scholarly journals Towards User Friendly Medication Mapping Using Entity-Boosted Two-Tower Neural Network

2021 ◽  
pp. 126-138
Author(s):  
Shaoqing Yuan ◽  
Parminder Bhatia ◽  
Busra Celikkaya ◽  
Haiyang Liu ◽  
Kyunghwan Choi
Keyword(s):  
Neural Network ◽  
User Friendly

scholarly journals A New “User-friendly” Blast Furnace Advisory Control System Using a Neural Network Temperature Profile Classifier

2010 ◽  
Vol 50 (5) ◽  
pp. 730-737 ◽  
Author(s):  
Francisco A. García ◽  
Pascual Campoy ◽  
Javier. Mochón ◽  
Iñigo Ruiz-Bustinza ◽  
Luis Felipe Verdeja ◽  
...  
Keyword(s):  
Neural Network ◽  
Blast Furnace ◽  
Control System ◽  
Temperature Profile ◽  
User Friendly

scholarly journals SUSSOL—Using Artificial Intelligence for Greener Solvent Selection and Substitution

Molecules ◽  
2020 ◽  
Vol 25 (13) ◽  
pp. 3037 ◽  
Author(s):  
Hannes Sels ◽  
Herwig De Smet ◽  
Jeroen Geuens
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Physical Properties ◽  
The Self ◽  
Self Organizing Map ◽  
Solvent Selection ◽  
Sustainable Solvents ◽  
Cluster A ◽  
User Friendly ◽  
Intuitive Sense

Solvents come in many shapes and types. Looking for solvents for a specific application can be hard, and looking for green alternatives for currently used nonbenign solvents can be even harder. We describe a new methodology for solvent selection and substitution, by applying Artificial Intelligence (AI) software to cluster a database of solvents based on their physical properties. The solvents are processed by a neural network, the Self-organizing Map of Kohonen, which results in a 2D map of clusters. The resulting clusters are validated both chemically and statistically and are presented in user-friendly visualizations by the SUSSOL (Sustainable Solvents Selection and Substitution Software) software. The software helps the user in exploring the solvent space and in generating and evaluating a list of possible alternatives for a specific solvent. The alternatives are ranked based on their safety, health, and environment scores. Cases are discussed to demonstrate the possibilities of our approach and to show that it can help in the search for more sustainable and greener solvents. The SUSSOL software makes intuitive sense and in most case studies, the software confirms the findings in literature, thus providing a sound platform for selecting the most sustainable solvent candidate.

scholarly journals An Educational Visual C++ Program Aid to Estimate Fuel Consumption for Tillage Equipment Based on Trained Artificial Neural Network

2010 ◽  
Vol 67 (1) ◽  
Author(s):  
Abdulrahman AL-JANOBI ◽  
Saad AL-HAMED ◽  
Abdulwahed ABOUKARIMA
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuel Consumption ◽  
Undergraduate Students ◽  
Field Experiments ◽  
Soil Conditions ◽  
C Programming Language ◽  
C Programming ◽  
Artificial Neural ◽  
User Friendly

An educational program was developed to assist graduate and undergraduate students to estimate fuel consumption for tillage equipment. It supports to select the appropriate power of an agricultural tractor to operate with a particular tillage implement in specific operation and soil conditions to minimize fuel consumption. The program was written in visual C++ programming language. The program was based on training library of an artificial neural network. The program offers an educational help and clarification to most of the affecting parameters on fuel consumption. The program was validated by comparing predicted fuel consumption with the results obtained during field experiments. The program has proven to be very user-friendly and efficient to meet the requirement.

scholarly journals Accurate and Versatile 3D Segmentation of Plant Tissues at Cellular Resolution

2020 ◽  
Author(s):  
Adrian Wolny ◽  
Lorenzo Cerrone ◽  
Athul Vijayan ◽  
Rachele Tofanelli ◽  
Amaya Vilches Barro ◽  
...  
Keyword(s):  
Neural Network ◽  
Human Performance ◽  
Light Sheet ◽  
Plant Tissues ◽  
Graphical Interface ◽  
3D Segmentation ◽  
The Neural Network ◽  
Volumetric Images ◽  
Automated Algorithms ◽  
User Friendly

ABSTRACTQuantitative analysis of plant and animal morphogenesis requires accurate segmentation of individual cells in volumetric images of growing organs. In the last years, deep learning has provided robust automated algorithms that approach human performance, with applications to bio-image analysis now starting to emerge. Here, we present PlantSeg, a pipeline for volumetric segmentation of plant tissues into cells. PlantSeg employs a convolutional neural network to predict cell boundaries and graph partitioning to segment cells based on the neural network predictions. PlantSeg was trained on fixed and live plant organs imaged with confocal and light sheet microscopes. PlantSeg delivers accurate results and generalizes well across different tissues, scales, and acquisition settings. We present results of PlantSeg applications in diverse developmental contexts. PlantSeg is free and open-source, with both a command line and a user-friendly graphical interface.

