Performance Enhancement of Adaptive Neural Networks Based on Learning Rate

Deep learning is the process of determining parameters that reduce the cost function derived from the dataset.The optimization in neural networks at the time is known as the optimal parameters.To solve optimization,it initialize the parameters during the optimization process.There should be no variation in the cost function parameters at the global minimum.The momentum technique is a parameters optimization approach;however,it has difficulties stopping the parameter when the cost function value fulfills the global minimum(non-stop problem).Moreover,existing approaches use techniques;the learning rate is reduced during the iteration period.These techniques are monotonically reducing at a steady rate over time;our goal is to make the learning rate parameters.We present a method for determining the best parameters that adjust the learning rate in response to the cost function value.As a result,after the cost function has been optimized,the process of the rate Schedule is complete.This approach is shown to ensure convergence to the optimal parameters.This indicates that our strategy minimizes the cost function(or effective learning).The momentum approach is used in the proposed method.To solve the Momentum approach non-stop problem,we use the cost function of the parameter in our proposed method.As a result,this learning technique reduces the quantity of the parameter due to the impact of the cost function parameter.To verify that the learning works to test the strategy,we employed proof of convergence and empirical tests using current methods and the results are obtained using Python.

Design of ANN Based Non-Linear Network Using Interconnection of Parallel Processor

Suspicious mass traffic constantly evolves,making network behaviour tracing and structure more complex.Neural networks yield promising results by considering a sufficient number of processing elements with strong interconnections between them.They offer efficient computational Hopfield neural networks models and optimization constraints used by undergoing a good amount of parallelism to yield optimal results.Artificial neural network(ANN)offers optimal solutions in classifying and clustering the various reels of data,and the results obtained purely depend on identifying a problem.In this research work,the design of optimized applications is presented in an organized manner.In addition,this research work examines theoretical approaches to achieving optimized results using ANN.It mainly focuses on designing rules.The optimizing design approach of neural networks analyzes the internal process of the neural networks.Practices in developing the network are based on the interconnections among the hidden nodes and their learning parameters.The methodology is proven best for nonlinear resource allocation problems with a suitable design and complex issues.The ANN proposed here considers more or less 46k nodes hidden inside 49 million connections employed on full-fledged parallel processors.The proposed ANN offered optimal results in real-world application problems,and the results were obtained using MATLAB.

CFOA Based Band Pass and Band Stop Ladder Filter—A New Configuration

A new technique for the conversion of ladder based filter into CFOA based filter has been proposed. The technique uses signal flow graph and converts the existing LC ladder based filter into band pass & band stop configurations. The design of band pass and band stop filter has been realized using the proposed technique. The proposed configuration is implemented using CFOA as an active device and all the capacitors are grounded. CFOA based circuits have greater linearity, high dynamic rate, high slew rate and high signal bandwidth. Simulation has been carried out using simulation software P Spice (v10.1). The simulation results have been demonstrated and discussed.

A Novel Hybrid Clustering Based Transmission Protocol for Wireless Body Area Networks

Wireless sensor networks are a collection of intelligent sensor devices that are connected to one another and have the capability to exchange information packets amongst themselves.In recent years,this field of research has become increasingly popular due to the host of useful applications it can potentially serve.A deep analysis of the concepts associated with this domain reveals that the two main problems that are to be tackled here are throughput enhancement and network security improvement.The present article takes on one of these two issues namely the throughput enhancement.For the purpose of improving network productivity,a hybrid clustering based packet propagation protocol has been proposed.The protocol makes use of not only clustering mechanisms of machine learning but also utilizes the traditional forwarding function approach to arrive at an optimum model.The result of the simulation is a novel transmission protocol which significantly enhances network productivity and increases throughput value.

Exploiting DNA repair pathways for tumor sensitization,mitigation of resistance,and normal tissue protection in radiotherapy

More than half of cancer patients are treated with radiotherapy,which kills tumor cells by directly and indirectly inducing DNA damage,including cytotoxic DNA double-strand breaks(DSBs).Tumor cells respond to these threats by activating a complex signaling network termed the DNA damage response(DDR).The DDR arrests the cell cycle,upregulates DNA repair,and triggers apoptosis when damage is excessive.The DDR signaling and DNA repair pathways are fertile terrain for therapeutic intervention.This review highlights strategies to improve therapeutic gain by targeting DDR and DNA repair pathways to radiosensitize tumor cells,overcome intrinsic and acquired tumor radioresistance,and protect normal tissue.Many biological and environmental factors determine tumor and normal cell responses to ionizing radiation and genotoxic chemotherapeutics.These include cell type and cell cycle phase distribution;tissue/tumor microenvironment and oxygen levels;DNA damage load and quality;DNA repair capacity;and susceptibility to apoptosis or other active or passive cell death pathways.We provide an overview of radiobiological parameters associated with X-ray,proton,and carbon ion radiotherapy;DNA repair and DNA damage signaling pathways;and other factors that regulate tumor and normal cell responses to radiation.We then focus on recent studies exploiting DSB repair pathways to enhance radiotherapy therapeutic gain.