Search results for: heterogeneous massive data

Authors: Farhad Asadi, Mohammad Javad Mollakazemi

Abstract:

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

Procedia PDF Downloads 416

Authors: İbrahi̇m Kara, Seher Arslankaya

Abstract:

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 347

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

Abstract:

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

Procedia PDF Downloads 110

Authors: Faiza Mnasri, Kamilia Abahri, Mohammed El Ganaoui, Slimane Gabsi

Abstract:

Most of the building materials are considered porous, and composed of solid matrix and pores. In the pores, the moisture can be existed in two phases: liquid and vapor. Thus, the mass balance equation is comprised of various moisture driving potentials that translate the movement of the different existing phases occupying pores and the hygroscopic behavior of a porous construction material. This study suggests to resolve a hygrothermal mathematical model of heat and mass transfers in different porous building materials by a numerical investigation. Thereby, the evolution of temperature and moisture content fields has been processed. So, numerous series of hygrothermal calculation on several cases of wall are exposed. Firstly, a case of monolayer wall of massive wood has been treated. In this part, we have compared the numerical solution of the model on one and two dimensions and the effect of dimensional space has been evaluated. In the second case, three building materials (concrete, wood fiberboard and wooden insulation) are tested separately with the same boundary conditions and their hygrothermal behavior are compared. The evaluation of the exchange of heat and air at the interface between the wall and the interior ambiance is carried.

Keywords: building materials, heat transfer, moisture diffusion, numerical solution

Procedia PDF Downloads 285

Authors: John Gaber

Abstract:

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)

Procedia PDF Downloads 479

Authors: Chinedu Ejike

Abstract:

The United States is now energy self-sufficient due to the production of shale oil reserves. With more than half of it being tapped daily in the United States, these unconventional reserves are massive and provide immense potential for future energy demands. Drilling horizontal wells and fracking are the primary methods for developing these reserves. Regrettably, recovery efficiency is rarely greater than 10%. As a result, optimizing recuperation offers a significant benefit. Huff and puff gas flooding and cyclic gas injection have all been demonstrated to be more successful than tapping the remaining oil in place. Methane, nitrogen, and carbon (IV) oxide, among other high-pressure gases, can be injected. Operators use Darcy's law to assess a reservoir's productive capacity, but they are unaware that the law may not apply to shale oil reserves. This is due to the fact that, unlike pressure differences alone, diffusion, concentration, and gas selection all play a role in the flow of gas injected into the wellbore. The reservoir drainage and oil sweep efficiency rates are determined by the transport method. This research assesses the parameters that influence the gas injection transport mechanism. Understanding the process causing these factors could accelerate recovery by two to three times, according to peer-reviewed studies and effective field testing.

Keywords: enhanced oil recovery, gas injection, shale oil, transport mechanism, unconventional reserve

Procedia PDF Downloads 165

Authors: Xulu Yao, Moi Hoon Yap, Yanlong Zhang

Abstract:

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

Procedia PDF Downloads 172

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

Abstract:

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 164

Authors: komal verma, V. S. Moholkar

Abstract:

