Prediction Model Research Papers - Academia.edu (original) (raw)

Top Papers
Most Cited Papers
Most Downloaded Papers
Newest Papers
People
- by and +1
- •
- Stochastic Process, Food Science, Prediction, Numerical Simulation

Command Control, Communication Computer and Intelligence (C4I) systems enables modern military forces to achieve information superiority in the battlefield. C4I are complex System of systems (SOS) where individual systems interact locally to achieve global SOS behaviors. To build software for C4I systems conventional software engineering SwE process and practices have shortcomings and are not capable to support certain aspect of these systems. If C4I systems fail to operate as required due to the fact that SwE process was unable to fulfill its requirements, the consequences may not be tolerated because of the criticality of the mission of these systems in information warfare (IW). This paper highlights the distinguished characteristics and operational requirements of C4I systems which poses challenges to SwE process and practices. This paper also discuss the possible future research areas in order to enhance SwE process so that better software could drive these complex systems as required.

Günümüzde Yapay Sinir Ağları son dönemde popüler olan öngörü yöntemlerinden biridir. Bununla birlikte Yapay Sinir Ağları kendi içerisinde öngörü modelleri birkaç farklı yöntemle ayrılmaktadır. Bu makalede yapay sinir ağları yöntemlerinden en çok kullanılan 3 yöntemin karşılaştırmalı analizi yapılmıştır. Bu çalışmada en yüksek tahmin sonuçlarını veren yöntemin belirlenmesi amaçlanmıştır. Bu veri setinde 6215 veri, 6 farklı bağımsız değişken bulunmaktadır. Bu bağımsız değişkenler sırasıyla, açılış, kapanış, en yüksek olduğu nokta, en düşük olduğu nokta, açılış-kapanış arasındaki fark, en yüksek çıktığı değer-en çok düştüğü değer arasındaki farkın ortalamasıdır. Bu bağımsız değişkenler Yapay Sinir Ağları modelinin girdisi iken, bağımlı değişken olarak modelin tahmin gücü kullanılmıştır. Bu çalışmada üç farklı Yapay Sinir Ağı yöntemi kullanılacaktır, bunlar sırasıyla; doğrusal regresyon, rassal orman regresyon, karar ağacı öğrenmesi'dir. Yapay Sinir Ağları ile elde edilen sonuçlar en iyi tahmin modeli ortaya çıkaracaktır. Uygun veri setinin kullanımı ile birlikte en doğru sonuçları verebilmektedir. Bunun en önemli nedenlerinden bir tanesi Yapay Sinir Ağları, daha fazla veriye ihtiyaç duymakta ve veri sayısı arttıkça öğrenme ve eğitim aşamaları daha iyi sonuç ortaya koymaktadır.

Remote sensing is collecting information about an object without any direct physical contact with the
particular object. It is widely used in many fields such as oceanography, geology, ecology. Remote sensing
uses the Satellite to detect and classify the particular object or area. They also classify the object on the
earth surfaces which includes Vegetation, Building, Soil, Forest and Water. The approach uses the
classifiers of previous images to decrease the required number of training samples for the classifier
training of an incoming image. For each incoming image, a rough classifier is predicted first based on the
temporal trend of a set of previous classifiers. The predicted classifier is then fine-tuned into a more
accurate manner with current training samples. This approach can be further applied as sequential image
data, with only a small number of training samples, which are being required from each image. This
method uses LANSAT 8 images for Training and Testing processes. First, using the Classifier Prediction
technique the Signatures are being generated for the input images. The generated Signatures are used for
the Training purposes. SVM Classification is used for classifying the images. The final results describes
that the leverage of a priori information from previous images will provide advantageous improvement for
future images in multi temporal image classification.

