scholarly journals Detection of duplicate and non-face images in the eRecruitment applications using machine learning techniques

2021 ◽  
Vol 10 (2) ◽  
pp. 114
Author(s):  
Manjunath K. E. ◽  
Yogeen S. Honnavar ◽  
Rakesh Pritmani ◽  
Sethuraman K.
Keyword(s):  
Machine Learning ◽  
Detection Algorithm ◽  
Similarity Score ◽  
Machine Learning Techniques ◽  
Human Face ◽  
Face Images ◽  
Research Organisation ◽  
Learning Techniques ◽  
Satellite Centre ◽  
Recruitment Activities

The objective of this work is to develop methodologies to detect, and report the noncompliant images with respect to indian space research organisation (ISRO) recruitment requirements. The recruitment software hosted at U. R. rao satellite centre (URSC) is responsible for handling recruitment activities of ISRO. Large number of online applications are received for each post advertised. In many cases, it is observed that the candidates are uploading either wrong or non-compliant images of the required documents. By non-compliant images, we mean images which do not have faces or there is not enough clarity in the faces present in the images uploaded. In this work, we attempt to address two specific problems namely: 1) To recognise image uploaded to recruitment portal contains a human face or not. This is addressed using a face detection algorithm. 2) To check whether images uploaded by two or more applications are same or not. This is achieved by using machine learning (ML) algorithms to generate similarity score between two images, and then identify the duplicate images. Screening of valid applications becomes very challenging as the verification of such images using a manual process is very time consuming and requires large human efforts. Hence, we propose novel ML techniques to determine duplicate and non-face images in the applications received by the recruitment portal.

scholarly journals Disease Identification in Chilli Leaves using Machine Learning Techniques

2019 ◽  
Vol 9 (1S3) ◽  
pp. 325-329
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Detection System ◽  
Network Models ◽  
Detection Algorithm ◽  
Machine Learning Techniques ◽  
Neural Network Models ◽  
The Past ◽  
Learning Techniques ◽  
Proposed Model

Crop diseases reduce the yield of the crop or may even kill it. Over the past two years, as per the I.C.A.R, the production of chilies in the state of Goa has reduced drastically due to the presence of virus. Most of the plants flower very less or stop flowering completely. In rare cases when a plant manages to flower, the yield is substantially low. Proposed model detects the presence of disease in crops by examining the symptoms. The model uses an object detection algorithm and supervised image recognition and feature extraction using convolutional neural network to classify crops as infected or healthy. Google machine learning libraries, TensorFlow and Keras are used to build neural network models. An Android application is developed around the model for the ease of using the disease detection system.

scholarly journals Fast and accurate primary user detection with machine learning techniques for cognitive radio networks

2021 ◽  
Vol 21 (1) ◽  
pp. 472
Author(s):  
G. A. Pethunachiyar ◽  
B. Sankaragomathi
Keyword(s):  
Machine Learning ◽  
Spectrum Sensing ◽  
Orthogonal Frequency Division Multiplexing ◽  
Signal To Noise Ratio ◽  
Energy Detection ◽  
Primary User ◽  
Detection Algorithm ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques

<p class="IJASEITAbtract"><span>Spectrum decision is an important and crucial task for the secondary user to avail the unlicensed spectrum for transmission. Managing the spectrum is an efficient one for spectrum sensing. Determining the primary user presence in the spectrum is an essential work for using the licensed spectrum of primary user. The information which lacks in managing the spectrum are the information about the primary user presence, accuracy in determining the existence of user in the spectrum, the cost for computation and difficult in finding the user in low signal-to noise ratio (SNR) values. The proposed system overcomes the above limitations. In the proposed system, the various techniques of machine learning like decision tree, support vector machines, naive bayes, ensemble based trees, nearest neighbour’s and logistic regression are used for testing the algorithm. As a first step, the spectrum sensing is done in two stages with Orthogonal Frequency Division Multiplexing and Energy Detection algorithm at the various values of SNR. The results generated from the above algorithm is used for database generation. Next, the different machine learning techniques are trained and compared for the results produced by different algorithms with the characteristics like speed, time taken for training and accuracy in prediction. The accuracy and finding the presence of the user in the spectrum at low SNR values are achieved by all the algorithms. The computation cost of the algorithm differs from each other. Among the tested techniques, k-nearest neighbour (KNN) algorithm produces the better performance in a minimized time.</span></p>

Novel Cardiac Arrhythmia Processing using Machine Learning Techniques

2020 ◽  
Vol 20 (03) ◽  
pp. 2050023
Author(s):  
Navdeep Prashar ◽  
Meenakshi Sood ◽  
Shruti Jain
Keyword(s):  
Machine Learning ◽  
Optimization Technique ◽  
Detection Algorithm ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Ecg Signal ◽  
Incorrect Decision ◽  
Ecg Signals ◽  
Learning Techniques ◽  
Peak Detection Algorithm

Electrocardiography (ECG) signals provides assistance to the cardiologists for identification of various cardiovascular diseases (CVD). ECG machine records the electrical activity of the heart with the assistance of electrodes placed on the patient’s body. Qualitative characterization of ECG signal reflects its sensitiveness towards distinct artifacts that resulted in low diagnostic accuracy and may lead to incorrect decision of the clinician. The artifacts are removed utilizing a robust noise estimator employing DTCWT using various threshold values and functions. The segments and intervals of ECG signals are calculated using the peak detection algorithm followed by particle swarm optimization (PSO) and the proposed optimization technique to select the best features from a considerable pool of features. Out of the 12 features, the best four features are selected using PSO and the proposed optimization technique. Comparative analysis with other feature selection methods and state-of-the-art techniques demonstrated that the proposed algorithm precisely selects principle features for handling the ECG signal and attains better classification utilizing distinctive machine learning algorithms. The obtained accuracy using our proposed optimization technique is 95.71% employing [Formula: see text]-NN and neural networks. Also, 4% and 10% improvements have been observed while using [Formula: see text]-NN over ANN and SVM, respectively, when the PSO technique is executed. Similarly, a 14.16% improvement is achieved while using [Formula: see text]-NN and ANN over the SVM machine learning technique for the proposed optimization technique. Heart rate is calculated using the proposed estimator and optimization technique, which is in consensus with the gold standard.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

A Brief Survey on Text Classification Using Various Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Padmavathi .S ◽  
M. Chidambaram
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Fixed Number ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Rule Based ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

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