Search results for: count data

Authors: Mousouris Spiridon, Kavakli Evangelia

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This paper focuses on Digital Libraries (DLs) that contain and geovisualise cultural data, highlighting the need to define them as a separate category termed Cultural Mapping Libraries, based on their inherent connection of culture with geographic location and their design requirements in support of visual representation of cultural data on the map. An exploratory analysis of DLs that conform to the above definition brought forward the observation that existing Cultural Mapping Libraries fail to geovisualise the entirety of cultural data per point of interest thus resulting in a Cultural Snapshot phenomenon. The existence of this phenomenon was reinforced by the results of a systematic bibliographic research. In order to address the Cultural Snapshot, this paper proposes the use of the Semantic Web principles to efficiently interconnect spatial cultural data through time, per geographic location. In this way points of interest are transformed into scenery where culture evolves over time. This evolution is expressed as occurrences taking place chronologically, in an event oriented approach, a conceptualization also endorsed by the CIDOC Conceptual Reference Model (CIDOC CRM). In particular, we posit the use of CIDOC CRM as the baseline for defining the logic of Cultural Mapping Libraries as part of the Culture Domain in accordance with the Digital Library Reference Model, in order to define the rules of cultural data management by the system. Our future goal is to transform this conceptual definition in to inferencing rules that resolve the Cultural Snapshot and lead to a more complete geovisualisation of cultural data.

Keywords: digital libraries, semantic web, geovisualization, CIDOC-CRM

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Authors: Ibrahim Rabiu Darazo

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The research has been conducted on the impact of E-banking on the operational efficiency of Banks in Nigeria, A case of some selected banks (Diamond Bank Plc, GTBankPlc, and Fidelity Bank Plc) in Nigeria. The research is a quantitative research which uses both primary and secondary sources of data collection. Questionnaire were used to obtained accurate data, where 150 Questionnaire were distributed among staff and customers of the three Banks , and the data collected where analysed using chi-square, whereas the secondary data where obtained from relevant text books, journals and relevant web sites. It is clear from the findings that, the use of e-banking by the banks has improved the efficiency of these banks, in terms of providing efficient services to customers electronically, using Internet Banking, Telephone Banking ATMs, reducing time taking to serve customers, e-banking allow new customers to open an account online, customers have access to their account at all the time 24/7.E-banking provide access to customers information from the data base and cost of check and postage were eliminated using e-banking. The recommendation at the end of the research include; the Banks should try to update their electronic gadgets, e-fraud(internal & external) should also be controlled, Banks shall employ qualified man power, Biometric ATMs shall be introduce to reduce fraud using ATM Cards, as it is use in other countries like USA.

Keywords: banks, electronic banking, operational efficiency of banks, biometric ATMs

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Authors: Jennifer Leach, Umashanger Thayasivam

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The use of technology has benefited society in more ways than one ever thought possible. Unfortunately, though, as society’s knowledge of technology has advanced, so has its knowledge of ways to use technology to manipulate people. This has led to a simultaneous advancement in the world of fraud. Machine learning techniques can offer a possible solution to help decrease this advancement. This research explores how the use of various machine learning techniques can aid in detecting fraudulent activity across two different types of fraudulent data, and the accuracy, precision, recall, and F1 were recorded for each method. Each machine learning model was also tested across five different training and testing splits in order to discover which testing split and technique would lead to the most optimal results.

Keywords: data science, fraud detection, machine learning, supervised learning

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Authors: O. O. Aiyelokun, O. A. Agbede

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Numerical modelling of groundwater flow can be susceptible to calibration errors due to lack of adequate ground-based hydro-metrological stations in river basins. Groundwater resources management in Southwest Nigeria is currently challenged by overexploitation, lack of planning and monitoring, urbanization and climate change; hence to adopt models as decision support tools for sustainable management of groundwater; they must be adequately calibrated. Since river basins in Southwest Nigeria are characterized by missing data, and lack of adequate ground-based hydro-meteorological stations; the need for adopting satellite-based data for constructing distributed models is crucial. This study seeks to evaluate the suitability of satellite-based data as substitute for ground-based, for computing boundary conditions; by determining if ground and satellite based meteorological data fit well in Ogun and Oshun River basins. The Climate Forecast System Reanalysis (CFSR) global meteorological dataset was firstly obtained in daily form and converted to monthly form for the period of 432 months (January 1979 to June, 2014). Afterwards, ground-based meteorological data for Ikeja (1981-2010), Abeokuta (1983-2010), and Oshogbo (1981-2010) were compared with CFSR data using Goodness of Fit (GOF) statistics. The study revealed that based on mean absolute error (MEA), coefficient of correlation, (r) and coefficient of determination (R²); all meteorological variables except wind speed fit well. It was further revealed that maximum and minimum temperature, relative humidity and rainfall had high range of index of agreement (d) and ratio of standard deviation (rSD), implying that CFSR dataset could be used to compute boundary conditions such as groundwater recharge and potential evapotranspiration. The study concluded that satellite-based data such as the CFSR should be used as input when constructing groundwater flow models in river basins in Southwest Nigeria, where majority of the river basins are partially gaged and characterized with long missing hydro-metrological data.

