IEEE Transactions on Pattern Analysis and Machine Intelligence Acceptance Rate - ResearchHelpDesk - The IEEE Transactions on Pattern Analysis and Machine Intelligence publishes articles on all traditional areas of computer vision and image understanding, all traditional areas of pattern analysis and recognition, and selected areas of machine intelligence, with a particular emphasis on machine learning for pattern analysis. Areas such as techniques for visual search, document and handwriting analysis, medical image analysis, video and image sequence analysis, content-based retrieval of image and video, face and gesture recognition and relevant specialized hardware and/or software architectures are also covered.

International Journal of Engineering and Advanced Technology Acceptance Rate - 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 seek original and unpublished research papers based on theoretical or experimental works for the publication globally.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

This dataset serves a support for the Pattern Recognition and Image Analysis course Kaggle notebooks.

In this dataset one can find: - A video (frame by frame, with colors) representing fishes (for segmentation exercises) - That same video, but with GT extraction (binary images) - a fork of the INFO-H500 Master course repository (May 2025 version) - a fork of the INFO-H501 Master course repository (May 2025 version)

Happy coding!

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

1. Source Information: (a) Authors: Martin Pleschberger: martin.pleschberger@k-ai.at Michael Scheiber: michael.scheiber@k-ai.at Stefan Schrunner: stefan.schrunner@k-ai.at KAI - Kompetenzzentrum Automobil- und Industrie- elektronik GmbH, Villach, Austria (b) Date: November, 2018 (c) Acknowledgement: The work has been performed in the project Power Semi- conductor and Electronics Manufacturing 4.0 (SemI40), under grant agreement No 692466. The project is co-funded by grants from Austria (BMVIT-IKT der Zukunft, FFG project no. 853338), Germany, Italy, France, Portugal and - Electronic Component Systems for European Leadership Joint Undertaking (ECSEL JU).

2. Past Usage: () M. Pleschberger: "Runtime Optimization for Automated Pattern Analysis", Master Thesis, Alpen-Adria Universit�t, Klagenfurt, 2018. (*) S. Schrunner, O. Bluder, A. Zernig, A. Kaestner, R. Kern: "A Comparison of Supervised Approaches for Process Pattern Recognition in Analog Semiconductor Wafer Test Data", IEEE International Conference on Machine Learning and Applications, Florida, USA, 2018.

3. Relevant Information:

   This data set was generated in accordance with the semiconductor industry and contains simulated electrical test data of manufactured devices, like resistances, voltages, etc. Devices are manufactured on round wafers, i.e. slices of semiconductor material. If the test values of devices form an interesting spatial pattern on the wafer, this might result from deviations during processing and hence, must be traced.

   A bunch of wafers is aggregated to a so-called lot. The dataset contains 4 training lots and 1 test lot � 200 wafers. In this dataset, each wafer contains approx. 17000 devices. Each device is assigned 10 different tests, which show one out of 5 patterns each, simulated with (ring, spot, trend, twospots and crescent) and without ('_pure' suffix) a randomized amount of Gaussian white noise.

   For pattern recognition, each test column on the wafer is regarded as a spatial image, the so-called wafermap. In particular, a wafermap is an image, where the position of each pixel is uniquely identified through the x- and y-coordinates and the coloring is described by a value of one of the test columns.

   Each wafermap in a lot is uniquely identified by its wafer number. Classes or clusters should not be assigned to each data row separately, but rather to wafermaps. The data represents wafermaps as spatial objects, with x- and y- coordinates. The values of such a map are characterized by all values in the corresponding test column for given lot and wafer numbers.

   The five classes (patterns) are characterized as follows: 1.) ring: a ring pattern along the border of the wafer 2.) spot: a single circular or elliptic spot, placed randomly on the wafer 3.) trend: a constant gradient over the wafer (with changes w.r.t. direction) 4.) twospots: two spots at the left and right edge of the wafer 5.) crescent: a crescent-shaped area on the right edge of the wafer

   Additional information on electrical wafer test data, their background and structure, can be found in the sources () and (*) (referred in 'Past Usage').

   The data is provided in 4 training and 1 test CSV file. Each dataset contains data of one lot.

Dataset de imágenes de la baraja francesa con dimensiones 150x150. Está organizado en 3 carpetas (train, valid, test), preparado para su uso en entrenamiento de una red neuronal. Dicha red neuronal se entrena para distinguir entre As, de 2 al 10, jack, reina y rey.

