Search results for: data databases

Authors: Iman Abu-Rub

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This research paper examines if Artificial Intelligence is, in fact, racist or not. Different studies from all around the world, and covering different communities were analyzed to further understand AI’s true implications over different communities. The black community, Asian community, and Muslim community were all analyzed and discussed in the paper to figure out if AI is biased or unbiased towards these specific communities. It was found that the biggest problem AI faces is the biased distribution of data collection. Most of the data inserted and coded into AI are of a white male, which significantly affects the other communities in terms of reliable cultural, political, or medical research. Nonetheless, there are various research was done that help increase awareness of this issue, but also solve it completely if done correctly. Governments and big corporations are able to implement different strategies into their AI inventions to avoid any racist results, which could cause hatred culturally but also unreliable data, medically, for example. Overall, Artificial Intelligence is not racist per se, but the data implementation and current racist culture online manipulate AI to become racist.

Keywords: social media, artificial intelligence, racism, discrimination

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Authors: Chico Horacio Jose Sambo

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Recently neural network has gained popularity when come to solve complex nonlinear problems. Permeability is one of fundamental reservoir characteristics system that are anisotropic distributed and non-linear manner. For this reason, permeability prediction from well log data is well suited by using neural networks and other computer-based techniques. The main goal of this paper is to predict reservoir permeability from well logs data by using neural network approach. A multi-layered perceptron trained by back propagation algorithm was used to build the predictive model. The performance of the model on net results was measured by correlation coefficient. The correlation coefficient from testing, training, validation and all data sets was evaluated. The results show that neural network was capable of reproducing permeability with accuracy in all cases, so that the calculated correlation coefficients for training, testing and validation permeability were 0.96273, 0.89991 and 0.87858, respectively. The generalization of the results to other field can be made after examining new data, and a regional study might be possible to study reservoir properties with cheap and very fast constructed models.

Keywords: neural network, permeability, multilayer perceptron, well log

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Authors: Usman Qamar, Younus Javed

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Frequent pattern mining is the process of finding a pattern (a set of items, subsequences, substructures, etc.) that occurs frequently in a data set. It was proposed in the context of frequent itemsets and association rule mining. Frequent pattern mining is used to find inherent regularities in data. What products were often purchased together? Its applications include basket data analysis, cross-marketing, catalog design, sale campaign analysis, Web log (click stream) analysis, and DNA sequence analysis. However, one of the bottlenecks of frequent itemset mining is that as the data increase the amount of time and resources required to mining the data increases at an exponential rate. In this investigation a new algorithm is proposed which can be uses as a pre-processor for frequent itemset mining. FASTER (FeAture SelecTion using Entropy and Rough sets) is a hybrid pre-processor algorithm which utilizes entropy and rough-sets to carry out record reduction and feature (attribute) selection respectively. FASTER for frequent itemset mining can produce a speed up of 3.1 times when compared to original algorithm while maintaining an accuracy of 71%.

Keywords: rough-sets, classification, feature selection, entropy, outliers, frequent itemset mining

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Authors: Salihah Alghamdi, Surajit Ray

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Space-time data can be observed over irregularly shaped manifolds, which might have complex boundaries or interior gaps. Most of the existing methods do not consider the shape of the data, and as a result, it is difficult to model irregularly shaped data accommodating the complex domain. We used a method that can deal with space-time data that are distributed over non-planner shaped regions. The method is based on partial differential equations and finite element analysis. The model can be estimated using a penalized least squares approach with a regularization term that controls the over-fitting. The model is regularized using two roughness penalties, which consider the spatial and temporal regularities separately. The integrated square of the second derivative of the basis function is used as temporal penalty. While the spatial penalty consists of the integrated square of Laplace operator, which is integrated exclusively over the domain of interest that is determined using finite element technique. In this paper, we applied a spatio-temporal regression model with partial differential equations regularization (ST-PDE) approach to analyze a remote sensing data measuring the greenness of vegetation, measure by an index called enhanced vegetation index (EVI). The EVI data consist of measurements that take values between -1 and 1 reflecting the level of greenness of some region over a period of time. We applied (ST-PDE) approach to irregular shaped region of the EVI data. The approach efficiently accommodates the irregular shaped regions taking into account the complex boundaries rather than smoothing across the boundaries. Furthermore, the approach succeeds in capturing the temporal variation in the data.

