Search results for: data storage

Authors: Nazli Hardy

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Multidisciplinary application of computer science, interactive database-driven web application, the Internet of Things (IoT) represents a digital ecosystem that has pervasive technological, social, and economic, impact on the human population. It is a long-term technology, and its development is built around the connection of everyday objects, to the Internet. It is estimated that by 2020, with billions of people connected to the Internet, the number of connected devices will exceed 50 billion, and thus IoT represents a paradigm shift in in our current interconnected ecosystem, a communication shift that will unavoidably affect people, businesses, consumers, clients, employees. By nature, in order to provide a cohesive and integrated service, connected devices need to collect, aggregate, store, mine, process personal and personalized data on individuals and corporations in a variety of contexts and environments. A significant factor in this paradigm shift is the necessity for secure and appropriate transmission, processing and storage of the data. Thus, while benefits of the applications appear to be boundless, these same opportunities are bounded by concerns such as trust, privacy, security, loss of control, and related issues. This poster and presentation look at a multi-factor authentication (MFA) mechanisms that need to change from the login-password tuple to an Identity and Access Management (IAM) model, to the more cohesive to Identity Relationship Management (IRM) standard. It also compares and contrasts messaging protocols that are appropriate for the IoT ecosystem.

Keywords: Internet of Things (IoT), authentication, protocols, survey

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Authors: Amy Gooden

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When human genomic data is considered, this is often done through only one dimension of the law, or the interplay between the various dimensions is not considered, thus providing an incomplete picture of the legal framework. This research considers and analyzes the various dimensions in South African law applicable to genomic sequence data – including property rights, personality rights, and intellectual property rights. The effective use of personal genomic sequence data requires the acknowledgement and harmonization of the rights applicable to such data.

Keywords: artificial intelligence, data, law, genomics, rights

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Authors: Abhishek Jain, Kavish Kejriwal, V. Balaji Rao, Abhigna Chavda

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Increasing environmental and social concerns are forcing companies to take a fresh view of the impact of supply chain operations on environment and society when designing a supply chain. A challenging task in today’s food industry is the distribution of high-quality food items throughout the food supply chain. Improper storage and unwanted transportation are the major hurdles in food supply chain and can be tackled by making dynamic storage facility location decisions with the distribution network. Since food supply chain in India is one of the biggest supply chains in the world, the companies should also consider environmental impact caused by the supply chain. This project proposes a multi-objective optimization model by integrating sustainability in decision-making, on distribution in a food supply chain network (SCN). A Multi-Objective Mixed-Integer Linear Programming (MOMILP) model between overall cost and environmental impact caused by the SCN is formulated for the problem. The goal of MOMILP is to determine the pareto solutions for overall cost and environmental impact caused by the supply chain. This is solved by using GAMS with CPLEX as third party solver. The outcomes of the project are pareto solutions for overall cost and environmental impact, facilities to be operated and the amount to be transferred to each warehouse during the time horizon.

Keywords: multi-objective mixed linear programming, food supply chain network, GAMS, multi-product, multi-period, environment

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Authors: X. Gumara Rigol, I. Martínez de Apellaniz Anzuola, A. Garcia Serrano, A. Franzi Cros, O. Vidal Calbet, A. Al Maruf

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Traditional federated architectures for data warehousing work well when corporations have existing regional data warehouses and there is a need to aggregate data at a global level. Schibsted Media Group has been maturing from a decentralised organisation into a more globalised one and needed to build both some of the regional data warehouses for some brands at the same time as the global one. In this paper, we present the architectural alternatives studied and why a custom federated approach was the notable recommendation to go further with the implementation. Although the data warehouses are logically federated, the implementation uses a single database system which presented many advantages like: cost reduction and improved data access to global users allowing consumers of the data to have a common data model for detailed analysis across different geographies and a flexible layer for local specific needs in the same place.

Keywords: data integration, data warehousing, federated architecture, Online Analytical Processing (OLAP)

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Authors: Mazyar Ahrari, Ramin Khajavi, Mehdi Kamali Dolatabadi, Tayebeh Toliyat, Abosaeed Rashidi

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Fabric as the first and most common layer that is in permanent contact with human skin is a very good interface to provide coverage, as well as heat and cold insulation. Phase change materials (PCMs) are organic and inorganic compounds which have the capability of absorbing and releasing noticeable amounts of latent heat during phase transitions between solid and liquid phases at a low temperature range. PCMs come across phase changes (liquid-solid and solid-liquid transitions) during absorbing and releasing thermal heat; so, in order to use them for a long time, they should have been encapsulated in polymeric shells, so-called microcapsules. Microencapsulation and nanoencapsulation methods have been developed in order to reduce the reactivity of a PCM with outside environment, promoting the ease of handling, decreasing the diffusion and evaporation rates. Methods of incorporation of PCMs in textiles such as electrospinning and determining thermal properties had been summarized. Paraffin waxes catch a lot of attention due to their high thermal storage density, repeatability of phase change, thermal stability, small volume change during phase transition, chemical stability, non-toxicity, non-flammability, non-corrosive and low cost and they seem to play a key role in confronting with climate change and global warming. In this article, we aimed to review the researches concentrating on the characteristics of PCMs and new materials and methods of microencapsulation.

