21,097 Hits in 7.0 sec

Human-in-the-Loop Processes as Enabler for Data Analytics in Digitalized Organizations [article]

Thomas Thiele, Thorsten Sommer, Stefan Schröder, Anja Richert, Sabina Jeschke
2016 Mensch & Computer  
As a key driver for innovation in science, economy and society, digitalization affects almost every aspect of our daily working and living environments.  ...  Lastly, a brief concept about evolution as a method for further optimizations and its implications for the human-in-the-loop process is outlined.  ...  High-Wage Countries" at RWTH Aachen University, as well as the Federal Ministry of Education and Research as part of the project "ELLI -Excellent Teaching and Learning in Engineering Sciences".  ... 
doi:10.18420/muc2016-ws11-0004 dblp:conf/mc/ThieleSSRJ16 fatcat:2w6k7ybp6zgu3k6nghdwdqainy

Digital Library Information Integration System Based on Big Data and Deep Learning

Xiao Lin, Ying Zhang, Jiangong Wang, C. Venkatesan
2022 Journal of Sensors  
and uses the word vector method to represent text features, the convolutional neural network in the deep learning model is used to extract the essential features of text information, and experimental verification  ...  In order to solve the defects of traditional text classification in digital library, the author proposes a method based on deep learning in the field of big data and artificial intelligence, which is applied  ...  Acknowledgments The second batch of science and technology projects of Fuzhou Science and Technology Bureau: digital library knowledge service platform based on WeChat official account's open source big  ... 
doi:10.1155/2022/9953787 fatcat:k2rx2jggafbhrddrnhe3jvq5py

Introduction: Rethinking AI. Neural Networks, Biometrics and the New Artificial Intelligence

Mathias Fuchs, Ramón Reichert, Mediarep, Philipps Universität Marburg
2020 Digital Culture & Society  
In the military field, neural networks are used in automatic image analysis for target recognition.  ...  Artificial neural networks are used in applied computer science and mathematics because they allow alternative formalizations of computability.  ... 
doi:10.25969/mediarep/13522 fatcat:gvro5uk76jf5rjgkq6d77h3pmm

Detailed Technical Programme Schedule

2020 2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)  
Framework for sharing COVID Blockchain and IPFS 5 1570675305 A Convolutional Neural Network Approach for The Diagnosis of Session-08.2: Neural Networks and Deep Learning based Methods for Digital  ...  and Deep Learning based Methods for Digital Image Ashwani Kumar, Vardhaman Engineering College, Hyderabad, Telangana , Jawaharlal Nehru University, New Delhi Neural Networks and Deep Learning based Methods  ... 
doi:10.1109/pdgc50313.2020.9315322 fatcat:4ndwytytovb7xkntz7bmaurj6a


2022 International Research Journal of Modernization in Engineering Technology and Science  
Using a combination of Convolution neural networks and Recurrent Neural Networks the project successfully came about to recognize text and hand written information.  ...  This project introduces a way to recognize text or handwritten information covert into machine readable text in a form the computer can process, store and edit as a text file or as a part of a data entry  ...  WaveNet is a deep neural network for generating raw audio, was used for this purpose.  ... 
doi:10.56726/irjmets29660 fatcat:qf3vsnkrsnbc3mydkrbadhn5d4

Digital, big data and computational forensics

Zeno Geradts
2018 Forensic Sciences Research  
The term "deep learning" is often used to refer to certain kinds of neural networks. The first publications on biological neural networks and the brain date back to the late 1800s [3] .  ...  We assume here that these algorithms are used in forensic science as tools and their results are critically verified by human experts.  ... 
doi:10.1080/20961790.2018.1500078 pmid:30483667 pmcid:PMC6201797 fatcat:273jz5z3xnde3fslzq3rw57rxi

Handwritten Digit Recognition System

Shubham Mendapara, Krish Pabani, Yash Paneliya
2021 International Journal of Scientific Research in Computer Science Engineering and Information Technology  
The main aim of this article is to use the neural network approach for recognizing handwritten digits. The Convolution Neural Network has become the center of all deep learning strategies.  ...  Apart from this, deep learning has brought a major turnaround in machine learning, which was the main reason it attracted many researchers. We can use it in many applications.  ...  METHODOLOGY AND CLASSIFICATIONFor developing the deep learning model, we will be using Convolutional Neural Network.  ... 
doi:10.32628/cseit217536 fatcat:tbcbrb6f2jdcdn3vrpmjwffaou