Fuente de las imágenes: https://www.kaggle.com/datasets/gpiosenka/cards-image-datasetclassification/code

The Great Isaiah Scroll (1QIsa^a) data set for writer identification This data set is collected for the ERC project:
The Hands that Wrote the Bible: Digital Palaeography and Scribal Culture of the Dead Sea Scrolls
PI: Mladen Popović
Grant agreement ID: 640497 Project website: https://cordis.europa.eu/project/id/640497

Copyright (c) University of Groningen, 2021. All rights reserved.
Disclaimer and copyright notice for all data contained on this .tar.gz file: 1) permission is hereby granted to use the data for research purposes. It is not allowed to distribute this data for commercial purposes. 2) provider gives no express or implied warranty of any kind, and any implied warranties of merchantability and fitness for purpose are disclaimed. 3) provider shall not be liable for any direct, indirect, special, incidental, or consequential damages arising out of any use of this data. 4) the user should refer to the first public article on this data set:

Popović, M., Dhali, M. A., & Schomaker, L. (2020). Artificial intelligence-based writer identification generates new evidence for the unknown scribes of the Dead Sea Scrolls exemplified by the Great Isaiah Scroll (1QIsa^a). arXiv preprint arXiv:2010.14476.

BibTeX:

@article{popovic2020artificial, title={Artificial intelligence based writer identification generates new evidence for the unknown scribes of the Dead Sea Scrolls exemplified by the Great Isaiah Scroll (1QIsaa)}, author={Popovi{\'c}, Mladen and Dhali, Maruf A and Schomaker, Lambert}, journal={arXiv preprint arXiv:2010.14476}, year={2020} }

5) the recipient should refrain from proliferating the data set to third parties external to his/her local research group. Please refer interested researchers to this site for obtaining their own copy. Organisation of the data: The .tar.gz file contains three directories: images, features, and plots. The included 'README' file contains all the instructions. The 'images' directory contains NetPBM images of the columns of 1QIsa^a. The NetPBM format is chosen because of its simplicity. Additionally, there is no doubt about lossy compression in the processing chain. There are two images for each of the Great Isaiah Scroll columns: one is the direct binarized output from the BiNet (arxiv.org/abs/1911.07930) system, and the other one is the manually cleaned version of the binarized output. The file names for the direct binarized output are of the format '1QIsaa_col

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,

Quantum-Enabled Pattern Recognition Market Outlook

According to our latest research, the Quantum-Enabled Pattern Recognition market size stood at USD 1.23 billion in 2024, reflecting the rapid adoption of quantum computing solutions in advanced analytics. The market is expected to grow at a robust CAGR of 38.4% from 2025 to 2033, reaching a projected value of USD 21.16 billion by 2033. This unprecedented growth is driven by the increasing demand for high-performance computing in sectors such as healthcare, finance, and automotive, where rapid and accurate pattern recognition is crucial for innovation and competitive advantage.

One of the primary growth factors fueling the Quantum-Enabled Pattern Recognition market is the exponential increase in data complexity and volume across industries. Traditional pattern recognition algorithms, while effective in certain domains, are struggling to keep pace with the demands of big data analytics, particularly in fields like genomics, financial modeling, and autonomous vehicles. Quantum computing, with its ability to process vast datasets in parallel and solve optimization problems at unprecedented speeds, is enabling breakthroughs in pattern recognition accuracy and efficiency. This capability is especially vital for applications such as medical diagnostics, where early and accurate pattern recognition can be life-saving, and in fraud detection, where real-time analysis of complex transaction patterns is essential.

Another significant driver in the Quantum-Enabled Pattern Recognition market is the surge in investments from both public and private sectors aimed at quantum technology research and development. Governments across North America, Europe, and Asia Pacific have launched major initiatives and funding programs to accelerate quantum innovation. Simultaneously, leading technology firms and start-ups are forming strategic partnerships to integrate quantum algorithms into existing AI and machine learning frameworks. The convergence of quantum computing and artificial intelligence is creating a new paradigm for pattern recognition, enabling solutions that are not only faster but also more adaptive and resilient to noise and uncertainty. This is fostering the emergence of new business models and services, particularly in sectors where data-driven decision-making is mission-critical.

Furthermore, the growing adoption of cloud-based quantum computing platforms is democratizing access to quantum-enabled pattern recognition tools. Cloud deployment models are lowering the barriers to entry for small and medium enterprises (SMEs), allowing them to leverage quantum capabilities without significant upfront investments in hardware. As a result, the ecosystem for quantum-enabled solutions is rapidly expanding, with a proliferation of software libraries, APIs, and development kits tailored for pattern recognition applications. This shift is also accelerating time-to-market for innovative solutions in image recognition, speech analysis, and data mining, further propelling market growth.