Keywords: thermoregulation, microencapsulation, phase change materials, thermal energy storage, nanoencapsulation

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Authors: Sikander Singh Cheema, Jasmeen Kaur

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In this paper, the concept of data mining is summarized and its one of the important process i.e KDD is summarized. The data mining based on Genetic Algorithm is researched in and ways to achieve the data mining Genetic Algorithm are surveyed. This paper also conducts a formal review on the area of data mining tasks and genetic algorithm in various fields.

Keywords: data mining, KDD, genetic algorithm, descriptive mining, predictive mining

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Authors: Maha Alaoui Ismaili, Bouchta Saidi, Mohamed Zahar, Abed Hamama

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Thirty one samples of raw camel milk obtained from the region of Laâyoune (South of Morocco) were examined for their microbial quality and presence of some pathogenic bacteria (Staphylococcus aureus and Salmonella sp.). pH of the samples ranged from 6.31 to 6.64 and their titratable acidity had a mean value of 18.56 °Dornic. Data obtained showed a strong microbial contamination with an average total aerobic flora of 1.76 108 ufc ml-1 and a very high fecal counts: 1.82 107 ; 3.25 106 and 3.75 106 ufc.ml-1 in average for total coliforms, fecal coliforms and enterococci respectively. Yeasts and moulds were also found at average respective levels of 3.13 106 and 1.60 105 ufc.ml-1. Salmonella sp. and S. aureus was detected respectively in 13% and 30% of the milk samples. These results indicate clearly the lack of hygienic conditions of camel milk production and storage in this region. Lactic acid bacteria were found at the following average numbers: 4.25 107 ; 4.45 107 and 3.55 107 ufc.ml-1 for Lactococci, Leuconostocs and Lactobacilli respectively.

Keywords: camel milk, microbial quality, Salmonella, Staphylococcus aureus

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Authors: Seung-Lock Seo

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This work presents a data-mining empirical monitoring scheme for industrial processes with partially unbalanced data. Measurement data of good operations are relatively easy to gather, but in unusual special events or faults it is generally difficult to collect process information or almost impossible to analyze some noisy data of industrial processes. At this time some noise filtering techniques can be used to enhance process monitoring performance in a real-time basis. In addition, pre-processing of raw process data is helpful to eliminate unwanted variation of industrial process data. In this work, the performance of various monitoring schemes was tested and demonstrated for discrete batch process data. It showed that the monitoring performance was improved significantly in terms of monitoring success rate of given process faults.

Keywords: data mining, process data, monitoring, safety, industrial processes

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Authors: Chen-Yu Chen, Ping-Hsueh We, Wei-Mon Yan

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Over the past few centuries, human requirements for energy have been met by burning fossil fuels. However, exploiting this resource has led to global warming and innumerable environmental issues. Thus, finding alternative solutions to the growing demands for energy has recently been driving the development of low-carbon and even zero-carbon energy sources. Wind power and solar energy are good options but they have the problem of unstable power output due to unpredictable weather conditions. To overcome this problem, a reliable and efficient energy storage sub-system is required in future distributed-power systems. Among all kinds of energy storage technologies, the fuel cell system with hydrogen storage is a promising option because it is suitable for large-scale and long-term energy storage. The high-temperature proton exchange membrane fuel cell (HT-PEMFC) with metallic bipolar plates is a promising fuel cell system because an HT-PEMFC can tolerate a higher CO concentration and the utilization of metallic bipolar plates can reduce the cost of the fuel cell stack. However, the operating life of metallic bipolar plates is a critical issue because of the corrosion phenomenon. As a result, in this work, we try to apply different coating layer on the metal surface and to investigate the protection performance of the coating layers. The tested bipolar plates include uncoated SS304 bipolar plates, titanium nitride (TiN) coated SS304 bipolar plates and chromium nitride (CrN) coated SS304 bipolar plates. The results show that the TiN coated SS304 bipolar plate has the lowest contact resistance and through-plane resistance and has the best cell performance and operating life among all tested bipolar plates. The long-term in-situ fuel cell tests show that the HT-PEMFC with TiN coated SS304 bipolar plates has the lowest performance decay rate. The second lowest is CrN coated SS304 bipolar plate. The uncoated SS304 bipolar plate has the worst performance decay rate. The performance decay rates with TiN coated SS304, CrN coated SS304 and uncoated SS304 bipolar plates are 5.324×10⁻³ % h⁻¹, 4.513×10⁻² % h⁻¹ and 7.870×10⁻² % h⁻¹, respectively. In addition, the EIS results indicate that the uncoated SS304 bipolar plate has the highest growth rate of ohmic resistance. However, the ohmic resistance with the TiN coated SS304 bipolar plates only increases slightly with time. The growth rate of ohmic resistances with TiN coated SS304, CrN coated SS304 and SS304 bipolar plates are 2.85×10⁻³ h⁻¹, 3.56×10⁻³ h⁻¹, and 4.33×10⁻³ h⁻¹, respectively. On the other hand, the charge transfer resistances with these three bipolar plates all increase with time, but the growth rates are all similar. In addition, the effective catalyst surface areas with all bipolar plates do not change significantly with time. Thus, it is inferred that the major reason for the performance degradation is the elevated ohmic resistance with time, which is associated with the corrosion and oxidation phenomena on the surface of the stainless steel bipolar plates.

