Search results for: shelf life prediction
9141 Aggregate Angularity on the Permanent Deformation Zones of Hot Mix Asphalt
Authors: Lee P. Leon, Raymond Charles
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This paper presents a method of evaluating the effect of aggregate angularity on hot mix asphalt (HMA) properties and its relationship to the Permanent Deformation resistance. The research concluded that aggregate particle angularity had a significant effect on the Permanent Deformation performance, and also that with an increase in coarse aggregate angularity there was an increase in the resistance of mixes to Permanent Deformation. A comparison between the measured data and predictive data of permanent deformation predictive models showed the limits of existing prediction models. The numerical analysis described the permanent deformation zones and concluded that angularity has an effect of the onset of these zones. Prediction of permanent deformation help road agencies and by extension economists and engineers determine the best approach for maintenance, rehabilitation, and new construction works of the road infrastructure.Keywords: aggregate angularity, asphalt concrete, permanent deformation, rutting prediction
Procedia PDF Downloads 3969140 Use of Multistage Transition Regression Models for Credit Card Income Prediction
Authors: Denys Osipenko, Jonathan Crook
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Because of the variety of the card holders’ behaviour types and income sources each consumer account can be transferred to a variety of states. Each consumer account can be inactive, transactor, revolver, delinquent, defaulted and requires an individual model for the income prediction. The estimation of transition probabilities between statuses at the account level helps to avoid the memorylessness of the Markov Chains approach. This paper investigates the transition probabilities estimation approaches to credit cards income prediction at the account level. The key question of empirical research is which approach gives more accurate results: multinomial logistic regression or multistage conditional logistic regression with binary target. Both models have shown moderate predictive power. Prediction accuracy for conditional logistic regression depends on the order of stages for the conditional binary logistic regression. On the other hand, multinomial logistic regression is easier for usage and gives integrate estimations for all states without priorities. Thus further investigations can be concentrated on alternative modeling approaches such as discrete choice models.Keywords: multinomial regression, conditional logistic regression, credit account state, transition probability
Procedia PDF Downloads 4789139 Mobile Based Long Range Weather Prediction System for the Farmers of Rural Areas of Pakistan
Authors: Zeeshan Muzammal, Usama Latif, Fouzia Younas, Syed Muhammad Hassan, Samia Razaq
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Unexpected rainfall has always been an issue in the lifetime of crops and brings destruction for the farmers who harvest them. Unfortunately, Pakistan is one of the countries in which untimely rain impacts badly on crops like wash out of seeds and pesticides etc. Pakistan’s GDP is related to agriculture, especially in rural areas farmers sometimes quit farming because leverage of huge loss to their crops. Through our surveys and research, we came to know that farmers in the rural areas of Pakistan need rain information to avoid damages to their crops from rain. We developed a prototype using ICTs to inform the farmers about rain one week in advance. Our proposed solution has two ways of informing the farmers. In first we send daily messages about weekly prediction and also designed a helpline where they can call us to ask about possibility of rain.Keywords: ICTD, farmers, mobile based, Pakistan, rural areas, weather prediction
Procedia PDF Downloads 5669138 Changing from Crude (Rudimentary) to Modern Method of Cassava Processing in the Ngwo Village of Njikwa Sub Division of North West Region of Cameroon
Authors: Loveline Ambo Angwah
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The processing of cassava from tubers or roots into food using crude and rudimentary method (hand peeling, grating, frying and to sun drying) is a very cumbersome and difficult process. The crude methods are time consuming and labour intensive. While on the other hand, modern processing method, that is using machines to perform the various processes as washing, peeling, grinding, oven drying, fermentation and frying is easier, less time consuming, and less labour intensive. Rudimentarily, cassava roots are processed into numerous products and utilized in various ways according to local customs and preferences. For the people of Ngwo village, cassava is transformed locally into flour or powder form called ‘cumcum’. It is also sucked into water to give a kind of food call ‘water fufu’ and fried to give ‘garri’. The leaves are consumed as vegetables. Added to these, its relative high yields; ability to stay underground after maturity for long periods give cassava considerable advantage as a commodity that is being used by poor rural folks in the community, to fight poverty. It plays a major role in efforts to alleviate the food crisis because of its efficient production of food energy, year-round availability, tolerance to extreme stress conditions, and suitability to present farming and food systems in Africa. Improvement of cassava processing and utilization techniques would greatly increase labor efficiency, incomes, and living standards of cassava farmers and the rural poor, as well as enhance the-shelf life of products, facilitate their transportation, increase marketing opportunities, and help improve human and livestock nutrition. This paper presents a general overview of crude ways in cassava processing and utilization methods now used by subsistence and small-scale farmers in Ngwo village of the North West region in Cameroon, and examine the opportunities of improving processing technologies. Cassava needs processing because the roots cannot be stored for long because they rot within 3-4 days of harvest. They are bulky with about 70% moisture content, and therefore transportation of the tubers to markets is difficult and expensive. The roots and leaves contain varying amounts of cyanide which is toxic to humans and animals, while the raw cassava roots and uncooked leaves are not palatable. Therefore, cassava must be processed into various forms in order to increase the shelf life of the products, facilitate transportation and marketing, reduce cyanide content and improve palatability.Keywords: cassava roots, crude ways, food system, poverty
