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23rd International Conference on Computational Science, ICCS 2023

作者： Ruszczak, Bogdan Wijata, Agata M. Nalepa, Jakub Faculty of Electrical Engineering Automatic Control and Informatics Department of Informatics Opole University of Technology Opole Poland Faculty of Biomedical Engineering Silesian University of Technology Zabrze Poland Faculty of Automatic Control Electronics and Computer Science Department of Algorithmics and Software Silesian University of Technology Gliwice Poland KP Labs Gliwice Poland

ISBN: (纸本)9783031360206

Non-invasive estimation of chlorophyll content in plants plays an important role in precision agriculture. This task may be tackled using hyperspectral imaging that acquires numerous narrow bands of the electromagnetic spectrum, which may reflect subtle features of the plant, and inherently offers spatial scalability. Such imagery is, however, high-dimensional, therefore it is challenging to transfer from the imaging device, store and investigate. We propose a machine learning pipeline for estimating chlorophyll content from hyperspectral data. It benefits from the Savitzky-Golay filtering to smooth the (potentially noisy) spectral curves, and from gradient-based features extracted from such a smoothed signal. The experiments revealed that our approach significantly outperforms the state of the art according to the widely-established estimation quality metrics obtained for four chlorophyll-related parameters. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Integrity constraints for probabilistic spatio-temporal knowledgebases

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8th International Conference on Scalable Uncertainty Management, SUM 2014

作者： Parisi, Francesco Grant, John Department of Informatics Modeling Electronics and System Engineering University of Calabria Italy Department of Computer Science and UMIACS University of Maryland College Park United States

ISBN: (纸本)9783319115078

We formally introduce integrity constraints for probabilistic spatio-temporal knowledgebases. We start by defining the syntax and semantics of PST knowledgebases. This definition generalizes the SPOT framework which is a declarative framework for the representation and processing of probabilistic spatio-temporal data where probability is represented as an interval because of uncertainty. We augment the previous definition by adding a type of non-atomic formula that expresses integrity constraints. The result is a highly expressive formalism for knowledge representation dealing with probabilistic spatio-temporal data. Our main results concern the complexity of checking the consistency of PST knowledgebases. © Springer International Publishing Switzerland 2014.

关键词： Knowledge representation

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A resource conscious human action recognition framework using 26-layered deep convolutional neural network

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第28-29期80卷 35827-35849页

作者： Khan, Muhammad Attique Zhang, Yu-Dong Khan, Sajid Ali Attique, Muhammad Rehman, Amjad Seo, Sanghyun Department of Computer Science HITEC University Taxila Pakistan Department of Informatics University of Leicester Leicester UK Department of Software Engineering Foundation University Islamabad Islamabad Pakistan Department of Software Sejong University Seoul South Korea Department of Information Sciences Prince Sultan University Riyadh Saudi Arabia School of Computer Art College of Art and Technology Chung-Ang University Anseong Republic of Korea

Vision-based human action recognition (HAR) is a hot topic of research from the decade due to a few popular applications such as visual surveillance and robotics. For correct action recognition, various local and global points are requires known as features. These features modified during the variation in human movement. But due to a bit change in several human actions, the features of these actions are mixed that degrade the recognition performance. In this article, we design a new 26-layered Convolutional Neural Network (CNN) architecture for accurate complex action recognition. The features are extracted from the global average pooling layer and fully connected (FC) layer, and fused by a proposed high entropy-based approach. Further, we propose a feature selection method name Poisson distribution along with Univariate Measures (PDaUM). Few of fused CNN features are irrelevant, and few of them are redundant that makes the incorrect prediction among complex human actions. Therefore, the proposed PDaUM based approach selects only the strongest features that later passed to the Extreme Learning Machine (ELM) and Softmax for final recognition. Four datasets are using for experimental analysis - HMDB51 (51 classes), UCF Sports (10 classes), KTH (6 classes), and Weizmann (10 classes). On these datasets, the ELM classifier gives an improved performance as compared to a Softmax classifier. The achieved accuracy on each dataset is 81.4%, 99.2%, 98.3%, and 98.7%, respectively. Comparison with existing techniques, it is shown that the proposed architecture gives better performance in terms of accuracy and testing time.

关键词： Action recognition CNN architecture Features fusion Features selection ELM

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Technical light-field setup for 3D imaging of the human nerve head validated with an eye model

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Current Directions in Biomedical engineering 2021年第2期7卷 433-436页

作者： Schramm, Stefan Dietzel, Alexander Blum, Maren-Christina Link, Dietmar Klee, Sascha Institute of Biomedical Engineering and Informatics Faculty of Computer Sciences and Automation Technische Universität Ilmenau Ilmenau Germany Department of General Health Studies Division Biostatistics and Data Science Karl Landsteiner University of Health Sciences Krems an der Donau Austria