Keywords: boundary condition, goodness of fit, groundwater, satellite-based data

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Authors: Zhaopeng Zhu, Xianzhi Song, Gensheng Li, Shuo Zhu, Shiming Duan, Xuezhe Yao

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Accurate calculation of wellbore pressure is of great significance to prevent wellbore risk during drilling. The traditional mechanism model needs a lot of iterative solving procedures in the calculation process, which reduces the calculation efficiency and is difficult to meet the demand of dynamic control of wellbore pressure. In recent years, many scholars have introduced artificial intelligence algorithms into wellbore pressure calculation, which significantly improves the calculation efficiency and accuracy of wellbore pressure. However, due to the ‘black box’ property of intelligent algorithm, the existing intelligent calculation model of wellbore pressure is difficult to play a role outside the scope of training data and overreacts to data noise, often resulting in abnormal calculation results. In this study, the multi-phase flow mechanism is embedded into the objective function of the neural network model as a constraint condition, and an intelligent prediction model of wellbore pressure under the constraint condition is established based on more than 400,000 sets of pressure measurement while drilling (MPD) data. The constraint of the multi-phase flow mechanism makes the prediction results of the neural network model more consistent with the distribution law of wellbore pressure, which overcomes the black-box attribute of the neural network model to some extent. The main performance is that the accuracy of the independent test data set is further improved, and the abnormal calculation values basically disappear. This method is a prediction method driven by MPD data and multi-phase flow mechanism, and it is the main way to predict wellbore pressure accurately and efficiently in the future.

Keywords: multiphase flow mechanism, pressure while drilling data, wellbore pressure, mechanism constraints, combined drive

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Authors: Jan-Peter Mund, Christian Kind

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In view of the ongoing climate change and global warming, fires along railways in Germany are occurring more frequently, with sometimes massive consequences for railway operations and affected railroad infrastructure. In the absence of systematic studies within the infrastructure network of German Rail, little is known about the causes of such embankment fires. Since a further increase in these hazards is to be expected in the near future, there is a need for a sound knowledge of triggers and drivers for embankment fires as well as methodical knowledge of prediction tools. Two predictable future trends speak for the increasing relevance of the topic: through the intensification of the use of rail for passenger and freight transport (e.g..: doubling of annual passenger numbers by 2030, compared to 2019), there will be more rail traffic and also more maintenance and construction work on the railways. This research project approach uses satellite data to identify historical embankment fires along rail network infrastructure. The team links data from these fires with infrastructure and weather data and trains a machine-learning model with the aim of predicting fire hazards on sections of the track. Companies reflect on the results and use them on a pilot basis in precautionary measures.

Keywords: embankment fires, railway maintenance, machine learning, remote sensing, VIIRS data

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Authors: Shivam Dwivedi, Sumit Prakash Gupta, Durga Toshniwal

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It is a challenging task in today’s date to keep defence forces in the highest state of combat readiness with budgetary constraints. A huge amount of time and money is squandered in the unnecessary and expensive traditional maintenance activities. To overcome this limitation Defence Intelligent Mission Readiness and Maintenance Scheduling System has been proposed, which ameliorates the maintenance system by diagnosing the condition and predicting the maintenance requirements. Based on new data mining algorithms, this system intelligently optimises mission readiness for imminent operations and maintenance scheduling in repair echelons. With modified data mining algorithms such as Weighted Feature Ranking Genetic Algorithm and SVM-Random Forest Linear ensemble, it improves the reliability, availability and safety, alongside reducing maintenance cost and Equipment Out of Action (EOA) time. The results clearly conclude that the introduced algorithms have an edge over the conventional data mining algorithms. The system utilizing the intelligent condition-based maintenance approach improves the operational and maintenance decision strategy of the defence force.

Keywords: condition based maintenance, data mining, defence maintenance, ensemble, genetic algorithms, maintenance scheduling, mission capability

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Authors: Tyler Gill, Sophia Daniels

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The paper examines how accessing the spatial-temporal constrains of data will help inform policymakers and law enforcement officials. The authors utilize Chicago crime data from 2006-2016 to demonstrate how the Emerging Hot Spot Tool is an ideal hot spot clustering approach to analyze crime data. Traditional approaches include density maps or creating a spatial weights matrix to include the spatial-temporal constrains. This new approach utilizes a space-time implementation of the Getis-Ord Gi* statistic to visualize the data more quickly to make better decisions. The research will help complement socio-cultural research to find key patterns to help frame future policies and evaluate the implementation of prior strategies. Through this analysis, homicide trends and patterns are found more effectively and recommendations for use by non-traditional users of GIS are offered for real life implementation.

