Search results for: Data Mining

Authors: V. Ibujes, A. Guevara, P. Barreto

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Concentration of arsenic represents a serious threat to human health. It is a bioaccumulable toxic element and is transferred through the food chain. In Ecuador, values of 0.0423 mg/kg As are registered in potatoes of the skirts of the Tungurahua volcano. The increase of arsenic contamination in Ecuador is mainly due to mining activity, since the process of gold extraction generates toxic tailings with mercury. In the Province of Azuay, due to the mining activity, the soil reaches concentrations of 2,500 to 6,420 mg/kg As whereas in the province of Tungurahua it can be found arsenic concentrations of 6.9 to 198.7 mg/kg due to volcanic eruptions. Since the contamination by arsenic, the present investigation is directed to the remediation of the soils in the provinces of Azuay and Tungurahua by phytoremediation technique and the definition of a methodology of extraction by means of analysis of arsenic in the system soil-plant. The methodology consists in selection of two types of plants that have the best arsenic removal capacity in synthetic solutions 60 μM As, a lower percentage of mortality and hydroponics resistance. The arsenic concentrations in each plant were obtained from taking 10 ml aliquots and the subsequent analysis of the ICP-OES (inductively coupled plasma-optical emission spectrometry) equipment. Soils were contaminated with synthetic solutions of arsenic with the capillarity method to achieve arsenic concentration of 13 and 15 mg/kg. Subsequently, two types of plants were evaluated to reduce the concentration of arsenic in soils for 7 weeks. The global variance for soil types was obtained with the InfoStat program. To measure the changes in arsenic concentration in the soil-plant system, the Rhizo and Wenzel arsenic extraction methodology was used and subsequently analyzed with the ICP-OES (optima 8000 Pekin Elmer). As a result, the selected plants were bluegrass and llanten, due to the high percentages of arsenic removal of 55% and 67% and low mortality rates of 9% and 8% respectively. In conclusion, Azuay soil with an initial concentration of 13 mg/kg As reached the concentrations of 11.49 and 11.04 mg/kg As for bluegrass and llanten respectively, and for the initial concentration of 15 mg/kg As reached 11.79 and 11.10 mg/kg As for blue grass and llanten after 7 weeks. For the Tungurahua soil with an initial concentration of 13 mg/kg As it reached the concentrations of 11.56 and 12.16 mg/kg As for the bluegrass and llanten respectively, and for the initial concentration of 15 mg/kg As reached 11.97 and 12.27 mg/kg Ace for bluegrass and llanten after 7 weeks. The best arsenic extraction methodology of soil-plant system is Wenzel.

Keywords: blue grass, llanten, phytoremediation, soil of Azuay, soil of Tungurahua, synthetic arsenic solution

Procedia PDF Downloads 93

Authors: Sarisa Pinkham, Kanyarat Bussaban

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The research aims to approximate the amount of daily rainfall by using a pixel value data approach. The daily rainfall maps from the Thailand Meteorological Department in period of time from January to December 2013 were the data used in this study. The results showed that this approach can approximate the amount of daily rainfall with RMSE=3.343.

Keywords: daily rainfall, image processing, approximation, pixel value data

Procedia PDF Downloads 383

Authors: Mohammad Javad Mollakazemi, Farhad Asadi, Aref Ghafouri

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In this paper, we considered and applied parametric modeling for some experimental data of dynamical system. In this study, we investigated the different distribution of output measurement from some dynamical systems. Also, with variance processing in experimental data we obtained the region of nonlinearity in experimental data and then identification of output section is applied in different situation and data distribution. Finally, the effect of the spanning the measurement such as variance to identification and limitation of this approach is explained.

