Search results for: retrieval practice

Authors: B. Ramesh Naik, T. Venugopal

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This paper presents a novel approach which improves the high-level semantics of images based on machine learning approach. The contemporary approaches for image retrieval and object recognition includes Fourier transforms, Wavelets, SIFT and HoG. Though these descriptors helpful in a wide range of applications, they exploit zero order statistics, and this lacks high descriptiveness of image features. These descriptors usually take benefit of primitive visual features such as shape, color, texture and spatial locations to describe images. These features do not adequate to describe high-level semantics of the images. This leads to a gap in semantic content caused to unacceptable performance in image retrieval system. A novel method has been proposed referred as discriminative learning which is derived from machine learning approach that efficiently discriminates image features. The analysis and results of proposed approach were validated thoroughly on WANG and Caltech-101 Databases. The results proved that this approach is very competitive in content-based image retrieval.

Keywords: CBIR, discriminative learning, region weight learning, scale invariant feature transforms

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Authors: Bruno T. Gomes, José A. Menezes, Giordano Cabral

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With the popularity of smartphones, mobile apps emerge to meet the diverse needs, however the resources at the disposal are limited, either by the hardware, due to the low computing power, or the software, that does not have the same robustness of desktop environment. For example, in automatic audio classification (AC) tasks, musical information retrieval (MIR) subarea, is required a fast processing and a good success rate. However the mobile platform has limited computing power and the best AC tools are only available for desktop. To solve these problems the fast classifier suits, to mobile environments, the most widespread MIR technologies, seeking a balance in terms of speed and robustness. At the end we found that it is possible to enjoy the best of MIR for mobile environments. This paper presents the results obtained and the difficulties encountered.

Keywords: audio classification, audio extraction, environment mobile, musical information retrieval

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Authors: Munaf Rashid

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For content-based human action retrieval systems, search accuracy is often inferior because of the following two reasons 1) global information pertaining to videos is totally ignored, only low level motion descriptors are considered as a significant feature to match the similarity between query and database videos, and 2) the semantic gap between the high level user concept and low level visual features. Hence, in this paper, we propose a method that will address these two issues and in doing so, this paper contributes in two ways. Firstly, we introduce a method that uses both global and local information in one framework for an action retrieval task. Secondly, to minimize the semantic gap, a user concept is involved by incorporating features weight updating (FWU) Relevance Feedback (RF) approach. We use statistical characteristics to dynamically update weights of the feature descriptors so that after every RF iteration feature space is modified accordingly. For testing and validation purpose two human action recognition datasets have been utilized, namely Weizmann and UCF. Results show that even with a number of visual challenges the proposed approach performs well.

Keywords: relevance feedback (RF), action retrieval, semantic gap, feature descriptor, codebook

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Authors: Suleiman Musa

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The utilization of CD-ROM as a storage and retrieval system by Nasarawa State University Keffi (NSUK) Library is crucial in preserving and dissemination of information to students and staff. This study investigated the utilization of CD-ROM Database storage and retrieval system by students of NUSK. Data was generated using structure questionnaire. One thousand and fifty two (1052) respondents were randomly selected among post-graduate and under-graduate students. Eight hundred and ten (810) questionnaires were returned, but only five hundred and ninety three (593) questionnaires were well completed and useful. The study found that post-graduate students use CD-ROM Databases more often than the under-graduate students in NSUK. The result of the study revealed that knowledge about CD-ROM Database 33.22% got it through library staff. 29.69% use CD-ROM once a month. Large number of users 45.70% purposely uses CD-ROM Databases for study and research. In fact, lack of users’ orientation amount to 58.35% of problems faced, while 31.20% lack of trained staff make it more difficult for utilization of CD-ROM Database. Major numbers of users 38.28% are neither satisfied nor dissatisfied, while a good number of them 27.99% are satisfied. Then 1.52% is highly dissatisfied but could not give reasons why. However, to ensure effective utilization of CD-ROM Database storage and retrieval system by students of NSUK, the following recommendations are made: effort should be made to encourage under-graduate in using CD-ROM Database. The institution should conduct orientation/induction course for students on CD-ROM Databases in the library. There is need for NSUK to produce in house databases on their CD-ROM for easy access by users.

