Search results for: real-time data visualization

Authors: Hitesh Tahbildar

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The cost of test data generation for a program is computationally very high. In general case, no algorithm to generate test data for all types of algorithms has been found. The cost of generating test data for different types of algorithm is different. Till date, people are emphasizing the need to generate test data for different types of programming constructs rather than different types of algorithms. The test data generation methods have been implemented to find heuristics for different types of algorithms. Some algorithms that includes divide and conquer, backtracking, greedy approach, dynamic programming to find the minimum cost of test data generation have been tested. Our experimental results say that some of these types of algorithm can be used as a necessary condition for selecting heuristics and programming constructs are sufficient condition for selecting our heuristics. Finally we recommend the different heuristics for test data generation to be selected for different types of algorithms.

Keywords: ongest path, saturation point, lmax, kL, kS

Procedia PDF Downloads 392

Authors: Hatice Kübra Koç

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For academia, collecting reliable and valid data is one of the most significant issues for researchers. However, it is not the same procedure for all different target groups; meanwhile, during data collection from teenagers, young adults, or adults, researchers can use common data collection tools such as questionnaires, interviews, and semi-structured interviews; yet, for young learners and very young ones, these reliable and valid data collection tools cannot be easily designed or applied by the researchers. In this study, firstly, common data collection tools are examined for ‘very young’ and ‘young learners’ participant groups since it is thought that the quality and efficiency of an academic study is mainly based on its valid and correct data collection and data analysis procedure. Secondly, two different data collection instruments for very young and young learners are stated as discussing the efficacy of them. Finally, a suggested data collection tool – a performance-based questionnaire- which is specifically developed for ‘very young’ and ‘young learners’ participant groups in the field of teaching English to young learners as a foreign language is presented in this current study. The designing procedure and suggested items/factors for the suggested data collection tool are accordingly revealed at the end of the study to help researchers have studied with young and very learners.

Keywords: data collection instruments, performance-based questionnaire, young learners, very young learners

Procedia PDF Downloads 77

Authors: Yaxin Bi

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Accurate prediction and understanding of the evolution mechanisms of earthquakes remain challenging in the fields of geology, geophysics, and seismology. This study leverages Long Short-Term Memory (LSTM) networks and Generative Adversarial Networks (GANs), a generative model tailored to time-series data, for generating synthetic time series data based on Swarm satellite data, which will be used for detecting seismic anomalies. LSTMs demonstrated commendable predictive performance in generating synthetic data across multiple countries. In contrast, the GAN models struggled to generate synthetic data, often producing non-informative values, although they were able to capture the data distribution of the time series. These findings highlight both the promise and challenges associated with applying deep learning techniques to generate synthetic data, underscoring the potential of deep learning in generating synthetic electromagnetic satellite data.

Keywords: LSTM, GAN, earthquake, synthetic data, generative AI, seismic precursors

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Authors: I. Székács, Á. Fejes, S. Klátyik, E. Takács, D. Patkó, J. Pomóthy, M. Mörtl, R. Horváth, E. Madarász, B. Darvas, A. Székács

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Environmental and toxicological characteristics of formulated pesticides may substantially differ from those of their active ingredients or other components alone. This phenomenon is demonstrated in the case of the herbicide active ingredient glyphosate. Due to its extensive application, this active ingredient was found in surface and ground water samples collected in Békés County, Hungary, in the concentration range of 0.54–0.98 ng/ml. The occurrence of glyphosate appeared to be somewhat higher at areas under intensive agriculture, industrial activities and public road services, but the compound was detected at areas under organic (ecological) farming or natural grasslands, indicating environmental mobility. Increased toxicity of the formulated herbicide product Roundup, compared to that of glyphosate was observed on the indicator aquatic organism Daphnia magna Straus. Acute LC50 values of Roundup and its formulating adjuvant Polyethoxylated Tallowamine (POEA) exceeded 20 and 3.1 mg/ml, respectively, while that of glyphosate (as isopropyl salt) was found to be substantially lower (690-900 mg/ml) showing good agreement with literature data. Cytotoxicity of Roundup, POEA and glyphosate has been determined on the neuroectodermal cell line, NE-4C measured both by cell viability test and holographic microscopy. Acute toxicity (LC50) of Roundup, POEA and glyphosate on NE-4C cells was found to be 0.013±0.002%, 0.017±0.009% and 6.46±2.25%, respectively (in equivalents of diluted Roundup solution), corresponding to 0.022±0.003 and 53.1±18.5 mg/ml for POEA and glyphosate, respectively, indicating no statistical difference between Roundup and POEA and 2.5 orders of magnitude difference between these and glyphosate. The same order of cellular toxicity seen in average cell area has been indicated under quantitative cell visualization. The results indicate that toxicity of the formulated herbicide is caused by the formulating agent, but in some parameters toxicological synergy occurs between POEA and glyphosate.