Keywords: Gaussian process, nonlinearity distribution, particle filter, system identification

Procedia PDF Downloads 503

Authors: Larissa Bolliger, Junoš Lukan, Mitja Lustrek, Dirk De Bacquer, Els Clays

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Introduction and Aim: Chronic workplace stress and its health-related consequences like mental and cardiovascular diseases have been widely investigated. This project focuses on the sources and context of psychosocial daily workplace stress in a real-world setting. The main objective is to analyze and model real-time relationships between (1) psychosocial stress experiences within the natural work environment, (2) micro-level work activities and events, and (3) physiological signals and behaviors in office workers. Methods: An Ecological Momentary Assessment (EMA) protocol has been developed, partly building on machine learning techniques. Empatica® wristbands will be used for real-life detection of stress from physiological signals; micro-level activities and events at work will be based on smartphone registrations, further processed according to an automated computer algorithm. A field study including 100 office-based workers with high-level problem-solving tasks like managers and researchers will be implemented in Slovenia and Belgium (50 in each country). Data mining and state-of-the-art statistical methods – mainly multilevel statistical modelling for repeated data – will be used. Expected Results and Impact: The project findings will provide novel contributions to the field of occupational health research. While traditional assessments provide information about global perceived state of chronic stress exposure, the EMA approach is expected to bring new insights about daily fluctuating work stress experiences, especially micro-level events and activities at work that induce acute physiological stress responses. The project is therefore likely to generate further evidence on relevant stressors in a real-time working environment and hence make it possible to advise on workplace procedures and policies for reducing stress.

Keywords: ecological momentary assessment, real-time, stress, work

Procedia PDF Downloads 152

Authors: Jaya Mathew

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Many organizations are faced with the challenge of how to analyze and build Machine Learning models using their sensitive telemetry data. In this paper, we discuss how users can leverage the power of R without having to move their big data around as well as a cloud based solution for organizations willing to host their data in the cloud. By using ScaleR technology to benefit from parallelization and remote computing or R Services on premise or in the cloud, users can leverage the power of R at scale without having to move their data around.

Keywords: predictive maintenance, machine learning, big data, cloud based, on premise solution, R

Procedia PDF Downloads 370

Authors: K. Thiagarajan, K. Saranya, A. Veeraiah, B. Sudha

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This paper presents a novel algorithm for secure, reliable and flexible transmission of big data in two hop wireless networks using cooperative jamming scheme. Two hop wireless networks consist of source, relay and destination nodes. Big data has to transmit from source to relay and from relay to destination by deploying security in physical layer. Cooperative jamming scheme determines transmission of big data in more secure manner by protecting it from eavesdroppers and malicious nodes of unknown location. The novel algorithm that ensures secure and energy balance transmission of big data, includes selection of data transmitting region, segmenting the selected region, determining probability ratio for each node (capture node, non-capture and eavesdropper node) in every segment, evaluating the probability using binary based evaluation. If it is secure transmission resume with the two- hop transmission of big data, otherwise prevent the attackers by cooperative jamming scheme and transmit the data in two-hop transmission.

Keywords: big data, two-hop transmission, physical layer wireless security, cooperative jamming, energy balance

Procedia PDF Downloads 482

Authors: Hiromi Takafuji

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It is a well-known fact that health care and welfare are the foundation of human activities, and human service professionals such as nurses and child care workers support these activities. COVID-19 pandemic has made the severity of the working environment in these fields even more known. It is high time to discuss the work of human service workers for the sustainable development of the human environment. Early turnover has been recognized as a long-standing issue in these fields. In Japan, the attrition rate within three years of graduation for these occupations has remained high at about 40% for more than 20 years. One of the reasons for this is Reality Shock: RS, which refers to the stress caused by the gap between pre-employment expectations and the post-employment reality experienced by new workers. The purpose of this study was to academically elucidate the mechanism of RS among human service professionals and to contribute to countermeasures against it. Firstly, to explore the structure of the relationship between professionalism and workers' RS, an exploratory interview survey was conducted and analyzed by text mining and content analysis. The results showed that the expectation of professionalism influences RS as a pre-employment job expectation. Next, the expectations of professionalism were quantified and categorized, and the responses of a total of 282 human service work professionals, nurses, child care workers, and caregivers; were finalized for data analysis. The data were analyzed using exploratory factor analysis, confirmatory factor analysis, multiple regression analysis, and structural equation modeling techniques. The results revealed that self-control orientation and authority orientation by qualification had a direct positive significant impact on RS. On the other hand, interpersonal helping orientation and altruistic orientation were found to have a direct negative significant impact and an indirect positive significant impact on RS.; we were able to clarify the structure of work expectations that affect the RS of welfare professionals, which had not been clarified in previous studies. We also explained the limitations, practical implications, and directions for future research.