Keywords: utilization, CD-ROM databases, storage, retrieval, students

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Authors: Ahmadu Maidorawa, Idrissa Djibo, Muhammad Tella

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This paper presents a method for detecting flow chart and figure plagiarism based on shape of image processing and multimedia retrieval. The method managed to retrieve flowcharts with ranked similarity according to different matching sets. Plagiarism detection is well known phenomenon in the academic arena. Copying other people is considered as serious offense that needs to be checked. There are many plagiarism detection systems such as turn-it-in that has been developed to provide these checks. Most, if not all, discard the figures and charts before checking for plagiarism. Discarding the figures and charts result in look holes that people can take advantage. That means people can plagiarize figures and charts easily without the current plagiarism systems detecting it. There are very few papers which talks about flowcharts plagiarism detection. Therefore, there is a need to develop a system that will detect plagiarism in figures and charts.

Keywords: flowchart, multimedia retrieval, figures similarity, image comparison, figure retrieval

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Authors: Nikat Parveen, M. Ananthi

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Big data is one of the emerging technology, which collects the data from various sensors and those data will be used in many fields. Data retrieval is one of the major issue where there is a need to extract the exact data as per the need. In this paper, large amount of data set is processed by using the feature selection. Feature selection helps to choose the data which are actually needed to process and execute the task. The key value is the one which helps to point out exact data available in the storage space. Here the available data is streamed and R-Center is proposed to achieve this task.

Keywords: big data, key value, feature selection, retrieval, performance

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Authors: Ibrahim Abdullahi Mohammad

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A library catalogue exists not only as an inventory of the collections of the particular library, but also as a retrieval device. It is provided to assist the library user in finding whatever information or information resources they may be looking for. The paper proposes that this location objective of the library catalogue can only be fulfilled, if the library catalogue is constructed, bearing in mind the information needs and searching behavior of the library user. Comparing AACR2 and RDA viz-a-viz the changes RDA has introduced into bibliographic standards, the paper tries to establish the level of viability of RDA in relation to AACR2.

Keywords: library catalogue, information retrieval, AACR2, RDA

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Authors: K. W. Nam, B. J. Kim, S. J. Lee

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Along with the development of network infrastructures, such as high-speed Internet and mobile environment, the explosion of multimedia data is expanding the range of multimedia services beyond voice and data services. Amid this flow, research is actively being done on the creation, management, and transmission of metadata on digital content to provide different services to users. This paper proposes a system for the insertion, storage, and retrieval of metadata about digital content. The metadata server with Binary XML was implemented for efficient storage space and retrieval speeds, and the transport data size required for metadata retrieval was simplified. With the proposed system, the metadata could be inserted into the moving objects in the video, and the unnecessary overlap could be minimized by improving the storage structure of the metadata. The proposed system can assemble metadata into one relevant topic, even if it is expressed in different media or in different forms. It is expected that the proposed system will handle complex network types of data.

Keywords: video, multimedia, metadata, editing tool, XML

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Authors: Rabia Bashir, Sajjad Akbar

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There is an inevitable demand of web mining due to rapid increase of huge information on the Internet, but the striking variety of web structures has made required content retrieval a difficult task. To counter this issue, Web Content Mining (WCM) emerges as a potential candidate which extracts and integrates suitable resources of data to users. In past few years, research has been done on several extraction techniques for WCM i.e. agent-based, template-based, assumption-based, statistic-based, wrapper-based and machine learning. However, it is still unclear that either these approaches are efficiently tackling the significant challenges of WCM or not. To answer this question, this paper identifies these challenges such as language independency, structure flexibility, performance, automation, dynamicity, redundancy handling, intelligence, relevant content retrieval, and privacy. Further, mapping of these challenges is done with existing extraction mechanisms which helps to adopt the most suitable WCM approach, given some conditions and characteristics at hand.

Keywords: content mining challenges, web content mining, web content extraction approaches, web information retrieval

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Authors: Mohammed Hasan Salahat, Stave Wade

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This paper presents an application of a “Systematic Soft Domain Driven Design Framework” as a soft systems approach to domain-driven design of information systems development. The framework combining techniques from Soft Systems Methodology (SSM), the Unified Modeling Language (UML), and an implementation pattern knows as ‘Naked Objects’. This framework have been used in action research projects that have involved the investigation and modeling of business processes using object-oriented domain models and the implementation of software systems based on those domain models. Within this framework, Soft Systems Methodology (SSM) is used as a guiding methodology to explore the problem situation and to develop the domain model using UML for the given business domain. The framework is proposed and evaluated in our previous works, and a real case study ‘Information Retrieval System for Academic Research’ is used, in this paper, to show further practice and evaluation of the framework in different business domain. We argue that there are advantages from combining and using techniques from different methodologies in this way for business domain modeling. The framework is overviewed and justified as multi-methodology using Mingers Multi-Methodology ideas.

