Search results for: data portal

Authors: Abraham Undu, Stephen Akuma

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Websites are becoming a major component of an organization’s success in our ever globalizing competitive world. The website symbolizes an organization, interacting or projecting an organization’s principles, culture, values, vision, and perspectives. It is an interface connecting organizations and their clients. The university, as an academic institution, makes use of a website to communicate and offer computing services to its stakeholders (students, staff, host community, university management etc). Unfortunately, website designers often give more consideration to the technology, organizational structure and business objectives of the university than to the usability of the site. Website designers end up designing university websites which do not meet the needs of the primary users. This empirical study investigated the Benue State University website from the point view of students. This research was realized by using a standardized website usability questionnaire based on the five factors of usability defined by WAMMI (Website Analysis and Measurement Inventory): attractiveness, controllability, efficiency, learnability and helpfulness. The result of the investigation showed that the university website (https://portal.bsum.edu.ng/) has neutral usability level because of the usability issues associated with the website. The research recommended feasible solutions to improve the usability of the website from the users’ perspective and also provided a modified usability model that will be used for better evaluation of the Benue State University website.

Keywords: Benue State University, modified usability model, usability, usability factors

Procedia PDF Downloads 152

Authors: Tonya P. Bowers

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Background: The community/patient-focused quality improvement (QI) project has resolved a clinical problem using a quantitative design evaluating behavior change practices in a convenience sample from a multi-cultural congregation in a faith-based setting. AD is a legal document that speaks for the patient when they are unable to speak for themselves. The AD provides detailed information regarding critical medical decisions on behalf of the patient if they’re unable to make decisions themselves. The goal of an AD is to improve EOL care renderings that align with the patient’s desires. The AD diminishes anxiety and stress associated with making difficult EOL care decisions for patients and their families. Method: The project has two intervention strategies: pre-intervention and post-intervention formative surveys and a final summative survey. Most of the data collection takes place during implementation. The Let’s Talk About It Program utilized an online meeting platform for presentation. Participants were asked to complete informed consent and surveys via an online portal. Education included slide presentation, Advance Directive demonstration, video clips, discussions and 1:1 assistance with AD completion with a project manager. Results: Considering the overwhelming likelihood responses where 87.5% identified they “definitely would” hold an End-Of-Life conversation with their healthcare provider or family, and 81.25% indicated their likelihood that they “definitely would” complete an advance directive. In addition, the final summative post-intervention survey (n-14) also demonstrated an overwhelming 93% positive response. Which undoubtedly demonstrates favorable outcomes for the project. Conclusion: the Let’s Talk About It Program demonstrated effectiveness in improving participants' attitudes and acceptance towards Advance Directives and expanding End-of-Life care discussions. Emphasis on program sustainment within the church is imperative in fostering continued awareness and improved health outcomes for the local community with low health literacy.

Keywords: advance directive, end of life, advance care planning, palliative care, low health literacy, faith-based

Procedia PDF Downloads 212

Authors: Mengdi Zhang, Zhenji Gao, Ning Kang, Rongmei Liu

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Management and application of geological big data is an important part of China's national big data strategy. With the implementation of a national big data strategy, geological big data management becomes more and more critical. At present, there are still a lot of technology barriers as well as cognition chaos in many aspects of geological big data management and application, such as data sharing, intellectual property protection, and application technology. Therefore, it’s a key task to make better use of new technologies for deeper delving and wider application of geological big data. In this paper, we briefly introduce the basic principle of blockchain technology at the beginning and then make an analysis of the application dilemma of geological data. Based on the current analysis, we bring forward some feasible patterns and scenarios for the blockchain application in geological big data and put forward serval suggestions for future work in geological big data management.

Keywords: blockchain, intellectual property protection, geological data, big data management

Procedia PDF Downloads 92

Authors: Tamanna Jethava, Rahul Joshi

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Frequent Item sets play an essential role in many data Mining tasks that try to find interesting patterns from the database. Typically it refers to a set of items that frequently appear together in transaction dataset. There are several mining algorithm being used for frequent item set mining, yet most do not scale to the type of data we presented with today, so called “BIG DATA”. Big Data is a collection of large data sets. Our approach is to work on the frequent item set mining over the large dataset with scalable and speedy way. Big Data basically works with Map Reduce along with HDFS is used to find out frequent item sets from Big Data on large cluster. This paper focuses on using pre-processing & mining algorithm as hybrid approach for big data over Hadoop platform.

