Biomedical researchers must often deal with large amounts of raw data, and analysis of this data might provide significant insights. However, if the raw data size is large, it might be difficult to uncover these insights. In this paper, a data framework named BioLake is presented that provides minimalist interactive methods to help researchers conduct bioinformatics data analysis. Unlike some existing analytical tools on the market, BioLake supports a wide range of web-based bioinformatics data analysis for public datasets, while allowing researchers to analyze their private datasets instantly. The tool also significantly enhances result interpretability by providing the source code and detailed instructions. In terms of data storage design, BioLake adopts the data lakehouse architecture to provide storage scalability and analysis flexibility. To further enhance the analysis efficiency, BioLake supports online analysis for custom data, allowing researchers to upload their own data via a designed procedure without waiting for server-side approval. BioLake allows a one-time upload of custom data of up to 500 MB to ensure that researchers avoid issues with data being too large for upload. In terms of the built-in dataset, BioLake applies reactive continuous data integration, helping the analysis pipeline to get rid of most preprocessing steps. The only pre-built-in dataset of BioLake in the first public version is TCGA-PRAD mRNA expression data for prostate cancer research, which is the primary focus of the development team of BioLake. In summary, BioLake offers a lightweight online tool to facilitate bioinformatic mRNA data analysis with the support of custom online data processing.

Change point detection is crucial for identifying state transitions and anomalies in dynamic systems, with applications in network security, health care, and social network analysis. Dynamic systems are represented by dynamic graphs with spatial and temporal dimensions. As objects and their relations in a dynamic graph change over time, detecting these changes is essential. Numerous methods for change point detection in dynamic graphs have been developed, but no systematic review exists. This paper addresses this gap by introducing change point detection tasks in dynamic graphs, discussing two tasks based on input data types: detection in graph snapshot series (focusing on graph topology changes) and time series on graphs (focusing on changes in graph entities with temporal dynamics). We then present related challenges and applications, provide a comprehensive taxonomy of surveyed methods, including datasets and evaluation metrics, and discuss promising research directions.

An exponential growth in the literature in general and the medical literature in particular raises a need for effective intelligent analysis strategies and tools to provide valuable insights to researchers about the current evolving literature. While existing applications provide more specific approaches to the problem, such as focusing on particular genome or protein information, in this paper, the proposed application provides effective and detailed analysis of PubMed. The developed tool, named KoExPubMed, follows a more generalized and holistic way by taking into consideration different types of information such as authors, countries, genes, and the interactions between them. The developed application consists of four main components; (1) keyword search and ID extraction, (2) PubMed article information and abstract retrieval, (3) country and address extraction, and (4) gene information extraction. In addition to the fundamental components, the tool provides a variety of visualization options for showing the extracted information and the related associations, including line charts for densities and countries, chord charts for collaborations of authors, network graphs for the genes mentioned together, bubble charts for gene frequencies, etc. By addressing the need for a generalized data mining tool, we propose a comprehensive application which is capable of employing data mining and machine learning techniques to extract from PubMed knowledge valuable to researchers and practitioners who are interested in closely investigating the achievements of others.

Molecular subtyping is a method of separating tumor clusters in a cancer type with common features according to molecular data and classification models. Genome datasets are taken from many different people and some genetic material, more precisely genetic markers, are obtained to predict the presence of a disease. In addition, breast cancer occurs due to mutation or modification observed in cells. miRNAs and lncRNAs take participation in cell cycle, regulation, and even chromatic inhibition of cell. For example, miRNAs function in cell cycle regulation as the degradation of mRNAs. Therefore, the aim of this work is to investigate the roles of miRNAs and lncRNAs in prognosis and characterizing the subtypes of Breast Cancer.

The aim of this work is to employ face recognition for creating learning profiles of the analysed persons who are students in this study. Generating education profiles will help experts in the diagnosis of Attention Deficit Hyperactivity Disorder (ADHD), which is a serious problem in children. Children with ADHD often have the ability and potential to learn. However, it may be difficult to reveal their capabilities and skills. Accordingly, a suffering child may have a hard time succeeding in real life when he/she is ignored and expected to mix with other children. The unrealized gap and deficiency may lead to other problems and more complicated situation with unpredictable consequences. Thanks to the system developed in this study, and the like, which will help in diagnosing the ADHD disease, and hence suffering individuals will be able to recognize their deficiencies, understand their ability to learn and adapt when approached differently in a way which suits his/her situation. This personalized handling of infected students will be an excellent guide to advance their potential and integration within the society carefully and smoothly. The system analyzes the face of a student to inspire his/her emotional state. The reported test results demonstrate how the system works well and produces high accuracy under a variety of severe conditions such as skewed angle, less illumination, accessories etc.

