Search results for: data databases

Authors: Omwoyo Bosire Onyancha

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The Journal Citation Reports (JCR) of the Thomson Reuters has, for decades, provided the data for bibliometrically assessing the impact of journals. In their criticism of the journal impact factor (JIF), a number of scholars such as Priem, Taraborelli, Groth and Neylon (2010) observe that the “JIF is often incorrectly used to assess the impact of individual articles. It is troubling that the exact details of the JIF are a trade secret, and that significant gaming is relatively easy”. The emergence of alternative metrics (Altmetrics) has introduced another dimension of re-assessing how the impact of journals (and other units such as articles and even individual researchers) can be measured. Altmetrics is premised upon the fact that research is increasingly being disseminated through social network sites such as ResearchGate, Mendeley, Twitter, Facebook, LinkedIn, and ImpactStory, among others. This paper adopts informetrics (including altmetrics) techniques to report on the findings of a study conducted to investigate and compare the social media impact of 274 South Africa Post Secondary Education (SAPSE)-accredited journals, which are recognized and accredited by the Department of Higher Education and Training (DHET) of South Africa (SA). We used multiple sources to extract data for the study, namely Altmetric.com and the Thomson Reuters’ Journal Citation Reports. Data was analyzed in order to determine South African journals’ presence and impact on social media as well as contrast the social media impact with Thomson Reuters’ citation impact. The Spearman correlation test was performed to compare the journals’ social media impact and JCR citation impact. Preliminary findings reveal that a total of 6360 articles published in 96 South African journals have received some attention in social media; the most commonly used social media platform was Twitter, followed by Mendeley, Facebook, News outlets, and CiteULike; there were 29 SA journals covered in the JCR in 2008 and this number has grown to 53 journals in 2014; the journals indexed in the Thomson Reuters performed much better, in terms of their altmetrics, than those journals that are not indexed in Thomson Reuters databases; nevertheless, there was high correlation among journals that featured in both datasets; the journals with the highest scores in Altmetric.com included the South African Medical Journal, African Journal of Marine Science, and Transactions of the Royal Society of South Africa while the journals with high impact factors in JCR were South African Medical Journal, Onderstepoort: Journal of Veterinary Research, and Sahara: Journal of Social Aspects of HIV-AIDS; and that Twitter has emerged as a strong avenue of sharing and communicating research published in the South African journals. Implications of the results of the study for the dissemination of research conducted in South Africa are offered. Discussions based on the research findings as well as conclusions and recommendations are offered in the full text paper.

Keywords: altmetrics, citation impact, journal citation reports, journal impact factor, journals, research, scholarly publishing, social media impact, South Africa

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Authors: Kuni Vergauwen, Ivan P. J. Huijnen, Astrid Depuydt, Jasmine Van Regenmortel, Mira Meeus

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A lower activity level and imbalanced activity pattern are frequently observed in persons with chronic fatigue syndrome (CFS) / myalgic encephalomyelitis (ME) due to debilitating fatigue and post-exertional malaise (PEM). Identification of measurement instruments to evaluate the activity level and pattern is therefore important. The objective is to identify measurement instruments suited to evaluate the activity level and/or pattern in patients with CFS/ME and review their psychometric properties. A systematic literature search was performed in the electronic databases PubMed and Web of Science until 12 October 2016. Articles including relevant measurement instruments were identified and included for further analysis. The psychometric properties of relevant measurement instruments were extracted from the included articles and rated based on the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) checklist. The review was performed and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. A total of 49 articles and 15 unique measurement instruments were found, but only three instruments were evaluated in patients with CFS/ME: the Chronic Fatigue Syndrome-Activity Questionnaire (CFS-AQ), Activity Pattern Interview (API) and International Physical Activity Questionnaire-Short Form (IPAQ-SF), three self-report instruments measuring the physical activity level. The IPAQ-SF, CFS-AQ and API are all equally capable of evaluating the physical activity level, but none of the three measurement instruments are optimal to use. No studies about the psychometric properties of activity monitors in patients with CFS/ME were found, although they are often used as the gold standard to measure the physical activity pattern. More research is needed to evaluate the psychometric properties of existing instruments, including the use of activity monitors.

