scholarly journals TRANSFER LEARNING ON DEEP NEURAL NETWORK: A CASE STUDY ON FOOD-101 FOOD CLASSIFIER

2021 ◽  
Vol 5 (9) ◽  
Author(s):  
Prakhar Tripathi
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Deep Learning ◽  
Transfer Learning ◽  
Small Data ◽  
Long Period ◽  
Long Time ◽  
Nested Hierarchy ◽  
Analyze Data

—In the era of the ‘Big-Data’ we hear a lot about machine learning for working on this big data. Machine learning helps us to predict and analyze data with better accuracy and least human intervention. Machine learning is autonomous but susceptible to errors. This is due to biased prediction when previously trained on small data. This leads to chain of errors that can`t be determined easily for long period of time. And when recognized takes lot time to recognize source. There comes the idea of deep learning which achieves the flexibility by using use nested hierarchy of concept to define the world. But deep learning has setback of taking very long time to train data which could be reduced by using transfer learning.

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.

scholarly journals Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4736
Author(s):  
Sk. Tanzir Mehedi ◽  
Adnan Anwar ◽  
Ziaur Rahman ◽  
Kawsar Ahmed
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Real Time ◽  
Transfer Learning ◽  
Security Requirements ◽  
Detection Accuracy ◽  
Area Network ◽  
Complex Data ◽  
Network Intrusion

The Controller Area Network (CAN) bus works as an important protocol in the real-time In-Vehicle Network (IVN) systems for its simple, suitable, and robust architecture. The risk of IVN devices has still been insecure and vulnerable due to the complex data-intensive architectures which greatly increase the accessibility to unauthorized networks and the possibility of various types of cyberattacks. Therefore, the detection of cyberattacks in IVN devices has become a growing interest. With the rapid development of IVNs and evolving threat types, the traditional machine learning-based IDS has to update to cope with the security requirements of the current environment. Nowadays, the progression of deep learning, deep transfer learning, and its impactful outcome in several areas has guided as an effective solution for network intrusion detection. This manuscript proposes a deep transfer learning-based IDS model for IVN along with improved performance in comparison to several other existing models. The unique contributions include effective attribute selection which is best suited to identify malicious CAN messages and accurately detect the normal and abnormal activities, designing a deep transfer learning-based LeNet model, and evaluating considering real-world data. To this end, an extensive experimental performance evaluation has been conducted. The architecture along with empirical analyses shows that the proposed IDS greatly improves the detection accuracy over the mainstream machine learning, deep learning, and benchmark deep transfer learning models and has demonstrated better performance for real-time IVN security.

Transfer learning based convolution neural net for authentication and classification of emotions from natural and stimulated speech signals

2021 ◽  
pp. 1-12
Author(s):  
Mukul Kumar ◽  
Nipun Katyal ◽  
Nersisson Ruban ◽  
Elena Lyakso ◽  
A. Mary Mekala ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Transfer Learning ◽  
Research Work ◽  
Oral Communication ◽  
Neural Net ◽  
Emotion Classification ◽  
Style Transfer ◽  
Conventional Machine ◽  
Neural Network Algorithm

Over the years the need for differentiating various emotions from oral communication plays an important role in emotion based studies. There have been different algorithms to classify the kinds of emotion. Although there is no measure of fidelity of the emotion under consideration, which is primarily due to the reason that most of the readily available datasets that are annotated are produced by actors and not generated in real-world scenarios. Therefore, the predicted emotion lacks an important aspect called authenticity, which is whether an emotion is actual or stimulated. In this research work, we have developed a transfer learning and style transfer based hybrid convolutional neural network algorithm to classify the emotion as well as the fidelity of the emotion. The model is trained on features extracted from a dataset that contains stimulated as well as actual utterances. We have compared the developed algorithm with conventional machine learning and deep learning techniques by few metrics like accuracy, Precision, Recall and F1 score. The developed model performs much better than the conventional machine learning and deep learning models. The research aims to dive deeper into human emotion and make a model that understands it like humans do with precision, recall, F1 score values of 0.994, 0.996, 0.995 for speech authenticity and 0.992, 0.989, 0.99 for speech emotion classification respectively.