Keywords: crime mapping, emerging hot spot analysis, Getis-Ord Gi*, spatial-temporal analysis

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Authors: Promothes Saha

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Recently, transportation engineering industry members at the study university showed concern that students lacked the skills needed to solve real-world engineering problems using spreadsheet data analysis. In response to the concerns shown by industry members, this study investigated how to engage students in a better way by incorporating spreadsheet analysis during class - also, help them learn the course topics. Helping students link theoretical knowledge to real-world problems can be a challenge. In this effort, in-class activities and worksheets were redesigned to integrate with Excel to solve example problems using built-in tools including cell referencing, equations, data analysis tool pack, solver tool, conditional formatting, charts, etc. The effectiveness of this technique was investigated using students’ evaluations of the course, enrollment data, and students’ comments. Based on the data of those criteria, it is evident that the spreadsheet activities may increase student learning.

Keywords: civil, engineering, active learning, transportation

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Authors: Sabina Akter, Osiris Valdez Banda, Pentti Kujala, Jani Romanoff

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This paper examines the relationship between on-board environmental factors and customer overall satisfaction in the context of the cruise on-board experience. The on-board environmental factors considered are ambient, layout/design, social, product/service and on-board enjoyment factors. The study presents a data-driven framework and model for the on-board cruise experience. The data are collected from 893 respondents in an application of a self-administered online questionnaire of their cruise experience. This study reveals the cruise passengers’ on-board experience through the customer decision journey based on the publicly available data. Pearson correlation and regression analysis have been applied, and the results show a positive and a significant relationship between the environmental factors and on-board experience. These data help understand the cruise passengers’ on-board experience, which will be used for the ultimate decision-making process in cruise ship design.

Keywords: cruise behavior, customer activities, on-board environmental factors, on-board experience, user or customer satisfaction

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Authors: Cinzia Carrodano, Dimitri Konstantas

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Risk is part of our lives. In today’s society risk is connected to our safety and safety has become a major priority in our life. Each person lives his/her life based on the evaluation of the risk he/she is ready to accept and sustain, and the level of safety he/she wishes to reach, based on highly personal criteria. The assessment of risk a person takes in a complex environment and the impact of actions of other people’actions and events on our perception of risk are alements to be considered. The concept of Holistic Risk Assessment (HRA) aims in developing a methodology and a model that will allow us to take into account elements outside the direct influence of the individual, and provide a personalized risk assessment. The concept is based on the fact that in the near future, we will be able to gather and process extremely large amounts of data about an individual and his/her environment in real time. The interaction and correlation of these data is the key element of the holistic risk assessment. In this paper, we present the HRA concept and describe the most important elements and considerations.

Keywords: continuous data, dynamic risk, holistic risk assessment, risk concept

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Authors: Abdullah Al Farwan, Ya Zhang

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In today’s educational arena, it is critical to understand educational data and be able to evaluate important aspects, particularly data on student achievement. Educational Data Mining (EDM) is a research area that focusing on uncovering patterns and information in data from educational institutions. Teachers, if they are able to predict their students' class performance, can use this information to improve their teaching abilities. It has evolved into valuable knowledge that can be used for a wide range of objectives; for example, a strategic plan can be used to generate high-quality education. Based on previous data, this paper recommends employing data mining techniques to forecast students' final grades. In this study, five data mining methods, Decision Tree, JRip, Naive Bayes, Multi-layer Perceptron, and Random Forest with wrapper feature selection, were used on two datasets relating to Portuguese language and mathematics classes lessons. The results showed the effectiveness of using data mining learning methodologies in predicting student academic success. The classification accuracy achieved with selected algorithms lies in the range of 80-94%. Among all the selected classification algorithms, the lowest accuracy is achieved by the Multi-layer Perceptron algorithm, which is close to 70.45%, and the highest accuracy is achieved by the Random Forest algorithm, which is close to 94.10%. This proposed work can assist educational administrators to identify poor performing students at an early stage and perhaps implement motivational interventions to improve their academic success and prevent educational dropout.

