Deep Learning

2019 ◽  
pp. 108-134
Author(s):  
Amit Sinha ◽  
Suneet Kumar Gupta ◽  
Anurag Tiwari ◽  
Amrita Chaturvedi
Keyword(s):  
Deep Learning ◽  
Machine Vision ◽  
Plant Disease ◽  
Learning Algorithms ◽  
The Other ◽  
Learning Approaches ◽  
Agricultural Field ◽  
Practical Applications ◽  
Other Hand

Deep learning approaches have been found to be suitable for the agricultural field with successful applications to vegetable infection through plant disease. In this chapter, the authors discuss some widely used deep learning architecture and their practical applications. Nowadays, in many typical applications of machine vision, there is a tendency to replace classical techniques with deep learning algorithms. The benefits are valuable; on one hand, it avoids the need of specialized handcrafted features extractors, and on the other hand, results are not damaged. Moreover, they typically get improved.

scholarly journals ML Based Virtual Personal Assistant

2020 ◽  
Vol 6 (12) ◽  
pp. 66-72
Author(s):  
Anshu Kumar Singh, Dr.Bhoomi Gupta
Keyword(s):  
Deep Learning ◽  
Learning Algorithms ◽  
The Other ◽  
Individual Data ◽  
Right Hand ◽  
Other Hand ◽  
Personal Assistant ◽  
Day By Day

The menial helper is shrewd programming installed in Smartphones and other associated gadgets that demonstrations like an individual right hand by helping you moderate your numerous assignments so you can focus on the more significant things. Savvy help is the need in the journey for an innovatively ahead society. With quite a rationale, our Virtual Assistant framework is proposed. The essential thought is to guarantee the security of individual data of clients and offer exact help for different day by day parts of living through a basic yet incredible framework. Such a framework utilizes Deep Learning Algorithms to accumulate results and fundamental chatterbot frameworks to go about as an ideal ally for humans. The assistant is prepared to build up its own awareness of the content, and you can show it how to speak with individuals. On the other hand, you can educate the assistant through film exchange or play contents. Be that as it may, a human-to-human discussion is a favored method to make the most ideal profound learning assistant. Keep in mind, the more information you have, the better the viability of AI will be.

scholarly journals Designing Image Based Captcha using Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 3678-3682
Author(s):  
Piyush Sable
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Computer Networks ◽  
The Other ◽  
Learning Approaches ◽  
Semantic Data ◽  
Other Hand ◽  
Learning Techniques ◽  
The Creation

Captchas, or Completely Automated Public Turing Tests to Tell Computers and Humans Apart, were created in response to programmers' ability to breach computer networks via computer attack programmes and bots. Because of its ease of development and use, the Text Captcha is the most well-known Captcha scheme. Hackers and programmers, on the other hand, have weakened the assumed security of Captchas, leaving websites vulnerable to assault. Text Captchas are still widely used since it is assumed that the attack speeds are moderate, typically two to five seconds for each image, and that this is not considered a significant concern. Style Area Captcha (SACaptcha) is a revolutionary image-based Captcha suggested in this paper, which relies on semantic data comprehension, pixel-level segmentation, and deep learning approaches. The suggested SACaptcha highlights the creation of image-based Captchas utilising deep learning techniques for boosting the security purpose, demonstrating that text Captchas are no longer secure.

scholarly journals Analysis of the Nosema Cells Identification for Microscopic Images

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3068
Author(s):  
Soumaya Dghim ◽  
Carlos M. Travieso-González ◽  
Radim Burget
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Processing ◽  
Deep Learning ◽  
The Other ◽  
Support Vector ◽  
Learning Approaches ◽  
Microscopic Images ◽  
Trained Neural Network ◽  
Nosema Disease

The use of image processing tools, machine learning, and deep learning approaches has become very useful and robust in recent years. This paper introduces the detection of the Nosema disease, which is considered to be one of the most economically significant diseases today. This work shows a solution for recognizing and identifying Nosema cells between the other existing objects in the microscopic image. Two main strategies are examined. The first strategy uses image processing tools to extract the most valuable information and features from the dataset of microscopic images. Then, machine learning methods are applied, such as a neural network (ANN) and support vector machine (SVM) for detecting and classifying the Nosema disease cells. The second strategy explores deep learning and transfers learning. Several approaches were examined, including a convolutional neural network (CNN) classifier and several methods of transfer learning (AlexNet, VGG-16 and VGG-19), which were fine-tuned and applied to the object sub-images in order to identify the Nosema images from the other object images. The best accuracy was reached by the VGG-16 pre-trained neural network with 96.25%.

