Evaluation of the robusticity of mutual fund performance in Ghana using Enhanced Resilient Backpropagation Neural Network(ERBPNN)and Fast Adaptive Neural Network Classifier(FANNC)

Mutual fund investment continues to play a very important role in the world financial markets especially in developing economies where the capital market is not very matured and tolerant of small scale investors.The total mutual fund asset globally as at the end of 2016 was in excess of$40.4 trillion.Despite its success there are uncertainties as to whether mutual funds in Ghana obtain optimal performance relative to their counterparts in United States,Luxembourg,Ireland,France,Australia,United Kingdom,Japan,China and Brazil.We contribute to the extant literature on mutual fund performance evaluation using a collection of more sophisticated econometric models.We selected six continuous historical years that is 2010-2011,2012-2013 and 2014-2015 to construct a mutual fund performance evaluation model utilizing the fast adaptive neural network classifier(FANNC),and to compare our results with those from an enhanced resilient back propagation neural networks(ERBPNN)model.Our FANNC model outperformed the existing models in terms of processing time and error rate.This makes it ideal for financial application that involves large volume of data and routine updates.

APPLICATIONS OF FAST SIMULATED ANNEALING IN NEURAL NETWORKS

Fast simulated annealing is implemented into the learning process of neural network to replace the traditional back-propagation algorithm. The new procedure exhibits performance fast in learning and accurate in prediction compared to the traditional neural networks. Two numerical data sets were used to illustrate its use in chemistry.

An optimized Parkinson’s disorder identification through evolutionary fast learning network

Purpose-Parkinson’s disease(PD)is a well-known complex neurodegenerative disease.Typically,its identification is based on motor disorders,while the computer estimation of its main symptoms with computational machine learning(ML)has a high exposure which is supported by researches conducted.Nevertheless,ML approaches required first to refine their parameters and then to work with the best model generated.This process often requires an expert user to oversee the performance of the algorithm.Therefore,an attention is required towards new approaches for better forecasting accuracy.Design/methodology/approach-To provide an available identification model for Parkinson disease as an auxiliary function for clinicians,the authors suggest a new evolutionary classification model.The core of the prediction model is a fast learning network(FLN)optimized by a genetic algorithm(GA).To get a better subset of features and parameters,a new coding architecture is introduced to improve GA for obtaining an optimal FLN model.Findings-The proposed model is intensively evaluated through a series of experiments based on Speech and HandPD benchmark datasets.The very popular wrappers induction models such as support vector machine(SVM),K-nearest neighbors(KNN)have been tested in the same condition.The results support that the proposed model can achieve the best performances in terms of accuracy and g-mean.Originality/value-A novel efficient PD detectionmodel is proposed,which is called A-W-FLN.The A-W-FLN utilizes FLN as the base classifier;in order to take its higher generalization ability,and identification capability is alsoembedded to discover themost suitable featuremodel in the detection process.Moreover,the proposedmethod automatically optimizes the FLN’s architecture to a smaller number of hidden nodes and solid connecting weights.This helps the network to train on complex PD datasets with non-linear features and yields superior result.

Pre-scan based fast handoff scheme for enterprise IEEE 802.11 networks

In IEEE 802.11 networks, many access points（APs） are required to cover a large area due to the limited coverage range of APs, and frequent handoffs may occur while a station（STA） is moving in an area covered by several APs. However, traditional handoff mechanisms employed at STAs introduce a few hundred milliseconds delay, which is far longer than what can be tolerated by some multimedia streams such as voice over Internet protocol（Vo IP）, it is a challenging issue for supporting seamless handoff service in IEEE 802.11 networks. In this paper, we propose a pre-scan based fast handoff scheme within an IEEE 802.11 enterprise wireless local area network（EWLAN） environment. The proposed scheme can help STA obtain the best alternative AP in advance after the pre-scan process, and when the handoff is actually triggered, STA can perform the authentication and reassociation process toward the alternative AP directly. Furthermore, we adopt Kalman filter to minimize the fluctuation of received signal strength（RSS）, thus reducing the unnecessary pre-scan process and handoffs. We performed simulations to evaluate performance, and the simulation results show that the proposed scheme can effectively reduce the handoff delay.

Fast Passenger Tracks Network

China’s fast passenger tracks network consists of four parts:express rail- way with speeds between 300km/h and 350 kin/h,passenger rail lines with speeds between 200 km/h and 250 km/h,intercity high-speed railways that

Analytical review and study on object detection techniques in the image

Object detection is the most fundamental but challenging issues in the field of computer vision.Object detection identifies the presence of various individual objects in an image.Great success is attained for object detection/recognition problems in the controlled environment,but still,the problem remains unsolved in the uncontrolled places,particularly,when the objects are placed in arbitrary poses in an occluded and cluttered environment.In the last few years,a lots of efforts are made by researchers to resolve this issue,because of its wide range of applications in computer vision tasks,like content-enabled image retrieval,event or activity recognition,scene understanding,and so on.This review provides a detailed survey of 50 research papers presenting the object detection techniques,like machine learning-based techniques,gradient-based techniques,Fast Region-based Convolutional Neural Network(Fast R-CNN)detector,and the foreground-based techniques.Here,the machine learning-based approaches are classified into deep learning-based approaches,random forest,Support Vector Machine(SVM),and so on.Moreover,the challenges faced by the existing techniques are explained in the gaps and issues section.The analysis based on the classification,toolset,datasets utilized,published year,and the performance metrics are discussed.The future dimension of the research is based on the gaps and issues identified from the existing research works.