Search results for: panel data models

Authors: Mohamed Elhakeem, A. N. Thanos Papanicolaou, Christopher Wilson, Yi-Jia Chang

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In this study, a physically-based, modelling framework was developed to predict saturated hydraulic conductivity (KSAT) dynamics in the Clear Creek Watershed (CCW), Iowa. The modelling framework integrated selected pedotransfer functions and watershed models with geospatial tools. A number of pedotransfer functions and agricultural watershed models were examined to select the appropriate models that represent the study site conditions. Models selection was based on statistical measures of the models’ errors compared to the KSAT field measurements conducted in the CCW under different soil, climate and land use conditions. The study has shown that the predictions of the combined pedotransfer function of Rosetta and the Water Erosion Prediction Project (WEPP) provided the best agreement to the measured KSAT values in the CCW compared to the other tested models. Therefore, Rosetta and WEPP were integrated with the Geographic Information System (GIS) tools for visualization of the data in forms of geospatial maps and prediction of KSAT variability in CCW due to the seasonal changes in climate and land use activities.

Keywords: saturated hydraulic conductivity, pedotransfer functions, watershed models, geospatial tools

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Authors: Ayodele Ajayi, John Ajayi

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This paper investigates the effect of corporate governance with a view to determining the relationship between board size and bank performance. Data for the study were obtained from the audited financial statements of five sampled banks listed on the Nigerian Stock Exchange. Panel data technique was adopted and analysis was carried out with the use of multiple regression and pooled ordinary least square. Results from the study show that the larger the board size, the greater the profit implying that corporate governance is positively correlated with bank performance.

Keywords: corporate governance, banks performance, board size, pooled data

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Authors: Neerav Kumar, Naumaan Nayyar, Sharath Kashyap

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Most companies see an increase in the adoption of machine learning (ML) applications across internal and external-facing use cases. ML applications vend output either in batch or real-time patterns. A complete batch ML pipeline architecture comprises data sourcing, feature engineering, model training, model deployment, model output vending into a data store for downstream application. Due to unclear role expectations, we have observed that scientists specializing in building and optimizing models are investing significant efforts into building the other components of the architecture, which we do not believe is the best use of scientists’ bandwidth. We propose a system architecture created using AWS services that bring industry best practices to managing the workflow and simplifies the process of model deployment and end-to-end data integration for an ML application. This narrows down the scope of scientists’ work to model building and refinement while specialized data engineers take over the deployment, pipeline orchestration, data quality, data permission system, etc. The pipeline infrastructure is built and deployed as code (using terraform, cdk, cloudformation, etc.) which makes it easy to replicate and/or extend the architecture to other models that are used in an organization.

Keywords: data pipeline, machine learning, AWS, architecture, batch machine learning

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Authors: Hossein Ramezani Ali-Akbari

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This paper presents an innovative method to control the rotational speed of a satellite solar panel during its deployment phase. A brushed DC motor has been utilized in the passive spring driven deployment mechanism to reduce the deployment speed. In order to use the DC motor as a damper, its connector terminals have been connected with an external resistance in a closed circuit. It means that, in this approach, there is no external power supply in the circuit. The working principle of this method is based on the back electromotive force (or back EMF) of the DC motor when an external torque (here the torque produced by the torsional springs) is coupled to the DC motor’s shaft. In fact, the DC motor converts to an electric generator and the current flows into the circuit and then produces the back EMF. Based on Lenz’s law, the generated current produced a torque which acts opposite to the applied external torque, and as a result, the deployment speed of the solar panel decreases. The main advantage of this method is to set an intended damping coefficient to the system via changing the external resistance. To produce the sufficient current, a gearbox has been assembled to the DC motor which magnifies the number of turns experienced by the DC motor. The coupled electro-mechanical equations of the system have been derived and solved, then, the obtained results have been presented. A full-scale prototype of the deployment mechanism has been built and tested. The potential application of brushed DC motors as a rotational speed damper has been successfully demonstrated.

Keywords: back electromotive force, brushed DC motor, rotational speed damper, satellite solar panel deployment mechanism

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Authors: Abdullahi Jibrin, Aishetu Abdulkadir

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The development of allometric models is crucial to accurate forest biomass/carbon stock assessment. The aim of this study was to develop a set of biomass prediction models that will enable the determination of total tree aboveground biomass for savannah woodland area in Niger State, Nigeria. Based on the data collected through biometric measurements of 1816 trees and destructive sampling of 36 trees, five species specific and one site specific models were developed. The sample size was distributed equally between the five most dominant species in the study site (Vitellaria paradoxa, Irvingia gabonensis, Parkia biglobosa, Anogeissus leiocarpus, Pterocarpus erinaceous). Firstly, the equations were developed for five individual species. Secondly these five species were mixed and were used to develop an allometric equation of mixed species. Overall, there was a strong positive relationship between total tree biomass and the stem diameter. The coefficient of determination (R2 values) ranging from 0.93 to 0.99 P < 0.001 were realised for the models; with considerable low standard error of the estimates (SEE) which confirms that the total tree above ground biomass has a significant relationship with the dbh. The F-test value for the biomass prediction models were also significant at p < 0.001 which indicates that the biomass prediction models are valid. This study recommends that for improved biomass estimates in the study site, the site specific biomass models should preferably be used instead of using generic models.