Keywords: classification algorithms, decision tree, feature selection, multi-layer perceptron, Naïve Bayes, random forest, students’ academic performance

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Authors: Efthymios Chondrogiannis, Vassiliki Andronikou, Efstathios Karanastasis, Theodora Varvarigou

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A large amount of data is typically stored in relational databases (DB). The latter can efficiently handle user queries which intend to elicit the appropriate information from data sources. However, direct access and use of this data requires the end users to have an adequate technical background, while they should also cope with the internal data structure and values presented. Consequently the information retrieval is a quite difficult process even for IT or DB experts, taking into account the limited contributions of relational databases from the conceptual point of view. Ontologies enable users to formally describe a domain of knowledge in terms of concepts and relations among them and hence they can be used for unambiguously specifying the information captured by the relational database. However, accessing information residing in a database using ontologies is feasible, provided that the users are keen on using semantic web technologies. For enabling users form different disciplines to retrieve the appropriate data, the design of a Graphical User Interface is necessary. In this work, we will present an interactive, ontology-based, semantically enable web tool that can be used for information retrieval purposes. The tool is totally based on the ontological representation of underlying database schema while it provides a user friendly environment through which the users can graphically form and execute their queries.

Keywords: ontologies, relational databases, SPARQL, web interface

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Authors: Komal Vig, Syamala Soumyakrishnan, Yadav Baral

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Bypass surgery, using the autologous vein has been one of the most effective treatments for cardiovascular diseases (CVD). More recently tissue engineering including engineered vascular grafts to synthesize blood vessels is gaining usage. Dacron and ePTFE has been employed for vascular grafts, however, these does not work well for small diameter grafts (<6 mm) due to intimal hyperplasia and thrombosis. In the present study PTFE was treated with LTP to improve the endothelialization of intimal surface of graft. Scaffolds were also modified with polyvinylpyrrolidone coated silver nanoparticles (Ag-PVP) and the antimicrobial peptides, p753 and p359. Human umbilical vein endothelial cells (HUVEC) were plated on the developed scaffolds and cell proliferation was determined by the MTT assay. Cells attachment on scaffolds was visualized by microscopy. mRNA expressions levels of different cell markers were investigated using quantitative real-time PCR (qPCR). X ray photoelectron spectroscopic confirmed the introduction of oxygenated functionalities from LTP air plasma. Microscopic and MTT assays indicated increase in cell viability in LTP treated scaffolds. Gene expression studies shows enhanced expression of cell adhesion marker Integrin- α 5 gene after LTP treatment. The KB test displayed a zone of inhibition for Ag-PVP, p753 and p359 of 19mm, 14mm, and 12mm respectively. To determine toxicity of antimicrobial agents to cells, MTT Assay was performed using HEK293 cells. MTT Assay exhibited that Ag-PVP and the peptides were non-toxic to cells at 100μg/mL and 50μg/mL, respectively. Live/dead analysis and plate count of treated bacteria exhibited bacterial inhibition on develop scaffold compared to non-treated scaffold. SEM was performed to analyze the structural changes of bacteria after treatment with antimicrobial agents. Gene expression studies were conducted on RNA from bacteria treated with Ag-PVP and peptides using qRT-PCR. Based on our initial results, more scaffolds alternatives will be developed and investigated for cell growth and vascularization studies.

Keywords: low temperature plasma, vascular graft, HUVEC cells, antimicrobial

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Authors: Salma Krafessi

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The financial markets have a multifaceted, intricate environment, and enormous volumes of data are produced every day. To find investment possibilities, possible fraudulent activity, and market oddities, accurate anomaly identification in this data is essential. Conventional methods for detecting anomalies frequently fail to capture the complex organization of financial data. In order to improve the identification of abnormalities in financial time series data, this study presents Tucker Decomposition as a reliable multi-way analysis approach. We start by gathering closing prices for the S&P 500 index across a number of decades. The information is converted to a three-dimensional tensor format, which contains internal characteristics and temporal sequences in a sliding window structure. The tensor is then broken down using Tucker Decomposition into a core tensor and matching factor matrices, allowing latent patterns and relationships in the data to be captured. A possible sign of abnormalities is the reconstruction error from Tucker's Decomposition. We are able to identify large deviations that indicate unusual behavior by setting a statistical threshold. A thorough examination that contrasts the Tucker-based method with traditional anomaly detection approaches validates our methodology. The outcomes demonstrate the superiority of Tucker's Decomposition in identifying intricate and subtle abnormalities that are otherwise missed. This work opens the door for more research into multi-way data analysis approaches across a range of disciplines and emphasizes the value of tensor-based methods in financial analysis.

