Search results for: data augmentation
24551 Bone Strengthening Effects of Deer Antler Extract
Authors: Hye Kyung Kim, Myung-Gyou Kim, Kang-Hyun Leem
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It has been reported that deer antler extract has bone-strengthening activity and effectively used in bone diseases therapy. However, little is known about the cellular and molecular mechanism of this effect. The upper section, mid section, and base of the antler has been known to exhibit different biological properties. Present study investigated the effects of these three parts of deer antler extracts on bone formation and resorption. The effects of deer antler extracts (DH) on bone formation were determined by cell proliferation, alkaline phosphatase (ALP) activity, collagen synthesis, and mineralization in human osteoblastic MG-63 cells. The effect on bone resorption was determined by osteoclastogenesis from bone marrow-derived precursor cells driven by RANKL. Ethanol extracts of DH (50 ~ 100 µg/ml) dose-dependently increased cell proliferation, and upper part increased the cell proliferation by 118.4% while mid and base parts increased proliferation by 107.8% and 102.3%, respectively. ALP activity was significantly increased by upper part of the DH treatment. After enhancement of ALP activity, significant augmentation of collagen synthesis and calcification assessed by Sirus red and Alzarin red staining, respectively, was observed in upper part of the DH treatment. The effect of DH on bone resorption was not observed in all three parts of the DH. These results could provide a mechanistic explanation for the bone-strengthening effects of DH.Keywords: alkaline phosphatase, collagen synthesis, deer antler, osteoblastic MG-63 cells
Procedia PDF Downloads 30724550 Ontological Modeling Approach for Statistical Databases Publication in Linked Open Data
Authors: Bourama Mane, Ibrahima Fall, Mamadou Samba Camara, Alassane Bah
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At the level of the National Statistical Institutes, there is a large volume of data which is generally in a format which conditions the method of publication of the information they contain. Each household or business data collection project includes a dissemination platform for its implementation. Thus, these dissemination methods previously used, do not promote rapid access to information and especially does not offer the option of being able to link data for in-depth processing. In this paper, we present an approach to modeling these data to publish them in a format intended for the Semantic Web. Our objective is to be able to publish all this data in a single platform and offer the option to link with other external data sources. An application of the approach will be made on data from major national surveys such as the one on employment, poverty, child labor and the general census of the population of Senegal.Keywords: Semantic Web, linked open data, database, statistic
Procedia PDF Downloads 17324549 The Role of Data Protection Officer in Managing Individual Data: Issues and Challenges
Authors: Nazura Abdul Manap, Siti Nur Farah Atiqah Salleh
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For decades, the misuse of personal data has been a critical issue. Malaysia has accepted responsibility by implementing the Malaysian Personal Data Protection Act 2010 to secure personal data (PDPA 2010). After more than a decade, this legislation is set to be revised by the current PDPA 2023 Amendment Bill to align with the world's key personal data protection regulations, such as the European Union General Data Protection Regulations (GDPR). Among the other suggested adjustments is the Data User's appointment of a Data Protection Officer (DPO) to ensure the commercial entity's compliance with the PDPA 2010 criteria. The change is expected to be enacted in parliament fairly soon; nevertheless, based on the experience of the Personal Data Protection Department (PDPD) in implementing the Act, it is projected that there will be a slew of additional concerns associated with the DPO mandate. Consequently, the goal of this article is to highlight the issues that the DPO will encounter and how the Personal Data Protection Department should respond to this subject. The study result was produced using a qualitative technique based on an examination of the current literature. This research reveals that there are probable obstacles experienced by the DPO, and thus, there should be a definite, clear guideline in place to aid DPO in executing their tasks. It is argued that appointing a DPO is a wise measure in ensuring that the legal data security requirements are met.Keywords: guideline, law, data protection officer, personal data
Procedia PDF Downloads 7324548 Energy Conservation in Heat Exchangers
Authors: Nadia Allouache
