Topic Analysis and Identification of Queries

2011 ◽  
pp. 345-358 ◽  
Author(s):  
Seda Ozmutlu ◽  
Huseyin C. Ozmutlu ◽  
Amanda Spink
Keyword(s):  
Information Retrieval ◽  
Literature Review ◽  
Real Time ◽  
Search Engine ◽  
Analytical Methods ◽  
Simple Task ◽  
Topic Analysis ◽  
Topic Identification ◽  
Time Requirements ◽  
User Queries

This chapter emphasizes topic analysis and identification of search engine user queries. Topic analysis and identification of queries is an important task related to the discipline of information retrieval which is a key element for the development of successful personalized search engines. Topic identification of text is also no simple task, and a problem yet unsolved. The problem is even harder for search engine user queries due to real-time requirements and the limited number of terms in the user queries. The chapter includes a detailed literature review on topic analysis and identification, with an emphasis on search engine user queries, a survey of the analytical methods that have been and can be used, and the challenges and research opportunities related to topic analysis and identification.

scholarly journals A Real Time Visual Exploratory Search Engine for Information Retrieval in a Cloud

2015 ◽  
Vol 4 (3) ◽  
pp. 216-220 ◽  
Author(s):  
Mohammed Najah Mahdi ◽  
◽  
Abdul Rahim Ahmad ◽  
Roslan Ismail ◽  
◽  
...  
Keyword(s):  
Information Retrieval ◽  
Real Time ◽  
Search Engine ◽  
Exploratory Search

Dampak Anemi Defisiensi Besi pada Kehamilan : a Literature Review

2020 ◽  
Vol 18 (1) ◽  
pp. 50
Author(s):  
Lilik Hidayanti ◽  
M.Zen Rahfiludin
Keyword(s):  
Literature Review ◽  
Search Engine ◽  
Electronic Search

Ibu hamil yang menderita anemia defisiensi besi dapat memberikan dampak negative pada kesehatan ibu maupun bayi yang dilahirkan sehingga dapat meningkatkan angka kematian ibu (AKI) dan angka kematian anak (AKB). Literature review ini bertujuan untuk melakukan telaah terhadap artikel-artikel hasil penelitian eksplanatori dan eksperimental yang dipublikasikan 10 tahun yang lalu atau mulai dari tahun 2008 terkait dengan dampak anemia defisiensi besi pada ibu saat hamil dengan outcome kehamilannya seperti prematuritas, BBLR, kejadian preeklamsi, perkembangan kognitif anak dan kematian bayi. Hasil penelusuran melalui 4 electronic search engine yaitu Proquest, CINAHL Medline, dan Scopus menemukan 220 artikel. Setelah dilakukan proses skrining berdasarkan kriteria inklusi yang ditetapkan, maka diperoleh 50 artikel yang akan ditelaah. Hasil telaah artikel yang telah kami lakukan menemukan bahwa paling banyak (21) artikel membahas mengenai dampak anemia defisiensi besi pada ibu hamil dengan kejadian BBLR, 18 artikel dengan kejadian prematuritas, 6 artikel dengan perkembangan mental anak, dan sisanya (5) artikel dengan kadar zat besi dalam tubuh bayi baru lahir dan ourcome kehamilan yang lainnya. Hasil penelitian di berbagai Negara baik Negara berkembang maupun Negara maju menunjukan bahwa anemia yang terjadi pada masa kehamilan dapat memberikan dampak kelahiran dengan BBLR, prematuritas, kematian neonatus, anemia neonatus, kelahiran denga  metode sectio, hambatan perkembangan mental, dan rendahnya skor APGAR.

scholarly journals Intelligent IoT systems for civil infrastructure health monitoring: a research roadmap

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
E. Bertino ◽  
M. R. Jahanshahi ◽  
A. Singla ◽  
R.-T. Wu
Keyword(s):  
Data Acquisition ◽  
Real Time ◽  
Original Data ◽  
Local Analysis ◽  
Civil Infrastructure ◽  
Time Requirements ◽  
Iot Devices ◽  
Learning Reinforcement ◽  
The Times ◽  
Acquisition Processes

AbstractThis paper addresses the problem of efficient and effective data collection and analytics for applications such as civil infrastructure monitoring and emergency management. Such problem requires the development of techniques by which data acquisition devices, such as IoT devices, can: (a) perform local analysis of collected data; and (b) based on the results of such analysis, autonomously decide further data acquisition. The ability to perform local analysis is critical in order to reduce the transmission costs and latency as the results of an analysis are usually smaller in size than the original data. As an example, in case of strict real-time requirements, the analysis results can be transmitted in real-time, whereas the actual collected data can be uploaded later on. The ability to autonomously decide about further data acquisition enhances scalability and reduces the need of real-time human involvement in data acquisition processes, especially in contexts with critical real-time requirements. The paper focuses on deep neural networks and discusses techniques for supporting transfer learning and pruning, so to reduce the times for training the networks and the size of the networks for deployment at IoT devices. We also discuss approaches based on machine learning reinforcement techniques enhancing the autonomy of IoT devices.