Keywords: irregularly shaped domain, partial differential equations, finite element analysis, complex boundray

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Authors: P. Daniels, T. Adonis, P. September-Brown, R. Comalie

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The community engagement unit at the University of the Western Cape was tasked with establishing a community engagement database. The database would store information of all community engagement projects related to the university. The wealth of knowledge obtained from the various disciplines would be used to facilitate interdisciplinary collaboration within the university, as well as facilitating community university partnership opportunities. The purpose of this qualitative study was to explore electronic data collection through the development of a database. Two types of electronic data collection platforms were used, namely online questionnaire and email. The semi structured questionnaire was used to collect data related to community engagement projects from different faculties and departments at the university. There are many benefits for using an electronic data collection platform, such as reduction of costs and time, ease in reaching large numbers of potential respondents, and the possibility of providing anonymity to participants. Despite all the advantages of using the electronic platform, there were as many challenges, as depicted in our findings. The findings suggest that certain barriers existed by using an electronic platform for data collection, even though it was in an academic environment, where knowledge and resources were in abundance. One of the challenges experienced in this process was the lack of dissemination of information via email to staff within faculties. The actual online software used for the questionnaire had its own limitations, such as only being able to access the questionnaire from the same electronic device. In a few cases, academics only completed the questionnaire after a telephonic prompt or face to face meeting about "Is higher education in South Africa ready to embrace electronic platform in data collection?"

Keywords: community engagement, database, data collection, electronic platform, electronic tools, knowledge sharing, university

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Authors: Divya Juneja, Sukhjeet Kaur Matharu

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Purpose: The paper is aimed at identifying the challenging factors experienced by the women entrepreneurs in India in operating their enterprises amidst the challenges posed by the COVID-19 pandemic. Methodology: The sample for the study comprised 396 women entrepreneurs from different regions of India. A purposive sampling technique was adopted for data collection. Data was collected through a self-administered questionnaire. Analysis was performed using the SPSS package for quantitative data analysis. Findings: The results of the study state that entrepreneurial characteristics, resourcefulness, networking, adaptability, and continuity have a positive influence on the resiliency of women entrepreneurs when faced with a crisis situation. Practical Implications: The findings of the study have some important implications for women entrepreneurs, organizations, government, and other institutions extending support to entrepreneurs.

Keywords: women entrepreneurs, analysis, data analysis, positive influence, resiliency

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Authors: Mohammed Abdullah Alshahrani

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The research focuses on investigating the use of partial least squares (PLS) methodology for addressing challenges associated with high-dimensional correlated data. Recent technological advancements have led to experiments producing data characterized by a large number of variables compared to observations, with substantial inter-variable correlations. Such data patterns are common in chemometrics, where near-infrared (NIR) spectrometer calibrations record chemical absorbance levels across hundreds of wavelengths, and in genomics, where thousands of genomic regions' copy number alterations (CNA) are recorded from cancer patients. PLS serves as a widely used method for analyzing high-dimensional data, functioning as a regression tool in chemometrics and a classification method in genomics. It handles data complexity by creating latent variables (components) from original variables. However, applying PLS can present challenges. The study investigates key areas to address these challenges, including unifying interpretations across three main PLS algorithms and exploring unusual negative shrinkage factors encountered during model fitting. The research presents an alternative approach to addressing the interpretation challenge of predictor weights associated with PLS. Sparse estimation of predictor weights is employed using a penalty function combining a lasso penalty for sparsity and a Cauchy distribution-based penalty to account for variable dependencies. The results demonstrate sparse and grouped weight estimates, aiding interpretation and prediction tasks in genomic data analysis. High-dimensional data scenarios, where predictors outnumber observations, are common in regression analysis applications. Ordinary least squares regression (OLS), the standard method, performs inadequately with high-dimensional and highly correlated data. Copy number alterations (CNA) in key genes have been linked to disease phenotypes, highlighting the importance of accurate classification of gene expression data in bioinformatics and biology using regularized methods like PLS for regression and classification.

Keywords: partial least square regression, genetics data, negative filter factors, high dimensional data, high correlated data

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Authors: Maisyatus Suadaa Irfana, Nove Eka Variant Anna, Dyah Puspitasari Sri Rahayu

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Technological developments provide convenience to all the people. Nowadays, the communication of human with the computer is done via text. With the development of technology, human and computer communications have been conducted with a voice like communication between human beings. It provides an easy facility for many people, especially those who have special needs. Voice search technology is applied in the search of book collections in the OPAC (Online Public Access Catalog), so library visitors will find it faster and easier to find books that they need. Integration with Google is needed to convert the voice into text. To optimize the time and the results of searching, Server will download all the book data that is available in the server database. Then, the data will be converted into JSON format. In addition, the incorporation of some algorithms is conducted including Decomposition (parse) in the form of array of JSON format, the index making, analyzer to the result. It aims to make the process of searching much faster than the usual searching in OPAC because the data are directly taken to the database for every search warrant. Data Update Menu is provided with the purpose to enable users perform their own data updates and get the latest data information.