Keywords: allometriy, biomass, carbon stock , model, regression equation, woodland, inventory

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Authors: Krishang Surapaneni

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The goal of this project is to determine how to predict important aspects of options, including the ask price. We want to compare different machine learning models to learn the best model and the best hyperparameters for that model for this purpose and data set. Option pricing is a relatively new field, and it can be very complicated and intimidating, especially to inexperienced people, so we want to create a machine learning model that can predict important aspects of an option stock, which can aid in future research. We tested multiple different models and experimented with hyperparameter tuning, trying to find some of the best parameters for a machine-learning model. We tested three different models: a Random Forest Regressor, a linear regressor, and an MLP (multi-layer perceptron) regressor. The most important feature in this experiment is the ask price; this is what we were trying to predict. In the field of stock pricing prediction, there is a large potential for error, so we are unable to determine the accuracy of the models based on if they predict the pricing perfectly. Due to this factor, we determined the accuracy of the model by finding the average percentage difference between the predicted and actual values. We tested the accuracy of the machine learning models by comparing the actual results in the testing data and the predictions made by the models. The linear regression model performed worst, with an average percentage error of 17.46%. The MLP regressor had an average percentage error of 11.45%, and the random forest regressor had an average percentage error of 7.42%

Keywords: finance, linear regression model, machine learning model, neural network, stock price

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Authors: Mostafa Gamal, Hoda K. Mohamed, Islam El-Maddah, Ali Hamdi

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In recent years, many digital documents on the web have been created due to the rapid growth of ’social applications’ communities or ’Data-intensive applications’. The evolution of online-based multimedia data poses new challenges in storing and querying large amounts of data for online recommender systems. Graph data models have been shown to be more efficient than relational data models for processing complex data. This paper will explain the key differences between graph and relational databases, their strengths and weaknesses, and why using graph databases is the best technology for building a realtime recommendation system. Also, The paper will discuss several similarity metrics algorithms that can be used to compute a similarity score of pairs of nodes based on their neighbourhoods or their properties. Finally, the paper will discover how NLP strategies offer the premise to improve the accuracy and coverage of realtime recommendations by extracting the information from the stored unstructured knowledge, which makes up the bulk of the world’s data to enrich the graph database with this information. As the size and number of data items are increasing rapidly, the proposed system should meet current and future needs.

Keywords: graph databases, NLP, recommendation systems, similarity metrics

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Authors: Chen Wang, Jared Evans, Yan Asmann

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With the quick evolvement of next-generation sequencing techniques, whole-exome or exome-panel data have become a cost-effective way for detection of small exonic mutations, but there has been a growing desire to accurately detect copy number variations (CNVs) as well. In order to address this research and clinical needs, we developed a sequencing coverage pattern-based method not only for copy number detections, data integrity checks, CNV calling, and visualization reports. The developed methodologies include complete automation to increase usability, genome content-coverage bias correction, CNV segmentation, data quality reports, and publication quality images. Automatic identification and removal of poor quality outlier samples were made automatically. Multiple experimental batches were routinely detected and further reduced for a clean subset of samples before analysis. Algorithm improvements were also made to improve somatic CNV detection as well as germline CNV detection in trio family. Additionally, a set of utilities was included to facilitate users for producing CNV plots in focused genes of interest. We demonstrate the somatic CNV enhancements by accurately detecting CNVs in whole exome-wide data from the cancer genome atlas cancer samples and a lymphoma case study with paired tumor and normal samples. We also showed our efficient reuses of existing exome sequencing data, for improved germline CNV calling in a family of the trio from the phase-III study of 1000 Genome to detect CNVs with various modes of inheritance. The performance of the developed method is evaluated by comparing CNV calling results with results from other orthogonal copy number platforms. Through our case studies, reuses of exome sequencing data for calling CNVs have several noticeable functionalities, including a better quality control for exome sequencing data, improved joint analysis with single nucleotide variant calls, and novel genomic discovery of under-utilized existing whole exome and custom exome panel data.