Keywords: chronic fatigue syndrome, data collection, physical activity, psychometrics

Procedia PDF Downloads 227

Authors: Nsimbe Allan

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The study examines the impact of Building Information Modeling (BIM) on the cost management of engineering projects, focusing specifically on the Mombasa Port Area Development Project. The objective of this research venture is to determine the mechanisms through which Building Information Modeling (BIM) facilitates stakeholder collaboration, reduces construction-related expenses, and enhances the precision of cost estimation. Furthermore, the study investigates barriers to execution, assesses the impact on the project's transparency, and suggests approaches to maximize resource utilization. The study, selected for its practical significance and intricate nature, conducted a Systematic Literature Review (SLR) using credible databases, including ScienceDirect and IEEE Xplore. To constitute the diverse sample, 69 individuals, including project managers, cost estimators, and BIM administrators, were selected via stratified random sampling. The data were obtained using a mixed-methods approach, which prioritized ethical considerations. SPSS and Microsoft Excel were applied to the analysis. The research emphasizes the crucial role that project managers, architects, and engineers play in the decision-making process (47% of respondents). Furthermore, a significant improvement in cost estimation accuracy was reported by 70% of the participants. It was found that the implementation of BIM resulted in enhanced project visibility, which in turn optimized resource allocation and facilitated the process of budgeting. In brief, the study highlights the positive impacts of Building Information Modeling (BIM) on collaborative decision-making and cost estimation, addresses challenges related to implementation, and provides solutions for the efficient assimilation and understanding of BIM principles.

Keywords: cost management, resource utilization, stakeholder collaboration, project transparency

Procedia PDF Downloads 67

Authors: Hatice Kübra Koç

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For academia, collecting reliable and valid data is one of the most significant issues for researchers. However, it is not the same procedure for all different target groups; meanwhile, during data collection from teenagers, young adults, or adults, researchers can use common data collection tools such as questionnaires, interviews, and semi-structured interviews; yet, for young learners and very young ones, these reliable and valid data collection tools cannot be easily designed or applied by the researchers. In this study, firstly, common data collection tools are examined for ‘very young’ and ‘young learners’ participant groups since it is thought that the quality and efficiency of an academic study is mainly based on its valid and correct data collection and data analysis procedure. Secondly, two different data collection instruments for very young and young learners are stated as discussing the efficacy of them. Finally, a suggested data collection tool – a performance-based questionnaire- which is specifically developed for ‘very young’ and ‘young learners’ participant groups in the field of teaching English to young learners as a foreign language is presented in this current study. The designing procedure and suggested items/factors for the suggested data collection tool are accordingly revealed at the end of the study to help researchers have studied with young and very learners.

Keywords: data collection instruments, performance-based questionnaire, young learners, very young learners

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Authors: Yaxin Bi

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Accurate prediction and understanding of the evolution mechanisms of earthquakes remain challenging in the fields of geology, geophysics, and seismology. This study leverages Long Short-Term Memory (LSTM) networks and Generative Adversarial Networks (GANs), a generative model tailored to time-series data, for generating synthetic time series data based on Swarm satellite data, which will be used for detecting seismic anomalies. LSTMs demonstrated commendable predictive performance in generating synthetic data across multiple countries. In contrast, the GAN models struggled to generate synthetic data, often producing non-informative values, although they were able to capture the data distribution of the time series. These findings highlight both the promise and challenges associated with applying deep learning techniques to generate synthetic data, underscoring the potential of deep learning in generating synthetic electromagnetic satellite data.

Keywords: LSTM, GAN, earthquake, synthetic data, generative AI, seismic precursors

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Authors: Manduth Ramchander

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Evaluating expressions involving multiplication and division can give rise to the fallacy that brackets can be arbitrarily inserted into expressions involving multiplication and division. The aim of this article was to draw upon mathematical theory to prove that brackets cannot be arbitrarily inserted into expressions involving multiplication and division and in particular in expressions where division precedes multiplication. In doing so, it demonstrates that the notion that two different answers are possible, when evaluating expressions involving multiplication and division, is indeed a false one. Searches conducted in a number of scholarly databases unearthed the rules to be applied when removing brackets from expressions, which revealed that consideration needs to be given to sign changes when brackets are removed. The rule pertaining to expressions involving multiplication and division was then extended upon, in its reverse format, to prove that brackets cannot be arbitrarily inserted into expressions involving multiplication and division. The application of the rule demonstrates that an expression involving multiplication and division can have only one correct answer. It is recommended that both the rule and its reverse be included in the curriculum, preferably at the juncture when manipulation with brackets is introduced.