- by David Shoham
- •
- Chronic kidney disease, Humans, United States, Female
- by Elke Bloem
- •
- Engineering, Earth Sciences, Computers, Soil Erosion
- by George Grass
- •
- Drug Discovery, Prediction Model, IDEA, ADME
- by James Chandler
- •
- Zoology, Ecology, Habitat Selection, Reproductive Success
- by YZ Zeng
- •
- Algorithms, Artificial Intelligence, Materials Science, Crystallography

A fuzzy logic-based system has been applied to a number of cases in medicine especially in the area of the development of diagnostic systems and has been discovered to produce accurate results. In this paper, a fuzzy logic-based system is presented which is used to simulate a prediction model for determining the likelihood of Sickle Cell Anemia (SCA) in individuals given a 3-tuple record containing the level of fetal haemoglobin, genotype and the degree of Anemia. Knowledge was elicited from an expert at Federal Medical Centre, Owo, Ondo State, Nigeria and was used in developing the rule-base and simulated the prediction model using the MATLAB software. The results of the fuzzification and defuzzification of variables, inference engine definition and model testing was also presented and showed that the fuzzy logic based model will be very useful in the prediction of the likelihood of Sickle Cell Anemia (SCA) among Nigerian patients.

- by Kehinde Williams
- •
- Fuzzy Logic, Sickle Cell Disease, Prediction Model, Likelihood

The aim of the research was to monitor and assess landslide hazards by remote sensing data processing and GIS spatial analysis. The automatic classification of remote sensing images provides many useful land use information to combine in a GIS environment with other spatial factors influencing the occurrence of landslide. The upper part of Susa Valley, in the Italian Western Alps, was chosen as test area because of a large variety of remote sensing data available by ISPRS WG VIII/2 with the aim to exchange information and experience in the field of geomatic techniques. It is well known that the occurrence of landslides is controlled by a lot of morphological, geological, and human factors. We have chosen, regarding the available data, the following factors: acclivity, aspect, lithology, land use and precipitations. We have built up a mathematical predictive model enabling actual/potential unstable slopes. It is a linear model where the hazard score depends on instability factors and...

- by Nora Tassetti
- •
- Geography, Spatial Analysis, Human Factors, Land management
- by Branko Popov
- •
- Engineering, Chemistry, Prediction Model, CHEMICAL SCIENCES
- by Andrea Versari
- •
- Chemistry, Food Engineering, Food, Prediction
- by Nazar Zaki
- •
- Bioinformatics, Algorithms, Machine Learning, Computational Biology
- by Richard Stenzel
- •
- Machine Learning, User profile, Support vector machine, Prediction Model

For centuries, land degradation triggered by deforestation has occurred in Ethiopia, in particular in the northern regional state Tigray, the area under study. In order to change this situation, the local government started to establish enclosures. In these sites, grazing is no longer permitted so that forest can naturally regenerate. In order to develop sustainable yield planning for forest rehabilitation

- by Christoph Kleinn
- •
- Case Study, Local governance, Natural Regeneration, Prediction Model
- by Romain Robbes
- •
- Prediction Model, Software Systems

Biologically based markers (biomarkers) are currently used to provide information on exposure, health effects, and individual susceptibility to chemical and radiological wastes. However, the development and validation of biomarkers are expensive and time consuming. To determine whether biomarker development and use offer potential improvements to risk models based on predictive relationships or assumed values, we explore the use of uncertainty analysis applied to exposure models for dietary methyl mercury intake. We compare exposure estimates based on self-reported fish intake and measured fish mercury concentrations with biomarker-based exposure estimates (i.e., hair or blood mercury concentrations) using a published data set covering 1 month of exposure. Such a comparison of exposure model predictions allowed estimation of bias and random error associated with each exposure model. From these analyses, both bias and random error were found to be important components of uncertainty regarding biomarker-based exposure estimates, while the diary-based exposure estimate was susceptible to bias. Application of the proposed methods to a simple case study demonstrates their utility in estimating the contribution of population variability and measurement error in specific applications of biomarkers to environmental exposure and risk assessment. Such analyses can guide risk analysts and managers in the appropriate validation, use, and interpretation of exposure biomarker information.

- by Scott Bartell and +2
- •
- Risk assessment, Humans, Case Study, Animals
- by quirico semeraro
- •
- Engineering, Materials Science, Mathematical Sciences, Specification Tests

Nowadays dynamic service management frameworks are proposed to ensure end-to-end QoS. To achieve this goal, it is necessary to manage Service Level Agreements (SLA) which specify quality parameters of the services operation such as... more

- by Jorge Mata
- •
- Markov Processes, Video Streaming, Service management, Quality of Service
- by Saumyadeep Roy
- •
- Statistical Analysis, Plant Biology, Agricultural Biotechnology, Salt Stress
- by Gary Lyman
- •
- Risk assessment, Multivariate Analysis, Humans, Blood Pressure
- by Silvia Siri and +1
- •
- Model Predictive Control (MPC), Traffic control, Accounting and Auditing, Traffic Flow
- by Emília Mendes
- •
- Web Engineering, Data Analysis, Web Development, Software effort estimation

