Search results for: Bayesian nets

Authors: Danielle M. Barna, Kolbjørn Engeland, Thordis Thorarinsdottir, Chong-Yu Xu

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Design flood values give estimates of flood magnitude within a given return period and are essential to making adaptive decisions around land use planning, infrastructure design, and disaster mitigation. Often design flood values are needed at locations with insufficient data. Additionally, in hydrologic applications where flood retention is important (e.g., floodplain management and reservoir design), design flood values are required at different flood durations. A statistical approach to this problem is a development of a regression model for extremes where some of the parameters are dependent on flood duration in addition to being covariate-dependent. In hydrology, this is called a regional flood-duration-frequency (regional-QDF) model. Typically, the underlying statistical distribution is chosen to be the Generalized Extreme Value (GEV) distribution. However, as the support of the GEV distribution depends on both its parameters and the range of the data, special care must be taken with the development of the regional model. In particular, we find that the GEV is problematic when developing a GAMLSS-type analysis due to the difficulty of proposing a link function that is independent of the unknown parameters and the observed data. We discuss these challenges in the context of developing a regional QDF model for Norway.

Keywords: design flood values, bayesian statistics, regression modeling of extremes, extreme value analysis, GEV

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Authors: J. J. Vargas, N. Prieto, L. A. Toro

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Control charts are commonly used to monitor processes involving either variable or attribute of quality characteristics and determining the control limits as a critical task for quality engineers to improve the processes. Nonetheless, in some applications it is necessary to include an estimation of efficiency. In this paper, the ability to define the efficiency of an industrial process was added to a control chart by means of incorporating a data envelopment analysis (DEA) approach. In depth, a Bayesian estimation was performed to calculate the posterior probability distribution of parameters as means and variance and covariance matrix. This technique allows to analyse the data set without the need of using the hypothetical large sample implied in the problem and to be treated as an approximation to the finite sample distribution. A rejection simulation method was carried out to generate random variables from the parameter functions. Each resulting vector was used by stochastic DEA model during several cycles for establishing the distribution of each efficiency measures for each DMU (decision making units). A control limit was calculated with model obtained and if a condition of a low level efficiency of DMU is presented, system efficiency is out of control. In the efficiency calculated a global optimum was reached, which ensures model reliability.

Keywords: data envelopment analysis, DEA, Multivariate control chart, rejection simulation method

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Authors: Destriyanti Sugiarti, Ririn A Wulandari

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Background: Malaria disease was widespread in many countries, both tropical and sub-tropical. Tasikmalaya is a region that experienced an increase in malaria cases over the last 5 years and highest in 2013, a total of 168 positive cases of malaria. Tasikmalaya region consists of coastal and mountain areas, it has a potential place for Anopheles mosquito breeding, i.e swamp, lagoon, andrice fields.The purpose of this study was to determine the correlation of environmental risk factors with the incidence of malaria in Tasikmalaya. Methods: The design of the study is case control study with 140 samples in 5 sub-district (Cineam, Cikatomas, Cipatujah, Salopa, and Jatiwaras). This study examines the environmental factors that influence the incidence of malaria in Tasikmalaya District in 2013. The research used 14 variables: individual characteristics (education, knowledge, occupation) and environmental risk factors (mobility to endemic areas, use mosquito nets, use of wire gauze at home, use mosquito repellent, repellent use, the presence of a large animal in a cage, breeding place, the presence of larvae, temperature and humidity chamber). Results: Results demonstrated an association between occupation (0.22; 0.10-0.47), the mobility of the population to the endemic areas (37.4; 14.29-98.18) ,the presence of larvae (5.26; 1.41-19.74), and the room temperature with optimum temperature for mosquito breeding is 25-30oC (3.25; 1.62- 6.50). Conclusion: The dominant factor affecting the spread of malaria in Tasikmalaya is the mobility of the population to endemic areas. The results of the study suggest migration survey conducted activity and health promotion for preventive efforts against malaria in malaria-endemic areas, and to encourage people to behave healthy life by freeing environment of mosquito larvae and protect themselves from mosquito bites.

Keywords: Environmental risk factors, malaria, correlation, Anopheles

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Authors: Aakansha Gupta, Neha Vadnere, Tapasvi Soni, M. Anbarsi

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Protein secondary structure prediction is one of the most important goals pursued by bioinformatics and theoretical chemistry; it is highly important in medicine and biotechnology. Some aspects of protein functions and genome analysis can be predicted by secondary structure prediction. This is used to help annotate sequences, classify proteins, identify domains, and recognize functional motifs. In this paper, we represent protein secondary structure as a mathematical model. To extract and predict the protein secondary structure from the primary structure, we require a set of parameters. Any constants appearing in the model are specified by these parameters, which also provide a mechanism for efficient and accurate use of data. To estimate these model parameters there are many algorithms out of which the most popular one is the EM algorithm or called the Expectation Maximization Algorithm. These model parameters are estimated with the use of protein datasets like RS126 by using the Bayesian Probabilistic method (data set being categorical). This paper can then be extended into comparing the efficiency of EM algorithm to the other algorithms for estimating the model parameters, which will in turn lead to an efficient component for the Protein Secondary Structure Prediction. Further this paper provides a scope to use these parameters for predicting secondary structure of proteins using machine learning techniques like neural networks and fuzzy logic. The ultimate objective will be to obtain greater accuracy better than the previously achieved.