Procedia PDF Downloads 1629137 Integration of Educational Data Mining Models to a Web-Based Support System for Predicting High School Student Performance
Authors: Sokkhey Phauk, Takeo Okazaki
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The challenging task in educational institutions is to maximize the high performance of students and minimize the failure rate of poor-performing students. An effective method to leverage this task is to know student learning patterns with highly influencing factors and get an early prediction of student learning outcomes at the timely stage for setting up policies for improvement. Educational data mining (EDM) is an emerging disciplinary field of data mining, statistics, and machine learning concerned with extracting useful knowledge and information for the sake of improvement and development in the education environment. The study is of this work is to propose techniques in EDM and integrate it into a web-based system for predicting poor-performing students. A comparative study of prediction models is conducted. Subsequently, high performing models are developed to get higher performance. The hybrid random forest (Hybrid RF) produces the most successful classification. For the context of intervention and improving the learning outcomes, a feature selection method MICHI, which is the combination of mutual information (MI) and chi-square (CHI) algorithms based on the ranked feature scores, is introduced to select a dominant feature set that improves the performance of prediction and uses the obtained dominant set as information for intervention. By using the proposed techniques of EDM, an academic performance prediction system (APPS) is subsequently developed for educational stockholders to get an early prediction of student learning outcomes for timely intervention. Experimental outcomes and evaluation surveys report the effectiveness and usefulness of the developed system. The system is used to help educational stakeholders and related individuals for intervening and improving student performance.Keywords: academic performance prediction system, educational data mining, dominant factors, feature selection method, prediction model, student performance
Procedia PDF Downloads 1039136 DNpro: A Deep Learning Network Approach to Predicting Protein Stability Changes Induced by Single-Site Mutations
Authors: Xiao Zhou, Jianlin Cheng
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A single amino acid mutation can have a significant impact on the stability of protein structure. Thus, the prediction of protein stability change induced by single site mutations is critical and useful for studying protein function and structure. Here, we presented a deep learning network with the dropout technique for predicting protein stability changes upon single amino acid substitution. While using only protein sequence as input, the overall prediction accuracy of the method on a standard benchmark is >85%, which is higher than existing sequence-based methods and is comparable to the methods that use not only protein sequence but also tertiary structure, pH value and temperature. The results demonstrate that deep learning is a promising technique for protein stability prediction. The good performance of this sequence-based method makes it a valuable tool for predicting the impact of mutations on most proteins whose experimental structures are not available. Both the downloadable software package and the user-friendly web server (DNpro) that implement the method for predicting protein stability changes induced by amino acid mutations are freely available for the community to use.Keywords: bioinformatics, deep learning, protein stability prediction, biological data mining
Procedia PDF Downloads 4549135 Hydro-Gravimetric Ann Model for Prediction of Groundwater Level
Authors: Jayanta Kumar Ghosh, Swastik Sunil Goriwale, Himangshu Sarkar
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Groundwater is one of the most valuable natural resources that society consumes for its domestic, industrial, and agricultural water supply. Its bulk and indiscriminate consumption affects the groundwater resource. Often, it has been found that the groundwater recharge rate is much lower than its demand. Thus, to maintain water and food security, it is necessary to monitor and management of groundwater storage. However, it is challenging to estimate groundwater storage (GWS) by making use of existing hydrological models. To overcome the difficulties, machine learning (ML) models are being introduced for the evaluation of groundwater level (GWL). Thus, the objective of this research work is to develop an ML-based model for the prediction of GWL. This objective has been realized through the development of an artificial neural network (ANN) model based on hydro-gravimetry. The model has been developed using training samples from field observations spread over 8 months. The developed model has been tested for the prediction of GWL in an observation well. The root means square error (RMSE) for the test samples has been found to be 0.390 meters. Thus, it can be concluded that the hydro-gravimetric-based ANN model can be used for the prediction of GWL. However, to improve the accuracy, more hydro-gravimetric parameter/s may be considered and tested in future.Keywords: machine learning, hydro-gravimetry, ground water level, predictive model
