18 datasets found
  1. f

    Data_Sheet_1_Generative Adversarial Networks for Augmenting Training Data of...

    • frontiersin.figshare.com
    pdf
    Updated Jun 1, 2023
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    Piotr Baniukiewicz; E. Josiah Lutton; Sharon Collier; Till Bretschneider (2023). Data_Sheet_1_Generative Adversarial Networks for Augmenting Training Data of Microscopic Cell Images.pdf [Dataset]. http://doi.org/10.3389/fcomp.2019.00010.s001
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    pdfAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    Frontiers
    Authors
    Piotr Baniukiewicz; E. Josiah Lutton; Sharon Collier; Till Bretschneider
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Generative adversarial networks (GANs) have recently been successfully used to create realistic synthetic microscopy cell images in 2D and predict intermediate cell stages. In the current paper we highlight that GANs can not only be used for creating synthetic cell images optimized for different fluorescent molecular labels, but that by using GANs for augmentation of training data involving scaling or other transformations the inherent length scale of biological structures is retained. In addition, GANs make it possible to create synthetic cells with specific shape features, which can be used, for example, to validate different methods for feature extraction. Here, we apply GANs to create 2D distributions of fluorescent markers for F-actin in the cell cortex of Dictyostelium cells (ABD), a membrane receptor (cAR1), and a cortex-membrane linker protein (TalA). The recent more widespread use of 3D lightsheet microscopy, where obtaining sufficient training data is considerably more difficult than in 2D, creates significant demand for novel approaches to data augmentation. We show that it is possible to directly generate synthetic 3D cell images using GANs, but limitations are excessive training times, dependence on high-quality segmentations of 3D images, and that the number of z-slices cannot be freely adjusted without retraining the network. We demonstrate that in the case of molecular labels that are highly correlated with cell shape, like F-actin in our example, 2D GANs can be used efficiently to create pseudo-3D synthetic cell data from individually generated 2D slices. Because high quality segmented 2D cell data are more readily available, this is an attractive alternative to using less efficient 3D networks.

  2. CPIQA: Climate Paper Image Question Answering Dataset for...

    • zenodo.org
    zip
    Updated Jun 24, 2025
    + more versions
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    Rudra Mutalik; Rudra Mutalik; Stuart E. Middleton; Stuart E. Middleton (2025). CPIQA: Climate Paper Image Question Answering Dataset for Retrieval-Augmented Generation with Context-based Query Expansion [Dataset]. http://doi.org/10.5281/zenodo.15374870
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    zipAvailable download formats
    Dataset updated
    Jun 24, 2025
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Rudra Mutalik; Rudra Mutalik; Stuart E. Middleton; Stuart E. Middleton
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    CPIQA is a large scale QA dataset focused on figured extracted from scientific research papers from various peer reviewed venues in the climate science domain. The figures extracted include tables, graphs and diagrams, which inform the generation of questions using large language models (LLMs). Notably this dataset includes questions for 3 audiences: general public, climate skeptic and climate expert. 4 types of questions are generated with various focusses including figures, numerical, text-only and general. This results in 12 questions generated per scientific paper. Alongside figures, descriptions of the figures generated using multimodal LLMs are included and used.

    This work was funded through the WCSSP South Africa project, a collaborative initiative between the Met Office, South African and UK partners, supported by the International Science Partnership Fund (ISPF) from the UK's Department for Science, Innovation and Technology (DSIT). It is also supported by the Natural Environment Research Council (grant NE/S015604/1) project GloSAT.

    Mutalik, R. Panchalingam, A. Loitongbam, G. Osborn, T. J. Hawkins, E. Middleton, S. E. CPIQA: Climate Paper Image Question Answering Dataset for Retrieval-Augmented Generation with Context-based Query Expansion, ClimateNLP-2025, ACL, 31st July 2025, https://nlp4climate.github.io/

  3. d

    6DOF pose estimation - synthetically generated dataset using BlenderProc

    • search.dataone.org
    • data.niaid.nih.gov
    • +1more
    Updated Nov 27, 2023
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    Divyam Sheth (2023). 6DOF pose estimation - synthetically generated dataset using BlenderProc [Dataset]. http://doi.org/10.5061/dryad.rbnzs7hj5
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    Dataset updated
    Nov 27, 2023
    Dataset provided by
    Dryad Digital Repository
    Authors
    Divyam Sheth
    Time period covered
    Jan 1, 2023
    Description

    Accurate and robust 6DOF (Six Degrees of Freedom) pose estimation is a critical task in various fields, including computer vision, robotics, and augmented reality. This research paper presents a novel approach to enhance the accuracy and reliability of 6DOF pose estimation by introducing a robust method for generating synthetic data and leveraging the ease of multi-class training using the generated dataset. The proposed method tackles the challenge of insufficient real-world annotated data by creating a large and diverse synthetic dataset that accurately mimics real-world scenarios. The proposed method only requires a CAD model of the object and there is no limit to the number of unique data that can be generated. Furthermore, a multi-class training strategy that harnesses the synthetic dataset's diversity is proposed and presented. This approach mitigates class imbalance issues and significantly boosts accuracy across varied object classes and poses. Experimental results underscore th..., This dataset has been synthetically generated using 3D software like Blender and APIs like Blendeproc., , # Data Repository README

    This repository contains data organized into a structured format. The data consists of three main folders and two files, each serving a specific purpose. The data contains two folders - Cat and Hand.

    Cat Dataset: 63492 labeled data with images, masks, and poses.

    Hand Dataset: 42418 labeled data with images, masks, and poses.

    Usage: The dataset is ready for use by simply extracting the contents of the zip file, whether for training in a segmentation task or a pose estimation task.

    To view .npy files you will need to use Python with the numpy package installed. In Python use the following commands.

    import numpy
    data = numpy.load('file.npy')
    print(data)

    What free/open software is appropriate for viewing the .ply files?
    These files can be opened using any 3D modeling software like Blender, Meshlab, etc.

    Camera Matrix Intrinstics Format :

    Fx 0 px 0 Fy py 0 0 0

    Below is an overview of the data organization:

    Folder Structure

    1. Rgb:
      • This ...
  4. f

    Data_Sheet_1_Inside out: transforming images of lab-grown plants for machine...

    • frontiersin.figshare.com
    pdf
    Updated Jul 6, 2023
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    Alexander E. Krosney; Parsa Sotoodeh; Christopher J. Henry; Michael A. Beck; Christopher P. Bidinosti (2023). Data_Sheet_1_Inside out: transforming images of lab-grown plants for machine learning applications in agriculture.pdf [Dataset]. http://doi.org/10.3389/frai.2023.1200977.s001
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    pdfAvailable download formats
    Dataset updated
    Jul 6, 2023
    Dataset provided by
    Frontiers
    Authors
    Alexander E. Krosney; Parsa Sotoodeh; Christopher J. Henry; Michael A. Beck; Christopher P. Bidinosti
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    IntroductionMachine learning tasks often require a significant amount of training data for the resultant network to perform suitably for a given problem in any domain. In agriculture, dataset sizes are further limited by phenotypical differences between two plants of the same genotype, often as a result of different growing conditions. Synthetically-augmented datasets have shown promise in improving existing models when real data is not available.MethodsIn this paper, we employ a contrastive unpaired translation (CUT) generative adversarial network (GAN) and simple image processing techniques to translate indoor plant images to appear as field images. While we train our network to translate an image containing only a single plant, we show that our method is easily extendable to produce multiple-plant field images.ResultsFurthermore, we use our synthetic multi-plant images to train several YoloV5 nano object detection models to perform the task of plant detection and measure the accuracy of the model on real field data images.DiscussionThe inclusion of training data generated by the CUT-GAN leads to better plant detection performance compared to a network trained solely on real data.