Keywords: coating layer, high-temperature proton exchange membrane fuel cell, metallic bipolar plate, performance degradation

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Authors: Abdulla Rahil, Rupert Gammon, Neil Brown

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The rapid growth of renewable energy sources and their integration into the grid have been motivated by the depletion of fossil fuels and environmental issues. Unfortunately, the grid is unable to cope with the predicted growth of renewable energy which would lead to its instability. To solve this problem, energy storage devices could be used. Electrolytic hydrogen production from an electrolyser is considered a promising option since it is a clean energy source (zero emissions). Choosing flexible operation of an electrolyser (producing hydrogen during the off-peak electricity period and stopping at other times) could bring about many benefits like reducing the cost of hydrogen and helping to balance the electric systems. This paper investigates the price of hydrogen during flexible operation compared with continuous operation, while serving the customer (hydrogen filling station) without interruption. The optimization algorithm is applied to investigate the hydrogen station in both cases (flexible and continuous operation). Three different scenarios are tested to see whether the off-peak electricity price could enhance the reduction of the hydrogen cost. These scenarios are: Standard tariff (1 tier system) during the day (assumed 12 p/kWh) while still satisfying the demand for hydrogen; using off-peak electricity at a lower price (assumed 5 p/kWh) and shutting down the electrolyser at other times; using lower price electricity at off-peak times and high price electricity at other times. This study looks at Derna city, which is located on the coast of the Mediterranean Sea (32° 46′ 0 N, 22° 38′ 0 E) with a high potential for wind resource. Hourly wind speed data which were collected over 24½ years from 1990 to 2014 were in addition to data on hourly radiation and hourly electricity demand collected over a one-year period, together with the petrol station data.

Keywords: hydrogen filling station off-peak electricity, renewable energy, off-peak electricity, electrolytic hydrogen

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Authors: Simon Keller, Rainer Mueller

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Continuous range queries are a common means to handle mobile clients in high-density areas. Most existing approaches focus on settings in which the range queries for location-based services are more or less static, whereas the mobile clients in the ranges move. We focus on a category called dynamic real-time range queries (DRRQ), assuming that both, clients requested by the query and the inquirers, are mobile. In consequence, the query parameters and the query results continuously change. This leads to two requirements: the ability to deal with an arbitrarily high number of mobile nodes (scalability) and the real-time delivery of range query results. In this paper, we present the highly decentralized solution adaptive quad streaming (AQS) for the requirements of DRRQs. AQS approximates the query results in favor of a controlled real-time delivery and guaranteed scalability. While prior works commonly optimize data structures on the involved servers, we use AQS to focus on a highly distributed cell structure without data structures automatically adapting to changing client distributions. Instead of the commonly used request-response approach, we apply a lightweight streaming method in which no bidirectional communication and no storage or maintenance of queries are required at all.

Keywords: approximation of client distributions, continuous spatial range queries, mobile objects, streaming-based decentralization in spatial mobile environments

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Authors: Chaimaa Messaoudi, Rachida Fissoune, Hassan Badir

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Technological advances of computer science and data analysis are helping to provide continuously huge volumes of biological data, which are available on the web. Such advances involve and require powerful techniques for data integration to extract pertinent knowledge and information for a specific question. Biomedical exploration of these big data often requires the use of complex queries across multiple autonomous, heterogeneous and distributed data sources. Semantic integration is an active area of research in several disciplines, such as databases, information-integration, and ontology. We provide a survey of some approaches and techniques for integrating biological data, we focus on those developed in the ontology community.