Keywords: model parameters, expectation maximization algorithm, protein secondary structure prediction, bioinformatics

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Authors: Dang Thuy Binh, Truong Thi Oanh, Le Phan Khanh Hung, Luong thi Tuong Vy

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One of the greatest environmental challenges facing Asia is the management and conservation of the marine biodiversity threaten by fisheries overexploitation, pollution, habitat destruction, and climate change. To date, a few molecular taxonomical studies has been conducted on marine fauna in Vietnam. The purpose of this study was to clarify the phylogeny of economic and ecological reef fish species in Vietnam Reef fish species covering Labridae, Scaridae, Nemipteridae, Serranidae, Acanthuridae, Lutjanidae, Lethrinidae, Mullidae, Balistidae, Pseudochromidae, Pinguipedidae, Fistulariidae, Holocentridae, Synodontidae, and Pomacentridae representing 28 genera were collected from South and Center, Vietnam. Combine with Genbank sequences, a phylogenetic tree was constructed based on 16S gene of mitochondrial DNA using maximum parsimony, maximum likelihood, and Bayesian inference approaches. The phylogram showed the well-resolved clades at genus and family level. Perciformes is the major order of reef fish species in Vietnam. The monophyly of Perciformes is not strongly supported as it was clustered in the same clade with Tetraodontiformes syngnathiformes and Beryciformes. Continue sampling of commercial fish species and classification based on morphology and genetics to build DNA barcoding of fish species in Vietnam is really necessary.

Keywords: reef fish, 16s rDNA, Vietnam, phylogeny

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Authors: Caglayan Hizal, Hasan Emre Demirci, Engin Aktas, Alper Sezer

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Offshore wind turbines consist of a long slender tower with a heavy fixed mass on the top of the tower (nacelle), together with a heavy rotating mass (blades and hub). They are always subjected to environmental loads including wind and wave loads in their service life. This study presents a three-stage methodology for reliability-based condition assessment of offshore wind-turbines against the seismic, wave and wind induced effects considering the soil-structure interaction. In this context, failure criterions are considered as serviceability limits of a monopile supporting an Offshore Wind Turbine: (a) allowable horizontal displacement at pile head should not exceed 0.2 m, (b) rotations at pile head should not exceed 0.5°. A Bayesian system identification framework is adapted to the classical reliability analysis procedure. Using this framework, a reliability assessment can be directly implemented to the updated finite element model without performing time-consuming methods. For numerical verification, simulation data of the finite model of a real offshore wind-turbine structure is investigated using the three-stage methodology.

Keywords: Offshore wind turbines, SHM, reliability assessment, soil-structure interaction

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Authors: Bel G Raggad

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An e-Learning setting is a distributed computing environment where information resources can be connected to any public network. Public networks are very unsecure which can compromise the reliability of an e-Learning environment. This study is only concerned with the intrusion detection aspect of e-Learning security and how incident responses are planned. The literature reported great advances in intrusion detection system (ids) but neglected to study an important ids weakness: suspected events are detected but an intrusion is not determined because it is not defined in ids databases. We propose an incident response generator (DIRG) that produces incident responses when the working ids system suspects an event that does not correspond to a known intrusion. Data involved in intrusion detection when ample uncertainty is present is often not suitable to formal statistical models including Bayesian. We instead adopt Dempster and Shafer theory to process intrusion data for the unknown event. The DIRG engine transforms data into a belief structure using incident scenarios deduced by the security administrator. Belief values associated with various incident scenarios are then derived and evaluated to choose the most appropriate scenario for which an automatic incident response is generated. This article provides a numerical example demonstrating the working of the DIRG system.

Keywords: decision support system, distributed computing, e-Learning security, incident response, intrusion detection, security risk, statefull inspection

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Authors: Opara Monica Onyinyechi, Ahmad Iqmer Nashriq Mohd Nazan, Suriani Ismail

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Introduction: Global estimates of malaria indicate that at least 3.3 billion people are at risk of being infected with malaria and 1.2 billion are at high risk. The review investigates the effectiveness of health education strategies to increase the level of malaria knowledge and ITN ownership among the populations of sub-Sahara African countries. Methods: A literature search was conducted using Science direct, CINAHL, PubMed, Prisma, Pico, Cochrane library and PsycINFO databases to retrieve articles published between 2000 until 2020. Eleven studies that reported on malaria prevention and intervention using health education strategies conducted in sub-Saharan Africa were included in the final review. Results: Four studies used educational interventions to teach appropriate ITN strategies and promote ITN usage. Two others focused on improving knowledge of malaria transmission, prevention, treatment, and its signs and symptoms. The remaining five studies assessed both ITN use and malaria knowledge. Of these, 10 were eligible for meta-analysis. On average, health education interventions significantly increase the odds of a person in the intervention group to report better malaria knowledge (odds ratio 1.30, 95% CI: 1.00 to 1.70, P= 0.05) and higher ITN ownership (odds ratio 1.53, 95% CI: 1.02 to 2.29, P= 0.004) compared to those in the control group. The odds of ITN ownership also substantially increases when the intervention was based on a theory or model (odds ratio 5.27, 95% CI: 3.24 to 8.58, P= 0.05). Conclusion: Our review highlights the various health education strategies used in sub-Saharan Africa to curb malaria over the past two decades. Meta-analysis findings show that health education intervention is moderately effective in improving malaria knowledge and ITN ownership and has contributed to the effort of global malaria strategy.