In this current study, the challenge of effectively treating and mineralizing industrial wastewater prior to its discharge into natural water bodies, such as rivers and lakes, is being addressed. Particularly, the focus is on the wastewater produced by chemical process industries, including refineries, petrochemicals, fertilizer, pharmaceuticals, pesticides, and dyestuff industries. These wastewaters often contain stubborn organic pollutants that conventional techniques, such as microbial processes cannot efficiently degrade. To tackle this issue, a ternary hybrid technique comprising of adsorption, heterogeneous Fenton process, and sonication has been employed. The study aims to evaluate the effectiveness of this approach for treating and mineralizing wastewater from a fertilizer industry located in Northeast India. The study comprises several key components, starting with the synthesis of the Fe3O4@AC nanocomposite using the co-precipitation method. The nanocomposite is then subjected to comprehensive characterization through various standard techniques, including FTIR, FE-SEM, EDX, TEM, BET surface area analysis, XRD, and magnetic property determination using VSM. Next, the process parameters of wastewater treatment are statistically optimized, focusing on achieving a high level of COD (Chemical Oxygen Demand) removal as the response variable. The Fe3O4@AC nanocomposite's adsorption characteristics and kinetics are also assessed in detail. The remarkable outcome of this study is the successful application of the ternary hybrid technique, combining adsorption, Fenton process, and sonication. This approach proves highly effective, leading to nearly complete mineralization (or TOC removal) of the fertilizer industry wastewater. The results highlight the potential of the Fe3O4@AC nanocomposite and the ternary hybrid technique as a promising solution for tackling challenging wastewater pollutants from various chemical process industries. This paper reports investigations in the mineralization of industrial wastewater (COD = 3246 mg/L, TOC = 2500 mg/L) using a ternary (ultrasound + Fenton + adsorption) hybrid advanced oxidation process. Fe3O4 decorated activated charcoal (Fe3O4@AC) nanocomposites (surface area = 538.88 m2/g; adsorption capacity = 294.31 mg/g) were synthesized using co-precipitation. The wastewater treatment process was optimized using central composite statistical design. At optimum conditions, viz. pH = 4.2, H2O2 loading = 0.71 M, adsorbent dose = 0.34 g/L, reduction in COD and TOC of wastewater were 94.75% and 89%, respectively. This result results from synergistic interactions among the adsorption of pollutants onto activated charcoal and surface Fenton reactions induced due to the leaching of Fe2+/Fe3+ ions from the Fe3O4 nanoparticles. Micro-convection generated due to sonication assisted faster mass transport (adsorption/desorption) of pollutants between Fe3O4@AC nanocomposite and the solution. The net result of this synergism was high interactions and reactions among and radicals and pollutants that resulted in the effective mineralization of wastewater. The Fe3O4@AC showed excellent recovery (> 90 wt%) and reusability (> 90% COD removal) in 5 successive cycles of treatment. LC-MS analysis revealed effective (> 50%) degradation of more than 25 significant contaminants (in the form of herbicides and pesticides) after the treatment with ternary hybrid AOP. Similarly, the toxicity analysis test using the seed germination technique revealed ~ 60% reduction in the toxicity of the wastewater after treatment.

Keywords: chemical oxygen demand (cod), fe3o4@ac nanocomposite, kinetics, lc-ms, rsm, toxicity

Procedia PDF Downloads 63

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 140

Authors: Dhaladhuli Pranavi, Amirtham Rajagopal

Abstract:

There are various failure modes in ceramic matrix composites; notable ones are fiber breakage, matrix cracking and fiber matrix debonding. Crack nucleation and propagation in microstructure of such composites requires an understanding of interaction of crack with the multiple inclusion heterogeneous system and interfaces. In order to assess structural integrity, the material parameters especially of the interface that governs the crack growth should be determined. In the present work, a nonlocal phase field approach is proposed to model the crack interface interaction in such composites. Nonlocal approaches help in understanding the complex mechanisms of delamination growth and mitigation and operates at a material length scale. The performance of the proposed formulation is illustrated through representative numerical examples. The model proposed is implemented in the framework of the finite element method. Several parametric studies on interface crack interaction are conducted. The proposed model is easy and simple to implement and works very well in modeling fracture in composite systems.

Keywords: composite, interface, nonlocal, phase field

Procedia PDF Downloads 136

Authors: Matthew Montebello

Abstract:

Student engagement within a virtual learning environment generates masses of data points that can significantly contribute to the learning analytics that lead to decision support. Ideally, similar data is collected during student interaction with a physical learning space, and as a consequence, data is present at a large scale, even in relatively small classes. In this paper, we report of such an occurrence during classes held in a HiFlex modality as we investigate the advantages of adopting such a methodology. We plan to take full advantage of the learner-generated data in an attempt to further enhance the effectiveness of the adopted learning environment. This could shed crucial light on operating modalities that higher education institutions around the world will switch to in a post-COVID era.

Keywords: HiFlex, big data in higher education, learning analytics, virtual learning environment

Procedia PDF Downloads 189

Authors: Shahzad Hassan, Kamran Saeed

Abstract:

People are always in search of Wi-Fi hotspots because Internet is a major demand nowadays. But like all other technologies, there is still room for improvement in the Wi-Fi technology with regards to the speed and quality of connectivity. In order to address these aspects, Harald Haas, a professor at the University of Edinburgh, proposed what we know as the Li-Fi (Light Fidelity). Li-Fi is a new technology in the field of wireless communication to provide connectivity within a network environment. It is a two-way mode of wireless communication using light. Basically, the data is transmitted through Light Emitting Diodes which can vary the intensity of light very fast, even faster than the blink of an eye. From the research and experiments conducted so far, it can be said that Li-Fi can increase the speed and reliability of the transfer of data. This paper pays particular attention on the assessment of the performance of this technology. In other words, it is a 5G technology which uses LED as the medium of data transfer. For coverage within the buildings, Wi-Fi is good but Li-Fi can be considered favorable in situations where large amounts of data are to be transferred in areas with electromagnetic interferences. It brings a lot of data related qualities such as efficiency, security as well as large throughputs to the table of wireless communication. All in all, it can be said that Li-Fi is going to be a future phenomenon where the presence of light will mean access to the Internet as well as speedy data transfer.

