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International conference on Research and Innovation in Information Systems (ICRIIS)

作者： Robab Saadatdoost Alex Tze Hiang Sim Hosein Jafarkarimi Faculty of Computer Science and Information Systems Universiti Teknologi Malaysia Johor Bahru Malaysia

This paper focuses on knowledge discovery among attributes of Iran Higher Education Institute using self organizing map (SOM); the key problem with massive volume of data is extracting knowledge and patterns that are hidden in data. Managerial needs to explore this data for the purpose of decision making and strategy making reveals its importance.. Furthermore it can be useful for researchers that study and research about higher education. Meanwhile planning for higher education has significant impact on developing of one society, successful planning needs to analysis some huge and historical data that is available in higher education institutes. SOM is a particular type of neural network used in clustering and helps discover patterns and relations without advanced knowledge about them. The steps of this approach can be discussed under five headings, which are (i) data Preparation (ii) data Loading, (iii) Initializing, (iv) Map training and (v) Interpretation of the results. The target dataset contains data of five universities located in Tehran, Iran affiliated to Medical Ministry of Iran and the most important attributes are program of study, learning style, study mode and degree. Results show that the number of enrolling students for Tehran medical university has decreased for the past 23 years from 1988 to 2005. This study also finds that Tehran University of Medical Science covers the majority of high degrees like MDdisplay(Doctor of Medicine) and PhD. The findings of this study can be used in improving of higher education decision making systems and the results of this study indicate SOM toolbox utility in similar institutes to knowledge discovery in a visualizing way.

关键词： Educational institutions Organizing data visualization data mining Training Color

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Human factors flight trial analysis for 2D moving map and 3D synthetic vision displays

Human factors flight trial analysis for 2D moving map and 3D...

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conference on Digital Avionics Systems (DASC)

作者： J. Schiefele C. Pschierer P. Wipplinger D. Howland J. Marts Jeppesen GmbH Neu-Isenburg Germany Jeppesen-Sanderson Denver CO

Two applications have been evaluated in this paper. The tested 2D moving map application displays the current flight plan together with relevant flight route information. It has Jeppesen chart compliant symbologies for high-enroute and low-enroute flights. All text and symbols are deconflicted. Additional information for chart objects can be obtained on request. Depiction of an own ship symbol is possible in either a track up or north up orientation. Terrain and obstacle data can also be displayed for enhanced situation awareness. The 3D SVS primary flight display provides an egocentric view out of the cockpit window with a depiction of primary flight data as well as a 3D depiction of airports, terrain and obstacles. Navigation procedures can be displayed by a dynamic tunnel-in-the-sky visualizing the selected approach procedure. Traditional chart color coding and Jeppesen symbology is adopted for this 3D display. The 3D depiction is used solely for flight guidance. This paper describes the human factors analysis of flight trials performed with these displays between Centennial, CO and Colorado Springs using a Piper Cheyenne from Marinvent Corp. Six pilots flew the Cheyenne in eighteen flights with completely randomized setups. Two different display concepts were evaluated. One concept under test was the electronic 2D enroute moving map which was compared to classic paper charts on the enroute segment between the two airports.

关键词： Human factors Three dimensional displays Two dimensional displays Airports Testing Marine vehicles Navigation data visualization Performance analysis Springs

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Bioinformatics approaches for cross-species liver cancer analysis based on microarray gene expression profiling

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BMC BIOINFORMATICS 2005年第s2期6卷 S6-S6页

作者： Fang, H Tong, W Perkins, R Shi, L Hong, H Cao, X Xie, Q Yim, SH Ward, JM Pitot, HC Dragan, YP US FDA Div Syst Toxicol Natl Ctr Toxicol Res Jefferson AR 72079 USA Z Tech Corp Div Bioinformat Jefferson AR 72079 USA NCI Lab Metab Ctr Canc Res Bethesda MD 20892 USA NCI Vet & Tumor Pathol Sect Ctr Canc Res Ft Detrick MD 21702 USA Univ Wisconsin McArdle Lab Canc Res Madison WI 53706 USA

