Search results for: data standardization

Authors: Mohammad Arabi

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The development of advanced technologies in the field of signal processing and vibration analysis has enabled more accurate analysis and fault detection in electrical systems. This research investigates the application of temporal and frequency features in detecting faults in electric motor bearings, aiming to enhance fault detection accuracy and prevent unexpected failures. The use of methods such as deep learning algorithms and neural networks in this process can yield better results. The main objective of this research is to evaluate the efficiency and accuracy of methods based on temporal and frequency features in identifying faults in electric motor bearings to prevent sudden breakdowns and operational issues. Additionally, the feasibility of using techniques such as machine learning and optimization algorithms to improve the fault detection process is also considered. This research employed an experimental method and random sampling. Vibration signals were collected from electric motors under normal and faulty conditions. After standardizing the data, temporal and frequency features were extracted. These features were then analyzed using statistical methods such as analysis of variance (ANOVA) and t-tests, as well as machine learning algorithms like artificial neural networks and support vector machines (SVM). The results showed that using temporal and frequency features significantly improves the accuracy of fault detection in electric motor bearings. ANOVA indicated significant differences between normal and faulty signals. Additionally, t-tests confirmed statistically significant differences between the features extracted from normal and faulty signals. Machine learning algorithms such as neural networks and SVM also significantly increased detection accuracy, demonstrating high effectiveness in timely and accurate fault detection. This study demonstrates that using temporal and frequency features combined with machine learning algorithms can serve as an effective tool for detecting faults in electric motor bearings. This approach not only enhances fault detection accuracy but also simplifies and streamlines the detection process. However, challenges such as data standardization and the cost of implementing advanced monitoring systems must also be considered. Utilizing temporal and frequency features in fault detection of electric motor bearings, along with advanced machine learning methods, offers an effective solution for preventing failures and ensuring the operational health of electric motors. Given the promising results of this research, it is recommended that this technology be more widely adopted in industrial maintenance processes.

Keywords: electric motor, fault detection, frequency features, temporal features

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Authors: Ali Youniss Aidbaiss, G. Unnikrishnan, Anoob Hakim

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Scope changes, scope creeps, cost and time overruns have become common in projects in the oil and gas sector. Even in repetitive projects, failure to implement lessons learnt and correct past mistakes have resulted in various setbacks. This paper describes the concept of reusing successfully implemented design packages as templates for repetitive projects, and thereby lowering the instances of project failures. Units or systems successfully installed in projects can be identified and taken up for preparing template design packages. Standardization of units and systems helps to develop templates from successful designs which can be repeatedly used with confidence. These packages can be used with minimum modifications for developing FEED packages faster, saving cost and other valuable resources. Lessons learnt from the completed project incorporated in the templates avoid repeating past mistakes during detailed design, procurement and execution. With template packages, consistent quality can be maintained for similar projects, avoiding scope creep and scope changes which will ultimately result in cost and time savings.

Keywords: engineering work package, repetitive construction, template design package, time saving in projects

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Authors: Ali Akbar Zeinali

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Lack of appropriate equivalences for the terms or technical words is the result of ineffective translation guidelines adopted in the translation processes. The increasing number of foreign words and specific terms incorporated into the native language are due to the ongoing development of technology and science. Many problems appear in medical translation when the Persian translators try to employ non-Persian or imported words in medical texts, in which multiple equivalents may be created for one particular word based on the individual preferences of authors and translators in the target language due to lack of standardization. The study attempted to discuss the findings based on the compatibility of the international naming criteria, considering the translation procedures. About 67% of 339 equivalents under this study were grouped as incompatible words while about 33% of them were compatible terms. The similarities and differences were investigated and discussed according to the compatibility status of the equivalents with Sager’s criteria. Such equivalents have been classified into several groups through bi-dimensional descriptions that were different features of translation procedures related to the international naming criteria. In review of the frequency distribution of compatibilities, the equivalents were divided into two categories of compatibles and incompatibles, indicating the effectiveness of the applied translation procedures.