Keywords: brackets, multiplications and division, operations, order

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Authors: H. Abas Nejad, A. R. Teymoori

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Facial expression recognition is one of the open problems in computer vision. Robust neutral face recognition in real time is a major challenge for various supervised learning based facial expression recognition methods. This is due to the fact that supervised methods cannot accommodate all appearance variability across the faces with respect to race, pose, lighting, facial biases, etc. in the limited amount of training data. Moreover, processing each and every frame to classify emotions is not required, as the user stays neutral for the majority of the time in usual applications like video chat or photo album/web browsing. Detecting neutral state at an early stage, thereby bypassing those frames from emotion classification would save the computational power. In this work, we propose a light-weight neutral vs. emotion classification engine, which acts as a preprocessor to the traditional supervised emotion classification approaches. It dynamically learns neutral appearance at Key Emotion (KE) points using a textural statistical model, constructed by a set of reference neutral frames for each user. The proposed method is made robust to various types of user head motions by accounting for affine distortions based on a textural statistical model. Robustness to dynamic shift of KE points is achieved by evaluating the similarities on a subset of neighborhood patches around each KE point using the prior information regarding the directionality of specific facial action units acting on the respective KE point. The proposed method, as a result, improves ER accuracy and simultaneously reduces the computational complexity of ER system, as validated on multiple databases.

Keywords: neutral vs. emotion classification, Constrained Local Model, procrustes analysis, Local Binary Pattern Histogram, statistical model

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Authors: Eliza F. Dunn

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Scripts are a hypothetical outline or roadmap to life as defined by culture. Sexual scripts are similarly a roadmap for what to expect in dating and sexual experiences. The articles for this review were found by searching three databases and refining 621 articles to 13 that are used in the results section. Some ways deaf sexual scripts vary from Traditional Sexual Scripts (TSS) are in the areas of gendered roles and sexual themes, which were both absent in deaf sexual scripts. Theories for why these differences exist are explored: the presence or absence of sexual education or the effects of intimate partner violence due to being a part of a disabled community. Finally, unique sexual flourishing for people who are d/Deaf found in studies was discussed, suggesting the needed perspective of resilience to be a focus of future research to fully understand deaf sexual scripts. Future research is discussed, noting the need for defining aspects of deaf sexual scripts in detail and studying the differences between these scripts and the TSS.

Keywords: deaf, deafness, sexual scripts, lift scripts, sexual flourishing

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Authors: Hyun-Woo Cho

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For ensuring the safety of a manufacturing process one should quickly identify an assignable cause of a fault in an on-line basis. To this end, many statistical techniques including linear and nonlinear methods have been frequently utilized. However, such methods possessed a major problem of small sample size, which is mostly attributed to the characteristics of empirical models used for reference models. This work presents a new method to overcome the insufficiency of measurement data in the monitoring and diagnosis tasks. Some quasi-measurement data are generated from existing data based on the two indices of similarity and importance. The performance of the method is demonstrated using a real data set. The results turn out that the presented methods are able to handle the insufficiency problem successfully. In addition, it is shown to be quite efficient in terms of computational speed and memory usage, and thus on-line implementation of the method is straightforward for monitoring and diagnosis purposes.

Keywords: data analysis, diagnosis, monitoring, process data, quality control

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Authors: Hsien-Tsen Wang

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Business Intelligence (BI) has long helped organizations make informed decisions based on data-driven insights and gain competitive advantages in the marketplace. In the past two decades, businesses witnessed not only the dramatically increasing volume and heterogeneity of business data but also the emergence of new technologies, such as Artificial Intelligence (AI), Semantic Web (SW), Cloud Computing, and Big Data. It is plausible that the convergence of these technologies would bring more value out of business data by establishing linked data frameworks and connecting in ways that enable advanced analytics and improved data utilization. In this paper, we first review and summarize current BI applications and methodology. Emerging technologies that can be integrated into BI applications are then discussed. Finally, we conclude with a proposed synergy framework that aims at achieving a more flexible, scalable, and intelligent BI solution.

Keywords: business intelligence, artificial intelligence, semantic web, big data, cloud computing

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Authors: Kamran Yazdanbakhsh, Somayeh Mahmoudi