Keywords: biological ontology, linked data, semantic data integration, semantic web

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Authors: Thrivikraman Aswathi, S. Advaith

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As there can be cases where the use of real data is somehow limited, such as when it is hard to get access to a large volume of real data, we need to go for synthetic data generation. This produces high-quality synthetic data while maintaining the statistical properties of a specific dataset. In the present work, a generative adversarial network (GAN) is trained to produce multivariate time series (MTS) data since the MTS is now being gathered more often in various real-world systems. Furthermore, the GAN-generated MTS data is fed into a transformer-based deep learning architecture that carries out the data categorization into predefined classes. Further, the model is evaluated across various distinct domains by generating corresponding MTS data.

Keywords: GAN, transformer, classification, multivariate time series

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Authors: Lakshmi Prayaga, Chandra Prayaga. Aaron Wade, Gopi Shankar Mallu, Harsha Satya Pola

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Synthetic data generated by Generative Adversarial Networks and Autoencoders is becoming more common to combat the problem of insufficient data for research purposes. However, generating synthetic data is a tedious task requiring extensive mathematical and programming background. Open-source platforms such as the Synthetic Data Vault (SDV) and Mostly AI have offered a platform that is user-friendly and accessible to non-technical professionals to generate synthetic data to augment existing data for further analysis. The SDV also provides for additions to the generic GAN, such as the Gaussian copula. We present the results from two synthetic data sets (CTGAN data and CTGAN with Gaussian Copula) generated by the SDV and report the findings. The results indicate that the ROC and AUC curves for the data generated by adding the layer of Gaussian copula are much higher than the data generated by the CTGAN.

Keywords: synthetic data generation, generative adversarial networks, conditional tabular GAN, Gaussian copula

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Authors: Gadiri Sabiha

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Introduction: Current allergy diagnostic tools using Multiplex technology have made it possible to increase the efficiency of the search for specific IgE. This opportunity is provided by the newly developed “Alex Biochip”, consisting of a panel of 282 allergens in native and molecular form, a CCD inhibitor, and the potential for detecting cross-reactive allergens. We evaluated the performance of this technology in detecting cross-reactivity in previously explored patients. Material/Method: The sera of 39 patients presenting sensitization and polysensitization profiles were explored. The search for specific IgE is carried out by the Alex ® IgE Biochip, and the results are analyzed by nature and by molecular family of allergens using specific software. Results/Discussion: The analysis gave a particular profile of cross-reactivity allergens: 33% for the Ole e1 family, 31% for NPC2, 26% for storage proteins, 20% for Tropomyosin, 10% for LTPs, 10% for Arginine Kinase and 10% for Uteroglobin CCDs were absent in all patients. The “Ole e1” allergen is responsible for a pollen-pollen cross allergy. The storage proteins found and LTP are not species-specific, causing cross-pollen-food allergy. The nDer p2 of the NPC2 family is responsible for cross-reactivity between mite species. Conclusion: The cross-reactivities responsible for mixed syndromes at diagnosis in our patients were dominated by pollen-pollen and pollen-food syndromes. They allow the identification of severity factors linked to the prognosis and the best-adapted immunotherapy.

Keywords: specific IgE, allergy, cross reactivity, molecular allergens

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Authors: Eman W. Boghdady, A. R. Shehata, M. A. Azem

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Database (DB) security demands permitting authorized users and prohibiting non-authorized users and intruders actions on the DB and the objects inside it. Organizations that are running successfully demand the confidentiality of their DBs. They do not allow the unauthorized access to their data/information. They also demand the assurance that their data is protected against any malicious or accidental modification. DB protection and confidentiality are the security concerns. There are four types of controls to obtain the DB protection, those include: access control, information flow control, inference control, and cryptographic. The cryptographic control is considered as the backbone for DB security, it secures the DB by encryption during storage and communications. Current cryptographic techniques are classified into two types: traditional classical cryptography using standard algorithms (DES, AES, IDEA, etc.) and chaos cryptography using continuous (Chau, Rossler, Lorenz, etc.) or discreet (Logistics, Henon, etc.) algorithms. The important characteristics of chaos are its extreme sensitivity to initial conditions of the system. In this paper, DB-security systems based on chaotic algorithms are described. The Pseudo Random Numbers Generators (PRNGs) from the different chaotic algorithms are implemented using Matlab and their statistical properties are evaluated using NIST and other statistical test-suits. Then, these algorithms are used to secure conventional DB (plaintext), where the statistical properties of the ciphertext are also tested. To increase the complexity of the PRNGs and to let pass all the NIST statistical tests, we propose two hybrid PRNGs: one based on two chaotic Logistic maps and another based on two chaotic Henon maps, where each chaotic algorithm is running side-by-side and starting from random independent initial conditions and parameters (encryption keys). The resulted hybrid PRNGs passed the NIST statistical test suit.