Procedia PDF Downloads 1209134 Prediction of Critical Flow Rate in Tubular Heat Exchangers for the Onset of Damaging Flow-Induced Vibrations
Authors: Y. Khulief, S. Bashmal, S. Said, D. Al-Otaibi, K. Mansour
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The prediction of flow rates at which the vibration-induced instability takes place in tubular heat exchangers due to cross-flow is of major importance to the performance and service life of such equipment. In this paper, the semi-analytical model for square tube arrays was extended and utilized to study the triangular tube patterns. A laboratory test rig with instrumented test section is used to measure the fluidelastic coefficients to be used for tuning the mathematical model. The test section can be made of any bundle pattern. In this study, two test sections were constructed for both the normal triangular and the rotated triangular tube arrays. The developed scheme is utilized in predicting the onset of flow-induced instability in the two triangular tube arrays. The results are compared to those obtained for two other bundle configurations. The results of the four different tube patterns are viewed in the light of TEMA predictions. The comparison demonstrated that TEMA guidelines are more conservative in all configurations consideredKeywords: fluid-structure interaction, cross-flow, heat exchangers,
Procedia PDF Downloads 2729133 Predicting Trapezoidal Weir Discharge Coefficient Using Evolutionary Algorithm
Authors: K. Roushanger, A. Soleymanzadeh
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Weirs are structures often used in irrigation techniques, sewer networks and flood protection. However, the hydraulic behavior of this type of weir is complex and difficult to predict accurately. An accurate flow prediction over a weir mainly depends on the proper estimation of discharge coefficient. In this study, the Genetic Expression Programming (GEP) approach was used for predicting trapezoidal and rectangular sharp-crested side weirs discharge coefficient. Three different performance indexes are used as comparing criteria for the evaluation of the model’s performances. The obtained results approved capability of GEP in prediction of trapezoidal and rectangular side weirs discharge coefficient. The results also revealed the influence of downstream Froude number for trapezoidal weir and upstream Froude number for rectangular weir in prediction of the discharge coefficient for both of side weirs.Keywords: discharge coefficient, genetic expression programming, trapezoidal weir
Procedia PDF Downloads 3829132 Dry Relaxation Shrinkage Prediction of Bordeaux Fiber Using a Feed Forward Neural
Authors: Baeza S. Roberto
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The knitted fabric suffers a deformation in its dimensions due to stretching and tension factors, transverse and longitudinal respectively, during the process in rectilinear knitting machines so it performs a dry relaxation shrinkage procedure and thermal action of prefixed to obtain stable conditions in the knitting. This paper presents a dry relaxation shrinkage prediction of Bordeaux fiber using a feed forward neural network and linear regression models. Six operational alternatives of shrinkage were predicted. A comparison of the results was performed finding neural network models with higher levels of explanation of the variability and prediction. The presence of different reposes are included. The models were obtained through a neural toolbox of Matlab and Minitab software with real data in a knitting company of Southern Guanajuato. The results allow predicting dry relaxation shrinkage of each alternative operation.Keywords: neural network, dry relaxation, knitting, linear regression
Procedia PDF Downloads 5779131 Use of Artificial Intelligence Based Models to Estimate the Use of a Spectral Band in Cognitive Radio
Authors: Danilo López, Edwin Rivas, Fernando Pedraza
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Currently, one of the major challenges in wireless networks is the optimal use of radio spectrum, which is managed inefficiently. One of the solutions to existing problem converges in the use of Cognitive Radio (CR), as an essential parameter so that the use of the available licensed spectrum is possible (by secondary users), well above the usage values that are currently detected; thus allowing the opportunistic use of the channel in the absence of primary users (PU). This article presents the results found when estimating or predicting the future use of a spectral transmission band (from the perspective of the PU) for a chaotic type channel arrival behavior. The time series prediction method (which the PU represents) used is ANFIS (Adaptive Neuro Fuzzy Inference System). The results obtained were compared to those delivered by the RNA (Artificial Neural Network) algorithm. The results show better performance in the characterization (modeling and prediction) with the ANFIS methodology.Keywords: ANFIS, cognitive radio, prediction primary user, RNA
Procedia PDF Downloads 4159130 Applied Complement of Probability and Information Entropy for Prediction in Student Learning
Authors: Kennedy Efosa Ehimwenma, Sujatha Krishnamoorthy, Safiya Al‑Sharji