Effect of Specimen Geometry on the Predicted Mechanical Behavior of Polyethylene Pipe Material

2014 ◽  
Author(s):  
Tarek M. A. A. El-Bagory ◽  
Tawfeeq A. R. Alkanhal ◽  
Maher Y. A. Younan
Keyword(s):  
Mechanical Behavior ◽  
Longitudinal Direction ◽  
Primary Objective ◽  
The Other ◽  
Pipe Material ◽  
Ring Specimen ◽  
Design Data ◽  
Practical Applications ◽  
Other Hand ◽  
Gauge Section

The primary objective of the present paper is to depict the mechanical behavior of high density polyethylene, (HDPE), pipes under different loading conditions with different specimen geometries to provide the designer with reliable design data relevant to practical applications. Therefore, it is necessary to study the effect of strain rate, ring configuration, and grip or fixture type on the mechanical behavior of dumb-bell-shaped, (DBS), and ring specimens made from HDPE pipe material. DBS and ring specimens are cut from the pipe in longitudinally, and circumferential (transverse) direction respectively. On the other hand, the ring specimen configuration is classified into two types; full ring, (FR), and notched ring, (NR) (equal double notch from two sides of notched ring specimen) specimens according to ASTM D 2290-12 standard. Tensile tests are conducted on specimens cut out from the pipe with thickness 10 mm at different crosshead speeds (10–1000 mm/min), and ambient temperature, Ta = 20 °C to investigate the mechanical properties of DBS, and ring specimens. In the case of test specimens taken from longitudinal direction from the pipe a necking phenomenon before failure appears at different locations along the gauge section. On the other hand, the fracture of NR specimens occurs at one notched side. The results demonstrated that the NR specimen has higher yield stress than DBS, and FR specimens at all crosshead speeds. The present experimental work reveals that the crosshead speed has a significant effect on the mechanical behavior of both DBS, and ring specimens. The fixture type plays an important role in the mechanical behavior for both FR and NR specimens at all crosshead speeds.

scholarly journals A Review on Advances in Automated Plant Disease Detection

2021 ◽  
Vol 11 (4) ◽  
pp. 251-264
Author(s):  
Radhika Bhagwat ◽  
Yogesh Dandawate
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Plant Disease ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Plant Diseases ◽  
Detection Methods ◽  
Economic Losses ◽  
Disease Detection ◽  
Range Images

Plant diseases cause major yield and economic losses. To detect plant disease at early stages, selecting appropriate techniques is imperative as it affects the cost, diagnosis time, and accuracy. This research gives a comprehensive review of various plant disease detection methods based on the images used and processing algorithms applied. It systematically analyzes various traditional machine learning and deep learning algorithms used for processing visible and spectral range images, and comparatively evaluates the work done in literature in terms of datasets used, various image processing techniques employed, models utilized, and efficiency achieved. The study discusses the benefits and restrictions of each method along with the challenges to be addressed for rapid and accurate plant disease detection. Results show that for plant disease detection, deep learning outperforms traditional machine learning algorithms while visible range images are more widely used compared to spectral images.

scholarly journals Stock Price Prediction Using Deep Learning Models

2021 ◽  
Author(s):  
Jaydip Sen ◽  
Sidra Mehtab ◽  
Gourab Nath
Keyword(s):  
Deep Learning ◽  
Stock Prices ◽  
Stock Price ◽  
Regression Models ◽  
Research Work ◽  
The Other ◽  
Learning Models ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Other Hand

Prediction of future movement of stock prices has been a subject matter of many research work. On one hand, we have proponents of the Efficient Market Hypothesis who claim that stock prices cannot be predicted, on the other hand, there are propositions illustrating that, if appropriately modeled, stock prices can be predicted with a high level of accuracy. There is also a gamut of literature on technical analysis of stock prices where the objective is to identify patterns in stock price movements and profit from it. In this work, we propose a hybrid approach for stock price prediction using five deep learning-based regression models. We select the NIFTY 50 index values of the National Stock Exchange (NSE) of India, over a period of December 29, 2014 to July 31, 2020. Based on the NIFTY data during December 29, 2014 to December 28, 2018, we build two regression models using <i>convolutional neural networks</i> (CNNs), and three regression models using <i>long-and-short-term memory</i> (LSTM) networks for predicting the <i>open</i> values of the NIFTY 50 index records for the period December 31, 2018 to July 31, 2020. We adopted a multi-step prediction technique with <i>walk-forward validation</i>. The parameters of the five deep learning models are optimized using the grid-search technique so that the validation losses of the models stabilize with an increasing number of epochs in the model training, and the training and validation accuracies converge. Extensive results are presented on various metrics for all the proposed regression models. The results indicate that while both CNN and LSTM-based regression models are very accurate in forecasting the NIFTY 50 <i>open</i> values, the CNN model that previous one week’s data as the input is the fastest in its execution. On the other hand, the encoder-decoder convolutional LSTM model uses the previous two weeks’ data as the input is found to be the most accurate in its forecasting results.

