Computation of Time-dependent Probabilities of Vesicle Release and Binding of Neurotransmitters of Postsynaptic Neuron

302-305When a postsynaptic neuron receives a spike from the axon, its synapse releases neurotransmitters to the synaptic cleft. The probability of vesicle release depends on the amount of calcium ions. The concentration of calcium ions keeps on changing with time. The opening and closing of these channels is controlled by the calcium ion gates operating at different rates. Similarly, the binding of neurotransmitters to the membrane depends on the number of receptors. The existing literature considers probabilities of vesicle release and binding of neurotransmitters as constants. In practice, these two probabilities are time-dependent. This issue is addressed in this paper and new derivations of the time-varying nature of these two probabilities are obtained from simulation study and analysis. The present investigation of estimation of these two time-dependent probabilities will help to develop improved nanoscale neuronal communication models

Computation of Time-dependent Probabilities of Vesicle Release and Binding of Neurotransmitters of Postsynaptic Neuron

When a postsynaptic neuron receives a spike from the axon, its synapse releases neurotransmitters to the synaptic cleft. The probability of vesicle release depends on the amount of calcium ions. The concentration of calcium ions keeps on changing with time. The opening and closing of these channels is controlled by the calcium ion gates operating at different rates. Similarly, the binding of neurotransmitters to the membrane depends on the number of receptors. The existing literature considers probabilities of vesicle release and binding of neurotransmitters as constants. In practice, these two probabilities are time-dependent. This issue is addressed in this paper and new derivations of the time-varying nature of these two probabilities are obtained from simulation study and analysis. The present investigation of estimation of these two time-dependent probabilities will help to develop improved nanoscale neuronal communication models

Sentiment Analysis of Twitter Data for Predicting Stock Market Movements

Predicting stock market movements is a well-known problem of interest. Now-a-days social media is perfectly representing the public sentiment and opinion about current events. Especially, twitter has attracted a lot of attention from researchers for studying the public sentiments. Stock market prediction on the basis of public sentiments expressed on twitter has been an intriguing field of research. Previous studies have concluded that the aggregate public mood collected from twitter may well be correlated with Dow Jones Industrial Average Index (DJIA). The thesis of this work is to observe how well the changes in stock prices of a company, the rises and falls, are correlated with the public opinions being expressed in tweets about that company. Understanding author's opinion from a piece of text is the objective of sentiment analysis. The present paper have employed two different textual representations, Word2vec and N-gram, for analyzing the public sentiments in tweets. In this paper, we have applied sentiment analysis and supervised machine learning principles to the tweets extracted from twitter and analyze the correlation between stock market movements of a company and sentiments in tweets. In an elaborate way, positive news and tweets in social media about a company would definitely encourage people to invest in the stocks of that company and as a result the stock price of that company would increase. At the end of the paper, it is shown that a strong correlation exists between the rise and falls in stock prices with the public sentiments in tweets.Comment: 6 pages 4 figures Conference Pape

Network Lifetime and Coverage Fraction Analysis for Wireless Sensor Networks

285-291In Wireless Sensor Networks, two crucial parameters are lifetime of the network and optimal coverage for sensed region. This paper addresses the issues and challenges pertaining to these parameters for further investigation, and provides a method to approximate the energy utilization and optimal coverage inside the bottleneck zone for wireless sensor networks. The proposed analytical framework calculates correctly the network lifetime upper bound of wireless sensor networks. The derivation of the network lifetime upper bound is carried out using (i) network coding and (ii) network coding with duty cycle. Based on that, an approximate derivation is made and the corresponding results are obtained from the simulation study. The comparison of the results of the previous study and those obtained in this paper reveals that the actual network lifetime upper bound is lower in the present case. This is due to the assumption made by authors of previous work, on coder nodes’ presence throughout the bottleneck zone instead of only one hop distance away from the sink. In addition, the effect of coverage fraction in case of node failure, on network lifetime upper bound is derived for the previously reported and present model. The simulated results obtained from new derivation show that the coverage fraction is lesser than that obtained by previous model