Keywords: linguistics, medical translation, naming, terminology

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Authors: M. Naidoo

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Artificial intelligence often requires large amounts of good quality data. Within important fields, such as healthcare, the training of AI systems predominately relies on health and personal data; however, the usage of this data is complicated by various layers of law and ethics that seek to protect individuals’ privacy rights. This research seeks to establish the challenges AI and data sciences pose to (i) informational rights, (ii) privacy rights, and (iii) data protection. To solve some of the issues presented, various methods are suggested, such as embedding values in technological development, proper balancing of rights and interests, and others.

Keywords: artificial intelligence, data science, law, policy

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Authors: Jing Zhao, Yoshiharu Ishikawa, Chuan Xiao, Kento Sugiura

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In order to analyze large-scale scientific data, research on data exploration and visualization has gained popularity. In this paper, we focus on the exploration and visualization of scientific simulation data, and define a spatial V-Optimal histogram for data summarization. We propose histogram construction algorithms based on a general binary hierarchical partitioning as well as a more specific one, the l-grid partitioning. For effective data summarization and efficient data visualization in scientific data analysis, we propose an optimal algorithm as well as a heuristic algorithm for histogram construction. To verify the effectiveness and efficiency of the proposed methods, we conduct experiments on the massive evacuation simulation data.

Keywords: simulation data, data summarization, spatial histograms, exploration, visualization

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Authors: Erin C. Rodenburg, Jennifer McWhirter, Andrew Papadopoulos

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Support workers for adults with developmental disabilities promote the care and wellbeing of a historically underserved population. Poor employment training and low work satisfaction for these disability support workers are linked to low productivity, poor quality of care, turnover, and intention to leave employment. Therefore, to improve the lives of those within disability support homes, both client and caregiver, it is vital to determine where improvements to training and support for those providing direct care can be made. The current study aims to explore disability support worker’s perceptions of the training received in their employment at the residential homes, how it prepared them for their role, and where there is room for improvement with the aim of developing recommendations for an improved training experience. Responses were collected from 85 disability support workers across 40 Ontario group homes. Findings suggest most disability support workers within the 40 support homes feel adequately trained in their responsibilities of employment. For those who did not feel adequately trained, the main issues expressed were a lack of standardization in training, a need for more continuous training, and a move away from trial and error in performing tasks to support clients with developmental disabilities.

Keywords: developmental disabilities, disability workers, support homes, training

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Authors: Raed El-Khalil, Hussein Zeaiter

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Managing and improving efficiency in the current highly competitive global automotive industry demands that companies adopt leaner and more flexible systems. During the past 20 years the domestic automotive industry in North America has been focusing on establishing new management strategies in order to meet market demands. 98The lean management process also known as Toyota Manufacturing Process (TPS) or lean manufacturing encompasses tools and techniques that were established in order to provide the best quality product with the fastest lead time at the lowest cost. The following paper presents a study that focused on improving labor efficiency at one of the Big Three (Ford, GM, Chrysler LLC) domestic automotive facility in North America. The objective of the study was to utilize several lean management tools in order to optimize the efficiency and utilization levels at the “Pre-Marriage” chassis area in a truck manufacturing and assembly facility. Utilizing three different lean tools (i.e. Standardization of work, 7 Wastes, and 5S) this research was able to improve efficiency by 51%, utilization by 246%, and reduce operations by 14%. The return on investment calculated based on the improvements made was 284%.

Keywords: lean manufacturing, standardized work, operation efficiency, utilization

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Authors: Vahid Bairami Rad

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Extracting knowledge from spatial data like GIS data is important to reduce the data and extract information. Therefore, the development of new techniques and tools that support the human in transforming data into useful knowledge has been the focus of the relatively new and interdisciplinary research area ‘knowledge discovery in databases’. Thus, we introduce a set of database primitives or basic operations for spatial data mining which are sufficient to express most of the spatial data mining algorithms from the literature. This approach has several advantages. Similar to the relational standard language SQL, the use of standard primitives will speed-up the development of new data mining algorithms and will also make them more portable. We introduced a database-oriented framework for spatial data mining which is based on the concepts of neighborhood graphs and paths. A small set of basic operations on these graphs and paths were defined as database primitives for spatial data mining. Furthermore, techniques to efficiently support the database primitives by a commercial DBMS were presented.