Keywords: glyphosate, polyethoxylated tallowamine, Roundup, combined aquatic and cellular toxicity, synergy

Procedia PDF Downloads 306

Authors: Hyun-Woo Cho

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For ensuring the safety of a manufacturing process one should quickly identify an assignable cause of a fault in an on-line basis. To this end, many statistical techniques including linear and nonlinear methods have been frequently utilized. However, such methods possessed a major problem of small sample size, which is mostly attributed to the characteristics of empirical models used for reference models. This work presents a new method to overcome the insufficiency of measurement data in the monitoring and diagnosis tasks. Some quasi-measurement data are generated from existing data based on the two indices of similarity and importance. The performance of the method is demonstrated using a real data set. The results turn out that the presented methods are able to handle the insufficiency problem successfully. In addition, it is shown to be quite efficient in terms of computational speed and memory usage, and thus on-line implementation of the method is straightforward for monitoring and diagnosis purposes.

Keywords: data analysis, diagnosis, monitoring, process data, quality control

Procedia PDF Downloads 469

Authors: Hsien-Tsen Wang

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Business Intelligence (BI) has long helped organizations make informed decisions based on data-driven insights and gain competitive advantages in the marketplace. In the past two decades, businesses witnessed not only the dramatically increasing volume and heterogeneity of business data but also the emergence of new technologies, such as Artificial Intelligence (AI), Semantic Web (SW), Cloud Computing, and Big Data. It is plausible that the convergence of these technologies would bring more value out of business data by establishing linked data frameworks and connecting in ways that enable advanced analytics and improved data utilization. In this paper, we first review and summarize current BI applications and methodology. Emerging technologies that can be integrated into BI applications are then discussed. Finally, we conclude with a proposed synergy framework that aims at achieving a more flexible, scalable, and intelligent BI solution.

Keywords: business intelligence, artificial intelligence, semantic web, big data, cloud computing

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Authors: John Q. Todd

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Given that modern equipment can provide comprehensive health, status, and error condition data via built-in sensors, maintenance organizations have a new and valuable source of insight to take advantage of. This presentation will expose what these data payloads might look like and how they can be filtered, visualized, calculated into metrics, used for machine learning, and generate alerts for further action.

Keywords: condition based maintenance, equipment data, metrics, alerts

Procedia PDF Downloads 169

Authors: Jhansi Rani Boda, G. Sunitha

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Worldwide, the financial markets are influenced by several factors such as the changes in economic and political processes that occur in the country and the globe, information diffusion and approachability and so on. Yet, the foremost important factor is the investor’s reaction and perception. For an individual investor, decision-making process can be perceived as a continuous process that has significant impact of their psychology while making investment decisions. Behavioral finance relies on research of human and social recognition and emotional tolerance studies to identify and understand the investment decisions. This article aims to report the research of individual investor’s financial behavior in a historical perspective. This article uncovers the investor’s psychology in investment decision making focusing on the investor’s rationality with an explanation of psychological and emotional factors that affect investing. The results of the study are revealed by means of Graphical visualization.