Keywords: malaria, health education, insecticide treated nets, sub-Saharan Africa, meta-analysis

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Authors: O. Abiodun Adeyinka, B. Adeyemo Adesesan

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The forensic use of handwriting depends on the analysis, comparison, and evaluation decisions made by forensic document examiners. When using biometric technology in forensic applications, it is necessary to compute Likelihood Ratio (LR) for quantifying strength of evidence under two competing hypotheses, namely the prosecution and the defense hypotheses wherein a set of assumptions and methods for a given data set will be made. It is therefore important to know how repeatable and reproducible our estimated LR is. This paper evaluated the accuracy and reproducibility of examiners' decisions. Confidence interval for the estimated LR were presented so as not get an incorrect estimate that will be used to deliver wrong judgment in the court of Law. The estimate of LR is fundamentally a Bayesian concept and we used two LR estimators, namely Logistic Regression (LoR) and Kernel Density Estimator (KDE) for this paper. The repeatability evaluation was carried out by retesting the initial experiment after an interval of six months to observe whether examiners would repeat their decisions for the estimated LR. The experimental results, which are based on handwriting dataset, show that LR has different confidence intervals which therefore implies that LR cannot be estimated with the same certainty everywhere. Though the LoR performed better than the KDE when tested using the same dataset, the two LR estimators investigated showed a consistent region in which LR value can be estimated confidently. These two findings advance our understanding of LR when used in computing the strength of evidence in handwriting using forensics.

Keywords: confidence interval, handwriting, kernel density estimator, KDE, logistic regression LoR, repeatability, reproducibility

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Authors: Ayadi Aya, Ghorbel Oussama, M. Obeid Abdulfattah, Abid Mohamed

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Nowadays, many wireless sensor networks have been distributed in the real world to collect valuable raw sensed data. The challenge is to extract high-level knowledge from this huge amount of data. However, the identification of outliers can lead to the discovery of useful and meaningful knowledge. In the field of wireless sensor networks, an outlier is defined as a measurement that deviates from the normal behavior of sensed data. Many detection techniques of outliers in WSNs have been extensively studied in the past decade and have focused on classic based algorithms. These techniques identify outlier in the real transaction dataset. This survey aims at providing a structured and comprehensive overview of the existing researches on classification based outlier detection techniques as applicable to WSNs. Thus, we have identified key hypotheses, which are used by these approaches to differentiate between normal and outlier behavior. In addition, this paper tries to provide an easier and a succinct understanding of the classification based techniques. Furthermore, we identified the advantages and disadvantages of different classification based techniques and we presented a comparative guide with useful paradigms for promoting outliers detection research in various WSN applications and suggested further opportunities for future research.

Keywords: bayesian networks, classification-based approaches, KPCA, neural networks, one-class SVM, outlier detection, wireless sensor networks

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Authors: Alexandra Amling, Emina Sadic

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The migration routes linking the Horn of Africa to Europe shift in response to political and humanitarian developments across the region. Abrupt changes to those routes can have profound effects on the relative ease of movement and the well-being of migrants. Somali migrants have traditionally been able to rely on a sophisticated, well-established, and reliable network of smugglers to facilitate their journey through the Sahel to Libya, but changes to the routes have undermined those networks. Recently, these shifts have made the journey from Somalia to Europe much more perilous. As the Libyan coast guard intensifies its efforts to stymie boats leaving its coast for Italian shores, arrivals in Spain are trending upwards. This paper thus, will examine how the instability in transit countries that are most commonly used by Somali migrants has had an impact on the reliability of their massive network of smuggling, and how resurgence in the Western route toward Spain provides a potentially new opportunity to reach Europe—a route that has rarely been used by the Somali migrant population in the past. First, the paper will discuss what scholars have called the pastoralist, nomadic tradition of Somalis which reportedly has allowed them to endure the long journeys from Somalia to their chosen destinations. Facilitated by relatives or clan affiliation, Somali migrants have historically been able to rely on a smuggling network that – at least tangentially – provided more security nets during their travels. Given the violence and chaos that unfolded both in Libya and Yemen in 2011 and 2015, respectively, the paper will, secondly, examine which actors in smuggling hubs increase the vulnerabilities of Somalis, pushing them to consider other routes. As a result, this paper will consider to what extent Somalis could follow the stream of other migrants to Algeria and Morocco to enter Europe via Spain. By examining one particular group of migrants and the nature and limitations of the networks associated with their movements, the paper will demonstrate the resilience and adaptability of both the migrants and the networks regardless of the ever-changing nature of migration routes and actors.

Keywords: Europe, migration, smuggling networks, Somalia

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Authors: Zehai Yu, Hui Zhu, Linglong Lin, Huawei Liang, Biao Yu, Weixin Huang

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With the development of intelligent vehicle systems, a high-precision road map is increasingly needed in many aspects. The automatic lane lines extraction and modeling are the most essential steps for the generation of a precise lane-level road map. In this paper, an automatic lane-level road map generation system is proposed. To extract the road markings on the ground, the multi-region Otsu thresholding method is applied, which calculates the intensity value of laser data that maximizes the variance between background and road markings. The extracted road marking points are then projected to the raster image and clustered using a two-stage clustering algorithm. Lane lines are subsequently recognized from these clusters by the shape features of their minimum bounding rectangle. To ensure the storage efficiency of the map, the lane lines are approximated to cubic polynomial curves using a Bayesian estimation approach. The proposed lane-level road map generation system has been tested on urban and expressway conditions in Hefei, China. The experimental results on the datasets show that our method can achieve excellent extraction and clustering effect, and the fitted lines can reach a high position accuracy with an error of less than 10 cm.

