Automated Neural-ware System for Stock Market Prediction

Abstract-This article uses neural networks in forecasting stock market prices. With their ability to discover patterns in nonlinear and chaotic systems, neural networks offer the ability to predict market directions more accuratcly than current techniques such as technical analysis, fundamental analysis, and regression comparcd with neural network performance. Proposed intclligcnt stock market prediction system is based on the Quantitative and Qualitativc factors. Three feedforward neural models can be used to analyze these factors. Input data to the neural network proposed are quantitative factors. Input data to the neural network proposcd for qualitative factors can be factors related to the political cffcct considered. Third neural network consists of decision integration in which input data will be the outputs of above-mentioned neural networks. This facilitates to make right decision whether stock market is influenced by quantitative or qualitative factors. I N T R O D U C T I O N A . Introduction f u r Neural-Ware System The Stock market is always one of the most popular investments due to its high profit. However, higher profit tends to higher risk too. Thus, various research works intended to develop models in order to provide the investors an optimum prediction. Among the traditional research, time series analysis techniques and multiple regression models were used. Recently due to the computational speed, Artificial NeuraI Networks (ANN) has been also used in this area. Through various models have been proposed, they only concentrated quantitative factors. However, in developing countries, like Sri Lanka, sometime non-quantitative factors are more important than qualitative factors. Therefore, proposed intelligent stock market prediction system intends the inclusion of both factors. Therefore, proposed intelligent stock market prediction system intends the inclusion of both factors such that right decision Intelligent stock market prediction is based on the systems integration. B. lnrelligent Stock Mcrrkst Prediction Related factors collection for the stock market environment. Factors Collection In order to make right decision, collecting the effective information regarding the predicted object is crucial

Nanorobot: Modelling And Simulation.

This research addresses the state of the art in nanorobot design and simulation focusing on the leukemia disease as well as ongoing applications on addressing the challenges posed by cancer treatment, especially chemotherapy

The application of ANFIS prediction models for thermal error compensation on CNC machine tools

Thermal errors can have significant effects on CNC machine tool accuracy. The errors come from thermal deformations of the machine elements caused by heat sources within the machine structure or from ambient temperature change. The effect of temperature can be reduced by error avoidance or numerical compensation. The performance of a thermal error compensation system essentially depends upon the accuracy and robustness of the thermal error model and its input measurements. This paper first reviews different methods of designing thermal error models, before concentrating on employing an adaptive neuro fuzzy inference system (ANFIS) to design two thermal prediction models: ANFIS by dividing the data space into rectangular sub-spaces (ANFIS-Grid model) and ANFIS by using the fuzzy c-means clustering method (ANFIS-FCM model). Grey system theory is used to obtain the influence ranking of all possible temperature sensors on the thermal response of the machine structure. All the influence weightings of the thermal sensors are clustered into groups using the fuzzy c-means (FCM) clustering method, the groups then being further reduced by correlation analysis. A study of a small CNC milling machine is used to provide training data for the proposed models and then to provide independent testing data sets. The results of the study show that the ANFIS-FCM model is superior in terms of the accuracy of its predictive ability with the benefit of fewer rules. The residual value of the proposed model is smaller than ±4 μm. This combined methodology can provide improved accuracy and robustness of a thermal error compensation system

Development of an Area Scan Step Length Measuring System Using a Polynomial Estimate of the Heel Cloud Point

Due to impaired mobility caused by aging, it is very important to employ early detection and monitoring of gait parameters to prevent the inevitable huge amount of medical cost at a later age. For gait training and potential tele-monitoring application outside clinical settings, low-cost yet highly reliable gait analysis systems are needed. This research proposes using a single LiDAR system to perform automatic gait analysis with polynomial fitting. The experimental setup for this study consists of two different walking speeds, fast walk and normal walk, along a 5-m straight line. There were ten test subjects (mean age 28, SD 5.2) who voluntarily participated in the study. We performed polynomial fitting to estimate the step length from the heel projection cloud point laser data as the subject walks forwards and compared the values with the visual inspection method. The results showed that the visual inspection method is accurate up to 6 cm while the polynomial method achieves 8 cm in the worst case (fast walking). With the accuracy difference estimated to be at most 2 cm, the polynomial method provides reliability of heel location estimation as compared with the observational gait analysis. The proposed method in this study presents an improvement accuracy of 4% as opposed to the proposed dual-laser range sensor method that reported 57.87 cm ± 10.48, an error of 10%. Meanwhile, our proposed method reported ±0.0633 m, a 6% error for normal walking