  5. r

    International Journal of Engineering and Advanced Technology Publication fee...

    • researchhelpdesk.org
    Updated Jun 25, 2022
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    Research Help Desk (2022). International Journal of Engineering and Advanced Technology Publication fee - ResearchHelpDesk [Dataset]. https://www.researchhelpdesk.org/journal/publication-fee/552/international-journal-of-engineering-and-advanced-technology
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    Dataset updated
    Jun 25, 2022
    Dataset authored and provided by
    Research Help Desk
    Description

    International Journal of Engineering and Advanced Technology Publication fee - ResearchHelpDesk - International Journal of Engineering and Advanced Technology (IJEAT) is having Online-ISSN 2249-8958, bi-monthly international journal, being published in the months of February, April, June, August, October, and December by Blue Eyes Intelligence Engineering & Sciences Publication (BEIESP) Bhopal (M.P.), India since the year 2011. It is academic, online, open access, double-blind, peer-reviewed international journal. It aims to publish original, theoretical and practical advances in Computer Science & Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. All submitted papers will be reviewed by the board of committee of IJEAT. Aim of IJEAT Journal disseminate original, scientific, theoretical or applied research in the field of Engineering and allied fields. dispense a platform for publishing results and research with a strong empirical component. aqueduct the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research. seek original and unpublished research papers based on theoretical or experimental works for the publication globally. publish original, theoretical and practical advances in Computer Science & Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. impart a platform for publishing results and research with a strong empirical component. create a bridge for a significant gap between research and practice by promoting the publication of original, novel, industry-relevant research. solicit original and unpublished research papers, based on theoretical or experimental works. Scope of IJEAT International Journal of Engineering and Advanced Technology (IJEAT) covers all topics of all engineering branches. Some of them are Computer Science & Engineering, Information Technology, Electronics & Communication, Electrical and Electronics, Electronics and Telecommunication, Civil Engineering, Mechanical Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. The main topic includes but not limited to: 1. Smart Computing and Information Processing Signal and Speech Processing Image Processing and Pattern Recognition WSN Artificial Intelligence and machine learning Data mining and warehousing Data Analytics Deep learning Bioinformatics High Performance computing Advanced Computer networking Cloud Computing IoT Parallel Computing on GPU Human Computer Interactions 2. Recent Trends in Microelectronics and VLSI Design Process & Device Technologies Low-power design Nanometer-scale integrated circuits Application specific ICs (ASICs) FPGAs Nanotechnology Nano electronics and Quantum Computing 3. Challenges of Industry and their Solutions, Communications Advanced Manufacturing Technologies Artificial Intelligence Autonomous Robots Augmented Reality Big Data Analytics and Business Intelligence Cyber Physical Systems (CPS) Digital Clone or Simulation Industrial Internet of Things (IIoT) Manufacturing IOT Plant Cyber security Smart Solutions – Wearable Sensors and Smart Glasses System Integration Small Batch Manufacturing Visual Analytics Virtual Reality 3D Printing 4. Internet of Things (IoT) Internet of Things (IoT) & IoE & Edge Computing Distributed Mobile Applications Utilizing IoT Security, Privacy and Trust in IoT & IoE Standards for IoT Applications Ubiquitous Computing Block Chain-enabled IoT Device and Data Security and Privacy Application of WSN in IoT Cloud Resources Utilization in IoT Wireless Access Technologies for IoT Mobile Applications and Services for IoT Machine/ Deep Learning with IoT & IoE Smart Sensors and Internet of Things for Smart City Logic, Functional programming and Microcontrollers for IoT Sensor Networks, Actuators for Internet of Things Data Visualization using IoT IoT Application and Communication Protocol Big Data Analytics for Social Networking using IoT IoT Applications for Smart Cities Emulation and Simulation Methodologies for IoT IoT Applied for Digital Contents 5. Microwaves and Photonics Microwave filter Micro Strip antenna Microwave Link design Microwave oscillator Frequency selective surface Microwave Antenna Microwave Photonics Radio over fiber Optical communication Optical oscillator Optical Link design Optical phase lock loop Optical devices 6. Computation Intelligence and Analytics Soft Computing Advance Ubiquitous Computing Parallel Computing Distributed Computing Machine Learning Information Retrieval Expert Systems Data Mining Text Mining Data Warehousing Predictive Analysis Data Management Big Data Analytics Big Data Security 7. Energy Harvesting and Wireless Power Transmission Energy harvesting and transfer for wireless sensor networks Economics of energy harvesting communications Waveform optimization for wireless power transfer RF Energy Harvesting Wireless Power Transmission Microstrip Antenna design and application Wearable Textile Antenna Luminescence Rectenna 8. Advance Concept of Networking and Database Computer Network Mobile Adhoc Network Image Security Application Artificial Intelligence and machine learning in the Field of Network and Database Data Analytic High performance computing Pattern Recognition 9. Machine Learning (ML) and Knowledge Mining (KM) Regression and prediction Problem solving and planning Clustering Classification Neural information processing Vision and speech perception Heterogeneous and streaming data Natural language processing Probabilistic Models and Methods Reasoning and inference Marketing and social sciences Data mining Knowledge Discovery Web mining Information retrieval Design and diagnosis Game playing Streaming data Music Modelling and Analysis Robotics and control Multi-agent systems Bioinformatics Social sciences Industrial, financial and scientific applications of all kind 10. Advanced Computer networking Computational Intelligence Data Management, Exploration, and Mining Robotics Artificial Intelligence and Machine Learning Computer Architecture and VLSI Computer Graphics, Simulation, and Modelling Digital System and Logic Design Natural Language Processing and Machine Translation Parallel and Distributed Algorithms Pattern Recognition and Analysis Systems and Software Engineering Nature Inspired Computing Signal and Image Processing Reconfigurable Computing Cloud, Cluster, Grid and P2P Computing Biomedical Computing Advanced Bioinformatics Green Computing Mobile Computing Nano Ubiquitous Computing Context Awareness and Personalization, Autonomic and Trusted Computing Cryptography and Applied Mathematics Security, Trust and Privacy Digital Rights Management Networked-Driven Multicourse Chips Internet Computing Agricultural Informatics and Communication Community Information Systems Computational Economics, Digital Photogrammetric Remote Sensing, GIS and GPS Disaster Management e-governance, e-Commerce, e-business, e-Learning Forest Genomics and Informatics Healthcare Informatics Information Ecology and Knowledge Management Irrigation Informatics Neuro-Informatics Open Source: Challenges and opportunities Web-Based Learning: Innovation and Challenges Soft computing Signal and Speech Processing Natural Language Processing 11. Communications Microstrip Antenna Microwave Radar and Satellite Smart Antenna MIMO Antenna Wireless Communication RFID Network and Applications 5G Communication 6G Communication 12. Algorithms and Complexity Sequential, Parallel And Distributed Algorithms And Data Structures Approximation And Randomized Algorithms Graph Algorithms And Graph Drawing On-Line And Streaming Algorithms Analysis Of Algorithms And Computational Complexity Algorithm Engineering Web Algorithms Exact And Parameterized Computation Algorithmic Game Theory Computational Biology Foundations Of Communication Networks Computational Geometry Discrete Optimization 13. Software Engineering and Knowledge Engineering Software Engineering Methodologies Agent-based software engineering Artificial intelligence approaches to software engineering Component-based software engineering Embedded and ubiquitous software engineering Aspect-based software engineering Empirical software engineering Search-Based Software engineering Automated software design and synthesis Computer-supported cooperative work Automated software specification Reverse engineering Software Engineering Techniques and Production Perspectives Requirements engineering Software analysis, design and modelling Software maintenance and evolution Software engineering tools and environments Software engineering decision support Software design patterns Software product lines Process and workflow management Reflection and metadata approaches Program understanding and system maintenance Software domain modelling and analysis Software economics Multimedia and hypermedia software engineering Software engineering case study and experience reports Enterprise software, middleware, and tools Artificial intelligent methods, models, techniques Artificial life and societies Swarm intelligence Smart Spaces Autonomic computing and agent-based systems Autonomic computing Adaptive Systems Agent architectures, ontologies, languages and protocols Multi-agent systems Agent-based learning and knowledge discovery Interface agents Agent-based auctions and marketplaces Secure mobile and multi-agent systems Mobile agents SOA and Service-Oriented Systems Service-centric software engineering Service oriented requirements engineering Service oriented architectures Middleware for service based systems Service discovery and composition Service level