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The probability computation of events is in the interval of [0, 1], which are values that are determined by the number of outcomes of events in a sample space S. The probability Pr(A) that an event A will never occur is 0. The probability Pr(B) that event B will certainly occur is 1. This makes both events A and B a certainty. Furthermore, the sum of probabilities Pr(E₁) + Pr(E₂) + … + Pr(Eₙ) of a finite set of events in a given sample space S equals 1. Conversely, the difference of the sum of two probabilities that will certainly occur is 0. This paper first discusses Bayes, the complement of probability, and the difference of probability for occurrences of learning-events before applying them in the prediction of learning objects in student learning. Given the sum of 1; to make a recommendation for student learning, this paper proposes that the difference of argMaxPr(S) and the probability of student-performance quantifies the weight of learning objects for students. Using a dataset of skill-set, the computational procedure demonstrates i) the probability of skill-set events that have occurred that would lead to higher-level learning; ii) the probability of the events that have not occurred that requires subject-matter relearning; iii) accuracy of the decision tree in the prediction of student performance into class labels and iv) information entropy about skill-set data and its implication on student cognitive performance and recommendation of learning.Keywords: complement of probability, Bayes’ rule, prediction, pre-assessments, computational education, information theory
Procedia PDF Downloads 1549129 Optimization of Technical and Technological Solutions for the Development of Offshore Hydrocarbon Fields in the Kaliningrad Region
Authors: Pavel Shcherban, Viktoria Ivanova, Alexander Neprokin, Vladislav Golovanov
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Currently, LLC «Lukoil-Kaliningradmorneft» is implementing a comprehensive program for the development of offshore fields of the Kaliningrad region. This is largely associated with the depletion of the resource base of land in the region, as well as the positive results of geological investigation surrounding the Baltic Sea area and the data on the volume of hydrocarbon recovery from a single offshore field are working on the Kaliningrad region – D-6 «Kravtsovskoye».The article analyzes the main stages of the LLC «Lukoil-Kaliningradmorneft»’s development program for the development of the hydrocarbon resources of the region's shelf and suggests an optimization algorithm that allows managing a multi-criteria process of development of shelf deposits. The algorithm is formed on the basis of the problem of sequential decision making, which is a section of dynamic programming. Application of the algorithm during the consolidation of the initial data, the elaboration of project documentation, the further exploration and development of offshore fields will allow to optimize the complex of technical and technological solutions and increase the economic efficiency of the field development project implemented by LLC «Lukoil-Kaliningradmorneft».Keywords: offshore fields of hydrocarbons of the Baltic Sea, development of offshore oil and gas fields, optimization of the field development scheme, solution of multicriteria tasks in oil and gas complex, quality management in oil and gas complex
Procedia PDF Downloads 1959128 A Deep-Learning Based Prediction of Pancreatic Adenocarcinoma with Electronic Health Records from the State of Maine
Authors: Xiaodong Li, Peng Gao, Chao-Jung Huang, Shiying Hao, Xuefeng B. Ling, Yongxia Han, Yaqi Zhang, Le Zheng, Chengyin Ye, Modi Liu, Minjie Xia, Changlin Fu, Bo Jin, Karl G. Sylvester, Eric Widen
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Predicting the risk of Pancreatic Adenocarcinoma (PA) in advance can benefit the quality of care and potentially reduce population mortality and morbidity. The aim of this study was to develop and prospectively validate a risk prediction model to identify patients at risk of new incident PA as early as 3 months before the onset of PA in a statewide, general population in Maine. The PA prediction model was developed using Deep Neural Networks, a deep learning algorithm, with a 2-year electronic-health-record (EHR) cohort. Prospective results showed that our model identified 54.35% of all inpatient episodes of PA, and 91.20% of all PA that required subsequent chemoradiotherapy, with a lead-time of up to 3 months and a true alert of 67.62%. The risk assessment tool has attained an improved discriminative ability. It can be immediately deployed to the health system to provide automatic early warnings to adults at risk of PA. It has potential to identify personalized risk factors to facilitate customized PA interventions.Keywords: cancer prediction, deep learning, electronic health records, pancreatic adenocarcinoma
Procedia PDF Downloads 1509127 Engineering Packaging for a Sustainable Food Chain
Authors: Ezekiel Olukayode Akintunde
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There is a high level of inadequate methods at all levels of food supply in the global food industry. The inadequacies have led to vast wastages of food. Hence there is a need to curb the wastages that can later affect natural resources, water resources, and energy to avoid negative impacts on the climate and the environment. There is a need to engage multifaceted engineering packaging approaches for a sustainable food chain to ensure active packaging, intelligent packaging, new packaging materials, and a sustainable packaging system. Packaging can be regarded as an indispensable component approach that can be applied to solve major problems of sustainable food consumption globally; this is about controlling the environmental impact of packed food. The creative innovation will ensure that packaged foods are free from food-borne diseases and food chemical pollution. This paper evaluates the key shortcomings that must be addressed by innovative food packaging to ensure a safe, natural environment that will preserve