Florentin Smarandache duce neutrosofia şi în Taiwan!

Vâlceano-americanul Florentin Smarandache se va afla în perioada 8-10 noiembrie 2011 într-un loc foarte exotic pentru români: oraşul Kaohsiung (1,5 milioane de locuitori), al doilea ca mărime din Taiwan, unde va prezida o secţiune la A VII-a Conferinţă Internaţional de Calcul Granular (notată prescurtat GrC 2011)

Optimized greenery configuration to mitigate urban heat: A decade systematic review

Urban vegetation is a nature-based solution for cooling cities. Under global warming and urban population growth, it is essential to optimize urban vegetation configuration in the urban area to bring maximum cooling benefit. This paper reviews 85 optimized urban vegetation configuration studies published from 2010 to 2020 to provide an insight into the most effective vegetation configuration for urban heat mitigation. Patterns and preferences in methods and the optimized greenery configurations are comprehensively analyzed. The results indicate that size, quantity, and layout of urban green space and the physiological characteristics and spatial arrangement of urban vegetation significantly influence their cooling effect. Additionally, two other research gaps were identified. First, more research needs to be done in southern hemisphere cities experiencing rapid urbanization and severe impacts of extreme weather. Second, a comprehensive method for quantifying interactions and cumulative effects of natural and artificial factors in the urban environment is required. Future study needs a holistic understanding of the interactive effects of vegetation spatial distribution on urban environment and climate for a more accurate analysis of optimal cooling greening layouts in large urban areas at multi-scales

Reducing Side Effects of Hiding Sensitive Itemsets in Privacy Preserving Data Mining

Data mining is traditionally adopted to retrieve and analyze knowledge from large amounts of data. Private or confidential data may be sanitized or suppressed before it is shared or published in public. Privacy preserving data mining (PPDM) has thus become an important issue in recent years. The most general way of PPDM is to sanitize the database to hide the sensitive information. In this paper, a novel hiding-missing-artificial utility (HMAU) algorithm is proposed to hide sensitive itemsets through transaction deletion. The transaction with the maximal ratio of sensitive to nonsensitive one is thus selected to be entirely deleted. Three side effects of hiding failures, missing itemsets, and artificial itemsets are considered to evaluate whether the transactions are required to be deleted for hiding sensitive itemsets. Three weights are also assigned as the importance to three factors, which can be set according to the requirement of users. Experiments are then conducted to show the performance of the proposed algorithm in execution time, number of deleted transactions, and number of side effects

INTRODUCTION TO NEUTROSOPHIC MEASURE, NEUTROSOPHIC INTEGRAL, AND NEUTROSOPHIC PROBABILITY

Neutrosophic Science means development and applications of neutrosophic logic/set/measure/integral/probability etc. and their applications in any field

Archipaper : dibujos desde el plano = blueprint drawings : [exposición]

Exposición de dibujos digitales, acuarelas, collages o papel sobre grafio para crear construcciones que reflexionan sobre su uso y la integración con el entorno y el ciudadano

An Intelligent Model for Pairs Trading Using Genetic Algorithms

Pairs trading is an important and challenging research area in computational finance, in which pairs of stocks are bought and sold in pair combinations for arbitrage opportunities. Traditional methods that solve this set of problems mostly rely on statistical methods such as regression. In contrast to the statistical approaches, recent advances in computational intelligence (CI) are leading to promising opportunities for solving problems in the financial applications more effectively. In this paper, we present a novel methodology for pairs trading using genetic algorithms (GA). Our results showed that the GA-based models are able to significantly outperform the benchmark and our proposed method is capable of generating robust models to tackle the dynamic characteristics in the financial application studied. Based upon the promising results obtained, we expect this GA-based method to advance the research in computational intelligence for finance and provide an effective solution to pairs trading for investment in practice

An Evolutionary Method for Financial Forecasting in Microscopic High-Speed Trading Environment

The advancement of information technology in financial applications nowadays have led to fast market-driven events that prompt flash decision-making and actions issued by computer algorithms. As a result, today’s markets experience intense activity in the highly dynamic environment where trading systems respond to others at a much faster pace than before. This new breed of technology involves the implementation of high-speed trading strategies which generate significant portion of activity in the financial markets and present researchers with a wealth of information not available in traditional low-speed trading environments. In this study, we aim at developing feasible computational intelligence methodologies, particularly genetic algorithms (GA), to shed light on high-speed trading research using price data of stocks on the microscopic level. Our empirical results show that the proposed GA-based system is able to improve the accuracy of the prediction significantly for price movement, and we expect this GA-based methodology to advance the current state of research for high-speed trading and other relevant financial applications

Binary Classification of Multigranulation Searching Algorithm Based on Probabilistic Decision

Multigranulation computing, which adequately embodies the model of human intelligence in process of solving complex problems, is aimed at decomposing the complex problem into many subproblems in different granularity spaces, and then the subproblems will be solved and synthesized for obtaining the solution of original problem. In this paper, an efficient binary classification of multigranulation searching algorithm which has optimal-mathematical expectation of classification times for classifying the objects of the whole domain is established. And it can solve the binary classification problems based on both multigranulation computing mechanism and probability statistic principle, such as the blood analysis case. Given the binary classifier, the negative sample ratio, and the total number of objects in domain, this model can search the minimum mathematical expectation of classification times and the optimal classification granularity spaces for mining all the negative samples. And the experimental results demonstrate that, with the granules divided into many subgranules, the efficiency of the proposed method gradually increases and tends to be stable. In addition, the complexity for solving problem is extremely reduced