  6. Augmented Sentinel 1-2

    • kaggle.com
    Updated Apr 20, 2025
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    Shamba Chowdhury (2025). Augmented Sentinel 1-2 [Dataset]. http://doi.org/10.34740/kaggle/dsv/11487561
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Apr 20, 2025
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Shamba Chowdhury
    License

    Attribution-ShareAlike 4.0 (CC BY-SA 4.0)https://creativecommons.org/licenses/by-sa/4.0/
    License information was derived automatically

    Description

    Dataset name

    AUGMENTED SEN1-2

    Year of publication

    2025

    Author

    Shamba Chowdhury Ankana Ghosh Shreyashi Ghosh

    License

    CC-BY The dataset contains Copernicus data (2024). Terms and conditions apply: https://scihub.copernicus.eu/twiki/pub/SciHubWebPortal/TermsConditions/TC_Sentinel_Data_31072014.pdf

    Associated publication

    TBA

    Links

    Dataset: https://www.kaggle.com/datasets/shambac/augmented-sentinel-1-2 Paper: TBA

    Dataset structure

    Top level: spring, summer, fall, winter - folders corresponding to four different random ROI distributions and to the four meteorological seasons Second level: s1_i, s2_i - folders corresponding to the scenes from which the patches were cut, with s1 indicating Sentinel-1 SAR images and s2 indicating Sentinel-2 optical images

    Dataset size

    No. of files: 616,148 Storage: 53,699 MB

    Description

    The Augmented SEN1-2 dataset contains 308,074 pairs of corresponding SAR and optical image patches acquired by the Sentinel-1 and Sentinel-2 satellites, respectively. The patches are distributed across the land masses of the Earth and spread over all four meteorological seasons. This is reflected by the dataset structure. For the SAR patches 8-bit single-channel images representing the sigma nought backscatter value in dB scale are provided. For the optical patches 8-bit color images representing the bands 4, 3, and 2 are used. Along with the patches, this dataset has been augmented with heatzone data of the locations to further aid in research. If you use the dataset, please cite the associated publication mentioned above.

  7. r

    International Journal of Engineering and Advanced Technology FAQ -...

    • researchhelpdesk.org
    Updated May 28, 2022
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    Research Help Desk (2022). International Journal of Engineering and Advanced Technology FAQ - ResearchHelpDesk [Dataset]. https://www.researchhelpdesk.org/journal/faq/552/international-journal-of-engineering-and-advanced-technology
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    Dataset updated
    May 28, 2022
    Dataset authored and provided by
    Research Help Desk
    Description

