Search results for: MSW quantity prediction

Authors: Autcha Araveeporn

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This paper presents a study about a nonparametric regression model consisting of a smoothing spline method and a penalized spline regression method. We also compare the techniques used for estimation and prediction of nonparametric regression model. We tried both methods with crude oil prices in dollars per barrel and the Stock Exchange of Thailand (SET) index. According to the results, it is concluded that smoothing spline method performs better than that of penalized spline regression method.

Keywords: nonparametric regression model, penalized spline regression method, smoothing spline method, Stock Exchange of Thailand (SET)

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Authors: Eitidal Albassam

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Cooling towers (CTs) in arid zone countries, used for heat rejection in water-cooled (WC) systems, consume a large quantity of water. Universally, water conservation is an issue because of the scarcity of fresh water and natural resources. Therefore, many studies have aimed to conserve fresh water and limit the water wasted. Nonetheless, all these methods are not related to improving the weather conditions around the entering air to CT. In Kuwait and other arid-zone countries, the dry bulb temperature (DBT) during the summer season is significantly greater than the incoming hot water temperature, and the air undergoes sensible cooling. This high DBT leads to extra heat transfer from air to water, demanding high water vaporization to achieve the required cooling of water. Thus, any reduction in ambient air temperature around the CT will minimize water consumption. This paper aims to discuss theoretically the effect of reducing the DBT around the cooling tower when considering the sun-shading barrier. The theoretical simulation model results show that if the DBT reduces by 2.8 °C approximately, the percentage of water evaporation savings in gallon per minute (GPM) starts from 6.48% when DBT reaches 51.67 °C till 9.80% for 37.78 °C. Moreover, the performance of the cooling tower will be improved when considering the appropriate shading barriers, which will not affect the existing wet-bulb temperature.

Keywords: dry-bulb temperature, entering air, water consumption, water vaporization

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Authors: H. Hosseini, B. V. Berdyshev, I. Iskopintsev

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This paper presents a rheological model for producing shape-memory thermoplastic polymers. Shape-memory occurs as a result of internal rearrangement of the structural elements of a polymer. A non-linear viscoelastic model was developed that allows qualitative and quantitative prediction of the stress-strain behavior of shape-memory polymers during heating. This research was done to develop a technique to determine the maximum possible change in size of heat-shrinkable products during heating. The rheological model used in this work was particularly suitable for defining process parameters and constructive parameters of the processing equipment.

Keywords: elastic deformation, heating, shape-memory polymers, stress-strain behavior, viscoelastic model

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Authors: Lukas Reznak, Maria Reznakova

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Recession of an economy has a profound negative effect on all involved stakeholders. It follows that timely prediction of recessions has been of utmost interest both in the theoretical research and in practical macroeconomic modelling. Current mainstream of recession prediction is based on standard OLS models of continuous GDP using macroeconomic data. This approach is not suitable for two reasons: the standard continuous models are proving to be obsolete and the macroeconomic data are unreliable, often revised many years retroactively. The aim of the paper is to explore a different branch of recession forecasting research theory and verify the findings on real data of the Czech Republic and Germany. In the paper, the authors present a family of discrete choice probit models with parameters estimated by the method of maximum likelihood. In the basic form, the probits model a univariate series of recessions and expansions in the economic cycle for a given country. The majority of the paper deals with more complex model structures, namely dynamic and bivariate extensions. The dynamic structure models the autoregressive nature of recessions, taking into consideration previous economic activity to predict the development in subsequent periods. Bivariate extensions utilize information from a foreign economy by incorporating correlation of error terms and thus modelling the dependencies of the two countries. Bivariate models predict a bivariate time series of economic states in both economies and thus enhance the predictive performance. A vital enabler of timely and successful recession forecasting are reliable and readily available data. Leading indicators, namely the yield curve and the stock market indices, represent an ideal data base, as the pieces of information is available in advance and do not undergo any retroactive revisions. As importantly, the combination of yield curve and stock market indices reflect a range of macroeconomic and financial market investors’ trends which influence the economic cycle. These theoretical approaches are applied on real data of Czech Republic and Germany. Two models for each country were identified – each for in-sample and out-of-sample predictive purposes. All four followed a bivariate structure, while three contained a dynamic component.