Keywords: OPAC, voice, searching, faster

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Authors: M. Uysal, M. Yilmaz, I. Tiryakioğlu

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Digital Terrain Model (DTM) is a digital numerical representation of the Earth's surface. DTMs have been applied to a diverse field of tasks, such as urban planning, military, glacier mapping, disaster management. In the expression of the Earth' surface as a mathematical model, an infinite number of point measurements are needed. Because of the impossibility of this case, the points at regular intervals are measured to characterize the Earth's surface and DTM of the Earth is generated. Hitherto, the classical measurement techniques and photogrammetry method have widespread use in the construction of DTM. At present, RADAR, LiDAR, and stereo satellite images are also used for the construction of DTM. In recent years, especially because of its superiorities, Airborne Light Detection and Ranging (LiDAR) has an increased use in DTM applications. A 3D point cloud is created with LiDAR technology by obtaining numerous point data. However recently, by the development in image mapping methods, the use of unmanned aerial vehicles (UAV) for photogrammetric data acquisition has increased DTM generation from image-based point cloud. The accuracy of the DTM depends on various factors such as data collection method, the distribution of elevation points, the point density, properties of the surface and interpolation methods. In this study, the random data reduction method is compared for DTMs generated from image based point cloud data. The original image based point cloud data set (100%) is reduced to a series of subsets by using random algorithm, representing the 75, 50, 25 and 5% of the original image based point cloud data set. Over the ANS campus of Afyon Kocatepe University as the test area, DTM constructed from the original image based point cloud data set is compared with DTMs interpolated from reduced data sets by Kriging interpolation method. The results show that the random data reduction method can be used to reduce the image based point cloud datasets to 50% density level while still maintaining the quality of DTM.

Keywords: DTM, Unmanned Aerial Vehicle (UAV), uniform, random, kriging

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Authors: Chen Chou, Feng-Tyan Lin

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Big Data has been attracted a lot of attentions in many fields for analyzing research issues based on a large number of maternal data. Electronic Toll Collection (ETC) is one of Intelligent Transportation System (ITS) applications in Taiwan, used to record starting point, end point, distance and travel time of vehicle on the national freeway. This study, taking advantage of ETC big data, combined with urban planning theory, attempts to explore various phenomena of inter-city transportation activities. ETC, one of government's open data, is numerous, complete and quick-update. One may recall that living area has been delimited with location, population, area and subjective consciousness. However, these factors cannot appropriately reflect what people’s movement path is in daily life. In this study, the concept of "Living Area" is replaced by "Influence Range" to show dynamic and variation with time and purposes of activities. This study uses data mining with Python and Excel, and visualizes the number of trips with GIS to explore influence range of Tainan city and the purpose of trips, and discuss living area delimited in current. It dialogues between the concepts of "Central Place Theory" and "Living Area", presents the new point of view, integrates the application of big data, urban planning and transportation. The finding will be valuable for resource allocation and land apportionment of spatial planning.

Keywords: Big Data, ITS, influence range, living area, central place theory, visualization

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Authors: Tapan Jain, Davender Singh Saini

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Clustering is a useful mechanism in wireless sensor networks which helps to cope with scalability and data transmission problems. The basic aim of our research work is to provide efficient clustering using Hierarchical agglomerative clustering (HAC). If the distance between the sensing nodes is calculated using their location then it’s quantitative HAC. This paper compares the various agglomerative clustering techniques applied in a wireless sensor network using the quantitative data. The simulations are done in MATLAB and the comparisons are made between the different protocols using dendrograms.

Keywords: routing, hierarchical clustering, agglomerative, quantitative, wireless sensor network

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Authors: Sophia Shi

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Acute kidney injury (AKI) is the sudden onset of kidney damage in which the kidneys cannot filter waste from the blood, requiring emergency hospitalization. AKI patient mortality rate is high in the ICU and is virtually impossible for doctors to predict because it is so unexpected. Currently, there is no hybrid model predicting AKI that takes advantage of two types of data. De-identified patient data from the MIMIC-III database and de-identified kidney images and corresponding patient records from the Beijing Hospital of the Ministry of Health were collected. Using data features including serum creatinine among others, two numeric models using MIMIC and Beijing Hospital data were built, and with the hospital ultrasounds, an image-only model was built. Convolutional neural networks (CNN) were used, VGG and Resnet for numeric data and Resnet for image data, and they were combined into a hybrid model by concatenating feature maps of both types of models to create a new input. This input enters another CNN block and then two fully connected layers, ending in a binary output after running through Softmax and additional code. The hybrid model successfully predicted AKI and the highest AUROC of the model was 0.953, achieving an accuracy of 90% and F1-score of 0.91. This model can be implemented into urgent clinical settings such as the ICU and aid doctors by assessing the risk of AKI shortly after the patient’s admission to the ICU, so that doctors can take preventative measures and diminish mortality risks and severe kidney damage.

Keywords: Acute kidney injury, Convolutional neural network, Hybrid deep learning, Patient record data, ResNet, Ultrasound kidney images, VGG

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Authors: Taner Ersoz, Filiz Ersoz

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This study was designed enable application of multivariate technique in the interpretation of categorical data for measuring health care services satisfaction in Turkey. The data was collected from a total of 17726 respondents. The establishment of the sample group and collection of the data were carried out by a joint team from The Ministry of Health and Turkish Statistical Institute (Turk Stat) of Turkey. The multiple correspondence analysis (MCA) was used on the data of 2882 respondents who answered the questionnaire in full. The multiple correspondence analysis indicated that, in the evaluation of health services females, public employees, younger and more highly educated individuals were more concerned and complainant than males, private sector employees, older and less educated individuals. Overall 53 % of the respondents were pleased with the improvements in health care services in the past three years. This study demonstrates the public consciousness in health services and health care satisfaction in Turkey. It was found that most the respondents were pleased with the improvements in health care services over the past three years. Awareness of health service quality increases with education levels. Older individuals and males would appear to have lower expectancies in health services.