Keywords: behavioral finance, psychology, investor’s behavior, psychological and emotional factors

Procedia PDF Downloads 285

Authors: Dragana Calic

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The openness, availability and the sheer volume of big data have provided, what some regard as, an invaluable and rich dataset. Researchers, businesses, advertising agencies, medical institutions, to name only a few, collect, share, and analyze this data to enable their processes and decision making. However, there are important ethical considerations associated with the use of big data. The rapidly evolving nature of online technologies has overtaken the many legislative, privacy, and ethical frameworks and principles that exist. For example, should we obtain consent to use people’s online data, and under what circumstances can privacy considerations be overridden? Current guidance on how to appropriately and ethically handle big data is inconsistent. Consequently, this paper focuses on two quite distinct but related ethical considerations that are at the core of the use of big data for research purposes. They include empowering the producers of data and empowering researchers who want to study big data. The first consideration focuses on informed consent which is at the core of empowering producers of data. In this paper, we discuss some of the complexities associated with informed consent and consider studies of producers’ perceptions to inform research ethics guidelines and practice. The second consideration focuses on the researcher. Similarly, we explore studies that focus on researchers’ perceptions and experiences.

Keywords: big data, ethics, producers’ perceptions, researchers’ perceptions

Procedia PDF Downloads 277

Authors: Walid Cherif

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Data mining has, over recent years, seen big advances because of the spread of internet, which generates everyday a tremendous volume of data, and also the immense advances in technologies which facilitate the analysis of these data. In particular, classification techniques are a subdomain of Data Mining which determines in which group each data instance is related within a given dataset. It is used to classify data into different classes according to desired criteria. Generally, a classification technique is either statistical or machine learning. Each type of these techniques has its own limits. Nowadays, current data are becoming increasingly heterogeneous; consequently, current classification techniques are encountering many difficulties. This paper defines new measure functions to quantify the resemblance between instances and then combines them in a new approach which is different from actual algorithms by its reliability computations. Results of the proposed approach exceeded most common classification techniques with an f-measure exceeding 97% on the IRIS Dataset.

Keywords: data mining, knowledge discovery, machine learning, similarity measurement, supervised classification

Procedia PDF Downloads 456

Authors: Ankur Mundhra, Shubhadeep Chakraborty, Y. R. Singh, Vishal Das

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Seismic data scaling affects the dynamic range of a data and with present day lower costs of storage and higher reliability of Hard Disk data, scaling is not suggested. However, in dealing with data of different vintages, which perhaps were processed in 16 bits or even 8 bits and are need to be processed with 32 bit available data, scaling is performed. Also, scaling amplifies low amplitude events in deeper region which disappear due to high amplitude shallow events that saturate amplitude scale. We have focused on significance of scaling data to aid interpretation. This study elucidates a proper seismic loading procedure in workstations without using default preset parameters as available in most software suites. Differences and distribution of amplitude values at different depth for seismic data are probed in this exercise. Proper loading parameters are identified and associated steps are explained that needs to be taken care of while loading data. Finally, the exercise interprets the un-certainties which might arise when correlating scaled and unscaled versions of seismic data with synthetics. As, seismic well tie correlates the seismic reflection events with well markers, for our study it is used to identify regions which are enhanced and/or affected by scaling parameter(s).

Keywords: clipping, compression, resolution, seismic scaling

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Authors: Rahul Jain, Pradhir Parmar, Dhruvesh Patel