Global climate change is altering the ecology of infectious agents and driving the emergence of disease in people, domestic animals, and wildlife. We present a novel, empirically based, predictive model for the impact of climate warming on development rates and availability of an important parasitic nematode of muskoxen in the Canadian Arctic, a region that is particularly vulnerable to climate

- by Susan Kutz
- •
- Climate Change, Ruminants, Global Warming, Biological Sciences

This paper shows how the Lorenz curve can be used, together with models of disease risk, to allocate scarce resources so as to optimize a health benefit. Consider the example of breast cancer mortality. If there were sufficient resources to provide all women with mammograms, a certain maximal number of lives could be saved. Suppose, however, that only a fraction of that amount of money is available for prevention activities. Suppose that a questionnaire could be given to assess a woman's risk of dying of breast cancer. Depending on the amount of money available, on the ratio of the cost of a questionnaire to the cost of a mammogram, and on the Lorenz curve of the distribution of risks of breast cancer mortality, I calculate the proportion of women who should be given questionnaires, the proportion of women given the questionnaires who should be given mammograms because they have high risks, and the proportion of women not given questionnaires who should be assigned to receive ma...

- by Mitchell Gail
- •
- Bioinformatics, Mathematics, Life Sciences, Medicine
- by Stefanos Koundouras
- •
- Plant Biology, Image Analysis, Prediction Model, Vitis Vinifera
- by William Mickelson
- •
- Psychology, School Psychology, Effect size, School

Keywords: MIMO antenna UWB antenna Model predictive Control Non-uniform microstrip line Printed monopole antenna a b s t r a c t A novel ultra wideband (UWB) printed monopole multiple-input multiple-output (MIMO) antenna with non-uniform transmission line using nonlinear model predictive control (NMPC) is presented. The proposed antenna is superior to conventional antennas in terms of dimensions, gain, and efficiency while maintaining the impedance bandwidth. In order to improve the results, a non-uniform transmission line has been used for impedance matching between the radiated patch element and the coaxial cable. For designing the non-uniform transmission line, it has been expanded using cosine terms. Regarding the presence of differential equation for the variation in the impedance of the transmission line and its transformation to the state-space equation, NMPC has been employed to design the transmission line and determine the cosine expansion coefficients. Two base antennas, as MIMO, were simulated configuration and fabricated. The surface area of the proposed MIMO antenna is 0.99 k 2 g , the wavelength has been obtained for the center frequency of the 3.16 GHz to 10.6 GHz range, and its mutual coupling, peak gain, channel capacity loss (CCL), total active reflection coefficient (TARC), mean effective gain (MEG) and diversity gain (DG), envelope correlation (ECC) are acceptable. The simulation and measurement results are in good agreement, and the proposed antenna is suitable for MIMO applications. Ó 2020 The ''Authors". Karabuk University. Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

- by Thomas Banhazi
- •
- Air Quality, Biomedical Engineering, Biosystems engineering, Prediction Model
- by Alessandro Saffiotti
- •
- Robotics, Algorithms, Artificial Intelligence, Pattern Recognition

This longitudinal study investigates parent and child predictors of adolescents' perceived social support from peers. Adolescents (285) and their parents filled out surveys when students were 11 and 15 years of age. Parent reports of their own social support and child reports of parental support to them, depression, and self-esteem were used as predictors of adolescents' peer social support. Path

- by Jacquelynne Eccles
- •
- Psychology, Social Support, Gender Difference, Prediction Model
- by Norman Fenton
- •
- Information Systems, Risk assessment, Bayesian Networks, Computer Software
- by P. Šandera
- •
- Condensed Matter Physics, Quantum Physics, Surface Science, Phosphorus

Accurately predicting Barrett's esophagus (BE) in patients with gastroesophageal reflux disease (GERD) is difficult. Using logistic regression analysis of symptom questionnaire scores we created a model to predict the presence of BE. We conducted a logistic regression analysis of symptom data collected prospectively on 517 GERD patients and created a prediction model based on patient gender, age, ethnicity, and symptom severity. There were 337 (65%) males and 180 (35%) females, of whom 99 (19%) had Barrett's esophagus (BE). Multiple logistic regression analysis was performed to determine the predictive ability of gender, age, and ethnicity along with symptoms of heartburn, nocturnal pain, odynophagia, presence of belching, dysphagia, relief of symptoms with food, and nausea. The only significant predictors (at the 0.05 level) were male gender, heartburn, nocturnal pain, and odynophagia (all with positive effects on the presence of BE) and dysphagia (which had a negative effe...