energy and sustain water resources. Certain solutions, including fabricating microbial biodegradable chemical compounds/polymers from agro-food waste remnants, appear a bright path to ensure a strong and innovative waste-based food packaging system. Over the years, depletion in the petroleum reserves has brought about the emergence of biodegradable polymers as a proper replacement for traditional plastics; moreover, the increase in the production of traditional plastics has raised serious concerns about environmental threats. Biodegradable polymers have proven to be biocompatible, which can also be processed for other useful applications. Therefore, this study will showcase a workable guiding framework for designing a sustainable food packaging system that will not constitute a danger to our present society and that will surely preserve natural water resources. Various assessment methods will be deployed at different stages of the packaging design to enhance the package's sustainability. Every decision that will be made must be facilitated with methods that will be engaged per stage to allow for corrective measures throughout the cycle of the design process. Basic performance appraisal of packaging innovations. Food wastage can result in inimical environmental impacts, and ethical practices must be carried out for food loss at home. An examination in West Africa quantified preventable food wastage over the entire food value chain at almost 180kg per person per year. That is preventable food wastage, 35% of which originated at the household level. Many food losses reported, which happened at the harvesting, storage, transportation, and processing stages, are not preventable and are without much environmental impact because such wastage can be used for feeding. Other surveys have shown that 15%-20% of household food losses can be traced to food packaging. Therefore, new innovative packaging systems can lessen the environmental effect of food wastage to extend shelf‐life to lower food loss in the process distribution chain and at the household level.Keywords: food packaging, biodegradable polymer, intelligent packaging, shelf-life
Procedia PDF Downloads 469126 Urban Big Data: An Experimental Approach to Building-Value Estimation Using Web-Based Data
Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin
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Current real-estate value estimation, difficult for laymen, usually is performed by specialists. This paper presents an automated estimation process based on big data and machine-learning technology that calculates influences of building conditions on real-estate price measurement. The present study analyzed actual building sales sample data for Nonhyeon-dong, Gangnam-gu, Seoul, Korea, measuring the major influencing factors among the various building conditions. Further to that analysis, a prediction model was established and applied using RapidMiner Studio, a graphical user interface (GUI)-based tool for derivation of machine-learning prototypes. The prediction model is formulated by reference to previous examples. When new examples are applied, it analyses and predicts accordingly. The analysis process discerns the crucial factors effecting price increases by calculation of weighted values. The model was verified, and its accuracy determined, by comparing its predicted values with actual price increases.Keywords: apartment complex, big data, life-cycle building value analysis, machine learning
Procedia PDF Downloads 3689125 Aerodynamic Coefficients Prediction from Minimum Computation Combinations Using OpenVSP Software
Authors: Marine Segui, Ruxandra Mihaela Botez
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OpenVSP is an aerodynamic solver developed by National Aeronautics and Space Administration (NASA) that allows building a reliable model of an aircraft. This software performs an aerodynamic simulation according to the angle of attack of the aircraft makes between the incoming airstream, and its speed. A reliable aerodynamic model of the Cessna Citation X was designed but it required a lot of computation time. As a consequence, a prediction method was established that allowed predicting lift and drag coefficients for all Mach numbers and for all angles of attack, exclusively for stall conditions, from a computation of three angles of attack and only one Mach number. Aerodynamic coefficients given by the prediction method for a Cessna Citation X model were finally compared with aerodynamics coefficients obtained using a complete OpenVSP study.Keywords: aerodynamic, coefficient, cruise, improving, longitudinal, openVSP, solver, time
Procedia PDF Downloads 2269124 Probabilistic Crash Prediction and Prevention of Vehicle Crash
Authors: Lavanya Annadi, Fahimeh Jafari
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Transportation brings immense benefits to society, but it also has its costs. Costs include such as the cost of infrastructure, personnel and equipment, but also the loss of life and property in traffic accidents on the road, delays in travel due to traffic congestion and various indirect costs in terms of air transport. More research has been done to identify the various factors that affect road accidents, such as road infrastructure, traffic, sociodemographic characteristics, land use, and the environment. The aim of this research is to predict the probabilistic crash prediction of vehicles using machine learning due to natural and structural reasons by excluding spontaneous reasons like overspeeding etc., in the United States. These factors range from weather factors, like weather conditions, precipitation, visibility, wind speed, wind direction, temperature, pressure, and humidity to human made structures like road structure factors like bump, roundabout, no exit, turning loop, give away, etc. Probabilities are dissected into ten different classes. All the predictions are based on multiclass classification techniques, which are supervised learning. This study considers all crashes that happened in all states collected by the US government. To calculate the probability, multinomial expected value was used and assigned a classification label as the crash probability. We applied three different classification models, including multiclass Logistic Regression, Random Forest and XGBoost. The numerical results show that XGBoost achieved a 75.2% accuracy rate which indicates the part that is being played by natural and structural reasons for the crash. The paper has provided in-deep insights through exploratory data analysis.Keywords: road safety, crash prediction, exploratory analysis, machine learning