Keywords: spatial data base, knowledge discovery database, data mining, spatial relationship, predictive data mining

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Authors: B. Suraj Aravind, M. H. M. Krishna Prasad

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There exist emerging applications of data streams that require association rule mining, such as network traffic monitoring, web click streams analysis, sensor data, data from satellites etc. Data streams typically arrive continuously in high speed with huge amount and changing data distribution. This raises new issues that need to be considered when developing association rule mining techniques for stream data. This paper proposes to introduce an improved data stream association rule mining algorithm by eliminating the limitation of resources. For this, the concept of cloud computing is used. Inclusion of this may lead to additional unknown problems which needs further research.

Keywords: data stream, association rule mining, cloud computing, frequent itemsets

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Authors: P. T. Collett, M. Kershaw

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Introduction: The discussion regarding which method of anesthesia provides better results for lower limb arthroplasty is a continuing debate. Multiple meta-analysis has been performed with no clear consensus. The current recommendation is to use neuraxial anesthesia for lower limb arthroplasty; however, the evidence to support this decision is weak. The Enhanced Recovery After Surgery (ERAS) society has recommended, either technique can be used as part of a multimodal anesthetic regimen. A local study was performed to see if the current anesthetic practice correlates with the current recommendations and to evaluate the efficacy of the different techniques utilized. Method: 90 patients who underwent total hip or total knee replacements at Nevill Hall Hospital between February 2019 to July 2019 were reviewed. Data collected included the anesthetic technique, day one opiate use, pain score, and length of stay. The data was collected from anesthetic charts, and the pain team follows up forms. Analysis: The average of patients undergoing lower limb arthroplasty was 70. Of those 83% (n=75) received a spinal anaesthetic and 17% (n=15) received a general anaesthetic. For patients undergoing knee replacement under general anesthetic the average day, one pain score was 2.29 and 1.94 if a spinal anesthetic was performed. For hip replacements, the scores were 1.87 and 1.8, respectively. There was no statistical significance between these scores. Day 1 opiate usage was significantly higher in knee replacement patients who were given a general anesthetic (45.7mg IV morphine equivalent) vs. those who were operated on under spinal anesthetic (19.7mg). This difference was not noticeable in hip replacement patients. There was no significant difference in length of stay between the two anesthetic techniques. Discussion: There was no significant difference in the day one pain score between the patients who received a general or spinal anesthetic for either knee or hip replacements. The higher pain scores in the knee replacement group overall are consistent with this being a more painful procedure. This is a small patient population, which means any difference between the two groups is unlikely to be representative of a larger population. The pain scale has 4 points, which means it is difficult to identify a significant difference between pain scores. Conclusion: There is currently little standardization between the different anesthetic approaches utilized in Nevill Hall Hospital. This is likely due to the lack of adherence to a standardized anesthetic regimen. In accordance with ERAS recommends a standard anesthetic protocol is a core component. The results of this study and the guidance from the ERAS society will support the implementation of a new health board wide ERAS protocol.

Keywords: anaesthesia, orthopaedics, intensive care, patient centered decision making, treatment escalation

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Authors: Tosin Ige

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Ethics must be a condition of the world, like logic. (Ludwig Wittgenstein, 1889-1951). As important as data mining is, it possess a significant threat to ethics, privacy, and legality, since data mining makes it difficult for an individual or consumer (in the case of a company) to control the accessibility and usage of his data. This research focuses on Current issues and the latest research and development on Privacy preserving data mining methods as at year 2022. It also discusses some advances in those techniques while at the same time highlighting and providing a new technique as a solution to an existing technique of privacy preserving data mining methods. This paper also bridges the wide gap between Data mining and the Web Application Programing Interface (web API), where research is urgently needed for an added layer of security in data mining while at the same time introducing a seamless and more efficient way of data mining.

Keywords: data, privacy, data mining, association rule, privacy preserving, mining technique

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Authors: A. Alexandru, C. A. Alexandru, D. Coardos, E. Tudora

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Big Data (BD) is associated with a new generation of technologies and architectures which can harness the value of extremely large volumes of very varied data through real time processing and analysis. It involves changes in (1) data types, (2) accumulation speed, and (3) data volume. This paper presents the main concepts related to the BD paradigm, and introduces architectures and technologies for BD and BD sets. The integration of BD with the Hadoop Framework is also underlined. BD has attracted a lot of attention in the public sector due to the newly emerging technologies that allow the availability of network access. The volume of different types of data has exponentially increased. Some applications of BD in the public sector in Romania are briefly presented.

