scholarly journals Deep Machine Learning and Neural Networks: An Overview

2017 ◽  
Vol 6 (2) ◽  
pp. 66 ◽  
Author(s):  
Chandrahas Mishra ◽  
D. L. Gupta
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Language Processing ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Application Area ◽  
Intelligent Machines ◽  
Different Types ◽  
Dynamic Examination

Deep learning is a technique of machine learning in artificial intelligence area. Deep learning in a refined "machine learning" algorithm that far surpasses a considerable lot of its forerunners in its capacities to perceive syllables and picture. Deep learning is as of now a greatly dynamic examination territory in machine learning and example acknowledgment society. It has increased colossal triumphs in an expansive zone of utilizations, for example, speech recognition, computer vision and natural language processing and numerous industry item. Neural network is used to implement the machine learning or to design intelligent machines. In this paper brief introduction to all machine learning paradigm and application area of deep machine learning and different types of neural networks with applications is discussed.

scholarly journals Vision-Based Skin Disease Identification Using Deep Learning

2019 ◽  
Vol 8 (6) ◽  
pp. 3784-3788
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Skin Disease ◽  
Medical Image ◽  
Skin Diseases ◽  
Learning Algorithm ◽  
Disease Prediction ◽  
Machine Learning Algorithm ◽  
Disease Identification ◽  
Different Types

Skin disease is the most common health problems worldwide.Human skin is one of the difficult areas topredict. The difficulty is due to rough areas, irregular skin tones, various factors like burns, moles. We have to identify the diseases excluding these factors.In a developing country like India, it is expensive for a large number of people to go to the dermatologist for their skin disease problem.Every year a large number of population in developing countries like India suffer due to different types of skin diseases. So the need for automatic skin disease prediction is increasing for the patients and as well as the dermatologist. In this paper, a method is proposed that uses computer vision-based techniques to detectvariouskinds of dermatological skin diseases. Inception_v3, Mobilenet, Resnetare three deep learning algorithms used for feature extraction in a medical image and machine learning algorithm namely Logistic Regression is used for training and testing the medical images.Using the combined architecture of the three convolutional neural networks considerable efficiency can be achieved.

scholarly journals Segmentation of Affected Crops using Deep Learning

2019 ◽  
Vol 8 (5S3) ◽  
pp. 232-234
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Monitoring System ◽  
Learning Algorithm ◽  
Target Area ◽  
Learning Technology ◽  
Machine Learning Algorithm ◽  
Normal Human ◽  
Human Monitoring ◽  
Disease Attack

Deep Learning technology can accurately predict the presence of diseases and pests in the agricultural farms. Upon this Machine learning algorithm, we can even predict accurately the chance of any disease and pest attacks in future For spraying the correct amount of fertilizer/pesticide to elimate host, the normal human monitoring system unable to predict accurately the total amount and ardent of pest and disease attack in farm. At the specified target area the artificial percepton tells the value accurately and give corrective measure and amount of fertilizers/ pesticides to be sprayed.

scholarly journals DEVELOPMENT OF A MACHINE LEARNING ALGORITHM TO PREDICT AUTHOR’S AGE FROM TEXT

2020 ◽  
Vol 7 (10) ◽  
pp. 380-389
Author(s):  
Asogwa D.C ◽  
Anigbogu S.O ◽  
Anigbogu G.N ◽  
Efozia F.N
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Machine Learning Algorithm ◽  
Age Group ◽  
Political Views ◽  
Learning Techniques ◽  
Age Prediction

Author's age prediction is the task of determining the author's age by studying the texts written by them. The prediction of author’s age can be enlightening about the different trends, opinions social and political views of an age group. Marketers always use this to encourage a product or a service to an age group following their conveyed interests and opinions. Methodologies in natural language processing have made it possible to predict author’s age from text by examining the variation of linguistic characteristics. Also, many machine learning algorithms have been used in author’s age prediction. However, in social networks, computational linguists are challenged with numerous issues just as machine learning techniques are performance driven with its own challenges in realistic scenarios. This work developed a model that can predict author's age from text with a machine learning algorithm (Naïve Bayes) using three types of features namely, content based, style based and topic based. The trained model gave a prediction accuracy of 80%.

scholarly journals Interpretation of Neural Networks Is Fragile

2019 ◽  
Vol 33 ◽  
pp. 3681-3688 ◽  
Author(s):  
Amirata Ghorbani ◽  
Abubakar Abid ◽  
James Zou
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Input Data ◽  
Learning Algorithm ◽  
Hessian Matrix ◽  
Machine Learning Algorithm ◽  
Feature Importance ◽  
Adversarial Attack ◽  
Measurement Biases

