Search results for: data databases

Authors: Darshana Lal, Sonu Rana

Abstract:

Learning Resource Centers (LRC) are acquiring different kinds of documents like books, journals, thesis, dissertations, standard, databases etc. in print and e-form. This article deals with the different types of sources available in LRC. It also discusses the concept of the web, as a tool, as a multimedia system and the different interfaces available on the web. The reasons for establishing LRC are highlighted along with the assignments of LRC. Different features of LRC‘S like self-learning and group learning are described. It also implements a group of activities like reading, learning, educational etc. The use of LRC by students and faculties are given and concluded with the benefits.

Keywords: internet, search engine, resource centre, opac, self-learning, group learning

Procedia PDF Downloads 366

Authors: Mariana Lima, Rodrigo Silva, Victor Stange, Teodiano Bastos

Abstract:

Due to the high reliability reached by DNA tests, since the 1980s this kind of test has allowed the identification of a growing number of criminal cases, including old cases that were unsolved, now having a chance to be solved with this technology. Currently, the use of genetic profiling databases is a typical method to increase the scope of genetic comparison. Forensic laboratories must process, analyze, and generate genetic profiles of a growing number of samples, which require time and great storage capacity. Therefore, it is essential to develop methodologies capable to organize and minimize the spent time for both biological sample processing and analysis of genetic profiles, using software tools. Thus, the present work aims the development of a software system solution for laboratories of forensics genetics, which allows sample, criminal case and local database management, minimizing the time spent in the workflow and helps to compare genetic profiles. For the development of this software system, all data related to the storage and processing of samples, workflows and requirements that incorporate the system have been considered. The system uses the following software languages: HTML, CSS, and JavaScript in Web technology, with NodeJS platform as server, which has great efficiency in the input and output of data. In addition, the data are stored in a relational database (MySQL), which is free, allowing a better acceptance for users. The software system here developed allows more agility to the workflow and analysis of samples, contributing to the rapid insertion of the genetic profiles in the national database and to increase resolution of crimes. The next step of this research is its validation, in order to operate in accordance with current Brazilian national legislation.

Keywords: database, forensic genetics, genetic analysis, sample management, software solution

Procedia PDF Downloads 357

Authors: Syed Iftikhar Hussain Shah, Vasilis Peristeras, Ioannis Magnisalis

Abstract:

Data that is high in volume, velocity, veracity and comes from a variety of sources is usually generated in all sectors including the government sector. Globally public administrations are pursuing (big) data as new technology and trying to adopt a data-centric architecture for hosting and sharing data. Properly executed, big data and data analytics in the government (big) data ecosystem can be led to data-driven government and have a direct impact on the way policymakers work and citizens interact with governments. In this research paper, we conduct a systematic literature review. The main aims of this paper are to highlight essential aspects of the government (big) data ecosystem and to explore the most critical socio-technical factors that contribute to the successful implementation of government (big) data ecosystem. The essential aspects of government (big) data ecosystem include definition, data types, data lifecycle models, and actors and their roles. We also discuss the potential impact of (big) data in public administration and gaps in the government data ecosystems literature. As this is a new topic, we did not find specific articles on government (big) data ecosystem and therefore focused our research on various relevant areas like humanitarian data, open government data, scientific research data, industry data, etc.

Keywords: applications of big data, big data, big data types. big data ecosystem, critical success factors, data-driven government, egovernment, gaps in data ecosystems, government (big) data, literature review, public administration, systematic review

Procedia PDF Downloads 211

Authors: Anli Du Preez, James Bekker

Abstract:

Data is currently one of the most critical and influential emerging technologies. However, the true potential of data is yet to be exploited since, currently, about 1% of generated data are ever actually analyzed for value creation. There is a data gap where data is not explored due to the lack of data analytics infrastructure and the required data analytics skills. This study developed a decision support framework for data analytics by following Jabareen’s framework development methodology. The study focused on machine learning algorithms, which is a subset of data analytics. The developed framework is designed to assist data analysts with little experience, in choosing the appropriate machine learning algorithm given the purpose of their application.

Keywords: Data analytics, Industrial engineering, Machine learning, Value creation

Procedia PDF Downloads 153

Authors: Priyesh Kumar Singh, S. K. Sharma, Sanjay Verma, C. Samuel

Abstract:

Supplier Relationship Management (SRM), is strategic planning and managing of all interactions with suppliers to maximize its value. Its application varies from construction industries to healthcare system and investment banks to aviation industries. Several buyer-supplier relationship models, as well as supplier selection and evaluation strategies, have been documented by many academicians and researchers. In this paper, through a comprehensive literature review of over 30 published papers, different theoretical models, empirical data and conclusions were analysed relating to SRM to find its role in establishing better supplier relationships. These journal articles were searched by using the keyword “supplier relationship management,” in databases of Mendeley Library, ProQuest, EBSCO and Google Scholar. This paper reviews the academic literature on different relationship models, supplier evaluation, and selection strategies to discuss its implications in different situations. It also describes the dominant factors responsible for buyer-supplier relationships such trust and power. Finally, conclusions have been drawn which can be validated by various researchers and can help practitioners in industries.

