Grey relational grades and neural networks : empirical evidence on vice funds

This research examines time-series predictability of Vice Funds Indices through the Grey Relational Analysis (GRA), and also applies three types of Artificial Neural Networks (ANN) model, namely, Back- propagation Perception Network (BPN), Recurrent Neural Network (RNN), and Radial Basis Function Neural Network (RBFNN) to capture nonlinear tendencies of Vice Funds indices. The study finds that among the three ANN models, BPN has the best predicting power. When the data is separated into 10%, 33% and 50% testing data sets to test the proficiency of the available forecasting information in the time- series of the predictors, the predictive power of the BPN model again dominated the findings 60% of the time. Traders, investors and fund manager can rely on BPN predicting power with large or even small data set. Nevertheless, the result also suggests the predicting power of both RNN and RBFNN model with smaller data sets. Overall, it is suggested that traders and fund managers have stronger chance of achieving more accurate forecasting using the BPN model in Vice Funds indices. Findings of this research have policy implications in the creation of forecasting and investing strategies by examining models that minimize errors in predicting Vice Funds indices.Cette recherche examine la prévisibilité des séries chronologiques des indices Vice Funds par le biais de l'analyse relationnelle grise (GRA), et applique également trois types de modèle de réseaux de neurones artificiels (ANN), à savoir le réseau de perception à propagation arrière (BPN), le réseau de neurones récurrents (RNN) ) et le Radial Basis Function Neural Network (RBFNN) pour capter les tendances non linéaires des indices Vice Funds. L'étude révèle que parmi les trois modèles ANN, BPN a le meilleur pouvoir de prédiction. Lorsque les données sont séparées en 10%, 33% et 50% de jeux de données pour tester la compétence des informations de prévision disponibles dans la série chronologique des prédicteurs, le pouvoir prédictif du modèle BPN de nouveau domine les résultats 60% du temps. Les traders, les investisseurs et le gestionnaire de fonds peuvent compter sur la puissance de prédiction de BPN avec des ensembles de données volumineux ou même petits. Néanmoins, le résultat suggère également la puissance de prédiction des modèles RNN et RBFNN avec des ensembles de données plus petits. Dans l'ensemble, il est suggéré que les traders et les gestionnaires de fonds ont plus de chances d'obtenir des prévisions plus précises en utilisant le modèle BPN dans les indices Vice Funds. Les résultats de cette recherche ont des implications politiques dans la création de stratégies de prévision et d'investissement en examinant des modèles qui minimisent les erreurs de prédiction des indices Vice Funds

Generating Buy/Sell Signals for an Equity Share Using Machine Learning

This study proposes a novel model for predicting 5 days’ ahead share price direction of GARAN (Garanti Bankasi A.Ş.), an equity share that is the top traded stock in BIST100, Istanbul Stock Exchange -Turkey. The first model includes global macroeconomic indicators as well as local inputs whereas the second model is focused more on local inputs. The performances of the two models are tested using Support Vector Machines (SVM), Neural Network with Back-Propagation (BPN), and Decision Tree (DT) algorithms. Though BPN and SVM have previously been used to predict BIST100 Index movement, DT has not been utilized before with this purpose. Forecasting is carried out tested for a time span of about 6 months on a rolling horizon basis, that is, algorithms are re-run weekly with updated data to generate daily buy/sell signals for the next week. A simple trading strategy is implemented based on buy/sell signals to calculate the rate of return on investment during the testing period. The results illustrate that DT having 80% prediction accuracy outperforms BPN and SVM that achieve 60% accuracy. Consequently, DT achieves a higher rate of return

Iot Based Alzheimer’s Disease Diagnosis Model for Providing Security Using Light Weight Hybrid Cryptography

Security in the Internet of things (IoT) is a broad yet active research area that focuses on securing the sensitive data being circulated in the network. The data involved in the IoT network comes from various organizations, hospitals, etc., that require a higher range of security from attacks and breaches. The common solution for security attacks is using traditional cryptographic algorithms that can protect the content through encryption and decryption operations. The existing solutions are suffering from major drawbacks, including computational complexities, time and space complexities, slower encryption, etc. Therefore, to overcome such drawbacks, this paper introduces an efficient light weight cryptographic mechanism to secure the images of Alzheimer’s disease (AD) being transmitted in the network. The mechanism involves major stages such as edge detection, key generation, encryption, and decryption. In the case of edge detection, the edge maps are detected using the Prewitt edge detection technique. Then the hybrid elliptic curve cryptography (HECC) algorithm is proposed to encrypt and secure the images being transmitted in the network. For encryption, the HECC algorithm combines blowfish with the elliptic curve algorithm to attain a higher range of security. Another significant advantage of the proposed method is selecting the ideal private key, which is achieved using the enhanced seagull optimization (ESO) algorithm. The proposed work has been tested in the Python tool, and the performance is evaluated with the Alzheimer’s dataset, and the outcomes proved its efficacy over the compared methods

A Predictive Analysis of the Indian FMCG Sector using Time Series Decomposition - Based Approach