Keywords: human service professional, new hire turnover, SEM, reality shock

Procedia PDF Downloads 96

Authors: Romeo Botes, Imelda Smit

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In today’s modern age of technology vast amounts of data needs to be processed in real-time to keep users satisfied. This data comes from various sources and in many formats, including electronic and mobile devices such as GPRS modems and GPS devices. They make use of different protocols including TCP, UDP, and HTTP/s for data communication to web servers and eventually to users. The data obtained from these devices may provide valuable information to users, but are mostly in an unreadable format which needs to be processed to provide information and business intelligence. This data is not always current, it is mostly historical data. The data is not subject to implementation of consistency and redundancy measures as most other data usually is. Most important to the users is that the data are to be pre-processed in a readable format when it is entered into the database. To accomplish this, programmers build processing programs and scripts to decode and process the information stored in databases. Programmers make use of various techniques in such programs to accomplish this, but sometimes neglect the effect some of these techniques may have on database performance. One of the techniques generally used,is to pull data from the database server, process it and push it back to the database server in one single step. Since the processing of the data usually takes some time, it keeps the database busy and locked for the period of time that the processing takes place. Because of this, it decreases the overall performance of the database server and therefore the system’s performance. This paper follows on a paper discussing the performance increase that may be achieved by utilizing array lists along with a pull-process-push data processing technique split in three steps. The purpose of this paper is to expand the number of clients when comparing the two techniques to establish the impact it may have on performance of the CPU storage and processing time.

Keywords: performance measures, algorithm techniques, data processing, push data, process data, array list

Procedia PDF Downloads 237

Authors: Nkongho Ayuketang Arreyndip, Ebobenow Joseph

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In this paper, temperature extremes are forecast by employing the block maxima method of the generalized extreme value (GEV) distribution to analyse temperature data from the Cameroon Development Corporation (CDC). By considering two sets of data (raw data and simulated data) and two (stationary and non-stationary) models of the GEV distribution, return levels analysis is carried out and it was found that in the stationary model, the return values are constant over time with the raw data, while in the simulated data the return values show an increasing trend with an upper bound. In the non-stationary model, the return levels of both the raw data and simulated data show an increasing trend with an upper bound. This clearly shows that although temperatures in the tropics show a sign of increase in the future, there is a maximum temperature at which there is no exceedance. The results of this paper are very vital in agricultural and environmental research.

Keywords: forecasting, generalized extreme value (GEV), meteorology, return level

Procedia PDF Downloads 469

Authors: Abdulrahman K. Alshegaifi, Chun-Hsi Huang

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Treating data based on its location in memory has received much attention in recent years due to its different properties, which offer important aspects for cache utilization. Stack data and non-stack data may interfere with each other’s locality in the data cache. One of the important aspects of stack data is that it has high spatial and temporal locality. In this work, we simulate non-unified cache design that split data cache into stack and non-stack caches in order to maintain stack data and non-stack data separate in different caches. We observe that the overall hit rate of non-unified cache design is sensitive to the size of non-stack cache. Then, we investigate the appropriate size and associativity for stack cache to achieve high hit ratio especially when over 99% of accesses are directed to stack cache. The result shows that on average more than 99% of stack cache accuracy is achieved by using 2KB of capacity and 1-way associativity. Further, we analyze the improvement in hit rate when adding small, fixed, size of stack cache at level1 to unified cache architecture. The result shows that the overall hit rate of unified cache design with adding 1KB of stack cache is improved by approximately, on average, 3.9% for Rijndael benchmark. The stack cache is simulated by using SimpleScalar toolset.