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Planning and drawing are the important aspects of civil engineering. For testing theories about spatial location and interaction between land uses and related activities the computer based solution of urban models are used. The planner’s primary interest is in creation of 3D models of building and to obtain the terrain surface so that he can do urban morphological mappings, virtual reality, disaster management, fly through generation, visualization etc. 3D city models have a variety of applications in urban studies. Gujarat International Finance Tec-City (GIFT) is an ongoing construction site between Ahmedabad and Gandhinagar, Gujarat, India. It will be built on 3590000 m2 having a geographical coordinates of North Latitude 23°9’5’’N to 23°10’55’’ and East Longitude 72°42’2’’E to 72°42’16’’E. Therefore to develop 3D city models of GIFT city, the base map of the city is collected from GIFT office. Differential Geographical Positioning System (DGPS) is used to collect the Ground Control Points (GCP) from the field. The GCP points are used for the registration of base map in QGIS. The registered map is projected in WGS 84/UTM zone 43N grid and digitized with the help of various shapefile tools in QGIS. The approximate height of the buildings that are going to build is collected from the GIFT office and placed on the attribute table of each layer created using shapefile tools. The Shuttle Radar Topography Mission (SRTM) 1 Arc-Second Global (30 m X 30 m) grid data is used to generate the terrain of GIFT city. The Google Satellite Map is used to place on the background to get the exact location of the GIFT city. Various plugins and tools in QGIS are used to convert the raster layer of the base map of GIFT city into 3D model. The fly through tool is used for capturing and viewing the entire area in 3D of the city. This paper discusses all techniques and their usefulness in 3D city model creation from the GCP, base map, SRTM and QGIS.

Keywords: 3D model, DGPS, GIFT City, QGIS, SRTM

Procedia PDF Downloads 233

Authors: Chiang-Ho Cheng, Hong-Yih Cheng, An-Shik Yang, Tung-Hsun Hsu

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This paper reports a novel actuating design that uses the shear deformation of a piezoelectric actuator to deflect a bulge-diaphragm for driving an array microdroplet ejector. In essence, we employed a circular-shaped actuator poled radial direction with remnant polarization normal to the actuating electric field for inducing the piezoelectric shear effect. The array microdroplet ejector consists of a shear type piezoelectric actuator, a vibration plate, two chamber plates, two channel plates and a nozzle plate. The vibration, chamber and nozzle plate components are fabricated using nickel electroforming technology, whereas the channel plate is fabricated by etching of stainless steel. The diaphragm displacement was measured by the laser two-dimensional scanning vibrometer. The ejected droplets of the microejector were also observed via an optic visualization system.

Keywords: actuator, nozzle, microejector, piezoelectric

Procedia PDF Downloads 418

Authors: Jorge Cortés, Alejandra Martínez, Carlos Pérez, Anaid Simón

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The presence of government, news media, and general citizenry in social media allows considering interactions between them as a form of political communication (i.e. the public exchange of contradictory discourses about politics). Twitter’s asymmetrical following model (users can follow, mention or reply to other users that do not follow them) could foster alternative democratic practices and have an impact on Mexican political culture, which has been marked by a lack of direct communication channels between these actors. The research aim is to assess Twitter’s role in political communication practices through the analysis of interaction dynamics between government, news media, and citizens by extracting and visualizing data from Twitter’s API to observe general behavior patterns. The hypothesis is that regardless the fact that Twitter’s features enable direct and horizontal interactions between actors, users repeat traditional dynamics of interaction, without taking full advantage of the possibilities of this medium. Through an interdisciplinary team including Communication Strategies, Information Design, and Interaction Systems, the activity on Twitter generated by the controversy over the presence of Uber in Mexico City was analysed; an issue of public interest, involving aspects such as public opinion, economic interests and a legal dimension. This research includes techniques from social network analysis (SNA), a methodological approach focused on the comprehension of the relationships between actors through the visual representation and measurement of network characteristics. The analysis of the Uber event comprised data extraction, data categorization, corpus construction, corpus visualization and analysis. On the recovery stage TAGS, a Google Sheet template, was used to extract tweets that included the hashtags #UberSeQueda and #UberSeVa, posts containing the string Uber and tweets directed to @uber_mx. Using scripts written in Python, the data was filtered, discarding tweets with no interaction (replies, retweets or mentions) and locations outside of México. Considerations regarding bots and the omission of anecdotal posts were also taken into account. The utility of graphs to observe interactions of political communication in general was confirmed by the analysis of visualizations generated with programs such as Gephi and NodeXL. However, some aspects require improvements to obtain more useful visual representations for this type of research. For example, link¬crossings complicates following the direction of an interaction forcing users to manipulate the graph to see it clearly. It was concluded that some practices prevalent in political communication in Mexico are replicated in Twitter. Media actors tend to group together instead of interact with others. The political system tends to tweet as an advertising strategy rather than to generate dialogue. However, some actors were identified as bridges establishing communication between the three spheres, generating a more democratic exercise and taking advantage of Twitter’s possibilities. Although interactions in Twitter could become an alternative to political communication, this potential depends on the intentions of the participants and to what extent they are aiming for collaborative and direct communications. Further research is needed to get a deeper understanding on the political behavior of Twitter users and the possibilities of SNA for its analysis.