Keywords: curve fitting, lane-level road map, line recognition, multi-thresholding, two-stage clustering

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Authors: Ofosuhene O. Apenteng, Noor Azina Ismail

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Over the last several years, concern has developed over how to minimize the spread of HIV/AIDS epidemic in many countries. AIDS epidemic has tremendously stimulated the development of mathematical models of infectious diseases. The transmission dynamics of HIV infection that eventually developed AIDS has taken a pivotal role of much on building mathematical models. From the initial HIV and AIDS models introduced in the 80s, various improvements have been taken into account as how to model HIV/AIDS frameworks. In this paper, we present the impact of migration on the spread of HIV/AIDS. Epidemic model is considered by a system of nonlinear differential equations to supplement the statistical method approach. The model is calibrated using HIV incidence data from Malaysia between 1986 and 2011. Bayesian inference based on Markov Chain Monte Carlo is used to validate the model by fitting it to the data and to estimate the unknown parameters for the model. The results suggest that the migrants stay for a long time contributes to the spread of HIV. The model also indicates that susceptible individual becomes infected and moved to HIV compartment at a rate that is more significant than the removal rate from HIV compartment to AIDS compartment. The disease-free steady state is unstable since the basic reproduction number is 1.627309. This is a big concern and not a good indicator from the public heath point of view since the aim is to stabilize the epidemic at the disease equilibrium.

Keywords: epidemic model, HIV, MCMC, parameter estimation

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Authors: V. Jamuna, A. K. Chakravarty, C. S. Patil, Vijay Kumar, M. A. Mir, Rakesh Kumar

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Intense selection of buffaloes for milk production at organized herds of the country without giving due attention to fertility traits viz. pregnancy rate has lead to deterioration in their performances. Aim of study is to develop an optimum model for predicting pregnancy rate and to assess the level of pregnancy rate with respect to milk production Murrah buffaloes. Data pertaining to 1224 lactation records of Murrah buffaloes spread over a period 21 years were analyzed and it was observed that pregnancy rate depicted negative phenotypic association with lactation milk yield (-0.08 ± 0.04). For developing optimum model for pregnancy rate in Murrah buffaloes seven simple and multiple regression models were developed. Among the seven models, model II having only Service period as an independent reproduction variable, was found to be the best prediction model, based on the four statistical criterions (high coefficient of determination (R 2), low mean sum of squares due to error (MSSe), conceptual predictive (CP) value, and Bayesian information criterion (BIC). For standardizing the level of fertility with milk production, pregnancy rate was classified into seven classes with the increment of 10% in all parities, life time and their corresponding average pregnancy rate in relation to the average lactation milk yield (MY).It was observed that to achieve around 2000 kg MY which can be considered optimum for Indian Murrah buffaloes, level of pregnancy rate should be in between 30-50%.

Keywords: life time, pregnancy rate, production, service period, standardization

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Authors: Rodolfo Lorbieski, Silvia Modesto Nassar

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Many supervised machine learning tasks require decision making across numerous different classes. Multi-class classification has several applications, such as face recognition, text recognition and medical diagnostics. The objective of this article is to analyze an adapted method of Stacking in multi-class problems, which combines ensembles within the ensemble itself. For this purpose, a training similar to Stacking was used, but with three levels, where the final decision-maker (level 2) performs its training by combining outputs from the tree-based pair of meta-classifiers (level 1) from Bayesian families. These are in turn trained by pairs of base classifiers (level 0) of the same family. This strategy seeks to promote diversity among the ensembles forming the meta-classifier level 2. Three performance measures were used: (1) accuracy, (2) area under the ROC curve, and (3) time for three factors: (a) datasets, (b) experiments and (c) levels. To compare the factors, ANOVA three-way test was executed for each performance measure, considering 5 datasets by 25 experiments by 3 levels. A triple interaction between factors was observed only in time. The accuracy and area under the ROC curve presented similar results, showing a double interaction between level and experiment, as well as for the dataset factor. It was concluded that level 2 had an average performance above the other levels and that the proposed method is especially efficient for multi-class problems when compared to binary problems.

Keywords: stacking, multi-layers, ensemble, multi-class

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Authors: Tuji Jemal Ahmed

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Food security and utilization are critical aspects of ensuring the well-being and prosperity of a nation. This paper examines the current state of food security and utilization in Ethiopia, focusing on the challenges, opportunities, and strategies employed to address the issue. Ethiopia, a country in East Africa, has made significant progress in recent years to improve food security and utilization for its population. However, persistent challenges such as recurrent droughts, limited access to resources, and low agricultural productivity continue to pose obstacles to achieving sustainable food security. The paper begins by providing an overview of the concept of food security, emphasizing its multidimensional nature and the importance of access, availability, utilization, and stability. It then explores the specific factors influencing food security and utilization in Ethiopia, including natural resources, climate variability, agricultural practices, infrastructure, and socio-economic factors. Furthermore, the paper highlights the initiatives and interventions implemented by the Ethiopian government, non-governmental organizations, and international partners to enhance food security and utilization. These efforts include agricultural extension programs, irrigation projects, investments in rural infrastructure, and social safety nets to protect vulnerable populations. The study also examines the role of technology and innovation in improving food security and utilization in Ethiopia. It explores the potential of sustainable agricultural practices, such as conservation agriculture, improved seed varieties, and precision farming techniques. Additionally, it discusses the role of digital technologies in enhancing access to market information, financial services, and agricultural inputs for smallholder farmers. Finally, the paper discusses the importance of collaboration and partnerships between stakeholders, including government agencies, development organizations, research institutions, and communities, in addressing food security and utilization challenges. It emphasizes the need for integrated and holistic approaches that consider both production and consumption aspects of the food system.