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Energy conservation is one of the major concerns in the modern high tech era due to the limited amount of energy resources and the increasing cost of energy. Predicting an efficient use of energy in thermal systems like heat exchangers can only be achieved if the second law of thermodynamics is accounted for. The performance of heat exchangers can be substantially improved by many passive heat transfer augmentation techniques. These letters permit to improve heat transfer rate and to increase exchange surface, but on the other side, they also increase the friction factor associated with the flow. This raises the question of how to employ these passive techniques in order to minimize the useful energy. The objective of this present study is to use a porous substrate attached to the walls as a passive enhancement technique in heat exchangers and to find the compromise between the hydrodynamic and thermal performances under turbulent flow conditions, by using a second law approach. A modified k- ε model is used to simulating the turbulent flow in the porous medium and the turbulent shear flow is accounted for in the entropy generation equation. A numerical modeling, based on the finite volume method is employed for discretizing the governing equations. Effects of several parameters are investigated such as the porous substrate properties and the flow conditions. Results show that under certain conditions of the porous layer thickness, its permeability, and its effective thermal conductivity the minimum rate of entropy production is obtained.Keywords: second law approach, annular heat exchanger, turbulent flow, porous medium, modified model, numerical analysis
Procedia PDF Downloads 27724547 Data Collection Based on the Questionnaire Survey In-Hospital Emergencies
Authors: Nouha Mhimdi, Wahiba Ben Abdessalem Karaa, Henda Ben Ghezala
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The methods identified in data collection are diverse: electronic media, focus group interviews and short-answer questionnaires [1]. The collection of poor-quality data resulting, for example, from poorly designed questionnaires, the absence of good translators or interpreters, and the incorrect recording of data allow conclusions to be drawn that are not supported by the data or to focus only on the average effect of the program or policy. There are several solutions to avoid or minimize the most frequent errors, including obtaining expert advice on the design or adaptation of data collection instruments; or use technologies allowing better "anonymity" in the responses [2]. In this context, we opted to collect good quality data by doing a sizeable questionnaire-based survey on hospital emergencies to improve emergency services and alleviate the problems encountered. At the level of this paper, we will present our study, and we will detail the steps followed to achieve the collection of relevant, consistent and practical data.Keywords: data collection, survey, questionnaire, database, data analysis, hospital emergencies
Procedia PDF Downloads 10224546 Federated Learning in Healthcare
Authors: Ananya Gangavarapu
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Convolutional Neural Networks (CNN) based models are providing diagnostic capabilities on par with the medical specialists in many specialty areas. However, collecting the medical data for training purposes is very challenging because of the increased regulations around data collections and privacy concerns around personal health data. The gathering of the data becomes even more difficult if the capture devices are edge-based mobile devices (like smartphones) with feeble wireless connectivity in rural/remote areas. In this paper, I would like to highlight Federated Learning approach to mitigate data privacy and security issues.Keywords: deep learning in healthcare, data privacy, federated learning, training in distributed environment
Procedia PDF Downloads 13424545 Chronic Hepatitis C Virus Screening: The Role, Strategies and Challenging of Primary Healthcare Faced to Augment and Identify Asymptomatic Infected Patients
Authors: Tarek K. Jalouta, Jolietta R. Holliman, Kathryn R. Burke, Kathleen M. Bewley-Thomas
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Background: Chronic hepatitis C virus (HCV) infection is one of the leading causes of liver cirrhosis and hepatocellular carcinoma. In the United States, HCV screening awareness, treatment, and linkage to care are under continues ascending progress. However, still millions of people are asymptomatically infected and undiagnosed yet. Through this community mission, we sought to identify the best and the newest strategies to identify those infected people to educate them, link them to care and cure them. Methods: We have identified patients that did not have a prior HCV screening in our Electronic medical record (EMR) including all our different hospital locations (South Suburban Chicago, Northern, Western and Central Indiana). Providing education to all Primary care/Gastroenterology/Infectious diseases providers and staff in the clinic to increase awareness of the HCV screening. Health-related quality of life, chronic clinical complications, and demographics data were collected for each patient. All outcomes of HCV antibody-reactive and HCV RNA–positive results were identified and statistically analyzed. Results: From July 2016 to July 2018 we screened 35,720 individuals of birth cohort in our different Franciscan’s health medical centers. Of the screened population, 986 (2.7%) individuals were HCV AB-reactive. Of those, 319 (1%) patients were HCV RNA-positive, and 264 patients were counseled and linked to providers. 34 patients initiated anti-HCV therapy with successful treatment. Conclusions: Our HCV screening augmentation project considered the largest screening program in the Midwest. Augmenting the HCV screening process through creating a Best Practice Alert (BPA) in the EMR (Epic Sys.) and point of care testing could be helpful. Although continued work is required, our team is working on increase screening through adding HCV test to CBC-Panels in Emergency Department settings, phone calls to all birth cohort individuals through Robo-Calling System aimed to reach 75,000 individuals by 2019. However, a better linkage to care and referral monitoring system to all HCV RNA positive patients is still needed, and access to therapy, especially for uninsured patients, is challenging.Keywords: chronic hepatitis C, chronic hepatitis C treatment, chronic hepatitis C screening, chronic hepatitis C prevention, liver cancer
