Feedforward backpropagation, genetic algorithm approaches for predicting reference evapotranspiration

Water scarcity is a global concern, as the demand for water is increasing tremendously and poor management of water resources will accelerates dramatically the depletion of available water. The precise prediction of evapotranspiration (ET), that consumes almost 100% of the supplied irrigation water, is one of the goals that should be adopted in order to avoid more squandering of water especially in arid and semiarid regions. The capabilities of feedforward backpropagation neural networks (FFBP) in predicting reference evapotranspiration (ET0) are evaluated in this paper in comparison with the empirical FAO Penman-Monteith (P-M) equation, later a model of FFBP+Genetic Algorithm (GA) is implemented for the same evaluation purpose. The study location is the main station in Iraq, namely Baghdad Station. Records of weather variables from the related meteorological station, including monthly mean records of maximum air temperature (Tmax), minimum air temperature (Tmin), sunshine hours (Rn), relative humidity (Rh) and wind speed (U2), from the related meteorological station are used in the prediction of ET0 values. The performance of both simulation models were evaluated using statistical coefficients such as the root of mean squared error (RMSE), mean absolute error (MAE) and coefficient of determination (R2). The results of both models are promising, however the hybrid model shows higher efficiency in predicting ET0 and could be recommended for modeling of ET0 in arid and semiarid regions

Optimized Kernel Extreme Learning Machine for Myoelectric Pattern Recognition

Myoelectric pattern recognition (MPR) is used to detect user’s intention to achieve a smooth interaction between human and machine. The performance of MPR is influenced by the features extracted and the classifier employed. A kernel extreme learning machine especially radial basis function extreme learning machine (RBF-ELM) has emerged as one of the potential classifiers for MPR. However, RBF-ELM should be optimized to work efficiently. This paper proposed an optimization of RBF-ELM parameters using hybridization of particle swarm optimization (PSO) and a wavelet function. These proposed systems are employed to classify finger movements on the amputees and able-bodied subjects using electromyography signals. The experimental results show that the accuracy of the optimized RBF-ELM is 95.71% and 94.27% in the healthy subjects and the amputees, respectively. Meanwhile, the optimization using PSO only attained the average accuracy of 95.53 %, and 92.55 %, on the healthy subjects and the amputees, respectively. The experimental results also show that SW-RBF-ELM achieved the accuracy that is better than other well-known classifiers such as support vector machine (SVM), linear discriminant analysis (LDA) and k-nearest neighbor (kNN)

Change detection of land use and land cover in an urban region with SPOT-5 images and partial Lanczos extreme learning machine

Satellite remote sensing technology and the science associated with evaluation of land use and land cover (LULC) in an urban region makes use of the wide range images and algorithms. Improved land management capacity is critically dependent on real-time or near real-time monitoring of land-use/land cover change (LUCC) to the extent to which solutions to a whole host of urban/rural interface development issues may be well managed promptly. Yet previous processing with LULC methods is often time-consuming, laborious, and tedious making the outputs unavailable within the required time window. This paper presents a new image classification approach based on a novel neural computing technique that is applied to identify the LULC patterns in a fast growing urban region with the aid of 2.5-meter resolution SPOT-5 image products. The classifier was constructed based on the partial Lanczos extreme learning machine (PL-ELM), which is a novel machine learning algorithm with fast learning speed and outstanding generalization performance. Since some different classes of LULC may be linked with similar spectral characteristics, texture features and vegetation indexes were extracted and included during the classification process to enhance the discernability. A validation procedure based on ground truth data and comparisons with some classic classifiers prove the credibility of the proposed PL-ELM classification approach in terms of the classification accuracy as well as the processing speed. A case study in Dalian Development Area (DDA) with the aid of the SPOT-5 satellite images collected in the year of 2003 and 2007 and PL-ELM fully supports the monitoring needs and aids in the rapid change detection with respect to both urban expansion and coastal land reclamations

Predicción de la estructura secundaria de las proteínas mediante redes neuronales

El trabajo está organizado como sigue: en el primer capítulo se realiza una introducción en el ámbito de la bioquímica para conocer el origen de las proteínas a partir del ADN y la secuenciación de aminoácidos. Además, se destacarán las diferentes estructuras secundarias de las proteínas ya que el objetivo del trabajo se fundamenta en la predicción de éstas. A continuación, en el capítulo 2 se introducirán las técnicas de Soft Computing, mencionando las más empleadas actualmente y haciendo especial hincapié en las Redes Neuronales, las cuales serán utilizadas para la resolución del trabajo. En el capítulo 3 comienza el desarrollo del procedimiento seguido: en primer lugar se realiza un análisis de la base de datos y herramienta de programación utilizadas. Posteriormente se comienza con la manipulación de datos y la formación de las muestras tanto de entrada como de salida. Como se podrá comprobar ulteriormente, se han hecho uso de tres esquemas de codificación para la obtención de datos (esquema ortogonal, estadístico y mediante grafos dirigidos). Para concluir con el capítulo, se desarrollan las decisiones tomadas respecto a la topología de la red neuronal, y así en el capítulo 4 analizar y discutir los resultados obtenidos en las diferentes redes neuronales. Además se realiza una clasificación de los resultados más significativos con intención de mejorar dichos resultados. Finalmente, se efectúan una serie de conclusiones de cierre en las que se incorporan futuras líneas de seguimiento para la máxima optimización del problema