From a regional perspective, North America continues to lead the Quantum-Enabled Pattern Recognition market in terms of revenue and innovation, accounting for more than 39% of the global market share in 2024. The region’s dominance is attributed to its strong technological infrastructure, robust funding ecosystem, and early adoption among key industries. Europe follows closely, driven by coordinated research efforts and public-private partnerships. Meanwhile, Asia Pacific is emerging as the fastest-growing region, with a projected CAGR of 41.2% through 2033, fueled by aggressive investments in quantum research and a rapidly digitizing economy. Latin America and the Middle East & Africa are gradually increasing their market presence, primarily through collaborations and pilot projects in sectors such as healthcare and financial services.

Component Analysis

The component segment of the Quantum-Enabled Pattern Recognition market is broadly categorized into hardware, software, and services. Hardware forms the foundational layer, encompassing quantum processors, qubits, and specialized quantum circuits required to perform high-speed computations. The demand for advanced quantum hardware is being driven by the need for scalable and stable quantum systems capable of supporting complex pattern recognition tasks. Leading hardware vendors are investing heavily in

AI in Pattern Recognition Market Outlook

According to our latest research, the AI in Pattern Recognition market size reached USD 7.8 billion in 2024 globally, driven by rapid advancements in artificial intelligence technologies and increasing demand across diverse industries. The market is projected to expand at a robust CAGR of 18.2% from 2025 to 2033, reaching an estimated value of USD 40.2 billion by 2033. This remarkable growth is attributed to the proliferation of big data, rising adoption of AI-powered solutions, and the need for enhanced automation and efficiency in business operations worldwide.

One of the primary growth factors fueling the AI in Pattern Recognition market is the exponential increase in data generation across industries such as healthcare, finance, retail, and manufacturing. Organizations are increasingly leveraging pattern recognition technologies to analyze massive datasets, extract actionable insights, and drive intelligent decision-making. The integration of AI with pattern recognition has enabled businesses to automate complex tasks, reduce human error, and optimize operational workflows. Moreover, the growing sophistication of machine learning and deep learning algorithms has significantly improved the accuracy and scalability of pattern recognition systems, making them indispensable tools for digital transformation initiatives.

Another pivotal driver is the expanding application landscape of AI in pattern recognition. Industries such as healthcare are utilizing AI-powered image and speech recognition for diagnostics and patient monitoring, while BFSI sectors employ these technologies for fraud detection and risk assessment. Retailers are enhancing customer experiences through personalized recommendations and inventory management, and automotive companies are integrating pattern recognition for autonomous driving and advanced driver-assistance systems. The convergence of AI with technologies like computer vision and natural language processing has opened new avenues for innovation, enabling organizations to address complex challenges with unprecedented efficiency and precision.

The surge in cloud computing adoption is also playing a crucial role in the growth of the AI in Pattern Recognition market. Cloud-based deployment models offer scalability, flexibility, and cost-efficiency, allowing businesses of all sizes to access advanced pattern recognition capabilities without heavy upfront investments in hardware or infrastructure. This democratization of AI technology is fostering innovation among small and medium enterprises, further accelerating market growth. Additionally, the increasing availability of AI-as-a-Service (AIaaS) platforms is simplifying the integration of pattern recognition solutions into existing business processes, thereby reducing time-to-market and enhancing competitiveness.

From a regional perspective, North America leads the global market for AI in pattern recognition, accounting for the largest revenue share in 2024. This dominance is attributed to the strong presence of leading technology companies, robust research and development activities, and early adoption of AI-driven solutions across key industries. Asia Pacific, on the other hand, is expected to witness the fastest growth over the forecast period, propelled by rapid digitalization, government initiatives supporting AI adoption, and a burgeoning startup ecosystem. Europe continues to invest heavily in AI research and regulatory frameworks, ensuring steady market expansion. Meanwhile, Latin America and the Middle East & Africa are gradually embracing AI technologies, although market penetration remains relatively lower compared to other regions.

