Milos Jovanovic

Using data mining on student behavior and cognitive style data for improving e-learning systems: a case study

Abstract In this research we applied classification models for prediction of students’ performance, and cluster models for grouping students based on their cognitive styles in e-learning environment. Classification models described in this paper should help: teachers, students and business people, for early engaging with students who are likely to become excellent on a selected topic. Clustering students based on cognitive styles and their overall performance should enable better adaption of the learning materials with respect to their learning styles. The approach is tested using well-established data mining algorithms, and evaluated by several evaluation measures. Model building process included data preprocessing, parameter optimization and attribute selection steps, which enhanced the overall performance. Additionally we propose a Moodle module that allows automatic extraction of data needed for educational data mining analysis and deploys models developed in this study.

Reusable components for partitioning clustering algorithms

Clustering algorithms are well-established and widely used for solving data-mining tasks. Every clustering algorithm is composed of several solutions for specific sub-problems in the clustering process. These solutions are linked together in a clustering algorithm, and they define the process and the structure of the algorithm. Frequently, many of these solutions occur in more than one clustering algorithm. Mostly, new clustering algorithms include frequently occurring solutions to typical sub-problems from clustering, as well as from other machine-learning algorithms. The problem is that these solutions are usually integrated in their algorithms, and that original algorithms are not designed to share solutions to sub-problems outside the original algorithm easily. We propose a way of designing cluster algorithms and to improve existing ones, based on reusable components. Reusable components are well-documented, frequently occurring solutions to specific sub-problems in a specific area. Thus we identify reusable components, first, as solutions to characteristic sub-problems in partitioning cluster algorithms, and, further, identify a generic structure for the design of partitioning cluster algorithms. We analyze some partitioning algorithms (K-means, X-means, MPCK-means, and Kohonen SOM), and identify reusable components in them. We give examples of how new cluster algorithms can be designed based on them.

An architecture for component-based design of representative-based clustering algorithms

We propose an architecture for the design of representative-based clustering algorithms based on reusable components. These components were derived from K-means-like algorithms and their extensions. With the suggested clustering design architecture, it is possible to reconstruct popular algorithms, but also to build new algorithms by exchanging components from original algorithms and their improvements. In this way, the design of a myriad of representative-based clustering algorithms and their fair comparison and evaluation are possible. In addition to the architecture, we show the usefulness of the proposed approach by providing experimental evaluation.

Building interpretable predictive models for pediatric hospital readmission using Tree-Lasso logistic regression.

OBJECTIVES Quantification and early identification of unplanned readmission risk have the potential to improve the quality of care during hospitalization and after discharge. However, high dimensionality, sparsity, and class imbalance of electronic health data and the complexity of risk quantification, challenge the development of accurate predictive models. Predictive models require a certain level of interpretability in order to be applicable in real settings and create actionable insights. This paper aims to develop accurate and interpretable predictive models for readmission in a general pediatric patient population, by integrating a data-driven model (sparse logistic regression) and domain knowledge based on the international classification of diseases 9th-revision clinical modification (ICD-9-CM) hierarchy of diseases. Additionally, we propose a way to quantify the interpretability of a model and inspect the stability of alternative solutions. MATERIALS AND METHODS The analysis was conducted on >66,000 pediatric hospital discharge records from California, State Inpatient Databases, Healthcare Cost and Utilization Project between 2009 and 2011. We incorporated domain knowledge based on the ICD-9-CM hierarchy in a data driven, Tree-Lasso regularized logistic regression model, providing the framework for model interpretation. This approach was compared with traditional Lasso logistic regression resulting in models that are easier to interpret by fewer high-level diagnoses, with comparable prediction accuracy. RESULTS The results revealed that the use of a Tree-Lasso model was as competitive in terms of accuracy (measured by area under the receiver operating characteristic curve-AUC) as the traditional Lasso logistic regression, but integration with the ICD-9-CM hierarchy of diseases provided more interpretable models in terms of high-level diagnoses. Additionally, interpretations of models are in accordance with existing medical understanding of pediatric readmission. Best performing models have similar performances reaching AUC values 0.783 and 0.779 for traditional Lasso and Tree-Lasso, respectfully. However, information loss of Lasso models is 0.35 bits higher compared to Tree-Lasso model. CONCLUSIONS We propose a method for building predictive models applicable for the detection of readmission risk based on Electronic Health records. Integration of domain knowledge (in the form of ICD-9-CM taxonomy) and a data-driven, sparse predictive algorithm (Tree-Lasso Logistic Regression) resulted in an increase of interpretability of the resulting model. The models are interpreted for the readmission prediction problem in general pediatric population in California, as well as several important subpopulations, and the interpretations of models comply with existing medical understanding of pediatric readmission. Finally, quantitative assessment of the interpretability of the models is given, that is beyond simple counts of selected low-level features.

Human-and-Humanoid Postures Under External Disturbances: Modeling, Simulation, and Robustness. Part 1: Modeling

It is a well-known fact that the growth of technology has radically changed our approach to biosciences and medicine. What is interesting is that in the last decade we have witnessed a reverse influence—a trend towards “biologically inspired” solutions to technical problems. This leads to a true symbiosis between bio and technical sciences. A good example is the intersection and overlapping of three distinct fields: sports, medicine, and robotics. This paper intends to apply sophisticated methods developed for mathematical modeling of humanoid robots in real human motions, particularly in posture stabilization and selection of appropriate postures for different situation in sports and every day life. A general simulation system is realized: following a deductive principle, the algorithm considers particular human/humanoid motions (like those occurring in different sports) as being just special cases of a general motion and impact theory. Simulation includes the interaction with the environment. Simulating a human/humanoid dynamics in a given task, all relevant characteristics could be found: trajectories, velocities and accelerations, loads of joints, power requirements, energy consumption, contact forces including ground reactions, impact effects, etc. Simulation is used in solving a problem that is important for both humans and humanoid robots, namely, the behavior of a posture (keeping stability or collapsing) when subject to different disturbances. Although “posture” is mainly a static term, maintaining its balance in the presence of disturbances is a truly dynamic problem. Typical postures from every day life and sports are considered, such as: upright standing, squat (and partial squat), and three karate postures. Two sorts of disturbances are applied to eventually, compromise the posture: external impulse and permanent external force. This paper does not aim to suggest some new control strategy but to develop the dynamic model and simulation algorithm, and apply them to compare the robustness of different postures to external disturbances.