The development in technology has led to the generation of huge amounts of data from various sources, including biological data, social networking data, etc. Accordingly, social network analysis has received considerable attention with the availability of more raw datasets which could be realized using a network structure. Most of the datasets can be represented as a social network which is a graph consisting of actors having relationships. Many tools exist for social network analysis inspired to extract knowledge from the networks. NetDriller has been developed as a social network extraction, manipulation and analysis tool to cover the lack that exists in other tools. It is capable of constructing social networks from raw data by employing a variety of data mining and machine learning techniques. In this paper, we describe an extend version of NetDriller, which has some new essential functions, including social network construction using data collection from Twitter, DBLP and IEEE. We also added (1) a new chart for viewing the network property and metrics, and (2) new graph manipulation techniques using GUI to keep the tool up to date with the huge volume of networks and the different types of raw data available on the web.

The initial announcement for the fourteenth ASONAM Conference invited the submission of research papers and special sessions proposals to the Social Networks Analysis and Mining (ASONAM 2022), Hague, Netherlands / 3-6 August 2022. However, the coronavirus epidemic is still affecting every human activity that included gatherings of people and travel is still limited for international conferences for many researchers. It was therefore decided to move the conference to a blended conference later in the 2022 and change the conference to a hybrid in-person conference and on-line conference at a different location. The final announcement was therefore ASONAM 2022-The 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 10-13 November 2022, Istanbul and Virtual on Zoom.

BACKGROUND: Drug repositioning is an emerging approach in pharmaceutical research for identifying novel therapeutic potentials for approved drugs and discover therapies for untreated diseases. Due to its time and cost efficiency, drug repositioning plays an instrumental role in optimizing the drug development process compared to the traditional de novo drug discovery process. Advances in the genomics, together with the enormous growth of large-scale publicly available data and the availability of high-performance computing capabilities, have further motivated the development of computational drug repositioning approaches. More recently, the rise of machine learning techniques, together with the availability of powerful computers, has made the area of computational drug repositioning an area of intense activities. RESULTS: In this study, a novel framework SNF-NN based on deep learning is presented, where novel drug-disease interactions are predicted using drug-related similarity information, disease-related similarity information, and known drug-disease interactions. Heterogeneous similarity information related to drugs and disease is fed to the proposed framework in order to predict novel drug-disease interactions. SNF-NN uses similarity selection, similarity network fusion, and a highly tuned novel neural network model to predict new drug-disease interactions. The robustness of SNF-NN is evaluated by comparing its performance with nine baseline machine learning methods. The proposed framework outperforms all baseline methods ([Formula: see text] = 0.867, and [Formula: see text]=0.876) using stratified 10-fold cross-validation. To further demonstrate the reliability and robustness of SNF-NN, two datasets are used to fairly validate the proposed framework's performance against seven recent state-of-the-art methods for drug-disease interaction prediction. SNF-NN achieves remarkable performance in stratified 10-fold cross-validation with [Formula: see text] ranging from 0.879 to 0.931 and [Formula: see text] from 0.856 to 0.903. Moreover, the efficiency of SNF-NN is verified by validating predicted unknown drug-disease interactions against clinical trials and published studies. CONCLUSION: In conclusion, computational drug repositioning research can significantly benefit from integrating similarity measures in heterogeneous networks and deep learning models for predicting novel drug-disease interactions. The data and implementation of SNF-NN are available at http://pages.cpsc.ucalgary.ca/ tnjarada/snf-nn.php .

The vascular structure of blood vessels is important in diagnosing retinal conditions such as glaucoma and diabetic retinopathy. Accurate segmentation of these vessels can help in detecting retinal objects such as the optic disc and optic cup and hence determine if there are damages to these areas. Moreover, the structure of the vessels can help in diagnosing glaucoma. The rapid development of digital imaging and computer-vision techniques has increased the potential for developing approaches for segmenting retinal vessels. In this paper, we propose an approach for segmenting retinal vessels that uses deep learning along with transfer learning. We adapted the U-Net structure to use a customized Inception V3 as the encoder and used multiple skip connections to form the decoder. Moreover, we used a weighted loss function to handle the issue of class imbalance in retinal images. Furthermore, we contributed a new dataset to this field. We tested our approach on six publicly available datasets and a newly created dataset. We achieved an average accuracy of 95.60 % and a Dice coefficient of 80.98 %. The results obtained from comprehensive experiments demonstrate the robustness of our approach to the segmentation of blood vessels in retinal images obtained from different sources. Our approach results in greater segmentation accuracy than other approaches.

Dota 2 is one of several multiplayer online battle arena games that have recently become extremely popular. A central feature of Dota 2 is that players select and purchase items that are used in the game and the selections strongly affect the outcome of the game. Item recommendations and purchase predictions in Dota 2 turn out to be interesting problems due to the many factors affecting the choice of items and due to the frequency by which items are purchased throughout the game. We present three recommender systems for recommending which items a player might buy throughout a match, based on commonly used purchasing strategies. These systems are a rule-based system, a logistic regression based system, and a logistic regression based system enhanced with clustering. Knowing only the player's hero and inventory items, the first two systems are able to predict the purchases by highly skilled players with accuracies ranging from 66.5% to 87.1%, depending on the hero where accuracy is defined as recommended items that were actually purchased in the next 5 min of the game. For the third system, the players are clustered by purchasing strategy which in turn group players by role and play style. Adding the clustering feature to the second version of the recommender system only improve the results slightly. The reason for this could be that the items in the player's inventory are already a strong indicator of a player's role and play style both of which were used to develop the first two recommender systems. An important question for future work is how the recommendations could be useful in practice for human or AI players.