Keywords: frequent item set mining, big data, Hadoop, MapReduce

Procedia PDF Downloads 439

Authors: M. Najib Osman, Hanafizan Hussain, Sri Kusuma Wati Mohd Daud

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Avatar was used as user’s symbol of identity in online communications such as Facebook, Twitter, online game, and portal community between unknown people. The development of this symbol is the use of animated character or avatar, which can engage learners in a way that draws them into the e-Learning experience. Immersive learning is one of the most effective learning techniques, and animated characters can help create an immersive environment. E-learning is an ideal learning environment using modern means of information technology, through the effective integration of information technology and the curriculum to achieve, a new learning style which can fully reflect the main role of the students to reform the traditional teaching structure thoroughly. Essential in any e-learning is the degree of interactivity for the learner, and whether the learner is able to study at any time, or whether there is a need for the learner to be online or in a classroom with other learners at the same time (synchronous learning). Ideally, e-learning should engage the learners, allowing them to interact with the course materials, obtaining feedback on their progress and assistance whenever it is required. However, the degree of interactivity in e-learning depends on how the course has been developed and is dependent on the software used for its development, and the way the material is delivered to the learner. Therefore, users’ accessibility that allows access to information at any time and places and their positive attitude towards e-learning such as having interacting with a good teacher and the creation of a more natural and friendly environment for e-learning should be enhanced. This is to motivate their learning enthusiasm and it has been the responsibility of educators to incorporate new technology into their ways of teaching.

Keywords: avatar, e-learning, higher education, students' perception

Procedia PDF Downloads 412

Authors: Hussaini Garba Mohammed

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Background/Significance: The lack of data gathering is affecting NGOs world-wide in general to have good data information about educational and health related issues among communities in any country and around the world. For example, HIV/AIDS smoking (Tuberculosis diseases) and COVID-19 virus carriers is becoming a serious public health problem, especially among old men and women. But there is no full details data survey assessment from communities, villages, and rural area in some countries to show the percentage of victims and patients, especial with this world COVID-19 virus among the people. These data are essential to inform programming targets, strategies, and priorities in getting good information about data gathering in any society.

Keywords: reliable information, data assessment, data mining, data communication

Procedia PDF Downloads 181

Authors: Liang Zhao, Jili Zhang, Chongquan Zhong

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Energy consumption data, in particular those involving public buildings, are impacted by many factors: the building structure, climate/environmental parameters, construction, system operating condition, and user behavior patterns. Traditional methods for data analysis are insufficient. This paper delves into the data mining technology to determine its application in the analysis of building energy consumption data including energy consumption prediction, fault diagnosis, and optimal operation. Recent literature are reviewed and summarized, the problems faced by data mining technology in the area of energy consumption data analysis are enumerated, and research points for future studies are given.

Keywords: data mining, data analysis, prediction, optimization, building operational performance

Procedia PDF Downloads 854

Authors: Shahnewaz Khan

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Machine learning (ML) techniques are often used in projects for creating data-driven applications. These tasks typically demand additional research and analysis. The proper technique and strategy must be chosen to ensure the success of data-driven projects. Otherwise, even exerting a lot of effort, the necessary development might not always be possible. In this post, an effort to examine the workflow of data-driven software development projects and its implementation process in order to describe how to manage a project successfully. Which will assist in minimizing the added workload.