Keywords: multiple correspondence analysis, multivariate categorical data, health care services, health satisfaction survey

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Authors: Monica Violeta Achim, Sorin Nicolae Borlea

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This aim of this work is to stress the needs for enhancing the role of forensic accounting in fighting economic and financial crime, in the context of the new international regulation movements in this area enhanced by the International Federation of Accountants (IFAC). Corruption, money laundering, tax evasion and other frauds significant hamper the economic growth and human development and, ultimately, the UN Sustainable Development Goals. The present paper also stresses the role of good governance in fighting the frauds, in order to achieve the most suitable sustainable development of the society. In this view, we made a bibliometric systematic review on forensic accounting and its contribution towards fraud detection and prevention and theirs relationship with good governance and Sustainable Developments Goals (SDGs). In this view, two powerful bibliometric visual software tools, VosViewer and CiteSpace are used in order to analyze published papers identifies in Scopus and Web of Science databases over the time. Our findings reveal the main red flags identified in literature as used tools by forensic accounting, the evolution in time of the interest of the topic, the distribution in space among world countries and connectivity with patterns of a good governance. Visual designs and scientific maps are useful to show these findings, in a visual way. Our findings are useful for managers and policy makers to provide important avenues that may help in reaching the 2030 Agenda for Sustainable Development, adopted by all United Nations Member States in 2015, in the area of using forensic accounting in preventing frauds.

Keywords: forensic accounting, frauds, red flags, SDGs

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Authors: Giacoma Pace

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The research seeks to evaluate whether students with impairments can achieve enhanced academic progress by actively engaging in collaborative problem-solving activities with teachers and peers, to overcome the obstacles rooted in socio-economic disparities. Furthermore, the research underscores the significance of fostering students' self-awareness regarding their learning process and encourages teachers to adopt a more interactive teaching approach. The research also posits that reducing conventional face-to-face lessons can motivate students to explore alternative learning methods, such as collaborative teamwork and peer education within the classroom. To address socio-cultural barriers it is imperative to assess their internet access and possession of technological devices, as these factors can contribute to a digital divide. The research features a case study of a Flipped Classroom Learning Unit, administered to six third-year high school classes: Scientific Lyceum, Technical School, and Vocational School, within the city of Turin, Italy. Data are about teachers and the students involved in the case study, some impaired students in each class, level of entry, students’ performance and attitude before using Flipped Classrooms, level of motivation, family’s involvement level, teachers’ attitude towards Flipped Classroom, goal obtained, the pros and cons of such activities, technology availability. The selected schools were contacted; meetings for the English teachers to gather information about their attitude and knowledge of the Flipped Classroom approach. Questionnaires to teachers and IT staff were administered. The information gathered, was used to outline the profile of the subjects involved in the study and was further compared with the second step of the study made up of a study conducted with the classes of the selected schools. The learning unit is the same, structure and content are decided together with the English colleagues of the classes involved. The pacing and content are matched in every lesson and all the classes participate in the same labs, use the same materials, homework, same assessment by summative and formative testing. Each step follows a precise scheme, in order to be as reliable as possible. The outcome of the case study will be statistically organised. The case study is accompanied by a study on the literature concerning EFL approaches and the Flipped Classroom. Document analysis method was employed, i.e. a qualitative research method in which printed and/or electronic documents containing information about the research subject are reviewed and evaluated with a systematic procedure. Articles in the Web of Science Core Collection, Education Resources Information Center (ERIC), Scopus and Science Direct databases were searched in order to determine the documents to be examined (years considered 2000-2022).

Keywords: flipped classroom, impaired, inclusivity, peer instruction

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Authors: Paul Dallyn, Ashraf El-Hamalawi, Alessandro Palmeri, Bob Knight

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In order to better understand the long term implications of the grout wear failure mode in large-diameter plain-sided grouted connections, a numerical model has been developed and calibrated that can take advantage of existing operational plant data to predict the wear accumulation for the actual load conditions experienced over a given period, thus limiting the need for expensive monitoring systems. This model has been derived and calibrated based on site structural condition monitoring (SCM) data and supervisory control and data acquisition systems (SCADA) data for two operational wind turbine generator substructures afflicted with this challenge, along with experimentally derived wear rates.