Keywords: tucker decomposition, financial markets, financial engineering, artificial intelligence, decomposition models

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Authors: Jose L. Diego, Antonio Berlanga, Gregorio López, Diana López

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The Rayuela method is a success story, as it is part of a project selected by the European Commission to face the challenge launched by itself for achieving a better understanding of human factors, as well as social and organisational aspects that are able to solve issues in fighting against crime. Rayuela's method specifically focuses on the drivers of cyber criminality, including approaches to prevent, investigate, and mitigate cybercriminal behavior. As the internet has become an integral part of young people’s lives, they are the key target of the Rayuela method because they (as a victim or as a perpetrator) are the most vulnerable link of the chain. Considering the increased time spent online and the control of their internet usage and the low level of awareness of cyber threats and their potential impact, it is understandable the proliferation of incidents due to human mistakes. 51% of Europeans feel not well informed about cyber threats, and 86% believe that the risk of becoming a victim of cybercrime is rapidly increasing. On the other hand, Law enforcement has noted that more and more young people are increasingly committing cybercrimes. This is an international problem that has considerable cost implications; it is estimated that crimes in cyberspace will cost the global economy $445B annually. Understanding all these phenomena drives to the necessity of a shift in focus from sanctions to deterrence and prevention. As a research project, Rayuela aims to bring together law enforcement agencies (LEAs), sociologists, psychologists, anthropologists, legal experts, computer scientists, and engineers, to develop novel methodologies that allow better understanding the factors affecting online behavior related to new ways of cyber criminality, as well as promoting the potential of these young talents for cybersecurity and technologies. Rayuela’s main goal is to better understand the drivers and human factors affecting certain relevant ways of cyber criminality, as well as empower and educate young people in the benefits, risks, and threats intrinsically linked to the use of the Internet by playing, thus preventing and mitigating cybercriminal behavior. In order to reach that goal it´s necessary an interdisciplinary consortium (formed by 17 international partners) carries out researches and actions like Profiling and case studies of cybercriminals and victims, risk assessments, studies on Internet of Things and its vulnerabilities, development of a serious gaming environment, training activities, data analysis and interpretation using Artificial intelligence, testing and piloting, etc. For facilitating the real implementation of the Rayuela method, as a community policing strategy, is crucial to count on a Police Force with a solid background in trust-building and community policing in order to do the piloting, specifically with young people. In this sense, Valencia Local Police is a pioneer Police Force working with young people in conflict solving, through providing police mediation and peer mediation services and advice. As an example, it is an official mediation institution, so agreements signed by their police mediators have once signed by the parties, the value of a judicial decision.

Keywords: fight against crime and insecurity, avert and prepare young people against aggression, ICT, serious gaming and artificial intelligence against cybercrime, conflict solving and mediation with young people

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Authors: Sirajdin Olagoke Adeshina, Haidi Ibrahim, Akeem Salawu

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large lecture theatres cannot be covered by a single camera but rather by a multicamera setup because of their size, shape, and seating arrangements. Although, classroom capture is achievable through a single camera. Therefore, a design and implementation of a multicamera setup for a large lecture hall were considered. Researchers have shown emphasis on the impact of class attendance taken on the academic performance of students. However, the traditional method of carrying out this exercise is below standard, especially for large lecture theatres, because of the student population, the time required, sophistication, exhaustiveness, and manipulative influence. An automated large classroom attendance system is, therefore, imperative. The common approach in this system is face detection and recognition, where known student faces are captured and stored for recognition purposes. This approach will require constant face database updates due to constant changes in the facial features. Alternatively, face counting can be performed by cropping the localized faces on the video or image into a folder and then count them. This research aims to develop a face localization-based approach to detect student faces in classroom images captured using a multicamera setup. A selected Haar-like feature cascade face detector trained with an asymmetric goal to minimize the False Rejection Rate (FRR) relative to the False Acceptance Rate (FAR) was applied on Raspberry Pi 4B. A relationship between the two factors (FRR and FAR) was established using a constant (λ) as a trade-off between the two factors for automatic adjustment during training. An evaluation of the proposed approach and the conventional AdaBoost on classroom datasets shows an improvement of 8% TPR (output result of low FRR) and 7% minimization of the FRR. The average learning speed of the proposed approach was improved with 1.19s execution time per image compared to 2.38s of the improved AdaBoost. Consequently, the proposed approach achieved 97% TPR with an overhead constraint time of 22.9s compared to 46.7s of the improved Adaboost when evaluated on images obtained from a large lecture hall (DK5) USM.

Keywords: automatic attendance, face detection, haar-like cascade, manual attendance

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Authors: Deniz Karagöz

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This study is attempt to comprehend the relationship between the spatial characteristics of cultural structure, activities and the tourism demand in Turkey. The analysis divided into four parts. The first part consisted of a cultural structure and cultural activity (CSCA) index provided by principal component analysis. The analysis determined four distinct dimensions, namely, cultural activity/structure, accessing culture, consumption, and cultural management. The exploratory spatial data analysis employed to determine the spatial models of cultural structure and cultural activities in 81 provinces in Turkey. Global Moran I indices is used to ascertain the cultural activities and the structural clusters. Finally, the relationship between the cultural activities/cultural structure and tourism demand was analyzed. The raw/original data of the study official databases. The data on the cultural structure and activities gathered from the Turkish Statistical Institute and the data related to the tourism demand was provided by the Republic of Turkey Ministry of Culture and Tourism.