Procedia PDF Downloads 12024544 The Utilization of Big Data in Knowledge Management Creation
Authors: Daniel Brian Thompson, Subarmaniam Kannan
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The huge weightage of knowledge in this world and within the repository of organizations has already reached immense capacity and is constantly increasing as time goes by. To accommodate these constraints, Big Data implementation and algorithms are utilized to obtain new or enhanced knowledge for decision-making. With the transition from data to knowledge provides the transformational changes which will provide tangible benefits to the individual implementing these practices. Today, various organization would derive knowledge from observations and intuitions where this information or data will be translated into best practices for knowledge acquisition, generation and sharing. Through the widespread usage of Big Data, the main intention is to provide information that has been cleaned and analyzed to nurture tangible insights for an organization to apply to their knowledge-creation practices based on facts and figures. The translation of data into knowledge will generate value for an organization to make decisive decisions to proceed with the transition of best practices. Without a strong foundation of knowledge and Big Data, businesses are not able to grow and be enhanced within the competitive environment.Keywords: big data, knowledge management, data driven, knowledge creation
Procedia PDF Downloads 10424543 Survey on Data Security Issues Through Cloud Computing Amongst Sme’s in Nairobi County, Kenya
Authors: Masese Chuma Benard, Martin Onsiro Ronald
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Businesses have been using cloud computing more frequently recently because they wish to take advantage of its advantages. However, employing cloud computing also introduces new security concerns, particularly with regard to data security, potential risks and weaknesses that could be exploited by attackers, and various tactics and strategies that could be used to lessen these risks. This study examines data security issues on cloud computing amongst sme’s in Nairobi county, Kenya. The study used the sample size of 48, the research approach was mixed methods, The findings show that data owner has no control over the cloud merchant's data management procedures, there is no way to ensure that data is handled legally. This implies that you will lose control over the data stored in the cloud. Data and information stored in the cloud may face a range of availability issues due to internet outages; this can represent a significant risk to data kept in shared clouds. Integrity, availability, and secrecy are all mentioned.Keywords: data security, cloud computing, information, information security, small and medium-sized firms (SMEs)
Procedia PDF Downloads 7924542 Cloud Design for Storing Large Amount of Data
Authors: M. Strémy, P. Závacký, P. Cuninka, M. Juhás
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Main goal of this paper is to introduce our design of private cloud for storing large amount of data, especially pictures, and to provide good technological backend for data analysis based on parallel processing and business intelligence. We have tested hypervisors, cloud management tools, storage for storing all data and Hadoop to provide data analysis on unstructured data. Providing high availability, virtual network management, logical separation of projects and also rapid deployment of physical servers to our environment was also needed.Keywords: cloud, glusterfs, hadoop, juju, kvm, maas, openstack, virtualization
Procedia PDF Downloads 34824541 Estimation of Missing Values in Aggregate Level Spatial Data
Authors: Amitha Puranik, V. S. Binu, Seena Biju
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Missing data is a common problem in spatial analysis especially at the aggregate level. Missing can either occur in covariate or in response variable or in both in a given location. Many missing data techniques are available to estimate the missing data values but not all of these methods can be applied on spatial data since the data are autocorrelated. Hence there is a need to develop a method that estimates the missing values in both response variable and covariates in spatial data by taking account of the spatial autocorrelation. The present study aims to develop a model to estimate the missing data points at the aggregate level in spatial data by accounting for (a) Spatial autocorrelation of the response variable (b) Spatial autocorrelation of covariates and (c) Correlation between covariates and the response variable. Estimating the missing values of spatial data requires a model that explicitly account for the spatial autocorrelation. The proposed model not only accounts for spatial autocorrelation but also utilizes the correlation that exists between covariates, within covariates and between a response variable and covariates. The precise estimation of the missing data points in spatial data will result in an increased precision of the estimated effects of independent variables on the response variable in spatial regression analysis.Keywords: spatial regression, missing data estimation, spatial autocorrelation, simulation analysis
