scholarly journals Traffic Congestion Detection from Camera Images using Deep Convolution Neural Networks

2018 ◽  
Vol 2672 (45) ◽  
pp. 222-231 ◽  
Author(s):  
Pranamesh Chakraborty ◽  
Yaw Okyere Adu-Gyamfi ◽  
Subhadipto Poddar ◽  
Vesal Ahsani ◽  
Anuj Sharma ◽  
...  
Keyword(s):  
Deep Learning ◽  
Traffic Congestion ◽  
Operating Characteristic ◽  
Support Vector ◽  
Receiver Operating Characteristic Curves ◽  
Radar Sensors ◽  
Congestion Detection ◽  
Learning Techniques ◽  
Deep Convolution Neural Network ◽  
Data Source

Recent improvements in machine vision algorithms have led to closed-circuit television (CCTV) cameras emerging as an important data source for determining of the state of traffic congestion. In this study we used two different deep learning techniques, you only look once (YOLO) and deep convolution neural network (DCNN), to detect traffic congestion from camera images. The support vector machine (SVM), a shallow algorithm, was also used as a comparison to determine the improvements obtained using deep learning algorithms. Occupancy data from nearby radar sensors were used to label congested images in the dataset and for training the models. YOLO and DCCN achieved 91.5% and 90.2% accuracy, respectively, whereas SVM’s accuracy was 85.2%. Receiver operating characteristic curves were used to determine the sensitivity of the models with regard to different camera configurations, light conditions, and so forth. Although poor camera conditions at night affected the accuracy of the models, the areas under the curve from the deep models were found to be greater than 0.9 for all conditions. This shows that the models can perform well in challenging conditions as well.

scholarly journals Deep learning para la clasificación de usos de suelo agrícola con Sentinel-2

2020 ◽  
pp. 35
Author(s):  
M. Campos-Taberner ◽  
F.J. García-Haro ◽  
B. Martínez ◽  
M.A. Gilabert
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Near Infrared ◽  
Spatial Information ◽  
Learning Algorithm ◽  
Support Vector ◽  
Sensing Applications ◽  
Learning Techniques ◽  
Remote Sensing Applications ◽  
Sentinel 2

<p class="p1">The use of deep learning techniques for remote sensing applications has recently increased. These algorithms have proven to be successful in estimation of parameters and classification of images. However, little effort has been made to make them understandable, leading to their implementation as “black boxes”. This work aims to evaluate the performance and clarify the operation of a deep learning algorithm, based on a bi-directional recurrent network of long short-term memory (2-BiLSTM). The land use classification in the Valencian Community based on Sentinel-2 image time series in the framework of the common agricultural policy (CAP) is used as an example. It is verified that the accuracy of the deep learning techniques is superior (98.6 % overall success) to that other algorithms such as decision trees (DT), k-nearest neighbors (k-NN), neural networks (NN), support vector machines (SVM) and random forests (RF). The performance of the classifier has been studied as a function of time and of the predictors used. It is concluded that, in the study area, the most relevant information used by the network in the classification are the images corresponding to summer and the spectral and spatial information derived from the red and near infrared bands. These results open the door to new studies in the field of the explainable deep learning in remote sensing applications.</p>

scholarly journals Sentiment Analysis using various Machine Learning and Deep Learning Techniques

2021 ◽  
pp. 385-394
Author(s):  
V Umarani ◽  
A Julian ◽  
J Deepa
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Analysis Process ◽  
Learning Techniques

Sentiment analysis has gained a lot of attention from researchers in the last year because it has been widely applied to a variety of application domains such as business, government, education, sports, tourism, biomedicine, and telecommunication services. Sentiment analysis is an automated computational method for studying or evaluating sentiments, feelings, and emotions expressed as comments, feedbacks, or critiques. The sentiment analysis process can be automated using machine learning techniques, which analyses text patterns faster. The supervised machine learning technique is the most used mechanism for sentiment analysis. The proposed work discusses the flow of sentiment analysis process and investigates the common supervised machine learning techniques such as multinomial naive bayes, Bernoulli naive bayes, logistic regression, support vector machine, random forest, K-nearest neighbor, decision tree, and deep learning techniques such as Long Short-Term Memory and Convolution Neural Network. The work examines such learning methods using standard data set and the experimental results of sentiment analysis demonstrate the performance of various classifiers taken in terms of the precision, recall, F1-score, RoC-Curve, accuracy, running time and k fold cross validation and helps in appreciating the novelty of the several deep learning techniques and also giving the user an overview of choosing the right technique for their application.

scholarly journals Deep Learning Assisted Neonatal Cry Classification via Support Vector Machine Models

2021 ◽  
Vol 9 ◽  
Author(s):  
Ashwini K ◽  
P. M. Durai Raj Vincent ◽  
Kathiravan Srinivasan ◽  
Chuan-Yu Chang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Support Vector ◽  
Svm Classifier ◽  
Infant Cry ◽  
Learning Techniques

