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

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.

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Sentiment Analysis of Lithuanian Texts Using Traditional and Deep Learning Approaches

Computers ◽

10.3390/computers8010004 ◽

2019 ◽

Vol 8 (1) ◽

pp. 4 ◽

Cited By ~ 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.

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Iterative machine learning applied to annotation of text datasets

10.5753/eniac.2021.18268 ◽

2021 ◽

Author(s):

Thiago Abdo ◽

Fabiano Silva

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Learning Algorithms ◽

Computational Cost ◽

Machine Learning Techniques ◽

Learning Approaches ◽

Learning Techniques ◽

The Creation ◽

The Impact ◽

High Computational Cost

The purpose of this paper is to analyze the use of different machine learning approaches and algorithms to be integrated as an automated assistance on a tool to aid the creation of new annotated datasets. We evaluate how they scale in an environment without dedicated machine learning hardware. In particular, we study the impact over a dataset with few examples and one that is being constructed. We experiment using deep learning algorithms (Bert) and classical learning algorithms with a lower computational cost (W2V and Glove combined with RF and SVM). Our experiments show that deep learning algorithms have a performance advantage over classical techniques. However, deep learning algorithms have a high computational cost, making them inadequate to an environment with reduced hardware resources. Simulations using Active and Iterative machine learning techniques to assist the creation of new datasets are conducted. For these simulations, we use the classical learning algorithms because of their computational cost. The knowledge gathered with our experimental evaluation aims to support the creation of a tool for building new text datasets.

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Deep Learning Approaches for Protein Structure Prediction

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i4.5.20037 ◽

2018 ◽

Vol 7 (4.5) ◽

pp. 168

Author(s):

Khatri Chandni ◽

Prof. Mrudang Pandya ◽

Dr. Sunil Jardosh

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Protein Structure ◽

Protein Structure Prediction ◽

Structure Prediction ◽

Machine Learning Techniques ◽

Great Promise ◽

Learning Approaches ◽

Learning Networks ◽

Learning Techniques

In recent years, Machine Learning techniques that are based on Deep Learning networks that show a great promise in research communities.Successful methods for deep learning involve Artificial Neural Networks and Machine Learning. Deep Learning solves severa problems in bioinformatics. Protein Structure Prediction is one of the most important fields that can be solving using Deep Learning approaches.These protein are categorized on basis of occurrence of amino acid patterns occur to extract the feature. In these paper aimed to review work based on protein structure prediction solve using Deep Learning Networks. Objective is to review motivate and facilitatethese deep learn the network for predicting protein sequences using Deep Learning.

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A Systematic Review of Deep Learning Approaches to Educational Data Mining

Complexity ◽

10.1155/2019/1306039 ◽

2019 ◽

Vol 2019 ◽

pp. 1-22 ◽

Cited By ~ 15

Author(s):

Antonio Hernández-Blanco ◽

Boris Herrera-Flores ◽

David Tomás ◽

Borja Navarro-Colorado

Keyword(s):

Machine Learning ◽

Data Mining ◽

Deep Learning ◽

Language Processing ◽

Educational Data Mining ◽

Research Field ◽

Machine Learning Techniques ◽

Mining Machine ◽

Learning Approaches ◽

Learning Techniques

Educational Data Mining (EDM) is a research field that focuses on the application of data mining, machine learning, and statistical methods to detect patterns in large collections of educational data. Different machine learning techniques have been applied in this field over the years, but it has been recently that Deep Learning has gained increasing attention in the educational domain. Deep Learning is a machine learning method based on neural network architectures with multiple layers of processing units, which has been successfully applied to a broad set of problems in the areas of image recognition and natural language processing. This paper surveys the research carried out in Deep Learning techniques applied to EDM, from its origins to the present day. The main goals of this study are to identify the EDM tasks that have benefited from Deep Learning and those that are pending to be explored, to describe the main datasets used, to provide an overview of the key concepts, main architectures, and configurations of Deep Learning and its applications to EDM, and to discuss current state-of-the-art and future directions on this area of research.

