Search results for: geographic data streams

Authors: Snezana Savoska, Blagoj Ristevski, Violeta Manevska, Zlatko Savoski, Ilija Jolevski

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Big data is widely accepted by the pharmaceutical companies as a result of business demands create through legal pressure. Pharmaceutical companies have many legal demands as well as standards’ demands and have to adapt their procedures to the legislation. To manage with these demands, they have to standardize the usage of the current information technology and use the latest software tools. This paper highlights some important aspects of experience with big data projects implementation in a pharmaceutical Macedonian company. These projects made improvements of their business processes by the help of new software tools selected to comply with legal and business demands. They use IT as a strategic tool to obtain competitive advantage on the market and to reengineer the processes towards new Internet economy and quality demands. The company is required to manage vast amounts of structured as well as unstructured data. For these reasons, they implement projects for emerging and appropriate software tools which have to deal with big data concepts accepted in the company.

Keywords: big data, unstructured data, SAP ERP, documentum

Procedia PDF Downloads 274

Authors: Adriano Araujo Carvalho, Arturo Alatrista Corrales

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This paper intends to show how electrical energy consumption and production data analysis were used to find opportunities to save energy at Taboada wastewater treatment plant in Callao, Peru. In order to access the data, it was used independent data networks for both electrical and process instruments, which were taken to analyze under an ISO 50001 energy audit, which considered, thus, Energy Performance Indexes for each process and a step-by-step guide presented in this text. Due to the use of aforementioned methodology and data mining techniques applied on information gathered through electronic multimeters (conveniently placed on substation switchboards connected to a cloud network), it was possible to identify thoroughly the performance of each process and thus, evidence saving opportunities which were previously hidden before. The data analysis brought both costs and energy reduction, allowing the plant to save significant resources and to be certified under ISO 50001.

Keywords: energy and production data analysis, energy management, ISO 50001, wastewater treatment plant energy analysis

Procedia PDF Downloads 201

Authors: Cosmin-Gabriel Porumb-Ghiurco, Dumitrana Fiț-Iordache, Szőke Árpád

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The literature incorporating geographical or tourist-geographical themes and explicit references to the Carpathian area is extremely abundant. Through this paper, we attempt to “undermine” the traditional, tourist-geographical approaches of the Carpathian Arch by targeting an aspect often regarded as marginal but which, if examined, even only empirically, takes the form of a vast problem with multidisciplinary vocation. Therefore, we propose a more extravagant yet pro-touristic approach to the Romanian Carpathian geo-space. Consequently, the explicit goal of this approach consists precisely in broadening the multidisciplinary, essentially geographic scope of the research, the vision and mental representation of the Carpathian area by advancing a lever that would symbolize a different kind of unification between geography and tourism on a more intimate, subtle, mythological and archetypal level. The spiritual and mercantile dimensions of the tourism field in general and of the local Carpathian tourism can meld harmoniously together in order to create a common territorial reality of referral and favorable perspectives for the consolidation of their symbiotic relationship.

Keywords: tourist image, mythical geography, collective imaginary, spiritual patrimony, Carpathians

Procedia PDF Downloads 96

Authors: Krishan Kumar, Mohit Mittal, Pramod Kumar

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Wireless sensor network is one of the most promising communication networks for monitoring remote environmental areas. In this network, all the sensor nodes are communicated with each other via radio signals. The sensor nodes have capability of sensing, data storage and processing. The sensor nodes collect the information through neighboring nodes to particular node. The data collection and processing is done by data aggregation techniques. For the data aggregation in sensor network, clustering technique is implemented in the sensor network by implementing self-organizing feature map (SOFM) neural network. Some of the sensor nodes are selected as cluster head nodes. The information aggregated to cluster head nodes from non-cluster head nodes and then this information is transferred to base station (or sink nodes). The aim of this paper is to manage the huge amount of data with the help of SOM neural network. Clustered data is selected to transfer to base station instead of whole information aggregated at cluster head nodes. This reduces the battery consumption over the huge data management. The network lifetime is enhanced at a greater extent.