Keywords: food security, utilization, Ethiopia, challenges

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Authors: Tuji Jemal Ahmed

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Food security and utilization are critical aspects of ensuring the well-being and prosperity of a nation. This paper examines the current state of food security and utilization in Ethiopia, focusing on the challenges, opportunities, and strategies employed to address the issue. Ethiopia, a country in East Africa, has made significant progress in recent years to improve food security and utilization for its population. However, persistent challenges such as recurrent droughts, limited access to resources, and low agricultural productivity continue to pose obstacles to achieving sustainable food security. The paper begins by providing an overview of the concept of food security, emphasizing its multidimensional nature and the importance of access, availability, utilization, and stability. It then explores the specific factors influencing food security and utilization in Ethiopia, including natural resources, climate variability, agricultural practices, infrastructure, and socio-economic factors. Furthermore, the paper highlights the initiatives and interventions implemented by the Ethiopian government, non-governmental organizations, and international partners to enhance food security and utilization. These efforts include agricultural extension programs, irrigation projects, investments in rural infrastructure, and social safety nets to protect vulnerable populations. The study also examines the role of technology and innovation in improving food security and utilization in Ethiopia. It explores the potential of sustainable agricultural practices, such as conservation agriculture, improved seed varieties, and precision farming techniques. Additionally, it discusses the role of digital technologies in enhancing access to market information, financial services, and agricultural inputs for smallholder farmers. Finally, the paper discusses the importance of collaboration and partnerships between stakeholders, including government agencies, development organizations, research institutions, and communities, in addressing food security and utilization challenges. It emphasizes the need for integrated and holistic approaches that consider both production and consumption aspects of the food system.

Keywords: food security, utilization, Ethiopia, challenges

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Authors: Mohamed Al-Badrashiny, Abdelghani Bellaachia

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This paper presents a classifier ensemble approach for predicting the survivability of the breast cancer patients using the latest database version of the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute. The system consists of two main components; features selection and classifier ensemble components. The features selection component divides the features in SEER database into four groups. After that it tries to find the most important features among the four groups that maximizes the weighted average F-score of a certain classification algorithm. The ensemble component uses three different classifiers, each of which models different set of features from SEER through the features selection module. On top of them, another classifier is used to give the final decision based on the output decisions and confidence scores from each of the underlying classifiers. Different classification algorithms have been examined; the best setup found is by using the decision tree, Bayesian network, and Na¨ıve Bayes algorithms for the underlying classifiers and Na¨ıve Bayes for the classifier ensemble step. The system outperforms all published systems to date when evaluated against the exact same data of SEER (period of 1973-2002). It gives 87.39% weighted average F-score compared to 85.82% and 81.34% of the other published systems. By increasing the data size to cover the whole database (period of 1973-2014), the overall weighted average F-score jumps to 92.4% on the held out unseen test set.

Keywords: classifier ensemble, breast cancer survivability, data mining, SEER

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Authors: Ghulam Haider Haidaree, Nsenda Lukumwena

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Today the world of research enjoys abundant data, available in virtually any field, technology, science, and business, politics, etc. This is commonly referred to as big data. This offers a great deal of precision and accuracy, supportive of an in-depth look at any decision-making process. When and if well used, Big Data affords its users with the opportunity to produce substantially well supported and good results. This paper leans extensively on big data to investigate possible smart solutions to urban mobility and related issues, namely road traffic accidents, its casualties, and fatalities based on multiple factors, including age, gender, location occurrences of accidents, etc. Multiple technologies were used in combination to produce an Information Communication Technology (ICT) based solution with embedded technology. Those technologies include principally Geographic Information System (GIS), Orange Data Mining Software, Bayesian Statistics, to name a few. The study uses the Leeds accident 2016 to illustrate the thinking process and extracts thereof a model that can be tested, evaluated, and replicated. The authors optimistically believe that the proposed model will significantly and smartly help to flatten the curve of road traffic accidents in the fast-growing population densities, which increases considerably motor-based mobility.

Keywords: accident factors, geographic information system, information communication technology, mobility

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Authors: Ofosuhene O. Apenteng, Noor Azina Ismail

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The human immunodeficiency virus (HIV) that causes acquired immune deficiency syndrome (AIDS) remains a global cause of morbidity and mortality. It has caused panic since its emergence. Relationships between migration and HIV/AIDS have become complex. In the absence of prospectively designed studies, dynamic mathematical models that take into account the migration movement which will give very useful information. We have explored the utility of mathematical models in understanding transmission dynamics of HIV and AIDS and in assessing the magnitude of how migration has impact on the disease. The model was calibrated to HIV and AIDS incidence data from Malaysia Ministry of Health from the period of 1986 to 2011 using Bayesian analysis with combination of Markov chain Monte Carlo method (MCMC) approach to estimate the model parameters. From the estimated parameters, the estimated basic reproduction number was 22.5812. The rate at which the susceptible individual moved to HIV compartment has the highest sensitivity value which is more significant as compared to the remaining parameters. Thus, the disease becomes unstable. This is a big concern and not good indicator from the public health point of view since the aim is to stabilize the epidemic at the disease-free equilibrium. However, these results suggest that the government as a policy maker should make further efforts to curb illegal activities performed by migrants. It is shown that our models reflect considerably the dynamic behavior of the HIV/AIDS epidemic in Malaysia and eventually could be used strategically for other countries.