Procedia PDF Downloads 1049123 The Use Support Vector Machine and Back Propagation Neural Network for Prediction of Daily Tidal Levels Along The Jeddah Coast, Saudi Arabia
Authors: E. A. Mlybari, M. S. Elbisy, A. H. Alshahri, O. M. Albarakati
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Sea level rise threatens to increase the impact of future storms and hurricanes on coastal communities. Accurate sea level change prediction and supplement is an important task in determining constructions and human activities in coastal and oceanic areas. In this study, support vector machines (SVM) is proposed to predict daily tidal levels along the Jeddah Coast, Saudi Arabia. The optimal parameter values of kernel function are determined using a genetic algorithm. The SVM results are compared with the field data and with back propagation (BP). Among the models, the SVM is superior to BPNN and has better generalization performance.Keywords: tides, prediction, support vector machines, genetic algorithm, back-propagation neural network, risk, hazards
Procedia PDF Downloads 4609122 Analysis of the Result for the Accelerated Life Cycle Test of the Motor for Washing Machine by Using Acceleration Factor
Authors: Youn-Sung Kim, Jin-Ho Jo, Mi-Sung Kim, Jae-Kun Lee
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Accelerated life cycle test is applied to various products or components in order to reduce the time of life cycle test in industry. It must be considered for many test conditions according to the product characteristics for the test and the selection of acceleration parameter is especially very important. We have carried out the general life cycle test and the accelerated life cycle test by applying the acceleration factor (AF) considering the characteristics of brushless DC (BLDC) motor for washing machine. The final purpose of this study is to verify the validity by analyzing the results of the general life cycle test and the accelerated life cycle test. It will make it possible to reduce the life test time through the reasonable accelerated life cycle test.Keywords: accelerated life cycle test, reliability test, motor for washing machine, brushless dc motor test
Procedia PDF Downloads 6049121 Mean Monthly Rainfall Prediction at Benina Station Using Artificial Neural Networks
Authors: Hasan G. Elmazoghi, Aisha I. Alzayani, Lubna S. Bentaher
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Rainfall is a highly non-linear phenomena, which requires application of powerful supervised data mining techniques for its accurate prediction. In this study the Artificial Neural Network (ANN) technique is used to predict the mean monthly historical rainfall data collected from BENINA station in Benghazi for 31 years, the period of “1977-2006” and the results are compared against the observed values. The specific objective to achieve this goal was to determine the best combination of weather variables to be used as inputs for the ANN model. Several statistical parameters were calculated and an uncertainty analysis for the results is also presented. The best ANN model is then applied to the data of one year (2007) as a case study in order to evaluate the performance of the model. Simulation results reveal that application of ANN technique is promising and can provide reliable estimates of rainfall.Keywords: neural networks, rainfall, prediction, climatic variables
Procedia PDF Downloads 4809120 Online Prediction of Nonlinear Signal Processing Problems Based Kernel Adaptive Filtering
Authors: Hamza Nejib, Okba Taouali
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This paper presents two of the most knowing kernel adaptive filtering (KAF) approaches, the kernel least mean squares and the kernel recursive least squares, in order to predict a new output of nonlinear signal processing. Both of these methods implement a nonlinear transfer function using kernel methods in a particular space named reproducing kernel Hilbert space (RKHS) where the model is a linear combination of kernel functions applied to transform the observed data from the input space to a high dimensional feature space of vectors, this idea known as the kernel trick. Then KAF is the developing filters in RKHS. We use two nonlinear signal processing problems, Mackey Glass chaotic time series prediction and nonlinear channel equalization to figure the performance of the approaches presented and finally to result which of them is the adapted one.Keywords: online prediction, KAF, signal processing, RKHS, Kernel methods, KRLS, KLMS
Procedia PDF Downloads 3919119 Stock Market Prediction by Regression Model with Social Moods
Authors: Masahiro Ohmura, Koh Kakusho, Takeshi Okadome
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This paper presents a regression model with autocorrelated errors in which the inputs are social moods obtained by analyzing the adjectives in Twitter posts using a document topic model. The regression model predicts Dow Jones Industrial Average (DJIA) more precisely than autoregressive moving-average models.Keywords: stock market prediction, social moods, regression model, DJIA