Keywords: bivariate probit, leading indicators, recession forecasting, Czech Republic, Germany

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Authors: Ameur Abdelkader, Abed Bouarfa Hafida

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Since its inception, predictive analysis has revolutionized the IT industry through its robustness and decision-making facilities. It involves the application of a set of data processing techniques and algorithms in order to create predictive models. Its principle is based on finding relationships between explanatory variables and the predicted variables. Past occurrences are exploited to predict and to derive the unknown outcome. With the advent of big data, many studies have suggested the use of predictive analytics in order to process and analyze big data. Nevertheless, they have been curbed by the limits of classical methods of predictive analysis in case of a large amount of data. In fact, because of their volumes, their nature (semi or unstructured) and their variety, it is impossible to analyze efficiently big data via classical methods of predictive analysis. The authors attribute this weakness to the fact that predictive analysis algorithms do not allow the parallelization and distribution of calculation. In this paper, we propose to extend the predictive analysis algorithm, Classification And Regression Trees (CART), in order to adapt it for big data analysis. The major changes of this algorithm are presented and then a version of the extended algorithm is defined in order to make it applicable for a huge quantity of data.

Keywords: predictive analysis, big data, predictive analysis algorithms, CART algorithm

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Authors: U. Chattaraj, K. Dhusiya, M. Raviteja

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Perception reaction time of drivers is an outcome of human thought process, which is vague and approximate in nature and also varies from driver to driver. So, in this study a fuzzy logic based model for prediction of the same has been presented, which seems suitable. The control factors, like, age, experience, intensity of driving of the driver, speed of the vehicle and distance of stimulus have been considered as premise variables in the model, in which the perception reaction time is the consequence variable. Results show that the model is able to explain the impacts of the control factors on perception reaction time properly.

Keywords: driver, fuzzy logic, perception reaction time, premise variable

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Authors: Chayarat Tantanasarit, Sandhya Babel

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Green mussels (Perna viridis) can effectively remove nutrients from seawater through their filtration process. This study aims to estimate 'net' nutrient removal rate by green mussel through calculation of nutrient uptake and release. Nutrients (carbon, nitrogen, and phosphorus) uptake was calculated based on the mussel filtration rate. Nutrient release was evaluated from carbon, nitrogen, and phosphorus released as mussel feces. By subtracting nutrient release from nutrient uptake, net nutrient removal by green mussel can be found as 3302, 380 and 124 mg/year/indv. Mass balance model was employed to simulate nutrient removal in actual green mussel farming conditions. Mussels farm area, seawater flow rate and amount of mussels were considered in the model. Results show that although larger quantity of green mussel farms lead to higher nutrient removal rate, the maximum green mussel cultivation should be taken into consideration as nutrients released through mussel excretion can strongly affect marine ecosystem.

Keywords: carbon, ecretion, filtration, nitrogen, phosphorus

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Authors: H. Hotait, X. Chiementin, L. Rasolofondraibe

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This paper presents a continuous work to detect the abnormal state in the rolling bearing by studying the vibration signature analysis and calculation of the remaining useful life. To achieve these aims, two methods; the first method is the classification to detect the degradation state by the AOM-OPTICS (Acousto-Optic Modulator) method. The second one is the prediction of the degradation state using least-squares support vector regression and then compared with the linear degradation model. An experimental investigation on ball-bearing was conducted to see the effectiveness of the used method by applying the acquired vibration signals. The proposed model for predicting the state of bearing gives us accurate results with the experimental and numerical data.

Keywords: bearings, automatization, optimization, prognosis, classification, defect detection

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Authors: Jaber H. Almutairi, Hosny Z. Abou-Ziyan, Issa F. Almesri, Mosab A. Alrahmani

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The present work investigates the behavior of fluid flow inside tunnel kilns using 3D-CFD (Computational Fluid Dynamics) simulations. The CFD simulations are carried out with the FLUENT software and validated against experimental results on fluid flow and heat transfer in tunnel kilns. A grid dependency study is conducted in the current work to improve the accuracy of the results. Three turbulence models k–ω, standard k–ε, and RNG k–ε are tested where k–ω model gives the best results in comparison with the experiment. The numerical results reveal an intriguing phenomenon where a long flow separation zone behind the setting is observed under different geometric and operation conditions. It was found that the uniformity of flow distribution can be substantially improved by rearranging the geometrical parameters of brick setting relative to kiln/setting. This improvement of flow distribution plays a critical role to enhance the quality and quantity of the production. It can be concluded that a better design and operation of tunnel kilns in terms of productivity and energy consumption can be obtained by taking into consideration the flow uniformity inside the tunnel kilns using CFD modelling.