Keywords: big data, big data analytics, Hadoop, cloud

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Authors: Manish Chand, Subhrojit Bagchi, R. Kumar

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A new dry tube (DT) has been installed in the tank of KAMINI research reactor, Kalpakkam India. This tube will be used for neutron activation analysis of small to large samples and testing of neutron detectors. DT tube is 375 cm height and 7.5 cm in diameter, located 35 cm away from the core centre. The experimental thermal flux at various axial positions inside the tube has been measured by irradiating the flux monitor (¹⁹⁷Au) at 20kW reactor power. The measured activity of ¹⁹⁸Au and the thermal cross section of ¹⁹⁷Au (n,γ) ¹⁹⁸Au reaction were used for experimental thermal flux measurement. The flux inside the tube varies from 10⁹ to 10¹⁰ and maximum flux was (1.02 ± 0.023) x10¹⁰ n cm⁻²s⁻¹ at 36 cm from the bottom of the tube. The Au and Zr foils without and with cadmium cover of 1-mm thickness were irradiated at the maximum flux position in the DT to find out the irradiation specific input parameters like sub-cadmium to epithermal neutron flux ratio (f) and the epithermal neutron flux shape factor (α). The f value was 143 ± 5, indicates about 99.3% thermal neutron component and α value was -0.2886 ± 0.0125, indicates hard epithermal neutron spectrum due to insufficient moderation. The measured flux profile has been validated using theoretical model of KAMINI reactor through Monte Carlo N-Particle Code (MCNP). In MCNP, the complex geometry of the entire reactor is modelled in 3D, ensuring minimum approximations for all the components. Continuous energy cross-section data from ENDF-B/VII.1 as well as S (α, β) thermal neutron scattering functions are considered. The neutron flux has been estimated at the corresponding axial locations of the DT using mesh tally. The thermal flux obtained from the experiment shows good agreement with the theoretically predicted values by MCNP, it was within ± 10%. It can be concluded that this MCNP model can be utilized for calculating other important parameters like neutron spectra, dose rate, etc. and multi elemental analysis can be carried out by irradiating the sample at maximum flux position using measured f and α parameters by k₀-NAA standardization.

Keywords: neutron flux, neutron activation analysis, neutron flux shape factor, MCNP, Monte Carlo N-Particle Code

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Authors: Aïcha Ben Salem, Faouzi Boufares, Sebastiao Correia

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The subject covered in this paper aims at assisting the user in its quality approach. The goal is to better extract, mix, interpret and reuse data. It deals with the semantic schema recognition of a data source. This enables the extraction of data semantics from all the available information, inculding the data and the metadata. Firstly, it consists of categorizing the data by assigning it to a category and possibly a sub-category, and secondly, of establishing relations between columns and possibly discovering the semantics of the manipulated data source. These links detected between columns offer a better understanding of the source and the alternatives for correcting data. This approach allows automatic detection of a large number of syntactic and semantic anomalies.

Keywords: schema recognition, semantic data profiling, meta-categorisation, semantic dependencies inter columns

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Authors: Winfred Okoe Addy, Jean Jacques Dominique Beraud

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Access control systems (ACs) are some of the most important components in safety areas. Inaccuracies of regulatory frameworks make personal policies and remedies more appropriate than standard models or protocols. This problem is exacerbated by the increasing complexity of software, such as integrated Big Data (BD) software for controlling large volumes of encrypted data and resources embedded in a dedicated BD production system. This paper proposes a general access control strategy system for the diffusion of Big Data domains since it is crucial to secure the data provided to data consumers (DC). We presented a general access control circulation strategy for the Big Data domain by describing the benefit of using designated access control for BD units and performance and taking into consideration the need for BD and AC system. We then presented a generic of Big Data access control system to improve the dissemination of Big Data.

