Backpropagation Artificial Neural Network To Detect Hyperthermic Seizures In Rats

A three-layered feed-forward back-propagation Artificial Neural Network was used to classify the seizure episodes in rats. Seizure patterns were induced by subjecting anesthetized rats to a Biological Oxygen Demand incubator at 45-47ºC for 30 to 60 minutes. Selected fast Fourier transform data of one second epochs of electroencephalogram were used to train and test the network for the classification of seizure and normal patterns. The results indicate that the present network with the architecture of 40-12-1 (input-hidden-output nodes) agrees with manual scoring of seizure and normal patterns with a high recognition rate of 98.6%

Efficient Oblivious Branching Programs for Threshold and Mod Functions

AbstractIn his survey paper on branching programs, Razborov asked the following question: Does every rectifier-switching network computing the majority ofnbits have sizen1+Ω(1)? We answer this question in the negative by constructing a simple oblivious branching program of sizeO[nlog3n/loglognlogloglogn] for computing any threshold function. This improves the previously best known upper bound ofO(n3/2) due to Lupanov. We also construct oblivious branching programs of sizeo(nlog4n) for computing general mod functions. All previously known constructions for computing general mod functions have sizeΩ(n3/2)

Asymptotically optimal declustering schemes for 2-dim range queries

AbstractDeclustering techniques have been widely adopted in parallel storage systems (e.g. disk arrays) to speed up bulk retrieval of multidimensional data. A declustering scheme distributes data items among multiple disks, thus enabling parallel data access and reducing query response time. We measure the performance of any declustering scheme as its worst case additive deviation from the ideal scheme. The goal thus is to design declustering schemes with as small an additive error as possible. We describe a number of declustering schemes with additive error O(logM) for 2-dimensional range queries, where M is the number of disks. These are the first results giving O(logM) upper bound for all values of M. Our second result is a lower bound on the additive error. It is known that except for a few stringent cases, additive error of any 2-dimensional declustering scheme is at least one. We strengthen this lower bound to Ω((logM)(d−1/2)) for d-dimensional schemes and to Ω(logM) for 2-dimensional schemes, thus proving that the 2-dimensional schemes described in this paper are (asymptotically) optimal. These results are obtained by establishing a connection to geometric discrepancy. We also present simulation results to evaluate the performance of these schemes in practice

Cancer Detection Using Neuro Fuzzy Classifier in CT Images

In this study, we have implemented an adaptive neuro fuzzy inference system (ANFIS) for detection of mass in CT images for early diagnosis of lung cancer. After completion of preprocessing and segmentation process four features have been extracted from images and given to ANFIS classifier as an input. The fuzzy system detects the severity of the lung nodules depends on IF-THEN rules. Feature based data set has been created with five fuzzy membership functions of each input. The proposed model is applied on more than 150 images and the computer added diagnosis (CAD) system achieved sensitivity of 97.27% and specificity of 95% with accuracy of 96.66%