Keywords: algorithms and data structure, DB security, encryption, chaotic algorithms, Matlab, NIST

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Authors: T. Topór, A. Derkowski, P. Ziemiański

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Formation of organic matter (OM)-hosted nanopores upon thermal maturation are one of the key factor controlling methane storage potential in unconventional shale-gas reservoirs. In this study, the subcritical CO₂ and N₂ gas adsorption measurements combined with scanning electron microscopy and supercritical methane adsorption have been used to characterize pore system and methane storage potential in black shales from the Baltic Basin (Poland). The samples were collected from a virtually equivalent Llandovery strata across the basin and represent a complete digenetic sequence, from thermally immature to overmature. The results demonstrate that the thermal maturation is a dominant mechanism controlling the formation of OM micro- and mesopores in the Baltic Basin shales. The formation of micro- and mesopores occurs in the oil window (vitrinite reflectance; leavedVR; ~0.5-0.9%) as a result of oil expulsion from kerogenleft OM highly porous. The generated hydrocarbons then turn into solid bitumen causing pore blocking and substantial decrease in micro- and mesopore volume in late-mature shales (VR ~0.9-1.2%). Both micro- and mesopores are regenerated in a middle of the catagenesis range (VR 1.4-1.9%) due to secondary cracking of OM and gas formation. The micropore volume in investigated shales is almost exclusively controlled by the OM content. The contribution of clay minerals to micropore volume is insignificant and masked by a strong contribution from OM. Methane adsorption capacity in the Baltic Basin shales is predominantly controlled by microporous OM with pores < 1.5 nm. The mesopore volume (2-50 nm) and mesopore surface area have no effect on methane sorption behavior. The adsorbed methane density equivalent, calculated as absolute methane adsorption divided by micropore volume, reviled a decrease of the methane loading potential in micropores with increasing maturity. The highest methane loading potential in micropores is observed for OM before metagenesis (VR < 2%), where the adsorbed methane density equivalent is greater than the density of liquid methane. This implies that, in addition to physical adsorption, absorption of methane in OM may occur before metagenesis. After OM content reduction using NaOCl solution methane adoption capacity substantially decreases, suggesting significantly greater adsorption potential for OM microstructure than for the clay minerals matrix.

Keywords: maturation, methane sorption, organic matter, porosity, shales

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Authors: Feng Tao, Ma Jing, Guo Xian, Wang Jing

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Named Data Networking (NDN) replaces IP address of traditional network with data name, and adopts dynamic cache mechanism. In the existing mechanism, however, only one-to-one search can be achieved because every data has a unique name corresponding to it. There is a certain mapping relationship between data content and data name, so if the data name is intercepted by an adversary, the privacy of the data content and user’s interest can hardly be guaranteed. In order to solve this problem, this paper proposes a one-to-many fuzzy search scheme based on order-preserving encryption to reduce the query overhead by optimizing the caching strategy. In this scheme, we use hash value to ensure the user’s query safe from each node in the process of search, so does the privacy of the requiring data content.

Keywords: NDN, order-preserving encryption, fuzzy search, privacy

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Authors: N. S. Risqina, G. Edijanti, P. S. Nurita, L. Endang, R. A. Siti, R. Tri