Network Lifetime and Coverage Fraction Analysis for Wireless Sensor Networks

In Wireless Sensor Networks, two crucial parameters are lifetime of the network and optimal coverage for sensed region. This paper addresses the issues and challenges pertaining to these parameters for further investigation, and provides a method to approximate the energy utilization and optimal coverage inside the bottleneck zone for wireless sensor networks. The proposed analytical framework calculates correctly the network lifetime upper bound of wireless sensor networks. The derivation of the network lifetime upper bound is carried out using (i) network coding and (ii) network coding with duty cycle. Based on that, an approximate derivation is made and the corresponding results are obtained from the simulation study.  The comparison of the results of the previous study and those obtained in this paper reveals that the actual network lifetime upper bound is lower in the present case. This is due to the assumption made by authors of previous work, on coder nodes’ presence throughout the bottleneck zone instead of only one hop distance away from the sink. In addition, the effect of coverage fraction in case of node failure, on network lifetime upper bound is derived for the previously reported and present model. The simulated results obtained from new derivation show that the coverage fraction is lesser than that obtained by previous model

Machine learning for the classification of breast cancer tumor: a comparative analysis

The detection and diagnosis of Breast cancer at an early stage is a challenging task. With the increase in emerging technologies such as data mining tools, along with machine learning algorithms, new prospects in the medical field for automatic diagnosis have been developed, with which the prediction of a disease at an early stage is possible. Early detection of the disease may increase the survival rate of patients. The main purpose of the study was to predict breast cancer disease as benign or malignant by using supervised machine learning algorithms such as the K-nearest neighbor (K-NN), multilayer perceptron (MLP), and random forest (RF) and to compare their performance in terms of the accuracy, precision, F1 score, support, and AUC. The experimental results demonstrated that the MLP achieved a high prediction accuracy of 99.4%, followed by random forest (96.4%) and K-NN (76.3%). The diagnosis rates of the MLP, random forest and K-NN were 99.9%, 99.6%, and 73%, respectively. The study provides a clear idea of the accomplishments of classification algorithms in terms of their prediction ability, which can aid healthcare professionals in diagnosing chronic breast cancer efficiently

A Comparative Performance Assessment of Evolutionary Fractional Order PID Controllers for Magnetic Levitation Plant with Time Delay

322-327Fractional Order controllers have been extensively applied to various fields of science and engineering, since several decades, because of the ability to control more parameters and consequent better control. However, to achieve this advantage, proper tuning of the associated parameters plays an important role. To achieve this objective, this paper employs a multi-agent symbiotic organisms search (MASOS) algorithm for appropriately tuning the parameters of fractional order proportional-integral-derivative (FOPID) controller for stabilizing a magnetic levitation plant (MLP) with time delay. Three different FOPID controllers have been precisely tuned and their performance has been evaluated and compared in this paper. The results demonstrate that the I-PD configuration produces the best performance in terms of time domain as well as frequency domain specifications, when compared with the other configurations

A Comparative Performance Assessment of Evolutionary Fractional Order PID Controllers for Magnetic Levitation Plant with Time Delay

Fractional Order controllers have been extensively applied to various fields of science and engineering, since several decades, because of the ability to control more parameters and consequent better control. However, to achieve this advantage, proper tuning of the associated parameters plays an important role. To achieve this objective, this paper employs a multi-agent symbiotic organisms search (MASOS) algorithm for appropriately tuning the parameters of fractional order proportional-integral-derivative (FOPID) controller for stabilizing a magnetic levitation plant (MLP) with time delay. Three different FOPID controllers have been precisely tuned and their performance has been evaluated and compared in this paper. The results demonstrate that the I-PD configuration produces the best performance in terms of time domain as well as frequency domain specifications, when compared with the other configurations

A Hybridized Forecasting Model for Metal Commodity Prices: An Empirical Model Evaluation

Appropriate decision on perfect commodity prediction under market’s constant fluctuations intensifies the need for efficient methods. The main objective of this study is to apply different optimization algorithms such as conventional particle swarm optimization (PSO), bat algorithm (BAT) and ant colony optimization (ACO) algorithms on back propagation neural network (BPNN) to enhance the accuracy of prediction and minimize the error. In this paper, a model has been proposed for volatility forecasting using PSO algorithm to train the BPNN and predict the commodities’ closing price. The proposed PSO-BPNN model is considered as best forecasting model compared to BPNN, BAT-BPNN and ACO-BPNN. The experiment has been carried out upon five publicly available metal datasets (gold, silver, lead, aluminium, and copper) to forecast the price return volatility of those five metals challenging the effectiveness. Here, three technical indicators and four filters, such as; moving average convergence/divergence (MACD), williams %R (W%), bollinger (B), least mean squares (LMS), finite impulse response (FIR), Kalmanand recursive least square (RLS) have been applied for providing an additional degree of freedom to train and test the classifiers. From the experimental result analysis it has been found that the proposed PSO-BPNN produces promising output while comparing with BPNN, BAT-BPNN and ACO-BPNN