Abstract. Stock price movements being random in its nature, prediction of stock prices using time series analysis presents a very difficult and challenging problem to the research community. However, over the last decade, due to rapid development and evolution of sophisticated algorithms for complex statistical analysis of large volume of time series data, and availability of high-performance hardware and parallel computing architecture, it has become possible to efficiently process and effectively analyze voluminous and highly diverse stock market time series data effectively, in real-time. Robust predictive models are being built for accurate forecasting of values of highly random variables such as stock price movements. This paper has presented a highly reliable and accurate forecasting framework for predicting the time series index values of the fast moving consumer goods (FMCG) sector in India. A time series decomposition approach is followed to understand the behavior of the FMCG sector time series for the period January 2010 till December 2016. Based on the structural analysis of the time series, six methods of forecast are designed. These methods are applied to predict the time series index values for the months of 2016. Extensive results are presented to demonstrate the effectiveness ofthe proposed decomposition approaches of time series and the efficiency of the six forecasting methods.Keywords. Time series decomposition, Trend, Seasonal, Random, Holt Winters Forecasting model, Auto Regression (AR), Moving Average (MA), Auto Regressive Integrated Moving Average (ARIMA), Partial Auto Correlation Function (PACF), Auto Correlation Function (ACF).JEL. G11, G14, G17, C63

Early and Late Stage Mechanisms for Vocalization Processing in the Human Auditory System

The human auditory system is able to rapidly process incoming acoustic information, actively filtering, categorizing, or suppressing different elements of the incoming acoustic stream. Vocalizations produced by other humans (conspecifics) likely represent the most ethologically-relevant sounds encountered by hearing individuals. Subtle acoustic characteristics of these vocalizations aid in determining the identity, emotional state, health, intent, etc. of the producer. The ability to assess vocalizations is likely subserved by a specialized network of structures and functional connections that are optimized for this stimulus class. Early elements of this network would show sensitivity to the most basic acoustic features of these sounds; later elements may show categorically-selective response patterns that represent high-level semantic organization of different classes of vocalizations. A combination of functional magnetic resonance imaging and electrophysiological studies were performed to investigate and describe some of the earlier and later stage mechanisms of conspecific vocalization processing in human auditory cortices. Using fMRI, cortical representations of harmonic signal content were found along the middle superior temporal gyri between primary auditory cortices along Heschl\u27s gyri and the superior temporal sulci, higher-order auditory regions. Additionally, electrophysiological findings also demonstrated a parametric response profile to harmonic signal content. Utilizing a novel class of vocalizations, human-mimicked versions of animal vocalizations, we demonstrated the presence of a left-lateralized cortical vocalization processing hierarchy to conspecific vocalizations, contrary to previous findings describing similar bilateral networks. This hierarchy originated near primary auditory cortices and was further supported by auditory evoked potential data that suggests differential temporal processing dynamics of conspecific human vocalizations versus those produced by other species. Taken together, these results suggest that there are auditory cortical networks that are highly optimized for processing utterances produced by the human vocal tract. Understanding the function and structure of these networks will be critical for advancing the development of novel communicative therapies and the design of future assistive hearing devices

An investigation into the use of neural networks for the prediction of the stock exchange of Thailand

Stock markets are affected by many interrelated factors such as economics and politics at both national and international levels. Predicting stock indices and determining the set of relevant factors for making accurate predictions are complicated tasks. Neural networks are one of the popular approaches used for research on stock market forecast. This study developed neural networks to predict the movement direction of the next trading day of the Stock Exchange of Thailand (SET) index. The SET has yet to be studied extensively and research focused on the SET will contribute to understanding its unique characteristics and will lead to identifying relevant information to assist investment in this stock market. Experiments were carried out to determine the best network architecture, training method, and input data to use for this task. With regards network architecture, feedforward networks with three layers were used - an input layer, a hidden layer and an output layer - and networks with different numbers of nodes in the hidden layers were tested and compared. With regards training method, neural networks were trained with back-propagation and with genetic algorithms. With regards input data, three set of inputs, namely internal indicators, external indicators and a combination of both were used. The internal indicators are based on calculations derived from the SET while the external indicators are deemed to be factors beyond the control of the Thailand such as the Down Jones Index

Selected Computing Research Papers Volume 2 June 2013

An Evaluation of Current Innovations for Solving Hard Disk Drive Vibration Problems (Isiaq Adeola) ........................................................................................................ 1 A Critical Evaluation of the Current User Interface Systems Used By the Blind and Visually Impaired (Amneet Ahluwalia) ................................................................................ 7 Current Research Aimed At Improving Bot Detection In Massive Multiplayer Online Games (Jamie Burnip) ........................................................................................................ 13 Evaluation Of Methods For Improving Network Security Against SIP Based DoS Attacks On VoIP Network Infrastructures (David Carney) ................................................ 21 An Evaluation of Current Database Encryption Security Research (Ohale Chidiebere) .... 29 A Critical Appreciation of Current SQL Injection Detection Methods (Lee David Glynn) .............................................................................................................. 37 An Analysis of Current Research into Music Piracy Prevention (Steven Hodgson) .......... 43 Real Time On-line Analytical Processing: Applicability Of Parallel Processing Techniques (Kushatha Kelebeng) ....................................................................................... 49 Evaluating Authentication And Authorisation Method Implementations To Create A More Secure System Within Cloud Computing Technologies (Josh Mallery) ................... 55 A Detailed Analysis Of Current Computing Research Aimed At Improving Facial Recognition Systems (Gary Adam Morrissey) ................................................................... 61 A Critical Analysis Of Current Research Into Stock Market Forecasting Using Artificial Neural Networks (Chris Olsen) ........................................................................... 69 Evaluation of User Authentication Schemes (Sukhdev Singh) .......................................... 77 An Evaluation of Biometric Security Methods for Use on Mobile Devices (Joe van de Bilt) .................................................................................................................. 8