Keywords: supplier relationship management, supplier performance, supplier evaluation, supplier selection strategies

Procedia PDF Downloads 250

Authors: Saeed Ullah Jan

Abstract:

This study has examined the e-readiness in libraries of public sector universities in Khyber Pakhtunkhwa. Efforts were made to evaluate the availability of human resources, electronic infrastructure, and network services and programs in the public sector university libraries. The population of the study was the twenty-seven public sector university libraries of Khyber Pakhtunkhwa. A quantitative approach was adopted, and a questionnaire-based survey was conducted to collect data from the librarian/in charge of public sector university libraries. The collected data were analyzed using Statistical Package for Social Sciences version 22 (SPSS). The mean score of the knowledge component interpreted magnitudes below three which indicates that the respondents are poorly or moderately satisfied regards knowledge of libraries. The satisfaction level of the respondents about the other components, such as electronic infrastructure, network services and programs, and enhancers of the networked world, was rated as average or below. The study suggested that major aspects of existing public-sector university libraries require significant transformation. For this purpose, the government should provide all the required resources and facilities to meet the population's informational and recreational demands. The Information Communication Technology (ICT) infrastructure of public university libraries needs improvement in terms of the availability of computer equipment, databases, network servers, multimedia projectors, digital cameras, uninterruptible power supply, scanners, and backup devices such as hard discs and Digital Video Disc/Compact Disc.

Keywords: ICT-libraries, e-readiness-libraries, e-readiness-university libraries, e-readiness-Pakistan

Procedia PDF Downloads 74

Authors: Yazan Al-Kofahi, Jamal Alqawasmi.

Abstract:

In this study, a comprehensive literature review (SLR) was conducted, with the main goal of assessing the existing literature about how artificial intelligence (AI), machine learning (ML), deep learning (DL) models are used in sustainable architecture applications and issues including thermal comfort satisfaction, energy efficiency, cost prediction and many others issues. For this reason, the search strategy was initiated by using different databases, including Scopus, Springer and Google Scholar. The inclusion criteria were used by two research strings related to DL, ML and sustainable architecture. Moreover, the timeframe for the inclusion of the papers was open, even though most of the papers were conducted in the previous four years. As a paper filtration strategy, conferences and books were excluded from database search results. Using these inclusion and exclusion criteria, the search was conducted, and a sample of 59 papers was selected as the final included papers in the analysis. The data extraction phase was basically to extract the needed data from these papers, which were analyzed and correlated. The results of this SLR showed that there are many applications of ML and DL in Sustainable buildings, and that this topic is currently trendy. It was found that most of the papers focused their discussions on addressing Environmental Sustainability issues and factors using machine learning predictive models, with a particular emphasis on the use of Decision Tree algorithms. Moreover, it was found that the Random Forest repressor demonstrates strong performance across all feature selection groups in terms of cost prediction of the building as a machine-learning predictive model.

Keywords: machine learning, deep learning, artificial intelligence, sustainable building

Procedia PDF Downloads 49

Authors: Santhani M. Selveindran

Abstract:

Background: Natural disasters are traumatic global events that are becoming increasing more common, with significant psychosocial impact on survivors. This impact results not only in psychosocial distress but, for many, can lead to psychosocial disorders and chronic psychopathology. While there are currently available interventions that seek to prevent and treat these psychosocial sequelae, their effectiveness is uncertain. The evidence-base is emerging with more primary studies evaluating the effectiveness of various psychosocial interventions for survivors of natural disasters, which remains to be synthesized. Aim of Review: To identify, critically appraise and synthesize the current evidence-base on the effectiveness of psychosocial interventions in preventing or treating Post-Traumatic Stress Disorder (PTSD), Major Depressive Disorder (MDD) and/or Generalized Anxiety Disorder (GAD) in adults and children who are survivors of natural disasters. Methods: A protocol was developed as a guide to carry out this review. A systematic search was conducted in eight international electronic databases, three grey literature databases, one dissertation and thesis repository, websites of six humanitarian and non-governmental organizations renowned for their work on natural disasters, as well as bibliographic and citation searching for eligible articles. Papers meeting the specific inclusion criteria underwent quality assessment using the Downs and Black checklist. Data were extracted from the included papers and analysed by way of narrative synthesis. Results: Database and website searching returned 3777 papers where 31 met the criteria for inclusion. Additional 2 papers were obtained through bibliographic and citation searching. Methodological quality of most papers was fair. Twenty-five studies evaluated psychological interventions, five, social interventions whereas three studies evaluated ‘mixed’ psychological and social interventions. All studies, irrespective of methodological quality, reported post-intervention reductions in symptom scores for PTSD, depression and/or anxiety and where assessed, reduced diagnosis of PTSD and MDD, and produced improvements in self-efficacy and quality of life. Statistically significant results were seen in 27 studies. However, three studies demonstrated that the evaluated interventions may not have been very beneficial. Conclusions: The overall positive results suggest that any psychosocial interventions are favourable and should be delivered to all natural disaster survivors, irrespective of age, country, and phase of disaster. Yet, heterogeneity and methodological shortcomings of the current evidence-base makes it difficult to draw definite conclusions needed to formulate categorical guidance or frameworks. Further, rigorously conducted research is needed in this area, although the feasibility of such, given the context and nature of the problem, is also recognized.