Component Analysis

The component segment of the AI in Pattern Recognition market is categorized into software, hardware, and services. Software solutions dominate the market, accounting for the largest share in 2024, as organizations prioritize investing in advanced AI algorithms, analytics platforms, and pattern recognition tools to streamline operations and enhance decision-making. The software segment encompasses machine learning frameworks, deep learning libraries, and specialized applications for image, speech, and text recognition. Continuous advancements in software development, such as the integration of explainable AI and automated machine learning, are further driving adoption across various industries. The scalability and flexibility offere

Windshield Rain Pattern Analysis Market Outlook

According to our latest research, the global Windshield Rain Pattern Analysis market size reached USD 1.12 billion in 2024, reflecting robust demand from the automotive, meteorological, and research sectors. The market is projected to expand at a CAGR of 13.4% from 2025 to 2033, reaching an estimated USD 3.61 billion by 2033. This growth is primarily driven by the increasing integration of advanced driver-assistance systems (ADAS) and autonomous vehicle technologies, which require precise environmental sensing and data analytics capabilities for enhanced safety and operational efficiency.

One of the most significant growth factors for the Windshield Rain Pattern Analysis market is the rapid adoption of autonomous and semi-autonomous vehicles globally. As automotive manufacturers race to develop vehicles that can safely navigate a wide range of weather conditions, the need for real-time, accurate rain pattern analysis has become paramount. The integration of image processing, sensor-based, and AI-driven technologies into vehicle systems enables better detection of rain intensity and distribution, improving wiper control, visibility, and overall safety. Regulatory mandates for advanced safety features in vehicles, especially in North America and Europe, further accelerate the adoption of these technologies. In addition, consumer demand for enhanced in-vehicle safety and comfort is prompting OEMs to invest heavily in innovative rain pattern analysis solutions, fueling market growth.

Another critical driver is the increasing deployment of windshield rain pattern analysis in weather monitoring and meteorological applications. Meteorological agencies and research institutes are leveraging these technologies to collect granular data on precipitation patterns, which is vital for urban planning, disaster management, and climate research. The proliferation of connected vehicles and IoT-enabled sensors provides a vast network for real-time data collection, enabling more accurate and localized weather forecasting. This synergy between automotive and meteorological industries not only broadens the application scope of rain pattern analysis systems but also opens up new revenue streams for technology providers, making the market more dynamic and competitive.

Furthermore, advancements in artificial intelligence and machine learning have significantly enhanced the capabilities of windshield rain pattern analysis systems. AI-based solutions can process vast amounts of sensor and image data, learning from diverse weather scenarios to deliver predictive insights and adaptive responses. This technological evolution is particularly crucial for autonomous vehicles, which must interpret complex environmental cues to make safe driving decisions. The continuous improvement of image processing algorithms, sensor accuracy, and AI models is lowering the margin of error in rain detection and pattern analysis, thereby increasing the reliability and adoption rate of these systems across various end-user segments.

Regionally, Asia Pacific is emerging as the fastest-growing market, driven by the rapid expansion of the automotive industry, urbanization, and increasing investments in smart transportation infrastructure. Countries like China, Japan, and South Korea are at the forefront of adopting advanced automotive safety technologies, supported by strong government initiatives and a robust manufacturing base. North America and Europe continue to dominate in terms of market share, owing to early adoption of ADAS, stringent safety regulations, and the presence of leading technology providers. Meanwhile, Latin America and the Middle East & Africa are witnessing gradual growth, fueled by rising vehicle sales and growing awareness of road safety. The global landscape is characterized by a blend of mature markets with high adoption rates and emerging regions with significant growth potential.

Technology Analysis

The Windshield Rain Pattern Analysis market is segmented by technology into image processing, sensor-based, and AI-based solutions, each playing a pivotal role in the evolution of rain detection and analysis systems. Image processing technology leverages advanced cameras and computer vision algorithms to monitor and interpret raindrop patterns on windshields in real time. This approach enables vehicles to adjust wiper speed and frequency dynamically, ensuring optimal visibility during inclement weather. The integration of high-resolution cameras with

Author Siming Zheng. Experiment CODES data(3 in 1).

Steam flooding is a complex process that has been considered as an effective enhanced oil recovery technique in both heavy oil and light oil reservoirs. Many studies have been conducted on different sets of steam flooding projects using the conventional data analysis methods, while the implementation of machine learning algorithms to find the hidden patterns is rarely found. In this study, a hierarchical clustering algorithm (HCA) coupled with principal component analysis is used to analyze the steam flooding projects worldwide. The goal of this research is to group similar steam flooding projects into the same cluster so that valuable operational design experiences and production performance from the analogue cases can be referenced for decision-making. Besides, hidden patterns embedded in steam flooding applications can be revealed based on data characteristics of each cluster for different reservoir/fluid conditions. In this research, principal component analysis is applied to project original data to a new feature space, which finds two principal components to represent the eight reservoir/fluid parameters (8D) but still retain about 90% of the variance. HCA is implemented with the optimized design of five clusters, Euclidean distance, and Ward’s linkage method. The results of the hierarchical clustering depict that each cluster detects a unique range of each property, and the analogue cases present that fields under similar reservoir/fluid conditions could share similar operational design and production performance.