Keywords: artificial neural network, data clustering, self organization feature map, wireless sensor network

Procedia PDF Downloads 519

Authors: Mohamed Thani Ibouroi, Ali Cheha, Claudine Montgelard, Veronique Arnal, Dawiyat Massoudi, Guillelme Astruc, Said Ali Ousseni Dhurham, Aurelien Besnard

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The Livingstone's flying fox (Pteropus livingstonii) and the Comorian fruit bat (P.seychellensis comorensis) are two endemic fruit bat species among the mostly threatened animals of the Comoros archipelagos. Despite their role as important ecosystem service providers like all flying fox species as pollinators and seed dispersers, little is known about their ecologies, population genetics and structures making difficult the development of evidence-based conservation strategies. In this study, we assess spatial distribution and ecological niche of both species using Species Distribution Modeling (SDM) based on the recent Ensemble of Small Models (ESMs) approach using presence-only data. Population structure and genetic diversity of the two species were assessed using both mitochondrial and microsatellite markers based on non-invasive genetic samples. Our ESMs highlight a clear niche partitioning of the two sympatric species. Livingstone’s flying fox has a very limited distribution, restricted on steep slope of natural forests at high elevation. On the contrary, the Comorian fruit bat has a relatively large geographic range spread over low elevations in farmlands and villages. Our genetic analysis shows a low genetic diversity for both fruit bats species. They also show that the Livingstone’s flying fox population of the two islands were genetically isolated while no evidence of genetic differentiation was detected for the Comorian fruit bats between islands. Our results support the idea that natural habitat loss, especially the natural forest loss and fragmentation are the important factors impacting the distribution of the Livingstone’s flying fox by limiting its foraging area and reducing its potential roosting sites. On the contrary, the Comorian fruit bats seem to be favored by human activities probably because its diets are less specialized. By this study, we concluded that the Livingstone’s flying fox species and its habitat are of high priority in term of conservation at the Comoros archipelagos scale.

Keywords: Comoros islands, ecological niche, habitat loss, population genetics, fruit bats, conservation biology

Procedia PDF Downloads 270

Authors: Suzan Wedyan

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Data mining is one of the main phases in the Knowledge Discovery Database (KDD) which is responsible of finding hidden and useful knowledge from databases. There are many different tasks for data mining including regression, pattern recognition, clustering, classification, and association rule. In recent years a promising data mining approach called associative classification (AC) has been proposed, AC integrates classification and association rule discovery to build classification models (classifiers). This paper surveys and critically compares several AC algorithms with reference of the different procedures are used in each algorithm, such as rule learning, rule sorting, rule pruning, classifier building, and class allocation for test cases.

Keywords: associative classification, classification, data mining, learning, rule ranking, rule pruning, prediction

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Authors: Rahma Souli-Jbali, Minyar Sassi Hidri, Rahma Ben Ayed

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Grid of computing nodes has emerged as a representative means of connecting distributed computers or resources scattered all over the world for the purpose of computing and distributed storage. Since fault tolerance becomes complex due to the availability of resources in decentralized grid environment, it can be used in connection with replication in data grids. The objective of our work is to present fault tolerance in data grids with data replication-driven model based on clustering. The performance of the protocol is evaluated with Omnet++ simulator. The computational results show the efficiency of our protocol in terms of recovery time and the number of process in rollbacks.

Keywords: data grids, fault tolerance, clustering, chandy-lamport

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Authors: Muhammet Dursun Kaya, Hasan Asil

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One of the most important factors of research and development is the deep insight into the evolutions of scientific development. The state-of-the-art tools and instruments can considerably assist the researchers, and many of the world organizations have become aware of the advantages of data mining for the acquisition of the knowledge required for the unstructured data. This paper was an attempt to review the articles on the information technology published in the past five years with the aid of data mining. A clustering approach was used to study these articles, and the research results revealed that three topics, namely health, innovation, and information systems, have captured the special attention of the researchers.

Keywords: information technology, data mining, scientific development, clustering

Procedia PDF Downloads 281

Authors: Mona Almansoori, Ahmed Mustafa, Ahmad Elshamy

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Wireless nodes in a sensor network gather and process critical information designed to process and communicate, information flooding through such network is critical for decision making and data processing, the integrity of such data is one of the most critical factors in wireless security without compromising the processing and transmission capability of the network. This paper presents mechanism to securely transmit data over a chain of sensor nodes without compromising the throughput of the network utilizing available battery resources available at the sensor node.