Keywords: epidemic model, reproduction number, HIV, MCMC, parameter estimation

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Authors: Suriya

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Rapid socio-economic development and urbanization have led to an increasingly serious air pollution problem in Ulaanbaatar (UB), the capital of Mongolia. PM₂.₅ pollution has become the most pressing aspect of UB air pollution. Therefore, monitoring and predicting PM₂.₅ concentration in UB is of great significance for the health of the local people and environmental management. As of yet, very few studies have used models to predict PM₂.₅ concentrations in UB. Using data from 0:00 on June 1, 2018, to 23:00 on April 30, 2020, we proposed two deep learning models based on Bayesian-optimized LSTM (Bayes-LSTM) and CNN-LSTM. We utilized hourly observed data, including Himawari8 (H8) aerosol optical depth (AOD), meteorology, and PM₂.₅ concentration, as input for the prediction of PM₂.₅ concentrations. The correlation strengths between meteorology, AOD, and PM₂.₅ were analyzed using the gray correlation analysis method; the comparison of the performance improvement of the model by using the AOD input value was tested, and the performance of these models was evaluated using mean absolute error (MAE) and root mean square error (RMSE). The prediction accuracies of Bayes-LSTM and CNN-LSTM deep learning models were both improved when AOD was included as an input parameter. Improvement of the prediction accuracy of the CNN-LSTM model was particularly enhanced in the non-heating season; in the heating season, the prediction accuracy of the Bayes-LSTM model slightly improved, while the prediction accuracy of the CNN-LSTM model slightly decreased. We propose two novel deep learning models for PM₂.₅ concentration prediction in UB, Bayes-LSTM, and CNN-LSTM deep learning models. Pioneering the use of AOD data from H8 and demonstrating the inclusion of AOD input data improves the performance of our two proposed deep learning models.

Keywords: deep learning, AOD, PM2.5, prediction, Ulaanbaatar

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Authors: Luna Degife, Desalegn Belay, Yoseph Worku, Tigist Tesfaye, Assefa Tufa, Abyot Bekele, Zegeye Hailemariam, Abay Hagos

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Half of the world’s population is at risk of Dengue Fever (DF), a highly under-recognized and underreported mosquito-borne viral disease with high prevalence in the tropical and subtropical regions. Globally, an estimated 50 to 200 million cases and 20, 000 DF deaths occur annually as per the world health organization report. In Ethiopia, the first outbreak occurred in 2013 in Diredawa administration city. Afterward, three outbreaks have been reported from the eastern part of the country. We received a report of the fifth DF outbreak for Ethiopia and the second for Diredawa city on October 4, 2015. We conducted the investigation to confirm the outbreak, identify the risk factors for the repeatedly occurrence of the disease and implement control measures. We conducted un- matched case-control study and defined a suspected DF case as any person with fever of 2-7 days and 2 or more of the following: a headache, arthralgia, myalgia, rash, or bleeding from any part of the body. Controls were residents of Diredawa city without DF symptoms. We interviewed 70 Cases and 140 controls from all health facilities in Diredawa city from October 7 to 15; 2015. Epi Info version 7.1.5.0 was used to analyze the data and multivariable logistic regression was conducted to assess risk factors for DF. Sixty-nine blood samples were collected for Laboratory confirmation.The mean age for cases was 23.7±9.5 standard deviation (SD) and for controls 31.2±13 SD. Close contact with DF patient (Adjusted odds ratio (AOR)=5.36, 95% confidence interval(CI): 2.75-10.44), nonuse of long-lasting insecticidal nets (AOR=2.74, 95% CI: 1.06-7.08) and availability of stagnant water in the village (AOR=3.61, 95% CI:1.31-9.93) were independent risk factors associated with higher rates of the disease. Forty-two samples were tested positive. Endemicity of DF is becoming a concern for Diredawa city after the first outbreak. Therefore, effective vector control activities need to be part of long-term preventive measures.

Keywords: dengue fever, Diredawa, outbreak, risk factors, second

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Authors: Leonard D. Agana, Wendell Ace Dela Cruz, Arjan C. Lingaya, Bonifacio T. Doma Jr.

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The purpose of this paper is to study the predict the fuel performance parameters, which include drivability index (DI), vapor lock index (VLI), and vapor lock potential using distillation curve and Reid vapor pressure (RVP) of dual alcohol-gasoline fuel blends. Distillation curve and Reid vapor pressure were predicted using artificial neural networks (ANN) with macroscopic properties such as boiling points, RVP, and molecular weights as the input layers. The ANN consists of 5 hidden layers and was trained using Bayesian regularization. The training mean square error (MSE) and R-value for the ANN of RVP are 91.4113 and 0.9151, respectively, while the training MSE and R-value for the distillation curve are 33.4867 and 0.9927. Fuel performance analysis of the dual alcohol–gasoline blends indicated that highly volatile gasoline blended with dual alcohols results in non-compliant fuel blends with D4814 standard. Mixtures of low-volatile gasoline and 10% methanol or 10% ethanol can still be blended with up to 10% C3 and C4 alcohols. Intermediate volatile gasoline containing 10% methanol or 10% ethanol can still be blended with C3 and C4 alcohols that have low RVPs, such as 1-propanol, 1-butanol, 2-butanol, and i-butanol. Biography: Graduate School of Chemical, Biological, and Materials Engineering and Sciences, Mapua University, Muralla St., Intramuros, Manila, 1002, Philippines

