Review on secure traditional and machine learning algorithms for age prediction using IRIS image

Object Recognition Using AI is a pioneering concept at the crossroads of artificial intelligence and computer vision, devoted to automating the identification and localization of objects within images and video streams. This paradigm harnesses machine learning algorithms to emulate human visual cognition, empowering systems to discern and precisely pinpoint diverse objects, transcending traditional image analysis. The essence of Object Recognition Using AI lies in its transformative impact across a multitude of applications. In retail, it revolutionizes inventory management by autonomously recognizing products on shelves. Within the security domain, it enhances surveillance systems, enabling real-time tracking of individuals and objects. In the automotive landscape, it empowers self-driving vehicles to interpret their surroundings, safeguarding passengers and pedestrians alike. Our research endeavors to advance the field of object recognition through the implementation of Mask R-CNN, a state-of-the-art deep learning architecture. We aim to achieve highly accurate object detection and segmentation by leveraging the power of convolutional neural networks. In pursuit of this, we conducted comprehensive experiments and evaluations. Our results showcase the effectiveness of Mask R-CNN in various real-world scenarios, with impressive precision and recall rates. In conclusion, this study contributes valuable insights and tools to the realm of computer vision, enabling enhanced object recognition applications. Keywords:Object Recognition, Mask R-CNN, Deep Learning, Convolutional Neural Networks, Computer Vision.

Section: Literature Surveymentioning

confidence: 99%

Object Recognition in AI

Mohan

2023

“…The study reports an accuracy of 85%. [19][20][21] used various feature extraction modules and implemented on currency [18] to extract features to detect fake currency.…”

Section: Literaturesurveymentioning

confidence: 99%

Counterfeit Currency Detection Using Machine Learning

Rao

2023

This research addresses the pervasive issue of counterfeit currency through a comprehensive approach integrating advanced image processing techniques and machine learning algorithms. The methodology encompasses crucial stages, including image comparison, segmentation, edge detection, feature extraction, and grayscale conversion, coupled with the implementation of machine learning models such as K-Nearest Neighbors (KNN),and theefficient MobileNetV2. In tackling the challenge of counterfeit currency, image processing techniques play a pivotal role by enabling the extraction and analysis of distinct features. From isolating patterns through segmentation to refining with edge detection and feature extraction, these techniques enhance the identification of intricate characteristics inherent in legitimate banknotes. Grayscale conversion further standardizes the representation for effective processing. Keywords: KNN, MobileNetV2, Image Processing, Machine learning algorithms, Feature extraction.

“…Author Swati et al [4] in "Review on secure traditional and machine learning algorithms for age prediction using IRIS image"discussed one hundred and one papers in the literature with various image segmentation, feature extraction and classification of the iris. This paper summarizes publicly available standard databases and various evaluation parameters, i.e., accuracy, precision, recall, f-score, etc.…”

Section: Iiliterature Surveymentioning

confidence: 99%

Identification of Software Clone Files Using Machine Learning

Swapna

2023

One technique to reduce the likelihood of introducing a bug is to identify code clone files. Refactoring, often known as removal, is the process of making software more understandable and maintainable. When additional clones are discovered in software programs, we need a method to help developers with refactored code and to enhance software quality. This method must tell developers which clone files require reworking. To provide developers with recommendations on which files require code refactoring, our research proposes a novel learning technique that automatically extracts features from the identified code clones and trains models to present a novel technique to enhance classification performance by transforming outliers into Unknown clone sets. The Eclipse dataset, which comprises 213 Java software files, was utilized for this project. Support Vector Machine (SVM), Random Forest, Bagging, and K-Nearest Neighbors (KNN) are the algorithms that are employed. We compare these four categorization models and recommend the model with the highest accuracy. Our tool can be developed and applied to reduce system bugs. By recognizing software clone files and eliminating duplication code, our study can enhance clone maintenance. Also, the possibility of bad design for a system, difficulty in a system improvement or modification, and introduction of a new bug can be decreased by identifying and refactoring clones. KEYWORDS: Cloning, Refactoring, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision trees.