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3rd International Conference on Trends in Electronics and Health informatics, TEHI 2023

作者： Farooqui, Nafees Akhter Mishra, Amit Kumar Ray, Kanad Mallik, Saurav Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Guntur India Karnataka Bengaluru India Department of Physics Amity School of Applied Sciences Amity University Rajasthan Rajasthan Jaipur India Amity Cognitive Computing and Brain Informatics Centre Amity University Rajasthan Rajasthan303002 India Department of Environmental Health Harvard T H Chan School of Public Health BostonMA02115 United States Pharmacology & Toxicology University of Arizona TucsonAZ85721 United States

ISBN: (纸本)9789819739363

Segmentation of leaves is an initial step in image-based plant phenotyping, which has received more attention recently. Leaf segmentation shows features of the leaf and growth stages at the leaf level. Due to the significant overlapping between leaves and fluctuating environmental circumstances, such as intensity change and blur due to wind, segmentation of plant tissues like leaves is a most important issue in plant phenotyping. The leaf segmentation work has more complicated problems, such as leaf surface, genotypes, length, structure, and thickness variability. This study proposes an ultra-modern deep gaining knowledge of architectures: UUNet++, a convolutional neural network for advance segmentation. A vital assessment is done using Computer Vision for Plant Phenotyping dataset (CVPPP). The plant phenotyping is performed on tobacco and Arabidopsis leaf of RGB images from plant village dataset. The proposed model needs less space as compared to the main UNet architecture. In contrast to typical deep learning image sets, this dataset contains a limited number of samples. Despite that, we achieve exceptional results in leaf segmentation, specifically in distinguishing the interior of leaves from the binary segmentation of the entire leaf. Furthermore, studies are necessary to accurately quantify the number of leaves. Without reducing performance measures, the disease classification inferences time is extremely efficient and accurate. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Plant diseases

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A Bibliometric and Social Media Analysis of the Nexus Between Blockchain Technology and Finance 2nd

A Bibliometric and Social Media Analysis of the Nexus Betwee...

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Ayodele, Olamide Emmanuel Ajibade, Temiloluwa Iyanuoluwa Oyebode, Oluwadare Joshua Akintoye, Kayode Akinlekan Ajayi, Babatunde Adedotun Adediran, Anthonia Oluwatosin Ajibade, Samuel-Soma M. Majeed, Anwar P. P. Abdul Luo, Yang Department of Banking and Finance Ekiti State University Ekiti State Ado Ekiti Nigeria Department of Business Management Scholar School System Birmingham United Kingdom Civil and Environmental Engineering Afe Babalola University Ekiti State Ado Ekiti Nigeria Department of Computer Science The Federal Polytechnic Ekiti State Ado Ekiti Nigeria Department of Cyber Security Faculty of Computing and Informatics Ladoke Akintola University of Technology Ogbomoso Nigeria Department of Real Estate Faculty of Built Environment Universiti Malaya KL Kuala Lumpur Malaysia Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University Selangor Malaysia Department of Computer Engineering Istanbul Ticaret University Istanbul Turkey Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

This study explores Blockchain technology in the global financial industry using trends analysis, bibliometric analysis, and a literature appraisal using Elsevier Scopus data from 2016 to 2023. The Blockchain in Finance (BCF) study produced 185 publications: 53.5% papers, 45.4% conference proceedings, and 1.1% reviews. Analysis of the subject area revealed three important Computer science, Engineering, and Decision sciences articles. However, source title analysis showed that ACM International Conference Proceeding Series, Sustainability Switzerland, and E3S Web of Conferences publish BCF research most often. According to the stakeholders study, Jon M. Truby and Qatar University (Qatar) are the top BCFin researchers and affiliations with 4% and % publications, respectively. China produces the most research on the topic. Due to active support from funding sources like the Chinese National Natural science Foundation (NNSF), 18 publications have been funded. Co-occurrence of keywords The investigation indicated that BCFin’s top keywords include Blockchain, Finance, and Supply Chain Finance. Three clusters with 4–8 keywords, 132 linkages, and 866 TLS were found via cluster analysis. Keywords classify the clusters as Integrated Business Solutions (Cluster 1), Digital Commerce Infrastructure (Cluster 2), and Decentralized Financial Network (Cluster 3). The findings indicate that BCFin is a dynamic and influential multidisciplinary study field. BCFin has transformed the global financial sector by improving trust, transaction security, and communication. It still struggles with interoperability, system integration, and sustainability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decentralized finance