Procedia PDF Downloads 37124540 Association Rules Mining and NOSQL Oriented Document in Big Data
Authors: Sarra Senhadji, Imene Benzeguimi, Zohra Yagoub
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Big Data represents the recent technology of manipulating voluminous and unstructured data sets over multiple sources. Therefore, NOSQL appears to handle the problem of unstructured data. Association rules mining is one of the popular techniques of data mining to extract hidden relationship from transactional databases. The algorithm for finding association dependencies is well-solved with Map Reduce. The goal of our work is to reduce the time of generating of frequent itemsets by using Map Reduce and NOSQL database oriented document. A comparative study is given to evaluate the performances of our algorithm with the classical algorithm Apriori.Keywords: Apriori, Association rules mining, Big Data, Data Mining, Hadoop, MapReduce, MongoDB, NoSQL
Procedia PDF Downloads 15424539 Immunization-Data-Quality in Public Health Facilities in the Pastoralist Communities: A Comparative Study Evidence from Afar and Somali Regional States, Ethiopia
Authors: Melaku Tsehay
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The Consortium of Christian Relief and Development Associations (CCRDA), and the CORE Group Polio Partners (CGPP) Secretariat have been working with Global Alliance for Vac-cines and Immunization (GAVI) to improve the immunization data quality in Afar and Somali Regional States. The main aim of this study was to compare the quality of immunization data before and after the above interventions in health facilities in the pastoralist communities in Ethiopia. To this end, a comparative-cross-sectional study was conducted on 51 health facilities. The baseline data was collected in May 2019, while the end line data in August 2021. The WHO data quality self-assessment tool (DQS) was used to collect data. A significant improvment was seen in the accuracy of the pentavalent vaccine (PT)1 (p = 0.012) data at the health posts (HP), while PT3 (p = 0.010), and Measles (p = 0.020) at the health centers (HC). Besides, a highly sig-nificant improvment was observed in the accuracy of tetanus toxoid (TT)2 data at HP (p < 0.001). The level of over- or under-reporting was found to be < 8%, at the HP, and < 10% at the HC for PT3. The data completeness was also increased from 72.09% to 88.89% at the HC. Nearly 74% of the health facilities timely reported their respective immunization data, which is much better than the baseline (7.1%) (p < 0.001). These findings may provide some hints for the policies and pro-grams targetting on improving immunization data qaulity in the pastoralist communities.Keywords: data quality, immunization, verification factor, pastoralist region
Procedia PDF Downloads 10124538 Identifying Critical Success Factors for Data Quality Management through a Delphi Study
Authors: Maria Paula Santos, Ana Lucas
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Organizations support their operations and decision making on the data they have at their disposal, so the quality of these data is remarkably important and Data Quality (DQ) is currently a relevant issue, the literature being unanimous in pointing out that poor DQ can result in large costs for organizations. The literature review identified and described 24 Critical Success Factors (CSF) for Data Quality Management (DQM) that were presented to a panel of experts, who ordered them according to their degree of importance, using the Delphi method with the Q-sort technique, based on an online questionnaire. The study shows that the five most important CSF for DQM are: definition of appropriate policies and standards, control of inputs, definition of a strategic plan for DQ, organizational culture focused on quality of the data and obtaining top management commitment and support.Keywords: critical success factors, data quality, data quality management, Delphi, Q-Sort
Procedia PDF Downloads 20824537 Data Mining in Medicine Domain Using Decision Trees and Vector Support Machine
Authors: Djamila Benhaddouche, Abdelkader Benyettou
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In this paper, we used data mining to extract biomedical knowledge. In general, complex biomedical data collected in studies of populations are treated by statistical methods, although they are robust, they are not sufficient in themselves to harness the potential wealth of data. For that you used in step two learning algorithms: the Decision Trees and Support Vector Machine (SVM). These supervised classification methods are used to make the diagnosis of thyroid disease. In this context, we propose to promote the study and use of symbolic data mining techniques.Keywords: biomedical data, learning, classifier, algorithms decision tree, knowledge extraction
Procedia PDF Downloads 54924536 Analysis of Different Classification Techniques Using WEKA for Diabetic Disease
Authors: Usama Ahmed
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Data mining is the process of analyze data which are used to predict helpful information. It is the field of research which solve various type of problem. In data mining, classification is an important technique to classify different kind of data. Diabetes is most common disease. This paper implements different classification technique using Waikato Environment for Knowledge Analysis (WEKA) on diabetes dataset and find which algorithm is suitable for working. The best classification algorithm based on diabetic data is Naïve Bayes. The accuracy of Naïve Bayes is 76.31% and take 0.06 seconds to build the model.Keywords: data mining, classification, diabetes, WEKA