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Skin is an important and vital organs and also as a mirror of health and life. Facial skin care is one of the main emphasis to get the beautiful, healthy, and fresh skin. Potentially antioxidant phenolic compounds shows, antimutagen, antitumor, anti-inflammatory, and anti-cancer. Flavonoids are a group of polyphenolic compounds that have the nature of free radicals, inhibiting the oxidative and hydrolytic enzymes as well as anti-inflammatory. Bitter melon (Momordica charantia Linn) is a plant that contains flavonoids, and phenolic antioxidant activity. Bitter melon has strong antioxidant activity that can counteract the free radicals.These compounds can prevent free radicals that cause premature aging. Gel masks including depth cleansing is the cosmetics which work in depth and could raise the dead skin cells. Measurement of antioxidant activity of the extract and gel mask is done by using the immersion method of DPPH. IC50 value of ethanol extract of bitter melon fruit of 287.932 ppm. The preparation of gel mask bitter melon fruit extract, necessary to test the effectiveness of antioxidants using DPPH method is done by measuring the inhibition of DPPH and using UV spectrophotometer at the wavelength of maximum DPPH solution. Tests conducted at the beginning and end of the evaluation (day 0 and day 28). The purpose of this study is to determine the antioxidant activity of the bitter melon's extract and to determine the antioxidant activity of ethanol extract gel mask pare in varying concentrations, ie 1xIC100 (0.295%), 2xIC100 (0.590%) and 4xIC100 (1.180%). Evaluation of physical properties of the preparation on (Day-0,7,14,21, and 28) and evaluation of antioxidant activity (day 0 and 28). Data were analyzed using One Way ANOVA to determine differences in the physical properties of each formula. The statistical results showed that differences in the formula and storage time affects the adhesion, dispersive power, dry time and pH it is shown on a significant value of p <0.05, but longer storage does not affect the pH because the significance value p> 0,05. The antioxidant test showed that there are differences in antioxidant activity in all formulas. Measurement of antioxidant activity of bitter melon fruit extract gel mask on day 0 with a concentration of 0.295%, 0.590%, and 1.180%, respectively, are 124,209.277 ppm, ppm 83819.223 and 47323.592 ppm, whereas day 28 consecutive 130 411, 495 ppm, and 53239.806 95561.645 ppm ppm. The Conclusions drawn that there are antioxidant activity in preparation gel mask of bitter melon fruit extract. The antioxidant activity of bitter melon fruit extract gel mask on the day 0 with a concentration of 0.295%, 0.590%, and 1.180%, respectively, are 124,209.277 ppm, ppm 83819.223 and 47323.592 ppm, whereas on day 28 of antioxidant activity gel mask bitter melon fruit extract with a concentration of 0.295%, 0.590%, and 1.180% in succession, namely: 130,411.495 ppm, ppm 95561.645 and 53239.806 ppm.

Keywords: antioxdant, bitter melon, gel mask, IC50

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Authors: Chellammal Surianarayanan

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Healthcare industry is generating large amounts of data driven by various needs such as record keeping, physician’s prescription, medical imaging, sensor data, Electronic Patient Record(EPR), laboratory, pharmacy, etc. Healthcare data is so big and complex that they cannot be managed by conventional hardware and software. The complexity of healthcare big data arises from large volume of data, the velocity with which the data is accumulated and different varieties such as structured, semi-structured and unstructured nature of data. Despite the complexity of big data, if the trends and patterns that exist within the big data are uncovered and analyzed, higher quality healthcare at lower cost can be provided. Hadoop is an open source software framework for distributed processing of large data sets across clusters of commodity hardware using a simple programming model. The core components of Hadoop include Hadoop Distributed File System which offers way to store large amount of data across multiple machines and MapReduce which offers way to process large data sets with a parallel, distributed algorithm on a cluster. Hadoop ecosystem also includes various other tools such as Hive (a SQL-like query language), Pig (a higher level query language for MapReduce), Hbase(a columnar data store), etc. In this paper an analysis has been done as how healthcare big data can be processed and analyzed using Hadoop ecosystem.

Keywords: big data analytics, Hadoop, healthcare data, towards quality healthcare

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Authors: Zakieh Piri, Shahla Damanabi, Peyman Rezaii Hachesoo

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Introduction: Healthcare organizations like other organizations suffer from a number of disorders such as Business Sponsor Disorder, Business Acceptance Disorder, Cultural/Political Disorder, Data Disorder, etc. As quality in healthcare care mostly depends on the quality of data, we aimed to identify data disorders and its symptoms in two teaching hospitals. Methods: Using a self-constructed questionnaire, we asked 20 questions in related to quality and usability of patient data stored in patient records. Research population consisted of 150 managers, physicians, nurses, medical record staff who were working at the time of study. We also asked their views about the symptoms and treatments for any data disorders they mentioned in the questionnaire. Using qualitative methods we analyzed the answers. Results: After classifying the answers, we found six main data disorders: incomplete data, missed data, late data, blurred data, manipulated data, illegible data. The majority of participants believed in their important roles in treatment of data disorders while others believed in health system problems. Discussion: As clinicians have important roles in producing of data, they can easily identify symptoms and disorders of patient data. Health information managers can also play important roles in early detection of data disorders by proactively monitoring and periodic check-ups of data.

Keywords: data disorders, quality, healthcare, treatment

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Authors: Byron Joseph A. Hallar, Annjeannette Alain D. Galang, Maria Visitacion N. Gumabay

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One of the unique challenges provided by the twenty-first century to Philippine higher education is the utilization of Big Data. The higher education system in the Philippines is generating burgeoning amounts of data that contains relevant data that can be used to generate the information and knowledge needed for accurate data-driven decision making. This study examines the status, relevance and future of Big Data and Analytics in Philippine higher education. The insights gained from the study may be relevant to other developing nations similarly situated as the Philippines.