Keywords: communication, LED, Li-Fi, Wi-Fi

Procedia PDF Downloads 332

Authors: Amir Hossein Ghaffari Nejad, Forouhar Ferdowsi, Reza Mashhadi

Abstract:

In the interest-free banking system of Iran, the savings of society are in the form of bank deposits, and banks using the Islamic contracts, allocate the resources to applicants for obtaining facilities and credit. In the meantime, the central bank, with the aim of introducing monetary policy, determines the maximum interest rate on bank deposits in terms of macroeconomic requirements. But in recent years, the country's economic constraints with the stagflation and the consequence of the institutional weaknesses of the financial market of Iran have resulted in massive disturbances in the balance sheet of the banking system, resulting in a period of mismatch maturity in the banks' assets and liabilities and the implementation of a Ponzi game. This issue caused determination of the interest rate in long-term bank deposit contracts to be associated with non-observance of the maximum rate set by the central bank. The result of this condition was in the allocation of new sources of equipment to meet past commitments towards the old depositors and, as a result, a significant part of the supply of equipment was leaked out of the facilitating cycle and credit crunch emerged. The purpose of this study is to identify the most important factors affecting the occurrence of non-confirmatory financial banking behavior using data from 19 public and private banks of Iran. For this purpose, the causes of this non-confirmatory behavior of banks have been investigated using the panel vector autoregression method (PVAR) for the period of 2007-2015. Granger's causality test results suggest that the return of parallel markets for bank deposits, non-performing loans and the high share of the ratio of facilities to banks' deposits are all a cause of the formation of non-confirmatory behavior. Also, according to the results of impulse response functions and variance decomposition, NPL and the ratio of facilities to deposits have the highest long-term effect and also have a high contribution to explaining the changes in banks' non-confirmatory behavior in determining the interest rate on deposits.

Keywords: non-conformity behavior, Ponzi Game, panel vector autoregression, nonperforming loans

Procedia PDF Downloads 211

Authors: Renata Kurpiewska-Korbut

Abstract:

Making an assumption that the use and sharing of data generated in humanitarian action constitute a core function of humanitarian organizations, the paper analyzes the position of the largest Polish humanitarian non-governmental organizations in the humanitarian data ecosystem in Ukraine and their approach to non-personal and personal data management since February of 2022. Both expert interviews and document analysis of non-profit organizations providing a direct response in the Ukrainian crisis context, i.e., the Polish Humanitarian Action, Caritas, Polish Medical Mission, Polish Red Cross, and the Polish Center for International Aid and the applicability of theoretical perspective of contingency theory – with its central point that the context or specific set of conditions determining the way of behavior and the choice of methods of action – help to examine the significance of data complexity and adaptive approach to data management by relief organizations in the humanitarian supply chain network. The purpose of this study is to determine how the existence of well-established and accurate internal procedures and good practices of using and sharing data (including safeguards for sensitive data) by the surveyed organizations with comparable human and technological capabilities are implemented and adjusted to Ukrainian humanitarian settings and data infrastructure. The study also poses a fundamental question of whether this crisis experience will have a determining effect on their future performance. The obtained finding indicate that Polish humanitarian organizations in Ukraine, which have their own unique code of conduct and effective managerial data practices determined by contingencies, have limited influence on improving the situational awareness of other assistance providers in the data ecosystem despite their attempts to undertake interagency work in the area of data sharing.

Keywords: humanitarian data ecosystem, humanitarian data management, polish NGOs, Ukraine

Procedia PDF Downloads 83

Authors: Daniel C. Bonzo

Abstract:

Practical considerations lead to the use of unit of analysis within subjects, e.g., bleeding episodes or treatment-related adverse events, in rare disease settings. This is coupled with data augmentation techniques such as extrapolation to enlarge the subject base. In general, one can think about extrapolation of data as extending information and conclusions from one estimand to another estimand. This approach induces hierarchichal clustered data with varying cluster sizes. Extrapolation of clinical trial data is being accepted increasingly by regulatory agencies as a means of generating data in diverse situations during drug development process. Under certain circumstances, data can be extrapolated to a different population, a different but related indication, and different but similar product. We consider here the problem of estimation (point and interval) using a mixed-models approach under an extrapolation. It is proposed that estimators (point and interval) be constructed using weighting schemes for the clusters, e.g., equally weighted and with weights proportional to cluster size. Simulated data generated under varying scenarios are then used to evaluate the performance of this approach. In conclusion, the evaluation result showed that the approach is a useful means for improving statistical inference in rare disease settings and thus aids not only signal detection but risk-benefit evaluation as well.