Keywords: hit rate, locality of program, stack cache, stack data

Procedia PDF Downloads 298

Authors: Wajahat Munir, Muhammad Haseeb, Adeel Anjum, Basit Raza, Ahmad Kamran Malik

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Databases are the key components of the software systems. Due to the exponential growth of data, it is the concern that the data should be accurate and available. The data in databases is vulnerable to internal and external threats, especially when it contains sensitive data like medical or military applications. Whenever the data is changed by malicious intent, data analysis result may lead to disastrous decisions. Autonomic self-healing is molded toward computer system after inspiring from the autonomic system of human body. In order to guarantee the accuracy and availability of data, we propose a technique which on a priority basis, tries to avoid any malicious transaction from execution and in case a malicious transaction affects the system, it heals the system in an isolated mode in such a way that the availability of system would not be compromised. Using this autonomic system, the management cost and time of DBAs can be minimized. In the end, we test our model and present the findings.

Keywords: autonomic computing, self-healing, threat avoidance, security

Procedia PDF Downloads 498

Authors: Mohammed R. Elkobaisi, Abdelsalam Maatuk

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The search engines are the large scale information retrieval tools from the Web that are currently freely available to all. This paper explains how to convert the raw resulted number of search engines into useful information. This represents a new method for data gathering comparing with traditional methods. When a query is submitted for a multiple numbers of keywords, this take a long time and effort, hence we develop a user interface program to automatic search by taking multi-keywords at the same time and leave this program to collect wanted data automatically. The collected raw data is processed using mathematical and statistical theories to eliminate unwanted data and converting it to usable data.

Keywords: search engines, information extraction, agent system

Procedia PDF Downloads 421

Authors: Yamma Rose, Kone Martine, Yonli Arsène, Wanko Ngnien Adrien

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Artisanal gold mining has seen a resurgence in recent years in Burkina Faso with its corollary of soil and water pollution. Indeed, in addition to visible impacts, it generates discharges rich in trace metal elements and acids. This pollution has significant environmental consequences, making these lands unusable while the population depends on the natural environment for its survival. The goal of this study is to assess the decontamination potential of Chrysopogon zizanioides on two artisanal gold processing sites in Burkina Faso. The cyanidation sites of Nebia (1Ha) and Nimbrogo (2Ha) located respectively in the Central West and Central South regions were selected. The soils were characterized to determine the initial pollution levels before the implementation of phytoremediation. After development of the site, parallel trenches equidistant 6 m apart, 30 cm deep, 40 cm wide and opposite to the water flow direction were dug and filled with earth amended with manure. The Chrysopogon zizanioides plants were transplanted 5 cm equidistant into the trenches. The mere fact that Chrysopogon zizanioides grew in the polluted soil is an indication that this plant tolerates and resists the toxicity of trace elements present on the site. The characterization shows sites very polluted with free cyanide 900 times higher than the national standard, the level of Hg in the soil is 5 times more than the limit value, iron and Zn are respectively 1000 times and 200 more than the tolerated environmental value. At time T1 (6 months) and T2 (12 months) of culture, Chrysopogon zizanioides showed less development on the Nimbrogo site than that of the Nebia site. Plant shoots and associated soil samples were collected and analyzed for total As, Hg, Fe and Zn concentration. The trace element content of the soil, the bioaccumulation factor and the hyper accumulation thresholds were also determined to assess the remediation potential. The concentration of As and Hg in the soil was below international risk thresholds, while that of Fe and Zn was well above these thresholds. The CN removal efficiency at the Nebia site is respectively 29.90% and 68.62% compared to 6.6% and 60.8% at Nimbrogo at time T1 and T2.

Keywords: chrysopogon zizanioides, in-situ phytoremediation, polluted soils, micropollutants

Procedia PDF Downloads 70

Authors: S. C. Sharma, Ankit Gambhir, Rajeev Arya

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In today’s era data security is an important concern and most demanding issues because it is essential for people using online banking, e-shopping, reservations etc. The two major techniques that are used for secure communication are Cryptography and Steganography. Cryptographic algorithms scramble the data so that intruder will not able to retrieve it; however steganography covers that data in some cover file so that presence of communication is hidden. This paper presents the implementation of Ron Rivest, Adi Shamir, and Leonard Adleman (RSA) Algorithm with Image and Audio Steganography and Data Encryption Standard (DES) Algorithm with Image and Audio Steganography. The coding for both the algorithms have been done using MATLAB and its observed that these techniques performed better than individual techniques. The risk of unauthorized access is alleviated up to a certain extent by using these techniques. These techniques could be used in Banks, RAW agencies etc, where highly confidential data is transferred. Finally, the comparisons of such two techniques are also given in tabular forms.

