Search results for: open data

Authors: Hamdi Sana, Emna Bouazizi, Sami Faiz

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In recent years, there is a huge increase in the use of spatio-temporal applications where data and queries are continuously moving. As a result, the need to process real-time spatio-temporal data seems clear and real-time stream data management becomes a hot topic. Sliding window model and frequent itemset mining over dynamic data are the most important problems in the context of data mining. Thus, sliding window model for frequent itemset mining is a widely used model for data stream mining due to its emphasis on recent data and its bounded memory requirement. These methods use the traditional transaction-based sliding window model where the window size is based on a fixed number of transactions. Actually, this model supposes that all transactions have a constant rate which is not suited for real-time applications. And the use of this model in such applications endangers their performance. Based on these observations, this paper relaxes the notion of window size and proposes the use of a timestamp-based sliding window model. In our proposed frequent itemset mining algorithm, support conditions are used to differentiate frequents and infrequent patterns. Thereafter, a tree is developed to incrementally maintain the essential information. We evaluate our contribution. The preliminary results are quite promising.

Keywords: real-time spatial big data, frequent itemset, transaction-based sliding window model, timestamp-based sliding window model, weighted frequent patterns, tree, stream query

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Authors: Iyad Ismail, Fathilatul Abdul Hamid

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This research was mainly investigated to recognize the extent of big data analysis by external auditors. This paper adopts grounded theory as a framework for conducting a series of semi-structured interviews with eighteen external auditors. The research findings comprised the availability extent of big data and big data analysis usage by the external auditors in Palestine, Gaza Strip. Considering the study's outcomes leads to a series of auditing procedures in order to improve the external auditing techniques, which leads to high-quality audit process. Also, this research is crucial for auditing firms by giving an insight into the mechanisms of auditing firms to identify the most important strategies that help in achieving competitive audit quality. These results are aims to instruct the auditing academic and professional institutions in developing techniques for external auditors in order to the big data analysis. This paper provides appropriate information for the decision-making process and a source of future information which affects technological auditing.

Keywords: big data analysis, external auditors, audit reliance, internal audit function

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Authors: Vasti Oelofse, Niekie van der Merwe, Dorita du Toit

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This study addresses the need for a unified best practice model for Physical Education across BRICS nations, as current research primarily offers individual country recommendations. Drawing on relevant literature within the framework of Bronfenbrenner’s Ecological Systems Theory, as well as data from open-ended questionnaires completed by Physical Education experts from the BRICS countries, , the study develops a best practice model based on identified challenges and effective practices in Physical Education. A model is proposed that incorporates flexible and resource-efficient strategies tailored to address PE challenges specific to these countries, enhancing outcomes for learners, empowering teachers, and fostering systemic collaboration among BRICS members. The proposed model comprises six key areas: “Curriculum and policy requirements”, “General approach”, “Theoretical basis”, “Strategies for presenting content”, “Teacher training”, and “Evaluation”. The “Strategies for presenting program content” area addresses both well-resourced and poorly resourced schools, adapting curriculum, teaching strategies, materials, and learner activities for varied socio-economic contexts. The model emphasizes a holistic approach to learner development, engaging environments, and continuous teacher training. A collaborative approach among BRICS countries, focusing on shared best practices and continuous improvement, is vital for the model's successful implementation, enhancing Physical Education programs and outcomes across these nations.

Keywords: BRICS countries, physical education, best practice model, ecological systems theory

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Authors: Ian D. Walker

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This paper describes the creation and teaching of an undergraduate course aimed at promoting critical thinking among the students in the course. The class, Robots in Business and Society, taught at Clemson University, is open to all undergraduate students of any discipline. It is taught as part of Clemson’s online class program and is structured to promote critical thinking via a series of interactive discussion boards and assignments. Critical thinking is measured via pre- and post-testing using a benchmark standardized test. The paper will detail the class organization, and describe and discuss the results and lessons learned with respect to improvement of student critical thinking from three offerings of the class.