Keywords: interaction, political communication, social network analysis, Twitter

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Authors: Diego Rodrigues, Marcelo Lisboa, Elismar Batista, Marcos Dias

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Based on data on child labor, this work arises questions about how to understand and locate the factors that make up the child labor rates, and which properties are important to analyze these cases. Using data mining techniques to discover valid patterns on Brazilian social databases were evaluated data of child labor in the State of Tocantins (located north of Brazil with a territory of 277000 km2 and comprises 139 counties). This work aims to detect factors that are deterministic for the practice of child labor and their relationships with financial indicators, educational, regional and social, generating information that is not explicit in the government database, thus enabling better monitoring and updating policies for this purpose.

Keywords: social data, government decision making, association of social data, data mining

Procedia PDF Downloads 359

Authors: Sadam Alwadi

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Outlier values become a problem that frequently occurs in the data observation or recording process. Thus, the need for data imputation has become an essential matter. In this work, it will make use of the methods described in the prior work to detect the outlier values based on a collection of stock market data. In order to implement the detection and find some solutions that maybe helpful for investors, real closed price data were obtained from the Amman Stock Exchange (ASE). Tukey and Maximum Overlapping Discrete Wavelet Transform (MODWT) methods will be used to impute the detect the outlier values.

Keywords: outlier values, imputation, stock market data, detecting, estimation

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Authors: P. Jayashree, S. Rajkumar

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With social networks and smart devices generating a multitude of data, effective data management is the need of the hour for networks and cloud applications. Some applications need effective storage while some other applications need effective communication over networks and data reduction comes as a handy solution to meet out both requirements. Most of the data compression techniques are based on data statistics and may result in either lossy or lossless data reductions. Though lossy reductions produce better compression ratios compared to lossless methods, many applications require data accuracy and miniature details to be preserved. A variety of data compression algorithms does exist in the literature for different forms of data like text, image, and multimedia data. In the proposed work, a generic progressive compression algorithm, based on probabilistic encoding, called PEINS is projected as an enhancement over irrational number stored coding technique to cater to storage issues of increasing data volumes as a cost effective solution, which also offers data security as a secondary outcome to some extent. The proposed work reveals cost effectiveness in terms of better compression ratio with no deterioration in compression time.

Keywords: compression ratio, generic compression, irrational number storage, probabilistic encoding

Procedia PDF Downloads 278

Authors: Femi Elegbeleye, Omobayo Esan, Muienge Mbodila, Patrick Bowe

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This paper focused on cost effective storage architecture using fog and cloud data storage gateway and presented the design of the framework for the data privacy model and data analytics framework on a real-time analysis when using machine learning method. The paper began with the system analysis, system architecture and its component design, as well as the overall system operations. The several results obtained from this study on data privacy model shows that when two or more data privacy model is combined we tend to have a more stronger privacy to our data, and when fog storage gateway have several advantages over using the traditional cloud storage, from our result shows fog has reduced latency/delay, low bandwidth consumption, and energy usage when been compare with cloud storage, therefore, fog storage will help to lessen excessive cost. This paper dwelt more on the system descriptions, the researchers focused on the research design and framework design for the data privacy model, data storage, and real-time analytics. This paper also shows the major system components and their framework specification. And lastly, the overall research system architecture was shown, its structure, and its interrelationships.