Keywords: SSM, UML, domain-driven design, soft domain-driven design, naked objects, soft language, information retrieval, multimethodology

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Authors: Kehinde Agbele, Longe Olumide, Daniel Ekong, Dele Seluwa, Akintoye Onamade

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One major problem of most existing information retrieval systems (IRS) is that they provide even access and retrieval results to individual users specially based on the query terms user issued to the system. When using IRS, users often present search queries made of ad-hoc keywords. It is then up to IRS to obtain a precise representation of user’s information need, and the context of the information. In effect, the volume and range of the Internet documents is growing exponentially and consequently causes difficulties for a user to obtain information that precisely matches the user interest. Diverse combination techniques are used to achieve the specific goal. This is due, firstly, to the fact that users often do not present queries to IRS that optimally represent the information they want, and secondly, the measure of a document's relevance is highly subjective between diverse users. In this paper, we address the problem by investigating the optimization of IRS to individual information needs in order of relevance. The paper addressed the development of algorithms that optimize the ranking of documents retrieved from IRS. This paper addresses this problem with a two-fold approach in order to retrieve domain-specific documents. Firstly, the design of context of information. The context of a query determines retrieved information relevance using personalization and context-awareness. Thus, executing the same query in diverse contexts often leads to diverse result rankings based on the user preferences. Secondly, the relevant context aspects should be incorporated in a way that supports the knowledge domain representing users’ interests. In this paper, the use of evolutionary algorithms is incorporated to improve the effectiveness of IRS. A context-based information retrieval system that learns individual needs from user-provided relevance feedback is developed whose retrieval effectiveness is evaluated using precision and recall metrics. The results demonstrate how to use attributes from user interaction behavior to improve the IR effectiveness.

Keywords: context, document relevance, information retrieval, personalization, user search behaviors

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Authors: Takanori Tanaka, Daisuke Kitao, Daisuke Ikeda

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The aurora is an attractive phenomenon but it is difficult to understand the whole mechanism of it. An approach of data-intensive science might be an effective approach to elucidate such a difficult phenomenon. To do that we need labeled data, which shows when and what types of auroras, have appeared. In this paper, we propose an image retrieval system for auroral all-sky images, some of which include discrete and diffuse aurora, and the other do not any aurora. The proposed system retrieves images which are similar to the query image by using a popular image recognition method. Using 300 all-sky images obtained at Tromso Norway, we evaluate two methods of image recognition methods with or without our original color filtering method. The best performance is achieved when SIFT with the color filtering is used and its accuracy is 81.7% for discrete auroras and 86.7% for diffuse auroras.

Keywords: data-intensive science, image classification, content-based image retrieval, aurora

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Authors: Sanjay Singh, Sushil Kumar, Rashmi Jain

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Quantum registering is another pattern in computational hypothesis and a quantum mechanical framework has a few helpful properties like Entanglement. We plan to store data concerning the structure and substance of a basic picture in a quantum framework. Consider a variety of n qubits which we propose to use as our memory stockpiling. In recent years classical processing is switched to quantum image processing. Quantum image processing is an elegant approach to overcome the problems of its classical counter parts. Image storage, retrieval and its processing on quantum machines is an emerging area. Although quantum machines do not exist in physical reality but theoretical algorithms developed based on quantum entangled states gives new insights to process the classical images in quantum domain. Here in the present work, we give the brief overview, such that how entangled states can be useful for quantum image storage and retrieval. We discuss the properties of tripartite Greenberger-Horne-Zeilinger and W states and their usefulness to store the shapes which may consist three vertices. We also propose the techniques to store shapes having more than three vertices.

Keywords: Greenberger-Horne-Zeilinger, image storage and retrieval, quantum entanglement, W states

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Authors: Alicia Curtin, Kathy Hall

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This paper explores research and practice shared on a masters University module entitled Children's Literature as Pedagogy. Issues explored include the meaning of childhood and literature; the definition of what counts as text, textual and literacy practice for children and adolescents. A sociocultural framework is used to define literacy practice from this perspective and student voice and experience remains central. Lessons from classroom experience and the use of innovative, multi modal and non traditional texts and pedagogical approaches are offered as examples of innovative and inclusive pedagogy in the field of literacy practice.