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Analysis On Deep Learning Approaches For Timely Detection Of Osteoarthritis

Turkish Journal of Computer and Mathematics Education (TURCOMAT) ◽

10.17762/turcomat.v12i3.1818 ◽

2021 ◽

Vol 12 (3) ◽

pp. 4396-4405

Author(s):

R Kanthavel Et.al

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Elastic Tissue ◽

Learning Approaches ◽

X Rays ◽

Imaging Analysis ◽

Bone Injury ◽

Learning Techniques

Osteoarthritis is mainly a familiar kind of arthritis when an elastic tissue named Cartilage that softens the tops of the bones, cracks down. The Person with osteoarthritis can encompass joint pain, inflexibility, or inflammation and there is no particular examination for osteoarthritis and physicians take the amalgamation of both medical cum clinical record and X-rays imaging analysis to make a diagnosis of the state. Osteoarthritis is generally only detected following ache and bone scratch and in advance, analysis could permit for ultimate involvement to avoid cartilage worsening and bone injury. Through machine-learning algorithms, the system can be trained to automatically distinguish among people who would develop osteoarthritis and persons who would not with the detection of exact biochemical variances in the midpoint of the knee’s cartilage. The outcome of the Machine learning Techniques will give the persons who are pre-symptomatic by the occasion of the baseline imaging and also the reduction in liquid concentration. In this study, we present the analysis of various deep learning techniques for timely detection of osteoarthritis disease. Several subsets of machine learning called deep learning techniques have been in use for the timely detection of osteoarthritis disease; and therefore analysis is needed highly to choose the best as far as accuracy and reliability are concerned.

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A Study On Deep Learning And Machine Learning Techniques On Detection Of Parkinson’s Disease

E3S Web of Conferences ◽

10.1051/e3sconf/202130901008 ◽

2021 ◽

Vol 309 ◽

pp. 01008

Author(s):

P. Mounika ◽

S. Govinda Rao

Keyword(s):

Machine Learning ◽

Parkinson’S Disease ◽

Parkinson's Disease ◽

Deep Learning ◽

Early Stage ◽

Signs And Symptoms ◽

Machine Learning Techniques ◽

Learning Approaches ◽

Machine Learning Methods ◽

Learning Techniques

Parkinson’s disease (PD) is a sophisticated anxiety malady that impairs movement. Symptoms emerge gradually, initiating with a slight tremor in only one hand occasionally. Tremors are prevalent, although the condition is sometimes associated with stiffness or slowed mobility. In the early degrees of PD, your face can also additionally display very little expression. Your fingers won’t swing while you walk. Your speech can also additionally grow to be gentle or slurred. PD signs and symptoms get worse as your circumstance progresses over time. The goal of this study is to test the efficiency of deep learning and machine learning approaches in order to identify the most accurate strategy for sensing Parkinson’s disease at an early stage. In order to measure the average performance most accurately, we compared deep learning and machine learning methods.

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Comparing Classical and Modern Machine Learning Techniques for Monitoring Pedestrian Workers in Top-View Construction Site Video Sequences

Applied Sciences ◽

10.3390/app10238466 ◽

2020 ◽

Vol 10 (23) ◽

pp. 8466

Author(s):