Keywords: big data, data analytics, higher education, republic of the philippines, assessment

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Authors: G. Julius P. Roy, Prateek Saxena, Sanjeev Singh

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Power distribution utilities two decades ago would collect data from its customers not later than a period of at least one month. The origin of SmartGrid and AMI has subsequently increased the sampling frequency leading to 1000 to 10000 fold increase in data quantity. This increase is notable and this steered to coin the tern Big Data in utilities. Power distribution industry is one of the largest to handle huge and complex data for keeping history and also to turn the data in to significance. Majority of the utilities around the globe are adopting SmartGrid technologies as a mass implementation and are primarily focusing on strategic interdependence and synergies of the big data coming from new information sources like AMI and intelligent SCADA, there is a rising need for new models of data management and resurrected focus on analytics to dissect data into descriptive, predictive and dictatorial subsets. The goal of this paper is to is to bring load disaggregation into smart energy toolkit for commercial usage.

Keywords: data management, analytics, energy data analytics, smart grid, smart utilities

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Authors: P. Srinivasa Rao, K. Venkatesh Sharma, G. Sadhya Devi, V. Nagesh

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Privacy Preserving Data Publication is the main concern in present days because the data being published through the internet has been increasing day by day. This huge amount of data was named as Big Data by its size. This project deals the privacy preservation in the context of Big Data using a data warehousing solution called hive. We implemented Nearest Similarity Based Clustering (NSB) with Bottom-up generalization to achieve (v,l)-anonymity. (v,l)-Anonymity deals with the sensitivity vulnerabilities and ensures the individual privacy. We also calculate the sensitivity levels by simple comparison method using the index values, by classifying the different levels of sensitivity. The experiments were carried out on the hive environment to verify the efficiency of algorithms with Big Data. This framework also supports the execution of existing algorithms without any changes. The model in the paper outperforms than existing models.

Keywords: sensitivity, sensitive level, clustering, Privacy Preserving Data Publication (PPDP), bottom-up generalization, Big Data

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Authors: Walaa M.Abd-Elhafiez, Wajeb Gharibi

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In this paper a novel color image compression technique for efficient storage and delivery of data is proposed. The proposed compression technique started by RGB to YCbCr color transformation process. Secondly, the canny edge detection method is used to classify the blocks into edge and non-edge blocks. Each color component Y, Cb, and Cr compressed by discrete cosine transform (DCT) process, quantizing and coding step by step using adaptive arithmetic coding. Our technique is concerned with the compression ratio, bits per pixel and peak signal to noise ratio, and produce better results than JPEG and more recent published schemes (like, CBDCT-CABS and MHC). The provided experimental results illustrate the proposed technique which is efficient and feasible in terms of compression ratio, bits per pixel and peak signal to noise ratio.

Keywords: image compression, color image, q-coder, quantization, edge-detection

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Authors: Felib Ayman Shawky Salem

Abstract:

Nowadays crypto currency has become a global phenomenon known to most people. People using this alternative digital money to do a transaction in many ways (e.g. Used for online shopping, wealth management, and fundraising). However, this digital asset also widely used in criminal activities since its use decentralized control as opposed to centralized electronic money and central banking systems and this makes a user, who used this currency invisible. The high-value exchange of these digital currencies also has been a target to criminal activities. The crypto currency crimes have become a challenge for the law enforcement to analyze and to proof the evidence as criminal devices. In this paper, our focus is more on bitcoin crypto currency and the possible artifacts that can be obtained from the different type of digital wallet, which is software and browser-based application. The process memory and physical hard disk are examined with the aims of identifying and recovering potential digital evidence. The stage of data acquisition divided by three states which are the initial creation of the wallet, transaction that consists transfer and receiving a coin and the last state is after the wallet is being deleted. Findings from this study suggest that both data from software and browser type of wallet process memory is a valuable source of evidence, and many of the artifacts found in process memory are also available from the application and wallet files on the client computer storage.

Keywords: cryptocurrency, bitcoin, payment methods, blockchain, appropriation, online retailers, TOE framework, disappropriation, non-appropriationBitCoin, financial protection, crypto currency, money laundering cryptocurrency, digital wallet, digital forensics

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Authors: Behnam Tavakkol

Abstract:

Uncertain data mining algorithms use different ways to consider uncertainty in data such as by representing a data object as a sample of points or a probability distribution. Fuzzy methods have long been used for clustering traditional (certain) data objects. They are used to produce non-crisp cluster labels. For uncertain data, however, besides some uncertain fuzzy k-medoids algorithms, not many other fuzzy clustering methods have been developed. In this work, we develop a fuzzy kernel k-medoids algorithm for clustering uncertain data objects. The developed fuzzy kernel k-medoids algorithm is superior to existing fuzzy k-medoids algorithms in clustering data sets with non-linearly separable clusters.