Keywords: access control, security, Big Data, domain

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Authors: Michael Gidey Gebru

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Most of Data Envelopment Analysis models operate in a static environment with input and output parameters that are chosen by deterministic data. However, due to ambiguity brought on shifting market conditions, input and output data are not always precisely gathered in real-world scenarios. Fuzzy numbers can be used to address this kind of ambiguity in input and output data. Therefore, this work aims to expand crisp Data Envelopment Analysis into Data Envelopment Analysis with fuzzy environment. In this study, the input and output data are regarded as fuzzy triangular numbers. Then, the Data Envelopment Analysis model with fuzzy environment is solved using a multi-objective method to gauge the Decision Making Units' efficiency. Finally, the developed Data Envelopment Analysis model is illustrated with an application on real data 50 educational institutions.

Keywords: efficiency, Data Envelopment Analysis, fuzzy, higher education, input, output

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Authors: Izzudin Busnaina

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An important question for marketers in the international arena is whether the consumer’s responses (i.e., sentiment and behavioral aspects) toward the global marketing programs in developing countries depend on culture. In a study representing a large sample of consumers and four different home appliances country-of-origin global operators in Libya, the author explores the potential role of culture on Arab consumers' responses toward foreign marketing programs. Results indicate that although the foreign companies have a tendency to adopted standardization perspective, this does not impact on consumers’ responses in a single cultural context toward marketing. The findings reveal that buying behavior was more a function of individual difference than of national cultural context. Further, the results suggest that for mainstream home appliances, segmenting on the basis of nationality is probably unnecessary and that a standardized approach would likely be successful across an increasingly relevant Arab world; and that continuing perceptions of Arab insularity are likely to be misplaced. Faced with the effectiveness of globally efficient marketing programs, local manufacturers would need to work hard to identify particular niche segments where a culturally-specific appeal might be more successful.

Keywords: arab world, buyer’s characteristics, consumer behavior, home appliances, marketing program

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Authors: Kacper Tomasz Kröber-Mulawa

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This paper will address the topic of data ownership from an economic perspective, and examples of economic limitations of data property rights will be provided, which have been identified using methods and approaches of economic analysis of law. To properly build a background for the economic focus, in the beginning a short perspective of data and data ownership in the EU’s legal system will be provided. It will include a short introduction to its political and social importance and highlight relevant viewpoints. This will stress the importance of a Single Market for data but also far-reaching regulations of data governance and privacy (including the distinction of personal and non-personal data, data held by public bodies and private businesses). The main discussion of this paper will build upon the briefly referred to legal basis as well as methods and approaches of economic analysis of law.

Keywords: antitrust, data, data ownership, digital economy, property rights

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Authors: Abdullah Alsaeed

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Virtualized computing and cloud computing infrastructures are no longer fuzz or marketing term. They are a core reality in today’s corporate Information Technology (IT) organizations. Hence, developing an effective and efficient methodologies for data backup and data recovery is required more than any time. The purpose of data backup and recovery techniques are to assist the organizations to strategize the business continuity and disaster recovery approaches. In order to accomplish this strategic objective, a variety of mechanism were proposed in the recent years. This research paper will explore and examine the latest techniques and solutions to provide data backup and restoration for the cloud computing platforms.

Keywords: data backup, data recovery, cloud computing, business continuity, disaster recovery, cost-effective, data encryption.

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Authors: JaeHwan Yang, Da-Woon Jeong, Seung-Young Kho, Dong-Kyu Kim

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In terms of ITS, information on link characteristic is an essential factor for plan or operation. But in practical cases, not every link has installed sensors on it. The link that does not have data on it is called “Missing Link”. The purpose of this study is to impute data of these missing links. To get these data, this study applies the machine learning method. With the machine learning process, especially for the deep learning process, missing link data can be estimated from present link data. For deep learning process, this study uses “Recurrent Neural Network” to take time-series data of road. As input data, Dedicated Short-range Communications (DSRC) data of Dalgubul-daero of Daegu Metropolitan Area had been fed into the learning process. Neural Network structure has 17 links with present data as input, 2 hidden layers, for 1 missing link data. As a result, forecasted data of target link show about 94% of accuracy compared with actual data.