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Three reasons make good use of the decision theory in medicine: 1. Increased medical knowledge and their complexity makes it difficult treatment information effectively without resorting to sophisticated analytical methods, especially when it comes to detecting errors and identify opportunities for treatment from databases of large size. 2. There is a wide geographic variability of medical practice. In a context where medical costs are, at least in part, by the patient, these changes raise doubts about the relevance of the choices made by physicians. These differences are generally attributed to differences in estimates of probabilities of success of treatment involved, and differing assessments of the results on success or failure. Without explicit criteria for decision, it is difficult to identify precisely the sources of these variations in treatment. 3. Beyond the principle of informed consent, patients need to be involved in decision-making. For this, the decision process should be explained and broken down. A decision problem is to select the best option among a set of choices. The problem is what is meant by "best option ", or know what criteria guide the choice. The purpose of decision theory is to answer this question. The systematic use of decision models allows us to better understand the differences in medical practices, and facilitates the search for consensus. About this, there are three types of situations: situations certain, risky situations, and uncertain situations: 1. In certain situations, the consequence of each decision are certain. 2. In risky situations, every decision can have several consequences, the probability of each of these consequences is known. 3. In uncertain situations, each decision can have several consequences, the probability is not known. Our aim in this article is to show how decision theory can usefully be mobilized to meet the needs of physicians. The decision theory can make decisions more transparent: first, by clarifying the data systematically considered the problem and secondly by asking a few basic principles should guide the choice. Once the problem and clarified the decision theory provides operational tools to represent the available information and determine patient preferences, and thus assist the patient and doctor in their choices.

Keywords: decision making, medicine, treatment strategies, patient

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Authors: Darren Edwards, Thomas Pinna

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Background: Interoception and heart rate variability have been found to predict outcomes of mental health and well-being. However, these have usually been investigated independently of one another. Objectives: This review aimed to explore the associations between interoception and heart rate variability (HRV) with emotion regulation (ER) and ER strategies within the existing literature and utilizing systematic review methodology. Methods: The process of article retrieval and selection followed the preferred reporting items for systematic review and meta-analyses (PRISMA) guidelines. Databases PsychINFO, Web of Science, PubMed, CINAHL, and MEDLINE were scanned for papers published. Preliminary inclusion and exclusion criteria were specified following the patient, intervention, comparison, and outcome (PICO) framework, whilst the checklist for critical appraisal and data extraction for systematic reviews of prediction modeling studies (CHARMS) framework was used to help formulate the research question, and to critically assess for bias in the identified full-length articles. Results: 237 studies were identified after initial database searches. Of these, eight studies were included in the final selection. Six studies explored the associations between HRV and ER, whilst three investigated the associations between interoception and ER (one of which was included in the HRV selection too). Overall, the results seem to show that greater HRV and interoception are associated with better ER. Specifically, high parasympathetic activity largely predicted the use of adaptive ER strategies such as reappraisal, and better acceptance of emotions. High interoception, instead, was predictive of effective down-regulation of negative emotions and handling of social uncertainty, there was no association with any specific ER strategy. Conclusions: Awareness of one’s own bodily feelings and vagal activation seem to be of central importance for the effective regulation of emotional responses.

Keywords: emotional regulation, vagal tone, interoception, chronic conditions, health and well-being, psychological flexibility

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Authors: John Q. Todd

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Given that modern equipment can provide comprehensive health, status, and error condition data via built-in sensors, maintenance organizations have a new and valuable source of insight to take advantage of. This presentation will expose what these data payloads might look like and how they can be filtered, visualized, calculated into metrics, used for machine learning, and generate alerts for further action.

Keywords: condition based maintenance, equipment data, metrics, alerts

Procedia PDF Downloads 188

Authors: Joanna Grzybowska, Magdalena Igras, Mariusz Ziółko

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Association rule learning is an approach for discovering interesting relationships in large databases. The analysis of relations, invisible at first glance, is a source of new knowledge which can be subsequently used for prediction. We used this data mining technique (which is an automatic and objective method) to learn about interesting affects associations in a corpus of emergency phone calls. We also made an attempt to match revealed rules with their possible situational context. The corpus was collected and subjectively annotated by two researchers. Each of 3306 recordings contains information on emotion: (1) type (sadness, weariness, anxiety, surprise, stress, anger, frustration, calm, relief, compassion, contentment, amusement, joy) (2) valence (negative, neutral, or positive) (3) intensity (low, typical, alternating, high). Also, additional information, that is a clue to speaker’s emotional state, was annotated: speech rate (slow, normal, fast), characteristic vocabulary (filled pauses, repeated words) and conversation style (normal, chaotic). Exponentially many rules can be extracted from a set of items (an item is a previously annotated single information). To generate the rules in the form of an implication X → Y (where X and Y are frequent k-itemsets) the Apriori algorithm was used - it avoids performing needless computations. Then, two basic measures (Support and Confidence) and several additional symmetric and asymmetric objective measures (e.g. Laplace, Conviction, Interest Factor, Cosine, correlation coefficient) were calculated for each rule. Each applied interestingness measure revealed different rules - we selected some top rules for each measure. Owing to the specificity of the corpus (emergency situations), most of the strong rules contain only negative emotions. There are though strong rules including neutral or even positive emotions. Three examples of the strongest rules are: {sadness} → {anxiety}; {sadness, weariness, stress, frustration} → {anger}; {compassion} → {sadness}. Association rule learning revealed the strongest configurations of affects (as well as configurations of affects with affect-related information) in our emergency phone calls corpus. The acquired knowledge can be used for prediction to fulfill the emotional profile of a new caller. Furthermore, a rule-related possible context analysis may be a clue to the situation a caller is in.