Keywords: non traditional, pedagogy, practice, sociocultural

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Authors: Ashleigh Beales, Anne Whitworth, Jade Cartwright

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Background: While significant benefits of lexical retrieval intervention are evident within the Primary Progressive Aphasia (PPA) and Alzheimer’s disease (AD) literature, an understanding of the mechanisms that underlie change or improvement is limited. Change mechanisms have been explored in the non-progressive post-stroke literature that may offer insight into how interventions affect change with progressive language disorders. The potential influences of cognitive factors may also play a role here, interacting with the aims of intervention. Exploring how such processes have been applied is likely to grow our understanding of how interventions have, or have not, been effective, and how and why generalisation is likely, or not, to occur. Aims: This review of the literature aimed to (1) investigate the proposed mechanisms of change which underpin lexical interventions, mapping the PPA and AD lexical retrieval literature to theoretical accounts of mechanisms that underlie change within the broader intervention literature, (2) identify whether and which nonlinguistic cognitive functions have been engaged in intervention with these populations and any proposed influence, and (3) explore evidence of linguistic generalisation, with particular reference to change mechanisms employed in interventions. Main contribution: A search of Medline, PsycINFO, and CINAHL identified 36 articles that reported data for individuals with PPA or AD following lexical retrieval intervention. A review of the mechanisms of change identified 10 studies that used stimulation, 21 studies utilised relearning, three studies drew on reorganisation, and two studies used cognitive-relay. Significant treatment gains, predominantly based on linguistic performance measures, were reported for all client groups for each of the proposed mechanisms. Reorganisation and cognitive-relay change mechanisms were only targeted in PPA. Eighteen studies incorporated nonlinguistic cognitive functions in intervention; these were limited to autobiographical memory (16 studies), episodic memory (three studies), or both (one study). Linguistic generalisation outcomes were inconsistently reported in PPA and AD studies. Conclusion: This review highlights that individuals with PPA and AD may benefit from lexical retrieval intervention, irrespective of the mechanism of change. Thorough application of a theory of intervention is required to gain a greater understanding of the change mechanisms, as well as the interplay of nonlinguistic cognitive functions.

Keywords: Alzheimer's disease, lexical retrieval, mechanisms of change, primary progressive aphasia

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Authors: Mehdi Sheikhi, Marjan Nassiri-Asl, Esmail Abbasi, Mahsa Shafiee

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Various synthetic derivatives of natural flavonoids are known to have neuroactive properties. The present study aimed to investigate the effects of vitexin (5, 7, 4-trihydroxyflavone-8-glucoside), a flavonoid found in such plants as tartary buckwheat sprouts, wheat leaves phenolome, Mimosa pudica Linn and Passiflora spp, on scopolamine-induced memory impairment in rats. To achieve this goal, we assessed the effects of vitexin on memory retrieval in the presence or absence of scopolamine using a step-through passive avoidance trial. In the first part of the study, vitexin (25, 50, and 100 μM) was administered intracerebroventricularly (i.c.v.) before acquisition trials. In the second part, vitexin, at the same doses, was administered before scopolamine (10 μg, i.c.v.) and before the acquisition trials. During retention tests, vitexin (100 μM) in the absence of scopolamine significantly increased the stepthrough latencies compared to scopolamine. In addition, vitexin (100 μM) significantly reversed the shorter step-through latencies induced by scopolamine (P < 0.05). These results indicate that vitexin has a potential role in enhancing memory retrieval. A possible mechanism is modulation of cholinergic receptors; however, other mechanisms may be involved in its effects in acute exposure.

Keywords: flavonoid, memory retrieval, passive avoidance, scopolamine, vitexin

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Authors: Alan C. Lin, Ricky N. Joevan

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This paper focuses on developing a system to transfer mold base plates and standard parts faster during the stage of injection mold design. This system not only provides a way to compare the file version, but also it utilizes Siemens NX 10 to isolate the updated information into a single executable file (.dll), and then, the file can be transferred without the need of transferring the whole file. By this way, the system can help the user to download only necessary mold base plates and standard parts, and those parts downloaded are only the updated portions.