Keywords: hybrid protocol, data integrity, lightweight encryption, neighbor based key sharing, sensor node data processing, Z-MAC

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Authors: Seyed Mostafa Jafari Raad, Hassan Hassanzadeh

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Injection of impurities along with CO₂ into saline aquifers provides an exceptional prospect for low-cost carbon capture and storage technologies and can potentially accelerate large-scale implementation of geological storage of CO₂. We have conducted linear stability analyses and numerical simulations to investigate the effects of permitted impurities in CO₂ streams on the onset of natural convection and dynamics of subsequent convective mixing. We have shown that the rate of dissolution of an impure CO₂ stream with H₂S highly depends on the operating conditions such as temperature, pressure, and composition of impurity. Contrary to findings of previous studies, our results show that an impurity such as H₂S can potentially reduce the onset time of natural convection and can accelerate the subsequent convective mixing. However, at the later times, the rate of convective dissolution is adversely affected by the impurities. Therefore, the injection of an impure CO₂ stream can be engineered to improve the rate of dissolution of CO₂, which leads to higher storage security and efficiency. Accordingly, we have identified the most favorable CO₂ stream compositions based on the geophysical properties of target aquifers. Information related to the onset of natural convection such as the scaling relations and the most favorable operating conditions for CO₂ storage developed in this study are important in proper design, site screening, characterization and safety of geological storage. This information can be used to either identify future geological candidates for acid gas disposal or reviewing the current operating conditions of licensed injection sites.

Keywords: CO₂ storage, solubility trapping, convective dissolution, storage efficiency

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Authors: M. Sai Veerendra

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Today, money laundering (ML) poses a serious threat not only to financial institutions but also to the nation. This criminal activity is becoming more and more sophisticated and seems to have moved from the cliché of drug trafficking to financing terrorism and surely not forgetting personal gain. Most of the financial institutions internationally have been implementing anti-money laundering solutions (AML) to fight investment fraud activities. However, traditional investigative techniques consume numerous man-hours. Recently, data mining approaches have been developed and are considered as well-suited techniques for detecting ML activities. Within the scope of a collaboration project on developing a new data mining solution for AML Units in an international investment bank in Ireland, we survey recent data mining approaches for AML. In this paper, we present not only these approaches but also give an overview on the important factors in building data mining solutions for AML activities.

Keywords: data mining, clustering, money laundering, anti-money laundering solutions

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Authors: S. Sharma, Gaballa Aqeelah, Tawfig Alghbaili, Ali Elmessmari

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There were abrupt discontinuities in the Brute Stack in the northernmost locations during the acquisition of 2D (2007) and 3D (2021) seismic data in the northwest region of the Ghadames Basin, Libya. In both campaigns, complete fluid circulation loss was seen in these regions during up-hole drilling. Geophysics, sedimentology and shallow subsurface geology were all integrated to look into what was causing the seismic signal to disappear at shallow depths. The Upper Cretaceous Nalut Formation is the near-surface or surface formation in the studied area. It is distinguished by abnormally high resistivity in all the neighboring wells. The Nalut Formation in all the nearby wells from the present study and previous outcrop study suggests lithology of dolomite and chert/flint in nodular or layered forms. There are also reports of karstic caverns, vugs, and thick cracks, which all work together to produce the high resistivity. Four up-hole samples that were analyzed for microfacies revealed a near-coastal to tidal environment. Algal (Chara) infested deposits up to 30 feet thick and monotonous, very porous, are seen in two up-hole sediments; these deposits are interpreted to be scattered, continental algal travertine mounds. Chert/flint, dolomite, and calcite in varying amounts are confirmed by XRD analysis. Regional tracking of the high resistivity of the Nalut Formation, which is thought to be connected to the sea level drop that created the paleokarst layer, is possible. It is abruptly overlain by a blanket marine transgressive deposit caused by rapid sea level rise, which is a regional, relatively high radioactive layer of argillaceous limestone. The examined area's close proximity to the mountainous, E-W trending ridges of northern Libya made it easier for recent freshwater circulation, which later enhanced cavern development and mineralization in the paleokarst layer. Seismic signal loss at shallow depth is caused by extremely heterogeneous mineralogy of pore- filling or lack thereof. Scattering effect of shallow karstic layer on seismic signal has been well documented. Higher velocity inflection points at shallower depths in the northern part and deeper intervals in the southern part, in both cases at Nalut level, demonstrate the layer's influence on the seismic signal. During the Permian-Carboniferous, the Ghadames Basin underwent uplift and extensive erosion, which resulted in this karstic layer of the Nalut Formation uplifted to a shallow depth in the northern part of the studied area weakening the acoustic signal, whereas in the southern part of the 3D acquisition area the Nalut Formation remained at the deeper interval without affecting the seismic signal. Results from actions taken during seismic processing to deal with this signal loss are visible and have improved. This study recommends using denser spacing or dynamite to circumvent the karst layer in a comparable geographic area in order to prevent signal loss at lesser depths.