Keywords: grouted connection, numerical model, offshore structure, wear, wind energy

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Authors: Michael Josef Schwerer

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Background: The identification of unknown victims from mass disasters, violent crimes, or terrorist attacks is frequently facilitated through information from missing persons lists, portrait photos, old or recent pictures showing unique characteristics of a person such as scars or tattoos, or simply reference samples from blood relatives for DNA analysis. In contrast, the identification or at least the characterization of an unknown perpetrator from criminal or terrorist actions remains challenging, particularly in the absence of material or data for comparison, such as fingerprints, which had been previously stored in criminal records. In scenarios that result in high levels of destruction of the perpetrator’s corpse, for instance, blast or fire events, the chance for a positive identification using standard techniques is further impaired. Objectives: This study shows the forensic genetic procedures in the Legal Medicine Service of the German Air Force for the identification of unknown individuals, including such cases in which reference samples are not available. Scenarios requiring such efforts predominantly involve aircraft crash investigations, which are routinely carried out by the German Air Force Centre of Aerospace Medicine as one of the Institution’s essential missions. Further, casework by military police or military intelligence is supported based on administrative cooperation. In the talk, data from study projects, as well as examples from real casework, will be demonstrated and discussed with the audience. Methods: Forensic genetic identification in our laboratories involves the analysis of Short Tandem Repeats and Single Nucleotide Polymorphisms in nuclear DNA along with mitochondrial DNA haplotyping. Extended DNA analysis involves phenotypic markers for skin, hair, and eye color together with the investigation of a person’s biogeographic ancestry. Assessment of the biological age of an individual employs CpG-island methylation analysis using bisulfite-converted DNA. Forensic Investigative Genealogy assessment allows the detection of an unknown person’s blood relatives in reference databases. Technically, end-point-PCR, real-time PCR, capillary electrophoresis, pyrosequencing as well as next generation sequencing using flow-cell-based and chip-based systems are used. Results and Discussion: Optimization of DNA extraction from various sources, including difficult matrixes like formalin-fixed, paraffin-embedded tissues, degraded specimens from decomposed bodies or from decedents exposed to blast or fire events, provides soil for successful PCR amplification and subsequent genetic profiling. For cases with extremely low yields of extracted DNA, whole genome preamplification protocols are successfully used, particularly regarding genetic phenotyping. Improved primer design for CpG-methylation analysis, together with validated sampling strategies for the analyzed substrates from, e.g., lymphocyte-rich organs, allows successful biological age estimation even in bodies with highly degraded tissue material. Conclusions: Successful identification of unknown individuals or at least their phenotypic characterization using pigmentation markers together with age-informative methylation profiles, possibly supplemented by family tree search employing Forensic Investigative Genealogy, can be provided in specialized laboratories. However, standard laboratory procedures must be adapted to work with difficult and highly degraded sample materials.

Keywords: identification, forensic genetics, phenotypic markers, CPG methylation, biological age estimation, forensic investigative genealogy

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Authors: Ons Slimene, Aroua Taamallah, Maha Khemaja

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In recent years, human activity recognition (HAR) has been a key area of research due to its diverse applications. It has garnered increasing attention in the field of computer vision. HAR plays an important role in people’s daily lives as it has the ability to learn advanced knowledge about human activities from data. In HAR, activities are usually represented by exploiting different types of sensors, such as embedded sensors or visual sensors. However, these sensors have limitations, such as local obstacles, image-related obstacles, sensor unreliability, and consumer concerns. Recently, several deep learning-based approaches have been proposed for HAR and these approaches are classified into two categories based on the type of data used: vision-based approaches and sensor-based approaches. This research paper highlights the importance of multimodal data fusion from skeleton data obtained from videos and data generated by embedded sensors using deep neural networks for achieving HAR. We propose a deep multimodal fusion network based on a twostream architecture. These two streams use the Convolutional Neural Network combined with the Bidirectional LSTM (CNN BILSTM) to process skeleton data and data generated by embedded sensors and the fusion at the feature level is considered. The proposed model was evaluated on a public OPPORTUNITY++ dataset and produced a accuracy of 96.77%.

Keywords: human activity recognition, action recognition, sensors, vision, human-centric sensing, deep learning, context-awareness

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Authors: Shi-xia Xu, Zai-wen Zhang, Ru-xue Chen, Shan Zhou, Xiang-feng Tang

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Objective: The clinical influence of HLA-DPB1 mismatches on clinical outcome of HSCT is less clear. This is the first meta-analysis to study the HLA-DPB1 matching statues on clinical outcomes after unrelated donor HSCT. Methods: We searched the CIBMTR, Cochrane Central Register of Controlled Trials (CENTRAL) and related databases (1995.01–2017.06) for all relevant articles. Comparative studies were used to investigate the HLA-DPB1 loci mismatches on clinical outcomes after unrelated donor HSCT, such as the disease-free survival (DFS), overall survival, GVHD, relapse, and transplant-related mortality (TRM). We performed meta-analysis using Review Manager 5.2 software and funnel plot to assess the bias. Results: At first, 1246 articles were retrieved, and 18 studies totaling 26368 patients analyzed. Pooled comparisons of studies found that the HLA-DPB1 mismatched group had a lower rate of DFS than the DPB1-matched group, and lower OS in non-T cell depleted transplantation. The DPB1 mismatched group has a higher incidence of aGVHD and more severe ( ≥ III degree) aGvHD, lower rate of relapse and higher TRM. Moreover, compared with 1-antigen mismatch, 2-antigen mismatched led to a higher risk of TRM and lower relapse rate. Conclusions: This meta-analysis indicated HLA-DPB1 has important influence on survival and transplant-related complications during unrelated donor HSCT and HLA-DPB1 donor selection strategies have been proposed based on a personalized algorithm.

