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Defense modeling and simulation (DM&S) offers insights into the efficient operations of combat entities, e.g., soldiers and weapon systems. Most DM&S aim at exact description of military doctrines, but often the doctrines fails to provide detail action procedures about how the combat entities conduct military operations. Such unspecified descriptions are filled with the rational behaviors...
With high renewable integration and changing consumer patterns, it has become hard to predict fast load deviations using historic data. To ensure acceptable frequency, the industry must rely on many small end users providing synthetic reserves instead of on requiring regulation reserves by generators. In this paper, a distribution management system is introduced to support such regulation services...
The article deals with the constructing of the decision-making system using the modern software which supports the automation of reporting and management. For this purpose we will implement the procedure of quality estimation and risk-contributing factors forecasting. The system can be used for the formation of the development strategy of the high-technology enterprises.
Scholars and industrial professionals are committed to integrating traditional financial economics models and machine learning models to improve the prediction model for stock prices, which is still a challenging topic. However, there is few acceptable results reported. This study proposes a two-stage multi-view prediction method that provides a new integration perspective for the integration of finance...
Coping with emergency situations requires effective and prompt decision-making under constantly changing situations. The deployment of various kinds of sensors makes it possible to acquire vast amounts of information. At present, however, most information processing is not automated; therefore, the quality of decision-making depends heavily on the capacities of the decision maker. To solve this problem,...
Discrimination decisions are at the forefront of human cognition. For this reason, many different types of models aim to predict how they are made. In this research, we compared the discrimination capabilities of a Recurrent Associative Memory (RAM) with the predictions of an accumulator model to show that, although the discrimination processes of both model classes differs, both make similar predictions...
There are six main categories of breast cancer be existent. In this paper, we have taken the Type 1 carcinoma cancer to support the decision making. For this, a novel machine learning based cost optimization is applied to make an efficient decision from the samples. Moreover, we have applied our methodology on the real datasets to predict cancer with appropriate parameters using Pearson correlation...
Blockchain is an emerging technology for sharing transactional data and computation without using a central trusted third party. It is an architectural choice to use a blockchain instead of traditional databases or protocols, and this creates trade-offs between non-functional requirements such as performance, cost, and security. However, little is known about predicting the behaviour of blockchain-based...
Recently, Technical Debt (TD) has gained popularity in the Software Engineering community to describe design decisions that allow software development teams to achieve short term benefits such as expedited release of code. Technical debt accrued should be managed to avoid the disastrous consequences of these temporary workarounds. Management of technical debt involve documenting the debt item in the...
One of the most important aspects of the marketing is to determine what price is to be fixed to sell your products. Pricing is both an art and science that requires an experimental and statistical formula for creating a profile for the brand and the product in the market. There are minimalistic approaches used for pricing the products and to consider what will work for your business. Neural networks...
Aiming at the Smart meters failure prediction problem and based on historical failure data of smart meters in a region of Xinjiang, a smart meter fault identification model is proposed based on C5.0 algorithm: first, after data preprocessing of smart meters history failure database is divided into two parts, training set and testing set; secondly, using C5.0 algorithm for data mining of training set,...
This paper provides a review of research on the application of data mining techniques for decision making in agriculture. The paper reports the application of a number of data mining techniques including artificial neural networks, Bayesian networks and support vector machines. The review has outlined a number of promising techniques that have been used to understand the relationships of various climate...
Recent years have witnessed the boom of venture capital industry. Venture capitalists can attain great financial rewards if their invested companies exit successfully, via being acquired or going IPO (Initial Public Offering). The literature has revealed that, from both financial and managerial perspectives, decision-making process and successful rates of venture capital (VC) investments can be greatly...
Recommender systems are useful tools that help people to filter and explore massive information. While most recommender systems focus on providing recommendations for individuals, people's minds are easily altered and dominated by crowds, especially in a socialized environment. In addition to fulfill personalized intentions, more considerate recommendations, which maximize satisfactions of both individuals...
The complexity of simulation models has increased during the last years in semiconductor foundries. Manual and automated decisions have to be modeled in detail to make the right conclusions from them. We describe an approach that uses forward simulation to minimize modeling effort and mimics fab behavior to a high degree. The approach is applied to the problem of controlling time link chains. Results...
When we make operational decisions for high-tech manufacturing with products having short life times, there exist various challenges: (1) high uncertainty in the supply, production and demand; (2) limited amount of valid historical data; and (3) decision makers with a risk-averse attitude. We propose a simulation-based prediction framework to support real-time decision making. Specifically, we consider...
Emotion plays a significant role in consumer decision making. We recently conducted a study to explore how media-based information of aggregated market emotion influences consumers' expected demand of commodities, and how businesses can use media-based emotion indices to predict commodities' price. We implemented time series econometrics by analyzing a fourteen year daily observations of twelve major...
Modeling of decision-making behavior for discretionary lane-changing execution (DLCE) is fundamental to both movement simulation and controlling design of automatic vehicles. The existing gap acceptance models ingored the nonlinearity of drivers' DLCE decision-making behavior. Therefore, this study tries to analyze and simulate the DLCE decision-making behavior using the real trajectory data. First,...
Buying or selling a house is one of the important decisions in a person's life. Online listing websites like "zillow.com", "trulia.com", and "realtor.com" etc. provide significant and effective assistance during the buy/sell process. However, they fail to supply one important information of a house that is, approximately how long will it take for a house to be sold after...
Recently hospitals struggle to control the cost of care while maintaining optimal outcomes. To respond to this challenge, we developed an interactive web platform which utilizes a multiple linear regression model. The user can create and furthermore alter a clinical scenario, during a patient hospitalization to see the dynamic prediction of total charges, via interactive sessions. The R2 value of...
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