Analyzing the difference between deep learning and machine learning features of EEG signal using clustering techniques

2019 ◽  
Author(s):  
Anushri Saha ◽  
Sachin Singh Rathore ◽  
Shivam Sharma ◽  
Debasis Samanta
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Eeg Signal ◽  
Clustering Techniques ◽  
The Difference

scholarly journals Measuring accuracy of Dataset using Deep Learning Algorithm RMSProp Algorithm

2021 ◽  
Vol 9 (10) ◽  
pp. 99-112
Author(s):  
Sanjana Naidu Gedela
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Deep Learning Algorithm ◽  
Medical Centers ◽  
Clinical Methods ◽  
Costs Of Treatment ◽  
The Difference ◽  
Hidden Layer ◽  
Data Elements

Abstract: Over the last few years, there have been many significant improvements in the field of AI, machine learning, deep learning are being used in various industries and research. In order to train the deep learning models learning of parameters plays a major role, here the reduction of loss incurred during the training process is the main objective. In a supervised mode of learning, a model is given the data samples and their respective outcomes. When a model generates an output, it compares it with the desired output and then takes the difference of generated and desired outputs and then attempts to bring the generated output close to the desired output. This is achieved through optimization algorithms. Though many kinds of clinical methods have been employed to detect whether patients have heart disease or not by number of features from patients. but it’s still a challenging task due to the multitude of data elements involved. The motive of our project is to save human resources in medical centers and improve accuracy of diagnosis. In our project we used an RMS prop optimizer. The purpose is to decide how many hidden layers need to be selected and how many neurons need to be selected in each and every hidden layer by looking at the dataset and to give the application of deep learning to the health care sector so that we can minimize the costs of treatment and help in proactive actions. We want to show that we can increase the accuracy of the project by taking stability along with accuracy into consideration. Index Terms: RMS Prop, Machine Learning, Deep Learning, number of features, proactive actions

scholarly journals Proposed Model for Detecting Malware on Workstations

2020 ◽  
Vol 9 (6) ◽  
pp. 1069-1078
Keyword(s):  
Machine Learning ◽  
Operating System ◽  
Deep Learning ◽  
Profile Analysis ◽  
End Users ◽  
Abnormal Behavior ◽  
Network Attacks ◽  
Analysis Technique ◽  
Proposed Model ◽  
The Difference

The trend of network attacks through end-users are widely used by attackers today. One of them is the attack by distributing malware into users' computers to steal data or escalate to higher privileges. The technique of attack by distributing malware is a dangerous attack method that is difficult to detect and prevent. Therefore, the task of detecting the sign of malware and alerting it for the user or the system is very necessary today. Current studies and recommendations for detecting malware are often based on two main methods that are using a set of signs and analyzing abnormal behavior based on machine learning or deep learning. In this paper, we will propose a method to detect malware on users' computers using an Event ID profile analysis technique. Event IDs are signs and behaviors of malware that are tracked and collected on the operating system kernel of the workstation. The difference between our research and other published methods is the way to collect behaviors of the malware. We don't collect them through virtualization systems, but through direct processes in the operating system kernel. Therefore, even though malware uses hidden techniques, its actions are recorded by the operating system kernel and based on those processes, we use the Event ID analysis technique to conclude about the existence of malware in the system

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.

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 ◽  
Filip Ferdinand ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
Learning Methods ◽  
Comprehensive Review
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