Predicting trends in stock market prices has been an area of interest for researchers many years due to its complex and dynamic nature. Intrinsic volatility across the globe makes task prediction challenging. Forecasting diffusion modeling, although effective can't be panacea diverse range problems encountered prediction, short-term or otherwise. Market risk, strongly correlated with forecasting errors, needs minimized ensure minimal risk investment. The authors propose minimize error by...

Enterprises are enhancing investments in cloud services setting up data centers to meet growing demand. A typical investment is of the order millions dollars, infrastructure and recurring cost included. This paper proposes an algorithmic/analytical approach address issues optimal utilization resources towards a feasible profitable model. The economic sustainability such model accomplished via Cobb-Douglas production function. seeks answer questions on maximal revenue given set budgetary...

Inspired by chaotic firing of neurons in the brain, we propose ChaosNet-a novel chaos based artificial neural network architecture for classification tasks. ChaosNet is built using layers neurons, each which a 1D map known as Generalized Luröth Series (GLS) that has been shown earlier works to possess very useful properties compression, cryptography, and computing XOR other logical operations. In this work, design learning algorithm on exploits topological transitivity property GLS neurons....

Breast cancer is a deadly disease with high mortality rate among PAN cancers. The advancements in biomedical information retrieval techniques have been beneficial developing early prognosis and diagnosis systems for patients. These provide the oncologist plenty of from several modalities to make correct feasible treatment plan breast patients protect them unnecessary therapies their toxic side effects. patient's related can be collected using various like clinical, copy number variation,...

This paper introduces AdaSwarm, a novel gradient-free optimizer which has similar or even better performance than the Adam adopted in neural networks. In order to support our proposed Exponentially weighted Momentum Particle Swarm Optimizer (EMPSO), is proposed. The ability of AdaSwarm tackle optimization problems attributed its capability perform good gradient approximations. We show that, any function, differentiable not, can be approximated by using parameters EMPSO. technique simulate GD...

The advancement of medical research in the field cancer prognosis and diagnosis using various modalities has put oncologists under tremendous stress. complexity heterogeneity involved multiple their significantly varied clinical outcomes make it difficult to analyze disease provide correct treatment. Breast is major concern among all cancers worldwide, specifically for females. To help patients, breast survival estimation been proposed. It ranges from complex deep neural networks simple...

Abstract In Multi-Channel Multi-Radio Wireless Mesh Networks (MCMR-WMN), finding the optimal routing by satisfying Quality of Service (QoS) constraints is an ambitious task. Multiple paths are available from source node to gateway for reliability, and sometimes it necessary deal with failures link in WMN. A major challenge a MCMR-WMN QoS satisfied interference free path redundant paths, order transmit packets through this path. The Particle Swarm Optimization (PSO) optimization technique...

Low Birth weight (LBW) acts as an indicator of sickness in newborn babies. LBW is closely associated with infant mortality well various health outcomes later life. Various studies show strong correlation between maternal during pregnancy and the child's birth weight. This manuscript exploits machine learning techniques to gain useful information from indicators pregnant women for early detection potential cases. The forecasting problem has been reformulated a classification NOT-LBW classes...

In this paper, an attempt is made to apply few conventional methods of EEG feature extraction and classification compare their performance for a specific task. Two different are implemented classify the mental tasks signals from known dataset. For purpose, auto regression model wavelet transform used as extraction. A combined vector also evaluated on accuracy. The features extracted these applied k-nearest neighbour support machine classifiers separately. Each subject has ten trials each...

In recent years, multi-channel multi-radio wireless mesh networks are considered a reliable and cost effective way for internet access in wide area. A major research challenge this network is, selecting least interference channel from the available channels, efficiently assigning radio to selected channel, routing packets through path. Many algorithms methods have been developed assignment maximize throughput using orthogonal channels. Recent test-bed experiments proved that POC (Partially...