scholarly journals Visual Information Retrieval for Videos Based on Feature Extraction using Machine Learning Techniques

2019 ◽  
Vol 8 (10S) ◽  
pp. 29-34
Keyword(s):  
Machine Learning ◽  
Information Retrieval ◽  
Visual Information ◽  
Video Retrieval ◽  
Multimedia Data ◽  
Machine Learning Techniques ◽  
Multiple Sequence ◽  
Visual Information Retrieval ◽  
Learning Techniques ◽  
Efficient Retrieval

Information retrieval is one of the important areas of research with highest scope for data mining combined with machine learning. The proposed research focus on visual information retrieval by applying machine learning techniques. The usage of multimedia data such as text, images, videos are abundantly increasing day by day in this smart era. Also the need for information classification and retrieval are getting exponential demands to fulfill the research and end user requirements. The tech giants are conducting their researches to develop efficient retrieval systems for videos. Video retrieval is considered to be the toughest and challenging research in the recent times. Due to large storage space, lengthy play time, multiple sequence of frames, spatial temporal challenges, lack of visual relevancy, less hardware and processing support. The proposed visual information retrieval has got higher scope of research with the above listed problems.

An Overview of Semantic-Based Visual Information Retrieval

2011 ◽  
pp. 2978-2983
Author(s):  
Yu-Jin Zhang
Keyword(s):  
Information Retrieval ◽  
Visual Information ◽  
Domain Knowledge ◽  
Video Retrieval ◽  
Video Annotation ◽  
Future Research ◽  
Semantic Retrieval ◽  
Visual Information Retrieval ◽  
Process Framework ◽  
Future Research Directions

Content-based image retrieval (CBIR) could be described as a process framework for efficiently retrieving images from a collection by similarity. The retrieval relies on extracting the appropriate characteristic quantities describing the desired contents of images. Content-based video retrieval (CBVR) made its appearance in treating video in the similar means as CBIR treating images. Content-based visual information retrieval (CBVIR) combines CBIR and CBVR together (Zhang, 2003). With the progress of electronic equipments and computer techniques for visual information capturing and processing, a huge number of image and video records have been collected. Visual information becomes a well-known information format and a popular element in all aspects of our society. The large visual data make the dynamic research to be focused on the problem of how to efficiently capture, store, access, process, represent, describe, query, search, and retrieve their contents. In the last years, CBVIR has experienced significant growth and progress, resulting in a virtual explosion of published information. It has attracted many interests from image engineering, computer vision and the database community. The current focus of CBVIR is around capturing highlevel semantics, that is, the so-called Semantic-based Visual Information Retrieval (SBVIR). This article will first show some statistics about the research publications on SBVIR in recent years to give an idea about its developments statue. It then gives an overview on several current centers of attention, by summarizing results on subjects such as image and video annotation, human-computer interaction, models and tools for semantic retrieval, and miscellaneous techniques in applications. Finally, some future research directions, the domain knowledge and learning, relevance feedback and association feedback, as well as research at even high levels, such as cognitive level and affective level, are pointed out.

Information Retrieval in Conjunction With Deep Learning

2020 ◽  
pp. 300-311 ◽  
Author(s):  
Anu Bajaj ◽  
Tamanna Sharma ◽  
Om Prakash Sangwan
Keyword(s):  
Machine Learning ◽  
Information Retrieval ◽  
Deep Learning ◽  
Statistical Models ◽  
Web Search ◽  
Machine Learning Techniques ◽  
Trend Detection ◽  
Social Media Analytics ◽  
Learning Techniques ◽  
Long Time

Information is second level of abstraction after data and before knowledge. Information retrieval helps fill the gap between information and knowledge by storing, organizing, representing, maintaining, and disseminating information. Manual information retrieval leads to underutilization of resources, and it takes a long time to process, while machine learning techniques are implications of statistical models, which are flexible, adaptable, and fast to learn. Deep learning is the extension of machine learning with hierarchical levels of learning that make it suitable for complex tasks. Deep learning can be the best choice for information retrieval as it has numerous resources of information and large datasets for computation. In this chapter, the authors discuss applications of information retrieval with deep learning (e.g., web search by reducing the noise and collecting precise results, trend detection in social media analytics, anomaly detection in music datasets, and image retrieval).

scholarly journals Visualizing correlations among Parkinson biomedical data through information retrieval and machine learning techniques

2021 ◽  
Author(s):  
Maria Frasca ◽  
Genoveffa Tortora
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Information Retrieval ◽  
Semantic Analysis ◽  
Clustering Algorithms ◽  
Machine Learning Techniques ◽  
Biomedical Data ◽  
Learning Techniques ◽  
Medical Reports

AbstractIn the last few years, the integration of researches in Computer Science and medical fields has made available to the scientific community an enormous amount of data, stored in databases. In this paper, we analyze the data available in the Parkinson’s Progression Markers Initiative (PPMI), a comprehensive observational, multi-center study designed to identify progression biomarkers important for better treatments for Parkinson’s disease. The data of PPMI participants are collected through a comprehensive battery of tests and assessments including Magnetic Resonance Imaging and DATscan imaging, collection of blood, cerebral spinal fluid, and urine samples, as well as cognitive and motor evaluations. To this aim, we propose a technique to identify a correlation between the biomedical data in the PPMI dataset for verifying the consistency of medical reports formulated during the visits and allow to correctly categorize the various patients. To correlate the information of each patient’s medical report, Information Retrieval and Machine Learning techniques have been adopted, including the Latent Semantic Analysis, Text2Vec and Doc2Vec techniques. Then, patients are grouped and classified into affected or not by using clustering algorithms according to the similarity of medical reports. Finally, we have adopted a visualization system based on the D3 framework to visualize correlations among medical reports with an interactive chart, and to support the doctor in analyzing the chronological sequence of visits in order to diagnose Parkinson’s disease early.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

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