Keywords: critical thinking, pedagogy, robotics, undergraduate teaching

Procedia PDF Downloads 288

Authors: Mohammad Anwar, Shah Waliullah

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This study investigated panel data regression models. This paper used Bayesian and classical methods to study the impact of institutions on economic growth from data (1990-2014), especially in developing countries. Under the classical and Bayesian methodology, the two-panel data models were estimated, which are common effects and fixed effects. For the Bayesian approach, the prior information is used in this paper, and normal gamma prior is used for the panel data models. The analysis was done through WinBUGS14 software. The estimated results of the study showed that panel data models are valid models in Bayesian methodology. In the Bayesian approach, the effects of all independent variables were positively and significantly affected by the dependent variables. Based on the standard errors of all models, we must say that the fixed effect model is the best model in the Bayesian estimation of panel data models. Also, it was proved that the fixed effect model has the lowest value of standard error, as compared to other models.

Keywords: Bayesian approach, common effect, fixed effect, random effect, Dynamic Random Effect Model

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Authors: Ayeshmanthe Rathnayake, Ashutosh Hardikar

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Introduction: Lung cancer is the most common cause of cancer death in Australia, with more than 13000 cases per year. Until now, there has been a major deficiency of national comprehensive thoracic surgery data. The thoracic workload for surgeons as well as caseload per unit, is highly variable, with some centres performing less than 15 cases per annum, thus raising concerns about optimal care at low-volume sites. This is an attempt to review the outcomes of lung cancer surgery in Tasmania. Method: The objective of this study is to determine the surgical outcomes of lung cancer surgery at Royal Hobart Hospital (RHH) with the primary outcome of surgical mortality. Four hundred fifty-one cases were analysed retrospectively from 2010 to May 2022. Results: A total of 451 patients underwent thoracic surgery with a primary diagnosis of lung cancer. The primary outcome of 30-day mortality was <0.5%. The mean age was 65.3 years, with male predominance and a 4.2% prevalence of Indigenous Australians. The mean LOS was 7.5 days. The surgical approach was either VATS (50.3%) or Thoracotomy (49.7%), with a trend towards the former in recent years with an increase in the proportion of VATS from 18.2% to 51% (p<0.05) in complex resections since 2019. A corresponding reduction in conversion rate to open was observed (18% vs. 5.5%), and there were no deaths within this subgroup. Lung resections were divided into lobectomy (55.4%), wedge resection (36.8%), segmentectomy (2.9%) and pneumonectomy (4.9%). The RHH demonstrates good surgical outcomes for lung cancer and provides a sustainable service for Tasmania. Conclusion: This retrospective study reports the surgical outcomes of lung cancer surgery at the Royal Hobart Hospital, thereby providing insight into the surgical management of lung cancer in the state thus far. The state has been slow to catch up on the minimally invasive program, but the overall results have been comparable to most peers.

Keywords: lung cancer, thoracic surgery, lung resection, surgical outcomes

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Authors: Sule Yucelbas, Gulay Tezel, Cuneyt Yucelbas, Seral Ozsen

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In this study, it was tried to identify some heart rhythm disorders by electrocardiography (ECG) data that is taken from MIT-BIH arrhythmia database by subtracting the required features, presenting to artificial neural networks (ANN), artificial immune systems (AIS), artificial neural network based on artificial immune system (AIS-ANN) and particle swarm optimization based artificial neural network (PSO-NN) classifier systems. The main purpose of this study is to evaluate the performance of hybrid AIS-ANN and PSO-ANN classifiers with regard to the ANN and AIS. For this purpose, the normal sinus rhythm (NSR), atrial premature contraction (APC), sinus arrhythmia (SA), ventricular trigeminy (VTI), ventricular tachycardia (VTK) and atrial fibrillation (AF) data for each of the RR intervals were found. Then these data in the form of pairs (NSR-APC, NSR-SA, NSR-VTI, NSR-VTK and NSR-AF) is created by combining discrete wavelet transform which is applied to each of these two groups of data and two different data sets with 9 and 27 features were obtained from each of them after data reduction. Afterwards, the data randomly was firstly mixed within themselves, and then 4-fold cross validation method was applied to create the training and testing data. The training and testing accuracy rates and training time are compared with each other. As a result, performances of the hybrid classification systems, AIS-ANN and PSO-ANN were seen to be close to the performance of the ANN system. Also, the results of the hybrid systems were much better than AIS, too. However, ANN had much shorter period of training time than other systems. In terms of training times, ANN was followed by PSO-ANN, AIS-ANN and AIS systems respectively. Also, the features that extracted from the data affected the classification results significantly.