Keywords: audio steganography, data security, DES, image steganography, intruder, RSA, steganography

Procedia PDF Downloads 282

Authors: Anushtha Saxena

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This paper examines the process of data monetisation bye-commerce companies operating in India. Data monetisation is collecting, storing, and analysing consumers’ data to use further the data that is generated for profits, revenue, etc. Data monetisation enables e-commerce companies to get better businesses opportunities, innovative products and services, a competitive edge over others to the consumers, and generate millions of revenues. This paper analyses the issues and challenges that are faced due to the process of data monetisation. Some of the issues highlighted in the paper pertain to the right to privacy, protection of data of e-commerce consumers. At the same time, data monetisation cannot be prohibited, but it can be regulated and monitored by stringent laws and regulations. The right to privacy isa fundamental right guaranteed to the citizens of India through Article 21 of The Constitution of India. The Supreme Court of India recognized the Right to Privacy as a fundamental right in the landmark judgment of Justice K.S. Puttaswamy (Retd) and Another v. Union of India . This paper highlights the legal issue of how e-commerce businesses violate individuals’ right to privacy by using the data collected, stored by them for economic gains and monetisation and protection of data. The researcher has mainly focused on e-commerce companies like online shopping websitesto analyse the legal issue of data monetisation. In the Internet of Things and the digital age, people have shifted to online shopping as it is convenient, easy, flexible, comfortable, time-consuming, etc. But at the same time, the e-commerce companies store the data of their consumers and use it by selling to the third party or generating more data from the data stored with them. This violatesindividuals’ right to privacy because the consumers do not know anything while giving their data online. Many times, data is collected without the consent of individuals also. Data can be structured, unstructured, etc., that is used by analytics to monetise. The Indian legislation like The Information Technology Act, 2000, etc., does not effectively protect the e-consumers concerning their data and how it is used by e-commerce businesses to monetise and generate revenues from that data. The paper also examines the draft Data Protection Bill, 2021, pending in the Parliament of India, and how this Bill can make a huge impact on data monetisation. This paper also aims to study the European Union General Data Protection Regulation and how this legislation can be helpful in the Indian scenarioconcerning e-commerce businesses with respect to data monetisation.

Keywords: data monetization, e-commerce companies, regulatory framework, GDPR

Procedia PDF Downloads 110

Authors: Yan Cheng, Yijun Shao, James Rudolph, Charlene R. Weir, Beth Sahlmann, Qing Zeng-Treitler

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Supervised predictive models require labeled data for training purposes. Complete and accurate labeled data, i.e., a ‘gold standard’, is not always available, and imperfectly labeled data may need to serve as an alternative. An important question is if the accuracy of the labeled data creates a performance ceiling for the trained model. In this study, we trained several models to recognize the presence of delirium in clinical documents using data with annotations that are not completely accurate (i.e., weakly-supervised learning). In the external evaluation, the support vector machine model with a linear kernel performed best, achieving an area under the curve of 89.3% and accuracy of 88%, surpassing the 80% accuracy of the training sample. We then generated a set of simulated data and carried out a series of experiments which demonstrated that models trained on imperfect data can (but do not always) outperform the accuracy of the training data, e.g., the area under the curve for some models is higher than 80% when trained on the data with an error rate of 40%. Our experiments also showed that the error resistance of linear modeling is associated with larger sample size, error type, and linearity of the data (all p-values < 0.001). In conclusion, this study sheds light on the usefulness of imperfect data in clinical research via weakly-supervised learning.

Keywords: weakly-supervised learning, support vector machine, prediction, delirium, simulation

Procedia PDF Downloads 190

Authors: Dražen Cvitanić, Biljana Maljković

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This paper presents models for predicting operating speeds on tangent sections of two-lane rural roads developed on continuous speed data. The data corresponds to 20 drivers of different ages and driving experiences, driving their own cars along an 18 km long section of a state road. The data were first used for determination of maximum operating speeds on tangents and their comparison with speeds in the middle of tangents i.e. speed data used in most of operating speed studies. Analysis of continuous speed data indicated that the spot speed data are not reliable indicators of relevant speeds. After that, operating speed models for tangent sections were developed. There was no significant difference between models developed using speed data in the middle of tangent sections and models developed using maximum operating speeds on tangent sections. All developed models have higher coefficient of determination then models developed on spot speed data. Thus, it can be concluded that the method of measuring has more significant impact on the quality of operating speed model than the location of measurement.