Keywords: clustered data, estimand, extrapolation, mixed model

Procedia PDF Downloads 128

Authors: Celal Dudak, Aslı Utku, Burak Yağlioğlu

Abstract:

Uninterrupted and continuous satellite communication through the whole orbit time is becoming more indispensable every day. Data relay systems are developed and built for various high/low data rate information exchanges like TDRSS of USA and EDRSS of Europe. In these missions, a couple of task-dedicated communication satellites exist. In this regard, for Turkey a data relay system is attempted to be defined exchanging low data rate information (i.e. TTC) for Earth-observing LEO satellites appointing commercial GEO communication satellites all over the world. First, justification of this attempt is given, demonstrating duration enhancements in the link. Discussion of preference of RF communication is, also, given instead of laser communication. Then, preferred communication GEOs – including TURKSAT4A already belonging to Turkey- are given, together with the coverage enhancements through STK simulations and the corresponding link budget. Also, a block diagram of the communication system is given on the LEO satellite.

Keywords: communication, GEO satellite, data relay system, coverage

Procedia PDF Downloads 430

Authors: Tanawat Hongthai, Dusit Thanapatay

Abstract:

This paper presents the development of encrypted near field communication (NFC) data exchange format transmission in an NFC passive tag for the feasibility of implementing a genuine product authentication. We propose a research encryption and checking the genuine product into four major categories; concept, infrastructure, development and applications. This result shows the passive NFC-forum Type 2 tag can be configured to be compatible with the NFC data exchange format (NDEF), which can be automatically partially data updated when there is NFC field.

Keywords: near field communication, NFC data exchange format, checking the genuine product, encrypted NFC

Procedia PDF Downloads 269

Authors: Bishal Baniya

Abstract:

This paper proposes the sustainability related policy areas for decarbonisation and value addition at an organizational level. General and public sector organizations around the world are usually significant in terms of consuming resources and producing waste – powered through their massive procurement capacity. However, these organizations also possess huge potential to cut resource use and emission as many of these organizations controls supply chain of goods/services. They can therefore be a trend setter and can easily lead other major economic sectors such as manufacturing, construction and mining, transportation, etc. in pursuit towards paradigm shift for sustainability. Whilst the environmental and social awareness has improved in recent years and they have identified policy areas to improve the organizational environmental performance, value addition to the core business of the organization hasn’t been understood and interpreted correctly. This paper therefore investigates ways to align sustainability policy measures in a way that it creates better value proposition relative to benchmark by accounting both eco and social efficiency. Preliminary analysis shows co-benefits other than resource and cost savings fosters the business cases for organizations and this can be achieved by better aligning the policy measures and engaging stakeholders.

Keywords: policy measures, environmental performance, value proposition, organisational level

Procedia PDF Downloads 142

Authors: Kanokwan Tangchitnusorn, Patcharawalai Wongboonsin

Abstract:

Following the ‘Golden Age of Welfare’ which enabled post-war prosperity to European citizens in 1950s, the world has witnessed the increasing mobility across borders of older citizens of First World countries. Then, in 1990s, the international retirement migration (IRM) of older persons has become a prominent trend, in which, it requires the integration of several fields of knowledge to explain, i.e. migration studies, tourism studies, as well as, social gerontology. However, while the studies of the IRM to developed destinations in Europe (e.g. Spain, Malta, Portugal, Italy), and the IRM to developing countries like Mexico, Panama, and Morocco have been largely studied in recent decades due to their massive migration volume, the study of the IRM to remoter destinations has been far more relatively sparse and incomplete. Developing countries in Southeast Asia have noticed the increasing number of retired expats, particularly to Thailand, where the number of foreigners applying for retirement visa increased from 10,709 in 2005 to 60,046 in 2014. Additionally, it was evident that the majority of Thailand’s retirement visa applicants were Westerners, i.e. citizens of the United Kingdom, the United States, Germany, and the Nordic countries, respectively. As such trend just becoming popular in Thailand in recent decades, little is known about the IRM populations, their well-being, and their future migration plans. This study aimed to examine the subjective wellbeing or the self-evaluations of own well-being among Western retirees in Thailand, as well as, their future migration plans as whether they planned to stay here for life or otherwise. The author employed a mixed method to obtain both quantitative and qualitative data during October 2015 – May 2016, including 330 self-administered questionnaires (246 online and 84 hard-copied responses), and 21 in-depth interviews of the Western residents in Nan (2), Pattaya (4), and Chiang Mai (15). As derived from the integration of previous subjective well-being measurements (i.e. Personal Wellbeing Index (PWI), Global AgeWatch Index, and OECD guideline on measuring subjective wellbeing), this study would measure the subjective well-being of Western retirees in Thailand in 7 dimensions, including standard of living, health status, personal relationships, social connections, environmental quality, personal security and local infrastructure.