Marcel Neuhausen ◽

Dennis Pawlowski ◽

Markus König

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Kalman Filter ◽

Safety Management ◽

Machine Learning Techniques ◽

Training Dataset ◽

Learning Approaches ◽

Construction Sites ◽

Learning Techniques ◽

Different Characteristics

Keeping an overview of all ongoing processes on construction sites is almost unfeasible, especially for the construction workers executing their tasks. It is difficult for workers to concentrate on their work while paying attention to other processes. If their workflows in hazardous areas do not run properly, this can lead to dangerous accidents. Tracking pedestrian workers could improve the productivity and safety management on construction sites. For this, vision-based tracking approaches are suitable, but the training and evaluation of such a system requires a large amount of data originating from construction sites. These are rarely available, which complicates deep learning approaches. Thus, we use a small generic dataset and juxtapose a deep learning detector with an approach based on classical machine learning techniques. We identify workers using a YOLOv3 detector and compare its performance with an approach based on a soft cascaded classifier. Afterwards, tracking is done by a Kalman filter. In our experiments, the classical approach outperforms YOLOv3 on the detection task given a small training dataset. However, the Kalman filter is sufficiently robust to compensate for the drawbacks of YOLOv3. We found that both approaches generally yield a satisfying tracking performances but feature different characteristics.

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Recognition of Automated Hand-written Digits on Document Images Making Use of Machine Learning Techniques

European Journal of Engineering and Technology Research ◽

10.24018/ejers.2021.6.4.2460 ◽

2021 ◽

Vol 6 (4) ◽

pp. 37-44

Author(s):

Hiral Raja ◽

Aarti Gupta ◽

Rohit Miri

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Machine Learning Techniques ◽

Learning Approaches ◽

Digit Recognition ◽

Learning Techniques ◽

Effective System ◽

Handwritten Digit ◽

Digit String ◽

And Function

The purpose of this study is to create an automated framework that can recognize similar handwritten digit strings. For starting the experiment, the digits were separated into different numbers. The process of defining handwritten digit strings is then concluded by recognizing each digit recognition module's segmented digit. This research utilizes various machine learning techniques to produce a strong performance on the digit string recognition challenge, including SVM, ANN, and CNN architectures. These approaches use SVM, ANN, and CNN models of HOG feature vectors to train images of digit strings. Deep learning methods organize the pictures by moving a fixed-size monitor over them while categorizing each sub-image as a digit pass or fail. Following complete segmentation, complete recognition of handwritten digits is accomplished. To assess the methods' results, data must be used for machine learning training. Following that, the digit data is evaluated using the desired machine learning methodology. The Experiment findings indicate that SVM and ANN also have disadvantages in precision and efficiency in text picture recognition. Thus, the other process, CNN, performs better and is more accurate. This paper focuses on developing an effective system for automatically recognizing handwritten digits. This research would examine the adaptation of emerging machine learning and deep learning approaches to various datasets, like SVM, ANN, and CNN. The test results undeniably demonstrate that the CNN approach is significantly more effective than the ANN and SVM approaches, ranking 71% higher. The suggested architecture is composed of three major components: image pre-processing, attribute extraction, and classification. The purpose of this study is to enhance the precision of handwritten digit recognition significantly. As will be demonstrated, pre-processing and function extraction are significant elements of this study to obtain maximum consistency.

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Sentiment Analysis by using deep learning and Machine learning Techniques: A Review

International Journal of Advanced Trends in Computer Science and Engineering ◽

10.30534/ijatcse/2021/421022021 ◽

2021 ◽

Vol 10 (2) ◽

pp. 754-761

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Sentiment Analysis ◽

Online Social Networks ◽

Machine Learning Techniques ◽

Learning Approaches ◽

Growing Up ◽

Specific Subject ◽

Learning Techniques ◽

Day By Day

More and more individuals are now using online social networks and resources throughout this day and age to not only interact and to communicate but also for sharing their views, experiences, ideas, impression about anything. The analysis of sentiments is the identification and categorization of these views to evaluate public opinions on a specific subject, question, product, etc. Day by day, the relevance of sentiment analysis is growing up. Machine learning is an area or field of computer science where, without being specifically programmed, computers can learn. Deep learning is the part of machine learning and deals with the algorithm, which is most widely used as Neural network, neural belief, etc., in which neuronal implementations are considered. For sentiment analysis, it compares their performance and accuracy so then it can be inferred that deep learning techniques in most of the cases provide better results. The gap in the precision of these two approaches, however, is not as important enough in certain situations, and so it is best to apply and use the machine learning approaches and methods because these are simpler in terms of Implementation

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