Keywords: human leukocyte antigen, DPB1, transplant, meta-analysis, outcome

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Authors: Burcu Guvenek, Duygu Baysal Kurt

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The impact of foreign trade on economic growth has been discussed since the Classical Economists. Today, foreign trade has become more important for the country's economy with the increasing globalization. When it comes to foreign trade, policies which may vary from country to country and from time to time as protectionism or free trade are implemented. In general, the positive effect of foreign trade on economic growth is alleged. However, as studies supporting this general acceptance take place in the economics literature, there are also studies in the opposite direction. In this paper, the impact of foreign trade on economic growth will be investigated with the help of panel data analysis. For this research, 24 OECD countries’ GDP and foreign trade data, including the period of 1990 and 2010, will be used.

Keywords: foreign trade, economic growth, OECD countries, panel data analysis

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Authors: Emanuel Koseos

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Data-Driven Decision Making (DDDM) refers to making decisions that are based on historical data in order to inform practice, develop strategies and implement policies that benefit organizational settings. In educational technology, DDDM facilitates the implementation of differential educational learning approaches such as Educational Data Mining (EDM) and Competency-Based Education (CBE), which commonly target university classrooms. There is a current need for DDDM models applied to middle and secondary schools from a concern for assessing the needs, progress and performance of students and educators with respect to regional standards, policies and evolution of curriculums. To address these concerns, we propose a DDDM reference model developed using educational key process initiatives as inputs to a machine learning framework implemented with statistical software (SAS, R) to provide a best-practices, complex-free and automated approach for educators at their regional level. We assessed the efficiency of the model over a six-year period using data from 45 schools and grades K-12 in the Langley, BC, Canada regional school district. We concluded that the model has wider appeal, such as business learning systems.

Keywords: competency-based learning, data-driven decision making, machine learning, secondary schools

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Authors: Enerit Sacdanaku, Idriz Haxhiu

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This study was conducted in the area of Vlora Bay, Albania. Data about Sea Turtles Caretta caretta and Chelonia mydas, belonging to two periods of time (1984–1991; 2008–2014) are given. All data gathered were analyzed using recent methodologies. For all turtles captured (as by catch), the Curve Carapace Length (CCL) and Curved Carapace Width (CCW) were measured. These data were statistically analyzed, where the mean was 67.11 cm for CCL and 57.57 cm for CCW of all individuals studied (n=13). All untagged individuals of marine turtles were tagged using metallic tags (Stockbrand’s titanium tag) with an Albanian address. Sex was determined and resulted that 45.4% of individuals were females, 27.3% males and 27.3% juveniles. All turtles were studied for the presence of the epibionts. The area of Vlora Bay is used from marine turtles (Caretta caretta) as a migratory corridor to pass from the Mediterranean to the northern part of the Adriatic Sea.

Keywords: Caretta caretta, Chelonia mydas, CCL, CCW, tagging, Vlora Bay

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Authors: Farhat Imtiaz, Umar Farooq

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In this work, we developed a computational model to predict ply level failure in impacted composite laminates. Data obtained from physical testing from flat and round nose impacts of 8-, 16-, 24-ply laminates were considered. Routine inspections of the tested laminates were carried out to approximate ply by ply inflicted damage incurred. Plots consisting of load–time, load–deflection, and energy–time history were drawn to approximate the inflicted damages. Impact test generated unwanted data logged due to restrictions on testing and logging systems were also filtered. Conventional filters (built-in, statistical, and numerical) reliably predicted load thresholds for relatively thin laminates such as eight and sixteen ply panels. However, for relatively thick laminates such as twenty-four ply laminates impacted by flat nose impact generated clipped data which can just be de-noised using oscillatory algorithms. The literature search reveals that modern oscillatory data filtering and extrapolation algorithms have scarcely been utilized. This investigation reports applications of filtering and extrapolation of the clipped data utilising fast Fourier Convolution algorithm to predict load thresholds. Some of the results were related to the impact-induced damage areas identified with Ultrasonic C-scans and found to be in acceptable agreement. Based on consistent findings, utilizing of modern data filtering and extrapolation algorithms to data logged by the existing machines has efficiently enhanced data interpretations without resorting to extra resources. The algorithms could be useful for impact-induced damage approximations of similar cases.