Keywords: psychosocial interventions, natural disasters, survivors, effectiveness

Procedia PDF Downloads 140

Authors: Annapureddy Srikant Reddy, Atthanti Mahendra, Samala Chinni Krishna, N. Neelima

Abstract:

In our present world, we are generating a lot of data and we, need a specific device to store all these data. Generally, we store data in pen drives, hard drives, etc. Sometimes we may loss the data due to the corruption of devices. To overcome all these issues, we implemented a cloud space for storing the data, and it provides more security to the data. We can access the data with just using the internet from anywhere in the world. We implemented all these with the java using Net beans IDE. Once user uploads the data, he does not have any rights to change the data. Users uploaded files are stored in the cloud with the file name as system time and the directory will be created with some random words. Cloud accepts the data only if the size of the file is less than 2MB.

Keywords: cloud space, AES, FTP, NetBeans IDE

Procedia PDF Downloads 191

Authors: Sedat Simsek, Tugay Arat

Abstract:

Today's communication concept, which has a great revolution with the printing press which has been found by Gutenberg, has no boundary thanks to advanced communication devices and the internet. It is possible to take advantage in many areas, such as from medicine to social sciences or from mathematics to education, from the computers that was first produced for the purpose of military services. The use of these developing technologies in the field of education has created a great vision changes in both training and having education. Materials, which can be considered as basic communication resources and used in traditional education has begun to lose its significance, and some technologies have begun to replace them such as internet, computers, smart boards, projection devices and mobile phone. On the other hand, the programs and applications used in these technologies have also been developed. University students use virtual books instead of the traditional printed book, use cell phones instead of note books, use the internet and virtual databases instead of the library to research. They even submit their homework with interactive methods rather than printed materials. The traditional education system, these technologies, which increase productivity, have brought a new dimension to education. The aim of this study is to determine the influence of technologies in the learning process of students and to find whether is there any similarities and differences that arise from the their faculty that they have been educated and and their learning process. In addition to this, it is aimed to determine the level of ICT usage of students studying at the university level. In this context, the advantages and conveniences of the technology used by students are also scrutinized. In this study, we used surveys to collect data. The data were analyzed by using SPSS 16 statistical program with the appropriate testing.

Keywords: education, communication technologies, role of technology, teaching

Procedia PDF Downloads 291

Authors: Rokhmatul Insani, Hira Laksmiwati Soemitro

Abstract:

Business Intelligence is a methodology that exploits the data to produce information and knowledge systematically, business intelligence can support the decision-making process. Some methods in business intelligence are data warehouse and data mining. A data warehouse can store historical data from transactional data. For data modelling in data warehouse, we apply dimensional modelling by Kimball. While data mining is used to extracting patterns from the data and get insight from the data. Data mining has many techniques, one of which is segmentation. For profiling of telecommunication customer, we use customer segmentation according to customer’s usage of services, customer invoice and customer payment. Customers can be grouped according to their characteristics and can be identified the profitable customers. We apply K-Means Clustering Algorithm for segmentation. The input variable for that algorithm we use RFM (Recency, Frequency and Monetary) model. All process in data mining, we use tools IBM SPSS modeller.

Keywords: business intelligence, customer segmentation, data warehouse, data mining

Procedia PDF Downloads 462

Authors: Shivangi Somvanshi

Abstract:

Poor crop productivity in salt-affected environment in the country is due to insufficient and untimely canal supply to agricultural land and inefficient field water management practices. This could further degrade due to inadequate maintenance of canal network, ongoing secondary soil salinization and waterlogging, worsening of groundwater quality. Large patches of low productivity in irrigation commands are occurring due to waterlogging and salt-affected soil, particularly in the scarcity rainfall year. Satellite remote sensing has been used for mapping of areas of low crop productivity, waterlogging and salt in irrigation commands. The spatial results obtained for these problems so far are less reliable for further use due to rapid change in soil quality parameters over the years. The existing spatial databases of canal network and flow data, groundwater quality and salt-affected soil were obtained from the central and state line departments/agencies and were integrated with GIS. Therefore, an integrated methodology based on remote sensing and GIS has been developed in ArcGIS environment on the basis of canal supply status, groundwater quality, salt-affected soils, and satellite-derived vegetation index (NDVI), salinity index (NDSI) and waterlogging index (NSWI). This methodology was tested for identification and delineation of area of low productivity in the Gautam Buddha Nagar district (Uttar Pradesh). It was found that the area affected by this problem lies mainly in Dankaur and Jewar blocks of the district. The problem area was verified with ground data and was found to be approximately 78% accurate. The methodology has potential to be used in other irrigation commands in the country to obtain reliable spatial data on low crop productivity.