Keywords: bioinformatics, computational genetics, copy number variations, data reuse, exome sequencing, next generation sequencing

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Authors: Dzhavidan Zeinalov, Bugra Sen, Firangiz Aslanova

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Most text-to-speech models cannot operate well in low-resource languages and require a great amount of high-quality training data to be considered good enough. Yet, with the improvements made in ASR systems, it is now much easier than ever to collect data for the design of custom text-to-speech models. In this work, our work on using the ASR model to collect data to build a viable text-to-speech system for one of the leading financial institutions of Azerbaijan will be outlined. NVIDIA’s implementation of the Tacotron 2 model was utilized along with the HiFiGAN vocoder. As for the training, the model was first trained with high-quality audio data collected from the Internet, then fine-tuned on the bank’s single speaker call center data. The results were then evaluated by 50 different listeners and got a mean opinion score of 4.17, displaying that our method is indeed viable. With this, we have successfully designed the first text-to-speech model in Azerbaijani and publicly shared 12 hours of audiobook data for everyone to use.

Keywords: Azerbaijani language, HiFiGAN, Tacotron 2, text-to-speech, transfer learning, whisper

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Authors: Josip Visković, Josip Arnerić, Ante Rozga

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Based on the fact that volatility is time varying in high frequency data and that periods of high volatility tend to cluster, the most successful and popular models in modelling time varying volatility are GARCH type models. When financial returns exhibit sudden jumps that are due to structural breaks, standard GARCH models show high volatility persistence, i.e. integrated behaviour of the conditional variance. In such situations models in which the parameters are allowed to change over time are more appropriate. This paper compares different GARCH models in terms of their ability to describe structural changes in returns caused by financial crisis at stock markets of six selected central and east European countries. The empirical analysis demonstrates that Markov regime switching GARCH model resolves the problem of excessive persistence and outperforms uni-regime GARCH models in forecasting volatility when sudden switching occurs in response to financial crisis.

Keywords: central and east European countries, financial crisis, Markov switching GARCH model, transition probabilities

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Authors: Melodi Mbuyi Mata, Blessing Olamide Taiwo, Afolabi Ayodele David

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The purpose of this research is to create empirical models for assessing the profitability of granite aggregate production in Akure, Ondo state aggregate quarries. In addition, an artificial neural network (ANN) model and multivariate predicting models for granite profitability were developed in the study. A formal survey questionnaire was used to collect data for the study. The data extracted from the case study mine for this study includes granite marketing operations, royalty, production costs, and mine production information. The following methods were used to achieve the goal of this study: descriptive statistics, MATLAB 2017, and SPSS16.0 software in analyzing and modeling the data collected from granite traders in the study areas. The ANN and Multi Variant Regression models' prediction accuracy was compared using a coefficient of determination (R²), Root mean square error (RMSE), and mean square error (MSE). Due to the high prediction error, the model evaluation indices revealed that the ANN model was suitable for predicting generated profit in a typical quarry. More quarries in Nigeria's southwest region and other geopolitical zones should be considered to improve ANN prediction accuracy.

Keywords: national development, granite, profitability assessment, ANN models

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Authors: Awol Seid Ebrie

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HIV results as an incurable disease, AIDS. After a person is infected with virus, the virus gradually destroys all the infection fighting cells called CD4 cells and makes the individual susceptible to opportunistic infections which cause severe or fatal health problems. Several studies show that the CD4 cells count is the most determinant indicator of the effectiveness of the treatment or progression of the disease. The objective of this paper is to investigate the progression of the disease over time among patient under HAART treatment. Two main approaches of the generalized multilevel ordinal models; namely the proportional odds model and the nonproportional odds model have been applied to the HAART data. Also, the multilevel part of both models includes random intercepts and random coefficients. In general, four models are explored in the analysis and then the models are compared using the deviance information criteria. Of these models, the random coefficients nonproportional odds model is selected as the best model for the HAART data used as it has the smallest DIC value. The selected model shows that the progression of the disease increases as the time under the treatment increases. In addition, it reveals that gender, baseline clinical stage and functional status of the patient have a significant association with the progression of the disease.

Keywords: nonproportional odds model, proportional odds model, random coefficients model, random intercepts model

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Authors: Dalin Si, Azizan Aziz, Bertrand Lasternas

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To encourage building owners to purchase electricity at the wholesale market and reduce building peak demand, this study aims to develop models that predict day-ahead hourly electricity consumption and demand using artificial neural network (ANN) and support vector machine (SVM). All prediction models are built in Python, with tool Scikit-learn and Pybrain. The input data for both consumption and demand prediction are time stamp, outdoor dry bulb temperature, relative humidity, air handling unit (AHU), supply air temperature and solar radiation. Solar radiation, which is unavailable a day-ahead, is predicted at first, and then this estimation is used as an input to predict consumption and demand. Models to predict consumption and demand are trained in both SVM and ANN, and depend on cooling or heating, weekdays or weekends. The results show that ANN is the better option for both consumption and demand prediction. It can achieve 15.50% to 20.03% coefficient of variance of root mean square error (CVRMSE) for consumption prediction and 22.89% to 32.42% CVRMSE for demand prediction, respectively. To conclude, the presented models have potential to help building owners to purchase electricity at the wholesale market, but they are not robust when used in demand response control.