407 Explanatory analysis of polysomnography for the identification of sleep apnea hypopnea events using deep learning neural network

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A161-A162
Author(s):  
Soonhyun Yook ◽  
Chaitanya Gupte ◽  
Zhixian Han ◽  
Eun Yeon Joo ◽  
Hea Ree Park ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Deep Learning ◽  
Sleep Apnea ◽  
Learning Algorithms ◽  
Sleep Apnea Syndrome ◽  
Learning Approaches ◽  
Explanatory Analysis ◽  
Technology Advancement ◽  
Obstructive Sleep ◽  
Deep Learning Neural Network

Abstract Introduction Using deep learning algorithms, we investigated univariate and multivariate effects of four polysomnography features including heart rate (HR), electrocardiogram (ECG), oxygen saturation (SpO2) and nasal air flow (NAF) on the identification of sleep apnea and hypopnea events. This explanatory analysis that may clarify the sensitivity and specificity of those features to SAs and SHs have not been probed. Methods We studied 804 polysomonography samples from 704 patients with obstructive sleep apnea and 100 controls. The input data were converted into scalograms as 4-channel 2D images to train Xception networks. For training, 77,638 patches were sampled from the original 6-hour sleep data with 30-second time width. A 10% of these patches were segregated as the test-set. With each feature sets, we tested the following classifications: 1) normal vs apnea vs hypopnea; 2) normal vs. apnea+hypopnea; 3) normal vs. apnea; and 4) normal vs. hypopnea. Results SpO2 classified normal vs. apnea most accurately (98%), followed by NAF (85%), ECG (77%), and HR (63%). SpO2 also showed the highest accuracy in classifying normal vs. hypopnea (87%), and normal vs. apnea+hypopnea (96%) and three groups (82%). When the combination of four features were used, the classification accuracies were generally improved compared to use of SpO2 only (normal vs. apnea 99%; vs. hypopnea 89%; vs. apnea+hypopnea: 94%; three groups: 86%). Conclusion Deep learning with SpO2 or NAF feature most accurately classified apneas from normal sleep events, suggesting these features’ characterization of sleep apnea events. Oxygen desaturation, which is a typical pattern of hypopnea, was only the feature showing reliable accuracy in classifying hypopnea vs. normal. Nevertheless, combination of four polysomnography features could improve the identification of sleep apnea and hypopnea. Furthermore, classifying normal vs. apnea+hypopnea was more accurate than separately classifying three groups, suggesting deep learning approaches as the primary screen tool. Since the classification accuracy of using SpO2 was higher than any other features, developing a portable equipment measuring SpO2 and running deep learning algorithms has the potential for inexpensive, accurate diagnostics of obstructive sleep apnea syndrome. Support (if any) This study was supported by USC STEVENS CENTER FOR INNOVATION TECHNOLOGY ADVANCEMENT GRANTS (TAG), BrightFocus Foundation Award (A2019052S).

scholarly journals Method for a cloud based remaining-service-life-prediction for vehicle-gearboxes based on big-data-analysis and machine learning

2020 ◽  
Vol 84 (4) ◽  
pp. 305-314
Author(s):  
Daniel Vietze ◽  
Michael Hein ◽  
Karsten Stahl
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Service Life ◽  
Operating Time ◽  
The Other ◽  
Learning Approaches ◽  
State Of Health ◽  
Remaining Service Life ◽  
Other Hand ◽  
The One

AbstractMost vehicle-gearboxes operating today are designed for a limited service-life. On the one hand, this creates significant potential for decreasing cost and mass as well as reduction of the carbon-footprint. On the other hand, this causes a rising risk of failure with increasing operating time of the machine. Especially if a failure can result in a high economic loss, this fact creates a conflict of goals. On the one hand, the machine should only be maintained or replaced when necessary and, on the other hand, the probability of a failure increases with longer operating times. Therefore, a method is desirable, making it possible to predict the remaining service-life and state of health with as little effort as possible.Centerpiece of gearboxes are the gears. A failure of these components usually causes the whole gearbox to fail. The fatigue life analysis deals with the dimensioning of gears according to the expected loads and the required service-life. Unfortunately, there is very little possibility to validate the technical design during operation, today. Hence, the goal of this paper is to present a method, enabling the prediction of the remaining-service-life and state-of-health of gears during operation. Within this method big-data and machine-learning approaches are used. The method is designed in a way, enabling an easy transfer to other machine elements and kinds of machinery.

scholarly journals The limits of distribution-free conditional predictive inference

2020 ◽  
Author(s):  
Rina Foygel Barber ◽  
Emmanuel J Candès ◽  
Aaditya Ramdas ◽  
Ryan J Tibshirani
Keyword(s):  
The Other ◽  
Conditional Inference ◽  
Predictive Inference ◽  
Distribution Free ◽  
Conformal Prediction ◽  
Underlying Distribution ◽  
Practical Applications ◽  
Other Hand ◽  
Test Points ◽  
Exact Conditional Inference

Abstract We consider the problem of distribution-free predictive inference, with the goal of producing predictive coverage guarantees that hold conditionally rather than marginally. Existing methods such as conformal prediction offer marginal coverage guarantees, where predictive coverage holds on average over all possible test points, but this is not sufficient for many practical applications where we would like to know that our predictions are valid for a given individual, not merely on average over a population. On the other hand, exact conditional inference guarantees are known to be impossible without imposing assumptions on the underlying distribution. In this work, we aim to explore the space in between these two and examine what types of relaxations of the conditional coverage property would alleviate some of the practical concerns with marginal coverage guarantees while still being possible to achieve in a distribution-free setting.

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