Keywords: remote sensing, GIS, salt affected soil, crop productivity, Gautam Buddha Nagar

Procedia PDF Downloads 274

Authors: Chinonso Jude Ugwu

Abstract:

This study examines education as a global business, primarily focusing on the boom of college students worldwide. It adopts a mixed-technique approach, using primary and secondary data sources. Primary data was obtained using questionnaires and interviews focusing on international college students, academic staff, and recruitment corporations from pre-determined universities in the United States, the United Kingdom, and Australia. The secondary information was collected from relevant literature, professional reports, and databases. The study ascertained that the boom in worldwide college students is a huge trend within the training enterprise, arising primarily from the growing call for better education worldwide. The studies additionally found that different factors are responsible for the decision of international students to consider studying abroad, such as high schooling satisfaction, cultural exposure, professional opportunities, and the popularity of universities. Furthermore, the study highlights the challenges college students face worldwide, including economic difficulties, social and cultural adjustments, and visa regulations. Based on the findings, the study concludes that Education as a Global Business is a profitable enterprise with substantial potential. However, universities and governments should handle global college students’ demanding situations by creating welcoming surroundings promoting diversity and inclusivity. The study recommends that universities put money into programs and offerings that assist worldwide college students’ welfare. Governments should ease visa regulations to inspire more extraordinary worldwide college students to observe abroad.

Keywords: education, business, profitability, global students

Procedia PDF Downloads 65

Authors: Hushyar Azari

Abstract:

Aim and background: Deep brain stimulation (DBS) is regarded as an important therapeutic choice for Parkinson's disease (PD). The two most common targets for DBS are the subthalamic nucleus (STN) and globus pallidus (GPi). This review was conducted to compare the clinical effectiveness of these two targets. Methods: A systematic literature search in electronic databases: Embase, Cochrane Library and PubMed were restricted to English language publications 2010 to 2021. Specified MeSH terms were searched in all databases. Studies which evaluated the Unified Parkinson's Disease Rating Scale (UPDRS) III were selected by meeting the following criteria: (1) compared both GPi and STN DBS; (2) had at least three months follow-up period; (3)at least five participants in each group; (4)conducted after 2010. Study quality assessment was performed using the Modified Jadad Scale. Results: 3577 potentially relevant articles were identified, of these, 3569 were excluded based on title and abstract, duplicate and unsuitable article removal. Eight articles satisfied the inclusion criteria and were scrutinized (458 PD patients). According to Modified Jadad Scale, the majority of included studies had low evidence quality which was a limitation of this review. 5 studies reported no statistically significant between-group difference for improvements in UPDRS ш scores. At the same time, there were some results in terms of pain, action tremor, rigidity, and urinary symptoms, which indicated that STN DBS might be a better choice. Regarding the adverse effects, GPi was superior. Conclusion: It is clear that other larger randomized clinical trials with longer follow-up periods and control groups are needed to decide which target is more efficient for deep brain stimulation in Parkinson’s disease and imposes fewer adverse effects on the patients. Meanwhile, STN seems more reasonable according to the results of this systematic review.

Keywords: brain stimulation, globus pallidus, Parkinson's disease, subthalamic nucleus

Procedia PDF Downloads 171

Authors: Adriana C. Panayi, Riaz A. Agha, Brady A. Sieber, Dennis P. Orgill

Abstract:

Background: Increased rates of both breast cancer and obesity have resulted in more women seeking breast reconstruction. These women may be at increased risk for perioperative complications. A systematic review was conducted to assess the outcomes in obese women who have undergone breast reconstruction following mastectomy. Methods: Cochrane, PUBMED and EMBASE electronic databases were screened and data was extracted from included studies. The clinical outcomes assessed were surgical complications, medical complications, length of postoperative hospital stay, reoperation rate and patient satisfaction. Results: 33 studies met the inclusion criteria for the review and 29 provided enough data to be included in the meta-analysis (71368 patients, 20061 of which were obese). Obese women were 2.3 times more likely to experience surgical complications (95 percent CI 2.19 to 2.39; P < 0.00001), 2.8 times more likely to have medical complications (95 percent CI 2.41 to 3.26; P < 0.00001) and had a 1.9 times higher risk of reoperation (95 percent CI 1.75 to 2.07; P < 0.00001). The most common complication, wound dehiscence, was 2.5 times more likely in obese women (95 percent CI 1.80 to 3.52; P < 0.00001). Sensitivity analysis confirmed that obese women were more likely to experience surgical complications (RR 2.36, 95% CI 2.22–2.52; P < 0.00001). Conclusions: This study provides evidence that obesity increases the risk of complications in both implant and autologous reconstruction. Additional prospective and observational studies are needed to determine if weight reduction prior to reconstruction reduces the perioperative risks associated with obesity.

Keywords: autologous reconstruction, breast cancer, breast reconstruction, literature review, obesity, oncology, prosthetic reconstruction

Procedia PDF Downloads 291

Authors: Younies Saeed Hassan Mahmoud, Mai Mabrouk, Elsayed Sallam

Abstract:

Analysing DNA microarray data sets is a great challenge, which faces the bioinformaticians due to the complication of using statistical and machine learning techniques. The challenge will be doubled if the microarray data sets contain missing data, which happens regularly because these techniques cannot deal with missing data. One of the most important data analysis process on the microarray data set is feature selection. This process finds the most important genes that affect certain disease. In this paper, we introduce a technique for imputing the missing data in microarray data sets while performing feature selection.

Keywords: DNA microarray, feature selection, missing data, bioinformatics

Procedia PDF Downloads 557

Authors: Lutful Karim, Mohammed S. Al-kahtani

Abstract:

Sensors are being used in various applications such as agriculture, health monitoring, air and water pollution monitoring, traffic monitoring and control and hence, play the vital role in the growth of big data. However, sensors collect redundant data. Thus, aggregating and filtering sensors data are significantly important to design an efficient big data framework. Current researches do not focus on aggregating and filtering data at multiple layers of sensor-based big data framework. Thus, this paper introduces (i) three layers data aggregation and framework for big data and (ii) a priority-based, dynamic data aggregation scheme (PDDA) for the lowest layer at sensors. Simulation results show that the PDDA outperforms existing tree and cluster-based data aggregation scheme in terms of overall network energy consumptions and end-to-end data transmission delay.