Monitoring and management of large scale applications is already a complex task because of syntactic and unstructured nature of execution data. Traditional application monitoring and management solutions focused on employing analysis techniques on unstructured and syntactic log information become limited as unstructured information cannot be well utilized to find out related events information or correlate such information with other related information from applications. Our proposed solution of semantically formalized logging fills this gap by bringing formal semantics and combining it in a meaningful way to enable automated monitoring and management of applications. Such formalized and well-structured log information helps analytical solution to maximally automate the process of monitoring and management of applications. However, while formalizing and structuring the log information, we came across several missing and incomplete data which causes hindrance in this process. In this paper, we tackle this problem and propose a social network analysis based solution to handle incomplete and missing data from application execution, possibly compute it and use it by our proposed solution of semantically formalizing and structured logs with adapted data mining techniques to enable automated and effective application monitoring and management. We demonstrate from an industrial use-case application that how historical data from application execution is stored using semantic logging and utilized with standard social-network analysis techniques to find out missing values in incomplete data and perform application monitoring and management.

Automated Ranking is crucial in the process of automated Web Services execution. Often adaptation and ranking (used interchangeably) of discovered Web services is carried out using functional and non-functional information of Web Services. Existing approaches are either found to be only focusing on semantic modeling and representation only, or using data mining and machine learning based approaches on unstructured and raw data to perform discovery and ranking. We propose an approach to allow semantically formalized representation of logs during Web Service execution and then use such logs to perform ranking and adaptation of discovered Web Services. We have built Semantic FP-Tree based technique to perform association rule learning on functional and non-functional characteristics of Web Services. The process of automated execution of Web Services is improved in two steps, i.e., (1) we provide semantically formalized logs that maintain well-structured and formalized information about past interactions of Services Consumers and Web Services, (2) we perform an extended association rule mining on semantically formalized logs to find out any possible correlation in functional and non-functional characteristics of Web Services during past execution which is then used in automated ranking and adaptation of Web Services.

The Middleware is a piece of software lying between the operating system and the application layer. Distributed applications are gaining popularity with the widespread of reliable communication services. It is affordable to have data accessible 24/7 from almost anywhere, thanks to the well-developed mobile technology and handheld devices. Healthcare domain is one of the very demanding application areas that highly benefits from these developments. Actually, health clinic system is an evolving and promising area in which clients can easily log into a distant health clinic server and retrieve, add, or update the data and diagnosis of the patients using Common Object Request Broker Architecture (CORBA) Internet Inter-Orb Protocol (IIOP) middleware. In this paper, we describe the development and implementation of a middleware model that utilizes the effective connection between two different programming languages (java and .Net) to send and receive requested patients' data in a very efficient response time. This is achieved by using the reference of the object in the server. Based on our knowledge, our approach has the best response time compared to the existing works in the area. Our approach has been successfully tested and evaluated.

Genes are encoding regions that form essential building block within the cell and lead to proteins which are achieving various functions. However, some genes may be mutated due to internal or external factors and this is a main cause for various diseases. The latter case could be discovered by closely examining samples taken from patients to identify faulty genes. In other words, it is important to identify mutated genes as disease biomarkers. Then consider certain normal and infected samples to build a classifier model capable of successfully classifying new samples as infected or normal. The work described in this paper addresses this problem by introducing a comprehensive framework that incorporates the two stages of the process, namely feature selection and sample classification. In fact, high dimensionality in terms of the number of genes and small number of samples distinguishes gene expression data as an ideal application for the proposed framework. Reducing the dimensionality is essential to efficiently analysis the samples for effective knowledge discovery. Actually, there is a tradeoff between feature selection and maintaining acceptable accuracy. The target is to find the reduction level or compact set of features which once used for knowledge discovery will lead to improved performance and acceptable accuracy. For the first stage, we concentrate on four feature selection techniques, namely chi-square from statistics, frequent pattern mining and clustering from data mining, and community detection from network analysis. The effectiveness of the feature reduction techniques is demonstrated in the second stage by coupling them with classification techniques, namely associative classification, support vector machine and naive Bayesian classifier. Majority voting is applied for both stages. The results reported for four cancer datasets demonstrate the applicability and effectiveness of the proposed framework.