Keywords: international retirement migration, ageing, mobility, wellbeing, Western, Thailand

Procedia PDF Downloads 334

Authors: Yung Gi Wu

Abstract:

With the growing of computer and network, digital data can be spread to anywhere in the world quickly. In addition, digital data can also be copied or tampered easily so that the security issue becomes an important topic in the protection of digital data. Digital watermark is a method to protect the ownership of digital data. Embedding the watermark will influence the quality certainly. In this paper, Vector Quantization (VQ) is used to embed the watermark into the image to fulfill the goal of data hiding. This kind of watermarking is invisible which means that the users will not conscious the existing of embedded watermark even though the embedded image has tiny difference compared to the original image. Meanwhile, VQ needs a lot of computation burden so that we adopt a fast VQ encoding scheme by partial distortion searching (PDS) and mean approximation scheme to speed up the data hiding process. The watermarks we hide to the image could be gray, bi-level and color images. Texts are also can be regarded as watermark to embed. In order to test the robustness of the system, we adopt Photoshop to fulfill sharpen, cropping and altering to check if the extracted watermark is still recognizable. Experimental results demonstrate that the proposed system can resist the above three kinds of tampering in general cases.

Keywords: data hiding, vector quantization, watermark, color image

Procedia PDF Downloads 354

Authors: Victor Breux, Jérôme Boutet, Alain Goret, Viviane Cattin

Abstract:

Early detection of anomalies in data centers is important to reduce downtimes and the costs of periodic maintenance. However, there is little research on this topic and even fewer on the fusion of sensor data for the detection of abnormal events. The goal of this paper is to propose a method for anomaly detection in data centers by combining sensor data (temperature, humidity, power) and deep learning models. The model described in the paper uses one autoencoder per sensor to reconstruct the inputs. The auto-encoders contain Long-Short Term Memory (LSTM) layers and are trained using the normal samples of the relevant sensors selected by correlation analysis. The difference signal between the input and its reconstruction is then used to classify the samples using feature extraction and a random forest classifier. The data measured by the sensors of a data center between January 2019 and May 2020 are used to train the model, while the data between June 2020 and May 2021 are used to assess it. Performances of the model are assessed a posteriori through F1-score by comparing detected anomalies with the data center’s history. The proposed model outperforms the state-of-the-art reconstruction method, which uses only one autoencoder taking multivariate sequences and detects an anomaly with a threshold on the reconstruction error, with an F1-score of 83.60% compared to 24.16%.

Keywords: anomaly detection, autoencoder, data centers, deep learning

Procedia PDF Downloads 184

Authors: Ting Gao, Mingyue He

Abstract:

Reconfigurable intelligent surfaces (RISs) are expected to be an important part of next-generation wireless communication networks due to their potential to reduce the hardware cost and energy consumption of millimeter Wave (mmWave) massive multiple-input multiple-output (MIMO) technology. However, owing to the lack of signal processing abilities of the RIS, the perfect channel state information (CSI) in RIS-assisted communication systems is difficult to acquire. In this paper, the uplink channel estimation for mmWave systems with a hybrid active/passive RIS architecture is studied. Specifically, a deep learning-based estimation scheme is proposed to estimate the channel between the RIS and the user. In particular, the sparse structure of the mmWave channel is exploited to formulate the channel estimation as a sparse reconstruction problem. To this end, the proposed approach is derived to obtain the distribution of non-zero entries in a sparse channel. After that, the channel is reconstructed by utilizing the least-squares (LS) algorithm and compressed sensing (CS) theory. The simulation results demonstrate that the proposed channel estimation scheme is superior to existing solutions even in low signal-to-noise ratio (SNR) environments.

