Data Science Conference is one of the leading research topics in the international research conference domain. Data Science is a conference track under the Architecture and Urban Planning Conference which aims to bring together leading academic scientists, researchers and research scholars to exchange and share their experiences and research results on all aspects of Architecture and Urban Planning.
internationalconference.net provides a premier interdisciplinary platform for researchers, practitioners and educators to present and discuss the most recent innovations, trends, and concerns as well as practical challenges encountered and solutions adopted in the fields of (Architecture and Urban Planning).
Data Science is not just a call for academic papers on the topic; it can also include a conference, event, symposium, scientific meeting, academic, or workshop.
On behalf of the International Conference on Architecture and Urban Planning, we cordially invite participants to speak as a keynote speaker on advances in the field of Architecture and Urban Planning research at the conference. The research conference is attended by distinguished scholars, experts and researchers from all over the world.
The organizing committee would be grateful if keynote speakers share their expertise on their specialized topic with conference participants. As a keynote speaker, your knowledge would be an excellent addition to our program.
Thank you for considering our request and please do not hesitate to contact us if you have any questions.
The conference is organized by Global Event Services which is a full service worldwide organizer of scientific events, conferences, symposiums, workshops, meetings, exhibitions and convention-planning.
Global Event Services has 15 years of experience in events industry. By focusing on creating a solid academic research environment, Global Conference Services helps to bring together scholars, experts, researchers and those who seek out new ideas and strive for new achievements from all over the world.
The official language of the conference is English. Translation and interpreting services will not be available. The dress code is business casual to business attire. Meeting room temperatures may vary, so wear layered clothing to ensure your personal comfort. Please arrive at the conference room at least 30 minutes before your session begins. There may be changes to the conference program, for which participants will be notified in a timely manner.
Electrical outlets will not be available for use due to safety reasons. As a courtesy to speakers and other participants, mobile phones must be turned to silent before entering the sessions. Access to the conference room is available only to registered participants.
By registering for the conference, you grant permission to conference management to photograph, film or record and use your name, likeness, image, voice and comments and to publish, reproduce, exhibit, distribute, broadcast, edit and/or digitize the resulting images and materials in publications, advertising materials, or in any other form worldwide without compensation. Taking of photographs and/or videotaping during any session is prohibited.
Types of Presentation (Oral presentation, Poster presentation, Online presentation)
Oral presenters will be given 10 minutes to present their work and additional 5 minutes for questions and answers. Poster or Online presentations will be given 5 minutes to present their work (minimum five slides) and additional 3 minutes for questions and answers. Moderators will be strict about timing. Your presentation must be in PDF format. All presentations must be in standard ratio to match the size of the projection screen.
The conference room is equipped with overhead multimedia projector, large screen, laptop running Linux/Windows (with acrobat reader installed), wireless remote for slides control with laser pointer. Once the presentation is launched, you will control/advance the slides. There will be no internet access on the presentation computer. Presentations must be submitted in advance using the online submission form. Please bring a copy of your presentation to the conference on a USB memory stick as a backup. All presenters are encouraged to check and review their presentations in advance.
Scientific Review Committee
All the full-text papers, regardless of the presentation type, will be peer-reviewed by the International Journal of Medical, Medicine and Health Sciences committee members. Each paper is peer-reviewed by two anonymous, independent reviewers. First proofs will be emailed to the corresponding author after acceptance. Authors should check their first proofs and answer any queries that have arisen during copyediting and typesetting within two days. Authors must check proofs carefully, as no further changes can be made once the paper has been published online. The official language is English. Sending a full-text paper for publication is optional.
The final edited full-text papers will be published online at the International Journal of Medical, Medicine and Health Sciences. Final papers are published in finished form 2-3 weeks after receipt of corrected author proofs. Each full-text paper is, paginated independently, fully citable with an assigned digital object identifier (DOI). The journal’s full open access policy allows authors to share their article in digital format.
Papers must be minimum of 4-pages long in double column layout.
Previously Published Papers on "Data Science Conference"
Review of the Road Crash Data Availability in Iraq
Abeer K. Jameel,
Data availability, Iraq, road safety.
Abstract: Iraq is a middle income country where the road safety issue is considered one of the leading causes of deaths. To control the road risk issue, the Iraqi Ministry of Planning, General Statistical Organization started to organise a collection system of traffic accidents data with details related to their causes and severity. These data are published as an annual report. In this paper, a review of the available crash data in Iraq will be presented. The available data represent the rate of accidents in aggregated level and classified according to their types, road users’ details, and crash severity, type of vehicles, causes and number of causalities. The review is according to the types of models used in road safety studies and research, and according to the required road safety data in the road constructions tasks. The available data are also compared with the road safety dataset published in the United Kingdom as an example of developed country. It is concluded that the data in Iraq are suitable for descriptive and exploratory models, aggregated level comparison analysis, and evaluation and monitoring the progress of the overall traffic safety performance. However, important traffic safety studies require disaggregated level of data and details related to the factors of the likelihood of traffic crashes. Some studies require spatial geographic details such as the location of the accidents which is essential in ranking the roads according to their level of safety, and name the most dangerous roads in Iraq which requires tactic plan to control this issue. Global Road safety agencies interested in solve this problem in low and middle-income countries have designed road safety assessment methodologies which are basing on the road attributes data only. Therefore, in this research it is recommended to use one of these methodologies.