Keywords: well logging, seismic data acquisition, sesimic data processing, up-holes

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Authors: Hossein Rassi, Alaheh Gholami Roud-Majany, Zahra Razavi, Massoud Hoshmand

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Cervical cancer is multi step disease that is thought to result from an interaction between genetic background and environmental factors. Human papillomavirus (HPV) infection is the leading risk factor for cervical intraepithelial neoplasia (CIN)and cervical cancer. In other hand, some of hTERT and miRNA polymorphism may plays an important role in carcinogenesis. This study attempts to clarify the relation of hTERT genotypes and miR-146a genotypes in cervical cancer. Forty two archival samples with cervical lesion retired from Khatam hospital and 40 sample from healthy persons used as control group. A simple and rapid method was used to detect the simultaneous amplification of the HPV consensus L1 region and HPV-16,-18, -11, -31, 33 and -35 along with the b-globin gene as an internal control. We use Multiplex PCR for detection of hTERT and miR-146a rs2910164 genotypes in our lab. Finally, data analysis was performed using the 7 version of the Epi Info(TM) 2012 software and test chi-square(x2) for trend. Cervix lesions were collected from 42 patients with Squamous metaplasia, cervical intraepithelial neoplasia, and cervical carcinoma. Successful DNA extraction was assessed by PCR amplification of b-actin gene (99bp). According to the results, hTERT ( rs 2736098) GG genotype and miR-146a rs2910164 CC genotype was significantly associated with increased risk of cervical cancer in the study population. In this study, we detected 13 HPV 18 from 42 cervical cancer. The connection between several SNP polymorphism and human virus papilloma in rare researches were seen. The reason of these differences in researches' findings can result in different kinds of races and geographic situations and also differences in life grooves in every region. The present study provided preliminary evidence that a p53 GG genotype and miR-146a rs2910164 CC genotype may effect cervical cancer risk in the study population, interacting synergistically with HPV 18 genotype. Our results demonstrate that the testing of hTERT rs 2736098 genotypes and miR-146a rs2910164 genotypes in combination with HPV18 can serve as major risk factors in the early identification of cervical cancers. Furthermore, the results indicate the possibility of primary prevention of cervical cancer by vaccination against HPV18 in Iran.

Keywords: polymorphism of hTERT gene, miR-146a rs2910164 polymorphism, cervical cancer, virus

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Authors: Kisik Song, Kyuwoong Kim, Sungjoo Lee

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Many global firms and corporations derive new technology and opportunity by identifying vacant technology from patent analysis. However, previous studies failed to focus on technologies that promised continuous growth in industrial fields. Most studies that derive new technology opportunities do not test practical effectiveness. Since previous studies depended on expert judgment, it became costly and time-consuming to evaluate new technologies based on patent analysis. Therefore, research suggests a quantitative and systematic approach to technology evaluation indicators by using patent data to and from customer communities. The first step involves collecting two types of data. The data is used to construct evaluation indicators and apply these indicators to the evaluation of new technologies. This type of data mining allows a new method of technology evaluation and better predictor of how new technologies are adopted.

Keywords: data mining, evaluating new technology, technology opportunity, patent analysis

Procedia PDF Downloads 381

Authors: M. Kamel, A. Hoayek, M. Batton-Hubert

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With the increase of network virtualization and the disparity of vendors, the continuous monitoring and detection of anomalies cannot rely on static rules. An advanced analytical methodology is needed to discriminate between ordinary events and unusual anomalies. In this paper, we focus on log data (textual data), which is a crucial source of information for network performance. Then, we introduce an algorithm used as a pipeline to help with the pretreatment of such data, group it into patterns, and dynamically label each pattern as an anomaly or not. Such tools will provide users and experts with continuous real-time logs monitoring capability to detect anomalies and failures in the underlying system that can affect performance. An application of real-world data illustrates the algorithm.

