scholarly journals Learned Features are Better for Ethnicity Classification

2017 ◽  
Vol 17 (3) ◽  
pp. 152-164 ◽  
Author(s):  
Inzamam Anwar ◽  
Naeem Ul Islam
Keyword(s):  
African American ◽  
Vital Role ◽  
Support Vector ◽  
Human Beings ◽  
Novel Approach ◽  
Hierarchical Features ◽  
Facial Images ◽  
Learned Features ◽  
Ethnicity Classification ◽  
Demographic Attribute

Abstract Ethnicity is a key demographic attribute of human beings and it plays a vital role in automatic facial recognition and have extensive real world applications such as Human Computer Interaction (HCI); demographic based classification; biometric based recognition; security and defense to name a few. In this paper, we present a novel approach for extracting ethnicity from the facial images. The proposed method makes use of a pre trained Convolutional Neural Network (CNN) to extract the features, then Support Vector Machine (SVM) with linear kernel is used as a classifier. This technique uses translational invariant hierarchical features learned by the network, in contrast to previous works, which use hand crafted features such as Local Binary Pattern (LBP); Gabor, etc. Thorough experiments are presented on ten different facial databases, which strongly suggest that our approach is robust to different expressions and illuminations conditions. Here we consider ethnicity classification as a three class problem including Asian, African-American and Caucasian. Average classification accuracy over all databases is 98.28%, 99.66% and 99.05% for Asian, African-American and Caucasian respectively. All the codes are available for reproducing the results on request.

scholarly journals Automatic Prediction of Age-group from Frontal Facial Images

2019 ◽  
Vol 9 (1) ◽  
pp. 5356-5359
Keyword(s):  
African American ◽  
Age Groups ◽  
Asian Population ◽  
Support Vector ◽  
Age Group ◽  
Face Images ◽  
And Gender ◽  
Face Structure ◽  
Improved Accuracy ◽  
Facial Images

Methods to automatically assess the age group of a person using his/her frontal facial image are proposed in this paper. This work is done for three major ethnicities: African, American and Asian with five different age-groups such as (1-10 years), (11-30 years), (31-50years), (51-70 years), (71-100 years). The performances of the classifiers were tested with face images of African, American and Asian population belonging to both genders. For this, first the facial parts such as the left eye, right eye, nose, mouth etc., are detected using the well-known Viola Jones Object Detection technique.450 sample images of the FERET database were considered for this study. Histogram of Gradient (HoG) and face-structure features are extracted and modeled using ANN and SVM. The efficiency of the proposed methods was tested with the facial images of various races belonging to different age-group and gender. Artificial neural network gave an accuracy of 92.10% whereas support vector machine gave an improved accuracy of 94.60%.

SVM Kernel and Genetic Feature Selection Based Automated Diagnosis of Breast Cancer

2020 ◽  
Vol 13 ◽  
Author(s):  
Indu Singh ◽  
Shashank Garg ◽  
Shivam Arora ◽  
Nikhil Arora ◽  
Kripali Agrawal
Keyword(s):  
Breast Cancer ◽  
Genetic Algorithm ◽  
Feature Selection ◽  
Early Stage ◽  
Feature Space ◽  
Breast Cancer Diagnosis ◽  
Support Vector ◽  
Svm Classifier ◽  
Human Beings ◽  
Novel Approach

Background: Breast cancer is the development of a malignant tumor in the breast of human beings (especially females). If not detected at the initial stages, it can substantially lead to an inoperable construct. It is a reason for majority of cancer-related deaths throughout the world. Objectives: The main aim of our study is to diagnose the breast cancer at early stage so that required treatment can be provided for survival. The tumor is classified as malignant or benign accurately at early stage using a novel approach that includes an ensemble of Genetic Algorithm for feature selection and kernel selection for SVM-Classifier. Methods: The proposed GA-SVM (Genetic Algorithm – Support Vector Machine) algorithm in this paper optimally selects the most appropriate features for training with the SVM classifier. Genetic Programming is used to select the features and the kernel for the SVM classifier. Genetic Algorithm operates by exploring the optimal layout of features for breast cancer, thus, subjugating the problems faced in exponentially immense feature space. Results: The proposed approach accounts for a mean accuracy of 98.82% by using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset available on UCI with the training and testing ratio being 50:50 respectively. Conclusion: The results prove that our proposed model outperforms the previously designed models for breast cancer diagnosis. The outcome assures that the GA-SVM model may be used as an effective tool in assisting the doctors for treating the patients. Alternatively, it may be utilized as an alternate opinion in their eventual diagnosis.

