Predictive Modelling Research Papers - Academia.edu (original) (raw)

- by
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- Microbiology, Algorithms, Machine Learning, User Interface
- by Stéphane Aulagnier and +1
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- Neural Network, Multidisciplinary, Ecological Modelling, Brown trout

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 Brijesh Tiwari
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- Kinetics, Heat Treatment, Predictive Microbiology, Predictive Modelling

Fault detection and prediction in the semiconductor industry will enable organizations to optimize their resources and maintain a high production yield for semiconductors. However, the data generated from the sensors are producing an abundance amount of data being kept in the repository and it is difficult for process engineers to analyze and make use of the dataset to make timely and accurate fault prediction and detection. Hence, this research will focus on developing a predictive model to predict and detect faulty equipment in the semiconductor manufacturing process. The quality of the data is enhanced through various analyses, feature selection and dimensionality reduction techniques. Random Forest, Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Artificial Neural Network (ANN) will be the main algorithms used to develop the predictive models and their respective results compared to determine the best model to be used.

- by IAEME Publication
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- Logistic Regression, ANN, Random Forest, Predictive Modelling
- by Shiowshuh Sheen
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- Microbiology, Risk Management, Industrial Biotechnology, Food Microbiology

The Sunda clouded leopard Neofelis diardi is a medium sized (15–25 kg) cat, found only on the Sundaic islands of Borneo and Sumatra. In recent years intensive camera-trapping surveys in Borneo have begun to shed light on the habitat associations and basic ecology of this elusive wild cat, but its distribution on an island-wide scale remains very poorly known. Such information is an essential element in the assessment of the Sunda clouded leopard’s conservation status and in the development of conservation action. In this paper we use MaxEnt niche distribution modelling to make predictions regarding the current distribution of this cat on Borneo. We collected a total of 259 occurrence records for the Sunda clouded leopard, stemming from all Bornean regions apart from Brunei and South Kalimantan, of which 48 (Balanced Model) or 94 (Spatial Filtering Model) were used in our modelling. Our habitat suitability model suggests that this species has a widespread distribution over a large co...

- by Dave Augeri
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- Geography, Conservation Biology, Biology, Ecology
- by Alonzo Gabriel
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- Methodology, Response Surface Methodology, Food Chemistry, Multidisciplinary
- by Bruno Fabiano
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- Packaging, Modeling, Refrigeration, Heat Treatment
- by George-john Nychas and +1
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- Chemical Engineering, Microbiology, Meat Science, Machine Learning
- by Luisa Torri
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- Food composition, Animal Production, Food Preservation, SHELF LIFE
- by B. Fournier
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- Mechanical Engineering, Civil Engineering, Finite Element, Fatigue

Le riz est une matière première importante dans l’agriculture et le commerce dont le prix ne cesse d’augmenter de jour en jour. Le but de ce travail consiste à modéliser le prix du riz en fonction d’autres variables explicatives. Mais tout d’abord, nous procéderons à une petite présentation de cette plante, ses caractéristiques, sa production, sa consommation ainsi que
les échanges commerciaux.

- by Wajdi BEN SAAD
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- Economics, International Trade, Predictive Modelling, Commodity trade

Blood and blood products are extremely essential for the medical treatment of almost all age groups. The primary supply of blood products within the world is mainly from volunteer donors. Thus, donor accomplishment and donor retention are critical factors for a blood bank for maintaining its blood supply. We tend to propose that our system will have a better understanding of donors' motivations to donate and personalizing their donation objectives would improve a blood bank's ability to secure and collect a more robust supply of blood. The projected system puts forward a solution to predict whether a particular donor can donate the blood or not within the coming month or not. This can facilitate the blood banks to forecast their stock of various varieties of blood groups and prepare them consequently.

- by IJCSMC Journal and +1
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- Computer Science, Information Technology, Technology, Computer Engineering

Developments in Human Resources Management (HRM) are fast being integrated with corresponding changes in data and information processing, which are restructuring our environments. The domain of human resource analytics, which can be understood as a data and analytical thinking-centred approach to Human Resources Management, is fast becoming an indispensable part of organisational setups. The present study explores the existing literature in the field of HR analytics and their implications for predictive decision-making in organisations. This will also include critically reviewing the literature on the integration of HR analytics in organisational setups through the introduction of relevant IT infrastructure and provisions.