Keywords: data estimation, link data, machine learning, road network

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Authors: Monica Lia

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This article presents a customer data analysis model using business intelligence tools for data modelling, transforming, data visualization and dynamic reports building. Economic organizational customer’s analysis is made based on the information from the transactional systems of the organization. The paper presents how to develop the data model starting for the data that companies have inside their own operational systems. The owned data can be transformed into useful information about customers using business intelligence tool. For a mature market, knowing the information inside the data and making forecast for strategic decision become more important. Business Intelligence tools are used in business organization as support for decision-making.

Keywords: customer analysis, business intelligence, data warehouse, data mining, decisions, self-service reports, interactive visual analysis, and dynamic dashboards, use cases diagram, process modelling, logical data model, data mart, ETL, star schema, OLAP, data universes

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Authors: Jenny Jin

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Background: The therapeutic use of cannabis for cancer symptom control in Australia is gaining momentum, reflecting a broader global acceptance of its medicinal potential. Objective: This overview examines the historical context, current regulations, and clinical applications of cannabis in oncology within Australia. Methods: A historical analysis outlines the ancient and 19th-century medicinal uses of cannabis, followed by its prohibition in the early 20th century and subsequent resurgence in the late 20th century. The current legal framework under the therapeutic gods administration (TGA) is discussed. Results: Research indicates that cannabinoids, particularly THC and CBD, effectively alleviate pain, reduce chemotherapy-induced nausea and vomiting, stimulate appetite, and enhance overall quality of life for cancer patients. Despite these benefits, challenges such as dosing standardization, stigma, and access barriers persist. Conclusion: Continued clinical research, policy development, and educational initiatives are essential to optimize the use of cannabis in cancer care. A patient-centred approach, emphasizing interdisciplinary collaboration and informed decision-making, is crucial for improving therapeutic outcomes in this evolving field.

Keywords: historical context of cannabis, symptom control in oncology patients, therapeutic benefits, outcome and future

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Authors: K. Hardy, A. Maurushat

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Policy makers are increasingly looking to make evidence-based decisions. Evidence-based decisions have historically used rigorous methodologies of empirical studies by research institutes, as well as less reliable immediate survey/polls often with limited sample sizes. As we move into the era of Big Data analytics, policy makers are looking to different methodologies to deliver reliable empirics in real-time. The question is not why did these people do this for the last 10 years, but why are these people doing this now, and if the this is undesirable, and how can we have an impact to promote change immediately. Big data analytics rely heavily on government data that has been released in to the public domain. The open data movement promises greater productivity and more efficient delivery of services; however, Australian government agencies remain reluctant to release their data to the general public. This paper considers the barriers to releasing government data as open data, and how these barriers might be overcome.

Keywords: big data, open data, productivity, data governance

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Authors: Hema Bhardwaj, D. Srinivasa Rao

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Technology for analysing, processing, and extracting meaningful data from enormous and complicated datasets can be termed as "big data." The technique of big data mining and big data analysis is extremely helpful for business movements such as making decisions, building organisational plans, researching the market efficiently, improving sales, etc., because typical management tools cannot handle such complicated datasets. Special computational and statistical issues, such as measurement errors, noise accumulation, spurious correlation, and storage and scalability limitations, are brought on by big data. These unique problems call for new computational and statistical paradigms. This research paper offers an overview of the literature on big data mining, its process, along with problems and difficulties, with a focus on the unique characteristics of big data. Organizations have several difficulties when undertaking data mining, which has an impact on their decision-making. Every day, terabytes of data are produced, yet only around 1% of that data is really analyzed. The idea of the mining and analysis of data and knowledge discovery techniques that have recently been created with practical application systems is presented in this study. This article's conclusion also includes a list of issues and difficulties for further research in the area. The report discusses the management's main big data and data mining challenges.

Keywords: big data, data mining, data analysis, knowledge discovery techniques, data mining challenges

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Authors: Rimmy Yadav, Anmol Preet Kaur

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Big Data has demonstrated the vast potential in streamlining, deciding, spotting business drifts in different fields, for example, producing, fund, Information Technology. This paper gives a multi-disciplinary diagram of the research issues in enormous information and its procedures, instruments, and system identified with the privacy, data storage management, network and energy utilization, adaptation to non-critical failure and information representations. Other than this, result difficulties and openings accessible in this Big Data platform have made.