    International Journal of Engineering and Advanced Technology FAQ - ResearchHelpDesk - International Journal of Engineering and Advanced Technology (IJEAT) is having Online-ISSN 2249-8958, bi-monthly international journal, being published in the months of February, April, June, August, October, and December by Blue Eyes Intelligence Engineering & Sciences Publication (BEIESP) Bhopal (M.P.), India since the year 2011. It is academic, online, open access, double-blind, peer-reviewed international journal. It aims to publish original, theoretical and practical advances in Computer Science & Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. All submitted papers will be reviewed by the board of committee of IJEAT. Aim of IJEAT Journal disseminate original, scientific, theoretical or applied research in the field of Engineering and allied fields. dispense a platform for publishing results and research with a strong empirical component. aqueduct the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research. seek original and unpublished research papers based on theoretical or experimental works for the publication globally. publish original, theoretical and practical advances in Computer Science & Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. impart a platform for publishing results and research with a strong empirical component. create a bridge for a significant gap between research and practice by promoting the publication of original, novel, industry-relevant research. solicit original and unpublished research papers, based on theoretical or experimental works. Scope of IJEAT International Journal of Engineering and Advanced Technology (IJEAT) covers all topics of all engineering branches. Some of them are Computer Science & Engineering, Information Technology, Electronics & Communication, Electrical and Electronics, Electronics and Telecommunication, Civil Engineering, Mechanical Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. The main topic includes but not limited to: 1. Smart Computing and Information Processing Signal and Speech Processing Image Processing and Pattern Recognition WSN Artificial Intelligence and machine learning Data mining and warehousing Data Analytics Deep learning Bioinformatics High Performance computing Advanced Computer networking Cloud Computing IoT Parallel Computing on GPU Human Computer Interactions 2. Recent Trends in Microelectronics and VLSI Design Process & Device Technologies Low-power design Nanometer-scale integrated circuits Application specific ICs (ASICs) FPGAs Nanotechnology Nano electronics and Quantum Computing 3. Challenges of Industry and their Solutions, Communications Advanced Manufacturing Technologies Artificial Intelligence Autonomous Robots Augmented Reality Big Data Analytics and Business Intelligence Cyber Physical Systems (CPS) Digital Clone or Simulation Industrial Internet of Things (IIoT) Manufacturing IOT Plant Cyber security Smart Solutions – Wearable Sensors and Smart Glasses System Integration Small Batch Manufacturing Visual Analytics Virtual Reality 3D Printing 4. Internet of Things (IoT) Internet of Things (IoT) & IoE & Edge Computing Distributed Mobile Applications Utilizing IoT Security, Privacy and Trust in IoT & IoE Standards for IoT Applications Ubiquitous Computing Block Chain-enabled IoT Device and Data Security and Privacy Application of WSN in IoT Cloud Resources Utilization in IoT Wireless Access Technologies for IoT Mobile Applications and Services for IoT Machine/ Deep Learning with IoT & IoE Smart Sensors and Internet of Things for Smart City Logic, Functional programming and Microcontrollers for IoT Sensor Networks, Actuators for Internet of Things Data Visualization using IoT IoT Application and Communication Protocol Big Data Analytics for Social Networking using IoT IoT Applications for Smart Cities Emulation and Simulation Methodologies for IoT IoT Applied for Digital Contents 5. Microwaves and Photonics Microwave filter Micro Strip antenna Microwave Link design Microwave oscillator Frequency selective surface Microwave Antenna Microwave Photonics Radio over fiber Optical communication Optical oscillator Optical Link design Optical phase lock loop Optical devices 6. Computation Intelligence and Analytics Soft Computing Advance Ubiquitous Computing Parallel Computing Distributed Computing Machine Learning Information Retrieval Expert Systems Data Mining Text Mining Data Warehousing Predictive Analysis Data Management Big Data Analytics Big Data Security 7. Energy Harvesting and Wireless Power Transmission Energy harvesting and transfer for wireless sensor networks Economics of energy harvesting communications Waveform optimization for wireless power transfer RF Energy Harvesting Wireless Power Transmission Microstrip Antenna design and application Wearable Textile Antenna Luminescence Rectenna 8. Advance Concept of Networking and Database Computer Network Mobile Adhoc Network Image Security Application Artificial Intelligence and machine learning in the Field of Network and Database Data Analytic High performance computing Pattern Recognition 9. Machine Learning (ML) and Knowledge Mining (KM) Regression and prediction Problem solving and planning Clustering Classification Neural information processing Vision and speech perception Heterogeneous and streaming data Natural language processing Probabilistic Models and Methods Reasoning and inference Marketing and social sciences Data mining Knowledge Discovery Web mining Information retrieval Design and diagnosis Game playing Streaming data Music Modelling and Analysis Robotics and control Multi-agent systems Bioinformatics Social sciences Industrial, financial and scientific applications of all kind 10. Advanced Computer networking Computational Intelligence Data Management, Exploration, and Mining Robotics Artificial Intelligence and Machine Learning Computer Architecture and VLSI Computer Graphics, Simulation, and Modelling Digital System and Logic Design Natural Language Processing and Machine Translation Parallel and Distributed Algorithms Pattern Recognition and Analysis Systems and Software Engineering Nature Inspired Computing Signal and Image Processing Reconfigurable Computing Cloud, Cluster, Grid and P2P Computing Biomedical Computing Advanced Bioinformatics Green Computing Mobile Computing Nano Ubiquitous Computing Context Awareness and Personalization, Autonomic and Trusted Computing Cryptography and Applied Mathematics Security, Trust and Privacy Digital Rights Management Networked-Driven Multicourse Chips Internet Computing Agricultural Informatics and Communication Community Information Systems Computational Economics, Digital Photogrammetric Remote Sensing, GIS and GPS Disaster Management e-governance, e-Commerce, e-business, e-Learning Forest Genomics and Informatics Healthcare Informatics Information Ecology and Knowledge Management Irrigation Informatics Neuro-Informatics Open Source: Challenges and opportunities Web-Based Learning: Innovation and Challenges Soft computing Signal and Speech Processing Natural Language Processing 11. Communications Microstrip Antenna Microwave Radar and Satellite Smart Antenna MIMO Antenna Wireless Communication RFID Network and Applications 5G Communication 6G Communication 12. Algorithms and Complexity Sequential, Parallel And Distributed Algorithms And Data Structures Approximation And Randomized Algorithms Graph Algorithms And Graph Drawing On-Line And Streaming Algorithms Analysis Of Algorithms And Computational Complexity Algorithm Engineering Web Algorithms Exact And Parameterized Computation Algorithmic Game Theory Computational Biology Foundations Of Communication Networks Computational Geometry Discrete Optimization 13. Software Engineering and Knowledge Engineering Software Engineering Methodologies Agent-based software engineering Artificial intelligence approaches to software engineering Component-based software engineering Embedded and ubiquitous software engineering Aspect-based software engineering Empirical software engineering Search-Based Software engineering Automated software design and synthesis Computer-supported cooperative work Automated software specification Reverse engineering Software Engineering Techniques and Production Perspectives Requirements engineering Software analysis, design and modelling Software maintenance and evolution Software engineering tools and environments Software engineering decision support Software design patterns Software product lines Process and workflow management Reflection and metadata approaches Program understanding and system maintenance Software domain modelling and analysis Software economics Multimedia and hypermedia software engineering Software engineering case study and experience reports Enterprise software, middleware, and tools Artificial intelligent methods, models, techniques Artificial life and societies Swarm intelligence Smart Spaces Autonomic computing and agent-based systems Autonomic computing Adaptive Systems Agent architectures, ontologies, languages and protocols Multi-agent systems Agent-based learning and knowledge discovery Interface agents Agent-based auctions and marketplaces Secure mobile and multi-agent systems Mobile agents SOA and Service-Oriented Systems Service-centric software engineering Service oriented requirements engineering Service oriented architectures Middleware for service based systems Service discovery and composition Service level agreements (drafting,

  8. r

    International Journal of Engineering and Advanced Technology Impact Factor...

    • researchhelpdesk.org
    Updated Feb 23, 2022
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    Research Help Desk (2022). International Journal of Engineering and Advanced Technology Impact Factor 2024-2025 - ResearchHelpDesk [Dataset]. https://www.researchhelpdesk.org/journal/impact-factor-if/552/international-journal-of-engineering-and-advanced-technology
    Explore at:
    Dataset updated
    Feb 23, 2022
    Dataset authored and provided by
    Research Help Desk
    Description