Lace Pattern Recognition

## Overview

Lace Pattern Recognition is a dataset for instance segmentation tasks - it contains Pattern annotations for 206 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Type of application and time limits.

Unsupervised exploratory data analysis (EDA) is often the first step in understanding complex data sets. While summary statistics are among the most efficient and convenient tools for exploring and describing sets of data, they are often overlooked in EDA. In this paper, we show multiple case studies that compare the performance, including clustering, of a series of summary statistics in EDA. The summary statistics considered here are pattern recognition entropy (PRE), the mean, standard deviation (STD), 1-norm, range, sum of squares (SSQ), and X4, which are compared with principal component analysis (PCA), multivariate curve resolution (MCR), and/or cluster analysis. PRE and the other summary statistics are direct methods for analyzing datathey are not factor-based approaches. To quantify the performance of summary statistics, we use the concept of the “critical pair,” which is employed in chromatography. The data analyzed here come from different analytical methods. Hyperspectral images, including one of a biological material, are also analyzed. In general, PRE outperforms the other summary statistics, especially in image analysis, although a suite of summary statistics is useful in exploring complex data sets. While PRE results were generally comparable to those from PCA and MCR, PRE is easier to apply. For example, there is no need to determine the number of factors that describe a data set. Finally, we introduce the concept of divided spectrum-PRE (DS-PRE) as a new EDA method. DS-PRE increases the discrimination power of PRE. We also show that DS-PRE can be used to provide the inputs for the k-nearest neighbor (kNN) algorithm. We recommend PRE and DS-PRE as rapid new tools for unsupervised EDA.

This dataset features over 160,000 high-quality images of patterns sourced from photographers worldwide. Designed to support AI and machine learning applications, it provides a diverse and richly annotated collection of pattern imagery.

Key Features: 1. Comprehensive Metadata The dataset includes full EXIF data, detailing camera settings such as aperture, ISO, shutter speed, and focal length. Additionally, each image is pre-annotated with object and scene detection metadata, making it ideal for tasks like classification, detection, and segmentation. Popularity metrics, derived from engagement on our proprietary platform, are also included.

1. Unique Sourcing Capabilities The images are collected through a proprietary gamified platform for photographers. Competitions focused on pattern photography ensure fresh, relevant, and high-quality submissions. Custom datasets can be sourced on-demand within 72 hours, allowing for specific requirements such as particular pattern types (e.g., geometric, organic, textile) or stylistic preferences to be met efficiently.

2. Global Diversity Photographs have been sourced from contributors in over 100 countries, ensuring a vast array of visual patterns captured in various cultural, architectural, and natural contexts. The images feature varied environments, including fabric textures, wallpapers, cityscapes, fractals, and abstract art, offering a rich visual spectrum for training and analysis.

3. High-Quality Imagery The dataset includes images with resolutions ranging from standard to high-definition to meet the needs of various projects. Both professional and amateur photography styles are represented, offering a mix of artistic and practical perspectives suitable for a variety of applications.

4. Popularity Scores Each image is assigned a popularity score based on its performance in GuruShots competitions. This unique metric reflects how well the image resonates with a global audience, offering an additional layer of insight for AI models focused on user preferences or engagement trends.

5. AI-Ready Design This dataset is optimized for AI applications, making it ideal for training models in tasks such as pattern recognition, style classification, and image generation. It is compatible with a wide range of machine learning frameworks and workflows, ensuring seamless integration into your projects.

6. Licensing & Compliance The dataset complies fully with data privacy regulations and offers transparent licensing for both commercial and academic use.

Use Cases: 1. Training AI systems for visual pattern recognition and classification. 2. Enhancing fashion and interior design models through textile and decorative pattern analysis. 3. Building datasets for generative models and style transfer applications. 4. Supporting research in visual perception, cultural studies, and computational aesthetics.

This dataset offers a comprehensive, diverse, and high-quality resource for training AI and ML models, tailored to deliver exceptional performance for your projects. Customizations are available to suit specific project needs. Contact us to learn more!

Orchid Pattern Recognition

## Overview

Orchid Pattern Recognition is a dataset for classification tasks - it contains Stripe Spots Mixed annotations for 1,351 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Forecast: Total Number of Scientific Publications in Computer Vision and Pattern Recognition in India 2024 - 2028 Discover more data with ReportLinker!

Dataset

This dataset was created by Muhammad Irfan

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