Real-time, High-resolution Depth Upsampling on Embedded Accelerators

2021 ◽  
Vol 20 (3) ◽  
pp. 1-22
Author(s):  
David Langerman ◽  
Alan George
Keyword(s):  
High Resolution ◽  
Low Power ◽  
Real Time ◽  
Mixed Reality ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Reconfigurable Logic ◽  
Depth Sensors ◽  
Time Requirements ◽  
Graphics Processing

High-resolution, low-latency apps in computer vision are ubiquitous in today’s world of mixed-reality devices. These innovations provide a platform that can leverage the improving technology of depth sensors and embedded accelerators to enable higher-resolution, lower-latency processing for 3D scenes using depth-upsampling algorithms. This research demonstrates that filter-based upsampling algorithms are feasible for mixed-reality apps using low-power hardware accelerators. The authors parallelized and evaluated a depth-upsampling algorithm on two different devices: a reconfigurable-logic FPGA embedded within a low-power SoC; and a fixed-logic embedded graphics processing unit. We demonstrate that both accelerators can meet the real-time requirements of 11 ms latency for mixed-reality apps. 1

Overview of information retrieval in a hospital-based health technology assessment center in a Swedish region

2021 ◽  
Vol 37 (1) ◽  
Author(s):  
Ida Stadig ◽  
Therese Svanberg
Keyword(s):  
Information Retrieval ◽  
Literature Review ◽  
Health Technology Assessment ◽  
Technology Assessment ◽  
Full Text ◽  
Health Technology ◽  
University Hospital ◽  
Literature Searching ◽  
Medical Library ◽  
Full Text Screening

Abstract Objectives This article aims to provide a brief review of information retrieval and hospital-based health technology assessment (HB-HTA) and describe library experiences and working methods at a regional HB-HTA center from the center's inception to the present day. Methods For this brief literature review, searches in PubMed and LISTA were conducted to identify studies reporting on HB-HTA and information retrieval. The description of the library's involvement in the HTA center and its working methods is based on the authors’ experience and internal and/or unpublished documents. Results Region Västra Götaland is the second largest healthcare region in Sweden and has had a regional HB-HTA center since 2007 (HTA-centrum). Assessments are performed by clinicians supported by HTA methodologists. The medical library at Sahlgrenska University Hospital works closely with HTA-centrum, with one HTA librarian responsible for coordinating the work. Conclusion In the literature on HB-HTA, we found limited descriptions of the role librarians and information specialists play in different units. The librarians at HTA-centrum play an important role, not only in literature searching but also in abstract and full-text screening.

scholarly journals On the Performance of One-Stage and Two-Stage Object Detectors in Autonomous Vehicles Using Camera Data

2020 ◽  
Vol 13 (1) ◽  
pp. 89
Author(s):  
Manuel Carranza-García ◽  
Jesús Torres-Mateo ◽  
Pedro Lara-Benítez ◽  
Jorge García-Gutiérrez
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Remote Sensing Data ◽  
Autonomous Driving ◽  
Two Stage ◽  
Detection Systems ◽  
One Stage ◽  
Time Requirements ◽  
Speed Accuracy

Object detection using remote sensing data is a key task of the perception systems of self-driving vehicles. While many generic deep learning architectures have been proposed for this problem, there is little guidance on their suitability when using them in a particular scenario such as autonomous driving. In this work, we aim to assess the performance of existing 2D detection systems on a multi-class problem (vehicles, pedestrians, and cyclists) with images obtained from the on-board camera sensors of a car. We evaluate several one-stage (RetinaNet, FCOS, and YOLOv3) and two-stage (Faster R-CNN) deep learning meta-architectures under different image resolutions and feature extractors (ResNet, ResNeXt, Res2Net, DarkNet, and MobileNet). These models are trained using transfer learning and compared in terms of both precision and efficiency, with special attention to the real-time requirements of this context. For the experimental study, we use the Waymo Open Dataset, which is the largest existing benchmark. Despite the rising popularity of one-stage detectors, our findings show that two-stage detectors still provide the most robust performance. Faster R-CNN models outperform one-stage detectors in accuracy, being also more reliable in the detection of minority classes. Faster R-CNN Res2Net-101 achieves the best speed/accuracy tradeoff but needs lower resolution images to reach real-time speed. Furthermore, the anchor-free FCOS detector is a slightly faster alternative to RetinaNet, with similar precision and lower memory usage.

scholarly journals Deep-Framework: A Distributed, Scalable, and Edge-Oriented Framework for Real-Time Analysis of Video Streams

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4045
Author(s):  
Alessandro Sassu ◽  
Jose Francisco Saenz-Cogollo ◽  
Maurizio Agelli
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Video Data ◽  
Video Analytics ◽  
Web Based ◽  
Real Time Analysis ◽  
Open Source Framework ◽  
Cluster Configuration ◽  
Time Requirements ◽  
High Level

Edge computing is the best approach for meeting the exponential demand and the real-time requirements of many video analytics applications. Since most of the recent advances regarding the extraction of information from images and video rely on computation heavy deep learning algorithms, there is a growing need for solutions that allow the deployment and use of new models on scalable and flexible edge architectures. In this work, we present Deep-Framework, a novel open source framework for developing edge-oriented real-time video analytics applications based on deep learning. Deep-Framework has a scalable multi-stream architecture based on Docker and abstracts away from the user the complexity of cluster configuration, orchestration of services, and GPU resources allocation. It provides Python interfaces for integrating deep learning models developed with the most popular frameworks and also provides high-level APIs based on standard HTTP and WebRTC interfaces for consuming the extracted video data on clients running on browsers or any other web-based platform.

Sign in / Sign up

Export Citation Format

Share Document