Keywords: tunnel kilns, flow separation, flow uniformity, computational fluid dynamics

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Authors: Widyani Fatwa Dewi, Subroto Athor

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In a telecommunication service provider, quantity and interval of customer demand often difficult to predict due to high dependency on customer expansion strategy and technological development. Demand arrives when a customer needs to add capacity to an existing site or build a network in a new site. Because demand is uncertain for each period, and sometimes there is a null demand for several equipments, it is categorized as intermittent. This research aims to improve demand forecast quality in Indonesia's telecommunication service providers by using Artificial Neural Network. In Artificial Neural Network, the pattern or relationship within data will be analyzed using the training process, followed by the learning process as validation stage. Historical demand data for 36 periods is used to support this research. It is found that demand forecast by using Artificial Neural Network outperforms the existing method if it is reviewed on two criteria: the forecast accuracy, using Mean Absolute Deviation (MAD), Mean of the sum of the Squares of the Forecasting Error (MSE), Mean Error (ME) and service level which is shown through inventory cost. This research is expected to increase the reference for a telecommunication demand forecast, which is currently still limited.

Keywords: artificial neural network, demand forecast, forecast accuracy, intermittent, service level, telecommunication

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Authors: Martina Kanciruk, Jac J. W. Andrews, Tyrone Donnon

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The purpose of this study was to determine the significance of maternal smoking for the development of childhood overweight and/or obesity. Accordingly, a systematic literature review of English-language studies published from 1980 to 2012 using the following data bases: MEDLINE, PsychINFO, Cochrane Database of Systematic Reviews, and Dissertation Abstracts International was conducted. The following terms were used in the search: pregnancy, overweight, obesity, smoking, parents, childhood, risk factors. Eighteen studies of maternal smoking during pregnancy and obesity conducted in Europe, Asia, North America, and South America met the inclusion criteria. A meta-analysis of these studies indicated that maternal smoking during pregnancy is a significant risk factor for overweight and obesity; mothers who smoke during pregnancy are at a greater risk for developing obesity or overweight; the quantity of cigarettes consumed by the mother during pregnancy influenced the odds of offspring overweight and/or obesity. In addition, the results from moderator analyses suggest that part of the heterogeneity discovered between the studies can be explained by the region of world that the study occurred in and the age of the child at the time of weight assessment.

Keywords: childhood obesity, overweight, smoking, parents, risk factors

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Authors: Abirham Simeneh Ayalew, Seada Hussen Adem, Frie Ayalew Yimam

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Battery energy storage has a great role on storing energy harnessed from different alternative resources and greatly benefit the power sector by supply energy back to the system during outage and regular operation in power sectors. Most of the study shows that there is an exponential increase in the quantity of lithium - ion battery energy storage system due to their power density, economical aspects and its performance. But this lithium ion battery failures resulted in fire and explosion due to its having flammable electrolytes (chemicals) which can create those hazards. Hazards happen in these energy storage system lead to minimize battery life spans or efficiency. Identifying the real cause of these hazards and its mitigation techniques can be the solution to improve the efficiency of battery technologies and the electrode materials should have high electrical conductivity, large surface area, stable structure and low resistance. This paper asses the real causes of chemical hazards, its impact on efficiency, proposed solution for mitigating those hazards associated with efficiency improvement and summery of researchers new finding related to the field.

