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In the academic industry, students' early performance prediction is important to academic communities so that strategic intervention can be planned before students reach the final semester. This paper presents a study on Artificial Neural Network (ANN) model development in predicting academic performance of engineering students. Cumulative Grade Point Average (CGPA) was used to measure the academic...
A key challenge within the power sector is to address the issue of intermittency. It is the unavailability of energy at all times in order to meet the demand requirements. Intermittency is responsible for reducing the efficiency of the national infrastructure and can compromise energy security. Increasing use of renewable energy can cause the increasing intermittency. This is an important issue that...
This paper presents the non-parametric approach using Artificial Neural Network (ANN) in modelling and vibration control of a flexible beam structure. Elman Network structure has been selected to be used in dynamic modelling and system identification of the flexible beam. Several validation tests also have been carried out in order to measure the reliability of the nonparametric model developed. Later,...
Prediction of protein structural class has been a new area of research in the scientific community in the last decade. Various approaches has been adopted and analysed. However representing the raw amino acid sequence to preserve the property of proteins has posed a great challenge. Chou's pseudo amino acid composition feature representation method has fetched wide attention in this regard. In Chou's...
Cache locking is a cache management technique to preclude the replacement of locked cache contents. Cache locking is often used to improve cache access predictability in Worst-Case Execution Time (WCET) analysis. Static cache locking methods have been proposed recently to improve average system performance. This paper presents an approach, Branch Prediction directed Dynamic Cache Locking (BPDCL),...
World experience shows the effectiveness of continuous monitoring of the fire hazardous areas to carry out management for decisions to prevent emergencies [1]. Identification of fires with the help of the images obtained by remote sensing of the earth surface, is essential to the economy, since timely identification of fire gives a real opportunity to localize and eliminate it in a small area, thus...
This paper carries out a study of the suitability of Neural Networks (NN) as solutions for the problem of detecting Gaussian targets with unknown one-lag correlation coefficient (?s) in different radar clutter environments (Additive White Gaussian Noise (AWGN) and correlated Gaussian clutter plus AWGN). The optimum Neyman-Pearson detector is formulated assuming an uniform variation of ?s -- [0, 1]...
The detection of brain condition under different subjects is utmost important and it is a challenging task. EEG signals are such data that need to carefully analyze when it consists of series of different subjects. This paper explores the application of canonical correlation analysis with artificial neural networks for EEG data sets with different subjects and reference. We demonstrate the network's...
This paper aims to develop a methodology for choosing the inputs of a multilayer fuzzy inference system to forecast time series power demand values in a substation feeder. The forecast is done by analyzing past time series data. On an iteration process., older data with greater correlation with the previous forecast errors are the inputs of the fuzzy system., which has as output a future demand value...
Mobile phones are gaining particular importance in health care services. In fact, the incredible diffusion of mobile electronic devices has opened new scenarios for the assessment of stress-related events. We presented an Android Smartphone based experience sampling method and a real life data set containing stress related events. Data consisted of 12 weeks of 9 trial participants provided with Android...
Evapotranspiration (ET) is an essential parameter for estimation of irrigation water requirements. Climatic variables (CV) along with soil and plant variables are known to influence ET exhibiting completely different patterns at some locations because of multicollinearity (MC). These factors, coupled with the complex phenomenon of transpiration have been largely responsible for the development of...
Genre classification for musical documents is conventionally based on keywords, statistical features or low-level acoustic features. Such features are either lack of in-depth information of music content or incomprehensible for music professionals. This paper proposed a classification scheme based on the correlation analysis of the melodic patterns extracted from music documents. The extracted patterns...
In semiconductor manufacturing plants, monitoring physical properties of all wafers is fundamental in order to maintain good yield and high quality standards. However, such an approach is too costly and in practice only few wafers in a lot are actually monitored. Virtual Metrology (VM) systems allow to partly overcome the lack of physical metrology. In a VM scheme, tool data are used to predict, for...
Air quality information is increasingly becoming a public health concern, since some of the aerosol particles pose harmful effects to peoples health. One widely available metric of aerosol abundance is the aerosol optical depth (AOD). The AOD is the integrated light extinction coefficient over a vertical atmospheric column of unit cross section, which represents the extent to which the aerosols in...
A canonical correlation Radial Basis Function (RBF) artificial neural network model (CCRBF) which was used to test and predict atmosphere environmental quality was proposed by coupling canonical correlation analysis and RBF neural network based on various factors influencing urban atmosphere environmental quality. The various factors were analyzed and extracted by applying canonical correlation analysis,...
To predict the continuous value of target variable using the values of explanation variables, we often use multiple linear regression methods, and many applications have been successfully reported. However, in some data cases, multiple linear regression methods may not work because of strong local dependency of target variable to explanation variables. In such cases, the use of the k nearest-neighbor...
El Nino southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) have enormous effects on the precipitations around the world. Australian rainfall is also affected by these key modes of complex climate variables. Many studies have tried to establish the relationships of these large-scale climate indices among the rainfalls of different parts of Australia, particularly Western Australia, New South...
In recent years, various kinds of satellite-derived aerosol products have been used to air quality monitoring. However, satellites are not sensitive to the near surface aerosol which impacts human health but the entire aerosol column. In this paper, we establish an artificial neural network (ANN) instead of multiple regression technique to lessen the surface PM2.5 estimation uncertainty from remote...
This paper presents a new image encryption technique based on neural chaotic generator. This encryption technique includes two main operations, permutation at pixel level and masking and permutation at bit level. The chaotic generator used in the encryption of image is perturbed by a new technique done by artificial neural network. Simulations show that the proposed encryption technique is effective...
This paper described a novel speech emotion recognition approach aiming at improving speech emotion recognition rate. Seven discrete emotional states (anger, disgust, fear, joy, neutral, sadness, surprise) are classified throughout the work. Firstly, series preprocessing of speech signals are done. Secondly, extracting features are done, and then we consider incorporating Canonical Correlation Based...
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