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Em Português - Mestrado em Sistemas Integrados de Apoio à Decisão

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Data-Driven Approach for Incident Management in a Smart City

February 07, 2021

ISCTE-IUL ISTAR-IUL MIBIS

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2021 1
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Why study at ISCTE?

My interest in ISCTE-IUL began when I met the first students of ISCTE-IUL . I noticed that the materials created by the teachers were, in general, very good and that a lot of care was taken in accompanying the students. For having such a good idea of ISCTE-IUL , I influenced my daughter to do her higher studies at ISCTE-IUL . She did her bachelor's and master's degrees and finished in 2018/2019, with an average of 16 out of 20, without ever leaving subjects behind and never having needed a tutor. That's why, when I thought about going back to school, it was natural that I went to search ISCTE-IUL's website for the formative offer, which could interest me. I was interested in the Master in Integrated Business Intelligence Systems ( MIBIS ) and in the beginning of January 2020 I sent an email to the master's director, professor João Carlos Ferreira , explaining my interest and asking if I would be able to be accepted in that master's course. His answer was

IoT Power Monitoring System for Smart Environments

In this research work, we describe the development and subsequent validation of EnerMon a flexible, efficient, edge-computing based Internet of Things (IoT) LoRa (LongRange) System to monitor power consumption. This system provides real-time information and a descriptive analytics process to provide a ‘big picture’ about energy consumption over time and identify energetic waste. The solution is based on Arduinos, current transformer sensors, Raspberry Pi as an application server and LoRa communication alongside a description and information on what is to be expected of it, describing the development process from the design phase to the validation phase with all steps in between. Due to LoRa low debit communication, an edge computing approach was implemented to create a real-time monitoring process based on this technology. This solution, with the help of descriptive analysis, allows the creation of an energetic local footprint, using a low-cost developed solution for less than 80€ per

Named Entity Recognition for Sensitive Data Discovery in Portuguese

Here is an example of work you can learn to do by attending the Master in Integrated Business Intelligence Systems The process of protecting sensitive data is continually growing and becoming increasingly important, especially as a result of the directives and laws imposed by the European Union. The effort to create automatic systems is continuous, but, in most cases, the processes behind them are still manual or semi-automatic. In this work, we have developed a component that can extract and classify sensitive data, from unstructured text information in European Portuguese. The objective was to create a system that allows organizations to understand their data and comply with legal and security purposes. We studied a hybrid approach to the problem of Named Entity Recognition for the Portuguese language. This approach combines several techniques such as rule-based/lexical-based models, machine learning algorithms, and neural networks. The rule-based and lexical-based approaches were u

Machine learning for quality control system

Attending the Master in Integrated Business Intelligence Systems you can learn to do this, for example: "In this work, we propose and develop a classification model to be used in a quality control system for clothing manufacturing using machine learning algorithms. The system consists of using pictures taken through mobile devices to detect defects on production objects. In this work, a defect can be a missing component or a wrong component in a production object. Therefore, the function of the system is to classify the components that compose a production object through the use of a classification model. As a manufacturing business progresses, new objects are created, thus, the classification model must be able to learn the new classes without losing previous knowledge. However, most classification algorithms do not support an increase of classes, these need to be trained from scratch with all . Thus. In this work, we make use of an incremental learning algorithm to tackle thi

MIBIS - Open applications

Want to know more about: - Internet of Things (IoT) - Use of sensors of all types - Capturing information from sensors and social media - Store captured data in the cloud in real-time - Clean and structure the data (Data Wharehouse) - Extracting knowledge from the data - Design decision support systems (DSS) based on knowledge extraction and much more on current topics in the field of Artificial Intelligence (AI), Smart Cities, Smart Transportation, Smart Health,..., Smart Anything. This is the right master's degree to you, grow in knowledge and prepare a future full of good opportunities in a propitious learning environment, with all the support of teachers and colleagues. Don't miss the deadline! Master in Integrated Business Intelligence Systems (MIBIS) - ISCTE-IUL IoT Laboratory

Higher Education, ISCTE-IUL, Lisbon, Portugal - Information on the functioning of the school year 2020/2021

Because I am receiving questions, in social media, from both Portuguese and foreign students, I share here the email I received from the rectory on 31-07-2020. Citation: Preparation of the 2020/2021 school year The Iscte is also awaiting guidance from the Ministry of Science, Technology and Higher Education (MSTHE) and the Directorate General of Health (DGH) regarding the operating conditions for the next school year. However, for the start of the 2020/2021 school year, the Iscte has already defined the following principles: 1. in line with the practice of previous years, it is planned to start classes, for most courses, in September 2020. 2. In the case of the 1st year of undergraduate and integrated master's degrees, given the schedule of the National Access Competition, classes will begin only on October 6 and end in December, according to the school calendar. 3. It is anticipated that classes will be taught in face-to-face from the Iscte rooms. 4. Classes are expected to be he

Smart Health

Remote monitoring of patients becomes increasingly necessary, especially for patients with chronic diseases, the elderly, and others who need special support. Technology and data science are already sufficiently developed to not only enable remote monitoring but to make real-time decisions based on the extraction of knowledge from captured data. There is a great demand for people specialised in IoT, remote sensing, data mining. Here is a master's course where you can learn and practice on these subjects. Pictures from IoT for Smart Cities, Summer School 2020 , ISCTE , ISTAR-IUL João Ferreira , Assistant Professor and Master's Director Luís Rosário , Cardiologist at Santa Maria Hospital

Workshop IoT for Smart Cities

For anyone interested in IoT. Next Monday, 27/7, at ISCTE, Classroom C1.03 , as part of the Summer School: 11am - Luis Rosado Cardiologist at Hospital Santa Maria - Health Challenges 12h - Rui Marinheiro lecturer at ISCTE - Communication and Architectures for IoT This Workshop will be open to the public and, exceptionally, can also be attended by Zoom, here : https://videoconf-colibri.zoom.us/j/92189789220?pwd=M0pIMjlNeUhtQzN4Y3NUMEJiUlFxZz09

Data-Driven Approach for Incident Management in a Smart City

Abstract Buildings in Lisbon are often the victim of several types of events (such as accidents, fires, collapses, etc.). This study aims to apply a data-driven approach towards knowledge extraction from past incident data, nowadays available in the context of a Smart City. We apply a Cross Industry Standard Process for Data Mining (CRISP-DM) approach to perform incident management of the city of Lisbon. From this data-driven process, a descriptive and predictive analysis of an events dataset provided by the Lisbon Municipality was possible, together with other data obtained from the public domain, such as the temperature and humidity on the day of the events. The dataset provided contains events from 2011 to 2018 for the municipality of Lisbon. This data mining approach over past data identified patterns that provide useful knowledge for city incident managers. Additionally, the forecasts can be used for better city planning, and data correlations of variables can provide information

BIM in People2People and Things2People Interactive Process

In this research work, we present an IoT solution to environment variables using a LoRa transmission technology to give real-time information to users in a Things2People process and achieve savings by promoting behavior changes in a People2People process. These data are stored and later processed to identify patterns and integrate with visualization tools, which allow us to develop an environmental perception while using the system. In this project, we implemented a different approach based on the development of a 3D visualization tool that presents the system collected data, warnings, and other users’ perception in an interactive 3D model of the building. This data representation introduces a new People2People interaction approach to achieve savings in shared spaces like public buildings by combining sensor data with the users’ individual and collective perception. This approach was validated at the ISCTE-IUL University Campus, where this 3D IoT data representation was presented

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