scholarly journals Use of artificial intelligence and neural networks for analysis and gesture detection in electrical impedance tomography

2020 ◽  
Vol 6 (3) ◽  
pp. 489-492
Author(s):  
Christian Gibas ◽  
Luca Mülln ◽  
Rainer Brück
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Impedance Tomography ◽  
Precise Analysis ◽  
User Friendly ◽  
And Control

AbstractArtificial intelligence and neural networks are getting more and more relevant for several types of application. The field of prosthesis technology currently uses electromyography for controllable prosthesis. The precision of the control suffers from the use of EMG. More precise and more collected data with the help of EIT allows a much more precise analysis and control of the prosthesis. In this paper a neural network for gesture detection using EIT is developed and presented in a user-friendly way.

scholarly journals An Optimized Hybrid Neural Network Model for Detecting Depression among Twitter Users

2019 ◽  
Vol 8 (10) ◽  
pp. 2781-2795
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Research Work ◽  
Hybrid Neural Network ◽  
The Neural Network ◽  
Twitter Users ◽  
Trained Neural Network ◽  
User Friendly ◽  
Better Than

The proposed work is to extensively evaluate if a user is depressed or not using his Tweets on Twitter. With the omni presence of social media, this method should help in identifying the depression of users. We propose an Optimized Hybrid Neural Network model to evaluate the user tweets on Twitter to analyze if a user is depressed or not. Where Neural Network is trained using Tweets to predict the polarity of Tweets. The Neural Network is trained in such a way that at any point when presented with a Tweet the model outputs the polarity associated with the Tweet. Also, a user-friendly GUI is presented to the user that loads the trained neural network in no time and can be used to analyze the users’ state of depression. The aim of this research work is to provide an algorithm to evaluate users’ sentiment on Twitter in a way better than all other existing techniques

scholarly journals Accurate and versatile 3D segmentation of plant tissues at cellular resolution

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Adrian Wolny ◽  
Lorenzo Cerrone ◽  
Athul Vijayan ◽  
Rachele Tofanelli ◽  
Amaya Vilches Barro ◽  
...  
Keyword(s):  
Neural Network ◽  
Human Performance ◽  
Light Sheet ◽  
Plant Tissues ◽  
Graphical Interface ◽  
3D Segmentation ◽  
The Neural Network ◽  
Volumetric Images ◽  
Automated Algorithms ◽  
User Friendly

Quantitative analysis of plant and animal morphogenesis requires accurate segmentation of individual cells in volumetric images of growing organs. In the last years, deep learning has provided robust automated algorithms that approach human performance, with applications to bio-image analysis now starting to emerge. Here, we present PlantSeg, a pipeline for volumetric segmentation of plant tissues into cells. PlantSeg employs a convolutional neural network to predict cell boundaries and graph partitioning to segment cells based on the neural network predictions. PlantSeg was trained on fixed and live plant organs imaged with confocal and light sheet microscopes. PlantSeg delivers accurate results and generalizes well across different tissues, scales, acquisition settings even on non plant samples. We present results of PlantSeg applications in diverse developmental contexts. PlantSeg is free and open-source, with both a command line and a user-friendly graphical interface.

scholarly journals Smart Glass for Visual Impaired People

2019 ◽  
Vol 8 (12) ◽  
pp. 3411-3415
Keyword(s):  
Neural Network ◽  
Visually Impaired ◽  
Convolution Neural Network ◽  
Impaired People ◽  
User Friendly ◽  
Test Result ◽  
Electronic Travel Aids

To defeat the voyaging trouble for the outwardly debilitated individuals, this endeavor presents an ETA (Electronic Travel Aids)- canny controlling device in the condition of a few eyeglasses for provide these people guidance gainfully and safely. Unique in relation to existing works, a novel Convolution Neural Network(CNN) based deterrent keeping away from calculation is proposed, which uses Google's pre-prepared datasets of different classes to take care of the issues of identifying little impediments, and straight forward obstructions, for example bicycle. For absolutely visually impaired individuals, three sorts of voice guidelines to educate the bearing where they can proceed. For deaf and dumb people we integrate two servo motors to insist them through touch. The prototype consists of pair of servo motors and camera in the eye glass and its effectiveness and precision were tried by an ongoing snag. The test result demonstrates that the savvy controlling glass is effective in accuracy than any other traditional algorithms. Thus it serves as a user friendly device by its simplistic design.

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