Keywords: data mining, emergency phone calls, emotional profiles, rules

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Authors: Dragana Calic

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The openness, availability and the sheer volume of big data have provided, what some regard as, an invaluable and rich dataset. Researchers, businesses, advertising agencies, medical institutions, to name only a few, collect, share, and analyze this data to enable their processes and decision making. However, there are important ethical considerations associated with the use of big data. The rapidly evolving nature of online technologies has overtaken the many legislative, privacy, and ethical frameworks and principles that exist. For example, should we obtain consent to use people’s online data, and under what circumstances can privacy considerations be overridden? Current guidance on how to appropriately and ethically handle big data is inconsistent. Consequently, this paper focuses on two quite distinct but related ethical considerations that are at the core of the use of big data for research purposes. They include empowering the producers of data and empowering researchers who want to study big data. The first consideration focuses on informed consent which is at the core of empowering producers of data. In this paper, we discuss some of the complexities associated with informed consent and consider studies of producers’ perceptions to inform research ethics guidelines and practice. The second consideration focuses on the researcher. Similarly, we explore studies that focus on researchers’ perceptions and experiences.

Keywords: big data, ethics, producers’ perceptions, researchers’ perceptions

Procedia PDF Downloads 284

Authors: Walid Cherif

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Data mining has, over recent years, seen big advances because of the spread of internet, which generates everyday a tremendous volume of data, and also the immense advances in technologies which facilitate the analysis of these data. In particular, classification techniques are a subdomain of Data Mining which determines in which group each data instance is related within a given dataset. It is used to classify data into different classes according to desired criteria. Generally, a classification technique is either statistical or machine learning. Each type of these techniques has its own limits. Nowadays, current data are becoming increasingly heterogeneous; consequently, current classification techniques are encountering many difficulties. This paper defines new measure functions to quantify the resemblance between instances and then combines them in a new approach which is different from actual algorithms by its reliability computations. Results of the proposed approach exceeded most common classification techniques with an f-measure exceeding 97% on the IRIS Dataset.

Keywords: data mining, knowledge discovery, machine learning, similarity measurement, supervised classification

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Authors: Ankur Mundhra, Shubhadeep Chakraborty, Y. R. Singh, Vishal Das

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Seismic data scaling affects the dynamic range of a data and with present day lower costs of storage and higher reliability of Hard Disk data, scaling is not suggested. However, in dealing with data of different vintages, which perhaps were processed in 16 bits or even 8 bits and are need to be processed with 32 bit available data, scaling is performed. Also, scaling amplifies low amplitude events in deeper region which disappear due to high amplitude shallow events that saturate amplitude scale. We have focused on significance of scaling data to aid interpretation. This study elucidates a proper seismic loading procedure in workstations without using default preset parameters as available in most software suites. Differences and distribution of amplitude values at different depth for seismic data are probed in this exercise. Proper loading parameters are identified and associated steps are explained that needs to be taken care of while loading data. Finally, the exercise interprets the un-certainties which might arise when correlating scaled and unscaled versions of seismic data with synthetics. As, seismic well tie correlates the seismic reflection events with well markers, for our study it is used to identify regions which are enhanced and/or affected by scaling parameter(s).

Keywords: clipping, compression, resolution, seismic scaling

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Authors: Adistya Maulidya, Mujuna Abbas, Nur Assyifa, Putri Dewi Gutiyani

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Countries are moving forward to develop databases from electronic health records for monitoring and research. Since the issuance of Information and Electonic Transaction Constitution No. 11 of 2008 as well as Minister Regulation No. 269 of 2008, there has been a gradual progress of Indonesian hospitals adopting Electonic Health Record (EHR) in its systems. This paper is the result of a literature study about the progress that has been made in Indonesia to develop national health information infrastructure through EHR within the hospitals. The purpose of this study was to describe trends in adoption of EHR systems among hospitals in Indonesia from 2008 to 2015 as well as to assess the preparedness of Indonesian national health information infrastructure facing ASEAN Economic Community.