Keywords: logs, anomaly detection, ML, scoring, NLP

Procedia PDF Downloads 100

Authors: Haifa Ben Saber, Mourad Elloumi

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In a number of domains, like in DNA microarray data analysis, we need to cluster simultaneously rows (genes) and columns (conditions) of a data matrix to identify groups of constant rows with a group of columns. This kind of clustering is called biclustering. Biclustering algorithms are extensively used in DNA microarray data analysis. More effective biclustering algorithms are highly desirable and needed. We introduce a new algorithm called, Enumerative tree (EnumTree) for biclustering of binary microarray data. is an algorithm adopting the approach of enumerating biclusters. This algorithm extracts all biclusters consistent good quality. The main idea of ​​EnumLat is the construction of a new tree structure to represent adequately different biclusters discovered during the process of enumeration. This algorithm adopts the strategy of all biclusters at a time. The performance of the proposed algorithm is assessed using both synthetic and real DNA micryarray data, our algorithm outperforms other biclustering algorithms for binary microarray data. Biclusters with different numbers of rows. Moreover, we test the biological significance using a gene annotation web tool to show that our proposed method is able to produce biologically relevent biclusters.

Keywords: DNA microarray, biclustering, gene expression data, tree, datamining.

Procedia PDF Downloads 374

Authors: Lynn Ruggieri

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The worldwide pandemic has only increased sustainability awareness. The public is demanding that businesses be held accountable for their impact on the environment. While financial data enjoys uniformity in reporting requirements, there are no uniform reporting requirements for non-financial data. Europe is leading the way with some standards being implemented for reporting non-financial sustainability data; however, there is no uniformity globally. And without uniformity, there is not a clear understanding of what information to include and how to disclose it. Sustainability reporting will provide important information to stakeholders and will enable businesses to understand their impact on the environment. Therefore, there is a crucial need for this data. This paper looks at the history of sustainability reporting in the countries of the European Union and throughout the world and makes a case for worldwide reporting requirements for sustainability.

Keywords: financial reporting, non-financial data, sustainability, global financial reporting

Procedia PDF Downloads 183

Authors: Omar Farshad Jeelani, Makaire Njie, Viktoriia M. Korzhuk

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Recently, the application of AI-powered algorithms in healthcare continues to flourish. Particularly, access to healthcare information, including patient health history, diagnostic data, and PII (Personally Identifiable Information) is paramount in the delivery of efficient patient outcomes. However, as the exchange of healthcare information between patients and healthcare providers through AI-powered solutions increases, protecting a person’s information and their privacy has become even more important. Arguably, the increased adoption of healthcare AI has resulted in a significant concentration on the security risks and protection measures to the security and privacy of healthcare data, leading to escalated analyses and enforcement. Since these challenges are brought by the use of AI-based healthcare solutions to manage healthcare data, AI-based data protection measures are used to resolve the underlying problems. Consequently, this project proposes AI-powered safeguards and policies/laws to protect the privacy of healthcare data. The project presents the best-in-school techniques used to preserve the data privacy of AI-powered healthcare applications. Popular privacy-protecting methods like Federated learning, cryptographic techniques, differential privacy methods, and hybrid methods are discussed together with potential cyber threats, data security concerns, and prospects. Also, the project discusses some of the relevant data security acts/laws that govern the collection, storage, and processing of healthcare data to guarantee owners’ privacy is preserved. This inquiry discusses various gaps and uncertainties associated with healthcare AI data collection procedures and identifies potential correction/mitigation measures.

Keywords: data privacy, artificial intelligence (AI), healthcare AI, data sharing, healthcare organizations (HCOs)

Procedia PDF Downloads 100

Authors: Sahil Imtiyaz

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One of the biggest challenges has emerged from the ever-expanding, dynamic, and instantaneously changing space-Big Data; and to find a data point and inherit wisdom to this space is a hard task. In this paper, we reduce the space of big data in Hamiltonian formalism that is in concordance with Ising Model. For this formulation, we simulate the system using dye laser in FORTRAN and analyse the dynamics of the data point in energy well of rhodium atom. After mapping the photon intensity and pulse width with energy and potential we concluded that as we increase the energy there is also increase in probability of tunnelling up to some point and then it starts decreasing and then shows a randomizing behaviour. It is due to decoherence with the environment and hence there is a loss of ‘quantumness’. This interprets the efficiency parameter and the extent of quantum evolution. The results are strongly encouraging in favour of the use of ‘Topological Property’ as a source of information instead of the qubit.