Keywords: cultural activities, cultural structure, spatial characteristics, tourism demand, Turkey

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Authors: Sayor Ajfar Aaron, Sajjat Hossain Abir, Ashif Newaz, Mushfiqur Rahman

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Investigating the convergence of blockchain technology and artificial intelligence, this paper examines their synergistic effects on data integrity and decision-making within smart contracts. By implementing AI-driven analytics on blockchain-based platforms, the research identifies improvements in automated contract enforcement and decision accuracy. The paper presents a framework that leverages AI to enhance transparency and trust while blockchain ensures immutable record-keeping, culminating in significantly optimized operational efficiencies in various industries.

Keywords: artificial intelligence, blockchain, data integrity, smart contracts

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Authors: Eva Slaninova, Diana Cernayova, Zuzana Sedrlova, Katerina Mrazova, Petr Sedlacek, Jana Nebesarova, Stanislav Obruca

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Cyanobacteria are gram-negative prokaryotes belonging to a group of photosynthetic bacteria. In comparison with heterotrophic microorganisms, cyanobacteria utilize atmospheric nitrogen and carbon dioxide without any additional substrates. This ability of these microorganisms could be employed in biotechnology for the production of bioplastics, concretely polyhydroxyalkanoates (PHAs) which are primarily accumulated as a storage material in cells in the form of intracellular granules. In this study, there two cyanobacterial cultures from genera Synechocystis were used, namely Synechocystic sp. PCC 6803 and Synechocystis salina CCALA 192. There were optimized and used several various approaches, including microscopic techniques such as cryo-scanning electron microscopy (Cryo-SEM) and transmission electron microscopy (TEM), and fluorescence lifetime imaging microscopy using Nile red as a fluorescent probe (FLIM). Due to these instrumental techniques, the morphology of intracellular space and surface of cells were characterized. The next group of methods which were employed was spectroscopic techniques such as UV-Vis spectroscopy measured in two modes (turbidimetry and integration sphere) and Fourier transform infrared spectroscopy (FTIR). All these diverse techniques were used for the detection and characterization of pigments (chlorophylls, carotenoids, phycocyanin, etc.) and PHAs, in our case poly (3-hydroxybutyrate) (P3HB). To verify results, gas chromatography (GC) was employed concretely for the determination of the amount of P3HB in biomass. Cyanobacteria were also characterized as polyhydroxybutyrate producers by flow cytometer, which could count cells and at the same time distinguish cells including P3HB and without due to fluorescent probe called BODIPY and live/dead fluorescent probe SYTO Blue. Based on results, P3HB content in cyanobacteria cells was determined, as also the overall fitness of the cells. Acknowledgment: Funding: This study was partly funded by the projectGA19-29651L of the Czech Science Foundation (GACR) and partly funded by the Austrian Science Fund (FWF), project I 4082-B25.

Keywords: cyanobacteria, fluorescent probe, microscopic techniques, poly(3hydroxybutyrate), spectroscopy, chromatography

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Authors: Mahdi Daghmhehci Firoozjaei, Minchang Kim, Dima Alhadidi

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In this paper, we present a power profiling model for smart grid consumers based on real time load data acquired smart meters. It profiles consumers’ power consumption behaviour using the dynamic time warping (DTW) clustering algorithm. Due to the invariability of signal warping of this algorithm, time-disordered load data can be profiled and consumption features be extracted. Two load types are defined and the related load patterns are extracted for classifying consumption behaviour by DTW. The classification methodology is discussed in detail. To evaluate the performance of the method, we analyze the time-series load data measured by a smart meter in a real case. The results verify the effectiveness of the proposed profiling method with 90.91% true positive rate for load type clustering in the best case.

Keywords: power profiling, user privacy, dynamic time warping, smart grid

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Authors: Chulsang Yoo, Gildo Kim

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Dual polarization radar provides comprehensive information about rainfall by measuring multiple parameters. In Korea, for the rainfall estimation, JPOLE and CSU-HIDRO algorithms are generally used. This study evaluated the local applicability of JPOLE and CSU-HIDRO algorithms in Korea by using the observed rainfall data collected on August, 2014 by the Biseulsan dual polarization radar data and KMA AWS. A total of 11,372 pairs of radar-ground rain rate data were classified according to thresholds of synthetic algorithms into suitable and unsuitable data. Then, evaluation criteria were derived by comparing radar rain rate and ground rain rate, respectively, for entire, suitable, unsuitable data. The results are as follows: (1) The radar rain rate equation including KDP, was found better in the rainfall estimation than the other equations for both JPOLE and CSU-HIDRO algorithms. The thresholds were found to be adequately applied for both algorithms including specific differential phase. (2) The radar rain rate equation including horizontal reflectivity and differential reflectivity were found poor compared to the others. The result was not improved even when only the suitable data were applied. Acknowledgments: This work was supported by the Basic Science Research Program through the National Research Foundation of Korea, funded by the Ministry of Education (NRF-2013R1A1A2011012).