Keywords: fibre reinforced laminates, fast Fourier algorithms, mechanical testing, data filtering and extrapolation

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Authors: Befekadu Urga Wakayo

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Introduction: Bacterial infections represent major human and animal health problems in Ethiopia. In the face of poor antibiotic regulatory mechanisms, development of antimicrobial resistance (AMR) to commonly used drugs has become a growing health and livelihood threat in the country. Monitoring and control of AMR demand close coloration between human and veterinary services as well as other relevant stakeholders. However, risk of AMR transfer from animal to human population’s remains poorly explored in Ethiopia. This systematic research literature review attempted to give an overview on AMR challenges of bovine mastitis bacteria in Ethiopia. Methodology: A web based research literature search and analysis strategy was used. Databases are considered including; PubMed, Google Scholar, Ethiopian Veterinary Association (EVA) and Ethiopian Society of Animal Production (ESAP). The key search terms and phrases were; Ethiopia, dairy, cattle, mastitis, bacteria isolation, antibiotic sensitivity and antimicrobial resistance. Ultimately, 15 research reports were used for the current analysis. Data extraction was performed using a structured Microsoft Excel format. Frequency AMR prevalence (%) was registered directly or calculated from reported values. Statistical analysis was performed on SPSS – 16. Variables were summarized by giving frequencies (n or %), Mean ± SE and demonstrative box plots. One way ANOVA and independent t test were used to evaluate variations in AMR prevalence estimates (Ln transformed). Statistical significance was determined at p < 0.050). Results: AMR in bovine mastitis bacteria was investigated in a total of 592 in vitro antibiotic sensitivity trials involving 12 different mastitis bacteria (including 1126 Gram positive and 77 Gram negative isolates) and 14 antibiotics. Bovine mastitis bacteria exhibited AMR to most of the antibiotics tested. Gentamycin had the lowest average AMR in both Gram positive (2%) and negative (1.8%) bacteria. Gram negative mastitis bacteria showed higher mean in vitro resistance levels to; Erythromycin (72.6%), Tetracycline (56.65%), Amoxicillin (49.6%), Ampicillin (47.6%), Clindamycin (47.2%) and Penicillin (40.6%). Among Gram positive mastitis bacteria higher mean in vitro resistance was observed in; Ampicillin (32.8%), Amoxicillin (32.6%), Penicillin (24.9%), Streptomycin (20.2%), Penicillinase Resistant Penicillin’s (15.4%) and Tetracycline (14.9%). More specifically, S. aurues exhibited high mean AMR against Penicillin (76.3%) and Ampicillin (70.3%) followed by Amoxicillin (45%), Streptomycin (40.6%), Tetracycline (24.5%) and Clindamycin (23.5%). E. coli showed high mean AMR to Erythromycin (78.7%), Tetracycline (51.5%), Ampicillin (49.25%), Amoxicillin (43.3%), Clindamycin (38.4%) and Penicillin (33.8%). Streptococcus spp. demonstrated higher (p =0.005) mean AMR against Kanamycin (> 20%) and full sensitivity (100%) to Clindamycin. Overall, mean Tetracycline (p = 0.013), Gentamycin (p = 0.001), Polymixin (p = 0.034), Erythromycin (p = 0.011) and Ampicillin (p = 0.009) resistance increased from the 2010’s than the 2000’s. Conclusion; the review indicated a rising AMR challenge among bovine mastitis bacteria in Ethiopia. Corresponding, public health implications demand a deeper, integrated investigation.

Keywords: antimicrobial resistance, dairy cattle, Ethiopia, Mastitis bacteria

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Authors: Amon Olimov, Umarov Jamshid, Dae-Ho Kim, Chol-U Lee, Ryum-Duck Oh

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This paper proposes IoT virtualization platform based incident information system. IoT information based environment is the platform that was developed for the purpose of collecting a variety of data by managing regionally scattered IoT devices easily and conveniently in addition to analyzing data collected from roads. Moreover, this paper configured the platform for the purpose of providing incident information based on sensed data. It also provides the same input/output interface as UNIX and Linux by means of matching IoT devices with the directory of file system and also the files. In addition, it has a variety of approaches as to the devices. Thus, it can be applied to not only incident information but also other platforms. This paper proposes the incident information system that identifies and provides various data in real time as to urgent matters on roads based on the existing USN/M2M and IoT visualization platform.

Keywords: incident information system, IoT, virtualization platform, USN, M2M

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Authors: Sean McCarthy, Patrice M. Ludwig, Will Watson

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This exploratory case study explores the deployment of Social Network Analysis (SNA) in mapping community assets in an interdisciplinary, undergraduate, team-taught course focused on income insecure populations in a rural area in the US. Specifically, it analyzes how students were taught to collect data on community assets and to visualize the connections between those assets using Kumu, an SNA data visualization tool. Further, the case study shows how social network data was also collected about student teams via their written communications in Slack, an enterprise messaging tool, which enabled instructors to manage and guide student research activity throughout the semester. The discussion presents how SNA methods can simultaneously inform both community-based research and social innovation pedagogy through the use of data visualization and collaboration-focused communication technologies.