Procedia PDF Downloads 13824535 Comprehensive Study of Data Science
Authors: Asifa Amara, Prachi Singh, Kanishka, Debargho Pathak, Akshat Kumar, Jayakumar Eravelly
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Today's generation is totally dependent on technology that uses data as its fuel. The present study is all about innovations and developments in data science and gives an idea about how efficiently to use the data provided. This study will help to understand the core concepts of data science. The concept of artificial intelligence was introduced by Alan Turing in which the main principle was to create an artificial system that can run independently of human-given programs and can function with the help of analyzing data to understand the requirements of the users. Data science comprises business understanding, analyzing data, ethical concerns, understanding programming languages, various fields and sources of data, skills, etc. The usage of data science has evolved over the years. In this review article, we have covered a part of data science, i.e., machine learning. Machine learning uses data science for its work. Machines learn through their experience, which helps them to do any work more efficiently. This article includes a comparative study image between human understanding and machine understanding, advantages, applications, and real-time examples of machine learning. Data science is an important game changer in the life of human beings. Since the advent of data science, we have found its benefits and how it leads to a better understanding of people, and how it cherishes individual needs. It has improved business strategies, services provided by them, forecasting, the ability to attend sustainable developments, etc. This study also focuses on a better understanding of data science which will help us to create a better world.Keywords: data science, machine learning, data analytics, artificial intelligence
Procedia PDF Downloads 7524534 Evaluation of a Potential Metabolism-Mediated Drug-Drug Interaction between Carvedilol and Fluvoxamine in Rats
Authors: Ana-Maria Gheldiu, Bianca M. Abrudan, Maria A. Neag, Laurian Vlase, Dana M. Muntean
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Background information: The objective of this study was to investigate the effect of multiple-dose fluvoxamine on the pharmacokinetic profile of single-dose carvedilol in rats, in order to evaluate this possible drug-drug pharmacokinetic interaction. Methods: A preclinical study, in 28 white male Wistar rats, was conducted. Each rat was cannulated on the femoral vein, prior to being connected to BASi Culex ABC®. Carvedilol was orally administrated in rats (3.57 mg/kg body mass (b.m.)) in the absence of fluvoxamine or after a pre-treatment with multiple oral doses of fluvoxamine (14.28 mg/kg b.m.). The plasma concentrations of carvedilol were estimated by high performance liquid chromatography-tandem mass spectrometry. The pharmacokinetic parameters of carvedilol were analyzed by non-compartmental method. Results: After carvediol co-administration with fluvoxamine, an approximately 2-fold increase in the exposure of carvedilol was observed, considering the significantly elevated value of the total area under the concentration versus time curve (AUC₀₋∞). Moreover, an increase by approximately 145% of the peak plasma concentration was found, as well as an augmentation by approximately 230% of the half life time of carvedilol was observed. Conclusion: Fluvoxamine co-administration led to a significant alteration of carvedilol’s pharmacokinetic profile in rats, these effects could be explained by the existence of a drug-drug interaction mediated by CYP2D6 inhibition. Acknowledgement: This work was supported by CNCS Romania – project PNII-RU-TE-2014-4-0242.Keywords: carvedilol, fluvoxamine, drug-drug pharmacokinetic interaction, rats
Procedia PDF Downloads 26624533 Application of Artificial Neural Network Technique for Diagnosing Asthma
Authors: Azadeh Bashiri
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Introduction: Lack of proper diagnosis and inadequate treatment of asthma leads to physical and financial complications. This study aimed to use data mining techniques and creating a neural network intelligent system for diagnosis of asthma. Methods: The study population is the patients who had visited one of the Lung Clinics in Tehran. Data were analyzed using the SPSS statistical tool and the chi-square Pearson's coefficient was the basis of decision making for data ranking. The considered neural network is trained using back propagation learning technique. Results: According to the analysis performed by means of SPSS to select the top factors, 13 effective factors were selected, in different performances, data was mixed in various forms, so the different models were made for training the data and testing networks and in all different modes, the network was able to predict correctly 100% of all cases. Conclusion: Using data mining methods before the design structure of system, aimed to reduce the data dimension and the optimum choice of the data, will lead to a more accurate system. Therefore, considering the data mining approaches due to the nature of medical data is necessary.Keywords: asthma, data mining, Artificial Neural Network, intelligent system