The role of Record of Rights (Khatian) in determining title or possession: An overview

IIUC Studies ◽  
2020 ◽  
Vol 15 ◽  
pp. 33-46
Author(s):  
Kalim Ullah
Keyword(s):  
Human Life ◽  
Vital Role ◽  
Critical Element ◽  
Human Beings ◽  
Supportive Evidence ◽  
Land Administration ◽  
Holy Quran ◽  
The Holy Quran ◽  
Religious Systems

Human beings are deeply related to land. Human beings take birth on land, live on land, die on land and mixes with land ultimately. As stated in the holy Quran: ‘We (Allah) created you (human beings) from the soil, we shall make you return to the soil and We shall call you back again from the soil’ (20:55). Human life is surrounded by soil i.e. land. So, land is a highly completed issue of human life involving economic, social, political, cultural and often religious systems. Land administration is thus a critical element and often a pre-condition for peaceful society and sustainable development. In administrating land, Khatian or record of rights plays a vital role to determine the rights and interests of the respective parties as supportive evidence. In this article, discussion is mainly made on the fact that Khatian or record of rights is not a document of title solely but it may be an evidence of title as well as possession. IIUC Studies Vol.15(0) December 2018: 33-46

Novel Approach of Improving Secondary Electron Detector in FIB System

2018 ◽  
Author(s):  
Steve Wang ◽  
Jim McGinn ◽  
Peter Tvarozek ◽  
Amir Weiss
Keyword(s):  
Integrated Circuit ◽  
Secondary Electron ◽  
Focused Ion Beam ◽  
Ion Beam ◽  
Vital Role ◽  
Electron Detector ◽  
System A ◽  
Novel Approach

Abstract Secondary electron detector (SED) plays a vital role in a focused ion beam (FIB) system. A successful circuit edit requires a good effective detector. Novel approach is presented in this paper to improve the performance of such a detector, making circuit altering for the most advanced integrated circuit (IC) possible.

Application of Machine Learning in Animal Disease Analysis and Prediction

2020 ◽  
Vol 15 ◽  
Author(s):  
Shuwen Zhang ◽  
Qiang Su ◽  
Qin Chen
Keyword(s):  
Machine Learning ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Clustering Algorithm ◽  
Principal Component ◽  
Support Vector ◽  
Animal Disease ◽  
Human Beings ◽  
Animal Diseases ◽  
Disease Analysis

Abstract: Major animal diseases pose a great threat to animal husbandry and human beings. With the deepening of globalization and the abundance of data resources, the prediction and analysis of animal diseases by using big data are becoming more and more important. The focus of machine learning is to make computers learn how to learn from data and use the learned experience to analyze and predict. Firstly, this paper introduces the animal epidemic situation and machine learning. Then it briefly introduces the application of machine learning in animal disease analysis and prediction. Machine learning is mainly divided into supervised learning and unsupervised learning. Supervised learning includes support vector machines, naive bayes, decision trees, random forests, logistic regression, artificial neural networks, deep learning, and AdaBoost. Unsupervised learning has maximum expectation algorithm, principal component analysis hierarchical clustering algorithm and maxent. Through the discussion of this paper, people have a clearer concept of machine learning and understand its application prospect in animal diseases.

scholarly journals Automated Confirmation of Protein Annotation Using NLP and the UniProtKB Database

2020 ◽  
Vol 11 (1) ◽  
pp. 24
Author(s):  
Jin Tao ◽  
Kelly Brayton ◽  
Shira Broschat
Keyword(s):  
Language Processing ◽  
Fine Tuning ◽  
Support Vector ◽  
Protein Annotation ◽  
Computing Power ◽  
Journal Publication ◽  
Novel Approach ◽  
Uniprotkb Database ◽  
Public Repositories ◽  
Annotation Errors

Advances in genome sequencing technology and computing power have brought about the explosive growth of sequenced genomes in public repositories with a concomitant increase in annotation errors. Many protein sequences are annotated using computational analysis rather than experimental verification, leading to inaccuracies in annotation. Confirmation of existing protein annotations is urgently needed before misannotation becomes even more prevalent due to error propagation. In this work we present a novel approach for automatically confirming the existence of manually curated information with experimental evidence of protein annotation. Our ensemble learning method uses a combination of recurrent convolutional neural network, logistic regression, and support vector machine models. Natural language processing in the form of word embeddings is used with journal publication titles retrieved from the UniProtKB database. Importantly, we use recall as our most significant metric to ensure the maximum number of verifications possible; results are reported to a human curator for confirmation. Our ensemble model achieves 91.25% recall, 71.26% accuracy, 65.19% precision, and an F1 score of 76.05% and outperforms the Bidirectional Encoder Representations from Transformers for Biomedical Text Mining (BioBERT) model with fine-tuning using the same data.

New Multi-View Classification Method with Uncertain Data

2021 ◽  
Vol 16 (1) ◽  
pp. 1-23
Author(s):  
Bo Liu ◽  
Haowen Zhong ◽  
Yanshan Xiao
Keyword(s):  
Learning Strategy ◽  
State Of The Art ◽  
Uncertain Data ◽  
Real Life ◽  
Support Vector ◽  
Classification Methods ◽  
Complementary Information ◽  
Novel Approach ◽  
Svm Model ◽  
Iterative Framework