Keywords: clustering algorithm, fuzzy methods, kernel k-medoids, uncertain data

Procedia PDF Downloads 199

Authors: Gul Rahim Rahimi

Abstract:

The IoT-based soil moisture monitoring system for indoor plants is designed to address the challenges of maintaining optimal moisture levels in soil for plant growth and health. The system utilizes sensor technology to collect real-time data on soil moisture levels, which is then processed and analyzed using machine learning algorithms. This allows for accurate and timely monitoring of soil moisture levels, ensuring plants receive the appropriate amount of water to thrive. The main objectives of the system are twofold: to keep plants fresh and healthy by preventing water deficiency and to provide users with comprehensive insights into the water content of the soil on a daily and hourly basis. By monitoring soil moisture levels, users can identify patterns and trends in water consumption, allowing for more informed decision-making regarding watering schedules and plant care. The scope of the system extends to the agriculture industry, where it can be utilized to minimize the efforts required by farmers to monitor soil moisture levels manually. By automating the process of soil moisture monitoring, farmers can optimize water usage, improve crop yields, and reduce the risk of plant diseases associated with over or under-watering. Key technologies employed in the system include the Capacitive Soil Moisture Sensor V1.2 for accurate soil moisture measurement, the Node MCU ESP8266-12E Board for data transmission and communication, and the Arduino framework for programming and development. Additionally, machine learning algorithms are utilized to analyze the collected data and provide actionable insights. Cloud storage is utilized to store and manage the data collected from multiple sensors, allowing for easy access and retrieval of information. Overall, the IoT-based soil moisture monitoring system offers a scalable and efficient solution for indoor plant care, with potential applications in agriculture and beyond. By harnessing the power of IoT and machine learning, the system empowers users to make informed decisions about plant watering, leading to healthier and more vibrant indoor environments.

Keywords: IoT-based, soil moisture monitoring, indoor plants, water management

Procedia PDF Downloads 35

Authors: Adira Nofeadri Ryofi, Alvin Andrianus, Anna Khairunnisa, Anugrah Cahyo Widodo, Arbhyando Tri Putrananda, Arsy Imanda N. Raswati, Gita Rahmaningsih, Ina Agustina

Abstract:

The consumption level of fresh bananas from 2005 until 2010, increased from 8.2 to 10 kg/capita/year. The commercial scale of banana generally harvested when it still green to make the banana avoid physical damage, chemical, and disease after harvest and ripe fruit. That first metabolism activity can be used as a synthesis reaction. Ripening fruit was influenced by ethylene hormone that synthesized in fruit which is experiencing ripe and including hormone in the ripening fruit process in klimaterik phase. This ethylene hormone is affected by the respiration level that would speed up the restructuring of carbohydrates inside the fruit, so the weighting of fruit will be decreased. Compared to other klimaterik fruit, banana is a fruit that has a medium ethylene production rate and the rate of respiration is low. The salicylic acid can regulate the result number of the growth process or the development of fruits and plants. Salicylic acid serves to hinder biosynthesis ethylene and delay senses. The research aims to understand the influence of salicylic acid concentration that derived from the waste of avocado skin in inhibition process to ethylene production that the maturation can be controlled, so it can keep the quality of banana for storage. It is also to increase the potential value of the waste of avocado skin that were still used in industrial cosmetics.

Keywords: ethylene hormone, extract avocado skin, inhibitor, salicylic acid

Procedia PDF Downloads 222

Authors: Eugenia Jilinguirian

Abstract:

In the healthcare industry, data mining is essential since it transforms the field by collecting useful data from large datasets. Data mining is the process of applying advanced analytical methods to large patient records and medical histories in order to identify patterns, correlations, and trends. Healthcare professionals can improve diagnosis accuracy, uncover hidden linkages, and predict disease outcomes by carefully examining these statistics. Additionally, data mining supports personalized medicine by personalizing treatment according to the unique attributes of each patient. This proactive strategy helps allocate resources more efficiently, enhances patient care, and streamlines operations. However, to effectively apply data mining, however, and ensure the use of private healthcare information, issues like data privacy and security must be carefully considered. Data mining continues to be vital for searching for more effective, efficient, and individualized healthcare solutions as technology evolves.

Keywords: data mining, healthcare, big data, individualised healthcare, healthcare solutions, database

Procedia PDF Downloads 53