Keywords: big data, optimization, quantum evolution, hamiltonian, dye laser, fermionic computations

Procedia PDF Downloads 198

Authors: Muhammad Muhammad Suleiman

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Cloud Computing is a versatile concept that refers to a service that allows users to outsource their data without having to worry about local storage issues. However, the most pressing issues to be addressed are maintaining a secure and reliable data repository rather than relying on untrustworthy service providers. In this study, we look at how stenography approaches and collaboration with Digital Watermarking can greatly improve the system's effectiveness and data security when used for Cloud Computing. The main requirement of such frameworks, where data is transferred or exchanged between servers and users, is safe data management in cloud environments. Steganography is the cloud is among the most effective methods for safe communication. Steganography is a method of writing coded messages in such a way that only the sender and recipient can safely interpret and display the information hidden in the communication channel. This study presents a new text steganography method for hiding a loaded hidden English text file in a cover English text file to ensure data protection in cloud computing. Data protection, data hiding capability, and time were all improved using the proposed technique.

Keywords: cloud computing, steganography, information hiding, cloud storage, security

Procedia PDF Downloads 198

Authors: Farhad Asadi, Mohammad Javad Mollakazemi

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In this paper, Bayesian online inference in models of data series are constructed by change-points algorithm, which separated the observed time series into independent series and study the change and variation of the regime of the data with related statistical characteristics. variation of statistical characteristics of time series data often represent separated phenomena in the some dynamical system, like a change in state of brain dynamical reflected in EEG signal data measurement or a change in important regime of data in many dynamical system. In this paper, prediction algorithm for studying change point location in some time series data is simulated. It is verified that pattern of proposed distribution of data has important factor on simpler and smother fluctuation of hazard rate parameter and also for better identification of change point locations. Finally, the conditions of how the time series distribution effect on factors in this approach are explained and validated with different time series databases for some dynamical system.

Keywords: time series, fluctuation in statistical characteristics, optimal learning, change-point algorithm

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Authors: John Segars

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Built out of a real-world need to have better, more detailed, asset and infrastructure documentation, this project will lay out the case for using the database functionality of ArcGIS as a tool to track and maintain infrastructure location, status, maintenance and serviceability. Workflows and processes will be presented and detailed which may be applied to an organizations’ infrastructure needs that might allow them to make use of the robust tools which surround the ArcGIS platform. The end result is a value-added information system framework with a geographic component e.g., the spatial location of various I.T. assets, a detailed set of records which not only documents location but also captures the maintenance history for assets along with photographs and documentation of these various assets as attachments to the numerous feature class items. In addition to the asset location and documentation benefits, the staff will be able to log into the devices and pull SNMP (Simple Network Management Protocol) based query information from within the user interface. The entire collection of information may be displayed in ArcGIS, via a JavaScript based web application or via queries to the back-end database. The project is applicable to all organizations which maintain an IT infrastructure but specifically targets post-secondary educational institutions where access to ESRI resources is generally already available in house.

Keywords: ESRI, GIS, infrastructure, network documentation, PostgreSQL

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Authors: İbrahi̇m Kara, Seher Arslankaya

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Frequently preferred in the field of engineering in particular, data mining has now begun to be used in the field of health as well since the data in the health sector have reached great dimensions. With data mining, it is aimed to reveal models from the great amounts of raw data in agreement with the purpose and to search for the rules and relationships which will enable one to make predictions about the future from the large amount of data set. It helps the decision-maker to find the relationships among the data which form at the stage of decision-making. In this study, it is aimed to determine the risk of heart attack at the first stage, to control it, and to make its resource planning with the method of data mining. Through the early and correct diagnosis of heart attacks, it is aimed to reveal the factors which affect the diseases, to protect health and choose the right treatment methods, to reduce the costs in health expenditures, and to shorten the durations of patients’ stay at hospitals. In this way, the diagnosis and treatment costs of a heart attack will be scrutinized, which will be useful to determine the risk of the disease at the first stage, to control it, and to make its resource planning.