Keywords: building energy prediction, data mining, demand response, electricity market

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Authors: Verónica Diaz Quezada

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This research examines the teaching models used by secondary math teachers when teaching logarithmic, quadratic and exponential functions. For this, descriptive case studies have been carried out on 5 secondary teachers. These teachers have been chosen from 3 scientific-humanistic and technical schools, in Chile. Data have been obtained through non-participant class observation and the application of a questionnaire and a rubric to teachers. According to the results, the didactic model that prevails is the one that starts with an interactive strategy, moves to a more content-based structure, and ends with a reinforcement stage. Nonetheless, there is always influence from teachers, their methods, and the group of students.

Keywords: teaching models, math teachers, functions, secondary education

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Authors: Avishek Chanda, Nam Kyeun Kim, Debes Bhattacharyya

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The use of renewable substitutes in various semi-structural and structural applications has experienced an increase since the last few decades. Sandwich panels have been used for many decades, although research on understanding the effects of the core structures on the panels’ fire-reaction properties is limited. The current work investigates the fire-performance of a corrugated sandwich panel made from renewable, biodegradable, and sustainable material, plywood. The bench-scale fire testing apparatus, cone-calorimeter, was employed to evaluate the required fire-reaction properties of the sandwich core in a panel configuration, with three corrugated layers glued together with face-sheets under a heat irradiance of 50 kW/m². The study helped in documenting a unique heat release trend associated with the fire performance of the 3-layered corrugated sandwich panels and in understanding the structural stability of the samples in the event of a fire. Furthermore, the total peak heat release rate was observed to be around 421 kW/m², which is significantly low compared to many polymeric materials in the literature. The total smoke production was also perceived to be very limited compared to other structural materials, and the total heat release was also nominal. The time to ignition of 21.7 s further outlined the advantages of using the plywood component since polymeric composites, even with flame-retardant additives, tend to ignite faster. Overall, the corrugated plywood sandwich panels had significant fire-reaction properties and could have important structural applications. The possible use of structural panels made from bio-degradable material opens a new avenue for the use of similar structures in sandwich panel preparation.

Keywords: corrugated sandwich panel, fire-reaction properties, plywood, renewable material

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Authors: Ryan A. Black, Stacey A. McCaffrey

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Over the past few decades, great strides have been made towards improving the science in the measurement of psychological constructs. Item Response Theory (IRT) has been the foundation upon which statistical models have been derived to increase both precision and accuracy in psychological measurement. These models are now being used widely to develop and refine tests intended to measure an individual's level of academic achievement, aptitude, and intelligence. Recently, the field of clinical psychology has adopted IRT models to measure psychopathological phenomena such as depression, anxiety, and addiction. Because advances in IRT measurement models are being made so rapidly across various fields, it has become quite challenging for psychologists and other behavioral scientists to keep abreast of the most recent developments, much less learn how to employ and decide which models are the most appropriate to use in their line of work. In the same vein, IRT measurement models vary greatly in complexity in several interrelated ways including but not limited to the number of item-specific parameters estimated in a given model, the function which links the expected response and the predictor, response option formats, as well as dimensionality. As a result, inferior methods (a.k.a. Classical Test Theory methods) continue to be employed in efforts to measure psychological constructs, despite evidence showing that IRT methods yield more precise and accurate measurement. To increase the use of IRT methods, this study endeavors to provide a comprehensive overview of binary IRT models; that is, measurement models employed on test data consisting of binary response options (e.g., correct/incorrect, true/false, agree/disagree). Specifically, this study will cover the most basic binary IRT model, known as the 1-parameter logistic (1-PL) model dating back to over 50 years ago, up until the most recent complex, 4-parameter logistic (4-PL) model. Binary IRT models will be defined mathematically and the interpretation of each parameter will be provided. Next, all four binary IRT models will be employed on two sets of data: 1. Simulated data of N=500,000 subjects who responded to four dichotomous items and 2. A pilot analysis of real-world data collected from a sample of approximately 770 subjects who responded to four self-report dichotomous items pertaining to emotional consequences to alcohol use. Real-world data were based on responses collected on items administered to subjects as part of a scale-development study (NIDA Grant No. R44 DA023322). IRT analyses conducted on both the simulated data and analyses of real-world pilot will provide a clear demonstration of how to construct, evaluate, and compare binary IRT measurement models. All analyses will be performed using the new IRT procedure in SAS 9.4. SAS code to generate simulated data and analyses will be available upon request to allow for replication of results.