Keywords: CAD, injection molding, mold base, data retrieval

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Authors: U. S. N. Raju, Kothuri Sai Kiran, Meena G. Kamal, Vinay Nikhil Pabba, Suresh Kanaparthi

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There is huge amount of lecture video data available for public use, and many more lecture videos are being created and uploaded every day. Searching for videos on required topics from this huge database is a challenging task. Therefore, an efficient method for video retrieval is needed. An approach for automated video indexing and video search in large lecture video archives is presented. As the amount of video lecture data is huge, it is very inefficient to do the processing in a centralized computation framework. Hence, Hadoop Framework for distributed computing for Big Video Data is used. First, step in the process is automatic video segmentation and key-frame detection to offer a visual guideline for the video content navigation. In the next step, we extract textual metadata by applying video Optical Character Recognition (OCR) technology on key-frames. The OCR and detected slide text line types are adopted for keyword extraction, by which both video- and segment-level keywords are extracted for content-based video browsing and search. The performance of the indexing process can be improved for a large database by using distributed computing on Hadoop framework.

Keywords: video lectures, big video data, video retrieval, hadoop

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Authors: Daud Abdia, Aqsa Shamim, Farhan Tabassum

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In order to achieve specific goals, basketball players have to use different skills to enhance their motivation, one such skill is deliberate practice. The aim of this study was to explore the relationship between deliberate practice of dribbling and self-regulatory behavior of male basketball players. For this purpose, a sample of 108 basketball players using stratified sampling was taken from public and private sector universities. Sample was divided into two groups that are experimental (n=54) and control group (n=54) using comparative experimental design. Experimental group was involved in the training of deliberate practice of dribbling for 5 weeks. Amounts of weekly practice activity and Self-Regulation of Learning Self-Report Scale (SRL-SRS) were used for self-regulatory behavior to collect data after the deliberate practice. The reliability of amounts of weekly practice activity was found to be 0.852, whereas SRL-SRS was found to be 0.890. The results of the study indicated a strong positive correlation between deliberate practice of dribbling and self-regulatory behavior (r=0.755, n=54, p=.000). Whereas, paired sample t-test; t(53)=1.37, p < 0.005 shows statistically significant improvement in the self-regulatory behavior after the training program of deliberate practice from 3.02 ± 0.64m to 3.21 ± 0.75m (p < 0.005). It was concluded that in order to enhance the self-regulatory behavior of basketball players we should work on the deliberate practice of the players.

Keywords: self-regulatory behavior, deliberate practice, dribbling, basketball

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Authors: Hsu-Hui Cheng, Peng-Chian Chen, Shu-Yuan Chang, Jie-Ying Zhang

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Activities in the music practice rooms range from playing, listening, rehearsing to music performing. The good room acoustics in a music practice room enables a music teacher to teach more effectively subtle concepts such as intonation, articulation, balance, dynamics and tone production. A poor acoustical environment would deeply affect the development of basic musical skills of music students. Practicing in the music practice room is an essential daily activity for music students; consequently, music practice rooms are very important facilities in a music school or department. The purpose of this survey is to measure and analyze the acoustic condition of piano practice rooms at the department of music in Zhaoqing University and accordingly apply a more effective teaching method to music students. The volume of the music practice room is approximately 25 m³, and it has existing curtains and some wood hole sound-absorbing panels. When all small music practice rooms are in constant use for teaching, it was found that the values of the background noise at 45, 46, 42, 46, 45 dB(A) in the small music practice room ( the doors and windows were close), respectively. The noise levels in the small music practice room to higher than standard levels (35dB(A)).

Keywords: acoustical performance, music practice room, noise level, piano room

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Authors: Amel Hannech, Mehdi Adda, Hamid Mcheick

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In computer science, the purpose of finding useful information is still one of the most active and important research topics. The most popular application of information retrieval (IR) are Search Engines, they meet users' specific needs and aim to locate the effective information in the web. However, these search engines have some limitations related to the relevancy of the results and the ease to explore those results. In this context, we proposed in previous works a Multi-Space Search Engine model that is based on a multidimensional interpretation universe. In the present paper, we integrate an additional dimension that allows to offer users new research experiences. The added component is based on creating user profiles and calculating the similarity between them that then allow the use of collaborative filtering in retrieving search results. To evaluate the effectiveness of the proposed model, a prototype is developed. The experiments showed that the additional dimension has improved the relevancy of results by predicting the interesting items of users based on their experiences and the experiences of other similar users. The offered personalization service allows users to approve the pertinent items, which allows to enrich their profiles and further improve research.