Background: The completion of the sequencing of human, mouse and rat genomes and knowledge of cross-species gene homologies enables studies of differential gene expression in animal models. These types of studies have the potential to greatly enhance our understanding of diseases such as liver cancer in humans. Genes co-expressed across multiple species are most likely to have conserved functions. We have used various bioinformatics approaches to examine microarray expression profiles from liver neoplasms that arise in albumin-SV40 transgenic rats to elucidate genes, chromosome aberrations and pathways that might be associated with human liver cancer. Results: In this study, we first identified 2223 differentially expressed genes by comparing gene expression profiles for two control, two adenoma and two carcinoma samples using an F-test. These genes were subsequently mapped to the rat chromosomes using a novel visualization tool, the Chromosome Plot. Using the same plot, we further mapped the significant genes to orthologous chromosomal locations in human and mouse. Many genes expressed in rat 1q that are amplified in rat liver cancer map to the human chromosomes 10, 11 and 19 and to the mouse chromosomes 7, 17 and 19, which have been implicated in studies of human and mouse liver cancer. Using Comparative Genomics Microarray analysis (CGMA), we identified regions of potential aberrations in human. Lastly, a pathway analysis was conducted to predict altered human pathways based on statistical analysis and extrapolation from the rat data. All of the identified pathways have been known to be important in the etiology of human liver cancer, including cell cycle control, cell growth and differentiation, apoptosis, transcriptional regulation, and protein metabolism. Conclusion: The study demonstrates that the hepatic gene expression profiles from the albumin-SV40 transgenic rat model revealed genes, pathways and chromosome alterations consistent with experimental and cli

关键词： Liver Cancer Comparative Genomic Hybridization analysis Human Liver Cancer Cancer Genome Anatomy Project Cytogenetic Location

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Using Ontology in Hierarchical Information Clustering

Using Ontology in Hierarchical Information Clustering

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Annual Hawaii International conference on System Sciences (HICSS)

作者： T.D. Breaux J.W. Reed Department of Computer Science North Carolina State University USA Computation Sciences and Engineering Oak Ridge National Laboratory USA

The tools to analyze and visualize information from multiple, heterogeneous sources have often relied on innovations in statistical methods. The results from purely statistical methods, however, overlook relevant semantic features present within natural language and text-based information. Emerging research in ontology languages (e.g. RDF, RDFS, SUO-KIF, and OWL) offers promising avenues for overcoming these limitations by leveraging existing and future libraries of meta-data and semantic mark-up. Using semantic features (e.g. hypernyms, meronyms, synonyms, etc.) encoded in ontology languages, methods such as keyword search and clustering can be augmented to analyze and visualize documents at conceptually richer levels. We present findings from a hierarchical clustering system modified for ontological indexing and run on a topic-centric test collection of documents each with fewer than 200 words. Our findings show that ontologies can impose a complete interpretation or subjective clustering onto a document set that is at least as good as meta-word search.

关键词： Ontologies visualization Statistical analysis Resource description framework Information analysis Technological innovation Natural languages OWL Libraries Keyword search

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Research Trends of the Internet of Things in Relation to Business Model Innovation: Results from Co-Word and Content Analyses

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FUTURE INTERNET 2023年第2期15卷 81-81页

作者： Kulakli, Atik Arikan, Cenk Lacin Amer Univ Middle East Coll Business Adm Egaila 54200 Kuwait

In the era of the Internet of Things, innovative business model initiatives continue to deepen, and the trend of search domains continues to expand. This paper aims to scientifically analyze research trends of the Internet of Things in relation to Business Model Innovation through bibliometric studies. The data were collected using the Clarivate Web of Science (WoS) Core Collection (SSCI and SCI indexed) from 2005 to 2022 (November). However, the publications for the research domains started in 2015. The results show that scientific publications on the Internet of Things in relation to Business Model Innovation have increased gradually since 2019. The WoS database is utilized for analyses because it contains journals and conference proceedings deemed more relevant by the academic domain and highly reputable sources for bibliometric studies. The VOS viewer, R Language, and Microsoft Excel were also used to analyze and complete the study. Bibliometric and scientometric analyses were conducted to identify publication patterns, text analysis, most important keywords (co-word, word cloud, and co-occurrence), trends for the topicality, and content clustering for the publication periods. The visualization of the research trends of the Internet of Things in relation to Business Model Innovation resulted in four co-occurrence clusters leading to some of the topic areas mentioned as follows: (1) The Internet of Things, (2) Business model innovation, (3) Technology infrastructure, and (4) Digital transformation and capabilities. The results of this study will assist academics in identifying worldwide research trends related to the Internet of Things and Business Model Innovation as well as recommending future research areas.