Keywords: instrument development, item response theory, latent trait theory, psychometrics

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Authors: Elnaz Entezar, Reza Ezzati

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In recent decades, trade flows, capital, workforce, technology and information have increased between international borders and the globalization has turned to an undeniable process in international economics. Meanwhile, despite the positive aspects of globalization, the critics of globalization opine that the risks and costs of globalization for developing vulnerable economies and the world's impoverished people are high and significant. In this regard, this study by using the data of KOF Economic Institute and the World Bank for 113 different countries during the period 2002-2012, by taking advantage of panel smooth transition regression, and by taking the gross domestic product as transmission variables discuss the nonlinear relationship between research variables. The results have revealed that globalization in low regime (countries with low GDP) has negative impact whereas in high regime (countries with high GDP) has a positive impact. In spite of the fact that in the early stages of growth, control of corruption has a positive impact on economic growth, after a threshold has a negative impact on economic growth.

Keywords: globalization, corruption, panel smooth transition model, economic growth, threshold, economic convergence

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Authors: M. O. Oke, T. S. Workneh

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Drying behaviour of blanched sweet potato in a cabinet dryer using different five air temperatures (40-80oC) and ten sweet potato varieties sliced to 5 mm thickness were investigated. The drying data were fitted to eight models. The Modified Henderson and Pabis model gave the best fit to the experimental moisture ratio data obtained during the drying of all the varieties while Newton (Lewis) and Wang and Singh models gave the least fit. The values of Deff obtained for Bophelo variety (1.27 x 10-9 to 1.77 x 10-9 m2/s) was the least while that of S191 (1.93 x 10-9 to 2.47 x 10-9 m2/s) was the highest which indicates that moisture diffusivity in sweet potato is affected by the genetic factor. Activation energy values ranged from 0.27-6.54 kJ/mol. The lower activation energy indicates that drying of sweet potato slices requires less energy and is hence a cost and energy saving method. The drying behavior of blanched sweet potato was investigated in a cabinet dryer. Drying time decreased considerably with increase in hot air temperature. Out of the eight models fitted, the Modified Henderson and Pabis model gave the best fit to the experimental moisture ratio data on all the varieties while Newton, Wang and Singh models gave the least. The lower activation energy (0.27-6.54 kJ/mol) obtained indicates that drying of sweet potato slices requires less energy and is hence a cost and energy saving method.

Keywords: sweet potato slice, drying models, moisture ratio, moisture diffusivity, activation energy

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Authors: Sheraz Gul

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The requirements for progressing a compound to clinical trials is well established and relies on the results from in-vitro and in-vivo animal tests to indicate that it is likely to be safe and efficacious when testing in humans. The typical data package required will include demonstrating compound safety, toxicity, bioavailability, pharmacodynamics (potential effects of the compound on body systems) and pharmacokinetics (how the compound is potentially absorbed, distributed, metabolised and eliminated after dosing in humans). If the desired criteria are met and the compound meets the clinical Candidate criteria and is deemed worthy of further development, a submission to regulatory bodies such as the US Food & Drug Administration for an exploratory Investigational New Drug Study can be made. The purpose of this study is to collect data to establish that the compound will not expose humans to unreasonable risks when used in limited, early-stage clinical studies in patients or normal volunteer subjects (Phase I). These studies are also designed to determine the metabolism and pharmacologic actions of the drug in humans, the side effects associated with increasing doses, and, if possible, to gain early evidence on their effectiveness. In order to reach the above goals, we have developed a pre-clinical high throughput Absorption, Distribution, Metabolism and Excretion–Toxicity (ADME–Toxicity) panel of assays to identify compounds that are likely to meet the Lead and Candidate compound acceptance criteria. This panel includes solubility studies in a range of biological fluids, cell viability studies in cancer and primary cell-lines, mitochondrial toxicity, off-target effects (across the kinase, protease, histone deacetylase, phosphodiesterase and GPCR protein families), CYP450 inhibition (5 different CYP450 enzymes), CYP450 induction, cardio-toxicity (hERG) and gene-toxicity. This panel of assays has been applied to multiple compound series developed in a number of projects delivering Lead and clinical Candidates and examples from these will be presented.

Keywords: absorption, distribution, metabolism and excretion–toxicity , drug discovery, food and drug administration , pharmacodynamics

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Authors: A. A. A. Aboalnour, Ahmed M. Amasaib, Mohammed-Almujtaba A. Mohammed-Farah, Abdelhakam, A. Noreldien

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This paper presents a design and study of Parabolic Trough Solar Collector (PTC). Mathematical models were used in this work to find the direct and reflected solar radiation from the air layer on the surface of the earth per hour based on the total daily solar radiation on a horizontal surface. Also mathematical models had been used to calculate the radiation of the tilted surfaces. Most of the ingredients used in this project as previews data required on several solar energy applications, thermal simulation, and solar power systems. In addition, mathematical models had been used to study the flow of the fluid inside the tube (receiver), and study the effect of direct and reflected solar radiation on the pressure, temperature, speed, kinetic energy and forces of fluid inside the tube. Finally, the mathematical models had been used to study the (PTC) performances and estimate its thermal efficiency.