Keywords: AIS, ANN, ECG, hybrid classifiers, PSO

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Authors: Phumelele Kubheka, Pius Owolawi, Gbolahan Aiyetoro

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Twitter is one of the most popular social media platforms where users can share their opinions on different subjects. As of 2010, The Twitter platform generates more than 12 Terabytes of data daily, ~ 4.3 petabytes in a single year. For this reason, Twitter is a great source for big mining data. Many industries such as Telecommunication companies can leverage the availability of Twitter data to better understand their markets and make an appropriate business decision. This study performs topic modeling on Twitter data using Latent Dirichlet Allocation (LDA). The obtained results are benchmarked with another topic modeling technique, Latent Semantic Indexing (LSI). The study aims to retrieve topics on a Twitter dataset containing user tweets on South African Telcos. Results from this study show that LSI is much faster than LDA. However, LDA yields better results with higher topic coherence by 8% for the best-performing model represented in Table 1. A higher topic coherence score indicates better performance of the model.

Keywords: big data, latent Dirichlet allocation, latent semantic indexing, telco, topic modeling, twitter

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Authors: Yu Zhang, Pedro Desouza

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Since big data has become substantially more accessible and manageable due to the development of powerful tools for dealing with unstructured data, people are eager to mine information from social media resources that could not be handled in the past. Sentiment analysis, as a novel branch of text mining, has in the last decade become increasingly important in marketing analysis, customer risk prediction and other fields. Scientists and researchers have undertaken significant work in creating and improving their sentiment models. In this paper, we present a concept of selecting appropriate classifiers based on the features and qualities of data sources by comparing the performances of five classifiers with three popular social media data sources: Twitter, Amazon Customer Reviews, and Movie Reviews. We introduced a couple of innovative models that outperform traditional sentiment classifiers for these data sources, and provide insights on how to further improve the predictive power of sentiment analysis. The modelling and testing work was done in R and Greenplum in-database analytic tools.

Keywords: sentiment analysis, social media, Twitter, Amazon, data mining, machine learning, text mining

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Authors: Freda Adomaa, Samuel Oppong Boampong, Charles Gyamfi Rahman

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The living and working environment of migrants and their access to healthcare services induce good or poor health. This study was conducted to assess the factors associated with rural-urban migration and its associated health hazards among female adolescents. A sample size of two hundred (200) was chosen in which all responded to questionnaires comprising closed-ended questions, which were distributed to gather data from the respondents, after which it was analyzed using the Statistical Package for Social Sciences (SPSS) version 20. The utilized three causes of rural-urban migration thus political, economic and socio-cultural. The study revealed that political situations such as regional inequality (65.4%) and ethnic conflicts (78.2%) whereas economic factors such as lack of amenities (82.3%), lack of employment in rural communities (77.4%), lack of education (74%), and poverty (85.3%) as well as socio-cultural factors such as divorced parents (65.6%), media influence (79.1%), family conflicts (59.4%) and appealing urban informal sector (65.2%) are major causes of migration. Respondents’ encountered challenges such as poor remuneration for services (87.2%), being maltreated by a colleague or worker (69%), sleeping in open space (73.3%), and harassment by the task force (71.4%) and teenage pregnancies (58.5%). The study concluded that the three variables play a key role in adolescent migration and when they travel they end up getting involved in serious health hazardous behaviors such as rapes as well as physical and psychological harassments’. The study, therefore, recommends that vocational training of the rural people on small scale industries (non-farm) activities that could generate an income for the rural household should be introduced.

Keywords: rural, urban, migration, female health hazards

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Authors: Abbas Raza Ali

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Big Data technology is gradually becoming a dire need of large enterprises. These enterprises are generating massively large amount of off-line and streaming data in both structured and unstructured formats on daily basis. It is a challenging task to effectively extract useful insights from the large scale datasets, even though sometimes it becomes a technology constraint to manage transactional data history of more than a few months. This paper presents a framework to efficiently manage massively large and complex datasets. The framework has been tested on a communication service provider producing massively large complex streaming data in binary format. The communication industry is bound by the regulators to manage history of their subscribers’ call records where every action of a subscriber generates a record. Also, managing and analyzing transactional data allows service providers to better understand their customers’ behavior, for example, deep packet inspection requires transactional internet usage data to explain internet usage behaviour of the subscribers. However, current relational database systems limit service providers to only maintain history at semantic level which is aggregated at subscriber level. The framework addresses these challenges by leveraging Big Data technology which optimally manages and allows deep analysis of complex datasets. The framework has been applied to offload existing Intelligent Network Mediation and relational Data Warehouse of the service provider on Big Data. The service provider has 50+ million subscriber-base with yearly growth of 7-10%. The end-to-end process takes not more than 10 minutes which involves binary to ASCII decoding of call detail records, stitching of all the interrogations against a call (transformations) and aggregations of all the call records of a subscriber.