Keywords: channel estimation, reconfigurable intelligent surface, wireless communication, deep learning

Procedia PDF Downloads 134

Authors: Syed Irfan Raza Naqvi, Zheng Jiangbin, Ahmad Moshin, Pervez Akhter

Abstract:

Currently, the IoT system depends on a centralized client-servant architecture that causes various scalability and privacy vulnerabilities. Distributed ledger technology (DLT) introduces a set of opportunities for the IoT, which leads to practical ideas for existing components at all levels of existing architectures. Blockchain Technology (BCT) appears to be one approach to solving several IoT problems, like Bitcoin (BTC) and Ethereum, which offer multiple possibilities. Besides, IoTs are resource-constrained devices with insufficient capacity and computational overhead to process blockchain consensus mechanisms; the traditional BCT existing challenge for IoTs is poor scalability, energy efficiency, and transaction fees. IOTA is a distributed ledger based on Direct Acyclic Graph (DAG) that ensures M2M micro-transactions are free of charge. IOTA has the potential to address existing IoT-related difficulties such as infrastructure scalability, privacy and access control mechanisms. We proposed an architecture, SLDBI: A Scalable, lightweight DAG-based Blockchain Design for Intelligent IoT Systems, which adapts the DAG base Tangle and implements a lightweight message data model to address the IoT limitations. It enables the smooth integration of new IoT devices into a variety of apps. SLDBI enables comprehensive access control, energy efficiency, and scalability in IoT ecosystems by utilizing the Masked Authentication Message (MAM) protocol and the IOTA Smart Contract Protocol (ISCP). Furthermore, we suggest proof-of-work (PoW) computation on the full node in an energy-efficient way. Experiments have been carried out to show the capability of a tangle to achieve better scalability while maintaining energy efficiency. The findings show user access control management at granularity levels and ensure scale up to massive networks with thousands of IoT nodes, such as Smart Connected Buildings (SCBDs).

Keywords: blockchain, IOT, direct acyclic graphy, scalability, access control, architecture, smart contract, smart connected buildings

Procedia PDF Downloads 111

Authors: Pavel H. Llamocca, Victoria Lopez

Abstract:

The main objective of our work is the comparative analysis of environmental data from Open Data bases, belonging to different governments. This means that you have to integrate data from various different sources. Nowadays, many governments have the intention of publishing thousands of data sets for people and organizations to use them. In this way, the quantity of applications based on Open Data is increasing. However each government has its own procedures to publish its data, and it causes a variety of formats of data sets because there are no international standards to specify the formats of the data sets from Open Data bases. Due to this variety of formats, we must build a data integration process that is able to put together all kind of formats. There are some software tools developed in order to give support to the integration process, e.g. Data Tamer, Data Wrangler. The problem with these tools is that they need data scientist interaction to take part in the integration process as a final step. In our case we don’t want to depend on a data scientist, because environmental data are usually similar and these processes can be automated by programming. The main idea of our tool is to build Hadoop procedures adapted to data sources per each government in order to achieve an automated integration. Our work focus in environment data like temperature, energy consumption, air quality, solar radiation, speeds of wind, etc. Since 2 years, the government of Madrid is publishing its Open Data bases relative to environment indicators in real time. In the same way, other governments have published Open Data sets relative to the environment (like Andalucia or Bilbao). But all of those data sets have different formats and our solution is able to integrate all of them, furthermore it allows the user to make and visualize some analysis over the real-time data. Once the integration task is done, all the data from any government has the same format and the analysis process can be initiated in a computational better way. So the tool presented in this work has two goals: 1. Integration process; and 2. Graphic and analytic interface. As a first approach, the integration process was developed using Java and Oracle and the graphic and analytic interface with Java (jsp). However, in order to open our software tool, as second approach, we also developed an implementation with R language as mature open source technology. R is a really powerful open source programming language that allows us to process and analyze a huge amount of data with high performance. There are also some R libraries for the building of a graphic interface like shiny. A performance comparison between both implementations was made and no significant differences were found. In addition, our work provides with an Official Real-Time Integrated Data Set about Environment Data in Spain to any developer in order that they can build their own applications.

