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JournalISSN: 2395-4396

International Journal of Advance Research and Innovative Ideas in Education 

IJARIIE
About: International Journal of Advance Research and Innovative Ideas in Education is an academic journal. The journal publishes majorly in the area(s): Cloud computing & Encryption. It has an ISSN identifier of 2395-4396. Over the lifetime, 4178 publications have been published receiving 3800 citations.

Papers published on a yearly basis

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Journal Article
TL;DR: A novel geometric framework for analysing 3D faces, with the specific goals of comparing, matching, and averaging their shapes is proposed and elastic shape analysis of these curves is used to develop a Riemannian framework for analyseing shapes of full facial surfaces.
Abstract: We propose a novel geometric framework for analysing 3D faces, with the specific goals of comparing, matching, and averaging their shapes. Here we represent facial surfaces by radial curves emanating from the nose tips and use elastic shape analysis of these curves to develop a Riemannian framework for analysing shapes of full facial surfaces. This representation, along with the elastic Riemannian metric, seems natural for measuring facial deformations and is robust to challenges such as large facial expressions (especially those with open mouths), large pose variations, missing parts, and partial occlusions due to glasses, hair, and illustrates the use of radial facial curves on 3D meshes to mode facial deformation caused by expression, occlusion and variation in poses and to recognize faces despite large expression, in presence of occlusion and pose variations. Here we represent facial surface by indexed collection of radial geodesic curves on 3D face meshes emanating from nose tip to the boundary of mesh and compare the facial shapes by comparing shapes of their corresponding curves. We use elastic shape analysis for comparing shapes of facial curves because elastic matching seems natural for facial deformation and is robust to challenges such as large facial expressions (especially those with open mouths), large pose variations, missing parts, and partial occlusions due to glasses, hair, and so on. Our results match or improve upon the state-of-the-art methods on two prominent databases: GavabDB and Bosporus, each posing a different type of challenges.

54 citations

Journal Article
TL;DR: The vortex induced vibration aquatic clean energy (VIVACE) converter as mentioned in this paper is based on the idea of maximizing rather than spoiling vortex shedding and exploiting rather than suppressing VIV.
Abstract: Any device aiming to harness the abundant clean and renewable energy from ocean and other water resources must have high energy density, be unobtrusive, have low maintenance, be robust, meet life cycle cost targets, and have a 10–20 year life. The vortex induced vibration aquatic clean energy (VIVACE) converter invented by Bernitsas and Raghavan, patent pending through the University of Michigan satisfies those criteria. It converts ocean/river current hydrokinetic energy to a usable form of energy such as electricity using VIV successfully and efficiently for the first time. VIVACE is based on the idea of maximizing rather than spoiling vortex shedding and exploiting rather than suppressing VIV. It introduces optimal damping for energy conversion while maintaining VIV over a broad range of vortex shedding synchronization. VIV occurs over very broad ranges of Reynolds (Re) number. Only three transition regions suppress VIV. Thus, even from currents as slow as 0.25 m/ s, VIVACE can extract energy with high power conversion ratio making ocean/river current energy a more accessible and economically viable resource. In this paper, the underlying concepts of the VIVACE converter are discussed. The designs of the physical model and laboratory prototype are presented.

49 citations

Journal Article
TL;DR: This project has detected seven emotions of humans which are Happiness, Anger, Sadness, Disgust, Neutral, Surprise and fear, and found that HOG gives a better result the BOF than the Bag-Of-Features.
Abstract: This paper presents emotion recognition using facial expression. Emotion recognition is widely used in industrial applications where emotion of humans are used to derive conclusions on products and detection of suspective behaviour. In this project we have detected seven emotions of humans which are Happiness, Anger, Sadness, Disgust, Neutral, Surprise and fear. We have taken a set of still images, detected the facial region and the features are extracted. Features are extracted using Bag-Of-Features (BOF) and Histogram of Oriented Gradients (HOG). The feature vectors created by these techniques are used to train Support Vector Machines (SVM) and results are verified against a given test input. We have achieved satisfactory results for emotion recognition. We have found that HOG gives a better result the BOF

49 citations

Journal Article
TL;DR: The results show that the proposed policy achieves higher offloading and lower content-retrieval delays than existing state-of-the-art approaches.
Abstract: In this paper, we propose a novel policy for device caching that facilitates popular content exchange through highrate device-to-device (D2D) millimeter-wave (mmWave) communication.The D2D aware caching (DAC) policy splits the cacheable content into two content groups and distributes it randomly to the user equipment devices (UEs), with the goal to enable D2D connections. By exploiting the high-bandwidth availability and the directionality of mmWaves, we ensure high rates for the D2D transmissions, while mitigating the co-channel interference that limits the D2D-communication potentials in the sub-6 GHz bands. Furthermore, based on a stochasticgeometry approach for the modeling of the network topology, we analytically derive the offloading gain that is achieved by the proposed policy and the distribution of the content retrieval delay considering both half- and full-duplex mode for the D2D communication. The accuracy of the proposed analytical framework is validated through Monte-Carlo simulations. In addition, for a wide range of a content popularity indicator the results show that the proposed policy achieves higher offloading and lower content-retrieval delays than existing state-of-the-art approaches.

46 citations

Journal Article
TL;DR: Experimental evidence using large-scale real-world data from both the Foursquare location-based social network and the Movie Lens movie recommendation system reveals that LARS* is efficient, scalable, and capable of producing recommendations twice as accurate compared to existing recommendation approaches.
Abstract: The problem of hyper-local place ranking. Given a user location and query string (e.g., “Indian restaurant"), hyper-local ranking provides a list of top-k points of interest influenced by previously logged directional queries (e.g., map direction searches from point A to point B).This paper proposes LARS*, a location-aware recommender system that uses their location-based ratings to show recommendations. Traditional recommender systems do not have spatial properties of users nor items; LARS*, next, supports a taxonomy of three novel classes of location-based ratings, namely, spatial ratings for non-spatial items, non-spatial ratings for spatial items, and spatial ratings for spatial items. LARS* exploits user rating locations through user partitioning, a technique that influences recommendations with ratings spatially close to querying users in a manner that maximizes system scalability while not sacrificing recommendation quality. LARS* exploits item locations using travel penalty, a technique that favors recommendation candidates closer in travel distance to querying users in a way that avoids exhaustive access to all spatial items. LARS* can apply these techniques separately, or together, depending on the type of location-based rating available. Experimental evidence using large-scale real-world data from both the Foursquare location-based social network and the Movie Lens movie recommendation system reveals that LARS* is efficient, scalable, and capable of producing recommendations twice as accurate compared to existing recommendation approaches. Our proposed location-aware recommender system, tackles a problem untouched by traditional recommender systems by dealing with three types of location-based ratings: spatial ratings for non-spatial items, non-spatial ratings for spatial items, and spatial ratings for spatial items. LARS* employs user partitioning and travel penalty techniques to support spatial ratings and spatial items, respectively.

42 citations

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Performance
Metrics
No. of papers from the Journal in previous years
YearPapers
2021288
2020292
2019708
2018809
20171,084
2016750