Keywords: IoT, fog, cloud, data analysis, data privacy

Procedia PDF Downloads 87

Authors: Nordila Ahmad, Thamer Mohammad, Bruce W. Melville, Zuliziana Suif

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Current methods for predicting local scour at wide bridge piers, were developed on the basis of laboratory studies and very limited scour prediction were tested with field data. Laboratory wide pier scour equation from previous findings with field data were presented. A wide range of field data were used and it consists of both live-bed and clear-water scour. A method for assessing the quality of the data was developed and applied to the data set. Three other wide pier-scour equations from the literature were used to compare the performance of each predictive method. The best-performing scour equation were analyzed using statistical analysis. Comparisons of computed and observed scour depths indicate that the equation from the previous publication produced the smallest discrepancy ratio and RMSE value when compared with the large amount of laboratory and field data.

Keywords: field data, local scour, scour equation, wide piers

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Authors: Inkyu Kim, SangMan Moon

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This IEEE 802.11b protocol provides up to 11Mbps data rate, whereas aerospace industry wants to seek higher data rate COTS data link system in the UAV. The Total Maximum Throughput (TMT) and delay time are studied on many researchers in the past years This paper provides theoretical data throughput performance of UAV formation flight data link using the existing 802.11b performance theory. We operate the UAV formation flight with more than 30 quad copters with 802.11b protocol. We may be predicting that UAV formation flight numbers have to bound data link protocol performance limitations.

Keywords: UAV datalink, UAV formation flight datalink, UAV WLAN datalink application, UAV IEEE 802.11b datalink application

Procedia PDF Downloads 376

Authors: Radoslav Židek, Veronika Šidlová, Radovan Kasarda, Birgit Fuerst-Waltl

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Machine learning represents a set of topics dealing with the creation and evaluation of algorithms that facilitate pattern recognition, classification, and prediction, based on models derived from existing data. The data can present identification patterns which are used to classify into groups. The result of the analysis is the pattern which can be used for identification of data set without the need to obtain input data used for creation of this pattern. An important requirement in this process is careful data preparation validation of model used and its suitable interpretation. For breeders, it is important to know the origin of animals from the point of the genetic diversity. In case of missing pedigree information, other methods can be used for traceability of animal´s origin. Genetic diversity written in genetic data is holding relatively useful information to identify animals originated from individual countries. We can conclude that the application of data mining for molecular genetic data using supervised learning is an appropriate tool for hypothesis testing and identifying an individual.

Keywords: genetic data, Pinzgau cattle, supervised learning, machine learning

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Authors: Nidhi Gopal

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Routing is the process of moving a packet of data from source to destination and enables messages to pass from one computer to another and eventually reach the target machine. A router is a networking device that forwards data packets between computer networks. It is connected to two or more data lines from different networks (as opposed to a network switch, which connects data lines from one single network). This paper mainly emphasizes upon the study of router device, its top level architecture, and how various sub-modules of router i.e. Register, FIFO, FSM and Synchronizer are synthesized, and simulated and finally connected to its top module.

Keywords: data packets, networking, router, routing

Procedia PDF Downloads 790

Authors: Khelfaoui Rachid, Chekifi Tawfiq, Dennai Brahim, Maazouzi A. Hak

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A numerical and experimental studies of passive micro drops production have been presented. This paper focuses on the modeling of micro-oscillators systems which are composed by passive amplifier without moving part. The micro-system modeling is based on geometrical oscillators form. An asymmetric micro-oscillator design that is based on a bistable fluidic amplifier is proposed. The characteristic size of the channels is generally about 35 microns of depth. The numerical results indicate that the production and manipulation of microdrops are possible with passive device within a typical oscillators chamber of 2.25 mm diameter and 0.20 mm length when the Reynolds number is Re = 490. The novel micro drops method that is presented in this study provides a simple solution about the production of microdrops problems in micro system. We undertake an experimental step. The first part is based on the realisation of sample oscillator; the second part is consisted of visualization, production and manipulation of microdrops.