Keywords: data mining, decision support systems, heart attack, health sector

Procedia PDF Downloads 358

Authors: Akalu Banbeta, Emmanuel Lesaffre, Reynaldo Martina, Joost Van Rosmalen

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Including data from previous studies (historical data) in the analysis of the current study may reduce the sample size requirement and/or increase the power of analysis. The most common example is incorporating historical control data in the analysis of a current clinical trial. However, this only applies when the historical control dataare similar enough to the current control data. Recently, several Bayesian approaches for incorporating historical data have been proposed, such as the meta-analytic-predictive (MAP) prior and the modified power prior (MPP) both for single control as well as for multiple historical control arms. Here, we examine the performance of the MAP and the MPP approaches for the analysis of (over-dispersed) count data. To this end, we propose a computational method for the MPP approach for the Poisson and the negative binomial models. We conducted an extensive simulation study to assess the performance of Bayesian approaches. Additionally, we illustrate our approaches on an overactive bladder data set. For similar data across the control arms, the MPP approach outperformed the MAP approach with respect to thestatistical power. When the means across the control arms are different, the MPP yielded a slightly inflated type I error (TIE) rate, whereas the MAP did not. In contrast, when the dispersion parameters are different, the MAP gave an inflated TIE rate, whereas the MPP did not.We conclude that the MPP approach is more promising than the MAP approach for incorporating historical count data.

Keywords: count data, meta-analytic prior, negative binomial, poisson

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Authors: John Gaber

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Planners regularly use citizen input as empirical data to help them better understand community issues they know very little about. This type of community data is based on the lived experiences of local residents and is known as "emic" data. What is becoming more common practice for planners is their use of data from local experts and stakeholders (known as "etic" data or the outsider perspective) to help them fill in the gaps in their analysis of applied planning research projects. Utilizing international Health Impact Assessment (HIA) data, I look at who planners invite to their citizen input investigations. Research presented in this paper shows that planners access a wide range of emic and etic community perspectives in their search for the “community’s view.” The paper concludes with how planners can chart out a new empirical path in their execution of emic/etic citizen participation strategies in their applied planning research projects.

Keywords: citizen participation, emic data, etic data, Health Impact Assessment (HIA)

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Authors: Xulu Yao, Moi Hoon Yap, Yanlong Zhang

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As a branch of artificial neural network, deep learning is widely used in the field of image recognition, but the lack of its dataset leads to imperfect model learning. By analysing the data scale requirements of deep learning and aiming at the application in GUI generation, it is found that the collection of GUI dataset is a time-consuming and labor-consuming project, which is difficult to meet the needs of current deep learning network. To solve this problem, this paper proposes a semi-supervised deep learning model that relies on the original small-scale datasets to produce a large number of reliable data sets. By combining the cyclic neural network with the generated countermeasure network, the cyclic neural network can learn the sequence relationship and characteristics of data, make the generated countermeasure network generate reasonable data, and then expand the Rico dataset. Relying on the network structure, the characteristics of collected data can be well analysed, and a large number of reasonable data can be generated according to these characteristics. After data processing, a reliable dataset for model training can be formed, which alleviates the problem of dataset shortage in deep learning.

Keywords: GUI, deep learning, GAN, data augmentation

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Authors: S. Ravindran, Y.Liu, I. Gratchev, D.Jeng

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Rainfall-induced shallow landslides are more common in the northern New South Wales (NSW), Australia. From 2009 to 2017, around 105 rainfall-induced landslides occurred along the road corridors and caused temporary road closures in the northern NSW. Rainfall causing shallow landslides has different distributions of rainfall varying from uniform, normal, decreasing to increasing rainfall intensity. The duration of rainfall varied from one day to 18 days according to historical data. The objective of this research is to analyse slope instability of some of the sites in the northern NSW by varying cumulative rainfall using SLOPE/W and SEEP/W and compare with field data of rainfall causing shallow landslides. The rainfall data and topographical data from public authorities and soil data obtained from laboratory tests will be used for this modelling. There is a likelihood of shallow landslides if the cumulative rainfall is between 100 mm to 400 mm in accordance with field data.

