Fuzzy rule-based system applied to risk estimation of cardiovascular patients

Cardiovascular decision support is one area of increasing research interest. On-going collaborations between clinicians and computer scientists are looking at the application of knowledge discovery in databases to the area of patient diagnosis, based on clinical records. A fuzzy rule-based system for risk estimation of cardiovascular patients is proposed. It uses a group of fuzzy rules as a knowledge representation about data pertaining to cardiovascular patients. Several algorithms for the discovery of an easily readable and understandable group of fuzzy rules are formalized and analysed. The accuracy of risk estimation and the interpretability of fuzzy rules are discussed. Our study shows, in comparison to other algorithms used in knowledge discovery, that classifcation with a group of fuzzy rules is a useful technique for risk estimation of cardiovascular patients. © 2013 Old City Publishing, Inc

A computational framework for aesthetical navigation in musical search space

Paper presented at 3rd AISB symposium on computational creativity, AISB 2016, 4-6th April, Sheffield. Abstract. This article addresses aspects of an ongoing project in the generation of artificial Persian (-like) music. Liquid Persian Music software (LPM) is a cellular automata based audio generator. In this paper LPM is discussed from the view point of future potentials of algorithmic composition and creativity. Liquid Persian Music is a creative tool, enabling exploration of emergent audio through new dimensions of music composition. Various configurations of the system produce different voices which resemble musical motives in many respects. Aesthetical measurements are determined by Zipf’s law in an evolutionary environment. Arranging these voices together for producing a musical corpus can be considered as a search problem in the LPM outputs space of musical possibilities. On this account, the issues toward defining the search space for LPM is studied throughout this paper

Mining frequent biological sequences based on bitmap without candidate sequence generation

Biological sequences carry a lot of important genetic information of organisms. Furthermore, there is an inheritance law related to protein function and structure which is useful for applications such as disease prediction. Frequent sequence mining is a core technique for association rule discovery, but existing algorithms suffer from low efficiency or poor error rate because biological sequences differ from general sequences with more characteristics. In this paper, an algorithm for mining Frequent Biological Sequence based on Bitmap, FBSB, is proposed. FBSB uses bitmaps as the simple data structure and transforms each row into a quicksort list QS-list for sequence growth. For the continuity and accuracy requirement of biological sequence mining, tested sequences used during the mining process of FBSB are real ones instead of generated candidates, and all the frequent sequences can be mined without any errors. Comparing with other algorithms, the experimental results show that FBSB can achieve a better performance on both run time and scalability

Missing Value Imputation Using Stratified Supervised Learning for Cardiovascular Data

Legacy (and current) medical datasets are rich source of information and knowledge. However, the use of most legacy medical datasets is beset with problems. One of the most often faced is the problem of missing data, often due to oversights in data capture or data entry procedures. Algorithms commonly used in the analysis of data often depend on a complete data set. Missing value imputation offers a solution to this problem. This may result in the generation of synthetic data, with artificially induced missing values, but simply removing the incomplete data records often produces the best classifier results. With legacy data, simply removing the records from the original datasets can significantly reduce the data volume and often affect the class balance of the dataset. A suitable method for missing value imputation is very much needed to produce good quality datasets for better analysing data resulting from clinical trials. This paper proposes a framework for missing value imputation using stratified machine learning methods. We explore machine learning technique to predict missing value for incomplete clinical (cardiovascular) data, with experiments comparing this with other standard methods. Two machine learning (classifier) algorithms, fuzzy unordered rule induction algorithm and decision tree, plus other machine learning algorithms (for comparison purposes) are used to train on complete data and subsequently predict missing values for incomplete data. The complete datasets are classified using decision tree, neural network, K-NN and K-Mean clustering. The classification performances are evaluated using sensitivity, specificity, accuracy, positive predictive value and negative predictive value. The results show that final classifier performance can be significantly improved for all class labels when stratification was used with fuzzy unordered rule induction algorithm to predict missing attribute values