Keywords: operating speed, continuous speed data, tangent sections, spot speed, consistency

Procedia PDF Downloads 450

Authors: S. Nickolas, Shobha K.

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The treatment of incomplete data is an important step in the data pre-processing. Missing values creates a noisy environment in all applications and it is an unavoidable problem in big data management and analysis. Numerous techniques likes discarding rows with missing values, mean imputation, expectation maximization, neural networks with evolutionary algorithms or optimized techniques and hot deck imputation have been introduced by researchers for handling missing data. Among these, imputation techniques plays a positive role in filling missing values when it is necessary to use all records in the data and not to discard records with missing values. In this paper we propose a novel artificial neural network based clustering algorithm, Adaptive Resonance Theory-2(ART2) for imputation of missing values in mixed attribute data sets. The process of ART2 can recognize learned models fast and be adapted to new objects rapidly. It carries out model-based clustering by using competitive learning and self-steady mechanism in dynamic environment without supervision. The proposed approach not only imputes the missing values but also provides information about handling the outliers.

Keywords: ART2, data imputation, clustering, missing data, neural network, pre-processing

Procedia PDF Downloads 271

Authors: Ruixia Liu

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Qinghai-Tibet Plateau has an important influence on the precipitation of its lower reaches. Data from remote sensing has itself advantage and numerical prediction model which assimilates RS data will be better than other. We got the assimilation data of MHS and terrestrial and sounding from GSI, and introduced the result into WRF, then got the result of RH and precipitation forecast. We found that assimilating MHS and terrestrial and sounding made the forecast on precipitation, area and the center of the precipitation more accurate by comparing the result of 1h,6h,12h, and 24h. Analyzing the difference of the initial field, we knew that the data assimilating about Qinghai-Tibet Plateau influence its lower reaches forecast by affecting on initial temperature and RH.

Keywords: Qinghai-Tibet Plateau, precipitation, data assimilation, GSI

Procedia PDF Downloads 228

Authors: Sook Ching Yee, Angela Siew Hoong Lee

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Big data technologies have become a trend to exploit business opportunities and provide valuable business insights through the analysis of big data. However, there are still many organizations that have yet to adopt big data technologies especially small and medium organizations (SME). This study uses the technology acceptance model (TAM) to look into several constructs in the TAM and other additional constructs which are positive affect, negative affect, organizational factor and motivational factor. The conceptual model proposed in the study will be tested on the relationship and influence of positive affect, negative affect, organizational factor and motivational factor towards the intention to use big data technologies to produce an outcome. Empirical research is used in this study by conducting a survey to collect data.

Keywords: big data technologies, motivational factor, negative affect, organizational factor, positive affect, technology acceptance model (TAM)

Procedia PDF Downloads 353

Authors: Byung Ho Jung, Ji Eun Shin, Dong Hoon Lim

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Rhipe that integrates R and Hadoop environment made it possible to process and analyze massive amounts of data using a distributed processing environment. In this paper, we implemented multiple regression analysis using Rhipe with various data sizes of actual data. Experimental results for comparing the performance of our Rhipe with stats and biglm packages available on bigmemory, showed that our Rhipe was more fast than other packages owing to paralleling processing with increasing the number of map tasks as the size of data increases. We also compared the computing speeds of pseudo-distributed and fully-distributed modes for configuring Hadoop cluster. The results showed that fully-distributed mode was faster than pseudo-distributed mode, and computing speeds of fully-distributed mode were faster as the number of data nodes increases.