Keywords: CSU-HIDRO algorithm, dual polarization radar, JPOLE algorithm, radar rainfall estimation algorithm

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Authors: Ryan Messina, Mehedi Hasan

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This research is to examine the impacts of using data to generate performance requirements for automation in visual inspections using machine vision. These situations are intended for design and how projects can smooth the transfer of tacit knowledge to using an algorithm. We have proposed a framework when specifying machine vision systems. This framework utilizes varying levels of automation as contingency planning to reduce data processing complexity. Using data assists in extracting tacit knowledge from those who can perform the manual tasks to assist design the system; this means that real data from the system is always referenced and minimizes errors between participating parties. We propose using three indicators to know if the project has a high risk of failing to meet requirements related to accuracy and reliability. All systems tested achieved a better integration into operations after applying the framework.

Keywords: automation, contingency planning, continuous engineering, control theory, machine vision, system requirements, system thinking

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Authors: Margareta Rahayuningsih, Nugroho Edi K., Siti Alimah

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Wreathed Hornbill (Rhyticeros undulatus) is the one of hornbill species (Family: Bucerotidae) that found on Mount Ungaran. In the preservation or planning in situ conservation of Wreathed Hornbill require the habitat condition data. The objective of the research was to determine the land cover change on Mount Ungaran using satellite image data and GIS. Based on the land cover data on 1999-2009 the research showed that the primer forest on Mount Ungaran was decreased almost 50%, while the seconder forest, tea and coffee plantation, and the settlement were increased.

Keywords: GIS, Mount Ungaran, threatened habitat, Wreathed Hornbill (Rhyticeros undulatus)

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Authors: Thanh Nguyen, Andrei Doncescu, Pierre Siegel

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Classification is an important data mining technique and could be used as data filtering in artificial intelligence. The broad application of classification for all kind of data leads to be used in nearly every field of our modern life. Classification helps us to put together different items according to the feature items decided as interesting and useful. In this paper, we compare two classification methods Naïve Bayes and ADTree use to detect spam e-mail. This choice is motivated by the fact that Naive Bayes algorithm is based on probability calculus while ADTree algorithm is based on decision tree. The parameter settings of the above classifiers use the maximization of true positive rate and minimization of false positive rate. The experiment results present classification accuracy and cost analysis in view of optimal classifier choice for Spam Detection. It is point out the number of attributes to obtain a tradeoff between number of them and the classification accuracy.

Keywords: classification, data mining, spam filtering, naive bayes, decision tree

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Authors: Alfi Al Fahreizy

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Yogyakarta is one of the provinces in Indonesia that often get a power outage because of high load electrical consumption. The authors mapped the electrical energy consumption [GWh] for the province of Yogyakarta in 2014-2025 using LEAP (Long-range Energy Alternatives Planning system) software. This paper use BAU (Business As Usual) scenario. BAU scenario in which the projection is based on the assumption that growth in electricity consumption will run as normally as before. The goal is to be able to see the electrical energy consumption in the household sector, industry , business, social, government office building, and street lighting. The data is the data projected statistical population and consumption data electricity [GWh] 2010, 2011, 2012 in Yogyakarta province.

Keywords: LEAP, energy consumption, Yogyakarta, BAU

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Authors: Weiliang Wen, Xinyu Guo, Ying Zhang, Jianjun Du, Boxiang Xiao

Abstract:

Reconstructing and analyzing three-dimensional (3D) models from situ measured data is important for a number of researches and applications in plant science, including plant phenotyping, functional-structural plant modeling (FSPM), plant germplasm resources protection, agricultural technology popularization. It has many scales like cell, tissue, organ, plant and canopy from micro to macroscopic. The techniques currently used for data capture, feature analysis, and 3D reconstruction are quite different of different scales. In this context, morphological data acquisition, 3D analysis and modeling of plants on different scales are introduced systematically. The commonly used data capture equipment for these multiscale is introduced. Then hot issues and difficulties of different scales are described respectively. Some examples are also given, such as Micron-scale phenotyping quantification and 3D microstructure reconstruction of vascular bundles within maize stalks based on micro-CT scanning, 3D reconstruction of leaf surfaces and feature extraction from point cloud acquired by using 3D handheld scanner, plant modeling by combining parameter driven 3D organ templates. Several application examples by using the 3D models and analysis results of plants are also introduced. A 3D maize canopy was constructed, and light distribution was simulated within the canopy, which was used for the designation of ideal plant type. A grape tree model was constructed from 3D digital and point cloud data, which was used for the production of science content of 11th international conference on grapevine breeding and genetics. By using the tissue models of plants, a Google glass was used to look around visually inside the plant to understand the internal structure of plants. With the development of information technology, 3D data acquisition, and data processing techniques will play a greater role in plant science.

