scholarly journals A Deep Learning Approach for UAV Enabled NOMA Relaying System

2020 ◽  
Author(s):  
Messiah Josephine M ◽  
Ameelia Roseline A
Keyword(s):  
Deep Learning ◽  
Frequency Spectrum ◽  
Future Research ◽  
Learning Approach ◽  
Wireless Devices ◽  
5G Networks ◽  
Channel Interference ◽  
Low Efficiency ◽  
Increasing Demand ◽  
Relaying System

The Increasing Demand in Wireless Devices Leads to Low Data Rate and Low Efficiency to resolve this Problem 5g is evolved. NOMA Technique is proposed to Face Challenges and Difficulties Issues in 5g Networks. Same frequency spectrum can used by more than one user is a major advantage in NOMA. The LMMSE Algorithm with NOMA is proposed In this Research. thus by LMMSE with OFDM is Compared. The Inter Channel Interference Using Equalizer Results In NOMA than OFDM In The Future Research Novel Deep Leaning using UAV enabled NOMA will be analyzed.

Deep learning approach on channel selection strategy for minimizing co-channel interference in unlicensed channels

2020 ◽  
Vol 105 ◽  
pp. 113558
Author(s):  
Lei Yen ◽  
Abebe Belay Adege ◽  
Hsin-Piao Lin ◽  
Ching-Huai Ho ◽  
Ken Lever
Keyword(s):  
Deep Learning ◽  
Channel Selection ◽  
Selection Strategy ◽  
Learning Approach ◽  
Channel Interference

Extraction of drug–drug interaction using neural embedding

2018 ◽  
Vol 16 (06) ◽  
pp. 1840027 ◽  
Author(s):  
Wen Juan Hou ◽  
Bamfa Ceesay
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Drug Interaction ◽  
Relevant Information ◽  
Learning System ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Learning Approach ◽  
Learning Techniques ◽  
Drug Drug Interaction

Information on changes in a drug’s effect when taken in combination with a second drug, known as drug–drug interaction (DDI), is relevant in the pharmaceutical industry. DDIs can delay, decrease, or enhance absorption of either drug and thus decrease or increase their action or cause adverse effects. Information Extraction (IE) can be of great benefit in allowing identification and extraction of relevant information on DDIs. We here propose an approach for the extraction of DDI from text using neural word embedding to train a machine learning system. Results show that our system is competitive against other systems for the task of extracting DDIs, and that significant improvements can be achieved by learning from word features and using a deep-learning approach. Our study demonstrates that machine learning techniques such as neural networks and deep learning methods can efficiently aid in IE from text. Our proposed approach is well suited to play a significant role in future research.

An Analytical Review on Application of Deep Learning in Medical Big Data Analysis

2021 ◽  
pp. 38-41
Author(s):  
Subham Kumar ◽  
Dr. Farha Haneef
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Health Condition ◽  
Future Research ◽  
Learning Approach ◽  
Analytical Review ◽  
Medical Health ◽  
Medical Big Data ◽  
Feature Expression ◽  
Typical Model

The data of medical health has also incremented dramatically and methods of interpreting medical-driven huge big data have originated as the requirement with time, assisting in the reorganization of medical health condition intelligently the with the use of technologies of computer widely. Due to the heterogeneous, noisy, and unstructured nature of medical big data, it is still a difficult task to analyze medical big data. The conventional methods of machine learning can’t find out the major information involved in the medical big data efficiently, while deep learning designs a hierarchical model. It consists of effective features of extraction, potential feature expression, and typical model construction. This paper is dedicated to surveying different approaches for medical big data processing using a deep learning approach and extracting finding for future research scope

Comparison of various Activation Functions A Deep Learning Approach

2018 ◽  
Vol 6 (3) ◽  
pp. 122-126
Author(s):  
Mohammed Ibrahim Khan ◽  
◽  
Akansha Singh ◽  
Anand Handa ◽  
◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
Activation Functions

A Deep Learning based Arabic Script Recognition System: Benchmark on KHAT

2020 ◽  
Vol 17 (3) ◽  
pp. 299-305 ◽  
Author(s):  
Riaz Ahmad ◽  
Saeeda Naz ◽  
Muhammad Afzal ◽  
Sheikh Rashid ◽  
Marcus Liwicki ◽  
...  
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Recognition System ◽  
Learning Approach ◽  
Arabic Text ◽  
Data Set ◽  
Processing Step ◽  
Handwritten Arabic

This paper presents a deep learning benchmark on a complex dataset known as KFUPM Handwritten Arabic TexT (KHATT). The KHATT data-set consists of complex patterns of handwritten Arabic text-lines. This paper contributes mainly in three aspects i.e., (1) pre-processing, (2) deep learning based approach, and (3) data-augmentation. The pre-processing step includes pruning of white extra spaces plus de-skewing the skewed text-lines. We deploy a deep learning approach based on Multi-Dimensional Long Short-Term Memory (MDLSTM) networks and Connectionist Temporal Classification (CTC). The MDLSTM has the advantage of scanning the Arabic text-lines in all directions (horizontal and vertical) to cover dots, diacritics, strokes and fine inflammation. The data-augmentation with a deep learning approach proves to achieve better and promising improvement in results by gaining 80.02% Character Recognition (CR) over 75.08% as baseline.

Sign in / Sign up

Export Citation Format

Share Document