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The purpose of this paper is to propose an approach for predicting the movements of Chinese medicinal material price indexes using news based on text mining.

A research framework and three major methods, namely, domain dictionary construction, market convergence time calculation and dimensionality reduction integrating semantic analysis, are proposed for the approach. The proposed approach is applied in practice for predicting the price index movements of the top ten Chinese medicinal materials that receive the greatest media attention.

A set of experiments performed herein show that a predictive relationship exists between the news and the commodity market and that each of the three major methods improves the forecasting performance.

Because the field of Chinese medicinal materials lacks a corpus that can be used for sentiment analysis, the accuracy of a trained automatic sentiment classifier is lower than obtained by a manual method, which can cause the calculated convergence result to be inaccurate, thus affecting the final prediction model. The manual method of having people label news decreases the proposed method’s aspects of being intelligent and automatic.

Using the method proposed herein to predict the trends in Chinese medicinal materials prices helps farmers arrange a reasonable planting plan to pursue their best interests.

The method proposed herein to predict the trends in the prices of Chinese medicinal materials is conducive to the government arranging planned drug availabilities in order to avoid disasters in which herbs are looted.

The produced prediction result is meaningful in supporting farmers and investors to make better decisions in growing and trading Chinese medicinal material, which leads to financial returns on investments and the avoidance of severe losses.

Routing in ad hoc networks faces significant challenges due to node mobility and dynamic network topology. In this work we propose the use of mobility prediction to reduce the search space required for route discovery. A method of mobility prediction making use of a sectorized cluster structure is described with the proposal of the Prediction based Location Aided Routing (P‐LAR) protocol. Simulation study and analytical results of P‐LAR find it to offer considerable saving in the amount of routing traffic generated during the route discovery phase.

Forthcoming requirements in MiFID and RegNMS mean that buy‐side and sell‐side firms need to find ways of showing regulators that they are sifting through their trading volumes in a justifiable, methodical manner looking for anomalous trades and investigating them, in order to prove “best execution”. The objective was to see if a SVM/DAPR approach could help identify equity trade anomalies for compliance investigation.

A major stock exchange, a computer systems supplier, four brokers and a statistical firm undertook a cooperative research project to determine whether automated statistical processing of trade and order information could provide a tighter focus on the most likely trades for best execution compliance investigation.

The support vector machine approach worked on UK equities and has significant potential for other markets such as foreign exchange, fixed income and commodities.

The research has implications for risk professionals as a generic approach to trading anomaly detection. The prototype compliance workstation can be trialed.

Automated anomaly detection could transform the role of compliance and risk in financial institutions.

The presented work contributes to research in the field of advanced man-machine interaction and to research in the field of formalisation and verification of complex systems. This work was motivated by the need to provide a detailed and well understandable formal description of embedded brain reading (eBR). The paper aims to discuss these issues.

The paper first introduces eBR and points out its main features. Next, a general model for eBR is developed to describe the overall architecture, integral parts and dependencies between those parts. The model is developed and presented in a formal structured form that allows for application of optimisation as well as verification techniques.

The paper demonstrates using implementations that the application of the formal model allows to check for completeness and correctness to detect errors in implementation, which were invisible without formalising eBR. In summary, the presented work contributes a formal model for a complex system and shows that such a formal model can improve the overall system's functionality.

For future work, the results support the application of formal modelling and verification techniques at the system level and the development of methods to prove for correctness and completeness of complex systems during their development.

The paper describes for the first time eBR and presents a formal model for it. It illustrates how an error-prone approach like BR can be applied safely by embedding it into the control of a real system and by applying mechanisms that control for its correct function.

Sina Weibo and Twitter are the top microblogging platforms with billions of users. Accordingly, these two platforms could be used to understand the public mood. In this paper, the authors want to discuss how to generate and compare the public mood on Sina Weibo and Twitter. The predictive power of the public mood toward commodity markets is discussed, and the authors want to solve the problem that how to choose between Sina Weibo and Twitter when predicting crude oil prices.

An enhanced latent Dirichlet allocation model considering term weights is implemented to generate topics from Sina Weibo and Twitter. Granger causality test and a long short-term memory neural network model are used to demonstrate that the public mood on Sina Weibo and Twitter is correlated with commodity contracts.

By comparing the topics and the public mood on Sina Weibo and Twitter, the authors find significant differences in user behavior on these two websites. Besides, the authors demonstrate that public mood on Sina Weibo and Twitter is correlated with crude oil contract prices in Shanghai International Energy Exchange and New York Mercantile Exchange, respectively.

Two sentiment analysis methods for Chinese (Sina Weibo) and English (Twitter) posts are introduced, which can be reused for other semantic analysis tasks. Besides, the authors present a prediction model for the practical participants in the commodity markets and introduce a method to choose between Sina Weibo and Twitter for certain prediction tasks.

In this study, the central objective is to foresee stock market signals with the use of a proper structure to achieve the highest accuracy possible. For this purpose, three hybrid models have been developed for the stock markets which are a combination of support vector machine (SVM) with meta-heuristic algorithms of particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA).All the analyses are technical and are based on the Japanese candlestick model.