Procedia PDF Downloads 26624532 Interpreting Privacy Harms from a Non-Economic Perspective
Authors: Christopher Muhawe, Masooda Bashir
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With increased Internet Communication Technology(ICT), the virtual world has become the new normal. At the same time, there is an unprecedented collection of massive amounts of data by both private and public entities. Unfortunately, this increase in data collection has been in tandem with an increase in data misuse and data breach. Regrettably, the majority of data breach and data misuse claims have been unsuccessful in the United States courts for the failure of proof of direct injury to physical or economic interests. The requirement to express data privacy harms from an economic or physical stance negates the fact that not all data harms are physical or economic in nature. The challenge is compounded by the fact that data breach harms and risks do not attach immediately. This research will use a descriptive and normative approach to show that not all data harms can be expressed in economic or physical terms. Expressing privacy harms purely from an economic or physical harm perspective negates the fact that data insecurity may result into harms which run counter the functions of privacy in our lives. The promotion of liberty, selfhood, autonomy, promotion of human social relations and the furtherance of the existence of a free society. There is no economic value that can be placed on these functions of privacy. The proposed approach addresses data harms from a psychological and social perspective.Keywords: data breach and misuse, economic harms, privacy harms, psychological harms
Procedia PDF Downloads 19024531 Machine Learning Analysis of Student Success in Introductory Calculus Based Physics I Course
Authors: Chandra Prayaga, Aaron Wade, Lakshmi Prayaga, Gopi Shankar Mallu
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This paper presents the use of machine learning algorithms to predict the success of students in an introductory physics course. Data having 140 rows pertaining to the performance of two batches of students was used. The lack of sufficient data to train robust machine learning models was compensated for by generating synthetic data similar to the real data. CTGAN and CTGAN with Gaussian Copula (Gaussian) were used to generate synthetic data, with the real data as input. To check the similarity between the real data and each synthetic dataset, pair plots were made. The synthetic data was used to train machine learning models using the PyCaret package. For the CTGAN data, the Ada Boost Classifier (ADA) was found to be the ML model with the best fit, whereas the CTGAN with Gaussian Copula yielded Logistic Regression (LR) as the best model. Both models were then tested for accuracy with the real data. ROC-AUC analysis was performed for all the ten classes of the target variable (Grades A, A-, B+, B, B-, C+, C, C-, D, F). The ADA model with CTGAN data showed a mean AUC score of 0.4377, but the LR model with the Gaussian data showed a mean AUC score of 0.6149. ROC-AUC plots were obtained for each Grade value separately. The LR model with Gaussian data showed consistently better AUC scores compared to the ADA model with CTGAN data, except in two cases of the Grade value, C- and A-.Keywords: machine learning, student success, physics course, grades, synthetic data, CTGAN, gaussian copula CTGAN
Procedia PDF Downloads 3924530 Data Access, AI Intensity, and Scale Advantages
Authors: Chuping Lo
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This paper presents a simple model demonstrating that ceteris paribus countries with lower barriers to accessing global data tend to earn higher incomes than other countries. Therefore, large countries that inherently have greater data resources tend to have higher incomes than smaller countries, such that the former may be more hesitant than the latter to liberalize cross-border data flows to maintain this advantage. Furthermore, countries with higher artificial intelligence (AI) intensity in production technologies tend to benefit more from economies of scale in data aggregation, leading to higher income and more trade as they are better able to utilize global data.Keywords: digital intensity, digital divide, international trade, scale of economics
Procedia PDF Downloads 5724529 Secured Transmission and Reserving Space in Images Before Encryption to Embed Data
Authors: G. R. Navaneesh, E. Nagarajan, C. H. Rajam Raju
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Nowadays the multimedia data are used to store some secure information. All previous methods allocate a space in image for data embedding purpose after encryption. In this paper, we propose a novel method by reserving space in image with a boundary surrounded before encryption with a traditional RDH algorithm, which makes it easy for the data hider to reversibly embed data in the encrypted images. The proposed method can achieve real time performance, that is, data extraction and image recovery are free of any error. A secure transmission process is also discussed in this paper, which improves the efficiency by ten times compared to other processes as discussed.Keywords: secure communication, reserving room before encryption, least significant bits, image encryption, reversible data hiding
Procedia PDF Downloads 40824528 Identity Verification Using k-NN Classifiers and Autistic Genetic Data
Authors: Fuad M. Alkoot