Keywords: battery energy storage, battery energy storage efficiency, chemical hazards, lithium ion battery

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Authors: Gamal F.Attia

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The baselines are the distances and lengths of the chords between projections of the positions of the laser stations on the reference ellipsoid. For the satellite geodesy, it is very important to determine the optimal length of orbital arc along which laser measurements are to be carried out. It is clear that for the dynamical methods long arcs (one month or more) are to be used. According to which more errors of modeling of different physical forces such as earth's gravitational field, air drag, solar radiation pressure, and others that may influence the accuracy of the estimation of the satellites position, at the same time the measured errors con be almost completely excluded and high stability in determination of relative coordinate system can be achieved. It is possible to diminish the influence of the errors of modeling by using short-arcs of the satellite orbit (several revolutions or days), but the station's coordinates estimated by different arcs con differ from each other by a larger quantity than statistical zero. Under the semidynamical ‘short arc’ method one or several passes of the satellite in one of simultaneous visibility from both ends of the chord is known and the estimated parameter in this case is the length of the chord. The comparison of the same baselines calculated with long and short arcs methods shows a good agreement and even speaks in favor of the last one. In this paper the Short Arc technique has been explained and 3 baselines have been determined using the ‘short arc’ method.

Keywords: baselines, short arc, dynamical, gravitational field

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Authors: Bouzidi Yassine

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This experimental work was aimed to characterize the porosity of the concrete cover zone using the capillary absorption test, and establish the links between open porosity characterized by the initial absorption, the compressive strength and carbonation depth. Eight formulations of workability similar made from ordinary Portland cement (CEM I 42.5) and a compound cement (CEM II/B 42.5) four of each type are studied. The results allow us to highlight the effect of the cement type. Indeed, concretes-based cement CEM II/B 42.5 carbonatent approximately faster than concretes-based cement CEM I 42.5. This effect is attributed in part to the lower content of portlandite Ca(OH)2 of concretes-based cement CEM II/B 42.5, but also the impact of the cement type on the open porosity of the cover concrete. The open porosity of concretes-based cement CEM I 42.5 is lower than that of concretes-based cement CEM II/B 42.5. The carbonation depth is a decreasing function of the compressive strength at 28 days and increases with the initial absorption. Through the results obtained, correlations between the quantity of water absorbed in 1 h, the carbonation depth at 180 days and the compressive strength at 28 days were performed in an acceptable manner.

Keywords: initial absorption, cover concrete, compressive strength, carbonation depth

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Authors: Seung Cho Yang, Jae Sung Lee, Se Hee Park

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This study suggests the analysis method to predict the damages of tunnel concrete caused by fires. The result obtained from the analyses of concrete temperatures at a fire in a tunnel using ABAQUS was compared with the test result. After the reliability of the analysis method was verified, the temperatures of a tunnel at a real fire and those of concrete during the fire were estimated to predict fire damages. The temperatures inside the tunnel were estimated by FDS, a CFD model. It was deduced that the fire performance of tunnel lining and the fire damages of the structure at an actual fire could be estimated by the analysis method.

Keywords: fire resistance, heat transfer, numerical analysis, tunnel fire

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Authors: Elmira Davasaz Tabrizi, Müşteba Sevil, Ercan Arican

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In recent decades, there has been a sharp increase in the prevalence of several unrelated chronic diseases. The use of long-term antibiotics for chronic illnesses is increasing. The antibiotic resistance occurrence and its relationship with host microbiomes are still unclear. Properties of the identifying antibodies have been the focus of chronic disease research, such as prostatitis or autoimmune. The immune system is made up of a complicated but well-organized network of cell types that constantly monitor and maintain their surroundings. The regulated homeostatic interaction between immune system cells and their surrounding environment shapes the microbial flora. Researchers believe that the disappearance of special bacterial species from our ancestral microbiota might have altered the body flora that can cause a rise in disease during the human life span. This unpleasant pattern demonstrates the importance of focusing on discovering and revealing the root causes behind the disappearance or alteration of our microbiota. In this review, we gathered the results of some studies that reveal changes in the diversity and quantity of microorganisms that may affect chronic and autoimmune diseases. Additionally, a Ph.D. thesis that is still in process as Metagenomic studies in chronic prostatitis samples is mentioned.

Keywords: metagenomic, autoimmune, prostatitis, microbiome

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Authors: Khashayar Kazemzadeh, Mohammad Hanif Dasoomi

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According to the increasing volume of traffic, lane changing plays a crucial role in traffic flow. Lane changing in traffic depends on several factors including road geometrical design, speed, drivers’ behavioral characteristics, etc. A great deal of research has been carried out regarding these fields. Despite of the other significant factors, the drivers’ behavioral characteristics of lane changing has been emphasized in this paper. This paper has predicted the effective equation based on personal characteristics of lane changing by regression models.