Keywords: information retrieval, v-facets, user behavior analysis, user profiles, topical ontology, association rules, data personalization

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Authors: Ganfeng Yu, Yuefeng Ma, Shanliang Yang

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The word discovery is a key problem in text information retrieval technology. Methods in new word discovery tend to be closely related to words because they generally obtain new word results by analyzing words. With the popularity of social networks, individual netizens and online self-media have generated various network texts for the convenience of online life, including network words that are far from standard Chinese expression. How detect network words is one of the important goals in the field of text information retrieval today. In this paper, we integrate the word embedding model and clustering methods to propose a network word discovery framework based on sentence semantic similarity (S³-NWD) to detect network words effectively from the corpus. This framework constructs sentence semantic vectors through a distributed representation model, uses the similarity of sentence semantic vectors to determine the semantic relationship between sentences, and finally realizes network word discovery by the meaning of semantic replacement between sentences. The experiment verifies that the framework not only completes the rapid discovery of network words but also realizes the standard word meaning of the discovery of network words, which reflects the effectiveness of our work.

Keywords: text information retrieval, natural language processing, new word discovery, information extraction

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Authors: Kabil Boukhari

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In the context of information retrieval, the selection of the most relevant words is a very important step. In fact, the text cleaning allows keeping only the most representative words for a better use. In this paper, we propose a library for the purpose text preprocessing within an implemented application to facilitate this task. This study has two purposes. The first, is to present the related work of the various steps involved in text preprocessing, presenting the segmentation, stemming and lemmatization algorithms that could be efficient in the rest of study. The second, is to implement a developed tool for text preprocessing in French and English. This library accepts unstructured text as input and provides the preprocessed text as output, based on a set of rules and on a base of stop words for both languages. The proposed library has been made on different corpora and gave an interesting result.

Keywords: text preprocessing, segmentation, knowledge extraction, normalization, text generation, information retrieval

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Authors: Marianna Constantinou, Hana Burianova, Ala Yankouskaya

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Episodic memory retrieval benefits from arousal, with better performance linked to arousing to-be-remembered information. However, the enduring impact of arousal on subsequent memory processes, particularly for non-arousing stimuli, remains unclear. This functional Magnetic Resonance Imaging (fMRI) study examined the effects of arousal on episodic memory processes in young and old adults, focusing on memory of neutral information following arousal exposure. Neural activity was assessed at three distinct timepoints: during exposure to arousing and non-arousing stimuli, memory consolidation (with or without arousing stimulus exposure), and during memory retrieval (with or without arousing stimulus exposure). Behavioural results show that across both age groups, participants performed worse when retrieving episodic memories about a video preceded by a highly arousing negative image. Our fMRI findings reveal three key findings: i) the extension of the influence of negative arousal beyond encoding; ii) the presence of this influence in both young and old adults; iii) and the differential treatment of positive arousal between these age groups. Our findings emphasise valence-specific effects on memory processes and support the enduring impact of negative arousal. We further propose an age-related alteration in the old adult brain in differentiating between positive and negative arousal.

Keywords: episodic memory, ageing, fmri, arousal, valence

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Authors: Ben G. Weber, Michael Mateas

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We present a case-based reasoning technique for selecting build orders in a real-time strategy game. The case retrieval process generalizes features of the game state and selects cases using domain-specific recall methods, which perform exact matching on a subset of the case features. We demonstrate the performance of the technique by implementing it as a component of the integrated agent framework of McCoy and Mateas. Our results demonstrate that the technique outperforms nearest-neighbor retrieval when imperfect information is enforced in a real-time strategy game.

Keywords: case based reasoning, real time strategy systems, requirements elicitation, requirement analyst, artificial intelligence

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Authors: Ahmed Fradi

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In a digital world in perpetual evolution and acceleration, data more and more voluminous, rich and varied, the new software solutions emerged with the Big Data phenomenon offer new opportunities to the company enabling it not only to optimize its business and to evolve its production model, but also to reorganize itself to increase competitiveness and to identify new strategic axes. Design and manufacturing industrial companies, like the others, face these challenges, data represent a major asset, provided that they know how to capture, refine, combine and analyze them. The objective of our paper is to propose a solution allowing geometric and topological information extraction from 3D CAD model (precisely STEP files) databases, with specific algorithm based on the programming paradigm MapReduce. Our proposal is the first step of our future approach to 3D CAD object retrieval.