Keywords: adoption, Indonesian hospitals, electronic health record, ASEAN economic community

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Authors: Zhi Zhu, Boquan Zhang, Tian Jing, Jingjing Li, Tao Wang

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Data assimilation is a model and data hybrid-driven method that dynamically fuses new observation data with a numerical model to iteratively approach the real system state. It is widely used in state prediction and parameter inference of continuous systems. Because of the discrete event system’s non-linearity and non-Gaussianity, traditional Kalman Filter based on linear and Gaussian assumptions cannot perform data assimilation for such systems, so particle filter has gradually become a technical approach for discrete event simulation data assimilation. Hence, we proposed a particle filter-based discrete event simulation data assimilation method and took the unmanned aerial vehicle (UAV) maintenance service system as a proof of concept to conduct simulation experiments. The experimental results showed that the filtered state data is closer to the real state of the system, which verifies the effectiveness of the proposed method. This research can provide a reference framework for the data assimilation process of other complex nonlinear systems, such as discrete-time and agent simulation.

Keywords: discrete event simulation, data assimilation, particle filter, model and data-driven

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Authors: Nino Damenia

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E-commerce opens a new horizon for business development, which is why the presence of e-commerce is a necessary condition for the formation, growth, and development of the country's economy. Worldwide, e-commerce turnover is growing at a high rate every year, as the electronic environment provides great opportunities for product promotion. E-commerce in Georgia is developing at a fast pace, but it is still a relatively young direction in the country's economy. Movement restrictions and other public health measures caused by the COVID-19 pandemic have reduced economic activity in most economic sectors and countries, significantly affecting production, distribution, and consumption. The pandemic has accelerated digital transformation. Digital solutions enable people and businesses to continue part of their economic and social activities remotely. This has also led to the growth of e-commerce. According to the data of the National Statistics Service of Georgia, the share of online trade is higher in cities (27.4%) than in rural areas (9.1%). The COVID-19 pandemic has forced local businesses to expand their digital offerings. The size of the local market increased 3.2 times in 2020 to 138 million GEL. And in 2018-2020, the share of local e-commerce increased from 11% to 23%. In Georgia, the state is actively engaged in the promotion of activities based on information technologies. Many measures have been taken for this purpose, but compared to other countries, this process is slow in Georgia. The purpose of the study is to determine development prospects for the economy of Georgia based on the analysis of electronic commerce. Research was conducted around the issues using Georgian and foreign scientists' articles, works, reports of international organizations, collections of scientific conferences, and scientific electronic databases. The empirical base of the research is the data and annual reports of the National Statistical Service of Georgia, internet resources of world statistical materials, and others. While working on the article, a questionnaire was developed, based on which an electronic survey of certain types of respondents was conducted. The conducted research was related to determining how intensively Georgian citizens use online shopping, including which age category uses electronic commerce, for what purposes, and how satisfied they are. Various theoretical and methodological research tools, as well as analysis, synthesis, comparison, and other types of methods, are used to achieve the set goal in the research process. The research results and recommendations will contribute to the development of e-commerce in Georgia and economic growth based on it.

Keywords: e-commerce, information technology, pandemic, digital transformation

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Authors: Saeed Ullah Jan, Waheed Ullah Kha

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Purpose of the study: This study aims to assess faculty members' information literacy skills in public sector medical colleges in Khyber Pakhtunkhwa. Design/Methodology/approach: The descriptive research design was used to conduct and accomplish the study's objectives. The research population consisted of faculty members at public sector medical colleges in Khyber Pakhtunkhwa southern region. Professors, Associate Professors, Assistant Professors, Lecturers, and demonstrators comprise the faculty. The adapted questionnaires were modified and used as data collection instruments. Key findings: The majority of the public sector medical college faculty recognizes the various sources of information, and they use both printed and online materials to identify needed information. The majority of faculty at these medical colleges consults monographs/textbooks regularly, preceded by online journals/medical databases. A good number of medical faculty members opted to use the HEC digital library to locate and access their contents. Delimitations of the study: This study is delimited to three public sector medical colleges operate in southern districts: Khyber Medical University Institute of Medical Sciences (KIMS) in Kohat, the Gomal Medical College (GMC) in Dera Ismail Khan, and the Bannu Medical College (BMC) in Bannu. Practical implication(s): The findings of the study will motivate the policymakers and authorities of these three medical colleges in the southern region of Khyber Pakhtunkhwa to enhance the information literacy skills of medical faculty. This practice will result in an effective medical education in the province. Contribution to the knowledge: No significant work has been done on the Faculty's Information literacy skills at public sector medical colleges in Khyber Pakhtunkhwa. This study will add valuable literature to the literary world.

