scholarly journals Multimodal Biometrics Data Based Gender Classification using Machine Vision

2019 ◽  
Vol 8 (11) ◽  
pp. 1356-1363
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Machine Vision ◽  
Data Fusion ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Gender Classification ◽  
Criminal Records ◽  
Multimodal Biometrics ◽  
Significant Step

Gender classification from biometrics data is a significant step in forensics to categorize and minimize the suspects search from the criminal records. In this paper, we present multimodal biometrics data analysis for Gender Classification using machine learning algorithms which take input as a Face, Fingerprints and Iris images. Extensive experiments were conducted using feature level and synthesis of classifiers on the SDMULA-HMT and KVK-Multimodal datasets. Experimental results presented using multimodal biometrics data fusion schemes achieves high gender classification accuracies compared to the contemporary techniques stated in the literature.

scholarly journals A Comparative Analysis of Machine Learning Algorithms Modeled from Machine Vision-Based Lettuce Growth Stage Classification in Smart Aquaponics

2020 ◽  
Vol 11 (9) ◽  
pp. 442-449 ◽  
Author(s):  
Sandy C. Lauguico ◽  
◽  
Ronnie S. Concepcion II ◽  
Jonnel D. Alejandrino ◽  
Rogelio Ruzcko Tobias ◽  
...  
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Machine Vision ◽  
Nearest Neighbor ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Urban Farming ◽  
K Nearest Neighbor ◽  
Lettuce Growth

The arising problem on food scarcity drives the innovation of urban farming. One of the methods in urban farming is the smart aquaponics. However, for a smart aquaponics to yield crops successfully, it needs intensive monitoring, control, and automation. An efficient way of implementing this is the utilization of vision systems and machine learning algorithms to optimize the capabilities of the farming technique. To realize this, a comparative analysis of three machine learning estimators: Logistic Regression (LR), K-Nearest Neighbor (KNN), and Linear Support Vector Machine (L-SVM) was conducted. This was done by modeling each algorithm from the machine vision-feature extracted images of lettuce which were raised in a smart aquaponics setup. Each of the model was optimized to increase cross and hold-out validations. The results showed that KNN having the tuned hyperparameters of n_neighbors=24, weights='distance', algorithm='auto', leaf_size = 10 was the most effective model for the given dataset, yielding a cross-validation mean accuracy of 87.06% and a classification accuracy of 91.67%.

Machine learning algorithms for smart data analysis in the Internet of things: an overview

2021 ◽  
pp. 307-327
Author(s):  
Mohammed H. Alsharif ◽  
Anabi Hilary Kelechi ◽  
Imran Khan ◽  
Mahmoud A. Albreem ◽  
Abu Jahid ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Internet Of Things ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
The Internet ◽  
Smart Data ◽  
The Internet Of Things

scholarly journals Not All Attributes are Created Equal: dX -Private Mechanisms for Linear Queries

2020 ◽  
Vol 2020 (1) ◽  
pp. 103-125
Author(s):  
Parameswaran Kamalaruban ◽  
Victor Perrier ◽  
Hassan Jameel Asghar ◽  
Mohamed Ali Kaafar
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Differential Privacy ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Trade Off ◽  
Systematic Procedure ◽  
Privacy Budget ◽  
Sensitivity Vector

AbstractDifferential privacy provides strong privacy guarantees simultaneously enabling useful insights from sensitive datasets. However, it provides the same level of protection for all elements (individuals and attributes) in the data. There are practical scenarios where some data attributes need more/less protection than others. In this paper, we consider dX -privacy, an instantiation of the privacy notion introduced in [6], which allows this flexibility by specifying a separate privacy budget for each pair of elements in the data domain. We describe a systematic procedure to tailor any existing differentially private mechanism that assumes a query set and a sensitivity vector as input into its dX -private variant, specifically focusing on linear queries. Our proposed meta procedure has broad applications as linear queries form the basis of a range of data analysis and machine learning algorithms, and the ability to define a more flexible privacy budget across the data domain results in improved privacy/utility tradeoff in these applications. We propose several dX -private mechanisms, and provide theoretical guarantees on the trade-off between utility and privacy. We also experimentally demonstrate the effectiveness of our procedure, by evaluating our proposed dX -private Laplace mechanism on both synthetic and real datasets using a set of randomly generated linear queries.

Sign in / Sign up

Export Citation Format

Share Document