Understanding the molecular mechanism that govern drug protein interactions is essential for efficient drug design. The progress in drug development is very slow compared to the rising variation in human genomes and the discovery of new diseases. Thus the need to have more efficient and effective d rug discovery pipelines is becoming essential. In this paper, we study and analyze the relationships between drugs and proteins that they target. We consider some properties of the proteins that can be used to give weight to protein-protein relationships. We aim to identify protein properties that might guide the drug to proteins. Amino acid enrichment in target clusters is analyzed to assess if certain drugs prefer particular amino acids. The correlation between the net charge of the drug with the amino acids in the target protein is studies as well. Moreover, characterizing the functional components in drug target clusters is necessary to find drug preference. Sequence motifs and domains, post-translational modification and biological pathways of target proteins are analyzed to understand drug preference. This characterizes the drug target proteins from sequence and functional angles. Finally, we realized the importance of the social network model in analyzing any problem that can be modeled as a network. Fortunately, the problem tackled in this paper fits well the network requirement of the social network model. Hence, we analyze the correlations using the social network model where actors are drugs and proteins; our aim is to analyze the relations between drugs and proteins by benefiting from the rich metrics developed to analyze social networks. In this paper, we briefly mention the social network technique in order to demonstrate its applicability which will be detailed in a future publication.

The past decades have witnessed advances in genomic technology; and this has allowed laboratories to generate vast amount of biological data, including microarray gene expression data. Effective analysis of the data helps in better understanding the mechanisms behind the complex behavior of the cell. Actually, a huge body of research focuses on the role of gene regulatory networks (GRNs) in controlling the cell. However, studying the heterogeneous interactions between mRNA and miRNA has received less attention. Fortunately, revealing the targets of miRNAs started to gain some consideration from the research community. Further, integrating mRNA gene expression and miRNA expression data is receiving more attention; the target is to understand the role of miRNA in regulating mRNA in different cell contexts; this could lead to predicting miRNA targets and constructing miRNA-mRNA interaction networks. On the other hand, we have already demonstrated the power of constraint-based learning as a promising technique to learn the structure of GRN , which are homogeneous in the sense that they contain one type of nodes, namely, genes. In this study, we extend our previous work to show how constraint-based learning can be effectively applied to tackle a more challenging problem, namely, to learn the structure of heterogeneous networks, like mRNA-miRNA network. In other words, to build the whole picture of the heterogeneous interactions, we used constraint-based learning algorithms which usually perform well on sparse graphs to predict the interactions within heterogeneous networks, namely, miRNA-mRNA interactions. We are able to achieve this by extending our PCPDPr algorithm, which works on homogeneous networks. The extended version named htrPCPDPr is capable of handling networks connecting two heterogeneous sets of nodes into a bipartite graph. This way, we propose a new learning mechanism to predict miRNA targets from expression profiles of both mRNA and miRNA, in addition to sequence-based prior knowledge about the interactions. The method has been applied to different set of genes related to the Alzheimer disease; the results reported in this paper demonstrate the novelty, applicability, and effectiveness of the proposed approach.

For certain domains, e.g. bioinformatics, producing more real samples is costly, error prone and time consuming. Therefore, there is a need for an intelligent automated process capable of substituting the real samples by artificial samples that carry the same characteristics as the real samples and hence could be used for running comprehensive testing of new methodologies. Motivated by this need, we describe a novel approach that integrates Probabilistic Boolean Network and genetic algorithm based techniques into a framework that uses some existing real samples as input and successfully produces new samples as output. The new samples will inspire the characteristics of the existing samples without duplicating them. This leads to diversity in the samples and hence a more rich set of samples to be used in testing. The developed framework incorporates two models (perspectives) for sample generation. We illustrate its applicability for producing new gene expression data samples, a high demanding area that has not received attention. The two perspectives employed in the process are based on models that are not closely related, the independence eliminates the bias of having the produced approach covering only certain characteristics of the domain and leading to samples skewed towards one direction. The produced results are very promising in showing the effectiveness, usefulness and applicability of the proposed multi-model framework.

The application of social network analysis (SNA) and mining in health care domains has recently received a considerable attention for its key role in understanding how doctors form communities, and how they are socially connected with each other. This understanding helps enhance organizational structures and process flows. In this paper, we show how SNA techniques can solve issues in the medical referral system by analyzing the social network of general practitioners (GPs) and specialists (SPs) associated with a medical referral system in the Canadian healthcare system and the like. Various SNA and mining procedures are proposed backed by experimental results.

Ranking is an important step once automated discovery in Web Services is enabled, it allows for automated selection of the best matched service, out of the discovered ones. However, automated selection of the best matched service is not as simple as it may look like. Different service consumers may have different preferences to select the service providers, which may even depend upon their past interactions. Various approaches have been proposed that allow ranking of services based on different functional and non-functional aspects. However, we believe that the selection of services based on the analysis of the past interactions of service consumers or their social-network could be another effective way to rank the services for the benefit of service consumers. In this paper, we present a community-aware personalized approach for recommending and ranking Web Services for a service consumer. It is based on analysis of historical interactions among service consumers and service providers. We perform analysis and mining on the log information of service consumers and service providers, model their past interactions as social network, apply standard social-network analysis techniques, and use this information in ranking Web Services.