Keywords: social innovation, social network analysis, pedagogy, problem-based learning, data visualization, information communication technologies

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Authors: Farouk Lawan Gambo

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Data compression shrinks files into fewer bits then their original presentation. It has more advantage on internet because the smaller a file, the faster it can be transferred but learning most of the concepts in data compression are abstract in nature therefore making them difficult to digest by some students (Engineers in particular). To determine the best approach toward learning data compression technique, this paper first study the learning preference of engineering students who tend to have strong active, sensing, visual and sequential learning preferences, the paper also study the advantage that mobility of learning have experienced; Learning at the point of interest, efficiency, connection, and many more. A survey is carried out with some reasonable number of students, through random sampling to see whether considering the learning preference and advantages in mobility of learning will give a promising improvement over the traditional way of learning. Evidence from data analysis using Ms-Excel as a point of concern for error-free findings shows that there is significance different in the students after using learning content provided on smart phone, also the result of the findings presented in, bar charts and pie charts interpret that mobile learning has to be promising feature of learning.

Keywords: data analysis, compression techniques, learning content, traditional learning approach

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Authors: S. Abera, T. Gidey, W. Terefe

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Introduction: Testing for HIV is the key entry point to HIV prevention, treatment, and care and support services. Hence, predictive data mining techniques can greatly benefit to analyze and discover new patterns from huge datasets like that of EDHS 2011 data. Objectives: The objective of this study is to build a predictive modeling for HIV testing and identify determinants of HIV testing for adults with age above fourteen years using data mining techniques. Methods: Cross-Industry Standard Process for Data Mining (CRISP-DM) was used to predict the model for HIV testing and explore association rules between HIV testing and the selected attributes among adult Ethiopians. Decision tree, Naïve-Bayes, logistic regression and artificial neural networks of data mining techniques were used to build the predictive models. Results: The target dataset contained 30,625 study participants; of which 16, 515 (53.9%) were women. Nearly two-fifth; 17,719 (58%), have never been tested for HIV while the rest 12,906 (42%) had been tested. Ethiopians with higher wealth index, higher educational level, belonging 20 to 29 years old, having no stigmatizing attitude towards HIV positive person, urban residents, having HIV related knowledge, information about family planning on mass media and knowing a place where to get testing for HIV showed an increased patterns with respect to HIV testing. Conclusion and Recommendation: Public health interventions should consider the identified determinants to promote people to get testing for HIV.

Keywords: data mining, HIV, testing, ethiopia

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Authors: Fatemehsadat Mortazavizadeh, Amin Dehghani, Majid Mirzaei, Nurulhuda Binti Mohammad Ramli, Adnan Dehghani

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Flood is Malaysia's most common and serious natural disaster. Kelantan River Basin is a tropical basin that experiences a rainy season during North-East Monsoon from November to March. It is also one of the hardest hit areas in Peninsular Malaysia during the heavy monsoon rainfall. Considering the consequences of the flood events, it is essential to develop the flood inundation map as part of the mitigation approach. In this study, the delineation of flood inundation zone in the area of Kelantan River basin using a hydrodynamic model is done by HEC-RAS, QGIS and ArcMap. The streamflow data has been generated with the weather generator based on the observation data. Then, the data is statistically analyzed with the Extreme Value (EV1) method for 2-, 5-, 25-, 50- and 100-year return periods. The minimum depth, maximum depth, mean depth, and the standard deviation of all the scenarios, including the OBS, are observed and analyzed. Based on the results, generally, the value of the data increases with the return period for all the scenarios. However, there are certain scenarios that have different results, which not all the data obtained are increasing with the return period. Besides, OBS data resulted in the middle range within Scenario 1 to Scenario 40.

Keywords: flood inundation, kelantan river basin, hydrodynamic model, extreme value analysis

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Authors: Takele Nemomsa, Girma Mamo, Tesfaye Balemi

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Climate change refers to a change in the state of the climate that can be identified (e.g. using statistical tests) by changes in the mean and/or variance of its properties and that persists for an extended period, typically decades or longer. In Ethiopia; Maize production in relation to climate change at regional and sub- regional scales have not been studied in detail. Thus, this study was aimed to analyse the impact of climate change on maize yield in Ambo Districts, Central Ethiopia. To this effect, weather data, soil data and maize experimental data for Arganne hybrid were used. APSIM software was used to investigate the response of maize (Zea mays) yield to different agronomic management practices using current and future (2020s–2080s) climate data. The climate change projections data which were downscaled using SDSM were used as input of climate data for the impact analysis. Compared to agronomic practices the impact of climate change on Arganne in Central Ethiopia is minute. However, within 2020s-2080s in Ambo area; the yield of Arganne hybrid is projected to reduce by 1.06% to 2.02%, and in 2050s it is projected to reduce by 1.56 While in 2080s; it is projected to increase by 1.03% to 2.07%. Thus, to adapt to the changing climate; farmers should consider increasing plant density and fertilizer rate per hectare.

Keywords: APSIM, downscaling, response, SDSM

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