In order for machine learning to be trusted in many applications, it is critical to be able to reliably explain why the machine learning algorithm makes certain predictions. For this reason, a variety of methods have been developed recently to interpret neural network predictions by providing, for example, feature importance maps. For both scientific robustness and security reasons, it is important to know to what extent can the interpretations be altered by small systematic perturbations to the input data, which might be generated by adversaries or by measurement biases. In this paper, we demonstrate how to generate adversarial perturbations that produce perceptively indistinguishable inputs that are assigned the same predicted label, yet have very different interpretations. We systematically characterize the robustness of interpretations generated by several widely-used feature importance interpretation methods (feature importance maps, integrated gradients, and DeepLIFT) on ImageNet and CIFAR-10. In all cases, our experiments show that systematic perturbations can lead to dramatically different interpretations without changing the label. We extend these results to show that interpretations based on exemplars (e.g. influence functions) are similarly susceptible to adversarial attack. Our analysis of the geometry of the Hessian matrix gives insight on why robustness is a general challenge to current interpretation approaches.

scholarly journals Big data, machine learning and artificial intelligence: a neurologist’s guide

2020 ◽  
pp. practneurol-2020-002688
Author(s):  
Stephen D Auger ◽  
Benjamin M Jacobs ◽  
Ruth Dobson ◽  
Charles R Marshall ◽  
Alastair J Noyce
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Big Data ◽  
Clinical Practice ◽  
Clinical Data ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Basic Principles ◽  
Biological Neural Networks

Modern clinical practice requires the integration and interpretation of ever-expanding volumes of clinical data. There is, therefore, an imperative to develop efficient ways to process and understand these large amounts of data. Neurologists work to understand the function of biological neural networks, but artificial neural networks and other forms of machine learning algorithm are likely to be increasingly encountered in clinical practice. As their use increases, clinicians will need to understand the basic principles and common types of algorithm. We aim to provide a coherent introduction to this jargon-heavy subject and equip neurologists with the tools to understand, critically appraise and apply insights from this burgeoning field.

scholarly journals Image Spam Detection Using Machine Learning and Natural Language Processing

2020 ◽  
Vol 55 (2) ◽  
Author(s):  
Yaseen Khather Yaseen ◽  
Alaa Khudhair Abbas ◽  
Ahmed M. Sana
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Optical Character ◽  
Harmful Content

Today, images are a part of communication between people. However, images are being used to share information by hiding and embedding messages within it, and images that are received through social media or emails can contain harmful content that users are not able to see and therefore not aware of. This paper presents a model for detecting spam on images. The model is a combination of optical character recognition, natural language processing, and the machine learning algorithm. Optical character recognition extracts the text from images, and natural language processing uses linguistics capabilities to detect and classify the language, to distinguish between normal text and slang language. The features for selected images are then extracted using the bag-of-words model, and the machine learning algorithm is run to detect any kind of spam that may be on it. Finally, the model can predict whether or not the image contains any harmful content. The results show that the proposed method using a combination of the machine learning algorithm, optical character recognition, and natural language processing provides high detection accuracy compared to using machine learning alone.

scholarly journals Detecting The Speaker Language Using CNN Deep Learning Algorithm

2022 ◽  
pp. 43-52
Author(s):  
Fawziya M. Rammo ◽  
Mohammed N. Al-Hamdani
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Open Source ◽  
Convolutional Neural Networks ◽  
Learning Algorithm ◽  
Language Models ◽  
Mel Frequency Cepstral Coefficients ◽  
Deep Learning Algorithm ◽  
Time Frames

Many languages identification (LID) systems rely on language models that use machine learning (ML) approaches, LID systems utilize rather long recording periods to achieve satisfactory accuracy. This study aims to extract enough information from short recording intervals in order to successfully classify the spoken languages under test. The classification process is based on frames of (2-18) seconds where most of the previous LID systems were based on much longer time frames (from 3 seconds to 2 minutes). This research defined and implemented many low-level features using MFCC (Mel-frequency cepstral coefficients), containing speech files in five languages (English. French, German, Italian, Spanish), from voxforge.org an open-source corpus that consists of user-submitted audio clips in various languages, is the source of data used in this paper. A CNN (convolutional Neural Networks) algorithm applied in this paper for classification and the result was perfect, binary language classification had an accuracy of 100%, and five languages classification with six languages had an accuracy of 99.8%.

scholarly journals Recent advances of deep learning in psychiatric disorders

2020 ◽  
Vol 3 (3) ◽  
pp. 202-213
Author(s):  
Lu Chen ◽  
Chunchao Xia ◽  
Huaiqiang Sun
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Psychiatric Disorders ◽  
Language Processing ◽  
Visual Recognition ◽  
Learning Algorithm ◽  
Future Research ◽  
Academic World ◽  
Recent Advances ◽  
Conventional Machine

ABSTRACT Deep learning (DL) is a recently proposed subset of machine learning methods that has gained extensive attention in the academic world, breaking benchmark records in areas such as visual recognition and natural language processing. Different from conventional machine learning algorithm, DL is able to learn useful representations and features directly from raw data through hierarchical nonlinear transformations. Because of its ability to detect abstract and complex patterns, DL has been used in neuroimaging studies of psychiatric disorders, which are characterized by subtle and diffuse alterations. Here, we provide a brief review of recent advances and associated challenges in neuroimaging studies of DL applied to psychiatric disorders. The results of these studies indicate that DL could be a powerful tool in assisting the diagnosis of psychiatric diseases. We conclude our review by clarifying the main promises and challenges of DL application in psychiatric disorders, and possible directions for future research.

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