Homework 9 17-2011

financial modeling for risk management using portfolio is the study of how effectively an invester can diversify the investment risk by constructing optimal portfolio. this study construct an optimal portfolio using Sharpe's optimization model thereby an evaluative study of the effectiveness of sharpe's optimizaion model has happened in this study it compaes he optimal porfolio with three other portfolio's constructed by using randome weight , equal weight, and P/E ratio. and also predict the expected loss and income of portfolio by finding value at risk for 99% and confidance level and also for 95% of confidance level using monte Carlo simulation method

Homework 9 17-2011

d015125

Kailtyn and olivia cl

kaitlynliphardt

Reliance

lokesh alapati

Christos Ntitoras - Future Hotel on the Web (Horeca 2015)

Christos Ntitoras

Kailtyn and olivia cl

kaitlynliphardt

Homework 9 17-2011

d015125

The Morgan Legacy, Chapter VII: Determination

fireflowersims

Building and Sustaining Community Partnerships

Geoff Oman

№6 испания (айгуль,настя)

aigulescka

financial modeling

Ebin John

Resume Film 'ATAS NAMA'

Perjanjian Bagi Hasil Peternakan Sapi di Dusun Betakan

Mekanisme Operasional Modal Ventura

Anjuran Menikah

Implementasi Kerakteristik Kebersamaan dalam Hukum Adat

The proposed approach avoids the semantic gap in image retrieval by combining automatic relevance feedback and a modified stochastic algorithm. A visual feature database is constructed from the image database, using combined feature vector. Very few fast-computable features are included in this step. The user selects the query image, and based on that, the system ranks the whole dataset. The nearest images are retrieved and the first automatic relevance feedback is generated. The combined similarity of textual and visual feature space using Latent Semantic Indexing is evaluated and the images are labelled as relevant or irrelevant. The feedback drives a feature re-weighting process and is routed to the particle swarm optimizer. Instead of classical swarm update approach, the swarm is split, for each swarm to perform the search in parallel, thereby increasing the performance of the system. It provides a powerful optimization tool and an effective space exploration mechanism. The proposed approach aims to achieve the following goals without any human interaction - to cluster relevant images using meta-heuristics and to dynamically modify the feature space by feeding automatic relevance feedback.

Destaque (20)

Axiatel planete-urgence

Programa de Busqueda de errores

Raid level 4

Kailtyn and olivia cl

Eng cerme cave

Homework 9 17-2011

Kailtyn and olivia cl

Reliance

Christos Ntitoras - Future Hotel on the Web (Horeca 2015)

Kailtyn and olivia cl

Homework 9 17-2011

The Morgan Legacy, Chapter VII: Determination

Building and Sustaining Community Partnerships

№6 испания (айгуль,настя)

financial modeling

Resume Film 'ATAS NAMA'

Perjanjian Bagi Hasil Peternakan Sapi di Dusun Betakan

Mekanisme Operasional Modal Ventura

Anjuran Menikah

Implementasi Kerakteristik Kebersamaan dalam Hukum Adat

Semelhante a Homework 9 17-2011

META-HEURISTICS BASED ARF OPTIMIZATION FOR IMAGE RETRIEVAL

IJCSEIT Journal

The proposed work aims to create a smart application camera, with the intention of eliminating the need for a human presence to detect any unwanted sinister activities, such as theft in this case. Spread among the campus, are certain valuable biometric identification systems at arbitrary locations. The application monitosr these systems (hereafter referred to as “object”) using our smart camera system based on an OpenCV platform. By using OpenCV Haar Training, employing the Viola-Jones algorithm implementation in OpenCV, we teach the machine to identify the object in environmental conditions. An added feature of face recognition is based on Principal Component Analysis (PCA) to generate Eigen Faces and the test images are verified by using distance based algorithm against the eigenfaces, like Euclidean distance algorithm or Mahalanobis Algorithm. If the object is misplaced, or an unauthorized user is in the extreme vicinity of the object, an alarm signal is raised.

