scholarly journals Ecg Heartbeat Classification: Conceptual Understanding through Cnn & Rnn – A Machine Learning Approach

2020 ◽  
Vol 10 (2) ◽  
pp. 143-147
Author(s):  
P. Rama Santosh Naidu ◽  
K.Venkata Ramana ◽  
G. Lavanya Devi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Dominant Role ◽  
Machine Learning Techniques ◽  
Heartbeat Classification ◽  
Ecg Signals ◽  
The Past ◽  
Learning Techniques ◽  
Machine Learning Approach

In recent days Machine Learning has become major study aspect in various applications that includes medical care where convenient discovery of anomalies in ECG signals plays an important role in monitoring patient's condition regularly. This study concentrates on various MachineLearning techniques applied for classification of ECG signals which include CNN and RNN. In the past few years, it is being observed that CNN is playing a dominant role in feature extraction from which we can infer that machine learning techniques have been showing accuracy and progress in classification of ECG signals. Therefore, this paper includes Convolutional Neural Network and Recurrent Neural Network which is being classified into two types for better results from considerably increased depth.

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.

scholarly journals Machine Learning Techniques for Land Use/Land Cover Classification of Medium Resolution Optical Satellite Imagery Focusing on Temporary Inundated Areas

2020 ◽  
Vol 13 (1-2) ◽  
pp. 43-52
Author(s):  
Boudewijn van Leeuwen ◽  
Zalán Tobak ◽  
Ferenc Kovács
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Land Use ◽  
Land Cover ◽  
Classification Model ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Land Use Land Cover ◽  
Learning Techniques

AbstractClassification of multispectral optical satellite data using machine learning techniques to derive land use/land cover thematic data is important for many applications. Comparing the latest algorithms, our research aims to determine the best option to classify land use/land cover with special focus on temporary inundated land in a flat area in the south of Hungary. These inundations disrupt agricultural practices and can cause large financial loss. Sentinel 2 data with a high temporal and medium spatial resolution is classified using open source implementations of a random forest, support vector machine and an artificial neural network. Each classification model is applied to the same data set and the results are compared qualitatively and quantitatively. The accuracy of the results is high for all methods and does not show large overall differences. A quantitative spatial comparison demonstrates that the neural network gives the best results, but that all models are strongly influenced by atmospheric disturbances in the image.

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 Nationality swapping in the Olympic Games 1978–2017: A supervised machine learning approach to analysing discourses of citizenship and nationhood

2018 ◽  
Vol 54 (8) ◽  
pp. 971-988
Author(s):  
Joost Jansen
Keyword(s):  
Machine Learning ◽  
English Language ◽  
State Of The Art ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Olympic Athletes ◽  
The Past ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Media Reports

While the practice of nationality swapping in sports traces back as far as the Ancient Olympics, it seems to have increased over the past decades. Cases of Olympic athletes who switched their national allegiances are often surrounded with controversy. Two strands of thought could help explain this controversy. First, these cases are believed to be indicative of the marketisation of citizenship. Second, these cases challenge established discourses of national identity as the question ‘who may represent the nation?’ becomes contested. Using state-of-the-art machine learning techniques, I analysed 1534 English language newspaper articles about Olympic athletes who changed their nationalities (1978–2017). The results indicate: (i) that switching national allegiance has not necessarily become more controversial; (ii) that most media reports do not frame nationality switching in economic terms; and (iii) that nationality swapping often occurs fairly unnoticed. I therefore conclude that a marketisation of citizenship is less apparent in nationality switching than some claim. Moreover, nationality switches are often mentioned rather casually, indicating the generally banal character of nationalism. Only under certain conditions does ‘hot’ nationalism spark the issue of nationhood.

scholarly journals Machine Learning Methods to Classify Mushrooms for Edibility-A Review

2020 ◽  
Vol 06 (09) ◽  
pp. 54-58
Author(s):  
Rakesh Kumar Y and Dr. V. Chandrasekhar
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Literature Review ◽  
Edible Mushroom ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Applied Machine Learning ◽  
Learning Techniques ◽  
Artificial Neural Network Ann

There are thousands of species of Mushrooms in the world; they are edible and non-edible being poisonous. It is difficult for non-expertise person to Identify poisonous and edible mushroom of all the species manually. So a computer aided system with software or algorithm is required to classify poisonous and nonpoisonous mushrooms. In this paper a literature review is presented on classification of poisonous and nonpoisonous mushrooms. Most of the research works to classify the type of mushroom have applied, machine learning techniques like Naïve Bayes, K-Neural Network, Support vector Machine(SVM), Artificial Neural Network(ANN), Decision Tree techniques. In this literature review, a summary and comparisons of all different techniques of mushroom classification in terms of its performance parameters, merits and demerits faced during the classification of mushrooms using machine learning techniques.

scholarly journals Sentiment Analysis using LSTM

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1338-1340
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Sentiment Analysis ◽  
Machine Learning Techniques ◽  
Learning Approach ◽  
Problem Statement ◽  
The Neural Network ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Area Of Concern

Extracting the sentiment of the text using machine learning techniques like LSTM is our area of concern. Classifying the movie reviews using LSTM is our problem statement. The reviews dataset is taken from the IMDB movie review dataset. Here we will classify a review based on the memory in the neural network of a LSTM cell state. Movie reviews often contain sensible content which describe the movie. We can manually decide whether a movie is good or bad by going through these reviews. Using machine learning approach we are classifying the movie reviews such that we can say that a movie is good or bad. LSTM is effective than many other techniques like RNN and CNN.

scholarly journals A Machine-Learning Approach to Distinguish Passengers and Drivers Reading While Driving

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3174 ◽  
Author(s):  
Renato Torres ◽  
Orlando Ohashi ◽  
Gustavo Pessin
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Traffic Accidents ◽  
Driver Distraction ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Learning Approach ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Smartphone Sensors

Driver distraction is one of the major causes of traffic accidents. In recent years, given the advance in connectivity and social networks, the use of smartphones while driving has become more frequent and a serious problem for safety. Texting, calling, and reading while driving are types of distractions caused by the use of smartphones. In this paper, we propose a non-intrusive technique that uses only data from smartphone sensors and machine learning to automatically distinguish between drivers and passengers while reading a message in a vehicle. We model and evaluate seven cutting-edge machine-learning techniques in different scenarios. The Convolutional Neural Network and Gradient Boosting were the models with the best results in our experiments. Results show accuracy, precision, recall, F1-score, and kappa metrics superior to 0.95.

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.

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