Keywords: modelling, miscible, micro drops, production, oscillator sample, capillary

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Authors: Julius Onyancha, Valentina Plekhanova

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One of the significant issues facing web users is the amount of noise in web data which hinders the process of finding useful information in relation to their dynamic interests. Current research works consider noise as any data that does not form part of the main web page and propose noise web data reduction tools which mainly focus on eliminating noise in relation to the content and layout of web data. This paper argues that not all data that form part of the main web page is of a user interest and not all noise data is actually noise to a given user. Therefore, learning of noise web data allocated to the user requests ensures not only reduction of noisiness level in a web user profile, but also a decrease in the loss of useful information hence improves the quality of a web user profile. Noise Web Data Learning (NWDL) tool/algorithm capable of learning noise web data in web user profile is proposed. The proposed work considers elimination of noise data in relation to dynamic user interest. In order to validate the performance of the proposed work, an experimental design setup is presented. The results obtained are compared with the current algorithms applied in noise web data reduction process. The experimental results show that the proposed work considers the dynamic change of user interest prior to elimination of noise data. The proposed work contributes towards improving the quality of a web user profile by reducing the amount of useful information eliminated as noise.

Keywords: web log data, web user profile, user interest, noise web data learning, machine learning

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Authors: Zeba Mahmood

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The modern business organizations are adopting technological advancement to achieve competitive edge and satisfy their consumer. The development in the field of Information technology systems has changed the way of conducting business today. Business operations today rely more on the data they obtained and this data is continuously increasing in volume. The data stored in different locations is difficult to find and use without the effective implementation of Data mining and Knowledge management techniques. Organizations who smartly identify, obtain and then convert data in useful formats for their decision making and operational improvements create additional value for their customers and enhance their operational capabilities. Marketers and Customer relationship departments of firm use Data mining techniques to make relevant decisions, this paper emphasizes on the identification of different data mining and Knowledge management techniques that are applied to different business industries. The challenges and issues of execution of these techniques are also discussed and critically analyzed in this paper.

Keywords: knowledge, knowledge management, knowledge discovery in databases, business, operational, information, data mining

Procedia PDF Downloads 520

Authors: Sachil Kumar, Anoop Kumar Verma, Wahid Ali, Uma Shankar Singh

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Globally approximately 55.3 million people die each year. In the India there were 95 lakh annual deaths in 2013. The number of deaths resulted from homicides, suicides and unintentional injuries in the same period was about 5.7 lakh. The ever-increasing crime rate necessitated the development of methods for determining time since death. An erroneous time of death window can lead investigators down the wrong path or possibly focus a case on an innocent suspect. In this regard a research was carried out by analyzing the temperature dependent degradation of a Cardiac Troponin-T protein (cTnT) in the myocardium postmortem as a marker for time since death. Cardiac tissue samples were collected from (n=6) medico-legal autopsies, (in the Department of Forensic Medicine and Toxicology, King George’s Medical University, Lucknow India) after informed consent from the relatives and studied post-mortem degradation by incubation of the cardiac tissue at room temperature (20±2 OC), 12 0C, 25 0C and 37 0C for different time periods ((~5, 26, 50, 84, 132, 157, 180, 205, and 230 hours). The cases included were the subjects of road traffic accidents (RTA) without any prior history of disease who died in the hospital and their exact time of death was known. The analysis involved extraction of the protein, separation by denaturing gel electrophoresis (SDS-PAGE) and visualization by Western blot using cTnT specific monoclonal antibodies. The area of the bands within a lane was quantified by scanning and digitizing the image using Gel Doc. The data shows a distinct temporal profile corresponding to the degradation of cTnT by proteases found in cardiac muscle. The disappearance of intact cTnT and the appearance of lower molecular weight bands are easily observed. Western blot data clearly showed the intact protein at 42 kDa, two major (27 kDa, 10kDa) fragments, two additional minor fragments (32 kDa) and formation of low molecular weight fragments as time increases. At 12 0C the intensity of band (intact cTnT) decreased steadily as compared to RT, 25 0C and 37 0C. Overall, both PMI and temperature had a statistically significant effect where the greatest amount of protein breakdown was observed within the first 38 h and at the highest temperature, 37 0C. The combination of high temperature (37 0C) and long Postmortem interval (105.15 hrs) had the most drastic effect on the breakdown of cTnT. If the percent intact cTnT is calculated from the total area integrated within a Western blot lane, then the percent intact cTnT shows a pseudo-first order relationship when plotted against the log of the time postmortem. These plots show a good coefficient of correlation of r = 0.95 (p=0.003) for the regression of the human heart at different temperature conditions. The data presented demonstrates that this technique can provide an extended time range during which Postmortem interval can be more accurately estimated.