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Kernel methods for the approximation of the eigenfunctions of the Koopman operator

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Physica D: Nonlinear Phenomena 2025年 476卷

作者： Lee, Jonghyeon Hamzi, Boumediene Hou, Boya Owhadi, Houman Santin, Gabriele Vaidya, Umesh Department of Computing and Mathematical Sciences CaltechCA United States Alan Turing Institute London United Kingdom Carl R. Woese Institute for Genomic Biology University of Illinois Urbana-Champaign IL United States Department of Environmental Sciences Informatics and Statistics Ca’ Foscari University of Venice Italy Department of Mechanical Engineering Clemson University United States Department of Mathematics Imperial College London London United Kingdom Department of Mathematics Gulf University for Science and Technology Kuwait

The Koopman operator provides a linear framework to study nonlinear dynamical systems. Its spectra offer valuable insights into system dynamics, but the operator can exhibit both discrete and continuous spectra, complicating direct computations. In this paper, we introduce a kernel-based method to construct the principal eigenfunctions of the Koopman operator without explicitly computing the operator itself. These principal eigenfunctions are associated with the equilibrium dynamics, and their eigenvalues match those of the linearization of the nonlinear system at the equilibrium point. We exploit the structure of the principal eigenfunctions by decomposing them into linear and nonlinear components. The linear part corresponds to the left eigenvector of the system's linearization at the equilibrium, while the nonlinear part is obtained by solving a partial differential equation (PDE) using kernel methods. Our approach avoids common issues such as spectral pollution and spurious eigenvalues, which can arise in previous methods. We demonstrate the effectiveness of our algorithm through numerical examples. © 2025

关键词： Numerical methods

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A Bio-Inspired Metaheuristics Algorithm for Reduction of Coverage Holes in Optimizing the Performance of Next Generation Wireless Network (NGWN) 2nd

A Bio-Inspired Metaheuristics Algorithm for Reduction of Cov...

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Ajibade, Samuel-Soma M. Abdul Majeed, Anwar P. P. Oyebode, Oluwadare Joshua Jasser, Muhammed Basheer Ajayi, Babatunde Adedotun Alebiosu, David Olayemi Luo, Yang Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Darul Ehsan47500 Malaysia Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Darul Ehsan Petaling Jaya47500 Malaysia Civil and Environmental Engineering Afe Babalola University Ekiti Ado Ekiti Nigeria Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Darul Ehsan Petaling Jaya47500 Malaysia Department of Cyber Security Faculty of Computing and Informatics Ladoke Akintola University of Technology Ogbomoso Nigeria Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

While Next Generation Wireless Networks are widely used, controlling them can be challenging regardless of whether a network is fully connected, there may be coverage gaps. In this study, we provide a technique for placing nodes in a wireless network while making sure that there is a minimum coverage hole in the network coverage area. We use the firefly algorithm to close the coverage gap in order to increase connectivity. The Firefly algorithm provides a novel solution for overcoming the drawbacks of traditional overburden hole detection techniques, such as downtime, low accuracy, and high operating costs, by installing sensor nodes in the appropriate locations and scheduling those network devices. The algorithm looks for the best locations where the NGWN sensor may be placed inside the designated zone in order to reduce the coverage hole area. The algorithm will treat randomly generated solutions as fireflies, and it will determine how bright they are based on how well they perform in terms of the goal function. Simulation results show that the proposed method decreases the covering hole area by 96%. The provided approach was suitable for NGWN since it reduced computation time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Operating costs

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From ecological modeling to decentralized optimization of smart power grids 45

From ecological modeling to decentralized optimization of sm...

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45. Jahrestagung der Gesellschaft fur Informatik: Informatik, Energie und Umwelt, INFORMATIK 2015 - 45th Annual Meeting of the German informatics Society: Computer science, Energy and Environment, INFORMATIK 2015

作者： Sonnenschein, Michael Vogel, Ute Hinrichs, Christian University of Oldenburg Department of Computing Science Environmental Informatics Oldenburg26111 Germany

ISBN: (纸本)9783885796404

In this contribution we want to sum up how computer science, ecological modeling and control of distributed power systems mutually influence. First we show, how ecological modeling has been influenced by the availability of faster computers and easier to use programming environments. Then we argue that adaption and emergent behavior in biological systems observed e.g. at the hand of microscopic models can be interpreted as an optimization process. This observation led to new optimization (meta-) heuristics in computer science and inspired multi-agent based optimization and control methods. We motivate why these methods are an interesting choice for finding new solutions to planning and control problems in future electrical power systems. As an example we sketch a completely distributed, agent-based planning and control system for a so-called dynamic virtual power plant. Finally we discuss some directions of future research in the domain of agent-based control of distributed systems.