Keywords: CFD, experimental, mathematical models, parabolic trough, radiation

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Authors: Stepan Hysek, Premysl Sedivka, Petra Gajdacova

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Currently, structural composite materials contain cellulose-based particles (wood chips, fibers) bonded with synthetic adhesives containing formaldehyde (urea-formaldehyde, melamine-formaldehyde adhesives and others). Formaldehyde is classified as a volatile substance with provable carcinogenic effects on live organisms, and an emphasis has been put on continual reduction of its content in products. One potential solution could be the development of an agglomerated material which does not contain adhesives releasing formaldehyde. A potential alternative to formaldehyde-based adhesives could be polyurethane adhesives containing no formaldehyde. Such adhesives have been increasingly used in applications where a few years ago formaldehyde-based adhesives were the only option. Advantages of polyurethane adhesive in comparison with others in the industry include the high elasticity of the joint, which is able to resist dynamic stress, and resistance to increased humidity and climatic effects. These properties predict polyurethane adhesives to be used in OSB/EPS panel production. The objective of this paper is to develop an adhesive for bonding of sandwich panels made of material based on wood and other materials, e.g. SIP) and optimization of input components in order to obtain an adhesive with required properties suitable for bonding of the given materials without involvement of formaldehyde. It was found that polyurethane recyclate as a filler is suitable modification of polyurethane adhesive and results have clearly revealed that modified adhesive can be used for OSB/EPS panel production.

Keywords: adhesive, polyurethane, recyclate, SIP

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Authors: Fatma Ezzahra Kateb, Neila Boulila Taktak, Mohamed Kabir Hassan

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We examine the impact of Islamic accounting and auditing standards issued by the Accounting and Auditing Organization for Islamic Financial Institutions (AAOIFI) on the income smoothing perspective of Islamic financial institutions located in the Middle East and North Africa region between 2013 and 2018. Based on General Least square regression for panel data, we find a significant and positive relationship between intentional income smoothing and earning persistence and cash flow predictability in all models. However, we discovered that AAOIFI accounting standards (FAS) had a negative and significant effect on intentional income smoothing and earning persistence. As a result, the income smoothing efficiency is lower for IFIs that use FASs than IFIs that use IFRSs. Our findings emphasize the need for specific standards to enhance the relevance of financial reports disclosed by Islamic financial institutions.

Keywords: AAOIFI, financial reporting quality, income smoothing perspective, MENA countries

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Authors: Akinbo Razak Yinka, Adesanya Kehinde Kazeem, Oladokun Oluwagbenga Peter

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Time series data are sequences of observations collected over a period of time. Such data can be used to predict health outcomes, such as disease progression, mortality, hospitalization, etc. The Statistical approach is based on mathematical models that capture the patterns and trends of the data, such as autocorrelation, seasonality, and noise, while Neural methods are based on artificial neural networks, which are computational models that mimic the structure and function of biological neurons. This paper compared both parametric and non-parametric time series models of patients treated for malaria in Maternal and Child Health Centres in Lagos State, Nigeria. The forecast methods considered linear regression, Integrated Moving Average, ARIMA and SARIMA Modeling for the parametric approach, while Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) Network were used for the non-parametric model. The performance of each method is evaluated using the Mean Absolute Error (MAE), R-squared (R2) and Root Mean Square Error (RMSE) as criteria to determine the accuracy of each model. The study revealed that the best performance in terms of error was found in MLP, followed by the LSTM and ARIMA models. In addition, the Bootstrap Aggregating technique was used to make robust forecasts when there are uncertainties in the data.

Keywords: ARIMA, bootstrap aggregation, MLP, LSTM, SARIMA, time-series analysis

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Authors: Tae Young Jin, Jin Soo Kim