Frequent pattern mining and consequently association rule mining is a useful technique for discovering relationships between items in databases. However, as the size of the data to be analyzed increases or the values of the pruning thresholds decrease, larger number of frequent pattern and more association rules will be generated with little information about the association rules in relation to each other. This research paper discusses a method to segment rules into different sets with no internal conflicts. The goal is to establish an effective method to reduce the difficulty for businesses to review the association rules of different customer segments, and track the behaviors of market segments based on their buying behaviors. The method established in this paper has the advantage of not needing customer information, thus removing the need for businesses to obtain customer information. This removes the threat of intrusions into customer privacy. The method also generates the rule sets based on conflicting rules, and dividing rules based on customer behaviors is more accurate than customer characteristics. The proposed method has been validated by running some tests.

Matchmaking services connecting consumers and providers on the internet have become phenomenally important in today's world. Whereas the matchmaking was by traditional media such as print and television in the past it is now expected to include the internet. Consumers expect these services to be readily available as this can only be accomplished via the internet Providers that do not have an internet presence are therefore severely disadvantage in the competition for customers (consumers). The decline of the traditional physical music store and the ascendancy of the virtual iTunes store is a perfect example of this. As the traditional stores go bankrupt and go out of business has also become the largest music retailer in the United States of America. Consumers simply do not want to spend extra energy to get what they want. If the music can be purchased and downloaded from the comfort of their own home, the consumers (customers) will do just that. Therefore online matchmaking services are a hot topic of discussion for many companies, as they are finding ways to provide the fastest, cheapest and most efficient methods for consumers to easily access their services.

Mining sequential patterns is an important activity in computerized data analysis, and further analysis of discovered patterns can lead to more important findings in a post mining stage. Methods that integrate the traditional mining tasks with a know

The motivation for the study described in this paper is realizing the fact that organizational structure of a group is a key indicator in determining its strengths and weaknesses. A general knowledge of the prevalent models of terrorist organizations leads to a better understanding of their capabilities. Knowledge of the different labels and systems of classification that have been applied to groups and individuals aid us in discarding useless or irrelevant terms, and in understanding the purposes and usefulness of different terminologies. Previous studies in network analysis have mostly dealt with legal networks with transparent structures. Terrorist networks share some features with conventional (real world) networks, but they are harder to identify because they mostly hide their illicit activities. In this paper we describe a novel approach for extracting structural patterns of terrorist networks with the help of social network analysis measures and techniques. We propose a global measure for estimating the degree of organization of social networks; the measure is global in terms of being applied to the whole network as an entity and being extracted from the major well-known SNA measures. The importance of such research comes from the fact that individuals in organized intellectual networks and especially terrorist networks tend to hide their individual rules and thus there is a need to deal with such networks as a whole, discovering the degree of organization and thus its strengths and weaknesses.

Searching for the right information over the Web is not straight-forward. In the era of high speed internet, high capacity networks, and interactive Web applications, it has become even easier for the users to publish data online. A huge amount of data is published over the internet; every data is in the form of web pages, news, blogs and other material, etc. Similarly, for search engines like Google and Yahoo, it becomes rather hard to find out the right information, i.e., as per user's preferences; search results for same query differ in priority for different users. In this paper, we proposed a way to prioritize search results of search engines like Google, based on the personal interests and context of users. In order to find out personal interest and context, we follow a unique approach of (1) finding out activities of a user of his/her social-network, (2) finding out what information does the social networks (i.e., friends and community) provide to the user. Based on this information, we have developed a methodology that takes into account the information about social networks and prioritize search results from Web search engine.

This paper presents a comprehensive approach for the transformation of ODL Schemas into XML Schemas. The approach starts with an incomplete set of rules described in the literature to assist in the transformation process. The fact that the rules provided a solid foundation for expansion, as well as the fact that the rules only cover a small subset of ODL, was our main motivation for continuing the study of this topic. In this paper, we first analyze an existing set of nine transformation rules. After evaluating the correctness and completeness of the rules, we proceed to propose some improvements and extensions into a more complete set of rules that cover the whole transformation process. By modifying the existing rule set, we are able to handle a much wider variety of ODL. Finally, we discuss some ODL scenarios that the original rule set cannot handle. This is meant to justify the need for the proposed extension as described in this paper. The presented more complete rule set is capable of handling a larger subset of ODL (including dictionaries, global and local scope enumerations, and most importantly, inheritance).