Keywords: big data, communication service providers, enterprise data warehouse, stream computing, Telco IN Mediation

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Authors: Peter M. Maurer Maurer

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DGL is a context free grammar-based tool for generating random data. Many types of simulator input data require some computation to be placed in the proper format. For example, it might be necessary to generate ordered triples in which the third element is the sum of the first two elements, or it might be necessary to generate random numbers in some sorted order. Although DGL is universal in computational power, generating these types of data is extremely difficult. To overcome this problem, we have enhanced DGL to include features that permit direct computation within the structure of a context free grammar. The features have been implemented as special types of productions, preserving the context free flavor of DGL specifications.

Keywords: DGL, Enhanced Context Free Grammars, Programming Constructs, Random Data Generation

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Authors: Ouzayr Rabhi, Ibtissam Arrassen

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To provide a complete analysis of the organization and to help decision-making, leaders need to have relevant data; Data Warehouses (DW) are designed to meet such needs. However, designing DW is not trivial and there is no formal method to derive a multidimensional schema from heterogeneous databases. In this article, we present a Model-Driven based approach concerning the design of data warehouses. We describe a multidimensional meta-model and also specify a set of transformations starting from a Unified Modeling Language (UML) metamodel. In this approach, the UML metamodel and the multidimensional one are both considered as a platform-independent model (PIM). The first meta-model is mapped into the second one through transformation rules carried out by the Query View Transformation (QVT) language. This proposal is validated through the application of our approach to generating a multidimensional schema of a Balanced Scorecard (BSC) DW. We are interested in the BSC perspectives, which are highly linked to the vision and the strategies of an organization.

Keywords: data warehouse, meta-model, model-driven architecture, transformation, UML

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Authors: Maria Lita Sudo

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The debate is an effective tool in cultivating critical thinking skills in English classes. It involves writing evidence-based arguments about a resolution in a form of constructive speech and oral discussion using constructive speech, which will then be attacked and defended. In the process of writing, EFL learners may experience anxiety, an emotional problem that affects writing achievement and cognitive processing. Thus, this study explored the sources and effect of English writing anxiety in the context of debate writing with a view to providing EFL teachers pedagogical suggestions in alleviating English writing anxiety in debate writing. The participants of this study are 95 Japanese senior high school EFL learners and 3 Japanese senior high school English teachers. In selecting the participants, opportunity sampling was employed and consent from Japanese English teachers was sought. Data were collected thru (1) observation (2) open-ended questionnaire and (3) semi-structured interview. This study revealed that not all teachers of English in the context of this study recognize the existence of English writing anxiety among their students and that the very nature of the debate, in general, may also be a source of English writing anxiety in the context of debate writing. The interview revealed that English writing anxiety affects students’ ability to retrieve L2 vocabulary. Further, this study revealed different sources of writing anxiety in debate writing, which can be categorized into four main categories: (1) L2 linguistic ability-related factors (2) instructional –related factors, (3) interpersonal-related factors, and (4) debate- related factors. Based on the findings, recommendations for EFL teachers and EFL learners in managing writing anxiety in debate writing are provided.

Keywords: debate, EFL learners, English writing anxiety, sources

Procedia PDF Downloads 137

Authors: Butta Singh

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This paper presents a chaotic map based approach for secured embedding of patient’s confidential data in electrocardiogram (ECG) signal. The chaotic map generates predefined locations through the use of selective control parameters. The sample value difference method effectually hides the confidential data in ECG sample pairs at these predefined locations. Evaluation of proposed method on all 48 records of MIT-BIH arrhythmia ECG database demonstrates that the embedding does not alter the diagnostic features of cover ECG. The secret data imperceptibility in stego-ECG is evident through various statistical and clinical performance measures. Statistical metrics comprise of Percentage Root Mean Square Difference (PRD) and Peak Signal to Noise Ratio (PSNR). Further, a comparative analysis between proposed method and existing approaches was also performed. The results clearly demonstrated the superiority of proposed method.

