Day :
- Artificial Intelligence
Location: Webinar
Session Introduction
Abir GALLALA
University of Luxembourg, Luxembourg
Title: Augmented-Reality-based digital twin approach for robot manipulation
Biography:
Abir GALLALA has completed her master in 2016 in Artificial Intelligence from the national engineering school of Sousse in Tunisia and a bachelor in industrial computer science from the same school. Currentlz she is pursuing a doctoral degree PhD at the university of Luxembourg in Human-Robot Interaction within the engineering department.
Abstract:
In recent years, robotics research has been facing a significant growing. Industrial and research interests are moving from the development of robots for structured industrial environments to the development of collaborative and autonomous robots operating in hybrid environments. The biggest drawback of the introduction of these cobots (collaborative robots) is that they are not user friendly. The fourth industrial revolution has allowed the introduction of new technologies that enhance the humanrobot interaction such as Augmented Reality (AR). In this poster, we suggest our approach which is an Augmented Reality-based digital twin approach for an easy and user-friendly Human-Robot interaction. The presented system is a marker-based system that allows users to program collaborative robots. This model aims to manioulate a virtual model of the cobot using an AR head-mounted device
Billy Susilo
National Taiwan University of Science and Technology, Taipei, Taiwan
Title: Predicting Microbial Species in a River Based on Physicochemical Properties by Bio-Inspired Metaheuristic Optimized Machine Learning
Biography:
Billy Susilo obtained his B.Eng in civil construction engineering and project management department at Petra Christian University, Surabaya, Indonesia. Afterwards, he received full scholarship from Taiwan government to pursue his master degree at National Taiwan University of Science and Technology, Taipei, Taiwan, and did research collaboration with environmental engineering department of National Taiwan University, Taipei, Taiwan. Besides graduated as best graduated student during his master degree period, he also receive awards as one of the best speaker for his research in 22nd Symposium on Construction Engineering and Management. His research focuses on engineering informatics, optimization on machine learning algorithm and metaheuristic artificial intelligence techniques. Currently, he is a data supervisor senior engineer in one the biggest geotechnical real-time monitoring construction company in Taiwan.
Abstract:
The primary objective of the examination of microbial nature is to comprehend the connection between Earth's microbial network and their capacities in the earth. This paper presents a proof-of-idea exploration to build up a bioclimatic displaying approach that use man-made reasoning procedures to distinguish the microbial species in a stream as a component of physicochemical boundaries. Highlight decrease and determination are both used in the information preprocessing attributable to the scant of accessible information focuses gathered and missing estimations of physicochemical ascribes from a waterway in Southeast China. A bio-roused metaheuristic upgraded machine student, which bolsters the change in accordance with the numerous yield forecast structure, is utilized in bioclimatic demonstrating. The exactness of expectation and pertinence of the model can support microbiologists and scientists in measuring the anticipated microbial species for additional exploratory arranging with negligible use, which is gotten one of the most major issues when confronting emotional changes of natural conditions brought about by an Earth-wide temperature boost. This work exhibits a neoteric approach for expected use in anticipating primer microbial structures in nature.
Jayaraj P B
National Institute of Technology Calicut, India
Title: AI in Computational Drug Discovery
Biography:
He received his Ph.D in Computer Science from National Institute of Technology Calicut, India. His thesis was “GPU based Virtual Screening Techniques for Faster Drug Discovery”. Now he is an assistant professor at the CSE department, NIT Calicut, India. His research interests include Medical-informatics, Computational Drug Design and GPU Computing. He has published many journals as well as conference proceedings. He has attended an International spring school on High Performance Computing (HighPer 2018) at San Sebastian, Spain in April 2018.
Abstract:
Ordinary medication revelation strategies depend essentially in-vitro tries directed with an objective particle and an extremely huge arrangement of little atoms to pick a correct ligand. With the investigation space for the correct ligand being exceptionally huge, this methodology is profoundly tedious and requires high capital for assistance. Virtual screening, a computational method utilized for assessing an enormous gathering of particles to recognize lead atoms, can be utilized for this reason to accelerate the medication revelation measure. Ligand based medication configuration works by building an applied model of the objective protein. Ligand based virtual screening utilizes this model to assess and isolate dynamic particles for an objective protein. A classes of calculation in machine inclining called Classification calculation can be utilized to construct the above model. In this theoretical, 3 distinctive AI ways to deal with settle virtual screening is depicted. The principal strategy uses a proficient virtual screening method utilizing Random Forest (RF) classifier. Second procedure applies SVM classifier for virtual screening. The third technique shows the appropriateness of Self Organizing Map (SOM) as a classifier for screening ligand atoms, which is first of its sort around there according to the writing. The discussion end with looking at the in addition to and short of the three strategies. The GPU parallelisation of these techniques will be additionally clarified in subtleties
Neel Gandhi
Pandit Deendayal Petroleum University, India
Title: Application of Artificial Intelligence in field of urological cancer
Biography:
Neel Gandhi is pursing his Bachelor’s in Information and communication technology having experienced in Artificial Intellgience in Healthcare from Pandit Deendayal Petroleum University.He is associated with IEEE and has done projects in field of artificial intelligence.He has participated in many conferences and interested in research
Abstract:
Artificial intelligence(AI) techniques like artificial neural networks,Bayesian belief networks and neurological fuzzy systems are widely adopted in urology.AI approach are found to be more exploratory as well as accurate in the terms of prediction compared to conventional statistics.The model are complex mathematical based models derived from working of human brain.A detailed study conducted in the field of urology using a technique resulted in finding a new dynamic applications in the field of neurological cancer treatment.The results of machine learning techniques and implementation were focused on handling as well as prediction using artificial neural networks for the purpose of diagnosis,prognosis and treatment of cancer.Different techniques of AI depending on respective characteristics,where found suitable for different tasks.Also,the lack of transparency in neural networks was also overcome using neuro-fuzzy systems.
Biography:
Abstract:
In this presentation we described and implemented Image compression using recurrent neural network, the compression of image method is a type of information compression that will decrease the same amount of image to be transmitted, stored and evaluated, but without losing the information content. Here we are compressing image with one of most type of neural network i.e. Recurrent Neural Network (RNN). The architecture consist of recurrent neural network based encoder, binarizer, and decoder system. Using this reconstructed the image which is having better quality than the original image and along with this here we show the activation function i.e. Sigmoid, ReLU and tanh functions. And also we evaluated PSNR, MSE, CR, BPP and SSIM, MS-SSIM, parameters for comparing original and compressed images. For this we are taken selected images on the Kodak dataset images. And this work is performed by using python 3.6 version tool with some standard packages for AI functions. So this can demonstrates that our Deep learning achieves better generalization.
Biography:
Abstract:
In this presentation we described and implemented Image compression using recurrent neural network, the compression of image method is a type of information compression that will decrease the same amount of image to be transmitted, stored and evaluated, but without losing the information content. Here we are compressing image with one of most type of neural network i.e. Recurrent Neural Network (RNN). The architecture consist of recurrent neural network based encoder, binarizer, and decoder system. Using this reconstructed the image which is having better quality than the original image and along with this here we show the activation function i.e. Sigmoid, ReLU and tanh functions. And also we evaluated PSNR, MSE, CR, BPP and SSIM, MS-SSIM, parameters for comparing original and compressed images. For this we are taken selected images on the Kodak dataset images. And this work is performed by using python 3.6 version tool with some standard packages for AI functions. So this can demonstrates that our Deep learning achieves better generalization.