Keywords: big data, clustering, tree topology, data aggregation, sensor networks

Procedia PDF Downloads 326

Authors: Hanan Begali, Shahi Dost, Annett Ziegler, Markus Cornberg, Maria-Esther Vidal, Anke R. M. Kraft

Abstract:

Chronic hepatitis B virus (HBV) infection can be treated with nucleot(s)ide analog (NA), for example, which inhibits HBV replication. However, they have hardly any influence on the functional cure of HBV, which is defined by hepatitis B surface antigen (HBsAg) loss. NA needs to be taken life-long, which is not available for all patients worldwide. Additionally, NA-treated patients are still at risk of developing cirrhosis, liver failure, or hepatocellular carcinoma (HCC). Although each patient has the same components of the immune system, immune responses vary between patients. Therefore, a deeper understanding of the immune response against HBV in different patients is necessary to understand the parameters leading to HBV cure and to use this knowledge to optimize HBV therapies. This requires seamless integration of an enormous amount of diverse and fine-grained data from viral markers, e.g., hepatitis B core-related antigen (HBcrAg) and hepatitis B surface antigen (HBsAg). The data integration system relies on the assumption that profiling human immune systems requires the analysis of various variables (e.g., demographic data, treatments, pre-existing conditions, immune cell response, or HLA-typing) rather than only one. However, the values of these variables are collected independently. They are presented in a myriad of formats, e.g., excel files, textual descriptions, lab book notes, and images of flow cytometry dot plots. Additionally, patients can be identified differently in these analyses. This heterogeneity complicates the integration of variables, as data management techniques are needed to create a unified view in which individual formats and identifiers are transparent when profiling the human immune systems. The proposed study (HBsRE) aims at integrating heterogeneous data sets of 87 chronically HBV-infected patients, e.g., clinical data, immune cell response, and HLA-typing, with knowledge encoded in biomedical ontologies and open-source databases into a knowledge-driven framework. This new technique enables us to harmonize and standardize heterogeneous datasets in the defined modeling of the data integration system, which will be evaluated in the knowledge graph (KG). KGs are data structures that represent the knowledge and data as factual statements using a graph data model. Finally, the analytic data model will be applied on top of KG in order to develop a deeper understanding of the immune profiles among various patients and to evaluate factors playing a role in a holistic profile of patients with HBsAg level loss. Additionally, our objective is to utilize this unified approach to stratify patients for new effective treatments. This study is developed in the context of the project “Transforming big data into knowledge: for deep immune profiling in vaccination, infectious diseases, and transplantation (ImProVIT)”, which is a multidisciplinary team composed of computer scientists, infection biologists, and immunologists.

Keywords: chronic hepatitis B infection, immune response, knowledge graphs, ontology

Procedia PDF Downloads 98

Authors: Cherifi Mehdi, Lahdir Mourad, Ameur Soltane

Abstract:

Image compression is the heart of several multimedia techniques. It is used to reduce the number of bits required to represent an image. Meteosat Second Generation (MSG) satellite allows the acquisition of 12 image files every 15 minutes and that results in a large databases sizes. In this paper, a novel image compression method based on the arithmetic coding with the sorted Run Length Coding (SRLC) for MSG images is proposed. The SRLC allows us to find the occurrence of the consecutive pixels of the original image to create a sorted run. The arithmetic coding allows the encoding of the sorted data of the previous stage to retrieve a unique code word that represents a binary code stream in the sorted order to boost the compression ratio. Through this article, we show that our method can perform the best results concerning compression ratio and bit rate unlike the method based on the Run Length Coding (RLC) and the arithmetic coding. Evaluation criteria like the compression ratio and the bit rate allow the confirmation of the efficiency of our method of image compression.

Keywords: image compression, arithmetic coding, Run Length Coding, RLC, Sorted Run Length Coding, SRLC, Meteosat Second Generation, MSG

Procedia PDF Downloads 338

Authors: Atje Setiawan Abdullah, Budi Nurani Ruchjana, I. Gede Nyoman Mindra Jaya, Eddy Hermawan

Abstract:

Ethnoscience is used to see the culture with a scientific perspective, which may help to understand how people develop various forms of knowledge and belief, initially focusing on the ecology and history of the contributions that have been there. One of the areas studied in ethnoscience is etno-informatics, is the application of informatics in the culture. In this study the science of informatics used is data mining, a process to automatically extract knowledge from large databases, to obtain interesting patterns in order to obtain a knowledge. While the application of culture described by naming database village on the island of Java were obtained from Geographic Indonesia Information Agency (BIG), 2014. The purpose of this study is; first, to classify the naming of the village on the island of Java based on the structure of the word naming the village, including the prefix of the word, syllable contained, and complete word. Second to classify the meaning of naming the village based on specific categories, as well as its role in the community behavioral characteristics. Third, how to visualize the naming of the village to a map location, to see the similarity of naming villages in each province. In this research we have developed two theorems, i.e theorems area as a result of research studies have collected intersection naming villages in each province on the island of Java, and the composition of the wedge theorem sets the provinces in Java is used to view the peculiarities of a location study. The methodology in this study base on the method of Knowledge Discovery in Database (KDD) on data mining, the process includes preprocessing, data mining and post processing. The results showed that the Java community prioritizes merit in running his life, always working hard to achieve a more prosperous life, and love as well as water and environmental sustainment. Naming villages in each location adjacent province has a high degree of similarity, and influence each other. Cultural similarities in the province of Central Java, East Java and West Java-Banten have a high similarity, whereas in Jakarta-Yogyakarta has a low similarity. This research resulted in the cultural character of communities within the meaning of the naming of the village on the island of Java, this character is expected to serve as a guide in the behavior of people's daily life on the island of Java.

Keywords: ethnoscience, ethno-informatics, data mining, clustering, Java island culture

Procedia PDF Downloads 265

Authors: Parthiban Srinivasan

Abstract:

With recent advances in computational power and access to enough data in biosciences, artificial intelligence methods are increasingly being used in drug discovery research. These methods are essentially a series of advanced statistics based exercises that review the past to indicate the likely future. Our goal is to develop a model that accurately predicts biological activity and toxicity parameters for novel compounds. We have compiled a robust library of over 150,000 chemical compounds with different pharmacological properties from literature and public domain databases. The compounds are stored in simplified molecular-input line-entry system (SMILES), a commonly used text encoding for organic molecules. We utilize an automated process to generate an array of numerical descriptors (features) for each molecule. Redundant and irrelevant descriptors are eliminated iteratively. Our prediction engine is based on a portfolio of machine learning algorithms. We found Random Forest algorithm to be a better choice for this analysis. We captured non-linear relationship in the data and formed a prediction model with reasonable accuracy by averaging across a large number of randomized decision trees. Our next step is to apply deep neural network (DNN) algorithm to predict the biological activity and toxicity properties. We expect the DNN algorithm to give better results and improve the accuracy of the prediction. This presentation will review all these prominent machine learning and deep learning methods, our implementation protocols and discuss these techniques for their usefulness in biomedical and health informatics.

Keywords: deep learning, drug discovery, health informatics, machine learning, toxicity prediction

Procedia PDF Downloads 346

Authors: Anteneh Dagnachew Sewenet, Paola Pisano

Abstract:

The concept of circular economy, circularity, and sustainability are interconnected and promote a more sustainable future. However, previous studies have mainly focused on each concept individually, neglecting the relationships and gaps in the existing literature. This study aims to integrate and link these concepts to expand the theoretical and practical methods of scholars and professionals in pursuit of sustainability. The aim of this systematic literature review is to comprehensively analyze and summarize the interconnections between circular economy, circularity, and sustainability. Additionally, it seeks to develop a conceptual framework that can guide practitioners and serve as a basis for future research. The review employed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. A total of 78 articles were analyzed, utilizing the Scopus and Web of Science databases. The analysis involved summarizing and systematizing the conceptualizations of circularity and its relationship with the circular economy and long-term sustainability. The review provided a comprehensive overview of the interconnections between circular economy, circularity, and sustainability. Key themes, theoretical frameworks, empirical findings, and conceptual gaps in the literature were identified. Through a rigorous analysis of scholarly articles, the study highlighted the importance of integrating these concepts for a more sustainable future. This study contributes to the existing literature by integrating and linking the concepts of circular economy, circularity, and sustainability. It expands the theoretical understanding of how these concepts relate to each other and provides a conceptual framework that can guide future research in this field. The findings emphasize the need for a holistic approach in achieving sustainability goals. The data collection for this review involved identifying relevant articles from the Scopus and Web of Science databases. The selection of articles was made based on predefined inclusion and exclusion criteria. The PRISMA protocol guided the systematic analysis of the selected articles, including summarizing and systematizing their content. This study addressed the question of how circularity is conceptualized and related to both the circular economy and long-term sustainability. It aimed to identify the interconnections between these concepts and bridge the gap in the existing literature. The review provided a comprehensive analysis of the interconnections between the circular economy, circularity, and sustainability. It presented a conceptual framework that can guide practitioners in implementing circular economy strategies and serve as a basis for future research. By integrating these concepts, scholars, and professionals can enhance the theoretical and practical methods in pursuit of a more sustainable future. The findings emphasize the importance of taking a holistic approach to achieve sustainability goals and highlight conceptual gaps that can be addressed in future studies.