Keywords: effective equation, lane changing, drivers’ behavioral characteristics, regression models

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Authors: Toby Li, Julian Zhu

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In this paper, a model based on a simplified geometry is introduced to give a very conservative collision probability prediction for the Starlink satellite in its most densely clustered region. Under the model in this paper, the probability of collision for Starlink satellite where it clustered most densely is found to be 8.484 ∗ 10^−4. It is found that the predicted collision probability increased nonlinearly with the increased safety distance set. This simple model provides evidence that the continuous development of maneuver avoidance systems is necessary for the future of the orbital safety of satellites under the harsher Lower Earth Orbit environment.

Keywords: Starlink, collision probability, debris, geometry model

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Authors: F. Sabri, J. Jamali

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In this article, the wrinkling failure of orthotropic composite membranes due to in-plane shear deformation is investigated using nonlinear finite element analyses. A nonlinear post-buckling analysis is performed to show the evolution of shear-induced wrinkles. The method of investigation is based on the post-buckling finite element analysis adopted from commercial FEM code; ANSYS. The resulting wrinkling patterns, their amplitude and their wavelengths under the prescribed loads and boundary conditions were confirmed by experimental results. Our study reveals that wrinkles develop when both the magnitudes and coverage of the minimum principal stresses in the laminated composite laminates are sufficiently large to trigger wrinkling.

Keywords: composite, FEM, membrane, wrinkling

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Authors: V. Jagadish, Mathiyalagan V.

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Prediction of Door Touch temperature of a Cooking Oven using CFD Simulation. Self-Clean cycle is carried out in Cooking ovens to convert food spilling into ashes which makes cleaning easy. During this cycle cavity of oven is exposed to high temperature around 460 C. At this operating point the user may prone to touch the Door surfaces, Side Shield, Control Panel. To prevent heat experienced by user, cooling system is built in oven. The most effective cooling system is developed with existing design constraints through CFD Simulations. Cross Flow fan is used for Cooling system due to its cost effectiveness and it can give more air flow with low pressure drop.

Keywords: CFD, MRF, RBM, RANS, new product development, simulation, thermal analysis

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Authors: Saba Ebrahimi, Hedieh Ashrafi

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Predicting the cover type of forests is a challenge for natural resource managers. In this project, we aim to perform a comprehensive comparative study of two well-known classification methods, support vector machine (SVM) and decision tree (DT). The comparison is first performed among different types of each classifier, and then the best of each classifier will be compared by considering different evaluation metrics. The effect of boosting and bagging for decision trees is also explored. Furthermore, the effect of principal component analysis (PCA) and feature selection is also investigated. During the project, the forest cover-type dataset from the remote sensing and GIS program is used in all computations.

Keywords: classification methods, support vector machine, decision tree, forest cover-type dataset

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Authors: Reza Hedayati, Meysam Jahanbakhshi

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Fracture Mechanics is used to predict debonding propagation in adhesive joint between aluminum and composite plates. Three types of loadings and two types of glass-epoxy composite sequences: [0/90]2s and [0/45/-45/90]s are considered for the composite plate and their results are compared. It was seen that generally the cases with stacking sequence of [0/45/-45/90]s have much shorter lives than cases with [0/90]2s. It was also seen that in cases with λ=0 the ends of the debonding front propagates forward more than its middle, while in cases with λ=0.5 or λ=1 it is vice versa. Moreover, regardless of value of λ, the difference between the debonding propagations of the ends and the middle of the debonding front is very close in cases λ=0.5 and λ=1. Another main conclusion was the non-dimensionalized debonding front profile is almost independent of sequence type or the applied load value.