Keywords: open data, R language, data integration, environmental data

Procedia PDF Downloads 305

Authors: Zahid Ullah, Atlas Khan

Abstract:

The rapid growth of data in various domains has created a pressing need for effective methods to transform this data into meaningful knowledge. In this era of big data, mathematical and statistical innovations play a crucial role in unlocking insights and facilitating informed decision-making in data analytics. This abstract aims to explore the transformative potential of these innovations and their impact on converting raw data into actionable knowledge. Drawing upon a comprehensive review of existing literature, this research investigates the cutting-edge mathematical and statistical techniques that enable the conversion of data into knowledge. By evaluating their underlying principles, strengths, and limitations, we aim to identify the most promising innovations in data analytics. To demonstrate the practical applications of these innovations, real-world datasets will be utilized through case studies or simulations. This empirical approach will showcase how mathematical and statistical innovations can extract patterns, trends, and insights from complex data, enabling evidence-based decision-making across diverse domains. Furthermore, a comparative analysis will be conducted to assess the performance, scalability, interpretability, and adaptability of different innovations. By benchmarking against established techniques, we aim to validate the effectiveness and superiority of the proposed mathematical and statistical innovations in data analytics. Ethical considerations surrounding data analytics, such as privacy, security, bias, and fairness, will be addressed throughout the research. Guidelines and best practices will be developed to ensure the responsible and ethical use of mathematical and statistical innovations in data analytics. The expected contributions of this research include advancements in mathematical and statistical sciences, improved data analysis techniques, enhanced decision-making processes, and practical implications for industries and policymakers. The outcomes will guide the adoption and implementation of mathematical and statistical innovations, empowering stakeholders to transform data into actionable knowledge and drive meaningful outcomes.

Keywords: data analytics, mathematical innovations, knowledge extraction, decision-making

Procedia PDF Downloads 64

Authors: Lu Si, Jie Yu, Shasha Li, Jun Ma, Lei Luo, Qingbo Wu, Yongqi Ma, Zhengji Liu

Abstract:

Instance selection (IS) technique is used to reduce the data size to improve the performance of data mining methods. Recently, to process very large data set, several proposed methods divide the training set into some disjoint subsets and apply IS algorithms independently to each subset. In this paper, we analyze the limitation of these methods and give our viewpoint about how to divide and conquer in IS procedure. Then, based on fast condensed nearest neighbor (FCNN) rule, we propose a large data sets instance selection method with MapReduce framework. Besides ensuring the prediction accuracy and reduction rate, it has two desirable properties: First, it reduces the work load in the aggregation node; Second and most important, it produces the same result with the sequential version, which other parallel methods cannot achieve. We evaluate the performance of FCNN-MR on one small data set and two large data sets. The experimental results show that it is effective and practical.

Keywords: instance selection, data reduction, MapReduce, kNN

Procedia PDF Downloads 245

Authors: Thais O. de Paula, Marjorie R. A. Sarmiento, Francis M. Borges, Alessandra B. Ferreira-Machado, Juliana A. Resende, Dioneia E. Cesar, Vania L. Silva, Claudio G. Diniz

Abstract:

Obesity is currently a worldwide public health threat, being considered a pandemic multifactorial disease related to the human gut microbiota (GM). Add to that GM is considered an important reservoir of antimicrobial resistance genes (ARG) and little is known on GM and ARG in obesity, considering the altered physiology and xenobiotics intake. As regional and social behavior may play important roles in GM modulation, and most of the studies are based on small sample size and various methodological approaches resulting in difficulties for data comparisons, this study was focused on the investigation of GM bacterial diversity in obese (OB), overweight (OW) and eutrophic individuals (ET) considering their nutritional, clinical and social characteristics; and comparative screening of AGR related to their physiology and xenobiotics intake. Microbial community was accessed by FISH considering phyla as a taxonomic level, and PCR-DGGE followed by dendrograms evaluation (UPGMA method) from fecal metagenome of 72 volunteers classified according to their body mass index (BMI). Nutritional, clinical, social parameters and xenobiotics intake were recorded for correlation analysis. The fecal metagenome was also used as template for PCR targeting 59 different ARG. Overall, 62% of OB were hypertensive, and 12% or 4% were, regarding the OW and ET individuals. Most of the OB were rated as low income (80%). Lower relative bacterial densities were observed in the OB compared to ET for almost all studied taxa (p < 0.05) with Firmicutes/Bacteroidetes ratio increased in the OB group. OW individuals showed a bacterial density representative of GM more likely to the OB. All the participants were clustered in 3 different groups based on the PCR-DGGE fingerprint patterns (C1, C2, C3), being OB mostly grouped in C1 (83.3%) and ET mostly grouped in C3 (50%). The cluster C2 showed to be transitional. Among 27 ARG detected, a cluster of 17 was observed in all groups suggesting a common core. In general, ARG were observed mostly within OB individuals followed by OW and ET. The ratio between ARG and bacterial groups may suggest that AGR were more related to enterobacteria. Positive correlations were observed between ARG and BMI, calories and xenobiotics intake (especially use of sweeteners). As with nutritional and clinical characteristics, our data may suggest that GM of OW individuals behave in a heterogeneous pattern, occasionally more likely to the OB or to the ET. Regardless the regional and social behaviors of our population, the methodological approaches in this study were complementary and confirmatory. The imbalance of GM over the health-disease interface in obesity is a matter of fact, but its influence in host's physiology is still to be clearly elucidated to help understanding the multifactorial etiology of obesity. Although the results are in agreement with observations that GM is altered in obesity, the altered physiology in OB individuals seems to be also associated to the increased xenobiotics intake and may interfere with GM towards antimicrobial resistance, as observed by the fecal metagenome and ARG screening. Support: FAPEMIG, CNPQ, CAPES, PPGCBIO/UFJF.