Neonatal infants communicate with us through cries. The infant cry signals have distinct patterns depending on the purpose of the cries. Preprocessing, feature extraction, and feature selection need expert attention and take much effort in audio signals in recent days. In deep learning techniques, it automatically extracts and selects the most important features. For this, it requires an enormous amount of data for effective classification. This work mainly discriminates the neonatal cries into pain, hunger, and sleepiness. The neonatal cry auditory signals are transformed into a spectrogram image by utilizing the short-time Fourier transform (STFT) technique. The deep convolutional neural network (DCNN) technique takes the spectrogram images for input. The features are obtained from the convolutional neural network and are passed to the support vector machine (SVM) classifier. Machine learning technique classifies neonatal cries. This work combines the advantages of machine learning and deep learning techniques to get the best results even with a moderate number of data samples. The experimental result shows that CNN-based feature extraction and SVM classifier provides promising results. While comparing the SVM-based kernel techniques, namely radial basis function (RBF), linear and polynomial, it is found that SVM-RBF provides the highest accuracy of kernel-based infant cry classification system provides 88.89% accuracy.

A Study on Brain Tumor Detection and Segmentation Using Deep Learning Techniques

2020 ◽  
Vol 17 (4) ◽  
pp. 1925-1930
Author(s):  
Ambeshwar Kumar ◽  
R. Manikandan ◽  
Robbi Rahim
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Brain Tumor ◽  
Tumor Detection ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques ◽  
Convolution Algorithm ◽  
The Brain

It’s a new era technology in the field of medical engineering giving awareness about the various healthcare features. Deep learning is a part of machine learning, it is capable of handling high dimensional data and is efficient in concentrating on the right features. Tumor is an unbelievably complex disease: a multifaceted cell has more than hundred billion cells; each cell acquires mutation exclusively. Detection of tumor particles in experiment is easily done by MRI or CT. Brain tumors can also be detected by MRI, however, deep learning techniques give a better approach to segment the brain tumor images. Deep Learning models are imprecisely encouraged by information handling and communication designs in biological nervous system. Classification plays an significant role in brain tumor detection. Neural network is creating a well-organized rule for classification. To accomplish medical image data, neural network is trained to use the Convolution algorithm. Multilayer perceptron is intended for identification of a image. In this study article, the brain images are categorized into two types: normal and abnormal. This article emphasize the importance of classification and feature selection approach for predicting the brain tumor. This classification is done by machine learning techniques like Artificial Neural Networks, Support Vector Machine and Deep Neural Network. It could be noted that more than one technique can be applied for the segmentation of tumor. The several samples of brain tumor images are classified using deep learning algorithms, convolution neural network and multi-layer perceptron.

scholarly journals Human Activity Recognition Based on Deep Learning Techniques

Proceedings ◽  
2019 ◽  
Vol 42 (1) ◽  
pp. 15
Author(s):  
Manuel Gil-Martín ◽  
Marcos Sánchez-Hernández ◽  
Rubén San-Segundo
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Deep Neural Network ◽  
Human Activity Recognition ◽  
Support Vector ◽  
Daily Life Activities ◽  
Learning Techniques ◽  
The Fourier Transform

Deep learning techniques are being widely applied to Human Activity Recognition (HAR). This paper describes the implementation and evaluation of a HAR system for daily life activities using the accelerometer of an iPhone 6S. This system is based on a deep neural network including convolutional layers for feature extraction from accelerations and fully-connected layers for classification. Different transformations have been applied to the acceleration signals in order to find the appropriate input data to the deep neural network. This study has used acceleration recordings from the MotionSense dataset, where 24 subjects performed 6 activities: walking downstairs, walking upstairs, sitting, standing, walking and jogging. The evaluation has been performed using a subject-wise cross-validation: recordings from the same subject do not appear in training and testing sets at the same time. The proposed system has obtained a 9% improvement in accuracy compared to the baseline system based on Support Vector Machines. The best results have been obtained using raw data as input to a deep neural network composed of two convolutional and two max-pooling layers with decreasing kernel sizes. Results suggest that using the module of the Fourier transform as inputs provides better results when classifying only between dynamic activities.

scholarly journals Vehicular Traffic Congestion Classification by Visual Features and Deep Learning Approaches: A Comparison

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5213 ◽  
Author(s):  
Donato Impedovo ◽  
Fabrizio Balducci ◽  
Vincenzo Dentamaro ◽  
Giuseppe Pirlo
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Traffic Flow ◽  
Traffic Congestion ◽  
Machine Learning Techniques ◽  
Visual Features ◽  
Learning Approaches ◽  
Traffic State ◽  
State Classification ◽  
Learning Techniques