Further as per the results achieved, the most suitable algorithm is chosen to anticipate sell and buy signals. Moreover, the authors have compared the results of the designed model validations in this study with basic models in three articles conducted in the past years. Therefore, SVM is examined by PSO. It is used as a classification agent to search the problem-solving space precisely and at a faster pace. With regards to the second model, SVM and ICA are tested to stock market timing, in a way that ICA is used as an optimization agent for the SVM parameters. At last, in the third model, SVM and GA are studied, where GA acts as an optimizer and feature selection agent.

As per the results, it is observed that all new models can predict accurately for only 6 days; however, in comparison with the confusion matrix results, it is observed that the SVM-GA and SVM-ICA models have correctly predicted more sell signals, and the SCM-PSO model has correctly predicted more buy signals. However, SVM-ICA has shown better performance than other models considering executing the implemented models.

In this study, the data for stock market of the years 2013–2021 were analyzed; the long length of timeframe makes the input data analysis challenging as they must be moderated with respect to the conditions where they have been changed.

In this study, two methods have been developed in a candlestick model; they are raw-based and signal-based approaches in which the hit rate is determined by the percentage of correct evaluations of the stock market for a 16-day period.

Borsa Istanbul 100 Index, known as BIST100, is the main indicator to measure the performance of the 100 highest stocks publicly traded in Borsa Istanbul concerning market and trading volume. BIST 100 index prediction is a popular research domain for its complex data structure caused by stock price, commodity, interest rate and exchange rate effects. The study proposed hybrid models using both Genetic, Particle Swarm Optimization, Harmony Search and Greedy algorithms from metaheuristic algorithms approach for dimension reduction, and MARS for prediction.

This paper aims to model in the simplest way through metaheuristic algorithms hybridized with the MARS model the effects of stock, commodity, interest and exchange rate variables on BIST 100 during the Covid-19 pandemic period (in the process of closing) between January 2020 and June 2021.

The most suitable hybrid model was chosen as PSO & MARS by calculating the RMSE, MSE, GCV, MAE, MAD, MAPE and R2 measurements of training, test and overall dataset to check every model's efficiency. Empirical results demonstrated that the proposed PSO & MARS hybrid modeling procedure gave results both as good as the MARS model and a simpler and non-complex model structure.

Using metaheuristic algorithms as a supporting tool for variable selection can help to identify important independent variables and contribute to the establishment of more non-complex models.ing, test and overall dataset to check every model's efficiency.

New regulatory initiatives, principally MiFID and RegNMS, challenge wholesale financial firms to prove that they can provide best execution for their clients. This article aims to outline the background to the problem and suggest that current research into SVM/DAPR applications may provide a practical approach.

The article presents a desk review of current issues in “best execution” based on work with European brokers and others, followed by initial, promising trial of SVM/DAPR.

The article finds that brokers need automated tools, e.g. “sifting engines” that help them to focus compliance efforts on anomalous trades.

Although brokers appear to need assistance in identifying anomalous trades, whether they place significant effort in compliance depends on regulatory enforcement.

MiFID and RegNMS will require changes in current practice. SVM/DAPR approaches appear to be worth further investigation.

In this research, the main purpose is to use a suitable structure to predict the trading signals of the stock market with high accuracy. For this purpose, two models for the analysis of technical adaptation were used in this study.

It can be seen that support vector machine (SVM) is used with particle swarm optimization (PSO) where PSO is used as a fast and accurate classification to search the problem-solving space and finally the results are compared with the neural network performance.

Based on the result, the authors can say that both new models are trustworthy in 6 days, however, SVM-PSO is better than basic research. The hit rate of SVM-PSO is 77.5%, but the hit rate of neural networks (basic research) is 74.2.

In this research, two approaches (raw-based and signal-based) have been developed to generate input data for the model: raw-based and signal-based. For comparison, the hit rate is considered the percentage of correct predictions for 16 days.

This study aims to explore the impact of spatial configuration on behavioral patterns of visitors in the ground floor of health-care spaces.

In this study, the Space Syntax analysis was used to combine visibility graph analysis and axial line analysis with empirical observation of visitors’ activities. Two types of observation methods on visitors were conducted to discover the behavioral patterns of individuals, respectively, named “gate counts” and “people following.”

The outcomes of this research revealed that the spatial arrangements of pathways, public areas, vertical circulations, entrance space, lobby, emergency department, reception desk and pharmacy have a significant influence on the way that visitors perceive the health-care environment.

The current research is limited to two aspects of effective wayfinding (configuration of health care and geometry). Future work can investigate the other potential factors coupled with the current factor as an integrated research for enhancing wayfinding and sustaining accessibility. Another limitation is that the observation results for this study had been conducted before the COVID-19 pandemic and future studies can compare these results with the current COVID-19 situation within health care environments.

A large amount of research has focused on the needs of populations in developed countries. This topic has not been investigated thoroughly by professionals in developing countries such as Iran. Accordingly, this study benefits environmental psychologists and architects by revealing the effective characteristics of legible spaces in health-care environments.