Keywords: antimicrobial resistance, bacterial diversity, gut microbiota, obesity

Procedia PDF Downloads 156

Authors: Mona Elsalamawy, Ashraf Ragab Mohamed, Abdellatif Elsayed Abosen

Abstract:

This paper aims to evaluate the effectiveness of different crystalline coating materials concerning of chloride ions penetration. The concrete ages at the coating installation and its moisture conditions were addressed; where, these two factors may play a dominant role for the effectiveness of the used materials. Rapid chloride ions penetration test (RCPT) was conducted at different ages and moisture conditions according to the relevant standard. In addition, the contaminated area and the penetration depth of the chloride ions were investigated immediately after the RCPT test using chemical identifier, 0.1 M silver nitrate AgNO₃ solution. Results have shown that, the very low chloride ions penetrability, for the studied crystallization materials, were investigated only with the old age concrete (G1). The significant reduction in chloride ions’ penetrability was illustrated after 7 days of installing the crystalline coating layers. Using imageJ is more reliable to describe the contaminated area of chloride ions, where the distribution of aggregate and heterogeneous of cement mortar was considered in the images analysis.

Keywords: chloride permeability, contaminated area, crystalline waterproofing materials, RCPT, XRD

Procedia PDF Downloads 244

Authors: Trevor Toy, Josef Langerman

Abstract:

Around a quarter of the world’s data is generated by financial with an estimated 708.5 billion global non-cash transactions reached between 2018 and. And with Open Banking still a rapidly developing concept within the financial industry, there is an opportunity to create a secure mechanism for connecting its stakeholders to openly, legitimately and consensually share the data required to enable it. Integration and data sharing of anonymised transactional data are still operated in silos and centralised between the large corporate entities in the ecosystem that have the resources to do so. Smaller fintechs generating data and businesses looking to consume data are largely excluded from the process. Therefore there is a growing demand for accessible transactional data for analytical purposes and also to support the rapid global adoption of Open Banking. The following research has provided a solution framework that aims to provide a secure decentralised marketplace for 1.) data providers to list their transactional data, 2.) data consumers to find and access that data, and 3.) data subjects (the individuals making the transactions that generate the data) to manage and sell the data that relates to themselves. The platform also provides an integrated system for downstream transactional-related data from merchants, enriching the data product available to build a comprehensive view of a data subject’s spending habits. A robust and sustainable data market can be developed by providing a more accessible mechanism for data producers to monetise their data investments and encouraging data subjects to share their data through the same financial incentives. At the centre of the platform is the market mechanism that connects the data providers and their data subjects to the data consumers. This core component of the platform is developed on a decentralised blockchain contract with a market layer that manages transaction, user, pricing, payment, tagging, contract, control, and lineage features that pertain to the user interactions on the platform. One of the platform’s key features is enabling the participation and management of personal data by the individuals from whom the data is being generated. This framework developed a proof-of-concept on the Etheruem blockchain base where an individual can securely manage access to their own personal data and that individual’s identifiable relationship to the card-based transaction data provided by financial institutions. This gives data consumers access to a complete view of transactional spending behaviour in correlation to key demographic information. This platform solution can ultimately support the growth, prosperity, and development of economies, businesses, communities, and individuals by providing accessible and relevant transactional data for big data analytics and open banking.

Keywords: big data markets, open banking, blockchain, personal data management

Procedia PDF Downloads 66