Keywords: chaotic maps, ECG steganography, data embedding, electrocardiogram

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Authors: S. Turkan

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In this paper, the Turkey’s Top 500 Industrial Enterprises data in 2014 were analyzed by data envelopment analysis. Data envelopment analysis is used to detect efficient decision-making units such as universities, hospitals, schools etc. by using inputs and outputs. The decision-making units in this study are enterprises. To detect efficient enterprises, some financial ratios are determined as inputs and outputs. For this reason, financial indicators related to productivity of enterprises are considered. The efficient foreign weighted owned capital enterprises are detected via super efficiency model. According to the results, it is said that Mercedes-Benz is the most efficient foreign weighted owned capital enterprise in Turkey.

Keywords: data envelopment analysis, super efficiency, logistic regression, financial ratios

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Authors: Sricheta Chowdhury

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To require a woman to choose between her work and her personal life is the most acute form of discrimination that can be meted out against her. No woman should be given a choice to choose between her motherhood and her career at Bar, yet that is the most detrimental discrimination that has been happening in Indian Bar, which no one has questioned so far. The falling number of women in practice is a reality that isn’t garnering much attention given the sharp rise in women studying law but is not being able to continue in the profession. Moving from a colonial misogynist whim to a post-colonial “new-age construct of Indian woman” façade, the policymakers of the Indian Judiciary have done nothing so far to decolonize itself from its rudimentary understanding of ‘equality of gender’ when it comes to the legal profession. Therefore, when Indian jurisprudence was (and is) swooning to the sweeping effect of transformative constitutionalism in the understanding of equality as enshrined under the Indian Constitution, one cannot help but question why the legal profession remained out of brushing effect of achieving substantive equality. The Airline industry’s discriminatory policies were not spared from criticism, nor were the policies where women’s involvement in any establishment serving liquor (Anuj Garg case), but the judicial practice did not question the stereotypical bias of gender and unequal structural practices until recently. That necessitates the need to examine the existing Bar policies and the steps taken by the regulatory bodies in assessing the situations that are in favor or against the purpose of furthering women’s issues in present-day India. From a comparative feminist point of concern, South Africa’s pro-women Bar policies are attractive to assess their applicability and extent in terms of promoting inclusivity at the Bar. This article intends to tap on these two countries’ potential in carving a niche in giving women an equal platform to play a substantive role in designing governance policies through the Judiciary. The article analyses the current gender composition of the legal profession while endorsing the concept of substantive equality as a requisite in designing an appropriate appointment process of the judges. It studies the theoretical framework on gender equality, examines the international and regional instruments and analyses the scope of welfare policies that Indian legal and regulatory bodies can undertake towards a transformative initiative in re-modeling the Judiciary to a more diverse and inclusive institution. The methodology employs a comparative and analytical understanding of doctrinal resources. It makes quantitative use of secondary data and qualitative use of primary data collected for determining the present status of Indian women legal practitioners and judges. With respect to quantitative data, statistics on the representation of women as judges and chief justices and senior advocates from their official websites from 2018 till present have been utilized. In respect of qualitative data, results of the structured interviews conducted through open and close-ended questions with retired lady judges of the higher judiciary and senior advocates of the Supreme Court of India, contacted through snowball sampling, are utilized.

Keywords: gender, higher judiciary, legal profession, representation, substantive equality

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Authors: Hyun-Woo Cho

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The real-time fault monitoring and diagnosis of large scale production processes is helpful and necessary in order to operate industrial process safely and efficiently producing good final product quality. Unusual and abnormal events of the process may have a serious impact on the process such as malfunctions or breakdowns. This work try to utilize process measurement data obtained in an on-line basis for the safe and some fault-free operation of industrial processes. To this end, this work evaluated the proposed intelligent process data monitoring framework based on a simulation process. The monitoring scheme extracts the fault pattern in the reduced space for the reliable data representation. Moreover, this work shows the results of using linear and nonlinear techniques for the monitoring purpose. It has shown that the nonlinear technique produced more reliable monitoring results and outperforms linear methods. The adoption of the qualitative monitoring model helps to reduce the sensitivity of the fault pattern to noise.