Keywords: big data, Hadoop, Parallel regression analysis, R, Rhipe

Procedia PDF Downloads 493

Authors: Mona Almansoori, Ahmed Mustafa, Ahmad Elshamy

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Wireless sensor network was formed by a combination of nodes, systematically it transmitting the data to their base stations, this transmission data can be easily compromised if the limited processing power and the data consistency from these nodes are kept in mind; there is always a discussion to address the secure data transfer or transmission in actual time. This will present a mechanism to securely transmit the data over a chain of sensor nodes without compromising the throughput of the network by utilizing available battery resources available in the sensor node. Our methodology takes many different advantages of Z-MAC protocol for its efficiency, and it provides a unique key by sharing the mechanism using neighbor node MAC address. We present a light weighted data integrity layer which is embedded in the Z-MAC protocol to prove that our protocol performs well than Z-MAC when we introduce the different attack scenarios.

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

Procedia PDF Downloads 136

Authors: Elkington Sibusiso Mnguni

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In South Africa, mining has been a key economic sector since the discovery of gold in 1886 in the Witwatersrand region, where the city of Johannesburg is located. However, some mines have since been decommissioned, and the continuous pumping of acid mine drainage (AMD) also stopped causing the AMD to rise towards the ground surface. This posed a serious environmental risk to the groundwater resources and river systems in the region. This paper documents the development and extent of the environmental damage as well as the measures implemented by the government to alleviate such damage. The study will add to the body of knowledge on the subject of AMD treatment to prevent environmental degradation. The method used to gather and collate relevant data and information was the desktop study. The key findings include the social and environmental impact of the AMD, which include the pollution of water sources for domestic use leading to skin and other health problems and the loss of biodiversity in some areas. It was also found that the technical intervention of constructing a plant to pump and treat the AMD using the high-density sludge technology was the most effective short-term solution available while a long-term solution was being explored. Some successes and challenges experienced during the implementation of the project are also highlighted. The study will be a useful record of the current status of the AMD treatment interventions in the region.

Keywords: acid mine drainage, groundwater resources, pollution, river systems, technical intervention, high density sludge

Procedia PDF Downloads 182

Authors: Madaki Umar Yusuf, Mohd Rizam B. Abubakar

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The survival censored data with incomplete covariate data is a common occurrence in many studies in which the outcome is survival time. With model when the missing covariates are categorical, a useful technique for obtaining parameter estimates is the EM by the method of weights. The survival outcome for the class of generalized linear model is applied and this method requires the estimation of the parameters of the distribution of the covariates. In this paper, we propose some clinical trials with ve covariates, four of which have some missing values which clearly show that they were fully censored data.

Keywords: EM algorithm, incomplete categorical covariates, ignorable missing data, missing at random (MAR), Weibull Distribution

Procedia PDF Downloads 397

Authors: Mohammad Talei, Neda Ranjbar Nosheri, Reza Kazemi Gorzadini

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Today municipalities are searching for the new tools for increasing the public participation in different levels of urban planning. This approach of urban planning involves the community in planning process using participatory approaches instead of the long traditional top-down planning methods. These tools can be used to obtain the particular problems of urban furniture form the residents’ point of view. One of the tools that is designed with this goal is public participation GIS (PPGIS) that enables citizen to record and following up their feeling and spatial knowledge regarding main problems of the city, specifically urban furniture, in the form of maps. However, despite the good intentions of PPGIS, its practical implementation in developing countries faces many problems including the lack of basic supporting infrastructure and services and unavailability of sophisticated public participatory models. In this research we develop a PPGIS using of Web 2 to collect voluntary geodataand to perform spatial analysis based on Spatial OnLine Analytical Processing (SOLAP) and Spatial Data Mining (SDM). These tools provide urban planners with proper informationregarding the type, spatial distribution and the clusters of reported problems. This system is implemented in a case study area in Tehran, Iran and the challenges to make it applicable and its potential for real urban planning have been evaluated. It helps decision makers to better understand, plan and allocate scarce resources for providing most requested urban furniture.

Keywords: PPGIS, spatial information, urbanizing futures, urban planning

Procedia PDF Downloads 718

Authors: Abdeljalil Beniiche

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The limitation with smart contracts is that they cannot access external data that might be required to control the execution of business logic. Oracles can be used to provide external data to smart contracts. An oracle is an interface that delivers data from external data outside the blockchain to a smart contract to consume. Oracle can deliver different types of data depending on the industry and requirements. In this paper, we study and describe the widely used blockchain oracles. Then, we elaborate on his potential role, technical architecture, and design patterns. Finally, we discuss the human oracle and its key role in solving the truth problem by reaching a consensus about a certain inquiry and tasks.