Finding best algorithmic components for clustering microarray data

The analysis of microarray data is fundamental to microbiology. Although clustering has long been realized as central to the discovery of gene functions and disease diagnostic, researchers have found the construction of good algorithms a surprisingly difficult task. In this paper, we address this problem by using a component-based approach for clustering algorithm design, for class retrieval from microarray data. The idea is to break up existing algorithms into independent building blocks for typical sub-problems, which are in turn reassembled in new ways to generate yet unexplored methods. As a test, 432 algorithms were generated and evaluated on published microarray data sets. We found their top performers to be better than the original, component-providing ancestors and also competitive with a set of new algorithms recently proposed. Finally, we identified components that showed consistently good performance for clustering microarray data and that should be considered in further development of clustering algorithms.

Human-and-Humanoid Postures Under External Disturbances: Modeling, Simulation, and Robustness. Part 2: Simulation and Robustness

The sophisticated method for mathematical modeling of humanoid robots formulated in Part 1 of this paper is applied here to the dynamic task of keeping a posture under disturbance, which is equally important to humans and humanoid robots. The idea of this work is to develop and realize a simulator tool for dynamic analysis of human-or-humanoid behavior under disturbances. To show the potentials and verify this tool, we comparatively analyze the robustness of some postures to external disturbance. At this stage of research we do not conduct real experiments with humans/humanoids but try to verify our simulation tool by relying on available experience. Therefore, the postures for comparison are taken from everyday life and from sports: upright standing, squat posture, and three karate postures. As the external disturbance we choose an impulse and a permanent force, both with variable direction and magnitude.

Internal Evaluation Measures as Proxies for External Indices in Clustering Gene Expression Data

Several external indices that use information not present in the dataset were shown to be useful for evaluation of representative based clustering algorithms. However, such supervised measures are not directly useful for construction of better clustering algorithms when class labels are not provided. We propose a method for identifying internal cluster evaluation measures that use only information present in the dataset and are related to given external indices. We utilize these internal measures for the construction of representative based clustering algorithms. Both identification and utilization steps of the proposed method are enabled by use of a component-based clustering algorithm design. Experiments on 432 algorithms using gene expression data sets provide evidence that some internal measures could be used as surrogates for external indices proposed in the literature. Moreover, the obtained results suggest that internal measures correlated to selected external indices can guide the algorithms toward significantly better cluster models.

Scalable experimental platform for research, development and testing of networked robotic systems in informationally structured environments experimental testbed station for wireless robot-sensor networks

The paper regards building of a scalable experimental platform for research, development, testing and verification of navigation and control algorithms for Wireless Robot-Sensor Networked systems (WRSN). Concept of the corresponding experimental test-bed station, with appropriate hierarchy-distributed control structure, operated within informationally structured space (environment) is presented in the paper. Complementary software simulator (Virtual WRSN) to enable preliminary system analysis, control synthesis, tuning parameters and simulation is developed, too. Both, experimental and virtual testbed system provide complementary research and development tools for research and development of heterogeneous, scalable networked multi-agent robotic systems to be employed in industry, at home, in office, medical institutions, public buildings, etc., as service human-oriented robots in our everyday life.

Iron-sulfur cluster damage by the superoxide radical in neural tissues of the SOD1G93A ALS rat model

Extensive clinical investigations, in hand with biochemical and biophysical research, have associated brain iron accumulation with the pathogenesis of the amyotrophic lateral sclerosis (ALS) disease. The origin of iron is still not identified, but it is proposed that it forms redox active complexes that can participate in the Fenton reaction generating the toxic hydroxyl radical. In this paper, the state of iron in the neural tissues isolated from SOD1(G93A) transgenic rats was investigated using low temperature EPR spectroscopy and is compared with that of nontransgenic (NTg) littermates. The results showed that iron in neural tissues is present as high- and low-spin, heme and non-heme iron. It appears that the SOD1(G93A) rat neural tissues were most likely exposed in vivo to higher amounts of reactive oxygen species when compared to the corresponding NTg tissues, as they showed increased oxidized [3Fe-4S](1+) cluster content relative to [4Fe-4S](1+). Also, the activity of cytochrome c oxidase (CcO) was found to be reduced in these tissues, which may be associated with the observed uncoupling of heme a3 Fe and CuB in the O2-reduction site of the enzyme. Furthermore, the SOD1(G93A) rat spinal cords and brainstems contained more manganese, presumably from MnSOD, than those of NTg rats. The addition of potassium superoxide to all neural tissues ex vivo, led to the [4Fe-4S]→[3Fe-4S] cluster conversion and concurrent release of Fe. These results suggest that the superoxide anion may be the cause of the observed oxidative damage to SOD1(G93A) rat neural tissues and that the iron-sulfur clusters may be the source of poorly liganded redox active iron implicated in ALS pathogenesis. Low temperature EPR spectroscopy appears to be a valuable tool in assessing the role of metals in neurodegenerative diseases.