Keywords: degradation, postmortem interval, proteolysis, temperature, troponin

Procedia PDF Downloads 375

Authors: Jerry Q. Cheng

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Currently, in analyzing large-scale recurrent event data, there are many challenges such as memory limitations, unscalable computing time, etc. In this research, a divide-and-conquer method is proposed using parametric frailty models. Specifically, the data is randomly divided into many subsets, and the maximum likelihood estimator from each individual data set is obtained. Then a weighted method is proposed to combine these individual estimators as the final estimator. It is shown that this divide-and-conquer estimator is asymptotically equivalent to the estimator based on the full data. Simulation studies are conducted to demonstrate the performance of this proposed method. This approach is applied to a large real dataset of repeated heart failure hospitalizations.

Keywords: big data analytics, divide-and-conquer, recurrent event data, statistical computing

Procedia PDF Downloads 150

Authors: Ashiff Khan, A. Seetharaman, Abhijit Dasgupta

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The new era of big data (BD) is influencing chemical industries tremendously, providing several opportunities to reshape the way they operate and help them shift towards intelligent manufacturing. Given the availability of free software and the large amount of real-time data generated and stored in process plants, chemical industries are still in the early stages of big data adoption. The industry is just starting to realize the importance of the large amount of data it owns to make the right decisions and support its strategies. This article explores the importance of professional competencies and data science that influence BD in chemical industries to help it move towards intelligent manufacturing fast and reliable. This article utilizes a literature review and identifies potential applications in the chemical industry to move from conventional methods to a data-driven approach. The scope of this document is limited to the adoption of BD in chemical industries and the variables identified in this article. To achieve this objective, government, academia, and industry must work together to overcome all present and future challenges.

Keywords: chemical engineering, big data analytics, industrial revolution, professional competence, data science

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Authors: M. Sumana, K. S. Hareesha

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Secure computations are essential while performing privacy preserving data mining. Distributed privacy preserving data mining involve two to more sites that cannot pool in their data to a third party due to the violation of law regarding the individual. Hence in order to model the private data without compromising privacy and information loss, secure multiparty computations are used. Secure computations of product, mean, variance, dot product, sigmoid function using the additive and multiplicative homomorphic property is discussed. The computations are performed on vertically partitioned data with a single site holding the class value.

Keywords: homomorphic property, secure product, secure mean and variance, secure dot product, vertically partitioned data

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Authors: Roger L. Goodwin

Abstract:

The US Consumer Price Indices (CPIs) measures hundreds of items in the US economy. Many social programs and government benefits index to the CPIs. In mid to late 1990, much research went into changes to the CPI by a Congressional Advisory Committee. One thing can be said from the research is that, aside from there are alternative estimators for the CPI; any fundamental change to the CPI will affect many government programs. The purpose of this project is to modernize an existing process. This paper will show the development of a small, visual, software product that documents the Economic Price Adjustment (EPA) for long-term contracts. The existing workbook does not provide the flexibility to calculate EPAs where the base-month and the option-month are different. Nor does the workbook provide automated error checking. The small, visual, software product provides the additional flexibility and error checking. This paper presents the feedback to project.

Keywords: Consumer Price Index, Economic Price Adjustment, contracts, visualization tools, database, reports, forms, event procedures

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