Procedia PDF Downloads 5399118 A Comparative Analysis of the Performance of COSMO and WRF Models in Quantitative Rainfall Prediction
Authors: Isaac Mugume, Charles Basalirwa, Daniel Waiswa, Mary Nsabagwa, Triphonia Jacob Ngailo, Joachim Reuder, Sch¨attler Ulrich, Musa Semujju
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The Numerical weather prediction (NWP) models are considered powerful tools for guiding quantitative rainfall prediction. A couple of NWP models exist and are used at many operational weather prediction centers. This study considers two models namely the Consortium for Small–scale Modeling (COSMO) model and the Weather Research and Forecasting (WRF) model. It compares the models’ ability to predict rainfall over Uganda for the period 21st April 2013 to 10th May 2013 using the root mean square (RMSE) and the mean error (ME). In comparing the performance of the models, this study assesses their ability to predict light rainfall events and extreme rainfall events. All the experiments used the default parameterization configurations and with same horizontal resolution (7 Km). The results show that COSMO model had a tendency of largely predicting no rain which explained its under–prediction. The COSMO model (RMSE: 14.16; ME: -5.91) presented a significantly (p = 0.014) higher magnitude of error compared to the WRF model (RMSE: 11.86; ME: -1.09). However the COSMO model (RMSE: 3.85; ME: 1.39) performed significantly (p = 0.003) better than the WRF model (RMSE: 8.14; ME: 5.30) in simulating light rainfall events. All the models under–predicted extreme rainfall events with the COSMO model (RMSE: 43.63; ME: -39.58) presenting significantly higher error magnitudes than the WRF model (RMSE: 35.14; ME: -26.95). This study recommends additional diagnosis of the models’ treatment of deep convection over the tropics.Keywords: comparative performance, the COSMO model, the WRF model, light rainfall events, extreme rainfall events
Procedia PDF Downloads 2569117 Estimation of Transition and Emission Probabilities
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
Procedia PDF Downloads 4729116 Nonparametric Quantile Regression for Multivariate Spatial Data
Authors: S. H. Arnaud Kanga, O. Hili, S. Dabo-Niang
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Spatial prediction is an issue appealing and attracting several fields such as agriculture, environmental sciences, ecology, econometrics, and many others. Although multiple non-parametric prediction methods exist for spatial data, those are based on the conditional expectation. This paper took a different approach by examining a non-parametric spatial predictor of the conditional quantile. The study especially observes the stationary multidimensional spatial process over a rectangular domain. Indeed, the proposed quantile is obtained by inverting the conditional distribution function. Furthermore, the proposed estimator of the conditional distribution function depends on three kernels, where one of them controls the distance between spatial locations, while the other two control the distance between observations. In addition, the almost complete convergence and the convergence in mean order q of the kernel predictor are obtained when the sample considered is alpha-mixing. Such approach of the prediction method gives the advantage of accuracy as it overcomes sensitivity to extreme and outliers values.Keywords: conditional quantile, kernel, nonparametric, stationary
Procedia PDF Downloads 1489115 Effect of Genuine Missing Data Imputation on Prediction of Urinary Incontinence
Authors: Suzan Arslanturk, Mohammad-Reza Siadat, Theophilus Ogunyemi, Ananias Diokno
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Missing data is a common challenge in statistical analyses of most clinical survey datasets. A variety of methods have been developed to enable analysis of survey data to deal with missing values. Imputation is the most commonly used among the above methods. However, in order to minimize the bias introduced due to imputation, one must choose the right imputation technique and apply it to the correct type of missing data. In this paper, we have identified different types of missing values: missing data due to skip pattern (SPMD), undetermined missing data (UMD), and genuine missing data (GMD) and applied rough set imputation on only the GMD portion of the missing data. We have used rough set imputation to evaluate the effect of such imputation on prediction by generating several simulation datasets based on an existing epidemiological dataset (MESA). To measure how well each dataset lends itself to the prediction model (logistic regression), we have used p-values from the Wald test. To evaluate the accuracy of the prediction, we have considered the width of 95% confidence interval for the probability of incontinence. Both imputed and non-imputed simulation datasets were fit to the prediction model, and they both turned out to be significant (p-value < 0.05). However, the Wald score shows a better fit for the imputed compared to non-imputed datasets (28.7 vs. 23.4). The average confidence interval width was decreased by 10.4% when the imputed dataset was used, meaning higher precision. The results show that using the rough set method for missing data imputation on GMD data improve the predictive capability of the logistic regression. Further studies are required to generalize this conclusion to other clinical survey datasets.Keywords: rough set, imputation, clinical survey data simulation, genuine missing data, predictive index
Procedia PDF Downloads 1639114 A Deep Learning Based Integrated Model For Spatial Flood Prediction
Authors: Vinayaka Gude Divya Sampath
