model refinement. Specifically, we achieved an accuracy of 88.95% for the nano model, 97.10% for the small model, and 94.10% for the medium version, showcasing the incremental improvements finished via model scaling. Our findings highlight the big strides made through YOLOv8 in pushing the limits of computer vision, ensuring its function as a milestone within the subject of object detection. This study sheds light on how model scaling affects object recognition, increasing the concept of deep learning-based computer creative and prescient techniques.

DNA sequence classification is a major challenge in biological processing of data. The classification of DNA sequences is an important study field in bioinformatics since it allows researchers to perform genomic analysis. Through this research, we tried to analyze the performance of various machine learning (ML) algorithms in identifying DNA genes in humans and chimpanzees to find the most effective algorithm for classification of DNA sequences. The results of this research outperformed earlier studies, which had an accuracy around 98%. The dataset was preprocessed using k-mer counting, which assisted in finding key characteristics for the model's training. Naïve Bayes stood out, with an outstanding accuracy of 98.4% for human DNA and 91.4% for chimpanzee DNA. After improving the parameters, Naïve Bayes continued to improve, with human DNA accuracy increasing to 99% and chimpanzee DNA accuracy to 92% in all categories followed by Logistic Regression at 93.9% for human DNA sequences and Ridge Classifier at 90.2% for chimpanzee DNA sequences. K-Nearest Neighbors, Ridge Classifier, Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, Extra Trees, XGBoost, and Naïve Bayes were among the algorithms tested, with accuracy, F1 score, precision, and recall used in assessing their performance. These findings highlight the necessity of selecting the appropriate models and tweaking their parameters to improve performance.

This device is maid for the visually impaired people to roam around easily with nevigation help.

This is an androind application made for deaf and dumb people to communicate easily with other people in their language.

Idea is to detect deepfake images using AI in real-time so fraud can be prevented.

This hardware tool helps people who have lost their arm in accident. It helps them to complete their regular day-to-day tasks with artificial arm.