Mobility models play an important role in the evaluation of wireless ad hoc networks. However, most existing mobility models are limited to 1D or 2D user movement. In this paper, we propose a novel 2.5D mobility model suitable for modeling the movement of backcountry skiers in mountainous terrains. These skiers carry wireless devices for communication in the event of emergencies, such as an avalanche, and thus form a dynamic mobile wireless ad hoc network. Our model represents groups of skiers, with each group having an invisible leader who determines the general direction of movement. We calculate the acceleration, velocity, and position of the group leader based on properties of the terrain, gravitational force, and a randomized sine function. To simulate the coordinated mobility of group members, we use a flocking model, which applies cohesion, separation, and alignment forces to influence group members to follow their leader, while avoiding collisions with each other. To demonstrate the applicability of our model, we use simulation to evaluate the skier rescue probability in the event of an avalanche.

In this paper, a new framework to build an adaptive classifier is introduced. At first, a clustering algorithm, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is applied to a set of sample data to form initial set of clusters. The clusters are represented as classes. Using support vector machine (SVM), a classifier model is generated. In real world application, data comes in continuously. Therefore, if the model does not learn from the new data, the model may not perform as well with the new data especially when the model's training data is different from the test data. The new framework proposed in this paper rebuilds the classifier model using selected data from test data set to improve the accuracy of the model. A case study on intrusion detection data set has been performed to evaluate our methodology. The result shows that this approach lead to have more accurate classification models over time.

In this paper, we investigate and explore the process of analyzing log data of website visitor traffic in order to assist the owner of a website in understanding the behavior of its visitors. The developed approach involves the review of statistical data on the types of visitors that come to the website, as well as the steps they take to reach and satisfy the goal of their visit. The value added from the analysis of this data provides eCommerce and commercial website owners with the information needed to display targeted advertisements or messages to their customers. In the long run, this is expected to allow for the increase in sales and overall customer loyalty.

This paper addresses an important and vital problem within the general area of disease recognition, namely identifying disease biomarker genes. Given the complexity of this domain, the basic idea tacked in this paper is employing multiple agents to handle this problem. Though the developed methodology is general enough to be applied to any other domain, we concentrate on identifying cancer biomarkers in this paper. Our approach is mainly based on detecting the minimum set of genes that could successfully identify cancer samples. Multiple agents are involved in the process. After each agents applies its own rules and reports candidate cancer biomarkers, the agents negotiate to agree on the actual biomarkers. The latter process may require further investigation of the characteristics of each of the reported genes because some of them may have the same functionality and the target is a compromise of the best representative of each functionality. A degree of confidence in each candidate biomarker influences the negotiation process. The so far conducted experiments reported very encouraging results with high classification rate; none of the involved agents could alone achieve a close success rate.

Automated text summarization is important to for humans to better manage the massive information explosion. Several machine learning approaches could be successfully used to handle the problem. This paper reports the results of our study to compare the performance between neural networks and support vector machines for text summarization. Both models have the ability to discover non-linear data and are effective model when dealing with large datasets.

The great increase in online transactions and the thousands of online retailers has created a great demand for companies to gain competitive advantage. An easy way for a company to gain customer advantage is through the use of data mining. Due to this high demand we have developed a prototypical tool to help with the analysis of these online transactions. From the raw data generated by running these transactions we are able to find consumer trends and shopping patterns by using hierarchical clustering and association rules mining algorithm. The focus of this research is to demonstrate how this development can be useful and effective in a business situation for companies to gain competitive advantage.

This paper presents a social network modeling technique that models the data to be analyzed to create a social network as frequent closed patterns. Frequent closed patterns have the advantage that they successfully grab the inherent information content of the dataset and is applicable to a broader application domain. Entropies of the frequent closed patterns are used to keep the dimensionality of the feature vectors to a reasonable size. Experimental results presented in the paper shows that social network produced from these set of features successfully carries the community structure information.

Significant gene extraction from microarray data is a challenging problem which is of great interest to researchers in Computational Biology, Medicine, Computer Science and Statistics. A number of methods have been proposed for extracting the smallest number of genes which can accurately classify different samples. Most of these methods ignore the fact that microarray data is mostly noisy. For instance, only using a statistical t-test has been shown to be insufficient since it result in a high false discovery rate. Recently, a singular value decomposition (SVD) based approach was proposed for time series microarray data reduction, however it turned out not to be efficient for classifying microarray data. To overcome the shortcomings of these approaches, this paper proposes two methods to reduce false discovery rates. The first method involves an iterative t-test which finds the p-value for each gene under perturbation by eliminating one sample at a time. It eliminates weak noisy genes by dropping any gene which does not show significant p-value under all the conditions. The second method is a hybrid process which adapts a combination of the SVD and the t-test. It considers the entropy of all the data, and thus takes the correlation between genes into account. Classification accuracy is used to validate the significance of the extracted genes. The reported test results on two datasets demonstrate the applicability and effectiveness of the two proposed methods.