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The research introduces an integrated prediction model to assess the susceptibility of roads in a future flooding event. The model consists of deep learning algorithm for forecasting gauge height data and Flood Inundation Mapper (FIM) for spatial flooding. An optimal architecture for Long short-term memory network (LSTM) was identified for the gauge located on Tangipahoa River at Robert, LA. Dropout was applied to the model to evaluate the uncertainty associated with the predictions. The estimates are then used along with FIM to identify the spatial flooding. Further geoprocessing in ArcGIS provides the susceptibility values for different roads. The model was validated based on the devastating flood of August 2016. The paper discusses the challenges for generalization the methodology for other locations and also for various types of flooding. The developed model can be used by the transportation department and other emergency response organizations for effective disaster management.Keywords: deep learning, disaster management, flood prediction, urban flooding
Procedia PDF Downloads 1369113 Customer Acquisition through Time-Aware Marketing Campaign Analysis in Banking Industry
Authors: Harneet Walia, Morteza Zihayat
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Customer acquisition has become one of the critical issues of any business in the 21st century; having a healthy customer base is the essential asset of the bank business. Term deposits act as a major source of cheap funds for the banks to invest and benefit from interest rate arbitrage. To attract customers, the marketing campaigns at most financial institutions consist of multiple outbound telephonic calls with more than one contact to a customer which is a very time-consuming process. Therefore, customized direct marketing has become more critical than ever for attracting new clients. As customer acquisition is becoming more difficult to archive, having an intelligent and redefined list is necessary to sell a product smartly. Our aim of this research is to increase the effectiveness of campaigns by predicting customers who will most likely subscribe to the fixed deposit and suggest the most suitable month to reach out to customers. We design a Time Aware Upsell Prediction Framework (TAUPF) using two different approaches, with an aim to find the best approach and technique to build the prediction model. TAUPF is implemented using Upsell Prediction Approach (UPA) and Clustered Upsell Prediction Approach (CUPA). We also address the data imbalance problem by examining and comparing different methods of sampling (Up-sampling and down-sampling). Our results have shown building such a model is quite feasible and profitable for the financial institutions. The Time Aware Upsell Prediction Framework (TAUPF) can be easily used in any industry such as telecom, automobile, tourism, etc. where the TAUPF (Clustered Upsell Prediction Approach (CUPA) or Upsell Prediction Approach (UPA)) holds valid. In our case, CUPA books more reliable. As proven in our research, one of the most important challenges is to define measures which have enough predictive power as the subscription to a fixed deposit depends on highly ambiguous situations and cannot be easily isolated. While we have shown the practicality of time-aware upsell prediction model where financial institutions can benefit from contacting the customers at the specified month, further research needs to be done to understand the specific time of the day. In addition, a further empirical/pilot study on real live customer needs to be conducted to prove the effectiveness of the model in the real world.Keywords: customer acquisition, predictive analysis, targeted marketing, time-aware analysis
Procedia PDF Downloads 1169112 Uplift Segmentation Approach for Targeting Customers in a Churn Prediction Model
Authors: Shivahari Revathi Venkateswaran
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Segmenting customers plays a significant role in churn prediction. It helps the marketing team with proactive and reactive customer retention. For the reactive retention, the retention team reaches out to customers who already showed intent to disconnect by giving some special offers. When coming to proactive retention, the marketing team uses churn prediction model, which ranks each customer from rank 1 to 100, where 1 being more risk to churn/disconnect (high ranks have high propensity to churn). The churn prediction model is built by using XGBoost model. However, with the churn rank, the marketing team can only reach out to the customers based on their individual ranks. To profile different groups of customers and to frame different marketing strategies for targeted groups of customers are not possible with the churn ranks. For this, the customers must be grouped in different segments based on their profiles, like demographics and other non-controllable attributes. This helps the marketing team to frame different offer groups for the targeted audience and prevent them from disconnecting (proactive retention). For segmentation, machine learning approaches like k-mean clustering will not form unique customer segments that have customers with same attributes. This paper finds an alternate approach to find all the combination of unique segments that can be formed from the user attributes and then finds the segments who have uplift (churn rate higher than the baseline churn rate). For this, search algorithms like fast search and recursive search are used. Further, for each segment, all customers can be targeted using individual churn ranks from the churn prediction model. Finally, a UI (User Interface) is developed for the marketing team to interactively search for the meaningful segments that are formed and target the right set of audience for future marketing campaigns and prevent them from disconnecting.Keywords: churn prediction modeling, XGBoost model, uplift segments, proactive marketing, search algorithms, retention, k-mean clustering
Procedia PDF Downloads 67