关键词： Multi agent systems

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A method for optimizing a metapopulation

A method for optimizing a metapopulation

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2nd International ICSC Symposium on Information Technologies in environmental Engineering, ITEE 2005

作者： Grüning, Fabian Sonnenschein, Michael Carl von Ossietzky Universität Oldenburg Department of Computing Science Environmental Informatics Group D-26111 Oldenburg Germany

ISBN: (纸本)3832243623

Metapopulations are often used to gain a model of a certain species in a scattered landscape. These models can be used for analytical questions concerning the viability of that species. There is a strong interest in discovering modifications of the environment that have a positive influence in the survivability of the population. Automatically generated recommendations were hardly available mainly because of the high complexity of the problem. This paper shows the results of an implementation of an optimization algorithm for methods to increase the viability of metapopulations. It uses a metamodel of a metapopulation model for accomplishing the needed performance.

关键词： Ecological modelling Planning for environmental engineering (EE)

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Simulation and optimization of habitat network permeability

Environmental Science and Engineering

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environmental science and Engineering 2007年 433-444页

作者： Finke, Jens Sonnenschein, Michael Oldenburg University Department of Computing Science Environmental Informatics Group 26111 Oldenburg Germany

ISBN: (纸本)3540713344

Landscape fragmentation is a major threat to a lot of wildlife species with a large spatial activity range. To ease the migration through the landscape, a lot of different measurements can be implemented. Though, due to limited resources, these should be placed where they are most effective. The simulation framework SimapD presented in this paper is capable of assessing the network permeability and incorporates effects of anthropogenic obstacles, like roads, into the assessment. Through an individual-oriented simulation an overall permeability index for a habitat network is obtained. Based on this simulation approach, an algorithm is presented for optimizing such habitat networks concerning its permeability. It is intended to be applied to find optimal mitigation strategies. The complete procedure is illustrated by an example.

关键词： Ecosystems

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Adaptive consensus of discrete-time heterogeneous multi-agent systems

Adaptive consensus of discrete-time heterogeneous multi-agen...

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50th Annual Conference on Society of Instrument and Control Engineers, SICE 2011

作者： Okajima, Takashi Tsumura, Koji Hayakawa, Tomohisa Ishii, Hideaki Department of Information Physics and Computing University of Tokyo Tokyo Japan Department of Mechanical and Environmental Informatics Tokyo Institute of Technology Tokyo Japan Department of Computational Intelligence and Systems Science Tokyo Institute of Technology Yokohama Japan

ISBN: (纸本)9784907764395

In this paper, we study an adaptive consensus problem for discrete-time multi-agent systems with uncertain heterogeneous dynamics. A distributed adaptive controller is designed so that state consensus can be achieved asymptotically. By constructing a logarithmic Lyapunov function, we show that states of all agents converge to the state of the leader, and that the closed-loop system is Lyapunov stable, when the communication topology of agents satisfies some sufficient conditions. A numerical example is presented to demonstrate the efficacy of the proposed approach. © 2011 SICE.

关键词： Multi agent systems

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A Survey on Large Language Models: Applications, Challenges, Limitations, and Practical Usage

TechRxiv

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TechRxiv 2025年

作者： Hadi, Muhammad Usman Al-Tashi, Qasem Qureshi, Rizwan Shah, Abbas Muneer, Amgad Irfan, Muhammad Zafar, Anas Shaikh, Muhammad Bilal Akhtar, Naveed Al-Garadi, Mohammed Ali Hassan, Syed Zohaib Shoman, Maged Wu, Jia Mirjalili, Seyedali Shah, Mubarak School of Engineering Ulster University BelfastBT15 1AP United Kingdom Department of Imaging Physics The University of Texas MD Anderson Cancer Center HoustonTX77030 United States Department of Electronics Engineering Mehran University of Engineering and Technology Jamshoro76062 Pakistan of Engineering Sciences and Technology Swabi23460 Pakistan Department of Computer Science National University of Computer and Emerging Sciences Karachi Pakistan Edith Cowan University 270 Joondalup Drive Joondalup PerthWA6027 Australia Computing and Information Systems The University of Melbourne 700 Swanston Street CarltonVIC3010 Australia Department of Biomedical Informatics Vanderbilt University Medical Center NashvilleTN United States Department of Civil Environmental and Construction Engineering The University of Central Florida OrlandoFL United States Centre for Artificial Intelligence Research and Optimization Torrens University Australia Fortitude Valley BrisbaneQLD4006 Australia University Research and Innovation Center Obuda University Budapest1034 Hungary Center for Research in Computer Vision The University of Central Florida OrlandoFL United States