Machine Learning as a Silent Observer of Advanced Geoscience Interpretation

2021 ◽  
Author(s):  
Muhammad Sajid
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Transfer Learning ◽  
Learning Performance ◽  
Learning Approach ◽  
Seismic Fault ◽  
Facies Classification ◽  
The World ◽  
Learning Machine ◽  
Fault Interpretation

Abstract Machine learning is proving its successes in all fields of life including medical, automotive, planning, engineering, etc. In the world of geoscience, ML showed impressive results in seismic fault interpretation, advance seismic attributes analysis, facies classification, and geobodies extraction such as channels, carbonates, and salt, etc. One of the challenges faced in geoscience is the availability of label data which is one of the most time-consuming requirements in supervised deep learning. In this paper, an advanced learning approach is proposed for geoscience where the machine observes the seismic interpretation activities and learns simultaneously as the interpretation progresses. Initial testing showed that through the proposed method along with transfer learning, machine learning performance is highly effective, and the machine accurately predicts features requiring minor post prediction filtering to be accepted as the optimal interpretation.

Deep Learning and Medical Imaging

2019 ◽  
pp. 101-147
Author(s):  
Nourhan Mohamed Zayed ◽  
Heba A. Elnemr
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Medical Imaging ◽  
Medical Image Analysis ◽  
Future Research ◽  
Research Directions ◽  
Conventional Machine ◽  
Future Research Directions ◽  
Nested Hierarchy ◽  
Great Potency

Deep learning (DL) is a special type of machine learning that attains great potency and flexibility by learning to represent input raw data as a nested hierarchy of essences and representations. DL consists of more layers than conventional machine learning that permit higher levels of abstractions and improved prediction from data. More abstract representations computed in terms of less abstract ones. The goal of this chapter is to present an intensive survey of existing literature on DL techniques over the last years especially in the medical imaging analysis field. All these techniques and algorithms have their points of interest and constraints. Thus, analysis of various techniques and transformations, submitted prior in writing, for plan and utilization of DL methods from medical image analysis prospective will be discussed. The authors provide future research directions in DL area and set trends and identify challenges in the medical imaging field. Furthermore, as quantity of medicinal application demands increase, an extended study and investigation in DL area becomes very significant.

Machine Learning for Business Analytics

2021 ◽  
pp. 232-256
Author(s):  
Kağan Okatan
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Social Media ◽  
Big Data ◽  
Machine Learning Algorithms ◽  
Decision Makers ◽  
Business Analytics ◽  
Business Intelligence Systems ◽  
Long Time ◽  
Rules Of The Game

All these types of analytics have been answering business questions for a long time about the principal methods of investigating data warehouses. Especially data mining and business intelligence systems support decision makers to reach the information they want. Many existing systems are trying to keep up with a phenomenon that has changed the rules of the game in recent years. This is undoubtedly the undeniable attraction of 'big data'. In particular, the issue of evaluating the big data generated especially by social media is among the most up-to-date issues of business analytics, and this issue demonstrates the importance of integrating machine learning into business analytics. This section introduces the prominent machine learning algorithms that are increasingly used for business analytics and emphasizes their application areas.

Deep Learning Applications in Agriculture

2021 ◽  
pp. 325-345
Author(s):  
Hari Kishan Kondaveeti ◽  
Gonugunta Priyatham Brahma ◽  
Dandhibhotla Vijaya Sahithi
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Deep Learning ◽  
Rapid Growth ◽  
Agricultural Sector ◽  
The Other ◽  
Learning Methods ◽  
Agriculture Sector ◽  
Learning Techniques ◽  
Recent Trends

Deep learning (DL), a part of machine learning (ML), comprises a contemporary technique for processing the images and analyzing the big data with promising outcomes. Deep learning methods are successfully being used in various sectors to gain better results. Agriculture sector is one of the sectors that could be benefitted from the deep learning techniques since the current agriculture techniques cannot keep up with the rapid growth in population. In this chapter, the recent trends in the applications of deep learning techniques in the agricultural sector and the survey of the research efforts that employ deep learning techniques are going to be discussed. Also, the models that are implemented are going to be analyzed and compared with the other existing models.

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