Keywords: dual alcohol-gasoline blends, distillation curve, machine learning, reid vapor pressure

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Authors: Guldana Zhigerbayeva, Ming Yang

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Transportation of hazardous materials (HAZMAT) is inevitable in the process industries. The statistics show a significant number of accidents has occurred during the transportation of HAZMAT. This makes risk management of HAZMAT transportation an important topic. The tree-based methods including fault-trees, event-trees and cause-consequence analysis, and Bayesian network, have been applied to risk management of HAZMAT transportation. However, there is limited work on the development of a systematic approach. The existing approaches fail to build up the linkages between the regulatory requirements and the safety measures development. The analysis of historical data from the past accidents’ report databases would limit our focus on the specific incidents and their specific causes. Thus, we may overlook some essential elements in risk management, including regulatory compliance, field expert opinions, and suggestions. A systematic approach is needed to translate the regulatory requirements of HAZMAT transportation into specified safety measures (both technical and administrative) to support the risk management process. This study aims to first adapt the House of Quality (HoQ) to House of Safety (HoS) and proposes a new approach- Safety Function Deployment (SFD). The results of SFD will be used in a multi-criteria decision-support system to develop find an optimal route for HazMats transportation. The proposed approach will be demonstrated through a hypothetical transportation case in Kazakhstan.

Keywords: hazardous materials, risk assessment, risk management, quality function deployment

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Authors: Roman Graf, Sergiu Gordea, Heather M. Ryan

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This paper presents an approach for the classification of an unstructured format description for identification of file formats. The main contribution of this work is the employment of data mining techniques to support file format selection with just the unstructured text description that comprises the most important format features for a particular organisation. Subsequently, the file format indentification method employs file format classifier and associated configurations to support digital preservation experts with an estimation of required file format. Our goal is to make use of a format specification knowledge base aggregated from a different Web sources in order to select file format for a particular institution. Using the naive Bayes method, the decision support system recommends to an expert, the file format for his institution. The proposed methods facilitate the selection of file format and the quality of a digital preservation process. The presented approach is meant to facilitate decision making for the preservation of digital content in libraries and archives using domain expert knowledge and specifications of file formats. To facilitate decision-making, the aggregated information about the file formats is presented as a file format vocabulary that comprises most common terms that are characteristic for all researched formats. The goal is to suggest a particular file format based on this vocabulary for analysis by an expert. The sample file format calculation and the calculation results including probabilities are presented in the evaluation section.

Keywords: data mining, digital libraries, digital preservation, file format

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Authors: Erika Csanyi, Gyula Sandor

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In the course of the examination, we sought to answer the question of how and to what extent the home range and daily activity of a deer stag relocated from its habitual surroundings changes. We conducted the examination in two hunting areas in Hungary, about 50 km from one another. The control area was in the north of Somogy County, while the sample area was an area of similar features in terms of forest cover, tree stock, agricultural structure, altitude above sea level, climate, etc. in the south of Somogy County. Three middle-aged red deer stags were captured with rocket nets, immobilized and marked with GPS-Plus Collars manufactured by Vectronic Aerospace Gesellschaft mit beschränkter Haftung. One captured species was relocated. We monitored deer movements over 24-hour periods at 3 months. In the course of the examination, we analysed the behaviour of the relocated species and those that remained in their original habitat, as well as the temporal evolution of their behaviour. We examined the characteristics of the marked species’ daily activities and the hourly distance they covered. We intended to find out the difference between the behaviour of the species remaining in their original habitat and of those relocated to a more distant, but similar habitat. In summary, based on our findings, it can be established that such enforced relocations to a different habitat (e.g., game relocation) significantly increases the home range of the species in the months following relocation. Home ranges were calculated using the full data set and the minimum convex polygon (MCP) method. Relocation did not increase the nocturnal and diurnal movement activity of the animal in question. Our research found that the home range of the relocated species proved to be significantly higher than that of those species that were not relocated. The results have been presented in tabular form and have also been displayed on a map. Based on the results, it can be established that relocation inherently includes the risk of falling victim to poaching, vehicle collision. It was only in the third month following relocation that the home range of the relocated species subsided to the level of those species that were not relocated. It is advisable to take these observations into consideration in relocating red deer for nature conservation or game management purposes.

Keywords: Cervus elaphus, home range, relocation, red deer stag

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Authors: Anthony Townsend, Robyn Fasser

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While complete objectivity is a desirable scientific position from which to conduct a care and contact evaluation (CCE), it is precisely the recognition that we are inherently incapable of operating objectively that is the foundation of ethical practice and skilled assessment. Drawing upon recent research from Daniel Kahneman (2021) on the differences between noise and bias, as well as different inherent biases collectively termed “The Elephant in the Brain” by Kevin Simler and Robin Hanson (2019) from Oxford University, this presentation addresses both the various ways in which our judgments, perceptions and even procedures can be distorted and contaminated while conducting a CCE, but also considers the value of second order cybernetics and the psychodynamic concept of ‘ambivalence’ as a conceptual basis to inform our assessment methodologies to limit such errors or at least better identify them. Both a conceptual framework for ambivalence, our higher-order capacity to allow for the convergence and consideration of multiple emotional experiences and cognitive perceptions to inform our reasoning, and a practical methodology for assessment relying on data triangulation, Bayesian inference and hypothesis testing is presented as a means of promoting ethical practice for health care professionals conducting CCEs. An emphasis on widening awareness and perspective, limiting ‘splitting’, is demonstrated both in how this form of emotional processing plays out in alienating dynamics in families as well as the assessment thereof. In addressing this concept, this presentation aims to illuminate the value of ambivalence as foundational to ethical practice for assessors.