The distance measures used to determine the dissimilarities between high-dimensional feature vectors are often expensive to compute. To reduce the number of expensive distance calculations in the search process, Korn, et al [5] proposed a multi-step algorithm, which involves two stages: filtering and refinement. This algorithm was later improved by Seidl and Kriegel [8] to produce optimal-sized candidate set in the filtering stage; the improved algorithm is said to be filtering optimal, but can not produce the result incrementally in the refinement stage. In this paper, we propose an extended version of the algorithm that can produce the nearest neighbors incrementally in an optimal way. Our algorithm is both filtering and refinement optimal, and well serves real applications. We proved the optimality of the proposed extended algorithm.

ABSTRACT Plasmas are ubiquitous in the Universe. An understanding of plasma phenomena is therefore of importance in almost every area of astrophysics, from stellar atmospheres to star clusters. Plasmas also occur in daily life both in industrial processes and in consumer products. Recent groundbreaking data is making this the golden age of plasma science. Although direct observations and analysis of data provide important physical evidence for plasma phenomena, they do not necessarily explain the phenomena. Hence, recent discoveries in this area might not only arise out of observations, but also from visual simulations of the phenomena supported by advanced rendering technologies. This report describes the state of art of such simulations, and examines practical issues often overlooked in the literature. Educational and public outreach applications are also discussed. Although the emphasis is on the predictive rendering of plasma processes, the simulation guidelines and trade‐offs addressed in this report can be extended to other types of natural phenomena. The report closes with a discussion of further avenues of research involving the visual simulation of plasma phenomena.

Abstract This paper describes the modeling and simulation of the deformation of human skeletal muscle at different structural levels based on sound scientific principles, experimental evidence, and state‐of‐art muscle anatomy and physiology. The equations of a continuum model of a muscle with realistic architecture, including internal arrangement of muscle fibers and passive structures, and deformation, including activation relations, was developed and solved with the finite element method. The continuum model is used as the basis of a strategy for controlling muscle deformation using activation relations. In order to demonstrate the functionality of the model, it was used to investigate force production and structural changes during contraction of the human tibialis anterior for maximally and submaximally activated muscle behavior. From a comparison with experimental data obtained from ultrasound imaging, we concluded that the modeling and simulation of the continuum based on physiologically meaningful parameters as described in the paper is both an excellent predictor of force production observations and of changes in internal geometry under various test conditions. It is therefore a valuable tool for controlling muscle deformation during movement. Copyright © 2005 John Wiley & Sons, Ltd.

Simulating natural phenomena has always been a focal point for computer graphics research. Its importance goes beyond the production of appealing presentations, since research in this area can contribute to the scientific understanding of complex natural processes. The natural phenomena, known as the Aurora Borealis and Aurora Australis, are geomagnetic phenomena of impressive visual characteristics and remarkable scientific interest. Aurorae present a complex behavior that arises from interactions between plasma (hot, ionized gases composed of ions, electrons, and neutral atoms) and Earth's electromagnetic fields. Previous work on the visual simulation of auroral phenomena have focused on static physical models of their shape, modeled from primitives, like sine waves. In this article, we focus on the dynamic behavior of the aurora, and we present a physically-based model to perform 3D visual simulations. The model takes into account the physical parameters and processes directly associated with plasma flow, and can be extended to simulate the dynamics of other plasma phenomena as well as astrophysical phenomena. The partial differential equations associated with these processes are solved using a complete multigrid implementation of the electromagnetic interactions, leading to a simulation of the shape and motion of the auroral displays. In order to illustrate the applicability of our model, we provide simulation sequences rendered using a distributed forward mapping approach.

Abstract We present the first computer graphics algorithm designed to simulate the aurora, a natural phenomenon of great visual beauty and considerable scientific interest. The algorithm is based on the current understanding of the physical origin of this natural display. The aurorae are mainly caused by high‐energy electrons originating in the sun and entering the earth's atmosphere in narrow regions centered on the magnetic poles. These electrons collide with atmospheric atoms, which are excited to higher energy levels. The excited atoms emit rapidly varying visible light in a curtain‐like volume as they return to lower energy levels, thereby creating the aurora. By simulating these light emissions along with the spatial and temporal distribution of the entering electrons, we are able to render the major visual aspects of auroral displays. The applicability of this auroral model for rendering and scientific purposes is illustrated through comparisons of synthetic images with photographs of real auroral displays. Copyright © 2003 John Wiley & Sons, Ltd.

The sensitivity of computed results to implementations of algorithms in GIS is considered in this paper on the example of a precisely defined recursive variant of the Ramer-Douglas-Peucker line simplification algorithm (called the R-D-P algorithm). We establish a robust version of the R-D-P algorithm where the determination of the simplification is rounding error free if the data are already machine numbers. Under these assumptions, the results are reproducible which is not the case with other versions of the algorithm.

The main topic of the paper is virtual spectrophotometers, virtual spectrophotometry and strategies for reducing the number of samples required to obtain asymptotically convergent measurements. A brief section on applications of virtual spectrophotometry is presented. This is followed by a careful formulation of virtual spectrophotometers. A virtual spectrophotometer, being a virtual device, is only described by its geometry.