Keywords: data, data-driven projects, data science, NLP, software project

Procedia PDF Downloads 84

Authors: Ayda Youssef, Tarek Rafaat, Iman zaky

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Purpose: Langerhans’ cell histiocytosis (LCH) is not uncommon pathology that implies aberrant proliferation of a specific dendritic (Langerhans) cell. These atypical but mature cells of monoclonal origin can infiltrate many sites of the body and may occur as localized lesions or as widespread systemic disease. Liver is one of the uncommon sites of affection. The twofold objective of this study is to illustrate the radiological presentation of this disease, and to compare these results with previously reported series. Methods and Materials: Between 2007 and 2012, 150 patients with biopsy-proven LCH were treated in our hospital, a paediatric cancer tertiary care center. A retrospective review of radiographic images and reports was performed. There were 33 patients with liver affection are stratified. All patients underwent imaging studies, mostly US and CT. A chart review was performed to obtain demographic, clinical and radiological data. They were analyzed and compared to other published series. Results: Retrospective assessment of 150 patients with LCH was performed, among them 33 patients were identified who had liver involvement. All these patients developed multisystemic disease; They were 12 females and 21 males with (n= 32), seven of them had marked hepatomegaly. Diffuse hypodense liver parenchyma was encountered in five cases, the periportal location has a certain predilection in cases of focal affection where three cases has a hypodense periportal soft tissue sheets, one of them associated with dilated biliary radicals, only one case has multiple focal lesions unrelated to portal tracts. On follow up of the patients, two cases show abnormal morphology of liver with bossy outline. Conclusion: LCH is a not infrequent disease. A high-index suspicion should be raised in the context of diagnosis of liver affection. A biopsy is recommended in the presence of radiological suspicion. Chemotherapy is the preferred therapeutic modality. Liver histiocytosis are not disease specific features but should be interpreted in conjunction with the clinical history and the results of biopsy. Clinical Relevance/Application: Radiologist should be aware of different patterns of hepatobiliary histiocytosis, Thus early diagnosis and proper management of patient can be conducted.

Keywords: langerhans’ cell histiocytosis, liver, medical and health sciences, radiology

Procedia PDF Downloads 283

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

Procedia PDF Downloads 106

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

Procedia PDF Downloads 177

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

Procedia PDF Downloads 462

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

Procedia PDF Downloads 503

Authors: Jou-An Chen, Julio Castillo, Errol Duncan Cason, Gabre Kemp, Leana Esterhuizen, Angel Valverde Portal, Esta Van Heerden

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Emerging contaminants (EC) such as pharmaceutical and personal care products have been accumulating for years in water bodies all over the world. However, very little is known about the occurrences, levels, and effects of ECs in South African water resources. This study provides an initial assessment of the distribution of eight ECs (Acetaminophen, Atrazine, Terbuthlyazine, Carbamazepine, Phenyton, Sulfmethoxazole, Nevirapine and Fluconozole) in fifteen water sources from the Free State and Easter Cape provinces of South Africa. Overall, the physiochemical conditions were different in surface and groundwater samples, with concentrations of several elements such as B, Ca, Mg, Na, NO3, and TDS been statistically higher in groundwater. In contrast, ECs levels, quantified at ng/mL using the LC/MS/ESI, were much lower in groundwater samples. The ECs with higher contamination levels were Carbamazepine, Sulfmethoxazole, Nevirapine, and Terbuthlyazine, while the most widespread were Sulfmethoxazole and Fluconozole, detected in all surface and groundwater samples. Fecal and E. coli tests indicated that surface water was more contaminated than groundwater. Microbial communities, assessed using NGS, were dominated by the phyla Proteobacteria and Bacteroidetes, in both surface and groundwater. Actinobacteria, Planctomycetes, and Cyanobacteria, were more dominant in surface water, while Verrucomicrobia were overrepresented in groundwater. In conclusion, ECs contamination is closely associated with human activities (human wastes). The microbial diversity identified can suggest possible biodegradation processes.