Keywords: ambivalence, forensic, psychology, noise, bias, ethics

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Authors: Sara ElElimy, Samir Moustafa

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Mobile network operators start to face many challenges in the digital era, especially with high demands from customers. Since mobile network operators are considered a source of big data, traditional techniques are not effective with new era of big data, Internet of things (IoT) and 5G; as a result, handling effectively different big datasets becomes a vital task for operators with the continuous growth of data and moving from long term evolution (LTE) to 5G. So, there is an urgent need for effective Big data analytics to predict future demands, traffic, and network performance to full fill the requirements of the fifth generation of mobile network technology. In this paper, we introduce data science techniques using machine learning and deep learning algorithms: the autoregressive integrated moving average (ARIMA), Bayesian-based curve fitting, and recurrent neural network (RNN) are employed for a data-driven application to mobile network operators. The main framework included in models are identification parameters of each model, estimation, prediction, and final data-driven application of this prediction from business and network performance applications. These models are applied to Telecom Italia Big Data challenge call detail records (CDRs) datasets. The performance of these models is found out using a specific well-known evaluation criteria shows that ARIMA (machine learning-based model) is more accurate as a predictive model in such a dataset than the RNN (deep learning model).

Keywords: big data analytics, machine learning, CDRs, 5G

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Authors: Y. Mehdipour, S. Ebrahimi, A. Jahanpour, F. Seyedzaei, B. Sabayan, A. Karimi, H. Amirifard

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Ischemic stroke is one of the common reasons for disability and mortality. The fourth leading cause of death in the world and the third in some other sources. Only 1/3 of the patients with ischemic stroke fully recover, 1/3 of them end in permanent disability and 1/3 face death. Thus, the use of predictive models to predict stroke has a vital role in reducing the complications and costs related to this disease. Thus, the aim of this study was to specify the effective factors and predict ischemic stroke with the help of DM methods. The present study was a descriptive-analytic study. The population was 213 cases from among patients referring to Ali ibn Abi Talib (AS) Hospital in Zahedan. Data collection tool was a checklist with the validity and reliability confirmed. This study used DM algorithms of decision tree for modeling. Data analysis was performed using SPSS-19 and SPSS Modeler 14.2. The results of the comparison of algorithms showed that CHAID algorithm with 95.7% accuracy has the best performance. Moreover, based on the model created, factors such as anemia, diabetes mellitus, hyperlipidemia, transient ischemic attacks, coronary artery disease, and atherosclerosis are the most effective factors in stroke. Decision tree algorithms, especially CHAID algorithm, have acceptable precision and predictive ability to determine the factors affecting ischemic stroke. Thus, by creating predictive models through this algorithm, will play a significant role in decreasing the mortality and disability caused by ischemic stroke.

Keywords: data mining, ischemic stroke, decision tree, Bayesian network

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Authors: Phalla Miech, William Leung, Ty Chhay, Sina Vor, Arata Hidano

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Smallholder pig farms (SPFs) are prevalent in Cambodia but are vulnerable to disease impacts, as evidenced by the recent incursion of African swine fever into the region. As part of the ‘PigFluCam+’ project, we sought to provide an updated picture of pig husbandry and biosecurity practices among SPFs in south-central Cambodia. A multi-stage sampling design was adopted to select study districts and villages within four provinces: Phnom Penh, Kandal, Takeo, and Kampong Speu. Structured interviews were conductedbetween October 2020 - May 2021 among all consenting households keeping pigs in 16 target villages. Recruited SPFs (n=176) kept 6.8 pigs on average (s.d.=7.7), with most (88%) keeping cross-bred varieties of sows (77%), growers/finishers (39%), piglets/weaners (22%), and few keeping boars (5%). Chickens (83%) and waterfowl (56%) were commonly raised and could usually contact pigs directly (79%). Pigs were the primary source of household income for 28% of participants. While pigs tended to be housed individually (40%) or in groups (33%), 13% kept pigs free-ranging/tethered. Pigs were commonly fed agricultural by-products (80%), commercial feed (60%), and, notably, household waste (59%). Under half of SPFs vaccinated their pigs (e.g., against classical swine fever, Aujesky’s, and pasteurellosis, although the target disease was often unknown). Among 20 SPFs who experienced pig morbidities/mortalities within the past 6 months, only 3 (15%) reported to animal health workers, and disease etiology was rarely known. Common biosecurity measures included nets covering pig pens (62%) and restricting access to the site/pens (46%). Boot dips (0.6%) and PPE (1.2%) were rarely used. Pig smallholdings remain an important contributor to rural livelihoods. Current practices and biosecurity challenges increase risk pathways for a range of disease threats of both local and global concern. Ethnographic studies are needed to better understand local determinants and develop context-appropriate strategies.

Keywords: smallholder production, swine, biosecurity practices, Cambodia, African swine fever

Procedia PDF Downloads 147