    International Journal of Engineering and Advanced Technology Impact Factor 2024-2025 - ResearchHelpDesk - International Journal of Engineering and Advanced Technology (IJEAT) is having Online-ISSN 2249-8958, bi-monthly international journal, being published in the months of February, April, June, August, October, and December by Blue Eyes Intelligence Engineering & Sciences Publication (BEIESP) Bhopal (M.P.), India since the year 2011. It is academic, online, open access, double-blind, peer-reviewed international journal. It aims to publish original, theoretical and practical advances in Computer Science & Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. All submitted papers will be reviewed by the board of committee of IJEAT. Aim of IJEAT Journal disseminate original, scientific, theoretical or applied research in the field of Engineering and allied fields. dispense a platform for publishing results and research with a strong empirical component. aqueduct the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research. seek original and unpublished research papers based on theoretical or experimental works for the publication globally. publish original, theoretical and practical advances in Computer Science & Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. impart a platform for publishing results and research with a strong empirical component. create a bridge for a significant gap between research and practice by promoting the publication of original, novel, industry-relevant research. solicit original and unpublished research papers, based on theoretical or experimental works. Scope of IJEAT International Journal of Engineering and Advanced Technology (IJEAT) covers all topics of all engineering branches. Some of them are Computer Science & Engineering, Information Technology, Electronics & Communication, Electrical and Electronics, Electronics and Telecommunication, Civil Engineering, Mechanical Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. The main topic includes but not limited to: 1. Smart Computing and Information Processing Signal and Speech Processing Image Processing and Pattern Recognition WSN Artificial Intelligence and machine learning Data mining and warehousing Data Analytics Deep learning Bioinformatics High Performance computing Advanced Computer networking Cloud Computing IoT Parallel Computing on GPU Human Computer Interactions 2. Recent Trends in Microelectronics and VLSI Design Process & Device Technologies Low-power design Nanometer-scale integrated circuits Application specific ICs (ASICs) FPGAs Nanotechnology Nano electronics and Quantum Computing 3. Challenges of Industry and their Solutions, Communications Advanced Manufacturing Technologies Artificial Intelligence Autonomous Robots Augmented Reality Big Data Analytics and Business Intelligence Cyber Physical Systems (CPS) Digital Clone or Simulation Industrial Internet of Things (IIoT) Manufacturing IOT Plant Cyber security Smart Solutions – Wearable Sensors and Smart Glasses System Integration Small Batch Manufacturing Visual Analytics Virtual Reality 3D Printing 4. Internet of Things (IoT) Internet of Things (IoT) & IoE & Edge Computing Distributed Mobile Applications Utilizing IoT Security, Privacy and Trust in IoT & IoE Standards for IoT Applications Ubiquitous Computing Block Chain-enabled IoT Device and Data Security and Privacy Application of WSN in IoT Cloud Resources Utilization in IoT Wireless Access Technologies for IoT Mobile Applications and Services for IoT Machine/ Deep Learning with IoT & IoE Smart Sensors and Internet of Things for Smart City Logic, Functional programming and Microcontrollers for IoT Sensor Networks, Actuators for Internet of Things Data Visualization using IoT IoT Application and Communication Protocol Big Data Analytics for Social Networking using IoT IoT Applications for Smart Cities Emulation and Simulation Methodologies for IoT IoT Applied for Digital Contents 5. Microwaves and Photonics Microwave filter Micro Strip antenna Microwave Link design Microwave oscillator Frequency selective surface Microwave Antenna Microwave Photonics Radio over fiber Optical communication Optical oscillator Optical Link design Optical phase lock loop Optical devices 6. Computation Intelligence and Analytics Soft Computing Advance Ubiquitous Computing Parallel Computing Distributed Computing Machine Learning Information Retrieval Expert Systems Data Mining Text Mining Data Warehousing Predictive Analysis Data Management Big Data Analytics Big Data Security 7. Energy Harvesting and Wireless Power Transmission Energy harvesting and transfer for wireless sensor networks Economics of energy harvesting communications Waveform optimization for wireless power transfer RF Energy Harvesting Wireless Power Transmission Microstrip Antenna design and application Wearable Textile Antenna Luminescence Rectenna 8. Advance Concept of Networking and Database Computer Network Mobile Adhoc Network Image Security Application Artificial Intelligence and machine learning in the Field of Network and Database Data Analytic High performance computing Pattern Recognition 9. Machine Learning (ML) and Knowledge Mining (KM) Regression and prediction Problem solving and planning Clustering Classification Neural information processing Vision and speech perception Heterogeneous and streaming data Natural language processing Probabilistic Models and Methods Reasoning and inference Marketing and social sciences Data mining Knowledge Discovery Web mining Information retrieval Design and diagnosis Game playing Streaming data Music Modelling and Analysis Robotics and control Multi-agent systems Bioinformatics Social sciences Industrial, financial and scientific applications of all kind 10. Advanced Computer networking Computational Intelligence Data Management, Exploration, and Mining Robotics Artificial Intelligence and Machine Learning Computer Architecture and VLSI Computer Graphics, Simulation, and Modelling Digital System and Logic Design Natural Language Processing and Machine Translation Parallel and Distributed Algorithms Pattern Recognition and Analysis Systems and Software Engineering Nature Inspired Computing Signal and Image Processing Reconfigurable Computing Cloud, Cluster, Grid and P2P Computing Biomedical Computing Advanced Bioinformatics Green Computing Mobile Computing Nano Ubiquitous Computing Context Awareness and Personalization, Autonomic and Trusted Computing Cryptography and Applied Mathematics Security, Trust and Privacy Digital Rights Management Networked-Driven Multicourse Chips Internet Computing Agricultural Informatics and Communication Community Information Systems Computational Economics, Digital Photogrammetric Remote Sensing, GIS and GPS Disaster Management e-governance, e-Commerce, e-business, e-Learning Forest Genomics and Informatics Healthcare Informatics Information Ecology and Knowledge Management Irrigation Informatics Neuro-Informatics Open Source: Challenges and opportunities Web-Based Learning: Innovation and Challenges Soft computing Signal and Speech Processing Natural Language Processing 11. Communications Microstrip Antenna Microwave Radar and Satellite Smart Antenna MIMO Antenna Wireless Communication RFID Network and Applications 5G Communication 6G Communication 12. Algorithms and Complexity Sequential, Parallel And Distributed Algorithms And Data Structures Approximation And Randomized Algorithms Graph Algorithms And Graph Drawing On-Line And Streaming Algorithms Analysis Of Algorithms And Computational Complexity Algorithm Engineering Web Algorithms Exact And Parameterized Computation Algorithmic Game Theory Computational Biology Foundations Of Communication Networks Computational Geometry Discrete Optimization 13. Software Engineering and Knowledge Engineering Software Engineering Methodologies Agent-based software engineering Artificial intelligence approaches to software engineering Component-based software engineering Embedded and ubiquitous software engineering Aspect-based software engineering Empirical software engineering Search-Based Software engineering Automated software design and synthesis Computer-supported cooperative work Automated software specification Reverse engineering Software Engineering Techniques and Production Perspectives Requirements engineering Software analysis, design and modelling Software maintenance and evolution Software engineering tools and environments Software engineering decision support Software design patterns Software product lines Process and workflow management Reflection and metadata approaches Program understanding and system maintenance Software domain modelling and analysis Software economics Multimedia and hypermedia software engineering Software engineering case study and experience reports Enterprise software, middleware, and tools Artificial intelligent methods, models, techniques Artificial life and societies Swarm intelligence Smart Spaces Autonomic computing and agent-based systems Autonomic computing Adaptive Systems Agent architectures, ontologies, languages and protocols Multi-agent systems Agent-based learning and knowledge discovery Interface agents Agent-based auctions and marketplaces Secure mobile and multi-agent systems Mobile agents SOA and Service-Oriented Systems Service-centric software engineering Service oriented requirements engineering Service oriented architectures Middleware for service based systems Service discovery and composition Service level

  9. D

    PGA-NeuS: Physics and Geometry-Augmented Neural Implicit Surfaces for Rigid...

