Ascertaining Along With Taxonomy of Vegetation Folio Ailment Employing CNN besides LVQ Algorithm

In agriculture, early disease detection is crucial for increasing crop yield. The diseases Microbial Blotch, Late Blight, Septoria leaf spot, and yellow twisted leaves all have an impact on tomato crop productivity. Automatic plant illness classification systems can assist in taking action after ascertaining leaf disease symptoms. This paper emphasis on multi-classification of tomato crop illnesses employs Convolution Neural Network (CNN) model and Learning Vector Quantization (LVQ) algorithm-based methodology. The dataset includes 500 photographs of Tomato foliage with four clinical manifestations. CNN paradigm performs feature extraction and categorization in which color information is extensively used in plant leaf disease investigations. The model's filters have been applied to triple conduit similar tendency on RGB hues. The LVQ was fed during training by a yield countenance vector of the convolution component. The experimental results reveal that the proposed method accurately detects four types of solanaceous leaf diseases

Virtual environment trajectory analysis:a basis for navigational assistance and scene adaptivity

This paper describes the analysis and clustering of motion trajectories obtained while users navigate within a virtual environment (VE). It presents a neural network simulation that produces a set of five clusters which help to differentiate users on the basis of efficient and inefficient navigational strategies. The accuracy of classification carried out with a self-organising map algorithm was tested and improved to in excess of 85% by using learning vector quantisation. This paper considers how such user classifications could be utilised in the delivery of intelligent navigational support and the dynamic reconfiguration of scenes within such VEs. We explore how such intelligent assistance and system adaptivity could be delivered within a Multi-Agent Systems (MAS) context

Iris Image Recognition using Optimized Kohonen Self Organizing Neural Network

The pursuit to develop an effective people management system has widened over the years to manage the enormous increase in population. Any management system includes identification, verification and recognition stages. Iris recognition has become notable biometrics to support the management system due to its versatility and non-invasive approach. These systems help to identify the individual with the texture information distributed around the iris region. Many classification algorithms are available to help in iris recognition. But those are very sophisticated and require heavy computation. In this paper, an improved Kohonen self-organizing neural network (KSONN) is used to boost the performance of existing KSONN. This improvement is brought by the introduction of optimization technique into the learning phase of the KSONN. The proposed method shows improved accuracy of the recognition. Moreover, it also reduces the iterations required to train the network. From the experimental results, it is observed that the proposed method achieves a maximum accuracy of 98% in 85 iterations

Improving the self-organizing feature map algorithm using an efficient initialization scheme

[[abstract]]It is often reported in the technique literature that the success of the self-organizing feature map formation is critically dependent on the initial weights and the selection of main parameters (i.e. the learning-rate parameter and the neighborhood set) of the algorithm. They usually have to be counteracted by the trial-and-error method; therefore, often time consuming retraining procedures have to precede before a neighborhood preserving feature amp is obtained. In this paper, we propose an efficient initialization scheme to construct an initial map. We then use the self-organizing feature map algorithm to make small subsequent adjustments so as to improve the accuracy of the initial map. Several data sets are tested to illustrate the performance of the proposed method.[[notice]]補正完

Facial image morphing by self-organizing feature maps

[[abstract]]We propose a new facial image morphing algorithm based on the Kohonen self-organizing feature map (SOM) algorithm to generate a smooth 2D transformation that reflects anchor point correspondences. Using only a 2D face image and a small number of anchor points, we show that the proposed morphing algorithm provides a powerful mechanism for processing facial expressions.[[conferencetype]]國際[[conferencedate]]19990710~19990716[[booktype]]紙本[[iscallforpapers]]Y[[conferencelocation]]Washington, DC, US

Astrofüüsikaliste struktuuride uurimine klasteranalüüsi meetoditega

Väitekirja elektrooniline versioon ei sisalda publikatsiooneAntud doktoritöös uurime klasteranalüüsi meetodite abil kahte tüüpi astrofüüsikalisi andmeid – suureskaalalisi galaktikate punanihke vaatluseid ja suuri superarvuti simulatsioone turbulentsest kineetilisest plasmast. Töö esimeses pooles uurime Universumi struktuuri kõige domineerivamat elementi – galaktilisi filamente. Enamus Universumi galaktikaid asuvad nendes pikkades sildades, mis ühendavad sfäärilisi galaktikate parvi ja peaaegu tühjasid hoomamatuid tühikuid. Filamentvõrgustiku kaardistamine on väga olulise tähtsusega, sest see aitab meil mõista selles leiduvate galaktikate evolutsiooni ja galaktikatevahelist ainet. Antud töös leiame senini varjatud mustri galaktikate paiknemises piki filamente, mis viitab galaktikate evolutsiooni mõjutavatele keskkonna teguritele. Seejärel kinnitame uue galaktikateandmestiku ja filamentvõrgustiku ruumilise klasterdumise, mis kinnitab antud võrgustiku õigsust ja motiveerib neid uusi galaktikaid tuleviku modelleerimisel kasutama. Töö teises pooles uurime pilte, mis on saadud magneetiliselt domineeritud astrofüüsikalise plasma simulatsioonist. Antud mudel simuleerib füüsikalist fenomeni, mis leidub galaktikate klastrites, mustade aukude akretsiooniketastes, Päikese koroonas ja isegi tuumasünteesi reaktorites. Kõrgelt laetud osakesed väljuvad antud plasmast teatud füüsikaliste protsesside käigus, mida pole veel täielikult mõistetud. Selle mõistmiseks tuleb detekteerida erinevad füüsikalised struktuurid, mis plasmas leiduvad. Antud töös rakendame juhendamata masinõppe meetodit ning kaardistame plasmas olevad struktuurid piksli täpsusega. Sealhulgas need objektid, mis kiirendavad osakesi plasmast lahkuma. Töös arendatakse ka ansambelõppe raamistik, mis tõstab oluliselt struktuuride kaardistamise täpsust. Antud töö demonstreerib klasteranalüüsi algoritmide võimekust füüsikaliste fenomenide uurimisel.In this PhD thesis, two classes of astrophysical datasets – large scale galaxy redshift surveys and large supercomputer simulations of fully-kinetic turbulent plasma – are studied with clustering algorithms. In the first part we investigate the most dominant structure element of the Universe: the galaxy filaments. Majority of galaxies in the Universe reside in these galaxy filaments, which are long bridges connecting spherical high-density regions of galaxies and border immense voids almost without galaxies. Mapping the structure from observational galaxy datasets is of utmost importance for understanding the objects residing inside them, that is, galaxies and the intergalactic medium. In this work, we reveal a hidden pattern in the locations of galaxies residing inside these structures, which sheds light on environmental effects governing the evolution of galaxies. Then, we trace the detected galaxy filaments with a new observational dataset of galaxies, and prove the detected network. This motivates the use of these new datasets in the future modeling of the Universe. In the second part of this thesis we study images originating from simulations of turbulent magnetically dominated plasma, which models the physical phenomena observed in galaxy clusters, black hole accretion disks, solar corona, and even in fusion reactors. Physical phenomena responsible for the excitation of particles inside the plasma are not yet fully understood. In order to understand the underlying physics, the physical structures inside the plasma need to be detected. We apply an unsupervised machine learning algorithm on these images; and detect the physical structures pixel-by-pixel, including those responsible for the ejection of particles. We also develop an ensemble framework to improve the accuracy of the results. This thesis demonstrates the great potential and value of clustering analysis tools, from a wide spectrum of concepts, for revealing and understanding physical phenomena.https://www.ester.ee/record=b539540