Flood Damage Assessments Using Spatial Data Management Techniques

Paper by Darryl W. Davis and R. Pat Web

Risk estimation of cardiovascular patients using Weka

Cardiovascular diseases remain the most prevalent cause of deaths worldwideand their prevention requires major life-style changes using limited health-care resources.Remote decision support for cardiovascular patients seems to allow them to lead a productivelife and to minimize the costs of treatment. In this paper, risk estimation of cardiovascularpatients on the basis of collected data used in our developing decision-makingsupport system is described. The system makes use of some data mining techniqueswhich are implemented in open source software tool Weka - Waikato Environment forKnowledge Analysis. The integration of Weka with our system, a description of used riskestimation models based on data mining techniques, and experimental results showing theperformance of these models are also given

Reasoning with BDI robots: from simulation to physical environment – implementations and limitations

In this paper an overview of the state of research into cognitive robots is given. This is driven by insights arising from research that has moved from simulation to physical robots over the course of a number of sub-projects. A number of major issues arising from seminal research in the area are explored. In particular in the context of advances in the field of robotics and a slowly developing model of cognition and behaviour that is being mapped onto robot colonies. The work presented is ongoing but major themes such as the veracity of data and information, and their effect on robot control architectures are explored. A small number of case studies are presented where the theoretical framework has been used to implement control of physical robots. The limitations of the current research and the wider field of behavioral and cognitive robots are explored

Ionic Imbalances and Coupling in Synchronization of Responses in Neurons

Most neurodegenerative diseases (NDD) are a result of changes in the chemical composition of neurons. For example, Alzheimer's disease (AD) is the product of A? peptide deposition which results in changes in the ion concentration. These changes in ion concentration affect the responses of the neuron to stimuli and often result in inducing excessive excitation or inhibition. This paper investigates the dynamics of a single neuron as ion changes occur. These changes are incorporated using the Nernst equation. Within the central and peripheral nervous system, signals and hence rhythms, are propagated through the coupling of the neurons. It was found that under certain conditions the coupling strength between two neurons could mitigate changes in ion concentration. By defining the state of perfect synchrony, it was shown that the effect of ion imbalance in coupled neurons was reduced while in uncoupled neurons these changes had a more significant impact on the neuronal behavior

Machine Learning in XENON1T Analysis

In process of analyzing large amounts of quantitative data, it can be quite time consuming and challenging to uncover populations of interest contained amongst the background data. Therefore, the ability to partially automate the process while gaining additional insight into the interdependencies of key parameters via machine learning seems quite appealing. As of now, the primary means of reviewing the data is by manually plotting data in different parameter spaces to recognize key features, which is slow and error prone. In this experiment, many well-known machine learning algorithms were applied to a dataset to attempt to semi-automatically identify known populations, and potentially identify other features of interest such as detector artefacts. Additionally, using the results of the machine learning process it became possible to cross-check the results of the XENON1T selection cuts. Clustering algorithms were used to segment the dataset into populations, which then recursively split those into additional subpopulations. Upon capturing a subpopulation, a classifier was trained and used to predict if other data could potentially belong to the same population. From this process, it was observed that there were two clustering algorithms that were capable of identifying the electronic recoil band accurately. It was also seen that a few XENON1T selection cuts may need relaxed. These algorithms may be able to be used to tweak the cuts, or continue in search of artefacts. The process of automating the analysis stage by means of machine learning could be further extended by automating the recognition of waveforms using neural networks

An algorithm for fast mining top-rank-k frequent patterns based on node-list data structure

Frequent pattern mining usually requires much run time and memory usage. In some applications, only the patterns with top frequency rank are needed. Because of the limited pattern numbers, quality of the results is even more important than time and memory consumption. A Frequent Pattern algorithm for mining Top-rank-K patterns, FP_TopK, is proposed. It is based on a Node-list data structure extracted from FTPP-tree. Each node is with one or more triple sets, which contain supports, preorder and post-order transversal orders for candidate pattern generation and top-rank-k frequent pattern mining. FP_TopK uses the minimal support threshold for pruning strategy to guarantee that each pattern in the top-rank-k table is really frequent and this further improves the efficiency. Experiments are conducted to compare FP_TopK with iNTK and BTK on four datasets. The results show that FP_TopK achieves better performance