Microclimate Variations in Rio de Janeiro Related to Massive Public Transportation
Marco E. O. Jardim,
Frederico A. M. Souza,
Valeria M. Bastos,
Myrian C. A. Costa,
Nelson F. F. Ebecken,
Big data, IoT, public transportation, public health system.
Abstract: Urban public transportation in Rio de Janeiro is based on bus lines, powered by diesel, and four limited metro lines that support only some neighborhoods. This work presents an infrastructure built to better understand microclimate variations related to massive urban transportation in some specific areas of the city. The use of sensor nodes with small analytics capacity provides environmental information to population or public services. The analyses of data collected from a few small sensors positioned near some heavy traffic streets show the harmful impact due to poor bus route plan.
Automatic Thresholding for Data Gap Detection for a Set of Sensors in Instrumented Buildings
Mahendra Pratap Singh,
Mohamed Naceur Abdelkrim,
Building system, time series, diagnosis, outliers, delay,
Abstract: Building systems are highly vulnerable to different
kinds of faults and failures. In fact, various faults, failures and human
behaviors could affect the building performance. This paper tackles
the detection of unreliable sensors in buildings. Different literature
surveys on diagnosis techniques for sensor grids in buildings have
been published but all of them treat only bias and outliers. Occurences
of data gaps have also not been given an adequate span of attention
in the academia. The proposed methodology comprises the automatic thresholding
for data gap detection for a set of heterogeneous sensors in
instrumented buildings. Sensor measurements are considered to be
regular time series. However, in reality, sensor values are not
uniformly sampled. So, the issue to solve is from which delay each
sensor become faulty? The use of time series is required for detection of abnormalities on
the delays. The efficiency of the method is evaluated on measurements
obtained from a real power plant: an office at Grenoble Institute of
technology equipped by 30 sensors.
Estimation of Train Operation Using an Exponential Smoothing Method
Exponential smoothing method, open data, operation estimation, train schedule.
Abstract: The purpose of this research is to improve the convenience of waiting for trains at level crossings and stations and to prevent accidents resulting from forcible entry into level crossings, by providing level crossing users and passengers with information that tells them when the next train will pass through or arrive. For this paper, we proposed methods for estimating operation by means of an average value method, variable response smoothing method, and exponential smoothing method, on the basis of open data, which has low accuracy, but for which performance schedules are distributed in real time. We then examined the accuracy of the estimations. The results showed that the application of an exponential smoothing method is valid.
A Study of the Adaptive Reuse for School Land Use Strategy: An Application of the Analytic Network Process and Big Data
Adaptive reuse, analytic network process, big data, land use strategy.
Abstract: In today's popularity and progress of information technology, the big data set and its analysis are no longer a major conundrum. Now, we could not only use the relevant big data to analysis and emulate the possible status of urban development in the near future, but also provide more comprehensive and reasonable policy implementation basis for government units or decision-makers via the analysis and emulation results as mentioned above. In this research, we set Taipei City as the research scope, and use the relevant big data variables (e.g., population, facility utilization and related social policy ratings) and Analytic Network Process (ANP) approach to implement in-depth research and discussion for the possible reduction of land use in primary and secondary schools of Taipei City. In addition to enhance the prosperous urban activities for the urban public facility utilization, the final results of this research could help improve the efficiency of urban land use in the future. Furthermore, the assessment model and research framework established in this research also provide a good reference for schools or other public facilities land use and adaptive reuse strategies in the future.
Integration of Big Data to Predict Transportation for Smart Cities
Big data, bus headway prediction, machine learning, public transportation.
Abstract: The Intelligent transportation system is essential to build smarter cities. Machine learning based transportation prediction could be highly promising approach by delivering invisible aspect visible. In this context, this research aims to make a prototype model that predicts transportation network by using big data and machine learning technology. In detail, among urban transportation systems this research chooses bus system. The research problem that existing headway model cannot response dynamic transportation conditions. Thus, bus delay problem is often occurred. To overcome this problem, a prediction model is presented to fine patterns of bus delay by using a machine learning implementing the following data sets; traffics, weathers, and bus statues. This research presents a flexible headway model to predict bus delay and analyze the result. The prototyping model is composed by real-time data of buses. The data are gathered through public data portals and real time Application Program Interface (API) by the government. These data are fundamental resources to organize interval pattern models of bus operations as traffic environment factors (road speeds, station conditions, weathers, and bus information of operating in real-time). The prototyping model is designed by the machine learning tool (RapidMiner Studio) and conducted tests for bus delays prediction. This research presents experiments to increase prediction accuracy for bus headway by analyzing the urban big data. The big data analysis is important to predict the future and to find correlations by processing huge amount of data. Therefore, based on the analysis method, this research represents an effective use of the machine learning and urban big data to understand urban dynamics.