- by IAEME Publication
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- Decision-Making, Predictive Modelling, Hr Analytics, Predictive

Over the last few years or so, the use of artificial neural networks ( ANNs) has increased in many areas of engineering. In particular, ANNs have been applied to many geotechnical engineering problems and have demonstrated some degree of success. A review of the literature reveals that ANNs have been used successfully in pile capacity prediction, modelling soil behaviour, site characterisation, earth retaining structures, settlement of structures, slope stability, design of tunnels and underground openings, liquefaction, soil permeability and hydraulic conductivity, soil compaction, soil swelling and classification of soils. The objective of this paper is to provide a general view of some ANN applications for solving some types of geotechnical engineering problems. It is not intended to describe the ANNs modelling issues in geotechnical engineering. The paper also does not intend to cover every single application or scientific paper that found in the literature. For brevity, some wo...

- by Mohamed Shahin and +1
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- Civil Engineering, Geotechnical Engineering, Engineering Design, Slope Stability

Mathematics plays a critical role in many modeling and simulation applications, and whenever detailed representations of combat phenomenon are required (e.g., attrition, detection, etc.). The required mathematics can seem daunting, but a wealth of problems relevant to the M&S community can be addressed with efficient numerical algorithms and heuristics. We shall present a small sampling of these methods and relevant examples of their usage. Examples include combat attrition, target engagement, detection, and missile defense. Most of these can be implemented with a spreadsheet for demonstration.

- by Jeffrey Strickland
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- Data Mining, Operations Research, Applied Statistics, Monte Carlo Simulation

The archaeological potential of
Patras is explored following a
dual approach. First, excavation
data from construction sites that
contain or do not contain
archaeological finds is used to
implement a predictive model of
underlying antiquities and their
minimum expected depth of
discovery within the City Plan. Its
predictive value is tested in the
light of the current building
legislation both on private and
public land. Second, surface
archaeological surveys record the
archaeological background of the
peri-urban zone, before intense
urbanization transforms it. Here,
the archaeological research is
complemented with the
application of Remote Sensing
for exploring land cover changes
and for detecting threats, such as
landslides and unauthorized soil
removal.

- by Dim On
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- Predictive Modelling, Ph, Water Activity, Response Surface Methodology (RSM)

Domestication is a good model for the study of evolutionary processes because of the recent evolution of crop species (<12,000 years ago), the key role of selection in their origins, and good archaeological and historical data on their spread and diversification. Recent studies, such as quantitative trait locus mapping, genome-wide association studies and whole-genome resequencing studies, have identified genes that are associated with the initial domestication and subsequent diversification of crops. Together, these studies reveal the functions of genes that are involved in the evolution of crops that are under domestication, the types of mutations that occur during this process and the parallelism of mutations that occur in the same pathways and proteins, as well as the selective forces that are acting on these mutations and that are associated with geographical adaptation of crop species.

- by Rachel Meyer
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- Evolutionary Biology, Archaeobotany, Phytochemistry, Anthropology of Food
- by Keith Challis
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- Archaeology, Geophysics, LiDAR, Archaeological Prospection
- by Vicente Sanchis
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- Microbiology, Mathematics, Food Safety, Kinetics
- by Arho Virkki
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- Mathematical Modelling, Humans, Predictive Modelling, Sleep
- by Mirek Kraus
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- Archaeology, Early Medieval Archaeology, Archaeological GIS, Predictive Modelling
- by Robert Budny
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- Plasma Physics, Numerical Simulation, Computer Simulation, Nuclear Fusion
- by O. Gillia
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- Mechanical Engineering, Civil Engineering, Finite Element, Fatigue

Near-Miss incidents can be treated as events to signal the weakness of safety management system (SMS) at the workplace. Analyzing near-misses will provide relevant root causes behind such incidents so that effective safety related interventions can be developed beforehand. Despite having a huge potential towards workplace safety improvements, analysis of near-misses is scant in the literature owing to the fact that near-misses are often reported as text narratives. The aim of this study is therefore to explore text-mining for extraction of root causes of near-misses from the narrative text descriptions of such incidents and to measure their relationships probabilistically. Root causes were extracted by word cloud technique and causal model was constructed using a Bayesian network (BN). Finally, using BN’s inference mechanism, scenarios were evaluated and root causes were listed in a prioritized order. A case study in a steel plant validated the approach and raised concerns for variety of circumstances such as incidents related to collision, slip-trip-fall, and working at height.