Multi-view classification aims at designing a multi-view learning strategy to train a classifier from multi-view data, which are easily collected in practice. Most of the existing works focus on multi-view classification by assuming the multi-view data are collected with precise information. However, we always collect the uncertain multi-view data due to the collection process is corrupted with noise in real-life application. In this case, this article proposes a novel approach, called uncertain multi-view learning with support vector machine (UMV-SVM) to cope with the problem of multi-view learning with uncertain data. The method first enforces the agreement among all the views to seek complementary information of multi-view data and takes the uncertainty of the multi-view data into consideration by modeling reachability area of the noise. Then it proposes an iterative framework to solve the proposed UMV-SVM model such that we can obtain the multi-view classifier for prediction. Extensive experiments on real-life datasets have shown that the proposed UMV-SVM can achieve a better performance for uncertain multi-view classification in comparison to the state-of-the-art multi-view classification methods.

scholarly journals A Novel Approach to Oil Layer Recognition Model Using Whale Optimization Algorithm and Semi-Supervised SVM

Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 757
Author(s):  
Yongke Pan ◽  
Kewen Xia ◽  
Li Wang ◽  
Ziping He
Keyword(s):  
Optimization Algorithm ◽  
Oil And Gas ◽  
The Other ◽  
Whale Optimization Algorithm ◽  
Classification Model ◽  
Support Vector ◽  
Recognition Model ◽  
Novel Approach ◽  
Whale Optimization ◽  
Oil And Gas Reservoir

The dataset distribution of actual logging is asymmetric, as most logging data are unlabeled. With the traditional classification model, it is hard to predict the oil and gas reservoir accurately. Therefore, a novel approach to the oil layer recognition model using the improved whale swarm algorithm (WOA) and semi-supervised support vector machine (S3VM) is proposed in this paper. At first, in order to overcome the shortcomings of the Whale Optimization Algorithm applied in the parameter-optimization of the S3VM model, such as falling into a local optimization and low convergence precision, an improved WOA was proposed according to the adaptive cloud strategy and the catfish effect. Then, the improved WOA was used to optimize the kernel parameters of S3VM for oil layer recognition. In this paper, the improved WOA is used to test 15 benchmark functions of CEC2005 compared with five other algorithms. The IWOA–S3VM model is used to classify the five kinds of UCI datasets compared with the other two algorithms. Finally, the IWOA–S3VM model is used for oil layer recognition. The result shows that (1) the improved WOA has better convergence speed and optimization ability than the other five algorithms, and (2) the IWOA–S3VM model has better recognition precision when the dataset contains a labeled and unlabeled dataset in oil layer recognition.

scholarly journals The Value of Recreational Ecosystem Services in India

2020 ◽  
Vol 3 (1) ◽  
pp. 80
Author(s):  
Muniyandi Balasubramanian
Keyword(s):  
Ecosystem Services ◽  
National Park ◽  
Economic Value ◽  
Consumer Surplus ◽  
Well Being ◽  
Vital Role ◽  
Human Beings ◽  
Forest Ecosystem Services ◽  
The Individual ◽  
Mental Well Being

Forest ecosystem services have played a vital role in human well-being. Particularly, recreational ecosystem services are creating physical and mental well-being for human beings. Therefore, the main objective of the paper is to estimate the economic value of recreational ecosystem services provides by recreational sites such as Nandi Hills and Nagarhole National Park based on the individual travel cost method in Karnataka, India. This study has used a random sampling method for 300 tourist visitors to recreational sites. The present study has also estimated the consumer surplus of the visitors. The results of the study have found that (i) economic value of two creational sites has been estimated at US $323.05 million, (ii) the consumer surplus has been estimated for Nandi Hills at US $7.45 and Nagarhole National Park at US $3.16. The main implication of the study is to design the entry fees for the recreational site and sustainable utilization of recreational ecosystem services for the present and future generations.

scholarly journals Recognizing Human Races through Machine Learning—A Multi-Network, Multi-Features Study

Mathematics ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 195
Author(s):  
Adrian Sergiu Darabant ◽  
Diana Borza ◽  
Radu Danescu
Keyword(s):  
Race And Ethnicity ◽  
Large Scale ◽  
Recognition Rate ◽  
Social Signals ◽  
Image Characteristics ◽  
Race Effect ◽  
Race Ethnicity ◽  
The Impact ◽  
Facial Images ◽  
Ethnicity Classification

The human face holds a privileged position in multi-disciplinary research as it conveys much information—demographical attributes (age, race, gender, ethnicity), social signals, emotion expression, and so forth. Studies have shown that due to the distribution of ethnicity/race in training datasets, biometric algorithms suffer from “cross race effect”—their performance is better on subjects closer to the “country of origin” of the algorithm. The contributions of this paper are two-fold: (a) first, we gathered, annotated and made public a large-scale database of (over 175,000) facial images by automatically crawling the Internet for celebrities’ images belonging to various ethnicity/races, and (b) we trained and compared four state of the art convolutional neural networks on the problem of race and ethnicity classification. To the best of our knowledge, this is the largest, data-balanced, publicly-available face database annotated with race and ethnicity information. We also studied the impact of various face traits and image characteristics on the race/ethnicity deep learning classification methods and compared the obtained results with the ones extracted from psychological studies and anthropomorphic studies. Extensive tests were performed in order to determine the facial features to which the networks are sensitive to. These tests and a recognition rate of 96.64% on the problem of human race classification demonstrate the effectiveness of the proposed solution.

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