Pervasive Intelligence

Sebastian Vehlken
2018 Digital Culture & Society  
it considers possible consequences of a widespread use of SR with a focus on swarms of Unmanned Aerial Systems (Swarm UAS).  ...  As is shown on the basis of some generative examples from the field of UAS, robot swarms are imagined to literally penetrate space and control it.  ...  In the military field, neural networks are used in automatic image analysis for target recognition.  ... 
doi:10.14361/dcs-2018-0108 fatcat:rkuidqpnfjehpaychdcs3ccure

Conversation Engine for Deaf and Dumb

Monika K J
2021 International Journal for Research in Applied Science and Engineering Technology  
Deaf and hard hearing people use linguistic communication to exchange information between their own community and with others.  ...  Our project aims at taking the fundamental step in removing the communication gap between normal people, deaf and dumb people using language.  ...  Deep-learning architectures like deep neural networks, deep belief networks, graph neural networks, perennial neural networks and convolutional neural networks are applied to fields yet as pc vision, speech  ... 
doi:10.22214/ijraset.2021.36841 fatcat:pu4dckm5e5ft7npznebe2csl2m


Neil Yen, Hwa-Young Jeong, Kurosh Madani, Francisco Massetto
2021 Computer Science and Information Systems  
Recent prompt development in ICT (Information and Communications Technology) has caused a great impact to our daily lives.  ...  In the past, developers, and researchers as well, often provide services by predicting what, and how, target users would be expecting.  ...  Semantic network analysis, CONCOR analysis, and content analysis were used as data analysis methodology.  ... 
doi:10.2298/csis210200iy fatcat:4kn3awbwera73jq6obnegqhagy

Collaboration Robots as Digital Doubles of Person for Communication in Public Life and Space

Evgeniy Bryndin
2019 Budapest International Research in Exact Sciences (BirEx) Journal  
The approach combines existing scientific theories of human behavior with the flexibility of neural networks to make better decisions made by humans in space and in extreme situations.  ...  This approach provided pre-preparation of neural networks from accumulated data using existing behaviors.  ...  Deep Neural Networks is a multi-layer neural network in which "learned" data is passed through several layers of "neurons"; On each layer, the result of the previous layer is used as input information.  ... 
doi:10.33258/birex.v1i4.473 fatcat:egbcq3kwq5fmfgj2xmzymqldiy

Exploration of Landslide Detection Model using Deep Learning

R. Rajeswari, Borra Surendra, D. Dhilipkumar, S. Gogula Krishnan
2022 International Journal of Engineering Technology and Management Sciences  
The proposed solution for our project is to use deep learning technology to monitor and anticipate landslides.  ...  The landslide occurs in a matter of seconds, and although warning surface mine employees is simple, informing underground mining personnel is complicated and has a low likelihood of escape.  ...  The typical neural network has just 2-3 layers, but the deep neural network has 150 or more.  ... 
doi:10.46647/ijetms.2022.v06i05.011 fatcat:t5pfqtweizbmxk2eus45qx76l4

Recognition of Handwritten Digits using Convolutional Neural Network and Linear Binary Pattern

The human effort can be further more reduced in recognition, learning, predicting and many other areas using machine learning and deep learning.  ...  The proposed model includes Convolutional Neural Network (CNN), a deep learning approach with Linear Binary Pattern (LBP) used for feature extraction.  ...  *, Department of Information Science and digits.  ... 
doi:10.35940/ijitee.a5045.119119 fatcat:eajmmlfsufhx5iu27uycbdeyui

Image and Text Steganography Using Convolutional Neural Network

Vijay Kumar, Saloni Laddha, Aniket, Nitin Dogra
2021 TECNICA ITALIANA-Italian Journal of Engineering Science  
In this project, along with LSB encoding, deep learning modules using the Adam algorithm are used to train the model that comprises a hiding network and a reveal network.  ...  The goal in our project is to hide digital messages using modern steganography techniques.  ...  Deep Neural Networks (DNNs) are used to decide which bits to alter in LSB encoding and how to replace those bits with the text message [5, 6] .  ... 
doi:10.18280/ti-ijes.650104 fatcat:g6bgmzsmcncqray5zodmb7ns7e

Preface [chapter]

2020 Deep Learning  
Other variations are also in vogue where latent variables are organized layerwise in deep generative models. Representative examples include deep belief networks and deep Boltzmann machines.  ...  Convolutional neural networks (CNNs) have earned a lot of fame lately, especially in the domain of computer vision where in some cases its performance has beaten that of humans.  ...  with long-short-term memory layers, deep convolutional neural networks, and the recent state-of-the-art model for music and speech generation using WaveNets.  ... 
doi:10.1515/9783110670905-202 fatcat:h2cw6gegcrb5jhhgfzsddcexeu
« Previous Showing results 1 — 15 out of 21,097 results