- by George Triadafilopoulos
- •
- Forecasting, Adolescent, Prospective studies, Humans
- by Bojana Dalbelo
- •
- Computer Science, Customer Relationship Management, Retail Banking, Fuzzy
- by DANIEL BAUTISTA
- •
- Computed Tomography, Clinical Epidemiology, Logistic Regression, Humans
- by Dorina Petriu
- •
- Middleware, OPERATING SYSTEM, Prediction Model, Software Performance Engineering

Energy prediction of appliances requires identifying and predicting individual appliance energy consumption when combined in a closed chain environment. This experiment aims to provide insight into reducing energy consumption by identifying trends and appliances involved. The proposed model tries to formalize such an approach using a time series forecasting- based process that considers the correlation between
different appliances. The entire work has been conducted in two parts. The first part highlights and identifies the energy consumption trends. The second part focuses on the comparison and analysis of different algorithms. The main objective is to understand which algorithm provides a better result in predicting energy consumption. A
comparison of algorithms for appliance usage prediction using identification and direct consumption reading is presented in this paper. The work is presented on real data taken from the REMODECE database, which comprises 19,735 instances with 29 attributes. The data records the energy for 10 minutes over about 4.5 months.

- by Editor IJATCA
- •
- Algorithms, Energy, Prediction Model
- by Parvinder Sandhu
- •
- Computer Science, Machine Learning, Software Testing, Neural Network

Breast cancer is considered to be the second most common type of cancer affecting the female population worldwide. It is estimated that more than 508 000 women died in 2011 as a result of breast cancer. The survival rates of breast cancer are lower in less developed countries mainly due to the absence of early detection methods resulting in a great percentage of women showing with late-stage disease. Early detection and medical diagnosis are known to be the most effective solution to minimize the risk of tumor development and progression. There are different methods for Early detection of breast cancer which include screening tests and clinical breast exams performed by a well-trained health professional. Due to a lack of facilities and cost, many women in less developed countries may not be able to use the mentioned methods. The objective associated with this research was to achieve an affordable and cost-effective prediction model of breast cancer based on anthropometric data and parameters that can easily be collected in a routine and regular blood test. For every one of the 166 individuals number of clinical features such as age, Body Mass Index (MBI), serum glucose levels, plasma levels of insulin, etc. were measured and observed. Various learning algorithms including Support Vector Machines (SVM), K-Nearest Neighbors (K-NN) and logistic regression(LR), etc. have been applied and compared with one another. The result shows that SVM and K-NN models perform well and allow prediction of breast cancer in women with accuracy more than 78%, the sensitivity of 78% and 79%, and Specificity value is 77% and 79% respectively.

- by Journal of Epigenetics
- •
- Breast Cancer, Prediction Model, Svm, Anthropometric Data

Although the Road Safety Audit (RSA) process is gaining widespread application throughout North America, little is understood about the net benefits being derived for design-build projects. A better understanding of collision reduction and mitigation is necessary to allow an objective economic evaluation of the RSA process. This study attempted to quantify the benefits of RSAs through a retrospective case study of the first major design-build RSA that was conducted in Canada – the Fredericton-Moncton Highway project. The Fredericton-Moncton Highway’s safety performance since it has opened was contrasted against other collision rates considered to be representative of those expected for this type of facility. Any difference between observed and expected rates can, in part, be attributed to the RSA process. Expected collision rates were developed using data from five similar facilities within the region and the output of six collision prediction models. The comparison of average colli...

- by Eric Hildebrand
- •
- Engineering, Road safety, Case Study, Prediction Model
- by Tycho Heimbach
- •
- Modeling, Pharmacokinetics, Drug development, Risk assessment
- by William Krantz
- •
- Engineering, Thermodynamics, Membrane Science, Theory