- by Deeshant Rajput and +1
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- Artificial Intelligence, Decision Making, Machine Learning, Research Methodology
- by Alissa Mattei
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- Engineering, Stability, Predictive Modelling, Degradation
- by C. Petrogalli
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- Multidisciplinary, Rolling Contact Fatigue, Predictive Modelling, Crack Initiation
- by Geert Geeven
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- Archaeology, Geochemistry, Archaeological Science, Settlement Patterns

Predictive modelling of Slav settlement location in the middle-Elbe region. – A predictive model has been constructed of the northwestern part of the middle-Elbe region based on an analysis of the relationship between environmental factors and locations of Slav settlement sites. Initially, an evaluation was carried out of the archaeological data on which to base such a model, and the validation area of the model determined. Relevant environmental factors were selected empirically and tested as to their inﬂuence on location of settlement site. The determining factors were found to be (a) height above the local drainage line (thalweg) in combination with distance to the nearest surface water, and (b) distance to the nearest ecoboundary. The application of the predictive model with respect to cultural history, as well as its suitability as a basis for archaeological heritage management are likely to be conﬁrmed by future studies.

Studie představuje výsledky dvou menších záchranných archeologických výzkumů z let 2018-2019, při kterých se podařilo prozkoumat několik sídlištních objektů platěnické kultury. Nově získané poznatky jsou zasazeny do mikroregionálního kontextu vývoje halštatského osídlení v povodí říčky Okluky. Představen je aktualizovaný stav poznání hustoty osídlení a sídelní strategie. Ve studii je rovněž prezentován prediktivní model, jehož testováním by mohly být v budoucnu identifikovány prozatím neznámé lokality.

- by Jaroslav Bartík and +2
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- Archaeology, Prehistoric Archaeology, Settlement Patterns, Rescue Archaeology

Where the shrikes live: a predictive model of the distribution of the breeding Red backed Shrike, Lanius collurio, in Veneto (Passeriformes: Laniidae)
We present the results of a study aimed at developing a predictive model of the distribution of the breeding Red Backed Shrike in Veneto. We chose this species as it is thought to be a good biodiversity indicator for farmland areas. Moreover it has been declared as a important species by European directives. Presence and absence data were derived from local breeding bird atlases and were standardized on a 10x10 km grid. The data were analysed in relation to thirteen land-cover variables and two altitude variables using a generalized linear model (GLM). In order to avoid multicollinearity problems amongst the land cover variables we performed a Principal Component Analysis (PCA), and used the first two components as predictors. The model was evaluated using an Information Theoretic approach. This relatively new method in the field of ecology explores multiple models or hypotheses without considering the most parsimonious model as the best one. The major advantage in using this procedure is that as little information as possible about the real world is lost. The model was used to produce a map with the probability of occurrence of the Red Backed Shrike. We tested the predictive ability of the model using a leave-one-out validation and a ROC plot. The Area Under the Curve (AUC) was calculated from the ROC plot.
The second axis of the PCA was particularly important in determining the distribution of the Red backed Shrike, followed by maximum altitude, minimum altitude and the first axis. The predictive performance of the model was good, with an AUC value of 0.77. The results are discussed in relation the species’ ecology and conservation. Our study shows some the potential analyses which could be carried out using data from local breeding bird atlases.