AUTOMATIC THEFT SECURITY SYSTEM (SMART SURVEILLANCE CAMERA)

csandit

AUTOMATIC THEFT SECURITY SYSTEM (SMART SURVEILLANCE CAMERA)

cscpconf

In recent years, image retrieval process has increased artistically. An image retrieval system is a process for searching and retrieving images from large amount of the image dataset. Color, texture and edge have been the primitive low level image descriptors in content based image retrieval systems. In this paper we discover a system which splits the search process into two stages. In the query specify approach the feature descriptors of a query image we re-extracted and then used to check the similarity between the query image and those images which is in database. In the evolution stage, the most relevant images where retrieved by using the Interactive genetic algorithm. IGA help the users to retrieve the images that are most relevant to the usersÃ¢â‚¬â„¢ need and SVM will rank the image as their title and as par time of search. So that user can get search image as par their requirements.

An Enhance Image Retrieval of User Interest Using Query Specific Approach and...

IJSRD

IEEE Projects 2014-2015

Finalyear Projects

Captcha

Gopinath Jammula

This paper presents the application of multi dimensional feature reduction of Consistency Subset Evaluator (CSE) and Principal Component Analysis (PCA) and Unsupervised Expectation Maximization (UEM) classifier for imaging surveillance system. Recently, research in image processing has raised much interest in the security surveillance systems community. Weapon detection is one of the greatest challenges facing by the community recently. In order to overcome this issue, application of the UEM classifier is performed to focus on the need of detecting dangerous weapons. However, CSE and PCA are used to explore the usefulness of each feature and reduce the multi dimensional features to simplified features with no underlying hidden structure. In this paper, we take advantage of the simplified features and classifier to categorize images object with the hope to detect dangerous weapons effectively. In order to validate the effectiveness of the UEM classifier, several classifiers are used to compare the overall accuracy of the system with the compliment from the features reduction of CSE and PCA. These unsupervised classifiers include Farthest First, Densitybased Clustering and k-Means methods. The final outcome of this research clearly indicates that UEM has the ability in improving the classification accuracy using the extracted features from the multi-dimensional feature reduction of CSE. Besides, it is also shown that PCA is able to speed-up the computational time with the reduced dimensionality of the features compromising the slight decrease of accuracy.

Multi-Dimensional Features Reduction of Consistency Subset Evaluator on Unsup...

CSCJournals

The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.

Enhancing The Human Perceivable Image Content In CAPTCHA Based BF Algorithm

theijes

Abstract In recent days, several social networking sites are more popular with digitized images. It comprises the major portion of the databases which makes the search engines to face difficulty in searching. We present a proficient image retrieval technique, which achieves eminent retrieval efficiency. Most of the images are annotated manually, thus the visual content and tags may be mismatched. This leads to poor performance in Tag Based Image Retrieval (TBIR). Automatic Image Annotation (AIA) analyzes the missing and noisy tags and over-refines it to increase the performance of TBIR. AIA can be achieved using the Tag Completion algorithm. The images retrieved from the TBIR are ranked based on the relevancy of the tags and visual content of the images. The relevancy can be evaluated using Content Based Image Retrieval (CBIR) technique. Based on the ranks, the images are indexed in the Tag matrix. Thus the images that match the search query can be retrieved in an optimal way. Keywords: Image Retrieval, Automatic Image Annotation, Tag Based Image Retrieval (TBIR), Tag Completion Algorithm, Content Based Image Retrieval (CBIR), Tag Matrix

Tag based image retrieval (tbir) using automatic image annotation

eSAT Journals

IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology

Tag based image retrieval (tbir) using automatic image annotation

eSAT Publishing House

IRJET- Smart Classroom Attendance System: Survey

IRJET - Explicit Content Detection using Faster R-CNN and SSD Mobilenet V2

Due to the extensive use of information technology and the recent developments in multimedia systems, the amount of multimedia data available to users has increased exponentially. Video is an example of multimedia data as it contains several kinds of data such as text, image, meta-data, visual and audio. Content based video retrieval is an approach for facilitating the searching and browsing of large multimedia collections over WWW. In order to create an effective video retrieval system, visual perception must be taken into account. We conjectured that a technique which employs multiple features for indexing and retrieval would be more effective in the discrimination and search tasks of videos. In order to validate this, content based indexing and retrieval systems were implemented using color histogram, Texture feature (GLCM), edge density and motion..