We present an algorithm to simulate the aurora borealis, commonly known as the "northern lights", a natural phenomenon of great visual beauty and considerable scientific interest. The algorithm is based on the current understanding of the physical origin of the aurora. This natural display is mainly caused by high-energy electrons originating in the Sun and entering the Earth's atmosphere in narrow regions centered on the magnetic poles. These electrons collide with atmospheric atoms which are excited to higher energy levels. The excited atoms emit rapidly varying visible light in a curtain-like volume as they return to lower energy levels thereby creating the aurora. By simulating these light emissions along with the spatial and temporal distribution of the entering electrons, we are able to render the major visual aspects of auroral displays. This approach also allows the representation of time-dependent features that characterize the dynamic nature of the aurorae. The applicability of this auroral model for artistic and research purposes is illustrated through comparisons of synthetic images with photographs of real auroral displays.

Physically and biologically based reflectance and transmittance models add realism to image synthesis applications at the expense of a significant increase in rendering time. Current research efforts in this area focus on developing practical solutions to quickly access a BDF (which represents a combination of BRDF and BTDF) while preserving its original characteristics. In this paper an approach to reconstruct relatively complex isotropic BDFs is presented. The spectral curves obtained using the proposed approach are compared with measured spectral curves, and some issues regarding its performance and storage requirements are examined. Copyright © 1999 John Wiley & Sons, Ltd.

Physically and biologically based reflectance and transmittance models add realism to image synthesis applications at the expense of a significant increase in rendering time. Current research efforts in this area focus on developing practical solutions to quickly access a BDF (which represents a combination of BRDF and BTDF) while preserving its original characteristics. An approach to reconstruct arbitrary isotropic BDFs is presented. The spectral curves obtained using the proposed approach are compared with measured spectral curves, and some issues regarding its performance and storage requirements are examined.

Recent developments in rendering have provided very realistic images. However, these images rarely show organic objects. We believe that one of the main difficulties of rendering these objects realistically is the lack of reflectance and transmittance models oriented to organic materials. In this paper an algorithmic reflectance and transmittance model for plant tissue oriented to computer graphics applications is presented. The model accounts for the three components of light propagation in plant tissues, namely surface reflectance, subsurface reflectance and transmittance, and mechanisms of light absorption by pigments present in these tissues. The model design is based on the available biological information, and it is controlled by a small number of biologically meaningful parameters. Its formulation, based on standard Monte Carlo techniques, guarantees its easy incorporation into most rendering systems. The spectral curves of reflectance and transmittance computed by the model are compared with measured curves from actual experiments.

This paper is motivated by a special linear interpolation problem encountered in scan‐line algorithms for scan‐conversion of filled polygons. rounding‐up integral linear interpolation is defined and its efficient computation is discussed. The paper then incorporates rounding‐up integral linear interpolation into a scan‐line algorithm for filled polygons, and it discusses the implementation of the algorithm. This approach has the advantage of only requiring integer arithmetic in the calculations. Furthermore, the approach provides a unified treatment for calculating span extrema for left and right edges of the polygon that guarantees the mutual exclusiveness of the ownership of boundary pixels of two filled polygons sharing an edge.

Conventional algorithms for scan-conversion of circles select one pixel in each iteration. Run-length slice circle algorithms have therefore been suggested. These algorithms determine a run of pixels in each iteration. The speed of scan-conversion is therefore increased due to I/O. A hybrid approach to the scan-conversion of circles is presented. The new approach combines the advantages of the two methods into a hybrid algorithm. Speedup is achieved in the hybrid algorithm not only due to the reduction in the number of I/O operations, but also due to a reduction in the number of arithmetic operations.

A two-step incremental linear interpolation algorithm is derived and analyzed. It is shown that the algorithm is correct, that it is reversible, and that it is faster than previous single-step algorithms. An example is given of the execution of the algorithm.

Fat curves in two‐dimensional Euclidean space are discussed. Previous work on fat curves is reviewed and a new definition is given for a fat curve having a smooth axis. The joining of two fat curves is discussed and a technique for scan‐converting fat curves is presented.

A major bottleneck in many graphics displays is the time required to scan-convert straight line segments. Most manufacturers use hardware based on Bresenham's [5] line algorithm. In this paper an algorithm is developed based on the original Bresenham scan-conversion together with the symmetry first noted by Gardner [18] and a recent double-step technique [31]. This results in a speed-up of scan-conversion by a factor of approximately 4 as compared to the original Bresenham algorithm. Hardware implementations are simple and efficient since the property of using only shift and increment operations is preserved.

The connection between a continuous convex curve and its discrete image is investigated using an appropriate definition of discrete convexity. It is shown that the discrete image of continuous convex curve may not be convex; however, its deviation from convexity (in the discrete definition) is bounded by a small constant. The actual pixel patterns that are obtained by discretizing convex curves are studied. Certain constraints on the context of discrete images of continuous convex curves were discovered.>