UGM AI Center of Excellence received a visit by one of the most important people in Indonesia. He is the President Director of PT AKR Corporindo Tbk. (AKRA), Haryanto Adikoesoemo. He looked around and also talk about the development of Artificial Intelligence.

In order to welcome industry 4.0, Universitas Gadjah Mada inaugurated the Artificial Intelligence (AI) Center of Excellence which will accommodate productive and innovative research in the field of intelligent transportation technology, early detection of diseases and health monitoring, robotics and censorship systems, digital communication and social media, energy management, and customer experience based on artificial intelligence technology. UGM AI Center of Excellence was inaugurated by Ir. Budi Karya Sumadi, Minister of Transportation of the Republic of Indonesia, on Friday, February 22, 2019 at the Department of Electrical Engineering and Information Technology of the Faculty of Engineering, coincided with the commemoration of the 73rd Engineering Higher Education Day (HPTT). The inauguration began with the ribbon cutting by Ir. Budi Karya Sumadi, Minister of Transportation of the Republic of Indonesia; Ir. Airlangga Hartanto, M.B.A., M.M.T, Minister of Industry of the Republic of Indonesia who previously inaugurated the Toyota Laboratory in the Department of Mechanical Engineering and Industrial Engineering FT UGM; and Prof. Ir. Panut Mulyono, M.Eng., D.Eng, Chancellor of Gadjah Mada University. The inauguration agenda was continued by the signing of a digital inscription by Ir. Budi Karya Sumadi, Minister of Transportation of the Republic of Indonesia.

UGM AI Center of Excellence has research activities, namely intelligent vehicle automatic detection systems, research on intelligent systems for detecting the density of vehicles on a road or traffic light, research on Automatic License Plate Recognition, and research on intelligent visual-based systems to assist navigation automatic. Several national practitioners and scientists also contributed to the UGM advisory board and advisors AI Center of Excellence, including Dr. Ing. Ilham Akbar Habibie (CEO of Ilthabi Rekatama), Dr. Agus Hasan Sulistiono (Director of PT. Tira Austenite, Tbk.), Prof. Ir. Nizam, M.Sc., Ph.D. (Dean of the Faculty of Engineering UGM), Herman Widjaja (VP Engineering Tokopedia), Rene Indiarto Widjaja (Director of PT. Epsindo Prima Energi), and Dr. Ettikan K. Karuppiah (Director of Developer Ecosystem, NVIDIA).

Chairperson of the Department of Electrical Engineering and Information Technology of the Faculty of Engineering, Dr. Sarjiya on another occasion said that the AI ​​Center of Excellence would be the right place for the implementation of artificial intelligence technology research that had been carried out at UGM for the wider community. In addition, the AI ​​Center of Excellence will accelerate the opportunities for technological development which is the basis of the digital industry that is currently growing rapidly in Indonesia. Some UGM researchers who initiated the AI ​​Center of Excellence have collaborated with various academic and industrial institutions in the country and abroad in developing various kinds of artificial intelligence technology-based solutions. In 2022, the UGM AI Center of Excellence is expected to be a pioneer in artificial intelligence technology innovation in Indonesia as well as being a major contributor to the development of the digital intelligence technology-based digital industry. (efr)

There is an interesting article from Kdnuggets, showing how to understand the Deep Learning.

(Source: https://www.kdnuggets.com/2016/01/seven-steps-deep-learning.html)

One interesting article from Kdnuggets, talking about Deep Learning for image classification using Keras.

(Source: https://www.kdnuggets.com/2017/08/first-steps-learning-deep-learning-image-classification-keras.html)

This project is a collaboration research with Active Intelligent System Laboratory, Toyohashi University of Technology, Japan. The goal is to detect and track an unstructured road boundary, mainly for robotic and intelligent vehicle navigation system. It has been done by Mr. Kazuki Mano under join supervision of Prof. Jun Miura and Dr. Igi Ardiyanto.

Discovering and drawing out the relationship between users and items in a service-based companies or organizations are the essence of a recommendation system. Here we address a novel approach for the recommendation system, incorporating the means of collaborative aspect between the users internal hidden patterns and the items or goods to be recommended. Unlike the existing methods, our algorithm introduces a guiding factor between the user hidden state and the choice over the item set, such that it gives additional degree of freedom for the recommendation system to opt on which factor is more prominent.

Diabetic Retinopathy (DR) is a disease which affect the vision ability. The observation by an ophthalmologist usually conducted by analyzing the retinal images of the patient which are marked by some DR features. However some misdiagnosis are usually found due to human error. Here, a deep learning-based low-cost embedded system is established to assist the doctor for grading the severity of the DR from the retinal images. A compact deep learning algorithm named Deep-DR-Net which fits on a small embedded board is afterwards proposed for such purposes. In the heart of Deep-DR-Net, a cascaded encoder-classifier network is arranged using residual style for ensuring the small model size. The usage of different types of convolutional layers subsequently guarantees the features richness of the network for differentiating the grade of the DR. Experimental results show the capability of the proposed system for detecting the existence as well as grading the severity of the DR symptomps. I. Ardiyanto, H. A. Nugroho, R. L. B. Buana, “Deep Learning-based Diabetic Retinopathy Assessment on Embedded System”, The 39th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC 2017), pp. 1760-1763, Jeju Island, South Korea, 2017.

Road-scene segmentation is a part of general image segmentation problems which tries to characterize the road-scene and divides it into labeled area/objects, such as road, building, car, pedestrian, pavement, etc. This problem has a huge range of applications, e.g. Intelligent Vehicle and Advanced Driver Assistance System (ADAS). We develop RCC-Net, a deep learning-based algorithm for achieving a real-time road-scene segmentation for Advanced Driver Assistance System (ADAS). We have successfully built the RCC-Net on a low-cost embedded system, NVIDIA Jetson TK1, opening the possibilities for in-car deployment. I. Ardiyanto, T.B. Adji, “Deep Residual Coalesced Convolutional Network for Efficient Semantic Road Segmentation”, IPSJ Transactions on Computer Vision and Applications (IPSJ-CVA), vol. 9:6, Springer, 2017. (Invited paper of MVA 2017, ISSN: 1882-6695)