Ijcet 06 10_004

IAEME Publication

System analysis and design for multimedia retrieval systems

ijma

A SURVEY ON CONTENT BASED IMAGE RETRIEVAL USING MACHINE LEARNING

Computer vision algorithms are essential components of many systems in operation today. Predicting the robustness of such algorithms for different visual distortions is a task which can be approached with known image quality measures. We evaluate the impact of several image distortions on object segmentation, tracking and detection, and analyze the predictability of this impact given by image statistics, error parameters and image quality metrics. We observe that existing image quality metrics have shortcomings when predicting the visual quality of virtual or augmented reality scenarios. These shortcomings can be overcome by integrating computer vision approaches into image quality metrics. We thus show that image quality metrics can be used to predict the success of computer vision approaches, and computer vision can be employed to enhance the prediction capability of image quality metrics – a reciprocal relation.

Computer Vision Performance and Image Quality Metrics : A Reciprocal Relation

cscpconf

COMPUTER VISION PERFORMANCE AND IMAGE QUALITY METRICS: A RECIPROCAL RELATION

csandit

IRJET- Comparative Analysis of Video Processing Object Detection

Following the popularity of "Cloud Revolution: Exploring the New Wave of Serverless Spatial Data," we're thrilled to announce this much-anticipated encore webinar. In this sequel, we'll dive deeper into the Cloud-Native realm by uncovering practical applications and FME support for these new formats, including COGs, COPC, FlatGeoBuf, GeoParquet, STAC, and ZARR. Building on the foundation laid by industry leaders Michelle Roby of Radiant Earth and Chris Holmes of Planet in the first webinar, this second part offers an in-depth look at the real-world application and behind-the-scenes dynamics of these cutting-edge formats. We will spotlight specific use-cases and workflows, showcasing their efficiency and relevance in practical scenarios. Discover the vast possibilities each format holds, highlighted through detailed discussions and demonstrations. Our expert speakers will dissect the key aspects and provide critical takeaways for effective use, ensuring attendees leave with a thorough understanding of how to apply these formats in their own projects. Elevate your understanding of how FME supports these cutting-edge technologies, enhancing your ability to manage, share, and analyze spatial data. Whether you're building on knowledge from our initial session or are new to the serverless spatial data landscape, this webinar is your gateway to mastering cloud-native formats in your workflows.

Moving object detection using background subtraction algorithm using simulink

eSAT Publishing House

Mri brain image retrieval using multi support vector machine classifier

srilaxmi524

Semelhante a Homework 9 17-2011 (20)

META-HEURISTICS BASED ARF OPTIMIZATION FOR IMAGE RETRIEVAL

AUTOMATIC THEFT SECURITY SYSTEM (SMART SURVEILLANCE CAMERA)

An Enhance Image Retrieval of User Interest Using Query Specific Approach and...

IEEE Projects 2014-2015

Captcha

Multi-Dimensional Features Reduction of Consistency Subset Evaluator on Unsup...

Enhancing The Human Perceivable Image Content In CAPTCHA Based BF Algorithm

Tag based image retrieval (tbir) using automatic image annotation

IRJET- Smart Classroom Attendance System: Survey

IRJET - Explicit Content Detection using Faster R-CNN and SSD Mobilenet V2

Ijcet 06 10_004

System analysis and design for multimedia retrieval systems

A SURVEY ON CONTENT BASED IMAGE RETRIEVAL USING MACHINE LEARNING

Computer Vision Performance and Image Quality Metrics : A Reciprocal Relation

COMPUTER VISION PERFORMANCE AND IMAGE QUALITY METRICS: A RECIPROCAL RELATION

IRJET- Comparative Analysis of Video Processing Object Detection

Moving object detection using background subtraction algorithm using simulink

Mri brain image retrieval using multi support vector machine classifier

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Apidays Singapore 2024 - Scalable LLM APIs for AI and Generative AI Applicati...

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Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...

Scaling API-first – The story of a global engineering organization Ian Reasor, Senior Computer Scientist - Adobe Radu Cotescu, Senior Computer Scientist - Adobe Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe

Join our latest Connector Corner webinar to discover how UiPath Integration Service revolutionizes API-centric automation in a 'Quote to Cash' process—and how that automation empowers businesses to accelerate revenue generation. A comprehensive demo will explore connecting systems, GenAI, and people, through powerful pre-built connectors designed to speed process cycle times. Speakers: James Dickson, Senior Software Engineer Charlie Greenberg, Host, Product Marketing Manager

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Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...