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Since wireless sensor networks are usually used for long-term monitoring in harsh environments, sensor nodes are vulnerable to faults. Function fault can lead to immediate node breakdown, while data fault makes the node generate erroneous sensor data. Faulty data results in incorrect estimation of the environment and causes unnecessary consumption of the network resources. Therefore, it is necessary...
In this paper we consider systems which are nonlinear with respect to its variables and to its parameters. A particular case is considered here: an intensity/pressure converter which able us to command an artificial muscle. One of the main problems in control of such system is to have an accurate model and so to know with sufficiently accuracy the different parameter's values. When the system is nonlinear,...
Sexual assault and interpersonal violence affects university communities in disproportionate numbers to those of the general population. It is estimated that one in five women will be the victims of a sexual assault during their college years. In this study, we use kernel density estimation, logistic regression and random forest modeling to conduct spatial and temporal analysis of sexual assault at...
New developments in time series analysis can be used to determine a better spectral representation for unknown data. Any stationary process can be modeled accurately with one of the three model types: AR (autoregressive), MA (moving average) or the combined ARMA model. Generally, the best type is unknown. However, if the three models are estimated with suitable methods, a single time series model...
Hidden Markov Models (HMMs) are one of the most important techniques to model and classify sequential data. Maximum Likelihood (ML) and (parametric and non-parametric) Bayesian estimation of the HMM parameters suffers from local maxima and in massive datasets they can be specially time consuming. In this paper, we extend the spectral learning of HMMs, a moment matching learning technique free from...
Current technology facilitates and increases connections through social media, allowing individuals everywhere to spread their ideas to the world. One social media platform is Twitter. One characteristic of a tweet is the requirement of conveying a message in a limited number of words. Proverbs are a feature of language that convey messages effectively in the least number of words. Therefore, we selected...
The increasing number of elderly persons, in addition to the lack of infrastructures designed to manage them brings an awareness of the importance of maintaining them at home by developing assistive technologies. Recent research on the latter focused on Human Activity Recognition (HAR). HAR aims to recognize the sequence of actions by a specific resident at home using sensor readings. In eldercare...
In big data universities, an understanding of how the individual learning style and preferences interacts with the instructional medium presented is needed. In this study we examined the VARK learning style inventory using the variable-centered, person-centered and social approaches. We worked on a big “data set” which encompasses two data sources the first was LMS while the second was social media...
Data transformation (normalization) is a method used in data preprocessing to scale the range of values in the data within a uniform scale to improve the quality of the data; as a result, the prediction accuracy is improved. However, some scholars have questioned the efficacy of data normalization, arguing that it can destroy the structure in the original (raw) data. To address these arguments, we...
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...
Constructing accurate models that represent the underlying structure of Big Data is a costly process that usually constitutes a compromise between computation time and model accuracy. Methods addressing these issues often employ parallelisation to handle processing. Many of these methods target the Support Vector Machine (SVM) and provide a significant speed up over batch approaches. However, the...
We propose a novel method for analyzing acoustic scenes that can sophisticatedly estimate acoustic scenes from an acoustic event sequence with intermittent missing events. On the basis of the idea that acoustic events are temporally correlated, we model the transition of acoustic events using a hidden Markov model (HMM) and estimate missing acoustic events. Then, we incorporate the transition of acoustic...
Recent research on the TIMIT database suggests that longer-length acoustic units are better suited for modelling pronunciation variation and long-term temporal dependencies in speech than traditional phoneme-length units, yielding substantial improvements in recognition accuracy [9]. In this paper, we investigate whether similar improvements can be gained on another database, viz. excerpts from novels...
In this paper, we study performance measures for online advertising systems by empirical approach. The traditional statistical measure Critical Success Index (CSI), also known as Threat Score (TS), is modified as CSI-R to add the emphasis on recall. We then generate two realistic data sets to examine the effects of CSI and CSI-R in evaluation of a Real-Time Bidding (RTB) system for online advertising...
A point pattern matching algorithm based on Mahalanobis distance is proposed, which effect is analyzed and confirmed by experiments. Secondly, the Graph Transformation Matching algorithm and Weighted Graph Transformation Matching algorithm are studied deeply. To overcome the limitation of Mahalanobis distance and WGTM, a novel and robust point pattern matching algorithm based on Weighted Graph Transformation...
A central problem of fuzzy modelling is the generation of fuzzy rules that fit the data to the highest possible extent. In this study, we present a method for automatic generation of fuzzy rules from data. The main advantage of the proposed method is its ability to perform data clustering without the requirement of predefining any parameters including number of clusters. The proposed method creates...
The development schedule of software projects is mainly measured in months and it is a necessary and important phase, since the under prediction or over prediction of it at the planning stage can negatively impact budgets. Unfortunately, only 39 percent of software projects finish on time relative to their original plan. According to its development type, a software project can be classified as new,...
The aim of this paper is to propose a general methodology to improve the linguistic-accuracy trade-off of fuzzy models, applicable to any rule-based fuzzy model. Here, the neuro-fuzzy system FasArt (Fuzzy Adaptive System ART based) is used to obtain rule-based fuzzy models, as shown in previous papers and works. FasArt, however, has the usual drawbacks, from the linguistic point of view, of most (precise)...
The aim of this paper is focused on fuzzy models and simplification-interpretability ideas to generate simpler and more interpretable models in accordance with fuzzy logic. These concepts are not very common in (precise) fuzzy modeling methods, which are the most common approach in technical fields. Here, orthogonal transforms are considered for achieving this aim, defining a criteria set to guide...
In order to enhance the accuracy of the dynamic equivalence of wind farm (WF) under different wind conditions (WCs), this paper proposed a Dynamic Multi-Turbine Multi-State (DMTMS) Model of WF based on the historical wind data. The proposed model could represent the dynamic characteristics of WF under different WCs with high accuracy. Support vector clustering (SVC), whose cluster partition is completed...
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