Routing or Computing? The Paradigm Shift Towards Intelligent Computer Network Packet Transmission Based on Deep Learning

2017 ◽  
Vol 66 (11) ◽  
pp. 1946-1960 ◽  
Author(s):  
Bomin Mao ◽  
Zubair Md. Fadlullah ◽  
Fengxiao Tang ◽  
Nei Kato ◽  
Osamu Akashi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Paradigm Shift ◽  
Computer Network ◽  
Packet Transmission ◽  
Intelligent Computer

scholarly journals E-commerce Sites with Outfit Composition using Deep Learning Method

2019 ◽  
Vol 8 (10) ◽  
pp. 2991-2994
Keyword(s):  
Deep Learning ◽  
Electronic Communication ◽  
Computer Network ◽  
Traditional Approach ◽  
Fashion Industry ◽  
It Industry ◽  
Learning Framework ◽  
Visual Characteristics ◽  
Deep Convolution Network ◽  
Main Change

The fashion industry has developed in many fields and its growth is making an enormous promote in article of clothing company and e-commerce entity. The difficult task for IT industry in this field is designing the predictive system of data mining to model this. E-commerce uses electronic communication as well as information technology in many transactions for creating, transforming or for redefining the relationships between individuals and organizations. It simply means buying of products, services and information and selling them through computer network. It is totally changing the traditional approach of business. The main change in business is noticeable growth and it has many significant effects on environment as well. This is the reason why it is so preferred in business nowadays. The important part of the proposed system is to rate the fashionable outfit individual and it is considers appearances as well as meta-data. Our approach has first implemented a system of encoding visual characteristics with the help of deep convolution network for complicated contents because it is not possible to list or to label every attribute of a image. Secondly, we proposed a multi-model deep learning framework for rich contexts of fashion outfit. We propose a system which will recommend with review comments and which product should purchase and the system will display a rating of the product.

scholarly journals Deep Machine Learning provides state-of-the-art performance in image-based plant phenotyping

2016 ◽  
Author(s):  
Michael P. Pound ◽  
Alexandra J. Burgess ◽  
Michael H. Wilson ◽  
Jonathan A. Atkinson ◽  
Marcus Griffiths ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Analysis ◽  
Paradigm Shift ◽  
State Of The Art ◽  
Plant Phenotyping ◽  
Learning Approaches ◽  
Challenging Problem ◽  
Feature Identification ◽  
Art Performance

AbstractDeep learning is an emerging field that promises unparalleled results on many data analysis problems. We show the success offered by such techniques when applied to the challenging problem of image-based plant phenotyping, and demonstrate state-of-the-art results for root and shoot feature identification and localisation. We predict a paradigm shift in image-based phenotyping thanks to deep learning approaches.

scholarly journals Paradigm Shift: The Promise of Deep Learning in Molecular Systems Engineering and Design

2021 ◽  
Vol 3 ◽  
Author(s):  
Abdulelah S. Alshehri ◽  
Fengqi You
Keyword(s):  
Deep Learning ◽  
Systems Engineering ◽  
Paradigm Shift ◽  
Representation Learning ◽  
Research Needs ◽  
Molecular Systems ◽  
Property Estimation ◽  
Synthesis Planning ◽  
Research Problems ◽  
Learning Architectures

The application of deep learning to a diverse array of research problems has accelerated progress across many fields, bringing conventional paradigms to a new intelligent era. Just as the roles of instrumentation in the old chemical revolutions, we reinforce the necessity for integrating deep learning in molecular systems engineering and design as a transformative catalyst towards the next chemical revolution. To meet such research needs, we summarize advances and progress across several key elements of molecular systems: molecular representation, property estimation, representation learning, and synthesis planning. We further spotlight recent advances and promising directions for several deep learning architectures, methods, and optimization platforms. Our perspective is of interest to both computational and experimental researchers as it aims to chart a path forward for cross-disciplinary collaborations on synthesizing knowledge from available chemical data and guiding experimental efforts.

scholarly journals A Novel Approach for Network Intrusion Detection Using Multistage Deep Learning Image Recognition

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1854
Author(s):  
Jevgenijus Toldinas ◽  
Algimantas Venčkauskas ◽  
Robertas Damaševičius ◽  
Šarūnas Grigaliūnas ◽  
Nerijus Morkevičius ◽  
...  
Keyword(s):  
Deep Learning ◽  
Intrusion Detection ◽  
Image Recognition ◽  
Computer Network ◽  
Detection System ◽  
Network Intrusion Detection ◽  
Suggested Approach ◽  
Network Intrusion ◽  
Novel Approach ◽  
Benchmark Datasets

The current rise in hacking and computer network attacks throughout the world has heightened the demand for improved intrusion detection and prevention solutions. The intrusion detection system (IDS) is critical in identifying abnormalities and assaults on the network, which have grown in size and pervasiveness. The paper proposes a novel approach for network intrusion detection using multistage deep learning image recognition. The network features are transformed into four-channel (Red, Green, Blue, and Alpha) images. The images then are used for classification to train and test the pre-trained deep learning model ResNet50. The proposed approach is evaluated using two publicly available benchmark datasets, UNSW-NB15 and BOUN Ddos. On the UNSW-NB15 dataset, the proposed approach achieves 99.8% accuracy in the detection of the generic attack. On the BOUN DDos dataset, the suggested approach achieves 99.7% accuracy in the detection of the DDos attack and 99.7% accuracy in the detection of the normal traffic.

scholarly journals Analysis of a Frequency Response of a Noisy Optical Network for Its Self-adaptation

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Artur Rataj ◽  
Amira Kamli ◽  
Tulin Atmaca
Keyword(s):  
Frequency Response ◽  
Computer Network ◽  
Optical Network ◽  
Packet Transmission ◽  
Step Frequency ◽  
Safe Limits ◽  
Self Adaptation ◽  
Optical Ring ◽  
Adaptation Scheme

Abstract We study the quality of frequency response in a noisy optical network. Such a response can be useful in traditional frequency-domain industrial loop controllers. In particular, we analyse a (step, frequency) response of a simulated computer network, where the stimulus is one of the coefficients which regulate the network’s strategy of packet transmission, and the response is the network’s momentary performance. This way, we find a frequency range, where an instantaneous dependence between the stimulus and the response can direct a self-adaptation scheme of the proposed strategy due to changing network conditions. To stay in the safe limits of the network’s behaviour, we make the stimulus weak. We use a bursty traffic model to test the limits of this approach. We use a model of an optical ring of an experimental NGREEN network developed at NOKIA. The discussed technique was capable of optimising the network’s behaviour.

A Review of Deep Machine Learning

2016 ◽  
Vol 24 ◽  
pp. 124-136 ◽  
Author(s):  
Ben Bright Benuwa ◽  
Yong Zhao Zhan ◽  
Benjamin Ghansah ◽  
Dickson Keddy Wornyo ◽  
Frank Banaseka Kataka
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Paradigm Shift ◽  
State Of The Art ◽  
Learning Algorithms ◽  
Algorithm Design ◽  
Prior State ◽  
Art Techniques

The rapid increase of information and accessibility in recent years has activated a paradigm shift in algorithm design for artificial intelligence. Recently, deep learning (a surrogate of Machine Learning) have won several contests in pattern recognition and machine learning. This review comprehensively summarises relevant studies, much of it from prior state-of-the-art techniques. This paper also discusses the motivations and principles regarding learning algorithms for deep architectures.

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