    • researchdata.ntu.edu.sg
    Updated May 22, 2025
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    DR-NTU (Data) (2025). PGA-NeuS: Physics and Geometry-Augmented Neural Implicit Surfaces for Rigid Bodies [Dataset]. http://doi.org/10.21979/N9/LTXKFL
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    Dataset updated
    May 22, 2025
    Dataset provided by
    DR-NTU (Data)
    License

    https://researchdata.ntu.edu.sg/api/datasets/:persistentId/versions/1.0/customlicense?persistentId=doi:10.21979/N9/LTXKFLhttps://researchdata.ntu.edu.sg/api/datasets/:persistentId/versions/1.0/customlicense?persistentId=doi:10.21979/N9/LTXKFL

    Dataset funded by
    Guangdong Basic and Applied Basic Research Foundation
    Academic Research Fund Grant
    Industry Alignment Fund–Industry Collaboration Projects (IAF-ICP)
    National Science and Technology Major Project
    National Natural Science Foundation of China
    Description

    This paper tackles the challenges of physics-based simulation of rigid bodies in neural rendering, focusing on 3D model representation and collision handling. A synthetic and real-world dataset is also included in the paper.

  10. s

    Synthetic healthy brain aging MRIs with segmentation masks

    • datahub.aida.scilifelab.se
    • researchdata.se
    Updated Dec 1, 2023
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    Jingru Fu; Antonios Tzortzakakis; José Barroso; Eric Westman; Daniel Ferreira; Rodrigo Moreno (2023). Synthetic healthy brain aging MRIs with segmentation masks [Dataset]. http://doi.org/10.23698/aida/synthetic/shbamri
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    Dataset updated
    Dec 1, 2023
    Dataset provided by
    AIDA
    KTH
    AIDA Data Hub
    Authors
    Jingru Fu; Antonios Tzortzakakis; José Barroso; Eric Westman; Daniel Ferreira; Rodrigo Moreno
    Description

    This dataset is a collection that includes the 6448 synthetic aging brain T1 MRI scans derived from two data sets by our proposed methodology (the following paper [1]). We augmented the HEALTHY longitudinal brain MRI data with corresponding segmentations to simulate the access of a scan per subject every 6 months in these cohorts.

  11. r

    International Journal of Engineering and Advanced Technology CiteScore...

    • researchhelpdesk.org
    Updated Apr 5, 2022
    + more versions
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    Research Help Desk (2022). International Journal of Engineering and Advanced Technology CiteScore 2024-2025 - ResearchHelpDesk [Dataset]. https://www.researchhelpdesk.org/journal/sjr/552/international-journal-of-engineering-and-advanced-technology
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    Dataset updated
    Apr 5, 2022
    Dataset authored and provided by
    Research Help Desk
    Description

