Khin T. Lwin

A learning-guided multi-objective evolutionary algorithm for constrained portfolio optimization

Graphical abstractDisplay Omitted HighlightsA learning-guided multi-objective evolutionary algorithm for constrained portfolio optimization problem is proposed.Four practical constraints, cardinality, quantity, pre-assignment and round lot, are considered.Performance wise, the proposed algorithm is not only capable to deliver high-quality portfolios enriched by additional constraints but also able to efficiently solve a reasonable size of asset up to 1318. It significantly outperforms the existing state-of-the-art algorithms. Portfolio optimization involves the optimal assignment of limited capital to different available financial assets to achieve a reasonable trade-off between profit and risk objectives. In this paper, we studied the extended Markowitzs mean-variance portfolio optimization model. We considered the cardinality, quantity, pre-assignment and round lot constraints in the extended model. These four real-world constraints limit the number of assets in a portfolio, restrict the minimum and maximum proportions of assets held in the portfolio, require some specific assets to be included in the portfolio and require to invest the assets in units of a certain size respectively. An efficient learning-guided hybrid multi-objective evolutionary algorithm is proposed to solve the constrained portfolio optimization problem in the extended mean-variance framework. A learning-guided solution generation strategy is incorporated into the multi-objective optimization process to promote the efficient convergence by guiding the evolutionary search towards the promising regions of the search space. The proposed algorithm is compared against four existing state-of-the-art multi-objective evolutionary algorithms, namely Non-dominated Sorting Genetic Algorithm (NSGA-II), Strength Pareto Evolutionary Algorithm (SPEA-2), Pareto Envelope-based Selection Algorithm (PESA-II) and Pareto Archived Evolution Strategy (PAES). Computational results are reported for publicly available OR-library datasets from seven market indices involving up to 1318 assets. Experimental results on the constrained portfolio optimization problem demonstrate that the proposed algorithm significantly outperforms the four well-known multi-objective evolutionary algorithms with respect to the quality of obtained efficient frontier in the conducted experiments.

A hybrid algorithm for constrained portfolio selection problems

Since Markowitz’s seminal work on the mean-variance model in modern portfolio theory, many studies have been conducted on computational techniques and recently meta-heuristics for portfolio selection problems. In this work, we propose and investigate a new hybrid algorithm integrating the population based incremental learning and differential evolution algorithms for the portfolio selection problem. We consider the extended mean-variance model with practical trading constraints including the cardinality, floor and ceiling constraints. The proposed hybrid algorithm adopts a partially guided mutation and an elitist strategy to promote the quality of solution. The performance of the proposed hybrid algorithm has been evaluated on the extended benchmark datasets in the OR Library. The computational results demonstrate that the proposed hybrid algorithm is not only effective but also efficient in solving the mean-variance model with real world constraints.

Mean-VaR portfolio optimization: A nonparametric approach

Portfolio optimization involves the optimal assignment of limited capital to different available financial assets to achieve a reasonable trade-off between profit and risk. We consider an alternative Markowitz’s mean–variance model in which the variance is replaced with an industry standard risk measure, Value-at-Risk (VaR), in order to better assess market risk exposure associated with financial and commodity asset price fluctuations. Realistic portfolio optimization in the mean-VaR framework is a challenging problem since it leads to a non-convex NP-hard problem which is computationally intractable. In this work, an efficient learning-guided hybrid multi-objective evolutionary algorithm (MODE-GL) is proposed to solve mean-VaR portfolio optimization problems with real-world constraints such as cardinality, quantity, pre-assignment, round-lot and class constraints. A learning-guided solution generation strategy is incorporated into the multi-objective optimization process to promote efficient convergence by guiding the evolutionary search towards promising regions of the search space. The proposed algorithm is compared with the Non-dominated Sorting Genetic Algorithm (NSGA-II) and the Strength Pareto Evolutionary Algorithm (SPEA2). Experimental results using historical daily financial market data from S & P 100 and S & P 500 indices are presented. The results show that MODE-GL outperforms two existing techniques for this important class of portfolio investment problems in terms of solution quality and computational time. The results highlight that the proposed algorithm is able to solve the complex portfolio optimization without simplifications while obtaining good solutions in reasonable time and has significant potential for use in practice.

Computational complexity of image processing algorithms for an intelligent mobile enabled tongue diagnosis scheme

Tongue diagnosis is an auxiliary, effective and non-invasive technique to evaluate the condition of a patients internal organ in traditional East Asian medicine. The diagnosis process relies on experts opinion based on visual inspection of colour, substance, coating, form and motion of the tongue. This work explores the computational complexity of image processing techniques to analyse chromatic properties and textural features for tongue image segmentation. The dynamic and novel approach of this work involves consideration of skin colour covering various range of contrast diversity while image segmentation, making it distinct from existing works. The aim of this research is to seek for an algorithm with reduced computational complexity suitable to be implemented in an enhanced mobile enable solution. The algorithm for tongue image processing needs to be fast and less complex making the system apt for mobile devices executing automatic tongue diagnosis entailing clinical decision support system. Analysing the performance of different colour models, RGB was unveiled to have a better enactment than others. The performance of edge detection techniques were evaluated on images with close contrast difference based on segmentation result and processing time. The morphological processing showed better result to separate the tongue from its background which can be further employed for geometric shape based disease diagnosis.