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DNA data have been used in forensics for decades. However, current research looks at using the DNA as a biometric identity verification modality. The goal is to improve the speed of identification. We aim at using gene data that was initially used for autism detection to find if and how accurate is this data for identification applications. Mainly our goal is to find if our data preprocessing technique yields data useful as a biometric identification tool. We experiment with using the nearest neighbor classifier to identify subjects. Results show that optimal classification rate is achieved when the test set is corrupted by normally distributed noise with zero mean and standard deviation of 1. The classification rate is close to optimal at higher noise standard deviation reaching 3. This shows that the data can be used for identity verification with high accuracy using a simple classifier such as the k-nearest neighbor (k-NN).Keywords: biometrics, genetic data, identity verification, k nearest neighbor
Procedia PDF Downloads 24824527 A Review on Intelligent Systems for Geoscience
Authors: R Palson Kennedy, P.Kiran Sai
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This article introduces machine learning (ML) researchers to the hurdles that geoscience problems present, as well as the opportunities for improvement in both ML and geosciences. This article presents a review from the data life cycle perspective to meet that need. Numerous facets of geosciences present unique difficulties for the study of intelligent systems. Geosciences data is notoriously difficult to analyze since it is frequently unpredictable, intermittent, sparse, multi-resolution, and multi-scale. The first half addresses data science’s essential concepts and theoretical underpinnings, while the second section contains key themes and sharing experiences from current publications focused on each stage of the data life cycle. Finally, themes such as open science, smart data, and team science are considered.Keywords: Data science, intelligent system, machine learning, big data, data life cycle, recent development, geo science
Procedia PDF Downloads 12924526 Data Quality as a Pillar of Data-Driven Organizations: Exploring the Benefits of Data Mesh
Authors: Marc Bachelet, Abhijit Kumar Chatterjee, José Manuel Avila
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Data quality is a key component of any data-driven organization. Without data quality, organizations cannot effectively make data-driven decisions, which often leads to poor business performance. Therefore, it is important for an organization to ensure that the data they use is of high quality. This is where the concept of data mesh comes in. Data mesh is an organizational and architectural decentralized approach to data management that can help organizations improve the quality of data. The concept of data mesh was first introduced in 2020. Its purpose is to decentralize data ownership, making it easier for domain experts to manage the data. This can help organizations improve data quality by reducing the reliance on centralized data teams and allowing domain experts to take charge of their data. This paper intends to discuss how a set of elements, including data mesh, are tools capable of increasing data quality. One of the key benefits of data mesh is improved metadata management. In a traditional data architecture, metadata management is typically centralized, which can lead to data silos and poor data quality. With data mesh, metadata is managed in a decentralized manner, ensuring accurate and up-to-date metadata, thereby improving data quality. Another benefit of data mesh is the clarification of roles and responsibilities. In a traditional data architecture, data teams are responsible for managing all aspects of data, which can lead to confusion and ambiguity in responsibilities. With data mesh, domain experts are responsible for managing their own data, which can help provide clarity in roles and responsibilities and improve data quality. Additionally, data mesh can also contribute to a new form of organization that is more agile and adaptable. By decentralizing data ownership, organizations can respond more quickly to changes in their business environment, which in turn can help improve overall performance by allowing better insights into business as an effect of better reports and visualization tools. Monitoring and analytics are also important aspects of data quality. With data mesh, monitoring, and analytics are decentralized, allowing domain experts to monitor and analyze their own data. This will help in identifying and addressing data quality problems in quick time, leading to improved data quality. Data culture is another major aspect of data quality. With data mesh, domain experts are encouraged to take ownership of their data, which can help create a data-driven culture within the organization. This can lead to improved data quality and better business outcomes. Finally, the paper explores the contribution of AI in the coming years. AI can help enhance data quality by automating many data-related tasks, like data cleaning and data validation. By integrating AI into data mesh, organizations can further enhance the quality of their data. The concepts mentioned above are illustrated by AEKIDEN experience feedback. AEKIDEN is an international data-driven consultancy that has successfully implemented a data mesh approach. By sharing their experience, AEKIDEN can help other organizations understand the benefits and challenges of implementing data mesh and improving data quality.Keywords: data culture, data-driven organization, data mesh, data quality for business success