Automatic traffic flow classification is useful to reveal road congestions and accidents. Nowadays, roads and highways are equipped with a huge amount of surveillance cameras, which can be used for real-time vehicle identification, and thus providing traffic flow estimation. This research provides a comparative analysis of state-of-the-art object detectors, visual features, and classification models useful to implement traffic state estimations. More specifically, three different object detectors are compared to identify vehicles. Four machine learning techniques are successively employed to explore five visual features for classification aims. These classic machine learning approaches are compared with the deep learning techniques. This research demonstrates that, when methods and resources are properly implemented and tested, results are very encouraging for both methods, but the deep learning method is the most accurately performing one reaching an accuracy of 99.9% for binary traffic state classification and 98.6% for multiclass classification.

scholarly journals Sentiment Analysis of Lithuanian Texts Using Traditional and Deep Learning Approaches

Computers ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 4 ◽  
Author(s):  
Jurgita Kapočiūtė-Dzikienė ◽  
Robertas Damaševičius ◽  
Marcin Woźniak
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Short Term Memory ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Approaches ◽  
Full Dataset ◽  
Learning Techniques ◽  
Long Short Term Memory

We describe the sentiment analysis experiments that were performed on the Lithuanian Internet comment dataset using traditional machine learning (Naïve Bayes Multinomial—NBM and Support Vector Machine—SVM) and deep learning (Long Short-Term Memory—LSTM and Convolutional Neural Network—CNN) approaches. The traditional machine learning techniques were used with the features based on the lexical, morphological, and character information. The deep learning approaches were applied on the top of two types of word embeddings (Vord2Vec continuous bag-of-words with negative sampling and FastText). Both traditional and deep learning approaches had to solve the positive/negative/neutral sentiment classification task on the balanced and full dataset versions. The best deep learning results (reaching 0.706 of accuracy) were achieved on the full dataset with CNN applied on top of the FastText embeddings, replaced emoticons, and eliminated diacritics. The traditional machine learning approaches demonstrated the best performance (0.735 of accuracy) on the full dataset with the NBM method, replaced emoticons, restored diacritics, and lemma unigrams as features. Although traditional machine learning approaches were superior when compared to the deep learning methods; deep learning demonstrated good results when applied on the small datasets.

scholarly journals Classification of fruits and vegetables using ResNet model.

2021 ◽  
Author(s):  
P. Sukhetha ◽  
N. Hemalatha ◽  
Raji Sukumar
Keyword(s):  
Deep Learning ◽  
Fruits And Vegetables ◽  
Nearest Neighbor ◽  
Support Vector ◽  
Agricultural Field ◽  
K Nearest Neighbor ◽  
New Techniques ◽  
Learning Techniques ◽  
A Current

Abstract Agriculture is one of the important parts of Indian economy. Agricultural field has more contribution towards growth and stability of the nation. Therefore, a current technologies and innovations can help in order to experiment new techniques and methods in the agricultural field. At Present Artificial Intelligence (AI) is one of the main, effective, and widely used technology. Especially, Deep Learning (DL) has numerous functions due to its capability to learn robust interpretations from images. Convolutional Neural Networks (CNN) is the major Deep Learning architecture for image classification. This paper is mainly focus on the deep learning techniques to classify Fruits and Vegetables, the model creation and implementation to identify Fruits and Vegetables on the fruit360 dataset. The models created are Support Vector Machine (SVM), K Nearest Neighbor (KNN), Decision Tree (DT), ResNet Pretrained Model, Convolutional Neural Network (CNN), Multilayer Perceptron (MLP). Among the different models ResNet pretrained Model performed the best with an accuracy of 95.83%.

scholarly journals Airline Delay Prediction using Machine Learning and Deep Learning Techniques

2020 ◽  
Vol 9 (2) ◽  
pp. 1049-1054
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Random Forest ◽  
Short Term Memory ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Maximum Delay ◽  
Learning Techniques ◽  
Delay Prediction

In this paper, we have tried to predict flight delays using different machine learning and deep learning techniques. By using such a model it can be easier to predict whether the flight will be delayed or not. Factors like ‘WeatherDelay’, ‘NASDelay’, ‘Destination’, ‘Origin’ play a vital role in this model. Using machine learning algorithms like Random Forest, Support Vector Machine (SVM) and K-Nearest Neighbors (KNN), the f1-score, precision, recall, support and accuracy have been predicted. To add to the model, Long Short-Term Memory (LSTM) RNN architecture has also been employed. In the paper, the dataset from Bureau of Transportation Statistics (BTS) of the ‘Pittsburgh’ is being used. The results computed from the above mentioned algorithms have been compared. Further, the results were visualized for various airlines to find maximum delay and AUC-ROC curve has been plotted for Random Forest Algorithm. The aim of our research work is to predict the delay so as to minimize loses and increase customer satisfaction.

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