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Carbon emissions have emerged as global concerns. Intergovernmental Panel of Climate Change (IPCC) have published reports about Green House Gases (GHGs) emissions regularly. United Nations Framework Convention on Climate Change (UNFCCC) have held a conference yearly since 1995. Especially, COP21 held at December 2015 made the Paris agreement which have strong binding force differently from former COP. The Paris agreement was ratified as of 4 November 2016, they finally have legal binding. Participating countries set up their own Intended Nationally Determined Contributions (INDC), and will try to achieve this. Thus, carbon emissions must be reduced. The energy sector is one of most responsible for carbon emissions and fossil fuels particularly are. Thus, this paper attempted to examine the relationship between natural gas consumption and economic growth. To achieve this, we adopted the Cobb-Douglas production function that consists of natural gas consumption, economic growth, capital, and labor using dependent panel analysis. Data were preprocessed with Principal Component Analysis (PCA) to remove cross-sectional dependency which can disturb the panel results. After confirming the existence of time-trended component of each variable, we moved to cointegration test considering cross-sectional dependency and structural breaks to describe more realistic behavior of volatile international indicators. The cointegration test result indicates that there is long-run equilibrium relationship between selected variables. Long-run cointegrating vector and Granger causality test results show that while natural gas consumption can contribute economic growth in the short-run, adversely affect in the long-run. From these results, we made following policy implications. Since natural gas has positive economic effect in only short-run, the policy makers in developing countries must consider the gradual switching of major energy source, from natural gas to sustainable energy source. Second, the technology transfer and financing business suggested by COP must be accelerated. Acknowledgement—This work was supported by the Energy Efficiency & Resources Core Technology Program of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea (No. 20152510101880) and by the National Research Foundation of Korea Grant funded by the Korean Government (NRF-205S1A3A2046684).

Keywords: developing countries, economic growth, natural gas consumption, panel data analysis

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Authors: Elif Aykin Dincer, Mustafa Erbas

Abstract:

The vacuum drying behavior of beef slices (10 x 4 x 0.2 cm3) was experimentally investigated at the temperature of 60, 70, and 80°C under 25 mbar ultimate vacuum pressure and the mathematical models (Lewis, Page, Midilli, Two-term, Wangh and Singh and Modified Henderson and Pabis) were used to fit the vacuum drying of beef slices. The increase in drying air temperature resulted in a decrease in drying time. It took approximately 206, 180 and 157 min to dry beef slices from an initial moisture content to a final moisture content of 0.05 kg water/kg dry matter at 60, 70 and 80 °C of vacuum drying, respectively. It is also observed that the drying rate increased with increasing drying temperature. The coefficients (R2), the reduced chi-square (x²) and root mean square error (RMSE) values were obtained by application of six models to the experimental drying data. The best model with the highest R2 and, the lowest x² and RMSE values was selected to describe the drying characteristics of beef slices. The Page model has shown a better fit to the experimental drying data as compared to other models. In addition, the effective moisture diffusivities of beef slices in the vacuum drying at 60 - 80 °C varied in the range of 1.05 – 1.09 x 10-10 m2/s. Consequently, this results can be used to simulate vacuum drying process of beef slices and improve efficiency of the drying process.

Keywords: beef slice, drying models, effective diffusivity, vacuum

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Authors: Yu Zhang, Pedro Desouza

Abstract:

Since big data has become substantially more accessible and manageable due to the development of powerful tools for dealing with unstructured data, people are eager to mine information from social media resources that could not be handled in the past. Sentiment analysis, as a novel branch of text mining, has in the last decade become increasingly important in marketing analysis, customer risk prediction and other fields. Scientists and researchers have undertaken significant work in creating and improving their sentiment models. In this paper, we present a concept of selecting appropriate classifiers based on the features and qualities of data sources by comparing the performances of five classifiers with three popular social media data sources: Twitter, Amazon Customer Reviews, and Movie Reviews. We introduced a couple of innovative models that outperform traditional sentiment classifiers for these data sources, and provide insights on how to further improve the predictive power of sentiment analysis. The modelling and testing work was done in R and Greenplum in-database analytic tools.

Keywords: sentiment analysis, social media, Twitter, Amazon, data mining, machine learning, text mining

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Authors: Fazıl Gökgöz, Fahrettin Filiz

Abstract:

Load forecasting has become crucial in recent years and become popular in forecasting area. Many different power forecasting models have been tried out for this purpose. Electricity load forecasting is necessary for energy policies, healthy and reliable grid systems. Effective power forecasting of renewable energy load leads the decision makers to minimize the costs of electric utilities and power plants. Forecasting tools are required that can be used to predict how much renewable energy can be utilized. The purpose of this study is to explore the effectiveness of LSTM-based neural networks for estimating renewable energy loads. In this study, we present models for predicting renewable energy loads based on deep neural networks, especially the Long Term Memory (LSTM) algorithms. Deep learning allows multiple layers of models to learn representation of data. LSTM algorithms are able to store information for long periods of time. Deep learning models have recently been used to forecast the renewable energy sources such as predicting wind and solar energy power. Historical load and weather information represent the most important variables for the inputs within the power forecasting models. The dataset contained power consumption measurements are gathered between January 2016 and December 2017 with one-hour resolution. Models use publicly available data from the Turkish Renewable Energy Resources Support Mechanism. Forecasting studies have been carried out with these data via deep neural networks approach including LSTM technique for Turkish electricity markets. 432 different models are created by changing layers cell count and dropout. The adaptive moment estimation (ADAM) algorithm is used for training as a gradient-based optimizer instead of SGD (stochastic gradient). ADAM performed better than SGD in terms of faster convergence and lower error rates. Models performance is compared according to MAE (Mean Absolute Error) and MSE (Mean Squared Error). Best five MAE results out of 432 tested models are 0.66, 0.74, 0.85 and 1.09. The forecasting performance of the proposed LSTM models gives successful results compared to literature searches.