With the new technology of 3D light field (LF) imaging, fundus photography can be expanded to provide depth information. This increases the diagnostic possibilities and additionally improves image quality by digitally refocusing. To provide depth information in the human optic nerve head such as in glaucoma diagnostics, a mydriatic fundus camera was upgraded with an LF imager. The aim of the study presented here was the validation of the technical setup and resulting depth estimations with an appropriate eye model. The technical setup consisted of a mydriatic fundus camera (FF450, Carl Zeiss Meditec AG, Jena, Germany) and an LF imager (R12, Raytrix GmbH, Kiel, Germany). The field of view was set to 30°. The eye model (24.65 mm total length) consisted of a two-lens optical system and interchangeable fundus models with papilla excavations from 0.2 to 1 mm in steps of 0.2 mm. They were coated with red acrylic lacquer and vessels were drawn with a thin brush. 15 images were taken for each papilla depth illuminated with green light (wavelength 520 nm ± 20 nm). Papilla depth was measured from the papilla ground to the surrounding flat region. All 15 measurements for each papilla depth were averaged and compared to the printed depth. It was possible to perform 3D fundus imaging in an eye model by means of a novel LF-based optical setup. All LF images could be digitally refocused subsequently. Depth estimation in the eye model was successfully performed over a 30° field of view. The measured virtual depth and the printed model papilla depth is linear correlated. The presented LF setup allowed high-quality 3D one-shot imaging and depth estimation of the optic nerve head in an eye model. © 2021 by Walter de Gruyter Berlin/Boston.

关键词： Optical systems

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Editorial - Special issue on multimedia in ecology

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Ecological informatics 2014年 23卷 1-2页

作者： Spampinato, Concetto Mezaris, Vasileios Huet, Benoit van Ossenbruggen, Jacco Department of Computer Engineering University of Catania Catania Italy Centre for Research and Technology Hellas Thessaloniki Greece Multimedia Communications Department Institut Eurecom France Centre of Mathematics and Informatics Amsterdam Netherlands

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Fire Hotspots Mapping and Forecasting in Indonesia Using Deep Learning Algorithm 3

Fire Hotspots Mapping and Forecasting in Indonesia Using Dee...

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3rd International Conference on Electrical engineering and informatics, ICon EEI 2022

作者： Listia Rosa, Sri Abdul Kadir, Evizal Syukur, Abdul Irie, Hitoshi Wandri, Rizky Evizal, Muhammad Fikri Universitas Islam Riau Department of Informatics Engineering Pekanbaru28284 Indonesia National Taiwan University Science and Technology Computer Science and Information Engineering Taipei Taiwan Chiba University Centre for Environmental Remote Sensing Chiba263-0022 Japan National Dong Hwa University Computer Science and Information Engineering Hualien Taiwan

ISBN: (数字)9781665454346

ISBN: (纸本)9781665454346

Indonesia is one of the countries in South East Asia has significant forest fire with dangerous impact to neighboring countries of the emission of haze and carbon. In this research aims to do plotting and mapping location with high number fire hotspot then forecasting potential number of hotspots in future time based on previous of history data collected. The forecasting data achieve is very important and beneficial for the authorities as one of references for preventive action and avoid scattering of forest fire. Long Short-Term Memory (LSTM) algorithm implemented in this research for analysis and forecasting of fire hotspot number while Python programming used to plot hotspot point. The source of fire hotspot dataset is referred to The National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) recorded from year 2021 with total number is about 100,000 hotspots in Indonesia region. Results show the distribution of fire hotspot concentration most in Sumatra and Kalimantan Island because the typical of land which peat that potential for getting fire. Forecasting of number hotspot for the year 2022 has achieve with good results with error less than 5% which only 4.56%. © 2022 IEEE.

关键词： Forecasting

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Parallelization of the functional flow algorithm for prediction of protein function using protein-protein interaction networks

Parallelization of the functional flow algorithm for predict...

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International Conference on High Performance Computing & Simulation (HPCS)

作者： Emrah Akkoyun Tolga Can Department of Medical Informatics Informatics Institute Department of Computer Engineering Middle East Technical University Turkey

Protein-protein interaction networks provide important information about functions of proteins. There are various studies which analyze interaction networks and predict functions of novel proteins based on their network connectivity. However, all of these methods are sequential methods that do not utilize high performance computing. Functional flow is one of these methods that uses network connectivity, distance effect, and topology of the network with local and global views to predict protein function. With these advantages, the functional flow algorithm produces more accurate results compared to other techniques. However, due to lack of a parallelized version of the algorithm, the method cannot be practically applied on large scale networks of complex species. In this paper, we provide a parallel implementation of functional flow. We use Hadoop which is one of the open source map/reduce environments. For our experiments, we installed Hadoop on 18 hosts with eight cores each. The first map/reduce job distributes the protein interaction network as a format which allows parallel distributed computing on all the worker nodes. The other map/reduce jobs generate flows for each known protein function and the function of novel proteins are predicted by accumulating all of these generated flows. Our experiments show that the method can be distributed on worker nodes efficiently and the application can provide better performance as the number of resources increases.