- by Michele Pegorer
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- Ornithology, Predictive Modelling, Ornitologia, Averla piccola
- by Peter Vesk
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- Land Use Change, Biological Sciences, Environmental Sciences, Habitat Restoration

This paper formulates the problem of building a context-aware predictive model based on user diverse behavioral activities with smartphones. In the area of machine learning and data science, a tree-like model as that of decision tree is considered as one of the most popular classification techniques, which can be used to build a data-driven predictive model. The traditional decision tree model typically creates a number of leaf nodes as decision nodes that represent context-specific rigid decisions, and consequently may cause overfitting problem in behavior modeling. However, in many practical scenarios within the context-aware environment, the generalized outcomes could play an important role to effectively capture user behavior. In this paper, we propose a behavioral decision tree, "BehavDT" context-aware model that takes into account user behavior-oriented generalization according to individual preference level. The BehavDT model outputs not only the generalized decisions but also the context-specific decisions in relevant exceptional cases. The effectiveness of our BehavDT model is studied by conducting experiments on individual user real smartphone datasets. Our experimental results show that the proposed BehavDT context-aware model is more effective when compared with the traditional machine learning approaches, in predicting user diverse behaviors considering multi-dimensional contexts.

- by Dr. Iqbal H. Sarker and +1
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- Artificial Intelligence, User Modeling, Machine Learning, Data Mining

Indonesia as one of the favorite vacation destinations of domestic and foreign travelers made the value of investment in the tourism industry continued to grow significantly. This was created more Online Travel Agent business in recent years. However, it made a lot of business travel and Umrah travel in Indonesia is threatened with bankruptcy, after the online travel business activity is rampant in conventional business market ticket sales and travel tours. The research case study is different from the Online Travel Agent business in general, because it worked in real-time analytic using flight tickets and hotel rooms sales data to create prediction or recommendation model. Data mining, extraction of hidden predictive information from large databases, was a powerful technique with great potential to help companies focus on the most important information in their data warehouse. By using classification method in data mining, the objectives of this paper is to create predictive models from flight tickets and hotel rooms sales data using the decision tree classification approach. The result of this paper is beneficial for business that can be used as basic algorithm for programming in Online Travel Agent recommendation feature.

- by SAMIRA MOBAIED
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- Temporal dynamics, Logistic Regression, Global change, Biodiversity Conservation

It is widely considered that the physical layout of workplace environments has an influence on social interaction and therefore the social structure of an organisation. However, there is little accordance among scholars from different disciplines on exactly how the relationship between space and organisation is constituted. Empirical studies often come to different conclusions: for example, on the influence of an open-plan office on communication patterns among staff, as many studies report increases as report decreases or unchanged ...

- by Alan Penn
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- Space Syntax, Social Interaction, Social Structure, Spatial Configuration
- by V. Souchere
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- Geology, Predictive Modelling, Case Study, Catena
- by Tom McMeekin
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- Microbiology, Food Safety, Knowledge Management, Decision Making

To achieve a sustainable development, impacts on biodiversity of urbanisation, new infrastructure projects and other land use changes must be considered on landscape and regional scales. This requires that important decisions are made after a systematic evaluation of environmental impacts. Landscape ecology can provide a conceptual framework for the assessment of consequences of long-term development processes like urbanisation on biodiversity components, and for evaluating and visualising the impacts of alternative planning scenarios. The aim of this paper was to develop methods for integrating biodiversity issues in planning and strategic environmental assessment in an urbanising environment, on landscape and regional levels.In order to test developed methods, a case study was conducted in the region of Stockholm, the capital of Sweden, and the study area embraced the city centre, suburbs and peri-urban areas. Focal species were tested as indicators of habitat quality, quantity and connectivity in the landscape. Predictive modelling of habitat distribution in geographic information systems involved the modelling of focal species occurrences based on empirical data, incorporated in a landscape ecological decision support system. When habitat models were retrieved, they were applied on future planning scenarios in order to predict and assess the impacts on focal species. The scenario involving a diffuse exploitation pattern had the greatest negative impacts on the habitat networks of focal species. The scenarios with concentrated exploitation also had negative impacts, although they were possible to mitigate quite easily. The predictions of the impacts on habitats networks of focal species made it possible to quantify, integrate and visualise the effects of urbanisation scenarios on aspects of biodiversity on a landscape level.