关键词： internet of things IoT business model innovation bibliometric co-word analysis text mining analysis content analysis scientometric Web of Science

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A Visualized analysis of Service Assistive Robots for Families With Disabled Children Based on CiteSpace data

A Visualized Analysis of Service Assistive Robots for Famili...

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Service Robotics (ICoSR), International conference on

作者： Yanlong Ma Olive Tree International Academy BFSU Hangzhou China

ISBN: (数字)9798350367119

ISBN: (纸本)9798350367126

It has been estimated that about 15% of the global population, which represents more than one billion people are with disabilities, among them there are 250 million disabled children. In China, there are 3.87 million disabled children with over 200,000 new disabled children added to total population each year which is challenging for both those children as well as for their care providers and caregivers. Service assistive Robotics (SABs) can help families with disabled children (FWDCs) to solve barriers, difficulties and practical problems. This paper has analyzed the 426 articles of SABs for FWDCs from 1990 to 2023 by using CiteSpace visualized approach. It has analyzed that: (1) The history development of the SABs for FWDCs study can be summarized into 3 periods: the beginning period (1990–2004), the rapid developing period (2005–2015), and the diversified developing period (2016–2023). (2) The top keywords are feature and functions, mobile robot, investment game, educational game, educational settings, task analysis. (3) At present studying, the main topics and hotspots are educational sciences, demand and need, computer and manufacturing system, emotion, trust and empathy, facilities and equipment, artificial intelligence, etc,. As a conclusion, the studies of SABs for FWDCs are developing very fast in the past 33 years. The study topics in recent years becomes more detailed and diverse. But the cross-disciplinary and diversified study is still needed to be improved. This paper sincerely hopes that more efforts can be contributed to the studies of SABs for FWDCs in the coming future.

关键词： Bibliographies data visualization Games Assistive robots Mobile robots History Artificial intelligence Task analysis Manufacturing systems Investment

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Evaluating collinearity constraint for automatic range image registration

Evaluating collinearity constraint for automatic range image...

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International conference on 3D Imaging, Modeling, Processing, visualization and Transmission (3DIMPVT)

作者： Yonghuai Liu Longzhuang Li Baogang Wei Department Computer Science University of Wales Aberystwyth Ceredigion UK Department Computer Science Texas A and M University Corpus Christi TX USA Computer Science College University of Zhejiang Hangzhou China

ISBN: (纸本)0769523277

While most of the existing range image registration algorithms either have to extract and match structural (geometric or optical) features or have to estimate the motion parameters of interest from outliers corrupted point correspondence data for the elimination of false matches in the process of image registration, the registration error and the collinearity error derived directly from the traditional closest point criterion are also capable of doing the same job. However, the latter has an advantage of easy implementation. The purpose of this paper is to investigate which definition of collinearity is more accurate and stable in eliminating false matches inevitably introduced by the closest point criterion. The experiments based on real images show the advantages and disadvantages of different definitions of collinearity.

关键词： Image registration Iterative closest point algorithm Computer science Cameras Iterative algorithms Geometrical optics Laser modes Simultaneous localization and mapping Algorithm design and analysis Image segmentation

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Coping with trial-to-trial variability of event related signals: A Bayesian inference approach

Coping with trial-to-trial variability of event related sign...

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European Signal Processing conference (EUSIPCO)

作者： Mingzhou Ding Kevin Knuth Yonghong Chen Steven L. Bressler Charles E. Schroeder Department of Biomedical Engineering University of Florida Gainesville FL USA NASA Ames Research Center Moffett Field CA USA Center for Complex Systems and Brain Sciences Florida Atlantic University Boca Raton FL USA Nathan Kline Institute Orangeburg NY USA