Elemental Graph Data Model: A Semantic and Topological Representation of Building Elements
Yasmeen A. S. Essawy,
Building information modeling, elemental graph data model, geometric and topological data models, and graph theory.
Abstract: With the rapid increase of complexity in the building industry, professionals in the A/E/C industry were forced to adopt Building Information Modeling (BIM) in order to enhance the communication between the different project stakeholders throughout the project life cycle and create a semantic object-oriented building model that can support geometric-topological analysis of building elements during design and construction. This paper presents a model that extracts topological relationships and geometrical properties of building elements from an existing fully designed BIM, and maps this information into a directed acyclic Elemental Graph Data Model (EGDM). The model incorporates BIM-based search algorithms for automatic deduction of geometrical data and topological relationships for each building element type. Using graph search algorithms, such as Depth First Search (DFS) and topological sortings, all possible construction sequences can be generated and compared against production and construction rules to generate an optimized construction sequence and its associated schedule. The model is implemented in a C# platform.
Exploring the Activity Fabric of an Intelligent Environment with Hierarchical Hidden Markov Theory
Behavior, big data, hierarchical Hidden Markov Model, intelligent object.
Abstract: The Internet of Things (IoT) was designed for widespread convenience. With the smart tag and the sensing network, a large quantity of dynamic information is immediately presented in the IoT. Through the internal communication and interaction, meaningful objects provide real-time services for users. Therefore, the service with appropriate decision-making has become an essential issue. Based on the science of human behavior, this study employed the environment model to record the time sequences and locations of different behaviors and adopted the probability module of the hierarchical Hidden Markov Model for the inference. The statistical analysis was conducted to achieve the following objectives: First, define user behaviors and predict the user behavior routes with the environment model to analyze user purposes. Second, construct the hierarchical Hidden Markov Model according to the logic framework, and establish the sequential intensity among behaviors to get acquainted with the use and activity fabric of the intelligent environment. Third, establish the intensity of the relation between the probability of objects’ being used and the objects. The indicator can describe the possible limitations of the mechanism. As the process is recorded in the information of the system created in this study, these data can be reused to adjust the procedure of intelligent design services.
Assessment of the Number of Damaged Buildings from a Flood Event Using Remote Sensing Technique
Flooding extent, Sentinel-1A data, JOSM desktop, damaged buildings.
Abstract: The heavy rainfall from 3rd to 22th January 2017 had swamped much area of Ranot district in southern Thailand. Due to heavy rainfall, the district was flooded which had a lot of effects on economy and social loss. The major objective of this study is to detect flooding extent using Sentinel-1A data and identify a number of damaged buildings over there. The data were collected in two stages as pre-flooding and during flood event. Calibration, speckle filtering, geometric correction, and histogram thresholding were performed with the data, based on intensity spectral values to classify thematic maps. The maps were used to identify flooding extent using change detection, along with the buildings digitized and collected on JOSM desktop. The numbers of damaged buildings were counted within the flooding extent with respect to building data. The total flooded areas were observed as 181.45 sq.km. These areas were mostly occurred at Ban khao, Ranot, Takhria, and Phang Yang sub-districts, respectively. The Ban khao sub-district had more occurrence than the others because this area is located at lower altitude and close to Thale Noi and Thale Luang lakes than others. The numbers of damaged buildings were high in Khlong Daen (726 features), Tha Bon (645 features), and Ranot sub-district (604 features), respectively. The final flood extent map might be very useful for the plan, prevention and management of flood occurrence area. The map of building damage can be used for the quick response, recovery and mitigation to the affected areas for different concern organization.
Forthcoming Big Data on Smart Buildings and Cities: An Experimental Study on Correlations among Urban Data
Big data, correlation analysis, data recommendation system, urban data network.
Abstract: Cities are complex systems of diverse and inter-tangled activities. These activities and their complex interrelationships create diverse urban phenomena. And such urban phenomena have considerable influences on the lives of citizens. This research aimed to develop a method to reveal the causes and effects among diverse urban elements in order to enable better understanding of urban activities and, therefrom, to make better urban planning strategies. Specifically, this study was conducted to solve a data-recommendation problem found on a Korean public data homepage. First, a correlation analysis was conducted to find the correlations among random urban data. Then, based on the results of that correlation analysis, the weighted data network of each urban data was provided to people. It is expected that the weights of urban data thereby obtained will provide us with insights into cities and show us how diverse urban activities influence each other and induce feedback.