An Automated Colourimetric Test by Computational Chromaticity Analysis: A Case Study of Tuberculosis Test

This paper presents an investigation into a novel approach for an automated universal colourimetric test by chromaticity analysis. This work particularly focuses on how a well-adjusted harmony between computational complexity and biochemical analysis can reduce the associated cost and unlock the limit on conventional chemical practice. The proposed research goal encompasses the potential to the criteria- anytime anywhere access, low cost, rapid detection, better sensitivity, specificity and accuracy. Our method includes obtaining the amount of colour change for each instance by delta E calculation. The system can provide the result in any ambient condition from the trajectory of colour change using Euclidean distance in LAB colour space. The strategy is verified on plasmonic ELISA based diagnosis of tuberculosis (TB). TB detection by plasmonic ELISA is a challenging, demanding and a time-consuming diagnosis. Completing the computation in real time, we circumvent the obstacle liberating the TB diagnosis in less than 15 min.

Advances in automated tongue diagnosis techniques

Tongue diagnosis can be an effective, noninvasive method to perform an auxiliary diagnosis any time anywhere, which can support the global need in the primary healthcare system. This work reviews the recent advances in tongue diagnosis, which is a significant constituent of traditional oriental medicinal technology, and explores the literature to evaluate the works done on the various aspects of computerized tongue diagnosis, namely preprocessing, tongue detection, segmentation, feature extraction, tongue analysis, especially in traditional Chinese medicine (TCM). In spite of huge volume of work done on automatic tongue diagnosis (ATD), there is a lack of adequate survey, especially to combine it with the current diagnosis trends. This paper studies the merits, capabilities, and associated research gaps in current works on ATD systems. After exploring the algorithms used in tongue diagnosis, the current trend and global requirements in health domain motivates us to propose a conceptual framework for the automated tongue diagnostic system on mobile enabled platform. This framework will be able to connect tongue diagnosis with the future point-of-care health system.

Advances in Crowd Analysis for Urban Applications Through Urban Event Detection

The recent expansion of pervasive computing technology has contributed with novel means to pursue human activities in urban space. The urban dynamics unveiled by these means generate an enormous amount of data. These data are mainly endowed by portable and radio-frequency devices, transportation systems, video surveillance, satellites, unmanned aerial vehicles, and social networking services. This has opened a new avenue of opportunities, to understand and predict urban dynamics in detail, and plan various real-time services and applications in response to that. Over the last decade, certain aspects of the crowd, e.g., mobility, sentimental, size estimation and behavioral, have been analyzed in detail and the outcomes have been reported. This paper mainly conducted an extensive survey on various data sources used for different urban applications, the state-of-the-art on urban data generation techniques and associated processing methods in order to demonstrate their merits and capabilities. Then, available open-access crowd data sets for urban event detection are provided along with relevant application programming interfaces. In addition, an outlook on a support system for urban application is provided which fuses data from all the available pervasive technology sources and finally, some open challenges and promising research directions are outlined.

An intelligent mobile-enabled expert system for tuberculosis disease diagnosis in real time

Abstract This paper presents an investigation into the development of an intelligent mobile-enabled expert system to perform an automatic detection of tuberculosis (TB) disease in real-time. One third of the global population are infected with the TB bacterium, and the prevailing diagnosis methods are either resource-intensive or time consuming. Thus, a reliable and easy–to-use diagnosis system has become essential to make the world TB free by 2030, as envisioned by the World Health Organisation. In this work, the challenges in implementing an efficient image processing platform is presented to extract the images from plasmonic ELISAs for TB antigen-specific antibodies and analyse their features. The supervised machine learning techniques are utilised to attain binary classification from eighteen lower-order colour moments. The proposed system is trained off-line, followed by testing and validation using a separate set of images in real-time. Using an ensemble classifier, Random Forest, we demonstrated 98.4% accuracy in TB antigen-specific antibody detection on the mobile platform. Unlike the existing systems, the proposed intelligent system with real time processing capabilities and data portability can provide the prediction without any opto-mechanical attachment, which will undergo a clinical test in the next phase.

Session 1: Information management and applications: A network-based ranking approach to discover places visited by tourists from geo-located tweets

The article analyses the existing connections between public spaces in the city, by developing a new method based on the ranking of the information produced by citizens using social media and related to their movement in the urban space. We propose a Node-Rank algorithm, a modified version of the Page-Rank algorithm, which introduces a new reticular perspective as it consider both incoming link in a page, and outgoing link too. The proposed algorithm has been tested with a dataset of geolocated Tweets collected in a previous research. Results indicate that Node-Rank Algorithm offers excellent performance in identifying the places of greatest interest from the point of view of twitter users and it is useful to reconstruct the networking between public spaces in the city.

Reversible decision support system: Minimising cognitive dissonance in multi-criteria based complex system using fuzzy analytic hierarchy process

In a multi-criteria system, it is often required to optimize the decision according to the given problem set. In this paper, a reversible decision support system has been utilized in a human-machine system to select the best decision among the selected alternatives. A reversible decision support system is used to interchange the alternatives between inputs and outputs. It also examines the dissonance level while taking decisions and ranked alternatives according to the attributes and sub-attributes of each criterion. These decisions have been classified into two segments such as reversible and irreversible. This paper highlights the reversible aspects of the alternatives and how it can be performed better with minimum dissonance. Fuzzy analytic hierarchy process has been applied especially triangular membership function to evaluate interim judgments. Triangular fuzzy number is implemented to form the perception of alternatives with different weights for each factor and sub-factor. Moreover, this study reveals that cognitive dissonance can be reduced in a reversible environment to resolve multi-constraint. Finally, performance of the proposed reversible decision support system has been analysed through an experiment to demonstrate its merits and capabilities. The result shows that the final decision has less dissonance level and more satisfaction when users get the chance to reverse the decision.