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Background: Understanding and controlling the impact of change decides about the success or failure of evolving products. The problem magnifies for start-ups operating with limited resources. Their usual focus is on Minimum Viable Product (MVP's) providing specialized functionality, thus have little expense available for handling changes. Aims: Change Impact Analysis (CIA) refers to the identification...
It has become a general consensus that smart grid is one of the most important technical fields for big data technology application. Power network dispatching and planning is fundamental to power industry development and construction, however, massive power data is still manually filled in, dispatching method and planning design method relies heavily on experience and single data source. This paper...
Defect prediction has been the subject of a great deal of research over the last two decades. Despite this research it is increasingly clear that defect prediction has not transferred into industrial practice. One of the reasons defect prediction remains a largely academic activity is that there are no defect prediction tools that developers can use during their day-to-day development activities....
Accurately predicting driving service orders in different regions is an essential task for service companies, in order to improve the service quality. In this paper, a specific ensemble multi-view prediction framework is proposed to address this task. It ensembles several different multi-view-based models with a weighted linear combination. Specifically, we have designed three specific multi-view-based...
Code review is a key tool for quality assurance in software development. It is intended to find coding mistakes overlooked during development phase and lower risk of bugs in final product. In large and complex projects accurate code review is a challenging task. As code review depends on individual reviewer predisposition there is certain margin of source code changes that is not checked as it should...
Managing support tickets in large, multi-product organizations is difficult. Failure to meet the expectations of customers can lead to the escalation of support tickets, which is costly for IBM in terms of customer relationships and resources spent addressing the escalation. Keeping the customer happy is an important task in requirements engineering, which often comes in the form of handling their...
In practice, the data owners of source projects may need to share data without disclosing sensitive information. Therefore, privacy-preserving data-sharing becomes an important topic in cross-company defect prediction (CCDP). In this context, the challenge is how to achieve a high privacy-preserving level while ensuring the utility of the shared privatized data for CCDP. CLIFF&MORPH is a recently...
The problem of corporations bankruptcy risk forecasting is considered under uncertainty and unreliable information. For its solution the application of fuzzy neural networks (FNN) with inference of Mamdani and Tsukamoto is suggested. Fuzzy inference rules and forecasting algorithm are developed. The experimental investigations of FNN application for bankruptcy risk forecasting of Ukrainian enterprises...
Stock market or equity market have a profound impact in today's economy. A rise or fall in the share price has an important role in determining the investor's gain. The existing forecasting methods make use of both linear (AR, MA, ARIMA) and non-linear algorithms (ARCH, GARCH, Neural Networks), but they focus on predicting the stock index movement or price forecasting for a single company using the...
The design of effective financial early warning algorithm is of great significance to the financial management of the company. The weak classification algorithm can be improved to a high classification algorithm with high recognition rate through the ensemble learning. The algorithm can overcome the drawback of low classification accuracy of single classifier. Therefore, this paper combines decision...
At present, all developed countries employing public eProcurement systems create a corpus of generic public procurement fraud schemes. A selection of attributes with fraud suspicion signs is performed. It is necessary to accomplish, first of all, because fraud in the public procurement sphere is one of the most common kinds of frauds. With a view to improve the existing anti-corruption enforcement...
This paper presents a machine learning method for event-driven stock prediction, using L1 regularized Logistic regression model. It studies the stock price movement after listed companies make announcements. The model uses specific events extracted from these announcements and combine with financial indicators of listed companies, macro indicators, and technical indicators as dependent variables....
This study focuses on how the cumulative excess returns (CER) of corporate bonds in the Japanese market respond to simultaneous publications of current net earnings and management’s net earnings forecast. The estimation results using a regression model generalizing the interaction of the current net earnings and management’s net earnings forecast show that the CER of corporate bonds is influenced...
A numerical prediction algorithm called addition-subtraction frequency (ASF) algorithm is presented in this paper to predict potential valley-point dates of stock market. In the paper, we use historical valley-point date data of Shanghai Security Exchange (SSE) Composite Index as the input of ASF algorithm. According to the Royal Swedish Academy of Sciences, the stock prediction in the next three...
Stock price prediction is considered as one of the most challenging and important tasks. It is so complex and uncertainly, so that it is not enough to only use financial indicators for stock price prediction. As we known, patents is not only can protect the companies technologies' development and promote the advance of the core technologies, but can be used as one of the evaluation indicators to estimate...
Enterprise-level 2.0 applications (E2.0) built on cloud computing Web 2.0 infrastructure offer promising new business models. However, recent studies show that most E2.0 firms experience a low free-to-paid conversion rate. Based on accumulated archival data and literature on predictive models and data mining, in this paper, we develop a logit model to predict the likelihood of E2.0 user continuance...
Personalized recommendation aims to use the historical behavior of users to recommend new items that are likely to be of interest to them. Due to a tiny improvement of it can lead to a huge profits, lots of giant e-commerce companies, such as Amazon, Alibaba and eBay, have put their great effort on this field. In this paper, we formulate the problem of personalized recommendation as a tree based regression...
This work presents the evolution of a solution for predictive maintenance to a Big Data environment. The proposed adaptation aims for predicting failures on wind turbines using a data-driven solution deployed in the cloud and which is composed by three main modules. (i) A predictive model generator which generates predictive models for each monitored wind turbine by means of Random Forest algorithm...
The work covers the conceptual model supporting situation evaluation, forecast and control at power grid companies. The purpose of the paper is to design the intelligent instruments, which support making decisions by management of the Far Eastern power grid companies and regulators of their activities. We show the main stages of model development and provide the expanded algorithm for the model operation...
In a competitive market one of the key tasks of an industrial enterprise is to form and implement a development strategy. The purpose of this study is to formulate, justify and evaluate the financial and investment development strategy of an enterprise, to make its financial and economic forecast for a six-year period taking into account the selection and implementation of a major investment project...
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