Keywords: big data, privacy, data management, network and energy consumption

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Authors: Mansoureh Ghiasabadi Farahani, Samira Kalantary, Sara Taghi-Pour, Mahboubeh Shamsi

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Nowadays, the development of computers technology and its recent applications provide access to new types of data, which have not been considered by the traditional data analysts. Two particularly interesting characteristics of such data sets include their huge size and streaming nature .Incremental learning techniques have been used extensively to address the data stream classification problem. This paper presents a concise survey on the obstacles and the requirements issues classifying data streams with using decision tree. The most important issue is to maintain a balance between accuracy and efficiency, the algorithm should provide good classification performance with a reasonable time response.

Keywords: big data, data streams, classification, decision tree

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Authors: Aishwarya Shekhar, Himanshu Sharma

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Data deduplication is one of important data compression techniques for eliminating duplicate copies of repeating data, and has been widely used in cloud storage to reduce the amount of storage space and save bandwidth. In this process, duplicate data is expunged, leaving only one copy means single instance of the data to be accumulated. Though, indexing of each and every data is still maintained. Data deduplication is an approach for minimizing the part of storage space an organization required to retain its data. In most of the company, the storage systems carry identical copies of numerous pieces of data. Deduplication terminates these additional copies by saving just one copy of the data and exchanging the other copies with pointers that assist back to the primary copy. To ignore this duplication of the data and to preserve the confidentiality in the cloud here we are applying the concept of hybrid nature of cloud. A hybrid cloud is a fusion of minimally one public and private cloud. As a proof of concept, we implement a java code which provides security as well as removes all types of duplicated data from the cloud.

Keywords: confidentiality, deduplication, data compression, hybridity of cloud

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Authors: Devatha Kalyan Kumar, Aravindraj D., Sadathulla A.

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Big data are now rapidly expanding in all engineering and science and many other domains. The potential of large or massive data is undoubtedly significant, make sense to require new ways of thinking and learning techniques to address the various big data challenges. Machine learning is continuously unleashing its power in a wide range of applications. In this paper, the latest advances and advancements in the researches on machine learning for big data processing. First, the machine learning techniques methods in recent studies, such as deep learning, representation learning, transfer learning, active learning and distributed and parallel learning. Then focus on the challenges and possible solutions of machine learning for big data.

Keywords: active learning, big data, deep learning, machine learning

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Authors: Tomy Prihananto, Damar Apri Sudarmadi

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Indonesia recognizes the right to privacy as a human right. Indonesia provides legal protection against data management activities because the protection of personal data is a part of human rights. This paper aims to describe the arrangement of data management and data management in Indonesia. This paper is a descriptive research with qualitative approach and collecting data from literature study. Results of this paper are comprehensive arrangement of data that have been set up as a technical requirement of data protection by encryption methods. Arrangements on encryption and protection of personal data are mutually reinforcing arrangements in the protection of personal data. Indonesia has two important and immediately enacted laws that provide protection for the privacy of information that is part of human rights.

Keywords: Indonesia, protection, personal data, privacy, human rights, encryption

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Authors: Assia Amri, Naima Layachi, Ali Ladjama

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The characterization of some Algerian honey was carried out on the basis of their physico-chemical properties: moisture,hydroxy methyl furfural, diastase activity, pH,free, total and lactonic acidity, electrical conductivity, minerals and proline content. Studied samples are found to be low in moisture and therefore safe from fermentation, low in HMF level and high in diastase activity. Additionally the diastase activity and the HMF content are widely recognized parameters indicating the freshness of honey. Phenolic compounds present in honey are classified into two groups - simple phenols and polyphenols. The simple phenols in honey are various phenol acids, but polyphenols are various flavonoids and flavonides. The aim of our work was to determine antioxidant properties of various Algerian honey samples–the total phenol content, total flavonoids content, as well as honey anti radical activity.The quality of honey samples differs on account of various factors such as season, packaging and processing conditions, floral source, geographical origin and storage period. It is important that precautions should be taken to ensure standardization and rationalization of beekeeping techniques, manufacturing procedures and storing processes to improve honey quality.

Keywords: honey, physico-chemical characterization, phenolic coumpound, HMF, diastase activity

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