Keywords: information literacy skills-Khyber Pakhtunkhwa, information literacy skills-medical faculty-Khyber Pakhtunkhwa, medical sciences, information literacy, information-literacy-Pakistan

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Authors: Sadam Alwadi

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Outlier values become a problem that frequently occurs in the data observation or recording process. Thus, the need for data imputation has become an essential matter. In this work, it will make use of the methods described in the prior work to detect the outlier values based on a collection of stock market data. In order to implement the detection and find some solutions that maybe helpful for investors, real closed price data were obtained from the Amman Stock Exchange (ASE). Tukey and Maximum Overlapping Discrete Wavelet Transform (MODWT) methods will be used to impute the detect the outlier values.

Keywords: outlier values, imputation, stock market data, detecting, estimation

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Authors: P. Jayashree, S. Rajkumar

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With social networks and smart devices generating a multitude of data, effective data management is the need of the hour for networks and cloud applications. Some applications need effective storage while some other applications need effective communication over networks and data reduction comes as a handy solution to meet out both requirements. Most of the data compression techniques are based on data statistics and may result in either lossy or lossless data reductions. Though lossy reductions produce better compression ratios compared to lossless methods, many applications require data accuracy and miniature details to be preserved. A variety of data compression algorithms does exist in the literature for different forms of data like text, image, and multimedia data. In the proposed work, a generic progressive compression algorithm, based on probabilistic encoding, called PEINS is projected as an enhancement over irrational number stored coding technique to cater to storage issues of increasing data volumes as a cost effective solution, which also offers data security as a secondary outcome to some extent. The proposed work reveals cost effectiveness in terms of better compression ratio with no deterioration in compression time.

Keywords: compression ratio, generic compression, irrational number storage, probabilistic encoding

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Authors: Femi Elegbeleye, Omobayo Esan, Muienge Mbodila, Patrick Bowe

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This paper focused on cost effective storage architecture using fog and cloud data storage gateway and presented the design of the framework for the data privacy model and data analytics framework on a real-time analysis when using machine learning method. The paper began with the system analysis, system architecture and its component design, as well as the overall system operations. The several results obtained from this study on data privacy model shows that when two or more data privacy model is combined we tend to have a more stronger privacy to our data, and when fog storage gateway have several advantages over using the traditional cloud storage, from our result shows fog has reduced latency/delay, low bandwidth consumption, and energy usage when been compare with cloud storage, therefore, fog storage will help to lessen excessive cost. This paper dwelt more on the system descriptions, the researchers focused on the research design and framework design for the data privacy model, data storage, and real-time analytics. This paper also shows the major system components and their framework specification. And lastly, the overall research system architecture was shown, its structure, and its interrelationships.

Keywords: IoT, fog, cloud, data analysis, data privacy

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Authors: Sina Mahdavi

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Background and Objective: Multiple sclerosis (MS) is an inflammatory autoimmune disease of the CNS that affects the myelination process in the central nervous system (CNS). Complex interactions of various "environmental or infectious" factors may act as triggers in autoimmunity and disease progression. The association between viral infections, especially human endogenous retrovirus (HERV) and MS is one potential cause that is not well understood. This study aims to summarize the available data on HERV infection in MS disease progression. Materials and Methods: For this study, the keywords "Multiple sclerosis", "Human endogenous retrovirus", and "central nervous system" in the databases PubMed, Google Scholar, Sid, and MagIran between 2016 and 2022 were searched and 14 articles chosen, studied, and analyzed. Results: In the leptomeningeal cells of MS patients, a retrovirus-like element associated with reverse transcriptase (RT) activity called multiple sclerosis-associated retroviruses (MSRV) has been identified. HERVs are expressed in the human CNS despite mechanisms to suppress their expression. External factors, especially viral infections such as influenza virus, Epstein-Barr virus, and herpes simplex virus type 1, can activate HERV gene expression. The MSRV coat protein is activated by activating TLR4 at the brain surface, particularly in oligodendroglial progenitor cells and macrophages, leading to immune cascades followed by the downregulation of myelin protein expression. The HERV-K18 envelope gene (env) acts as a superantigen and induces inflammatory responses in patients with MS. Conclusion: There is a high expression of endogenous retroviruses during the course of MS, which indicates the relationship between HERV and MS, that this virus can play a role in the development of MS by creating an inflammatory state. Therefore, measures to modulate the expression of endogenous retroviruses may be effective in reducing inflammatory processes in demyelinated areas of MS patients.