Keywords: plant, three dimensional modeling, multi-scale, plant phenotyping, three dimensional data acquisition

Procedia PDF Downloads 277

Authors: Farzad Khajavi

Abstract:

Studies about the interaction between active pharmaceutical ingredients (API) and excipients are so important in the pre-formulation stage of development of all dosage forms. Analytical techniques such as differential scanning calorimetry (DSC), Thermal gravimetry (TG), and Furrier transform infrared spectroscopy (FTIR) are commonly used tools for investigating regarding compatibility and incompatibility of APIs with excipients. Sometimes the interpretation of data obtained from these techniques is difficult because of severe overlapping of API spectrum with excipients in their mixtures. Principal component analysis (PCA) as a powerful factor analytical method is used in these situations to resolve data matrices acquired from these analytical techniques. Binary mixtures of API and interested excipients are considered and produced. Peaks of FTIR, DSC, or TG of pure API and excipient and their mixtures at different mole ratios will construct the rows of the data matrix. By applying PCA on the data matrix, the number of principal components (PCs) is determined so that it contains the total variance of the data matrix. By plotting PCs or factors obtained from the score of the matrix in two-dimensional spaces if the pure API and its mixture with the excipient at the high amount of API and the 1:1mixture form a separate cluster and the other cluster comprise of the pure excipient and its blend with the API at the high amount of excipient. This confirms the existence of compatibility between API and the interested excipient. Otherwise, the incompatibility will overcome a mixture of API and excipient.

Keywords: API, compatibility, DSC, TG, interactions

Procedia PDF Downloads 133

Authors: Rock-Hyun Choi, Won-Seok Kang, Chang-Sik Son

Abstract:

Physical activity is important for healthy living. Recently wearable devices which motivate physical activity are quickly developing, and become cheaper and more comfortable. In particular, fitness trackers provide a variety of information and need to provide well-analyzed, and user-friendly results. In this study, frequency analysis was performed to classify various data sets of Fitbit into simple activity status. The data from Fitbit cloud server consists of 263 subjects who were healthy factory and office workers in Korea from March 7th to April 30th, 2016. In the results, we found assumptions of activity state classification seem to be sufficient and reasonable.

Keywords: activity status, fitness tracker, heart rate, steps

Procedia PDF Downloads 384

Authors: Praniil Nagaraj

Abstract:

This paper proposes a method to collect homeless data using crowdsourcing and presents an approach to analyze the data, demonstrating its potential to strengthen existing and future policies aimed at promoting socio-economic equilibrium. This paper's contributions can be categorized into three main areas. Firstly, a unique method for collecting homeless data is introduced, utilizing a user-friendly smartphone app (currently available for Android). The app enables the general public to quickly record information about homeless individuals, including the number of people and details about their living conditions. The collected data, including date, time, and location, is anonymized and securely transmitted to the cloud. It is anticipated that an increasing number of users motivated to contribute to society will adopt the app, thus expanding the data collection efforts. Duplicate data is addressed through simple classification methods, and historical data is utilized to fill in missing information. The second contribution of this paper is the description of data analysis techniques applied to the collected data. By combining this new data with existing information, statistical regression analysis is employed to gain insights into various aspects, such as distinguishing between unsheltered and sheltered homeless populations, as well as examining their correlation with factors like unemployment rates, housing affordability, and labor demand. Initial data is collected in San Francisco, while pre-existing information is drawn from three cities: San Francisco, New York City, and Washington D.C., facilitating the conduction of simulations. The third contribution focuses on demonstrating the practical implications of the data processing results. The challenges faced by key stakeholders, including charitable organizations and local city governments, are taken into consideration. Two case studies are presented as examples. The first case study explores improving the efficiency of food and necessities distribution, as well as medical assistance, driven by charitable organizations. The second case study examines the correlation between micro-geographic budget expenditure by local city governments and homeless information to justify budget allocation and expenditures. The ultimate objective of this endeavor is to enable the continuous enhancement of the quality of life for the underprivileged. It is hoped that through increased crowdsourcing of data from the public, the Generosity Curve and the Need Curve will intersect, leading to a better world for all.

Keywords: crowdsourcing, homelessness, socio-economic policies, statistical analysis

Procedia PDF Downloads 46