Keywords: process data, data mining, process operation, real-time monitoring

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Authors: Danzhao Cheng, Eugene Ch'ng

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Virtual Reality technologies can reconstruct cultural heritage objects and sites to a level of realism. Concentrating mostly on documenting authentic data and accurate representations of tangible contents, current virtual heritage is limited to accumulating visually presented objects. Such constructions, however, are fragmentary and may not convey the inherent significance of heritage in a meaningful way. In order to contextualise fragmentary historical contents where history can be told, a strategy is to create a guided narrative via role-playing. Such an approach can strengthen the logical connections of cultural elements and facilitate creative synthesis within the virtual world. This project successfully reconstructed the Ningbo Sanjiangkou VR site in Yuan Dynasty combining VR technology and role-play game approach. The results with 80 pairs of participants suggest that VR role-playing can be beneficial in a number of ways. Firstly, it creates thematic interactivity which encourages users to explore the virtual heritage in a more entertaining way with task-oriented goals. Secondly, the experience becomes highly engaging since users can interpret a historical context through the perspective of specific roles that exist in past societies. Thirdly, personalisation allows open-ended sequences of the expedition, reinforcing user’s acquisition of procedural knowledge relative to the cultural domain. To sum up, role-playing in VR poses great potential for experiential learning as it allows users to interpret a historical context in a more entertaining way.

Keywords: experiential learning, maritime silk road, role-playing, virtual heritage, virtual reality

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Authors: Srinivas Peri, Siva Abhishek Sirivella, Tejaswini Kallakuri, Uzair Ahmad

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Effective traffic management and infrastructure planning are crucial for the development of smart cities and intelligent transportation systems. This study addresses the challenge of data scarcity by generating realistic synthetic traffic data using the PeMS-Bay dataset, improving the accuracy and reliability of predictive modeling. Advanced synthetic data generation techniques, including TimeGAN, GaussianCopula, and PAR Synthesizer, are employed to produce synthetic data that replicates the statistical and structural characteristics of real-world traffic. Future integration of Spatial-Temporal Generative Adversarial Networks (ST-GAN) is planned to capture both spatial and temporal correlations, further improving data quality and realism. The performance of each synthetic data generation model is evaluated against real-world data to identify the best models for accurately replicating traffic patterns. Long Short-Term Memory (LSTM) networks are utilized to model and predict complex temporal dependencies within traffic patterns. This comprehensive approach aims to pinpoint areas with low vehicle counts, uncover underlying traffic issues, and inform targeted infrastructure interventions. By combining GAN-based synthetic data generation with LSTM-based traffic modeling, this study supports data-driven decision-making that enhances urban mobility, safety, and the overall efficiency of city planning initiatives.

Keywords: GAN, long short-term memory, synthetic data generation, traffic management

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Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

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The widespread use of machine learning applications in production is significantly accelerated by improved computing power and increasing data availability. Predictive quality enables the assurance of product quality by using machine learning models as a basis for decisions on test results. The use of real Bosch production data based on geometric gauge blocks from machining, mating data from assembly and hydraulic measurement data from final testing of directional valves is a promising approach to classifying the quality characteristics of workpieces.

Keywords: machine learning, classification, predictive quality, hydraulics, supervised learning

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Authors: Gehad S. Kaseb, Mona F. Ahmed

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Arabic Sentiment Analysis (SA) is one of the most common research fields with many open areas. Few studies apply SA to Arabic dialects. This paper proposes different pre-processing steps and a modified methodology to improve the accuracy using normal Support Vector Machine (SVM) classification. The paper works on two datasets, Arabic Sentiment Tweets Dataset (ASTD) and Extended Arabic Tweets Sentiment Dataset (Extended-AATSD), which are publicly available for academic use. The results show that the classification accuracy approaches 86%.

Keywords: Arabic, classification, sentiment analysis, tweets

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Authors: Suglo Tohari Luri

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Data analysis is an important aspect of business performance. With the application of artificial intelligence within databases, selecting a suitable database engine for an application design is also very crucial for business data analysis. The application of business intelligence (BI) software into some relational databases such as Neo4j has proved highly effective in terms of customer data analysis. Yet what remains of great concern is the fact that not all business organizations have the neo4j business intelligence software applications to implement for customer data analysis. Further, those with the BI software lack personnel with the requisite expertise to use it effectively with the neo4j database. The purpose of this research is to demonstrate how the Neo4j program code alone can be applied for the analysis of e-commerce website customer visits. As the neo4j database engine is optimized for handling and managing data relationships with the capability of building high performance and scalable systems to handle connected data nodes, it will ensure that business owners who advertise their products at websites using neo4j as a database are able to determine the number of visitors so as to know which products are visited at routine intervals for the necessary decision making. It will also help in knowing the best customer segments in relation to specific goods so as to place more emphasis on their advertisement on the said websites.