In electroneurophysiology, single-trial brain responses to a sensory stimulus or a motor act are commonly assumed to result from the linear superposition of a stereotypic event-related signal (e.g. the event-related potential or ERP) that is invariant across trials and some ongoing brain activity often referred to as noise. To extract the signal, one performs an ensemble average of the brain responses over many identical trials to attenuate the noise. To date, this simple signal-plus-noise (SPN) model has been the dominant approach in cognitive neuroscience. Mounting empirical evidence has shown that the assumptions underlying this model may be overly simplistic. More realistic models have been proposed that account for the trial-to-trial variability of the event-related signal as well as the possibility of multiple differentially varying components within a given ERP waveform. The variable-signal-plus-noise (VSPN) model, which has been demonstrated to provide the foundation for separation and characterization of multiple differentially varying components, has the potential to provide a rich source of information for questions related to neural functions that complement the SPN model. Thus, being able to estimate the amplitude and latency of each ERP component on a trial-by-trial basis provides a critical link between the perceived benefits of the VSPN model and its many concrete applications. In this paper we describe a Bayesian approach to deal with this issue and the resulting strategy is referred to as the differentially Variable Component analysis (dVCA).We compare the performance of dVCA on simulated data with Independent Component analysis (ICA) and analyze neurobiological recordings from monkeys performing cognitive tasks.

关键词： Brain modeling visualization Mathematical model Bayes methods Noise Detectors Standards

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Understand Corporate Rationales for Engaging in Reverse Stock Splits - A data Mining Application

Understand Corporate Rationales for Engaging in Reverse Stoc...

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Annual Hawaii International conference on System Sciences (HICSS)

作者： M.Y. Kiang D.M. Fisher S.A. Fisher R.T. Chi Information Systems Department College of Business Administration California State University Long Beach USA Information Systems Department School of Business and Public Administration California State University USA Department of Accountancy College of Business Administration California State University Long Beach USA

There has been much written on the individual topics of bankruptcy prediction, corporate performance, and reverse stock splits. However, there is little research into the relationship between reverse stock splits and corporate performance as well as bankruptcies. The purpose of this study is to provide and empirically support rationales for reverse splits by classifying reverse splitting firms into two groups, those declaring bankruptcy within 2 years and those remaining solvent. The apparent rationales for engaging in reverse splits differ between the two groups, i.e., weak firms attempting to increase their stock price while solid firms seeking to reposition their stock in the market. Two alternative approaches, Altman's Z-scores and artificial neural networks, are used for classifying reverse splitting firms into the two groups. A comparison is then made of the relative success of Z-scores and neural networks in the classification. This study should generate an understanding of corporate rationale for engaging in reverse splits and the relative success of Z-scores and artificial neural networks in forecasting the two groups.

关键词： data mining Artificial neural networks Neural networks Information systems Educational institutions Algorithm design and analysis Predictive models data visualization Forward contracts Investments

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visualization and analysis of Wireless Sensor Network data for Smart Civil Structure Applications Based On Spatial Correlation Technique

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AIP conference Proceedings 2009年第1期1146卷 113-122页

作者： Bhawani Shankar Chowdhry Neil M. White Jai Kumar Jeswani Khalil Dayo Manorma Rathi aMehran University of Engineering & Technology Jamshoro Pakistan bSchool of Electronics and Computer Science University of Southampton UK cRegular Associate The Abdus Salam ICTP Trieste ITALY

Disasters affecting infrastructure, such as the 2001 earthquakes in India, 2005 in Pakistan, 2008 in China and the 2004 tsunami in Asia, provide a common need for intelligent buildings and smart civil structures. Now, imagine massive reductions in time to get the infrastructure working again, realtime information on damage to buildings, massive reductions in cost and time to certify that structures are undamaged and can still be operated, reductions in the number of structures to be rebuilt (if they are known not to be damaged). Achieving these ideas would lead to huge, quantifiable, long‐term savings to government and industry. Wireless sensor networks (WSNs) can be deployed in buildings to make any civil structure both smart and intelligent. WSNs have recently gained much attention in both public and research communities because they are expected to bring a new paradigm to the interaction between humans, environment, and machines. This paper presents the deployment of WSN nodes in the Top Quality Centralized Instrumentation Centre (TQCIC). We created an ad hoc networking application to collect real‐time data sensed from the nodes that were randomly distributed throughout the building. If the sensors are relocated, then the application automatically reconfigures itself in the light of the new routing topology. WSNs are event‐based systems that rely on the collective effort of several micro‐sensor nodes, which are continuously observing a physical phenomenon. WSN applications require spatially dense sensor deployment in order to achieve satisfactory coverage. The degree of spatial correlation increases with the decreasing inter‐node separation. Energy consumption is reduced dramatically by having only those sensor nodes with unique readings transmit their data. We report on an algorithm based on a spatial correlation technique that assures high QoS (in terms of SNR) of the network as well as proper utilization of energy, by suppressing redundant data transmission. T

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