This paper presents a forecasting model designed using WSNs (Wireless Sensor Networks) to predict flood in rivers using simple and fast calculations to provide real-time results and save the lives of people who may be affected by the flood. Our prediction model uses multiple variable robust linear regression which is easy to understand and simple and cost effective in implementation, is speed efficient, but has low resource utilization and yet provides real time predictions with reliable accuracy, thus having features which are desirable in any real world algorithm. Our prediction model is independent of the number of parameters, i.e. any number of parameters may be added or removed based on the on-site requirements. When the water level rises, we represent it using a polynomial whose nature is used to determine if the water level may exceed the flood line in the near future. We compare our work with a contemporary algorithm to demonstrate our improvements over it. Then we present our simulation results for the predicted water level compared to the actual water level.

A Simple Flood Forecasting Scheme Using Wireless Sensor Networks

In a system comprising of human-machine interaction, the emotion recognition of speech has always been a wide area of research since the machines can never analyze the emotion of a speaker on its own. To recognize the speaker’s emotion, numerous systems were developed and tested. In this research study, an enhancedhuman speech emotion recognition system using a hybrid of PRNN and KNN algorithms is designed. The six basic emotions like neutral, anger, happiness, sadness, surprise and fear over the speech emotions are classified and studied for their accuracy with other previously developed systems. The database for this study is taken as the emotional speech samples of numbers. A cascaded system of Mel Frequency Cepstral Coefficient (MFCC) and Gray Level Co-occurrence Matrix (GLCM) was used for feature extraction process along with a Wiener filter for filtering the noise in speech. Also, a hybrid of Pattern Recognition Neural Network (PRNN) and K-Nearest Neighbour (KNN) is used for prediction accuracy of outcomes. The outcomes are compared with previously developed recognition systems and better efficiency is observed.

An Enhanced Human Speech Emotion Recognition Using Hybrid of PRNN and KNN

Parameter identification of proton exchange membrane fuel cell via Levenberg-Marquardt backpropagation algorithm

An artificial neural network (ANN) usually called neural network. It can be considered as a resemblance to a paradigm which is inspired by biological nervous system. In network the signals are transmitted by the means of connections links. The links possess an associated way which is multiplied along with the incoming signal. The output signal is obtained by applying activation to the net input NN are one of the most exciting and challenging research areas. As ANN mature into commercial systems, they are likely to be implemented in hardware. Their fault tolerance and reliability are therefore vital to the functioning of the system in which they are embedded. The pattern recognition system is implemented with Back propagation network and Hopfield network to remove the distortion from the input. The Hopfield network has high fault tolerance which supports this system to get the accurate output.

/pdf/pattern-recognition-using-artificial-neural-network-4kjpf010eq.pdf

Pattern Recognition using Artificial Neural Network

A constraint is defined as a logical relation among several unknown quantities or variables, each taking a value in a given domain. Constraint Programming (CP) is an emergent field in operations research. Constraint programming is based on feasibility which means finding a feasible solution rather than optimization which means finding an optimal solution and focuses on the constraints and variables domain rather than the objective functions. While defining a set of constraints, this may seem a simple way to model a real-world problem but finding a good model that works well with a chosen solver is not that easy. A model could be very hard to solve if it is poorly chosen.

/pdf/a-review-of-constraint-programming-4yu73a2h8q.pdf

A Review of Constraint Programming

This paper propose a method of personal identification based on finger-vein patterns. An image of a finger captured by the web camera under the IR light transmission contains not only the vein pattern itself; but also shade produced by various thickness of the finger muscles; bones; and tissue networks surrounding the vein. In this paper; we intro-duce preliminary process to enhance the image quality worsened by light effect and noise produced by the web camera; then segment the vein pattern by using adaptive threshold method and matched them using improved template matching. The main purposes of this paper are to investigate finger-vein technology; the applicable fields and whether finger-vein detection can solve the problems fingerprint detection imposes in certain industries.

/pdf/finger-vein-detection-using-gabor-filter-segmentation-and-4maov1w2co.pdf

Poonam Dabas

Papers

Pattern Recognition using Artificial Neural Network

A Review of Constraint Programming

Finger Vein Detection using Gabor Filter, Segmentation and Matched Filter