Keywords: circularity, circular economy, sustainability, innovation

Procedia PDF Downloads 82

Authors: Negarin Akbari, Leila Asadi, Behjat Khorsandi, Zahra Asadi, Javad Nadali

Abstract:

Background: Persistent and early pain experiences before the full development of the nervous system can lead to enduring behavioural changes in preterm infants. Purpose: This study was conducted to evaluate the effect of non-nutritive sucking on pain during heel sticks in preterm neonates. Data Sources: Databases include PubMed/MEDLINE, Scopus, Web of Science, Cochrane, and Google Scholar search engines were searched from January 2000 to May 2024. Study Selection: Inclusion criteria were clinical trials about the effect of non-nutritive sucking on pain management during heel stick focused on neonates. Articles must have been written in English. Data Extraction: Selected articles were assessed, and data was extracted by two independent reviewers based on inclusion criteria, and any disagreements were resolved through discussion and consensus. Results: Until May 25, 2024, a total of 6216 articles were retrieved. Following the screening, eight articles underwent a detailed assessment, focusing on 225 neonates—116 in the NNS group and 109 in the control group. Among the subjects, male neonates constituted 58.5% (n=131). The gestational age averaged 31.6 ± 2.02 weeks, with a mean birth weight of 1632.2 ± 322.8 grams. The findings revealed a statistically significant reduction in pain scores among neonates in the intervention group compared to the control group. While non-nutritive sucking exhibited a significant effect on pain and heart rate reduction, its impact on oxygen saturation in neonates was non-significant when compared to the control group. Implications for Practice and Research: Using the non-nutritive sucking method as a low-cost and accessible measure can reduce pain during painful procedures.

Keywords: non-nutritive sucking, heel stick, pain management, preterm neonates, non-pharmacologic interventions, systematic review, meta-analysis

Procedia PDF Downloads 5

Authors: Ricardo Pinto Mario Covele, Patricio V. Langa, Patrick Swanzy

Abstract:

The English language has been recognized as a universal medium of instruction in academia, especially in Higher Education Institutions (HEIs) hence exerting enormous influence within the context of research and publication. By extension, the English Language has been embraced by scholars from non-English speaking countries. The purpose of this review was to synthesize the discussions using four databases. Discussion in the English language in the career development of academics, particularly in non-English speaking universities, is largely less visible. This paper seeks to fill this gap and to improve the visibility of the English language in the career development of academics focusing on non-English language speaking universities by undertaking a systematic literature review. More specifically, the paper addresses the language policy, English language learning model as a second language, sociolinguistic field and career development, methods, as well as its main findings. This review analyzed 75 relevant resources sourced from Western Cape’s Library, Scopus, Google scholar, and web of science databases from November 2020 to July 2021 using the PQRS framework as an analytical lens. The paper’s findings demonstrate that, while higher education continues to be under-challenges of English language usage, literature targeting non-English speaking universities remains less discussed than it is often described. The findings also demonstrate the dominance of English language policy, both for knowledge production and dissemination of literature challenging emerging scholars from non-English speaking HEIs. Hence, the paper argues for the need to reconsider the context of non-English language speakers in the English language in the career development of academics’ research, both as empirical fields and as emerging knowledge producers. More importantly, the study reveals two bodies of literature: (1) the instrumentalist approach to English Language learning and (2) Intercultural approach to the English Language for career opportunities, classified as the appropriate to explain the English language learning process and how is it perceived towards scholars’ academic careers in HEIs.

Keywords: English language, public and private universities, language policy, career development, non-English speaking countries

Procedia PDF Downloads 134

Authors: Nicolas Galazis, Nikolina Docheva, Constantinos Simillis, Kypros Nicolaides

Abstract:

Background: Obese women are at increased risk for many pregnancy complications, and bariatric surgery (BS) before pregnancy has shown to improve some of these. Objectives: To review the current literature and quantitatively assess the obstetric and neonatal outcomes in pregnant women who have undergone BS. Search Strategy: MEDLINE, EMBASE and Cochrane databases were searched using relevant keywords to identify studies that reported on pregnancy outcomes after BS. Selection Criteria: Pregnancy outcome in firstly, women after BS compared to obese or BMI-matched women with no BS and secondly, women after BS compared to the same or different women before BS. Only observational studies were included. Data Collection and Analysis: Two investigators independently collected data on study characteristics and outcome measures of interest. These were analysed using the random effects model. Heterogeneity was assessed and sensitivity analysis was performed to account for publication bias. Main Results: The entry criteria were fulfilled by 17 non-randomised cohort or case-control studies, including seven with high methodological quality scores. In the BS group, compared to controls, there was a lower incidence of preeclampsia (OR, 0.45, 95% CI, 0.25-0.80; p=0.007), GDM (OR, 0.47, 95% CI, 0.40-0.56; P<0.001) and large neonates (OR 0.46, 95% CI 0.34-0.62; p<0.001) and a higher incidence of small neonates (OR 1.93, 95% CI 1.52-2.44; p<0.001), preterm birth (OR 1.31, 95% CI 1.08-1.58; p=0.006), admission for neonatal intensive care (OR 1.33, 95% CI 1.02-1.72; p=0.03) and maternal anaemia (OR 3.41, 95% CI 1.56-7.44, p=0.002). Conclusions: BS as a whole improves some pregnancy outcomes. Laparoscopic adjustable gastric banding does not appear to increase the rate of small neonates that was seen with other BS procedures. Obese women of childbearing age undergoing BS need to be aware of these outcomes.