Keywords: blockchain, oracles, oracles design, human oracles

Procedia PDF Downloads 123

Authors: Sandra Rizkallah, Amir F. Atiya

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This work presents a novel word to vector representation that is based on embedding the words into a sphere, whereby the dot product of the corresponding vectors represents the similarity between any two words. Embedding the vectors into a sphere enabled us to take into consideration the antonymity between words, not only the synonymity, because of the suitability to handle the polarity nature of words. For example, a word and its antonym can be represented as a vector and its negative. Moreover, we have managed to extract an adequate vocabulary. The obtained results show that the proposed approach can capture the essence of the language, and can be generalized to estimate a correct similarity of any new pair of words.

Keywords: natural language processing, word to vector, text similarity, text mining

Procedia PDF Downloads 265

Authors: Alhadi Bustaman, Soeganda Formalidin, Titin Siswantining

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DNA microarray technology is used to analyze thousand gene expression data simultaneously and a very important task for drug development and test, function annotation, and cancer diagnosis. Various clustering methods have been used for analyzing gene expression data. However, when analyzing very large and heterogeneous collections of gene expression data, conventional clustering methods often cannot produce a satisfactory solution. Biclustering algorithm has been used as an alternative approach to identifying structures from gene expression data. In this paper, we introduce a transform technique based on singular value decomposition to identify normalized matrix of gene expression data followed by Mixed-Clustering algorithm and the Lift algorithm, inspired in the node-deletion and node-addition phases proposed by Cheng and Church based on Agglomerative Hierarchical Clustering (AHC). Experimental study on standard datasets demonstrated the effectiveness of the algorithm in gene expression data.

Keywords: agglomerative hierarchical clustering (AHC), biclustering, gene expression data, lymphoma, singular value decomposition (SVD)

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Authors: Ramya P, Lino Abraham Varghese, S. Bose

Abstract:

By cloud storage services, the data can be stored in the cloud, and can be shared across multiple users. Due to the unexpected hardware/software failures and human errors, which make the data stored in the cloud be lost or corrupted easily it affected the integrity of data in cloud. Some mechanisms have been designed to allow both data owners and public verifiers to efficiently audit cloud data integrity without retrieving the entire data from the cloud server. But public auditing on the integrity of shared data with the existing mechanisms will unavoidably reveal confidential information such as identity of the person, to public verifiers. Here a privacy-preserving mechanism is proposed to support public auditing on shared data stored in the cloud. It uses group signatures to compute verification metadata needed to audit the correctness of shared data. The identity of the signer on each block in shared data is kept confidential from public verifiers, who are easily verifying shared data integrity without retrieving the entire file. But on demand, the signer of the each block is reveal to the owner alone. Group private key is generated once by the owner in the static group, where as in the dynamic group, the group private key is change when the users revoke from the group. When the users leave from the group the already signed blocks are resigned by cloud service provider instead of owner is efficiently handled by efficient proxy re-signature scheme.

Keywords: data integrity, dynamic group, group signature, public auditing

Procedia PDF Downloads 384

Authors: Nathainail Bashir, Neil Anderson

Abstract:

The objective of this study site was to investigate the current state of the practice with regards to karst detection methods and recommend the best method and pattern of arrays to acquire the desire results. Proper site investigation in karst prone regions is extremely valuable in determining the location of possible voids. Two geophysical techniques were employed: multichannel analysis of surface waves (MASW) and electric resistivity tomography (ERT).The MASW data was acquired at each test location using different array lengths and different array orientations (to increase the probability of getting interpretable data in karst terrain). The ERT data were acquired using a dipole-dipole array consisting of 168 electrodes. The MASW data was interpreted (re: estimated depth to physical top of rock) and used to constrain and verify the interpretation of the ERT data. The ERT data indicates poorer quality MASW data were acquired in areas where there was significant local variation in the depth to top of rock.

Keywords: dipole-dipole, ERT, Karst terrains, MASW

Procedia PDF Downloads 313