Keywords: data, engine, intelligence, customer, neo4j, database

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Authors: P. Bharathiraja

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Computer Aided decision making system is used to enhance diagnosis and prognosis of diseases and also to assist clinicians and junior doctors in clinical decision making. Medical Data used for decision making should be definite and consistent. Data Mining and soft computing techniques are used for cleaning the data and for incorporating human reasoning in decision making systems. Fuzzy rule based inference technique can be used for classification in order to incorporate human reasoning in the decision making process. In this work, missing values are imputed using the mean or mode of the attribute. The data are normalized using min-ma normalization to improve the design and efficiency of the fuzzy inference system. The fuzzy inference system is used to handle the uncertainties that exist in the medical data. Equal-width-partitioning is used to partition the attribute values into appropriate fuzzy intervals. Fuzzy rules are generated using Class Based Associative rule mining algorithm. The system is trained and tested using heart disease data set from the University of California at Irvine (UCI) Machine Learning Repository. The data was split using a hold out approach into training and testing data. From the experimental results it can be inferred that classification using fuzzy inference system performs better than trivial IF-THEN rule based classification approaches. Furthermore it is observed that the use of fuzzy logic and fuzzy inference mechanism handles uncertainty and also resembles human decision making. The system can be used in the absence of a clinical expert to assist junior doctors and clinicians in clinical decision making.

Keywords: decision making, data mining, normalization, fuzzy rule, classification

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Authors: Mohsin Raza, Rizwan Latif, Syed Adnan Qasim, Imran Shafi

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High torque low-speed diesel engine has a wide range of industrial and commercial applications. In literature, it’s found that lot of work has been done for the high-speed diesel engine and research on High Torque low-speed is rare. The fuel injection plays a key role in the efficiency of engine and reduction in exhaust emission. The fuel breakup plays a critical role in air-fuel mixture and spray combustion. The current study explains numerically an important phenomenon in spray combustion which is deformation and breakup of liquid drops in compression ignition internal combustion engine. The secondary breakup and its influence on spray and characteristics of compressed gas in-cylinder have been calculated by using simulation software in the backdrop of high torque low-speed diesel like conditions. The secondary spray breakup is modeled with KH - RT instabilities. The continuous field is described by turbulence model and dynamics of the dispersed droplet is modeled by Lagrangian tracking scheme. The results by using KH - RT model are compared against other default methods in OpenFOAM and published experimental data from research and implemented in CFD (Computational Fluid Dynamics). These numerical simulation, done in OpenFoam and Matlab, results are analyzed for the complete 720- degree 4 stroke engine cycle at a low engine speed, for favorable agreement to be achieved. Results thus obtained will be analyzed for better evaporation in near nozzle region. The proposed analyses will further help in better engine efficiency, low emission and improved fuel economy.

Keywords: diesel fuel, KH-RT, Lagrangian , Open FOAM, secondary breakup

Procedia PDF Downloads 265

Authors: Chandan Kumar

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Background and Context: Population growth and rapid urbanization resulted nearly 2 Lacs migrants along with families moving to Delhi each year in search of jobs. Most of these poor migrant families end up living in slums and constitute an estimated population of 1.87 lacs every year. Further, more than half (52 per cent) of Delhi’s population resides in places such as unauthorized and resettled colonies. Slum population is fully dependent on public toilet to defecate. In Public toilets, manholes either connected with Sewer line or septic tank. Septic tank connected public toilet faces major challenges to dispose of waste water. They have to dispose of waste water in outside open drain and waste water struck out side of public toilet complex and near to the slum area. As a result, outbreak diseases such as Malaria, Dengue and Chikungunya in slum area due to stagnated waste water. Intervention and Innovation took place by Save the Children in 21 Public Toilet Complexes of South Delhi and North Delhi. These public toilet complexes were facing same waste water disposal problem. They were disposing of minimum 1800 liters waste water every day in open drain. Which caused stagnated water-borne diseases among the nearest community. Construction of Soak Well: Construction of soak well in urban context was an innovative approach to minimizing the problem of waste water management and increased water table of existing borewell in toilet complex. This technique made solution in Ground water recharging system, and additional water was utilized in vegetable gardening within the complex premises. Soak well had constructed with multiple filter media with inlet and safeguarding bed on surrounding surface. After construction, soak well started exhausting 2000 liters of waste water to raise ground water level through different filter media. Finally, we brought a change in the communities by constructing soak well and with zero maintenance system. These Public Toilet Complexes were empowered by safe disposing waste water mechanism and reduced stagnated water-borne diseases.