Keywords: Big Data, MapReduce, 3D object retrieval, CAD, STEP format

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Authors: Ahmad FAROOQ, Xinfeng Zhang, Fahad Sabah, Raheem Sarwar

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In Traditional Chinese Medicine, tongue diagnosis is a vital inspection tool (TCM). In this study, we explore the potential of machine learning in tongue diagnosis. It begins with the cataloguing of the various classifications and characteristics of the human tongue. We infer 24 kinds of tongues from the material and coating of the tongue, and we identify 21 attributes of the tongue. The next step is to apply machine learning methods to the tongue dataset. We use the Weka machine learning platform to conduct the experiment for performance analysis. The 457 instances of the tongue dataset are used to test the performance of five different machine learning methods, including SVM, Random Forests, Decision Trees, and Naive Bayes. Based on accuracy and Area under the ROC Curve, the Support Vector Machine algorithm was shown to be the most effective for tongue diagnosis (AUC).

Keywords: medical imaging, image retrieval, machine learning, tongue

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Authors: Mahdi Salarian, Xi Xu, Rashid Ansari

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Accurate localization of mobile devices based on camera-acquired visual media information usually requires a search over a very large GPS-referenced image database. This paper proposes an efficient method for limiting the search space for image retrieval engine by extracting and leveraging additional media information about Estimated Positional Error (EP E) to address complexity and accuracy issues in the search, especially to be used for compensating GPS location inaccuracy in dense urban areas. The improved performance is achieved by up to a hundred-fold reduction in the search area used in available reference methods while providing improved accuracy. To test our procedure we created a database by acquiring Google Street View (GSV) images for down town of Chicago. Other available databases are not suitable for our approach due to lack of EP E for the query images. We tested the procedure using more than 200 query images along with EP E acquired mostly in the densest areas of Chicago with different phones and in different conditions such as low illumination and from under rail tracks. The effectiveness of our approach and the effect of size and sector angle of the search area are discussed and experimental results demonstrate how our proposed method can improve performance just by utilizing a data that is available for mobile systems such as smart phones.

Keywords: localization, retrieval, GPS uncertainty, bag of word

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Authors: Jonah Kenei, Elisha Opiyo

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Electronic health records, as a repository of patient information, is nowadays the most commonly used technology to record, store and review patient clinical records and perform other clinical tasks. However, the accurate identification and retrieval of relevant information from clinical records is a difficult task due to the unstructured nature of clinical documents, characterized in particular by a lack of clear structure. Therefore, medical practice is facing a challenge thanks to the rapid growth of health information in electronic health records (EHRs), mostly in narrative text form. As a result, it's becoming important to effectively manage the growing amount of data for a single patient. As a result, there is currently a requirement to visualize electronic health records (EHRs) in a way that aids physicians in clinical tasks and medical decision-making. Leveraging text visualization techniques to unstructured clinical narrative texts is a new area of research that aims to provide better information extraction and retrieval to support clinical decision support in scenarios where data generated continues to grow. Clinical datasets in electronic health records (EHR) offer a lot of potential for training accurate statistical models to classify facets of information which can then be used to improve patient care and outcomes. However, in many clinical note datasets, the unstructured nature of clinical texts is a common problem. This paper examines the very issue of getting raw clinical texts and mapping them into meaningful structures that can support healthcare professionals utilizing narrative texts. Our work is the result of a collaborative design process that was aided by empirical data collected through formal usability testing.

Keywords: classification, electronic health records, narrative texts, visualization

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Authors: Rutuja Desai

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Search engine is fundamentally a framework used to search the data which is pertinent to the client via WWW. Looking close-by spot identified with the keywords is an imperative concept in developing web advances. For such kind of searching, extent pursuit or closest neighbor is utilized. In range search the forecast is made whether the objects meet to query object. Nearest neighbor is the forecast of the focuses close to the query set by the client. Here, the nearest neighbor methodology is utilized where Data recovery R+ tree is utilized rather than IR2 tree. The disadvantages of IR2 tree is: The false hit number can surpass the limit and the mark in Information Retrieval R-tree must have Voice over IP bit for each one of a kind word in W set is recouped by Data recovery R+ tree. The inquiry is fundamentally subordinate upon the key words and the geometric directions.

Keywords: information retrieval, nearest neighbor search, keyword search, R+ tree

Procedia PDF Downloads 261