    International Journal of Engineering and Advanced Technology CiteScore 2024-2025 - ResearchHelpDesk - International Journal of Engineering and Advanced Technology (IJEAT) is having Online-ISSN 2249-8958, bi-monthly international journal, being published in the months of February, April, June, August, October, and December by Blue Eyes Intelligence Engineering & Sciences Publication (BEIESP) Bhopal (M.P.), India since the year 2011. It is academic, online, open access, double-blind, peer-reviewed international journal. It aims to publish original, theoretical and practical advances in Computer Science & Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. All submitted papers will be reviewed by the board of committee of IJEAT. Aim of IJEAT Journal disseminate original, scientific, theoretical or applied research in the field of Engineering and allied fields. dispense a platform for publishing results and research with a strong empirical component. aqueduct the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research. seek original and unpublished research papers based on theoretical or experimental works for the publication globally. publish original, theoretical and practical advances in Computer Science & Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. impart a platform for publishing results and research with a strong empirical component. create a bridge for a significant gap between research and practice by promoting the publication of original, novel, industry-relevant research. solicit original and unpublished research papers, based on theoretical or experimental works. Scope of IJEAT International Journal of Engineering and Advanced Technology (IJEAT) covers all topics of all engineering branches. Some of them are Computer Science & Engineering, Information Technology, Electronics & Communication, Electrical and Electronics, Electronics and Telecommunication, Civil Engineering, Mechanical Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. The main topic includes but not limited to: 1. Smart Computing and Information Processing Signal and Speech Processing Image Processing and Pattern Recognition WSN Artificial Intelligence and machine learning Data mining and warehousing Data Analytics Deep learning Bioinformatics High Performance computing Advanced Computer networking Cloud Computing IoT Parallel Computing on GPU Human Computer Interactions 2. Recent Trends in Microelectronics and VLSI Design Process & Device Technologies Low-power design Nanometer-scale integrated circuits Application specific ICs (ASICs) FPGAs Nanotechnology Nano electronics and Quantum Computing 3. Challenges of Industry and their Solutions, Communications Advanced Manufacturing Technologies Artificial Intelligence Autonomous Robots Augmented Reality Big Data Analytics and Business Intelligence Cyber Physical Systems (CPS) Digital Clone or Simulation Industrial Internet of Things (IIoT) Manufacturing IOT Plant Cyber security Smart Solutions – Wearable Sensors and Smart Glasses System Integration Small Batch Manufacturing Visual Analytics Virtual Reality 3D Printing 4. Internet of Things (IoT) Internet of Things (IoT) & IoE & Edge Computing Distributed Mobile Applications Utilizing IoT Security, Privacy and Trust in IoT & IoE Standards for IoT Applications Ubiquitous Computing Block Chain-enabled IoT Device and Data Security and Privacy Application of WSN in IoT Cloud Resources Utilization in IoT Wireless Access Technologies for IoT Mobile Applications and Services for IoT Machine/ Deep Learning with IoT & IoE Smart Sensors and Internet of Things for Smart City Logic, Functional programming and Microcontrollers for IoT Sensor Networks, Actuators for Internet of Things Data Visualization using IoT IoT Application and Communication Protocol Big Data Analytics for Social Networking using IoT IoT Applications for Smart Cities Emulation and Simulation Methodologies for IoT IoT Applied for Digital Contents 5. Microwaves and Photonics Microwave filter Micro Strip antenna Microwave Link design Microwave oscillator Frequency selective surface Microwave Antenna Microwave Photonics Radio over fiber Optical communication Optical oscillator Optical Link design Optical phase lock loop Optical devices 6. Computation Intelligence and Analytics Soft Computing Advance Ubiquitous Computing Parallel Computing Distributed Computing Machine Learning Information Retrieval Expert Systems Data Mining Text Mining Data Warehousing Predictive Analysis Data Management Big Data Analytics Big Data Security 7. Energy Harvesting and Wireless Power Transmission Energy harvesting and transfer for wireless sensor networks Economics of energy harvesting communications Waveform optimization for wireless power transfer RF Energy Harvesting Wireless Power Transmission Microstrip Antenna design and application Wearable Textile Antenna Luminescence Rectenna 8. Advance Concept of Networking and Database Computer Network Mobile Adhoc Network Image Security Application Artificial Intelligence and machine learning in the Field of Network and Database Data Analytic High performance computing Pattern Recognition 9. Machine Learning (ML) and Knowledge Mining (KM) Regression and prediction Problem solving and planning Clustering Classification Neural information processing Vision and speech perception Heterogeneous and streaming data Natural language processing Probabilistic Models and Methods Reasoning and inference Marketing and social sciences Data mining Knowledge Discovery Web mining Information retrieval Design and diagnosis Game playing Streaming data Music Modelling and Analysis Robotics and control Multi-agent systems Bioinformatics Social sciences Industrial, financial and scientific applications of all kind 10. Advanced Computer networking Computational Intelligence Data Management, Exploration, and Mining Robotics Artificial Intelligence and Machine Learning Computer Architecture and VLSI Computer Graphics, Simulation, and Modelling Digital System and Logic Design Natural Language Processing and Machine Translation Parallel and Distributed Algorithms Pattern Recognition and Analysis Systems and Software Engineering Nature Inspired Computing Signal and Image Processing Reconfigurable Computing Cloud, Cluster, Grid and P2P Computing Biomedical Computing Advanced Bioinformatics Green Computing Mobile Computing Nano Ubiquitous Computing Context Awareness and Personalization, Autonomic and Trusted Computing Cryptography and Applied Mathematics Security, Trust and Privacy Digital Rights Management Networked-Driven Multicourse Chips Internet Computing Agricultural Informatics and Communication Community Information Systems Computational Economics, Digital Photogrammetric Remote Sensing, GIS and GPS Disaster Management e-governance, e-Commerce, e-business, e-Learning Forest Genomics and Informatics Healthcare Informatics Information Ecology and Knowledge Management Irrigation Informatics Neuro-Informatics Open Source: Challenges and opportunities Web-Based Learning: Innovation and Challenges Soft computing Signal and Speech Processing Natural Language Processing 11. Communications Microstrip Antenna Microwave Radar and Satellite Smart Antenna MIMO Antenna Wireless Communication RFID Network and Applications 5G Communication 6G Communication 12. Algorithms and Complexity Sequential, Parallel And Distributed Algorithms And Data Structures Approximation And Randomized Algorithms Graph Algorithms And Graph Drawing On-Line And Streaming Algorithms Analysis Of Algorithms And Computational Complexity Algorithm Engineering Web Algorithms Exact And Parameterized Computation Algorithmic Game Theory Computational Biology Foundations Of Communication Networks Computational Geometry Discrete Optimization 13. Software Engineering and Knowledge Engineering Software Engineering Methodologies Agent-based software engineering Artificial intelligence approaches to software engineering Component-based software engineering Embedded and ubiquitous software engineering Aspect-based software engineering Empirical software engineering Search-Based Software engineering Automated software design and synthesis Computer-supported cooperative work Automated software specification Reverse engineering Software Engineering Techniques and Production Perspectives Requirements engineering Software analysis, design and modelling Software maintenance and evolution Software engineering tools and environments Software engineering decision support Software design patterns Software product lines Process and workflow management Reflection and metadata approaches Program understanding and system maintenance Software domain modelling and analysis Software economics Multimedia and hypermedia software engineering Software engineering case study and experience reports Enterprise software, middleware, and tools Artificial intelligent methods, models, techniques Artificial life and societies Swarm intelligence Smart Spaces Autonomic computing and agent-based systems Autonomic computing Adaptive Systems Agent architectures, ontologies, languages and protocols Multi-agent systems Agent-based learning and knowledge discovery Interface agents Agent-based auctions and marketplaces Secure mobile and multi-agent systems Mobile agents SOA and Service-Oriented Systems Service-centric software engineering Service oriented requirements engineering Service oriented architectures Middleware for service based systems Service discovery and composition Service level

  12. i

    NeuroBiomech Experiment Data Based on Public CSL500 / WLASL / Kinetics...

    • ieee-dataport.org
    Updated Jul 10, 2025
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    qinkun xiao (2025). NeuroBiomech Experiment Data Based on Public CSL500 / WLASL / Kinetics Datasets [Dataset]. https://ieee-dataport.org/documents/neurobiomech-experiment-data-based-public-csl500-wlasl-kinetics-datasets
    Explore at:
    Dataset updated
    Jul 10, 2025
    Authors
    qinkun xiao
    Description

    WLASL

  13. S

    Scene Recognition Report

    • datainsightsmarket.com
    doc, pdf, ppt
    Updated Jun 15, 2025
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    Data Insights Market (2025). Scene Recognition Report [Dataset]. https://www.datainsightsmarket.com/reports/scene-recognition-531039
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    pdf, ppt, docAvailable download formats
    Dataset updated
    Jun 15, 2025
    Dataset authored and provided by
    Data Insights Market
    License

    https://www.datainsightsmarket.com/privacy-policyhttps://www.datainsightsmarket.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The scene recognition market is experiencing robust growth, driven by the increasing adoption of artificial intelligence (AI) and computer vision technologies across diverse sectors. The market, estimated at $2 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 20% from 2025 to 2033, reaching an estimated value of $10 billion by 2033. This expansion is fueled by several key factors. The surge in demand for advanced image and video analysis capabilities in applications such as autonomous vehicles, robotics, security surveillance, and augmented reality (AR)/virtual reality (VR) is a significant driver. Furthermore, continuous improvements in deep learning algorithms and the availability of large-scale labeled datasets are enhancing the accuracy and efficiency of scene recognition systems. The market is witnessing a growing preference for cloud-based solutions, owing to their scalability, cost-effectiveness, and accessibility. However, challenges remain. High computational costs associated with processing large volumes of visual data, concerns about data privacy and security, and the need for robust algorithms capable of handling diverse lighting conditions and occlusions continue to hinder widespread adoption. Despite these restraints, market segmentation is expanding, with a significant focus on developing customized solutions for specific industry needs. The competitive landscape is dynamic, featuring established players like Nikon and AWS alongside innovative startups like VISUA and Catchoom Technologies. The ongoing research and development efforts are focused on enhancing the accuracy, speed, and robustness of scene recognition algorithms, pushing the market towards more sophisticated and reliable solutions for a wider array of applications.

  14. h

    text_parts_de

    • huggingface.co
    Updated May 27, 2025
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    Samir M'Sallem (2025). text_parts_de [Dataset]. https://huggingface.co/datasets/samirmsallem/text_parts_de
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    Dataset updated
    May 27, 2025
    Authors
    Samir M'Sallem
    License

    Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/
    License information was derived automatically

    Description

    Dataset Card for Text parts classification German

    The Text parts classification German dataset is a dataset to train language models to classify text parts in scientific papers. The dataset was created by importing around 10 conference papers and 10 theses and labelling the text parts based on the categories. The data set was then augmented using synthetic data. For this purpose, the text parts were duplicated using data augmentation. OpenThesaurus was then used to replace all… See the full description on the dataset page: https://huggingface.co/datasets/samirmsallem/text_parts_de.