Within the vast expanse of computerized language processing, a revolutionary entity known as Large Language Models (LLMs) has emerged, wielding immense power in its capacity to comprehend intricate linguistic patterns and conjure coherent and contextually fitting responses. LLMs are a type of artificial intelligence (AI) that have emerged as powerful tools for a wide range of tasks, including natural language processing (NLP), machine translation, vision applications, and question-answering. This survey provides a comprehensive overview of LLMs, including their history, architecture, datasets, training methods, applications, challenges, and future prospects. We begin by discussing the fundamental concepts of generative AI and the architecture of generative pre-trained transformers (GPT). We then provide an overview of the history of LLMs, their evolution over time, and the different training methods. We also present benchmark dataset for training and fine-tuning and evaluating LLMs. We then discuss the wide range of tasks where they are used and also discuss applications of LLMs in different domains, including medicine, education, finance, engineering, agriculture, media, entertainment, politics, and law. We also discuss how LLMs are shaping the future of AI and their increasing role in scientific discovery, and how they can be used to solve real-world problems. Next, we explore the challenges associated with deploying LLMs in real-world scenarios, including ethical considerations, model biases, interpretability, privacy concerns, and computational resource requirements. This survey also highlights techniques for enhancing the robustness and controllability of LLMs and addressing bias, fairness, and quality issues in Generative AI. Finally, we conclude by highlighting the future of LLM research and the challenges that need to be addressed in order to make this technology more reliable and useful. This survey is intended to provide researchers, practitioners, and ent

关键词： Natural language processing systems

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Spatial resolution for forest carbon maps

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science (New York, N.Y.) 2025年第6732期387卷 370-371页

作者： Laura Duncanson Neha Hunka Tommaso Jucker John Armston Nancy Harris Lola Fatoyinbo Christopher A Williams Jeff W Atkins Brett Raczka Shawn Serbin Michael Keller Ralph Dubayah Chad Babcock Mark A Cochrane Andrew Hudak George C Hurtt Paul M Montesano L Monika Moskal Taejin Park Sassan Saatchi Carlos A Silva Hao Tang Rodrigo Vargas Aaron Weiskittel Konrad Wessels Scott J Goetz Geographical Sciences University of Maryland College Park MD USA. School of Biological Sciences University of Bristol Bristol UK. World Resources Institute Washington DC USA. NASA Goddard Space Flight Center Greenbelt MD USA. Graduate School of Geography Clark University Worcester MA USA. US Department of Agriculture (USDA) Forest Service Southern Research Station New Ellenton SC USA. Department of Biology Virginia Commonwealth University Richmond VA USA. Computational and Information Systems Lab Data Assimilation Research Section National Center for Atmospheric Research Boulder CO USA. International Institute of Tropical Forestry USDA Forest Service Rio Piedras PR USA. Forest Resources Department University of Minnesota St. Paul MN USA. University of Maryland Center for Environmental Science Frostburg MD USA. Rocky Mountain Research Station USDA Forest Service Moscow ID USA. NASA Goddard Space Flight Center ADNET Systems Greenbelt MD USA. School of Environmental and Forest Sciences University of Washington WA USA. Bay Area Environmental Research Institute Moffett Field CA USA. NASA Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA. CTrees Pasadena CA USA. Forest Biometrics Remote Sensing and Artificial Intelligence Lab (SilvaLab) School of Forest Fisheries and Geomatics Science University of Florida Gainesville FL USA. Department of Geography National University of Singapore Singapore. Department of Plant and Soil Sciences University of Delaware Newark DE USA. Center for Research on Sustainable Forests University of Maine Orono ME USA. Geography and Geoinformation Science George Mason University Fairfax VA USA. School of Informatics Computing and Cyber Systems Northern Arizona University Flagstaff AZ USA.

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