Keywords: deep learning, long short term memory, energy, renewable energy load forecasting

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Authors: Zainab Mutlaq Ibrahim, Mohamed Bader-El-Den, Mihaela Cocea

Abstract:

Students' textual feedback can hold unique patterns and useful information about learning process, it can hold information about advantages and disadvantages of teaching methods, assessment components, facilities, and other aspects of teaching. The results of analysing such a feedback can form a key point for institutions’ decision makers to advance and update their systems accordingly. This paper proposes a data mining framework for analysing end of unit general textual feedback using part of speech feature (PoS) with four machine learning algorithms: support vector machines, decision tree, random forest, and naive bays. The proposed framework has two tasks: first, to use the above algorithms to build an optimal model that automatically classifies the whole data set into two subsets, one subset is tailored to assessment practices (assessment related), and the other one is the non-assessment related data. Second task to use the same algorithms to build an optimal model for whole data set, and the new data subsets to automatically detect their sentiment. The significance of this paper is to compare the performance of the above four algorithms using part of speech feature to the performance of the same algorithms using n-grams feature. The paper follows Knowledge Discovery and Data Mining (KDDM) framework to construct the classification and sentiment analysis models, which is understanding the assessment domain, cleaning and pre-processing the data set, selecting and running the data mining algorithm, interpreting mined patterns, and consolidating the discovered knowledge. The results of this paper experiments show that both models which used both features performed very well regarding first task. But regarding the second task, models that used part of speech feature has underperformed in comparison with models that used unigrams and bigrams.

Keywords: assessment, part of speech, sentiment analysis, student feedback

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Authors: Xiaogang Li, Jieqiong Miao

Abstract:

As for the problem of the grey forecasting model prediction accuracy is low, an improved grey prediction model is put forward. Firstly, use trigonometric function transform the original data sequence in order to improve the smoothness of data , this model called SGM( smoothness of grey prediction model), then combine the improved grey model with support vector machine , and put forward the grey support vector machine model (SGM - SVM).Before the establishment of the model, we use trigonometric functions and accumulation generation operation preprocessing data in order to enhance the smoothness of the data and weaken the randomness of the data, then use support vector machine (SVM) to establish a prediction model for pre-processed data and select model parameters using genetic algorithms to obtain the optimum value of the global search. Finally, restore data through the "regressive generate" operation to get forecasting data. In order to prove that the SGM-SVM model is superior to other models, we select the battery life data from calce. The presented model is used to predict life of battery and the predicted result was compared with that of grey model and support vector machines．For a more intuitive comparison of the three models, this paper presents root mean square error of this three different models .The results show that the effect of grey support vector machine (SGM-SVM) to predict life is optimal, and the root mean square error is only 3.18%. Keywords: grey forecasting model, trigonometric function, support vector machine, genetic algorithms, root mean square error

Keywords: Grey prediction model, trigonometric functions, support vector machines, genetic algorithms, root mean square error

Procedia PDF Downloads 454

Authors: A.D. Susanti, W. B. Sediawan, S.K. Wirawan, Budhijanto, Ritmaleni

Abstract:

Oryzanol content in rice bran oil (RBO) naturally has high antioxidant activity. Its reviewed has several health properties and high interested in pharmacy, cosmetics, and nutrition’s. Because of the low concentration of oryzanol in crude RBO (0.9-2.9%) then its need to be further processed for practical usage, such as via adsorption process. In this study, investigation and adjustment of adsorption equilibrium models were conducted through the kinetic data approachments. Mathematical modeling on kinetics of batch adsorption of oryzanol separation from RBO has been set-up and then applied for equilibrium results. The size of adsorbent particles used in this case are usually relatively small then the concentration in the adsorbent is assumed to be not different. Hence, the adsorption rate is controlled by the rate of oryzanol mass transfer from the bulk fluid of RBO to the surface of silica gel. In this approachments, the rate of mass transfer is assumed to be proportional to the concentration deviation from the equilibrium state. The equilibrium models applied were Langmuir, coefficient distribution, and Freundlich with the values of the parameters obtained from equilibrium results. It turned out that the models set-up can quantitatively describe the experimental kinetics data and the adjustment of the values of equilibrium isotherm parameters significantly improves the accuracy of the model. And then the value of mass transfer coefficient per unit adsorbent mass (kca) is obtained by curve fitting.

Keywords: adsorption equilibrium, adsorption kinetics, oryzanol, rice bran oil

Procedia PDF Downloads 316