Keywords: landslides, modelling, rainfall, suction

Procedia PDF Downloads 188

Authors: Nicholas Milburn, Yu Liang, Dalei Wu

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Athlete performance scoring within the climbing do-main presents interesting challenges as the sport does not have an objective way to assign skill. Assessing skill levels within any sport is valuable as it can be used to mark progress while training, and it can help an athlete choose appropriate climbs to attempt. Machine learning-based methods are popular for complex problems like this. The dataset available was composed of dynamic force data recorded during climbing; however, this dataset came with challenges such as data scarcity, imbalance, and it was temporally heterogeneous. Investigated solutions to these challenges include data augmentation, temporal normalization, conversion of time series to the spectral domain, and cross validation strategies. The investigated solutions to the classification problem included light weight machine classifiers KNN and SVM as well as the deep learning with CNN. The best performing model had an 80% accuracy. In conclusion, there seems to be enough information within climbing force data to accurately categorize climbers by skill.

Keywords: classification, climbing, data imbalance, data scarcity, machine learning, time sequence

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Authors: Julien Caudeville, Muriel Ismert

Abstract:

Analyzing the relationship between environment and health has become a major preoccupation for public health as evidenced by the emergence of the French national plans for health and environment. These plans have identified the following two priorities: (1) to identify and manage geographic areas, where hotspot exposures are suspected to generate a potential hazard to human health; (2) to reduce exposure inequalities. At a regional scale and fine resolution of exposure outcome prerequisite, environmental monitoring networks are not sufficient to characterize the multidimensionality of the exposure concept. In an attempt to increase representativeness of spatial exposure assessment approaches, risk composite indicators could be built using additional available databases and theoretical framework approaches to combine factor risks. To achieve those objectives, combining data process and transfer modeling with a spatial approach is a fundamental prerequisite that implies the need to first overcome different scientific limitations: to define interest variables and indicators that could be built to associate and describe the global source-effect chain; to link and process data from different sources and different spatial supports; to develop adapted methods in order to improve spatial data representativeness and resolution. A GIS-based modeling platform for quantifying human exposure to chemical substances (PLAINE: environmental inequalities analysis platform) was used to build health risk indicators within the Lorraine region (France). Those indicators combined chemical substances (in soil, air and water) and noise risk factors. Tools have been developed using modeling, spatial analysis and geostatistic methods to build and discretize interest variables from different supports and resolutions on a 1 km2 regular grid within the Lorraine region. By example, surface soil concentrations have been estimated by developing a Kriging method able to integrate surface and point spatial supports. Then, an exposure model developed by INERIS was used to assess the transfer from soil to individual exposure through ingestion pathways. We used distance from polluted soil site to build a proxy for contaminated site. Air indicator combined modeled concentrations and estimated emissions to take in account 30 polluants in the analysis. For water, drinking water concentrations were compared to drinking water standards to build a score spatialized using a distribution unit serve map. The Lden (day-evening-night) indicator was used to map noise around road infrastructures. Aggregation of the different factor risks was made using different methodologies to discuss weighting and aggregation procedures impact on the effectiveness of risk maps to take decisions for safeguarding citizen health. Results permit to identify pollutant sources, determinants of exposure, and potential hotspots areas. A diagnostic tool was developed for stakeholders to visualize and analyze the composite indicators in an operational and accurate manner. The designed support system will be used in many applications and contexts: (1) mapping environmental disparities throughout the Lorraine region; (2) identifying vulnerable population and determinants of exposure to set priorities and target for pollution prevention, regulation and remediation; (3) providing exposure database to quantify relationships between environmental indicators and cancer mortality data provided by French Regional Health Observatories.

Keywords: health risk, environment, composite indicator, hotspot areas

Procedia PDF Downloads 252

Authors: M. A. I Perera, C. R. Wijesinghe, A. R. Weerasinghe

Abstract:

his study was conducted to review and identify the unsupervised techniques that can be employed to analyze gene expression data in order to identify better subtypes of tumors. Identifying subtypes of cancer help in improving the efficacy and reducing the toxicity of the treatments by identifying clues to find target therapeutics. Process of gene expression data analysis described under three steps as preprocessing, clustering, and cluster validation. Feature selection is important since the genomic data are high dimensional with a large number of features compared to samples. Hierarchical clustering and K Means are often used in the analysis of gene expression data. There are several cluster validation techniques used in validating the clusters. Heatmaps are an effective external validation method that allows comparing the identified classes with clinical variables and visual analysis of the classes.

Keywords: cancer subtypes, gene expression data analysis, clustering, cluster validation

Procedia PDF Downloads 152