  15. h

    MathGenieData

    • huggingface.co
    Updated Aug 27, 2024
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    MathGenie (2024). MathGenieData [Dataset]. https://huggingface.co/datasets/MathGenie/MathGenieData
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Aug 27, 2024
    Authors
    MathGenie
    License

    Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
    License information was derived automatically

    Description

    MathGenieData

    This is the full mathematical training dataset introduced in the paper MathGenie: Generating Synthetic Data with Question Back-translation for Enhancing Mathematical Reasoning of LLMs.

    Project HomePage Github

      Note
    

    MathGenieData consists of 81 K code-integrated solutions and 30 K code-integrated verification rationales curated from GPT-4, as well as 170 K new problems and solutions augmented from GSM8K and MATH using the MathGenie pipeline. Note that… See the full description on the dataset page: https://huggingface.co/datasets/MathGenie/MathGenieData.

  16. Replication Data for: ``Impact of Parameterized Isopycnal Diffusivity on...

    • zenodo.org
    • explore.openaire.eu
    zip
    Updated Sep 23, 2022
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    Chenyue Xie; Chenyue Xie; Huaiyu Wei; Huaiyu Wei; Yan Wang; Yan Wang (2022). Replication Data for: ``Impact of Parameterized Isopycnal Diffusivity on Shelf-Ocean Exchanges under Upwelling-Favorable Winds: Offline Tracer Simulations Augmented by Artificial Neural Network'' [Dataset]. http://doi.org/10.5281/zenodo.7105701
    Explore at:
    zipAvailable download formats
    Dataset updated
    Sep 23, 2022
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Chenyue Xie; Chenyue Xie; Huaiyu Wei; Huaiyu Wei; Yan Wang; Yan Wang
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This dataset contains the modified MAMEBUS source code, configuration files for the MITgcm and MAMEBUS simulations, model diagnostics used in the paper, and scripts to train the Artificial Neural Networks.

  17. f

    Table 1_Augmented intelligence with voice assistance and automated machine...

    • frontiersin.figshare.com
    docx
    Updated Mar 4, 2025
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    Alexandros Bousdekis; Mina Foosherian; Mattheos Fikardos; Stefan Wellsandt; Katerina Lepenioti; Enrica Bosani; Gregoris Mentzas; Klaus-Dieter Thoben (2025). Table 1_Augmented intelligence with voice assistance and automated machine learning in Industry 5.0.docx [Dataset]. http://doi.org/10.3389/frai.2025.1538840.s001
    Explore at:
    docxAvailable download formats
    Dataset updated
    Mar 4, 2025
    Dataset provided by
    Frontiers
    Authors
    Alexandros Bousdekis; Mina Foosherian; Mattheos Fikardos; Stefan Wellsandt; Katerina Lepenioti; Enrica Bosani; Gregoris Mentzas; Klaus-Dieter Thoben
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Augmented intelligence puts together human and artificial agents to create a socio-technological system, so that they co-evolve by learning and optimizing decisions through intuitive interfaces, such as conversational, voice-enabled interfaces. However, existing research works on voice assistants relies on knowledge management and simulation methods instead of data-driven algorithms. In addition, practical application and evaluation in real-life scenarios are scarce and limited in scope. In this paper, we propose the integration of voice assistance technology with Automated Machine Learning (AutoML) in order to enable the realization of the augmented intelligence paradigm in the context of Industry 5.0. In this way, the user is able to interact with the assistant through Speech-To-Text (STT) and Text-To-Speech (TTS) technologies, and consequently with the Machine Learning (ML) pipelines that are automatically created with AutoML, through voice in order to receive immediate insights while performing their task. The proposed approach was evaluated in a real manufacturing environment. We followed a structured evaluation methodology, and we analyzed the results, which demonstrates the effectiveness of our proposed approach.

  18. Replication Data for: ``Impact of Parameterized Isopycnal Diffusivity on...

    • zenodo.org
    bin, zip
    Updated Sep 23, 2022
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    Chenyue Xie; Chenyue Xie; Huaiyu Wei; Huaiyu Wei; Yan Wang; Yan Wang (2022). Replication Data for: ``Impact of Parameterized Isopycnal Diffusivity on Shelf-Ocean Exchanges under Upwelling-Favorable Winds: Offline Tracer Simulations Augmented by Artificial Neural Network'' [Dataset]. http://doi.org/10.5281/zenodo.7046069
    Explore at:
    bin, zipAvailable download formats
    Dataset updated
    Sep 23, 2022
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Chenyue Xie; Chenyue Xie; Huaiyu Wei; Huaiyu Wei; Yan Wang; Yan Wang
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This dataset contains the MAMEBUS source code, configuration files for the MITgcm and MAMEBUS simulations, model diagnostics used in the paper, and scripts to train the Artificial Neural Networks.

  19. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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Piotr Baniukiewicz; E. Josiah Lutton; Sharon Collier; Till Bretschneider (2023). Data_Sheet_1_Generative Adversarial Networks for Augmenting Training Data of Microscopic Cell Images.pdf [Dataset]. http://doi.org/10.3389/fcomp.2019.00010.s001

Data_Sheet_1_Generative Adversarial Networks for Augmenting Training Data of Microscopic Cell Images.pdf

Related Article
Explore at:
pdfAvailable download formats
Dataset updated
Jun 1, 2023
Dataset provided by
Frontiers
Authors
Piotr Baniukiewicz; E. Josiah Lutton; Sharon Collier; Till Bretschneider
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

Generative adversarial networks (GANs) have recently been successfully used to create realistic synthetic microscopy cell images in 2D and predict intermediate cell stages. In the current paper we highlight that GANs can not only be used for creating synthetic cell images optimized for different fluorescent molecular labels, but that by using GANs for augmentation of training data involving scaling or other transformations the inherent length scale of biological structures is retained. In addition, GANs make it possible to create synthetic cells with specific shape features, which can be used, for example, to validate different methods for feature extraction. Here, we apply GANs to create 2D distributions of fluorescent markers for F-actin in the cell cortex of Dictyostelium cells (ABD), a membrane receptor (cAR1), and a cortex-membrane linker protein (TalA). The recent more widespread use of 3D lightsheet microscopy, where obtaining sufficient training data is considerably more difficult than in 2D, creates significant demand for novel approaches to data augmentation. We show that it is possible to directly generate synthetic 3D cell images using GANs, but limitations are excessive training times, dependence on high-quality segmentations of 3D images, and that the number of z-slices cannot be freely adjusted without retraining the network. We demonstrate that in the case of molecular labels that are highly correlated with cell shape, like F-actin in our example, 2D GANs can be used efficiently to create pseudo-3D synthetic cell data from individually generated 2D slices. Because high quality segmented 2D cell data are more readily available, this is an attractive alternative to using less efficient 3D networks.

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