关键词： Proteins Reservoirs computers Prediction algorithms Bioinformatics Distributed computing

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Agro-geoinformatics 1

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丛书名： Springer Remote Sensing/Photogrammetry

1000年

作者： Liping Di Berk Üstündağ

ISBN: (数字)9783030663872

ISBN: (纸本)9783030663865;9783030663896

This volume collects and presents the fundamentals, tools, and processes of utilizing geospatial information technologies to process remotely sensed data for use in agricultural monitoring and management. The issues related to handling digital agro-geoinformation, such as collecting (including field visits and remote sensing), processing, storing, archiving, preservation, retrieving, transmitting, accessing, visualization, analyzing, synthesizing, presenting, and disseminating agro-geoinformation have never before been systematically documented in one volume. The book is edited by International Conference on Agro-Geoinformatics organizers Dr. Liping Di (George Mason University), who coined the term “Agro-Geoinformatics” in 2012, and Dr. Berk Üstündağ (Istanbul Technical University) and are uniquely positioned to curate and edit this foundational text.;The book is composed of eighteen chapters that can each stand alone but also build on each other to give the reader a comprehensive understanding of agro-geoinformatics and what the tools and processes that compose the field can accomplish. Topics covered include land parcel identification, image processing in agricultural observation systems, databasing and managing agricultural data, crop status monitoring, moisture and evapotranspiration assessment, flood damage monitoring, agricultural decision support systems and more.

关键词： Geographical Information Systems/Cartography Agriculture Sustainable Development

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Product Recommendation Using Collaborative Filtering and K-Means Clustering 5

Product Recommendation Using Collaborative Filtering and K-M...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Kumar, M. Ranjith Vishnu, S. Roshen, G. Kumar, D. Niranjan Revathi, P. Baster, David Ricardo Ledo Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Mathematics Chennai India Amrita Vishwa Vidyapeetham Dept. of Computer Science & Engineering Amrita School of Computing Chennai India C. Abdul Hakeem College of Engineering and Technology Dept. of Computer Science & Engineering Vellore Melvisharam India University of Holguin Dept. of Informatics and Mathematics Cuba

ISBN: (纸本)9798350383522

The growth of e-commerce has altered how consumers shop, providing a digital space where convenience, vast product offerings, and competitive pricing converge. In today's world, e-commerce websites are transitioning from traditional search-driven methods to customized and intuitive approaches via product suggestions. Product recommendation systems are vital in e-commerce, from bringing new business to retaining existing ones. Our three-part recommendation system is designed so that new users have a great and engaging experience as the Product Popularity -Based System shows them carefully chosen products that are in demand. Collaborative Filtering Recommendations are highly personalized recommendations given to people who have already made their first purchases based on their prior actions and preferences. The K-Means Clustering-Based Recommendation System uses textual clustering analysis to deliver contextually relevant recommendations. We use a variety of evaluation metrics, such as click-through rates, user engagement, and the Silhouette Score, to assess the effectiveness and accuracy of our recommendation systems. Our findings show significant increases in user engagement, conversion rates, and relevant recommendations. Our findings demonstrate the transformative power of well-designed recommendation systems, which improve user experiences and retention and provide invaluable solutions for businesses entering the e-commerce space. This paper provides an in-depth examination of the multifaceted landscape of e-commerce recommendations, shedding light on their far-reaching implications for customer acquisition and retention in this dynamic digital era. © 2024 IEEE.

关键词： clustering collaborative filtering E-commerce systems K-means algorithm recommender system

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Novel Noise Filter Techniques and Dynamic Ensemble Selection for Classification

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Recent Advances in computer Science and Communications 2022年第1期15卷 48-59页

作者： Nguyen, Thi Ngoc A. Nhu, Quynh P. Solanki, Vijender K. School of Applied Mathematics and Informatics Hanoi University of Science and Technology Hanoi Viet Nam CMC Institute of Science and Technology 11 Duy Tan Cau Giay Hanoi Viet Nam Department of Computer Science and Engineering CMR Institute of Technology TS Hyderabad India

Background: Ensemble selection is one of the most researched topics for ensemble learning. Researchers have been attracted to selecting a subset of base classifiers that may perform more helpful than the whole ensemble system classifiers. Dynamic Ensemble Selection (DES) is one of the most effective techniques in classification problems. DES systems select the most appropriate classifiers from the candidate classifier pool. Ensemble models that balance diversity and accuracy in the training process improve performance than the whole classifiers. Objective: In this paper, novel techniques are proposed by combining Noise Filter (NF) and Dynamic Ensemble System (DES) to have better predictive accuracy. In other words, a noise filter and DES make the data cleaner and DES improves the performance of classification. Methods: The proposed NF-DES model, which was demonstrated on twelve datasets, especially has three credit scoring datasets and a performance measure accuracy. Results: The results show that our proposed model is better than other models. Conclusion: The novel noise filer and dynamic ensemble learning with the aim to improve the classification ability are presented. To improve the performance of classification, noise filter with dynamic ensemble learning makes the noise data toward the correct class. Then, novel dynamic ensemble learning chooses the appropriate subset classifiers in the pool of base classifiers. © 2022 Bentham Science Publishers.

关键词： Support vector machines

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