Fish Classification Based on Robust Features Selection Using Machine Learning Techniques

2015 ◽  
pp. 237-245 ◽  
Author(s):  
Than Thida Hnin ◽  
Khin Thidar Lynn
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Features Selection ◽  
Learning Techniques

scholarly journals Multirate Processing with Selective Subbands and Machine Learning for Efficient Arrhythmia Classification

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1511
Author(s):  
Saeed Mian Qaisar ◽  
Alaeddine Mihoub ◽  
Moez Krichen ◽  
Humaira Nisar
Keyword(s):  
Machine Learning ◽  
Signal Reconstruction ◽  
Computational Cost ◽  
Machine Learning Techniques ◽  
Features Selection ◽  
Frequency Content ◽  
Fixed Rate ◽  
Ecg Signals ◽  
Learning Techniques ◽  
Multirate Processing

The usage of wearable gadgets is growing in the cloud-based health monitoring systems. The signal compression, computational and power efficiencies play an imperative part in this scenario. In this context, we propose an efficient method for the diagnosis of cardiovascular diseases based on electrocardiogram (ECG) signals. The method combines multirate processing, wavelet decomposition and frequency content-based subband coefficient selection and machine learning techniques. Multirate processing and features selection is used to reduce the amount of information processed thus reducing the computational complexity of the proposed system relative to the equivalent fixed-rate solutions. Frequency content-dependent subband coefficient selection enhances the compression gain and reduces the transmission activity and computational cost of the post cloud-based classification. We have used MIT-BIH dataset for our experiments. To avoid overfitting and biasness, the performance of considered classifiers is studied by using five-fold cross validation (5CV) and a novel proposed partial blind protocol. The designed method achieves more than 12-fold computational gain while assuring an appropriate signal reconstruction. The compression gain is 13 times compared to fixed-rate counterparts and the highest classification accuracies are 97.06% and 92.08% for the 5CV and partial blind cases, respectively. Results suggest the feasibility of detecting cardiac arrhythmias using the proposed approach.

scholarly journals Video spam comment features selection using machine learning techniques

2019 ◽  
Vol 15 (2) ◽  
pp. 1046
Author(s):  
Nabilah Alias ◽  
Cik Feresa Mohd Foozy ◽  
Sofia Najwa Ramli ◽  
Naqliyah Zainuddin
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Random Forest ◽  
Feature Detection ◽  
Random Tree ◽  
Machine Learning Techniques ◽  
Decision Table ◽  
Features Selection ◽  
Video Sharing ◽  
Learning Techniques

<p>Nowadays, social media (e.g., YouTube and Facebook) provides connection and interaction between people by posting comments or videos. In fact, comments are a part of contents in a website that can attract spammer to spreading phishing, malware or advertising. Due to existing malicious users that can spread malware or phishing in the comments, this work proposes a technique used for video sharing spam comments feature detection. The first phase of the methodology used in this work is dataset collection. For this experiment, a dataset from UCI Machine Learning repository is used. In the next phase, the development of framework and experimentation. The dataset will be pre-processed using tokenization and lemmatization process. After that, the features to detect spam is selected and the experiments for classification were performed by using six classifiers which are Random Tree, Random Forest, Naïve Bayes, KStar, Decision Table, and Decision Stump. The result shows the highest accuracy is 90.57% and the lowest was 58.86%.</p>

scholarly journals Modeling and Forecasting Gender-Based Violence through Machine Learning Techniques

2020 ◽  
Vol 10 (22) ◽  
pp. 8244
Author(s):  
Ignacio Rodríguez-Rodríguez ◽  
José-Víctor Rodríguez ◽  
Domingo-Javier Pardo-Quiles ◽  
Purificación Heras-González ◽  
Ioannis Chatzigiannakis
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Features Selection ◽  
Gender Based Violence ◽  
Predictive Algorithm ◽  
Learning Techniques ◽  
Predictive Algorithms ◽  
Selection Of Variables ◽  
Different Populations ◽  
Gender Based

Gender-Based Violence (GBV) is a serious problem that societies and governments must address using all applicable resources. This requires adequate planning in order to optimize both resources and budget, which demands a thorough understanding of the magnitude of the problem, as well as analysis of its past impact in order to infer future incidence. On the other hand, for years, the rise of Machine Learning techniques and Big Data has led different countries to collect information on both GBV and other general social variables that in one way or another can affect violence levels. In this work, in order to forecast GBV, firstly, a database of features related to more than a decade’s worth of GBV is compiled and prepared from official sources available due to Spain’s open access. Then, secondly, a methodology is proposed that involves testing different methods of features selection so that, with each of the subsets generated, four techniques of predictive algorithms are applied and compared. The tests conducted indicate that it is possible to predict the number of GBV complaints presented to a court at a predictive horizon of six months with an accuracy (Root Median Squared Error) of 0.1686 complaints to the courts per 10,000 inhabitants—throughout the whole Spanish territory—with a Multi-Objective Evolutionary Search Strategy for the selection of variables, and with Random Forest as the predictive algorithm. The proposed methodology has also been successfully applied to three specific Spanish territories of different populations (large, medium, and small), pointing to the presented method’s possible use elsewhere in the world.

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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