Procedia PDF Downloads 12724525 The Effect of Surface Modified Nano-Hydroxyapatite Incorporation into Polymethylmethacrylate Cement on Biocompatibility and Mechanical Properties
Authors: Yu-Shan Wu, Po-Liang Lai, I-Ming Chu
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Poly(methylmethacrylate)(PMMA) is the most frequently used bone void filler for vertebral augmentation in osteoporotic fracture. PMMA bone cement not only exhibits strong mechanical properties but also can fabricate according to the shape of bone defect. However, the adhesion between the PMMA-based cement and the adjacent bone is usually weak and as PMMA bone cement is inherently bioinert. The combination of bioceramics and polymers as composites may increase cell adhesion and improve biocompatibility. The nano-hydroxyapatite(HAP) not only plays a significant role in maintaining the properties of the natural bone but also offers a favorable environment for osteoconduction, protein adhesion, and osteoblast proliferation. However, defects and cracks can form at the polymer/ceramics interface, resulting in uneven distribution of stress and subsequent inferior mechanical strength. Surface-modified HAP nano-crystals were prepared by chemically grafting poly(ε-caprolactone)(PCL) on surface-modified nano-HAP surface to increase the affinity of polymer/ceramic phases .Thus, incorporation of surface-modified nano-hydroxyapatite (EC-HAP) may not only improve the interfacial adhesion between cement and bone and between nanoparticles and cement, but also increase biocompatibility. In this research, PMMA mixing with 0, 5, 10, 15, 20, 25 and 30 wt% EC-HAP were examined. MC3T3-E1 cells were used for the biological evaluation of the response to the cements in vitro. Morphology was observed using scanning electron microscopy (SEM). Mechanical properties of HAP/PMMA and EC-HAP/PMMA cement were investigated by compression test. Surface wettability of the cements was measured by contact angles.Keywords: bone cement, biocompatibility, nano-hydroxyapatite, polycaprolactone, PMMA, surface grafting
Procedia PDF Downloads 39324524 Big Data Analysis with RHadoop
Authors: Ji Eun Shin, Byung Ho Jung, Dong Hoon Lim
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It is almost impossible to store or analyze big data increasing exponentially with traditional technologies. Hadoop is a new technology to make that possible. R programming language is by far the most popular statistical tool for big data analysis based on distributed processing with Hadoop technology. With RHadoop that integrates R and Hadoop environment, we implemented parallel multiple regression analysis with different sizes of actual data. Experimental results showed our RHadoop system was much faster as the number of data nodes increases. We also compared the performance of our RHadoop with lm function and big lm packages available on big memory. The results showed that our RHadoop was faster than other packages owing to paralleling processing with increasing the number of map tasks as the size of data increases.Keywords: big data, Hadoop, parallel regression analysis, R, RHadoop
Procedia PDF Downloads 42924523 Efficient Positioning of Data Aggregation Point for Wireless Sensor Network
Authors: Sifat Rahman Ahona, Rifat Tasnim, Naima Hassan
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Data aggregation is a helpful technique for reducing the data communication overhead in wireless sensor network. One of the important tasks of data aggregation is positioning of the aggregator points. There are a lot of works done on data aggregation. But, efficient positioning of the aggregators points is not focused so much. In this paper, authors are focusing on the positioning or the placement of the aggregation points in wireless sensor network. Authors proposed an algorithm to select the aggregators positions for a scenario where aggregator nodes are more powerful than sensor nodes.Keywords: aggregation point, data communication, data aggregation, wireless sensor network
Procedia PDF Downloads 14924522 Spatial Econometric Approaches for Count Data: An Overview and New Directions
Authors: Paula Simões, Isabel Natário
Abstract:
This paper reviews a number of theoretical aspects for implementing an explicit spatial perspective in econometrics for modelling non-continuous data, in general, and count data, in particular. It provides an overview of the several spatial econometric approaches that are available to model data that are collected with reference to location in space, from the classical spatial econometrics approaches to the recent developments on spatial econometrics to model count data, in a Bayesian hierarchical setting. Considerable attention is paid to the inferential framework, necessary for structural consistent spatial econometric count models, incorporating spatial lag autocorrelation, to the corresponding estimation and testing procedures for different assumptions, to the constrains and implications embedded in the various specifications in the literature. This review combines insights from the classical spatial econometrics literature as well as from hierarchical modeling and analysis of spatial data, in order to look for new possible directions on the processing of count data, in a spatial hierarchical Bayesian econometric context.Keywords: spatial data analysis, spatial econometrics, Bayesian hierarchical models, count data
Procedia PDF Downloads 586