Keywords: fatigue, debonding, Paris law, APDL, adhesive

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Authors: R. Tirado, G. Habert, A. Mailhac, S. Laurenceau

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The rapid development of cities implies significant material inflows and outflows. The construction sector is one of the main consumers of raw materials and producers of waste. The waste from the building sector, for its quantity and potential for recovery, constitutes significant deposits requiring major efforts, by combining different actors, to achieve the circular economy's objectives. It is necessary to understand and know the current construction actors' knowledge of stocks, urban metabolism, deposits, and recovery practices in this context. This article aims to explore the role of local governments in planning strategies by facilitating a circular economy. In particular, the principal opportunities and challenges of communities for applying the principles of the circular economy in the building sector will be identified. The approach used for the study was to conduct semi-structured interviews with those responsible for circular economy projects within local administrations of some communities in France. The results show territories' involvement in the inclusion and application of the principles of the circular economy in the building sector. The main challenges encountered are numerous, hence the importance of having identified and described them so that the different actors can work to meet them.

Keywords: building stock, circular economy, interview, local authorities

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Authors: Srushti Patil, Preethi Baligar, Gopalkrishna Joshi, Gururaj N. Bhadri

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In designing learning environments, the instructional strategies can be tailored to suit the learning style of an individual to ensure effective learning. In this study, the information shared on social media like Facebook is being used to predict learning style of a learner. Previous research studies have shown that Facebook data can be used to predict user personality. Users with a particular personality exhibit an inherent pattern in their digital footprint on Facebook. The proposed work aims to correlate the user's’ personality, predicted from Facebook data to the learning styles, predicted through questionnaires. For Millennial learners, Facebook has become a primary means for information sharing and interaction with peers. Thus, it can serve as a rich bed for research and direct the design of learning environments. The authors have conducted this study in an undergraduate freshman engineering course. Data from 320 freshmen Facebook users was collected. The same users also participated in the learning style and personality prediction survey. The Kolb’s Learning style questionnaires and Big 5 personality Inventory were adopted for the survey. The users have agreed to participate in this research and have signed individual consent forms. A specific page was created on Facebook to collect user data like personal details, status updates, comments, demographic characteristics and egocentric network parameters. This data was captured by an application created using Python program. The data captured from Facebook was subjected to text analysis process using the Linguistic Inquiry and Word Count dictionary. An analysis of the data collected from the questionnaires performed reveals individual student personality and learning style. The results obtained from analysis of Facebook, learning style and personality data were then fed into an automatic classifier that was trained by using the data mining techniques like Rule-based classifiers and Decision trees. This helps to predict the user personality and learning styles by analysing the common patterns. Rule-based classifiers applied for text analysis helps to categorize Facebook data into positive, negative and neutral. There were totally two models trained, one to predict the personality from Facebook data; another one to predict the learning styles from the personalities. The results show that the classifier model has high accuracy which makes the proposed method to be a reliable one for predicting the user personality and learning styles.

Keywords: educational data mining, Facebook, learning styles, personality traits

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Authors: Adel Hanna, Tahar Ayadat, Mohammad Etezad, Cyrille Cros

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A number of theoretical methods have been developed over the years to calculate the amount settlement of the soil reinforced with group of stone columns. The results deduced from these methods sometimes show large disagreement with the experimental observations. The reason of this divergence might be due to the fact that many of the previous methods assumed the deform shape of the columns which is different with the actual case. A new method to calculate settlement of the ground reinforced with group of stone columns is presented in this paper which overcomes the restrictions made by previous theories. This method is based on results deduced from numerical modeling. Results obtained from the model are validated.

Keywords: stone columns, group, soft soil, settlement, prediction

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Authors: Etienne K. Ngoy

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This analysis suggests that the comprehensive degradation caused by any environmental factor on fiber reinforced plastics under mechanical stress can be measured as a change in viscoelastic properties of the material. The change in viscoelastic characteristics is experimentally determined as a time-dependent function expressing the amplification of the stress relaxation. The variation of this experimental function provides a measure of the environmental degradation rate. Where real service environment conditions can be reliably simulated in the laboratory, it is possible to generate master curves that include environmental degradation effect and hence predict the durability of the fiber reinforced plastics under environmental degradation.