Keywords: diseases, ground water recharging system, soak well, toilet complex, waste water

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Authors: Yina F. Muñoz, Alexander Paz, Hanns De La Fuente-Mella, Joaquin V. Fariña, Guilherme M. Sales

Abstract:

The primary approach for estimating bridge deterioration uses Markov-chain models and regression analysis. Traditional Markov models have problems in estimating the required transition probabilities when a small sample size is used. Often, reliable bridge data have not been taken over large periods, thus large data sets may not be available. This study presents an important change to the traditional approach by using the Small Data Method to estimate transition probabilities. The results illustrate that the Small Data Method and traditional approach both provide similar estimates; however, the former method provides results that are more conservative. That is, Small Data Method provided slightly lower than expected bridge condition ratings compared with the traditional approach. Considering that bridges are critical infrastructures, the Small Data Method, which uses more information and provides more conservative estimates, may be more appropriate when the available sample size is small. In addition, regression analysis was used to calculate bridge deterioration. Condition ratings were determined for bridge groups, and the best regression model was selected for each group. The results obtained were very similar to those obtained when using Markov chains; however, it is desirable to use more data for better results.

Keywords: concrete bridges, deterioration, Markov chains, probability matrix

Procedia PDF Downloads 336

Authors: Fan Ye

Abstract:

Adverse weather conditions, particularly those with low visibility, are critical to the driving tasks. However, the direct relationship between visibility distances and traffic flow/roadway safety is uncertain due to the limitation of visibility data availability. The recent growth of deployment of Road Weather Information Systems (RWIS) makes segment-specific visibility information available which can be integrated with other Intelligent Transportation System, such as automated warning system and variable speed limit, to improve mobility and safety. Before applying the RWIS visibility measurements in traffic study and operations, it is critical to validate the data. Therefore, an attempt was made in the paper to examine the validity and viability of RWIS visibility data by comparing visibility measurements among RWIS, airport weather stations, and weather information recorded by police in crash reports, based on Ohio data. The results indicated that RWIS visibility measurements were significantly different from airport visibility data in Ohio, but no conclusion regarding the reliability of RWIS visibility could be drawn in the consideration of no verified ground truth in the comparisons. It was suggested that more objective methods are needed to validate the RWIS visibility measurements, such as continuous in-field measurements associated with various weather events using calibrated visibility sensors.

Keywords: RWIS, visibility distance, low visibility, adverse weather

Procedia PDF Downloads 251

Authors: Rahul Malhotra

Abstract:

Fiber based access networks can deliver performance that can support the increasing demands for high speed connections. One of the new technologies that have emerged in recent years is Passive Optical Networks. This paper is targeted to show the simultaneous delivery of triple play service (data, voice and video). The comparative investigation and suitability of various data rates is presented. It is demonstrated that as we increase the data rate, number of users to be accommodated decreases due to increase in bit error rate.

Keywords: BER, PON, TDMPON, GPON, CWDM, OLT, ONT

Procedia PDF Downloads 556

Authors: Vahid Bayrami Rad

Abstract:

In this research, common methods and techniques have been investigated with a focus on intelligent fault finding and monitoring systems in the oil industry. In fact, remote and intelligent control methods are considered a necessity for implementing various operations in the oil industry, but benefiting from the knowledge extracted from countless data generated with the help of data mining algorithms. It is a avoid way to speed up the operational process for monitoring and troubleshooting in today's big oil companies. Therefore, by comparing data mining algorithms and checking the efficiency and structure and how these algorithms respond in different conditions, The proposed (Bayesian) algorithm using data clustering and their analysis and data evaluation using a colored Petri net has provided an applicable and dynamic model from the point of view of reliability and response time. Therefore, by using this method, it is possible to achieve a dynamic and consistent model of the remote control system and prevent the occurrence of leakage in oil pipelines and refineries and reduce costs and human and financial errors. Statistical data The data obtained from the evaluation process shows an increase in reliability, availability and high speed compared to other previous methods in this proposed method.

Keywords: wireless sensors, petroleum equipment troubleshooting, Bayesian algorithm, colored Petri net, rapid miner, data mining-reliability

Procedia PDF Downloads 66