Keywords: multiple sclerosis, human endogenous retrovirus, central nervous system, MSRV

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Authors: Nordila Ahmad, Thamer Mohammad, Bruce W. Melville, Zuliziana Suif

Abstract:

Current methods for predicting local scour at wide bridge piers, were developed on the basis of laboratory studies and very limited scour prediction were tested with field data. Laboratory wide pier scour equation from previous findings with field data were presented. A wide range of field data were used and it consists of both live-bed and clear-water scour. A method for assessing the quality of the data was developed and applied to the data set. Three other wide pier-scour equations from the literature were used to compare the performance of each predictive method. The best-performing scour equation were analyzed using statistical analysis. Comparisons of computed and observed scour depths indicate that the equation from the previous publication produced the smallest discrepancy ratio and RMSE value when compared with the large amount of laboratory and field data.

Keywords: field data, local scour, scour equation, wide piers

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Authors: Inkyu Kim, SangMan Moon

Abstract:

This IEEE 802.11b protocol provides up to 11Mbps data rate, whereas aerospace industry wants to seek higher data rate COTS data link system in the UAV. The Total Maximum Throughput (TMT) and delay time are studied on many researchers in the past years This paper provides theoretical data throughput performance of UAV formation flight data link using the existing 802.11b performance theory. We operate the UAV formation flight with more than 30 quad copters with 802.11b protocol. We may be predicting that UAV formation flight numbers have to bound data link protocol performance limitations.

Keywords: UAV datalink, UAV formation flight datalink, UAV WLAN datalink application, UAV IEEE 802.11b datalink application

Procedia PDF Downloads 392

Authors: Radoslav Židek, Veronika Šidlová, Radovan Kasarda, Birgit Fuerst-Waltl

Abstract:

Machine learning represents a set of topics dealing with the creation and evaluation of algorithms that facilitate pattern recognition, classification, and prediction, based on models derived from existing data. The data can present identification patterns which are used to classify into groups. The result of the analysis is the pattern which can be used for identification of data set without the need to obtain input data used for creation of this pattern. An important requirement in this process is careful data preparation validation of model used and its suitable interpretation. For breeders, it is important to know the origin of animals from the point of the genetic diversity. In case of missing pedigree information, other methods can be used for traceability of animal´s origin. Genetic diversity written in genetic data is holding relatively useful information to identify animals originated from individual countries. We can conclude that the application of data mining for molecular genetic data using supervised learning is an appropriate tool for hypothesis testing and identifying an individual.

Keywords: genetic data, Pinzgau cattle, supervised learning, machine learning

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Authors: Nidhi Gopal

Abstract:

Routing is the process of moving a packet of data from source to destination and enables messages to pass from one computer to another and eventually reach the target machine. A router is a networking device that forwards data packets between computer networks. It is connected to two or more data lines from different networks (as opposed to a network switch, which connects data lines from one single network). This paper mainly emphasizes upon the study of router device, its top level architecture, and how various sub-modules of router i.e. Register, FIFO, FSM and Synchronizer are synthesized, and simulated and finally connected to its top module.

Keywords: data packets, networking, router, routing

Procedia PDF Downloads 812

Authors: Julius Onyancha, Valentina Plekhanova

Abstract:

One of the significant issues facing web users is the amount of noise in web data which hinders the process of finding useful information in relation to their dynamic interests. Current research works consider noise as any data that does not form part of the main web page and propose noise web data reduction tools which mainly focus on eliminating noise in relation to the content and layout of web data. This paper argues that not all data that form part of the main web page is of a user interest and not all noise data is actually noise to a given user. Therefore, learning of noise web data allocated to the user requests ensures not only reduction of noisiness level in a web user profile, but also a decrease in the loss of useful information hence improves the quality of a web user profile. Noise Web Data Learning (NWDL) tool/algorithm capable of learning noise web data in web user profile is proposed. The proposed work considers elimination of noise data in relation to dynamic user interest. In order to validate the performance of the proposed work, an experimental design setup is presented. The results obtained are compared with the current algorithms applied in noise web data reduction process. The experimental results show that the proposed work considers the dynamic change of user interest prior to elimination of noise data. The proposed work contributes towards improving the quality of a web user profile by reducing the amount of useful information eliminated as noise.

Keywords: web log data, web user profile, user interest, noise web data learning, machine learning

Procedia PDF Downloads 265