Keywords: environmental effects, fiber reinforced plastics durability, prediction, stress effect

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Authors: Mehran Dizbadi, Daniyal Safarzadeh, Behrooz Arastoo, Ansgar Brunn

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Rapid and uncontrolled expansion of cities has led to unplanned aerial development. In this way, modeling and predicting the urban growth of a city helps decision-makers. In this study, the aspect of sustainable urban development has been studied for the city of Mashhad. In general, the prediction of urban aerial development is one of the most important topics of modern town management. In this research, using the Cellular Automaton (CA) model developed for geo data of Geographic Information Systems (GIS) and presenting a simple and powerful model, a simulation of complex urban processes has been done.

Keywords: urban modeling, sustainable development, fuzzy cellular automaton, geo-information system

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Authors: Suzan Wedyan

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Data mining is one of the main phases in the Knowledge Discovery Database (KDD) which is responsible of finding hidden and useful knowledge from databases. There are many different tasks for data mining including regression, pattern recognition, clustering, classification, and association rule. In recent years a promising data mining approach called associative classification (AC) has been proposed, AC integrates classification and association rule discovery to build classification models (classifiers). This paper surveys and critically compares several AC algorithms with reference of the different procedures are used in each algorithm, such as rule learning, rule sorting, rule pruning, classifier building, and class allocation for test cases.

Keywords: associative classification, classification, data mining, learning, rule ranking, rule pruning, prediction

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Authors: Azimollah Aleshzadeh, Enver Vural Yavuz

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The main goal of this study is to produce landslide susceptibility maps using different data-driven bivariate statistical approaches; namely, entropy weight method (EWM), evidence belief function (EBF), and information content model (ICM), at Uzundere county, Erzurum province, in the north-eastern part of Turkey. Past landslide occurrences were identified and mapped from an interpretation of high-resolution satellite images, and earlier reports as well as by carrying out field surveys. In total, 42 landslide incidence polygons were mapped using ArcGIS 10.4.1 software and randomly split into a construction dataset 70 % (30 landslide incidences) for building the EWM, EBF, and ICM models and the remaining 30 % (12 landslides incidences) were used for verification purposes. Twelve layers of landslide-predisposing parameters were prepared, including total surface radiation, maximum relief, soil groups, standard curvature, distance to stream/river sites, distance to the road network, surface roughness, land use pattern, engineering geological rock group, topographical elevation, the orientation of slope, and terrain slope gradient. The relationships between the landslide-predisposing parameters and the landslide inventory map were determined using different statistical models (EWM, EBF, and ICM). The model results were validated with landslide incidences, which were not used during the model construction. In addition, receiver operating characteristic curves were applied, and the area under the curve (AUC) was determined for the different susceptibility maps using the success (construction data) and prediction (verification data) rate curves. The results revealed that the AUC for success rates are 0.7055, 0.7221, and 0.7368, while the prediction rates are 0.6811, 0.6997, and 0.7105 for EWM, EBF, and ICM models, respectively. Consequently, landslide susceptibility maps were classified into five susceptibility classes, including very low, low, moderate, high, and very high. Additionally, the portion of construction and verification landslides incidences in high and very high landslide susceptibility classes in each map was determined. The results showed that the EWM, EBF, and ICM models produced satisfactory accuracy. The obtained landslide susceptibility maps may be useful for future natural hazard mitigation studies and planning purposes for environmental protection.

Keywords: entropy weight method, evidence belief function, information content model, landslide susceptibility mapping

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Authors: O. D. A. N. Perera, H. G. Wanigasinghe

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Tender jackfruit is an underutilized biomass, which still has a good consumer demand. Valorization of this ingredient into meat analog would obtain greater consumer acceptance due to concerns about health, the environment, and living sustainably of mankind have increased significantly in this decade, opening the market for meat substitutes. The objective of this research was to create a plant-based meat substitute with a structure similar to meat products. In this study, three different combinations of tender jackfruit were used to create vegan burger patties, which were then examined for their textural, physico-chemical, and sensory qualities. The developed burger patties have been compared with store-bought chicken patties. The developed vegan burger patties P1, P2, and P3 had a comparable flavor preference to the control and demonstrated considerable general acceptability (p >.05). P3 has a high quantity of protein (17.10 ± 0.02%) and fiber (6.40 ± 0.06%). At the same time, the vegan burger patty resulted in less fat, high fiber, and high protein which meets the vegan consumer requirements.

Keywords: underutilized, high fibre, soya protein isolate, cooking yield

Procedia PDF Downloads 65