Fast learning optimized prediction methodology for protein secondary structure prediction, relative solvent accessibility prediction and phosphorylation prediction

Computational methods are rapidly gaining importance in the field of structural biology, mostly due to the explosive progress in genome sequencing projects and the large disparity between the number of sequences and the number of structures. There has been an exponential growth in the number of available protein sequences and a slower growth in the number of structures. There is therefore an urgent need to develop computed structures and identify the functions of these sequences. Developing methods that will satisfy these needs both efficiently and accurately is of paramount importance for advances in many biomedical fields, for a better basic understanding of aberrant states of stress and disease, including drug discovery and discovery of biomarkers. Several aspects of secondary structure predictions and other protein structure-related predictions are investigated using different types of information such as data obtained from knowledge-based potentials derived from amino acids in protein sequences, physicochemical properties of amino acids and propensities of amino acids to appear at the ends of secondary structures. Investigating the performance of these secondary structure predictions by type of amino acid highlights some interesting aspects relating to the influences of the individual amino acid types on formation of secondary structures and points toward ways to make further gains. Other research areas include Relative Solvent Accessibility (RSA) predictions and predictions of phosphorylation sites, which is one of the Post-Translational Modification (PTM) sites in proteins. Protein secondary structures and other features of proteins are predicted efficiently, reliably, less expensively and more accurately. A novel method called Fast Learning Optimized PREDiction (FLOPRED) Methodology is proposed for predicting protein secondary structures and other features, using knowledge-based potentials, a Neural Network based Extreme Learning Machine (ELM) and advanced Particle Swarm Optimization (PSO) techniques that yield better and faster convergence to produce more accurate results. These techniques yield superior classification of secondary structures, with a training accuracy of 93.33% and a testing accuracy of 92.24% with a standard deviation of 0.48% obtained for a small group of 84 proteins. We have a Matthew\u27s correlation-coefficient ranging between 80.58% and 84.30% for these secondary structures. Accuracies for individual amino acids range between 83% and 92% with an average standard deviation between 0.3% and 2.9% for the 20 amino acids. On a larger set of 415 proteins, we obtain a testing accuracy of 86.5% with a standard deviation of 1.38%. These results are significantly higher than those found in the literature. Prediction of protein secondary structure based on amino acid sequence is a common technique used to predict its 3-D structure. Additional information such as the biophysical properties of the amino acids can help improve the results of secondary structure prediction. A database of protein physicochemical properties is used as features to encode protein sequences and this data is used for secondary structure prediction using FLOPRED. Preliminary studies using a Genetic Algorithm (GA) for feature selection, Principal Component Analysis (PCA) for feature reduction and FLOPRED for classification give promising results. Some amino acids appear more often at the ends of secondary structures than others. A preliminary study has indicated that secondary structure accuracy can be improved as much as 6% by including these effects for those residues present at the ends of alpha-helix, beta-strand and coil. A study on RSA prediction using ELM shows large gains in processing speed compared to using support vector machines for classification. This indicates that ELM yields a distinct advantage in terms of processing speed and performance for RSA. Additional gains in accuracies are possible when the more advanced FLOPRED algorithm and PSO optimization are implemented. Phosphorylation is a post-translational modification on proteins often controls and regulates their activities. It is an important mechanism for regulation. Phosphorylated sites are known to be present often in intrinsically disordered regions of proteins lacking unique tertiary structures, and thus less information is available about the structures of phosphorylated sites. It is important to be able to computationally predict phosphorylation sites in protein sequences obtained from mass-scale sequencing of genomes. Phosphorylation sites may aid in the determination of the functions of a protein and to better understanding the mechanisms of protein functions in healthy and diseased states. FLOPRED is used to model and predict experimentally determined phosphorylation sites in protein sequences. Our new PSO optimization included in FLOPRED enable the prediction of phosphorylation sites with higher accuracy and with better generalization. Our preliminary studies on 984 sequences demonstrate that this model can predict phosphorylation sites with a training accuracy of 92.53% , a testing accuracy 91.42% and Matthew\u27s correlation coefficient of 83.9%. In summary, secondary structure prediction, Relative Solvent Accessibility and phosphorylation site prediction have been carried out on multiple sets of data, encoded with a variety of information drawn from proteins and the physicochemical properties of their constituent amino acids. Improved and efficient algorithms called S-ELM and FLOPRED, which are based on Neural Networks and Particle Swarm Optimization are used for classifying and predicting protein sequences. Analysis of the results of these studies provide new and interesting insights into the influence of amino acids on secondary structure prediction. S-ELM and FLOPRED have also proven to be robust and efficient for predicting relative solvent accessibility of proteins and phosphorylation sites. These studies show that our method is robust and resilient and can be applied for a variety of purposes. It can be expected to yield higher classification accuracy and better generalization performance compared to previous methods