Keywords: emerging contaminants, EC, personal care products, pharmaceuticals, natural attenuation process

Procedia PDF Downloads 219

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

Procedia PDF Downloads 173

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

Procedia PDF Downloads 312

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

Procedia PDF Downloads 418

Authors: Samah Oda, Asmaa Khafaga, Mohammed Hashim, Asmaa Khamis

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Toxicity of hydroxyurea (HU), a treatment for certain tumors, polycythemia, and thrombocytosis, was evaluated in rats in one-month toxicity study. Sixty male albino rats were equally classified into four groups. Rats received daily oral gavage of HU in 0, 250, 500, and 750 mg/kg b.wt. Chemical and histopathological assessment of liver, lung, spleen, and bone marrow was performed at 10, 20, and 30 days of the experiment. No significant change was reported in alanine aminotransferase (ALT), aspartate aminotransferase (AST), globulin, and albumin/ globulin ratio during the experiment. Significant decreases in alkaline phosphatase (ALP) and total albumin were reported in rats received 500 and 750 mg/kg b.wt of HU. In addition, total cholesterol level increased significantly after 10 days; however, it significantly decreased after 20 and 30 days of the experiment. Moreover, hepatocytic vacuolation and necrosis with portal inflammatory infiltrates were reported along experimental periods. Pulmonary congestion, hemorrhage, interstitial mononuclear infiltration, peribronchitis, and bronchial epithelial necrosis were also reported. Severe lymphocytic necrosis in spleen and severe loss of hematopoietic cells and replacement with corresponding adipose tissue in bone marrow tissues was demonstrated. In conclusion, HU could be able to induce severe dose and time-dependent hepato-pulmonary toxicity and lymphoid depression in rats.

Keywords: hydroxyurea, hepato-pulmonary toxicity, lymphoid depression, histopathology

Procedia PDF Downloads 150

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

Procedia PDF Downloads 134

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

Procedia PDF Downloads 62

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

Procedia PDF Downloads 83

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.

Procedia PDF Downloads 89

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

Procedia PDF Downloads 510

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

Procedia PDF Downloads 434

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

Procedia PDF Downloads 372

Authors: Getenet Dessie, Jinhu Li, Son Nghiem, Tinh Doan

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Background: Concerns regarding malnutrition have evolved from focusing solely on single forms to addressing the simultaneous occurrence of multiple types, commonly referred to as the double or triple burden of malnutrition. Nevertheless, data concerning the concurrent occurrence of various types of malnutrition are scarce. Therefore, this systematic review and meta-analysis aims to assess the pooled prevalence of the double burden of malnutrition among children under five in Sub-Saharan Africa and other least-developed countries (LDCs). Methods: Electronic, web-based searches were conducted from January 15 to June 28, 2023, across several databases, including PubMed, Embase, Google Scholar, and the World Health Organization's Hinari portal, as well as other search engines, to identify primary studies published up to June 28, 2023. Laboratory-based cross-sectional studies on children under the age of five were included. Two independent authors assessed the risk of bias and the quality of the identified articles. The primary outcomes of this study were micronutrient deficiencies and the comorbidity of stunting and anemia, as well as wasting and anemia. The random-effects model was utilized for analysis. The association of identified variables with the various forms of malnutrition was also assessed using adjusted odds ratios (AOR) with a 95% confidence interval (CI). This review was registered in PROSPERO with the reference number CRD42023409483. Findings: The electronic search generated 6,087 articles, 93 of which matched the inclusion criteria for the final meta-analysis. Micronutrient deficiencies were prevalent among children under five in Sub-Saharan Africa and other LDCs, with rates ranging from 16.63% among 25,169 participants for vitamin A deficiency to 50.90% among 3,936 participants for iodine deficiency. Iron deficiency anemia affected 20.56% of the 63,121 participants. The combined prevalence of wasting anemia and stunting anemia was 5.41% among 64,709 participants and 19.98% among 66,016 participants, respectively. Both stunting and vitamin A supplementation were associated with vitamin A and iron deficiencies, with adjusted odds ratios (AOR) of 1.54 (95% CI: 1.01, 2.37) and 1.37 (95% CI: 1.21, 1.55), respectively. Interpretation: The prevalence of the double burden of malnutrition among children under the age of five was notably high in Sub-Saharan Africa and other LDCs. These findings indicate a need for increased attention and a focus on understanding the factors influencing this double burden of malnutrition.

Keywords: children, Sub-Saharan Africa, least developed countries, double burden of malnutrition, systematic review, meta-analysis

Procedia PDF Downloads 82

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

Procedia PDF Downloads 110

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

Procedia PDF Downloads 313

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

Procedia PDF Downloads 384