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On an average 9 out of 10 startups fail(industry standard). Several reasons are responsible for the failure of a startup including bad management, lack of funds, etc. This work aims to create a predictive model for startups based on many key things involved at various stages in the life of a startup. It is highly desirable to increase the success rate of startups and not much work have been done to...
Even though the electricity HPFC (Hourly Price Forward Curve) is still surprisingly under-researched the prediction of electricity prices is highly important in order to keep power plants profitable or in order to optimize the electricity purchases based on future customers demand. In this work two methods to model and predict HPFC based on neural networks will be proposed and compared to more common...
The future power grid will need to incorporate systems and processes with a higher degree of variability and randomness due to the penetration of renewable energy resources and the increase of energy demand. Forecasting variables in a more uncertain environment poses new challenges and revisions of the existing forecasting methodologies will have to be made to maintain forecasting accuracy. This paper...
MARA Junior Science College (MRSM) Lenggong is one of the educational institutes under Majlis Amanah Rakyat (MARA). Based on the current academic performance and selected criteria of 6A's in the Penilaian Menengah Rendah (PMR, now it is known as PT3), rationally there should be no reason for the failure to achieve excellent results in the Sijil Pelajaran Malaysia (SPM). However, every time the results...
Building's energy demand is influenced by many factors, such as: weather conditions, building structure and characteristics, energy consumption of components (lighting and HVAC systems), level of occupancy and user's behavior. As consequence of multi-variable impact on building's energy consumption, theoretical models based on first principles are not able to forecast actual energy demand of a generic...
Rainfall forecasting is one of the most imperative and demanding operational responsibilities carried out by meteorological services all over the world. The task is complicated since all decisions are to be taken in the visage of uncertainty. In this article, the traditional data pre-processing technique, moving average is coupled with Artificial Neural Network as MA - ANN to improve the prediction...
Oil is the lifeblood of the global economy. Recently, oil prices have witnessed fluctuations and the prediction of oil prices has become a challenge for researchers. The aim of this research is to design a model that is able to predict the prices of crude oil with good accuracy. We used the daily data from 1999 to 2012 with 14 input factors to predict the price of West Texas Intermediate (WTI), which...
The widespread adoption of ubiquitous devices does not only facilitate the connection of billions of people, but has also fuelled a culture of sharing rich, high resolution locations through check-ins. Despite the profusion of GPS and WiFi driven location prediction techniques, the sparse and random nature of check-in data generation have ushered diverse problems, which have prompted the prediction...
In this paper, we apply learning techniques to predict link quality evolution in a WSN and take advantage of wireless links with the best possible quality to improve the packet delivery rate. We model this problem as a forecaster prediction game based on the advice of several experts. The forecaster learns on-line how to adjust its prediction to better fit the environment metric values. Simulations...
Depression is a disorder that has a huge impact on both the patient and its environment. An effective treatment of depression is of crucial importance. Currently, Internet-based self-help therapies are the state-of-the-art among therapies that do not involve a human therapist. However, these interventions are not tailored towards individual patient needs. The utilization of pervasive technology, including...
In this paper, a modified partial least-squares (PLS) regression modeling method is proposed. The proposed method can build a modified regression model to extract the useful information in residual subspace, which is helpful to predict the output variables. With this method, more accurate quality variables are predicted. In simulation experiment, penicillin fermentation process is used to test the...
With the impact of climate change in India, majority of the agricultural crops are being badly affected interms of their performance over a period of last two decades. Predicting the crop yield well ahead of its harvest would help the policy makers and farmers for taking appropriate measures for marketing and storage. Such predictions will also help the associated industries for planning the logistics...
The randomness of the wind velocity causes the fluctuation of wind power. Therefore, It is necessary to forecast the wind power in a certain time. In this paper, the ultra-short term predication of wind power has been carried out based on the Auto-Regressive-and-Moving-Average (ARMA) model. The wind power was predicted by prediction steps of ARMA in section II. According to the corresponding national...
The prediction of future locations is of enormous research interest, partly due to the fast growing number of users of pervasive devices, as well as the tons of spatiotemporal data generated by such devices. In this paper, we propose a novel enhanced Next Location prediction technique which utilizes a trajectory model called Time Mobility Context Correlation Pattern (TMC-Pattern) and sequence alignment...
This article proposes an approach to predict the result of binarization algorithms on a given document image according to its state of degradation. Indeed, historical documents suffer from different types of degradation which result in binarization errors. We intend to characterize the degradation of a document image by using different features based on the intensity, quantity and location of the...
The high failure rates of many programming courses means there is a need to identify struggling students as early as possible. Prior research has focused upon using a set of tests to assess the use of a student's demographic, psychological and cognitive traits as predictors of performance. But these traits are static in nature, and therefore fail to encapsulate changes in a student's learning progress...
Due to high competition in today's business and the need for satisfactory communication with customers, companies understand the inevitable necessity to focus not only on preventing customer churn but also on predicting their needs and providing the best services for them. The purpose of this article is to predict future services needed by wireless users, with data mining techniques. For this purpose,...
In this paper we present a mathematical model for collaborative filtering implementation in stock market predictions. In popular literature collaborative filtering, also known as Wisdom of Crowds, assumes that group has a greater knowledge than the individual while each individual can improve groups performance by its specific information input. There are commercially available tools for collaborative...
News articles are a captivating type of online content that capture a significant amount of Internet users' interest. They are particularly consumed by mobile users and extremely diffused through online social platforms. As a result, there is an increased interest in promptly identifying the articles that will receive a significant amount of user attention. This task falls under the broad scope of...
Fraud in public companies has a large financialimpact, and yet is only weakly detected by those who look for it, many incidents have been detected only when whistleblowers have come forward. We examine the problem of detecting fraud from the textual component of the quarterly and annual reports that public companies are required to file. Using an empirically derived set of words, we achieve prediction...
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