Exploring Machine Learning and Deep Learning Frameworks for Task-Oriented Dialogue Act Classification

2019 ◽  
Author(s):  
Tulika Saha ◽  
Saurabh Srivastava ◽  
Mauajama Firdaus ◽  
Sriparna Saha ◽  
Asif Ekbal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Task Oriented ◽  
Learning Frameworks

Deep Learning Models for the Detection of Plant Leaf Diseases: A Systematic Review

2019 ◽  
Author(s):  
Malusi Sibiya ◽  
Mbuyu Sumbwanyambe
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Systems ◽  
Learning Models ◽  
Plant Leaves ◽  
Plant Leaf ◽  
Learning Framework ◽  
Software Frameworks ◽  
Software Packages ◽  
Learning Frameworks

Machine learning systems use different algorithms to detect the diseases affecting the plant leaves. Nevertheless, selecting a suitable machine learning framework differs from study to study, depending on the features and complexity of the software packages. This paper introduces a taxonomic inspection of the literature in deep learning frameworks for the detection of plant leaf diseases. The objective of this study is to identify the dominating software frameworks in the literature for modelling machine learning plant leaf disease detecting systems.

scholarly journals Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey

2019 ◽  
Vol 52 (1) ◽  
pp. 77-124 ◽  
Author(s):  
Giang Nguyen ◽  
Stefan Dlugolinsky ◽  
Martin Bobák ◽  
Viet Tran ◽  
Álvaro López García ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Deep Learning ◽  
Large Scale ◽  
Large Scale Data ◽  
Learning Frameworks ◽  
Scale Data

Current Trends in Deep Learning Frameworks With Opportunities and Future Prospectus

2020 ◽  
pp. 63-77
Author(s):  
Chitra A. Dhawale ◽  
Krtika Dhawale
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Real Time ◽  
End Users ◽  
The Other ◽  
Qualitative Review ◽  
Current Trends ◽  
Real Time Applications ◽  
Learning Frameworks

Artificial Intelligence (AI) is going through its golden era by playing an important role in various real-time applications. Most AI applications are using Machine learning and it represents the most promising path to strong AI. On the other hand, Deep Learning (DL), which is itself a kind of Machine Learning (ML), is becoming more and more popular and successful at different use cases, and is at the peak of developments. Hence, DL is becoming a leader in this domain. To foster the growth of the DL community to a greater extent, many open source frameworks are available which implemented DL algorithms. Each framework is based on an algorithm with specific applications. This chapter provides a brief qualitative review of the most popular and comprehensive DL frameworks, and informs end users of trends in DL Frameworks. This helps them make an informed decision to choose the best DL framework that suits their needs, resources, and applications so they choose a proper career.

scholarly journals Prediction of Social Trends using Twitter

2020 ◽  
Vol 9 (4) ◽  
pp. 1726-1729
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Social Trends ◽  
Learning Frameworks

In this study project, we deal with the application of sentimental analysis on a dataset consisting of tweets. We implement a number of machine learning and deep learning frameworks to perform the analysis. In the end, we use a majority based method to show 4 of our best models for the dataset to achieve the best result.

Deep Learning Based High-Resolution Remote Sensing Image classification

2017 ◽  
Vol 7 (10) ◽  
pp. 22
Author(s):  
Sumit Kaur
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Image Classification ◽  
Language Processing ◽  
Object Perception ◽  
Remote Sensing Image ◽  
Research Area ◽  
Remote Sensing Image Classification ◽  
Unsupervised Algorithms

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification. 

Data science in economics: comprehensive review of advanced machine learning and deep learning methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Ferdinand Filip ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
State Of The Art ◽  
Science Methods ◽  
Learning Models ◽  
Diverse Range ◽  
Hybrid Machine ◽  
Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

Deep Learning for text in limted data settings

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Text Data ◽  
Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

Sign in / Sign up

Export Citation Format

Share Document