Keywords: bariatric surgery, pregnancy, preeclampsia, gestational diabetes, birth weight

Procedia PDF Downloads 398

Authors: Shizra Waris, Saleem Akhtar

Abstract:

Big data is a research area receiving attention from academia and IT communities. In the digital world, the amounts of data produced and stored have within a short period of time. Consequently this fast increasing rate of data has created many challenges. In this paper, we use functionalism and structuralism paradigms to analyze the genesis of big data applications and its current trends. This paper presents a complete discussion on state-of-the-art big data technologies based on group and stream data processing. Moreover, strengths and weaknesses of these technologies are analyzed. This study also covers big data analytics techniques, processing methods, some reported case studies from different vendor, several open research challenges and the chances brought about by big data. The similarities and differences of these techniques and technologies based on important limitations are also investigated. Emerging technologies are suggested as a solution for big data problems.

Keywords: computer, it community, industry, big data

Procedia PDF Downloads 180

Authors: Bensaid A., Mostephaoui T., Nedjai R.

Abstract:

Climatic factors are the subject of considerable research, both methodologically and instrumentally. Under the effect of climate change, the approach to climate parameters with precision remains one of the main objectives of the scientific community. This is from the perspective of assessing climate change and its repercussions on humans and the environment. However, many regions of the world suffer from a severe lack of reliable instruments that can make up for this deficit. Alternatively, the use of empirical methods becomes the only way to assess certain parameters that can act as climate indicators. Several scientific methods are used for the evaluation of evapotranspiration which leads to its evaluation either directly at the level of the climatic stations or by empirical methods. All these methods make a point approach and, in no case, allow the spatial variation of this parameter. We, therefore, propose in this paper the use of three sources of information (network of weather stations of Meteo France, World Databases, and Moodis satellite images) to evaluate spatial evapotranspiration (ETP) using the Turc method. This first step will reflect the degree of relevance of the indirect (satellite) methods and their generalization to sites without stations. The spatial variation representation of this parameter using the geographical information system (GIS) accounts for the heterogeneity of the behaviour of this parameter. This heterogeneity is due to the influence of site morphological factors and will make it possible to appreciate the role of certain topographic and hydrological parameters. A phase of predicting the evolution over the medium and long term of evapotranspiration under the effect of climate change by the application of the Intergovernmental Panel on Climate Change (IPCC) scenarios gives a realistic overview as to the contribution of aquatic systems to the scale of the region.

Keywords: climate change, ETP, MODIS, GIEC scenarios

Procedia PDF Downloads 86

Authors: Ibrahim Gomaa, Hoda M. O. Mokhtar

Abstract:

Although most of the existing skyline queries algorithms focused basically on querying static points through static databases; with the expanding number of sensors, wireless communications and mobile applications, the demand for continuous skyline queries has increased. Unlike traditional skyline queries which only consider static attributes, continuous skyline queries include dynamic attributes, as well as the static ones. However, as skyline queries computation is based on checking the domination of skyline points over all dimensions, considering both the static and dynamic attributes without separation is required. In this paper, we present an efficient algorithm for computing continuous skyline queries without discriminating between static and dynamic attributes. Our algorithm in brief proceeds as follows: First, it excludes the points which will not be in the initial skyline result; this pruning phase reduces the required number of comparisons. Second, the association between the spatial positions of data points is examined; this phase gives an idea of where changes in the result might occur and consequently enables us to efficiently update the skyline result (continuous update) rather than computing the skyline from scratch. Finally, experimental evaluation is provided which demonstrates the accuracy, performance and efficiency of our algorithm over other existing approaches.

Keywords: continuous query processing, dynamic database, moving object, skyline queries

Procedia PDF Downloads 199

Authors: Arpine Maghakyan

Abstract:

The purpose of this paper is to study the relationship between the level of industry digitalization and audit fees, especially, the relationship between Big 4 auditor fees and industry digitalization level. On the one hand, automation of business processes decreases internal control weakness and manual mistakes; increases work effectiveness and integrations. On the other hand, it may cause serious misstatements, high business risks or even bankruptcy, typically in early stages of automation. Incomplete automation can bring high audit risk especially if the auditor does not fully understand client’s business automation model. Higher audit risk consequently will cause higher audit fees. Higher audit fees for clients with high automation level are more highlighted in Big 4 auditor’s behavior. Using data of US firms from 2005-2015, we found that industry level digitalization is an interaction for the auditor quality on audit fees. Moreover, the choice of Big4 or non-Big4 is correlated with client’s industry digitalization level. Big4 client, which has higher digitalization level, pays more than one with low digitalization level. In addition, a high-digitalized firm that has Big 4 auditor pays higher audit fee than non-Big 4 client. We use audit fees and firm-specific variables from Audit Analytics and Compustat databases. We analyze collected data by using fixed effects regression methods and Wald tests for sensitivity check. We use fixed effects regression models for firms for determination of the connections between technology use in business and audit fees. We control for firm size, complexity, inherent risk, profitability and auditor quality. We chose fixed effects model as it makes possible to control for variables that have not or cannot be measured.

Keywords: audit fees, auditor quality, digitalization, Big4

Procedia PDF Downloads 287

Authors: Branci Sarra, Branci Saadia

Abstract:

Because of the multiplied data growth, many computer science tools have been developed to process and analyze these Big Data. High-performance computing architectures have been designed to meet the treatment needs of Big Data (view transaction processing standpoint, strategic, and tactical analytics). The purpose of this article is to provide a historical and global perspective on the recent trend of high-performance computing architectures especially what has a relation with Analytics and Data Mining.

Keywords: high performance computing, HPC, big data, data analysis

Procedia PDF Downloads 505