International Journal of Computational Intelligence Systems Impact Factor 2024-2025 - ResearchHelpDesk - The International Journal of Computational Intelligence Systems is an international peer reviewed journal and the official publication of the European Society for Fuzzy Logic and Technologies (EUSFLAT). The journal publishes original research on all aspects of applied computational intelligence, especially targeting papers demonstrating the use of techniques and methods originating from computational intelligence theory. This is an open access journal, i.e. all articles are immediately and permanently free to read, download, copy & distribute. The journal is published under the CC BY-NC 4.0 user license which defines the permitted 3rd-party reuse of its articles. Aims & Scope The International Journal of Computational Intelligence Systems publishes original research on all aspects of applied computational intelligence, especially targeting papers demonstrating the use of techniques and methods originating from computational intelligence theory. The core theories of computational intelligence are fuzzy logic, neural networks, evolutionary computation and probabilistic reasoning. The journal publishes only articles related to the use of computational intelligence and broadly covers the following topics: Autonomous reasoning Bio-informatics Cloud computing Condition monitoring Data science Data mining Data visualization Decision support systems Fault diagnosis Intelligent information retrieval Human-machine interaction and interfaces Image processing Internet and networks Noise analysis Pattern recognition Prediction systems Power (nuclear) safety systems Process and system control Real-time systems Risk analysis and safety-related issues Robotics Signal and image processing IoT and smart environments Systems integration System control System modelling and optimization Telecommunications Time series prediction Warning systems Virtual reality Web intelligence Deep learning

Artificial neural networks (ANNs) have demonstrated encouraging results in many applications, but when compared to biological neural networks (BNNs), they still fall short in many aspects Spiking neural networks (SNNs) bridge the gap between ANNs and BNNs by leveraging biologically realistic neurons. Spiking neural networks are of great interest in machine learning because they can achieve high-performance computing with low power consumption. This article provides a comparative analysis of ANN and SNN.

This is a dataset of 637 journal papers applying neural networks for various tasks in seismology spanning January 1988 to January 2022. The dataset mainly includes peer reviewed papers and does not contain duplicated works. It follows a hierarchical classification of papers based on seismological tasks (i.e. category, sub_category_I, sub_category_II, task, and sub_task). For each paper following information are provided: 1) first author's last name, 2) publication year, 3) paper's title, 4) journal 's name, 5) machine learning method used, 6) the type of used neural network, 7) the name of neural network architecture, 8) the number of neurons/kernels in each hidden layer, 9) type of training process, i.e. supervised, semi-supervised, etc, 10) input data into the network, 11) output data, 12) data domain, i.e. time, frequency, feature, etc, 13) the type of data used for training, e.g. synthetic or real data, 14) the size of training set, 15) the metrics used to measure the performance, 16) performance scores, 17) the baseline method used for evaluation, and 18) a short note summarizing the paper's objective, its approach, and its significance. An updating version of the dataset can be find from here: https://smousavi05.github.io/dl_seismology/ and here:https://github.com/smousavi05/dl_seismology/tree/main/docs. An updating glossary of seismological tasks and relevant machine learning techniques and papers are provided here: https://smousavi05.gitbook.io/mlseismology/

IETE journal of research Impact Factor 2024-2025 - ResearchHelpDesk - IETE Journal of Research is a bimonthly journal published by the Institution of Electronics and Telecommunication Engineers (IETE), India. It publishes scientific and technical papers describing original research work or novel product/process development. Occasionally special issues are brought out on new and emerging research areas. This journal is useful to researchers, engineers, scientists, teachers, managers, and students who are interested in keeping track of original research and development work being carried out in the broad area of electronics, telecommunications, computer science, and engineering and information technology. Subjects covered by this journal are: Communications: Digital and analog communication, Digital signal processing, Image processing, Satellite communication, Secure communication, Speech and audio processing, Space communication, Vehicular communications, Wireless communication. Computers and Computing: Algorithms, Artificial intelligence, Computer graphics, Compiler programming and languages, Computer vision, Data mining, High-performance computing, Information technology, Internet computing, Multimedia, Networks, Network Security, Operating systems, Quantum learning systems, Pattern Recognition, Sensor networks, Soft computing. Control Engineering: Control theory and practice- Conventional control, Non-linear control, Adaptive control, Robust Control, Reinforcement learning control, Soft computing tools in control application- Fuzzy logic systems, Neural Networks, Support vector machines, Intelligent control. Electromagnetics: Antennas and arrays, Bio-electromagnetics, Computational electromagnetics, Electromagnetic interference, Electromagnetic compatibility, Metamaterials, Millimeter-wave and Terahertz circuits and systems, Microwave measurements, Microwave Photonics, Passive, active and tunable microwave circuits, Propagation studies, Radar and remote sensing, Radio wave propagation and scattering, RFID, RF MEMS, Solid-state microwave devices and tubes, UWB circuits and systems. Electronic Circuits, Devices, and Components: Analog and Digital circuits, Display Technology, Embedded Systems VLSI Design, Microelectronics technology and device characterization, MEMS, Nano-electronics, Nanotechnology, Physics and technology of CMOS devices, Sensors, Semiconductor device modeling, Space electronics, Solid state devices, and modeling. Instrumentation and Measurements: Automated instruments and measurement techniques, Industrial Electronics, Non-destructive characterization and testing, Sensors. Medical Electronics: Bio-informatics, Biomedical electronics, Bio-MEMS, Medical Instrumentation. Opto-Electronics: Fibre optics, Holography and optical data storage, Optical sensors Quantum Electronics, Quantum optics. Power Electronics: AC-DC/DC-DC/DC-AC/AC-AC converters, Battery chargers, Custom power devices, Distributed power generation, Electric vehicles, Electrochemical processes, Electronic blast, Flexible AC transmission systems, Heating/welding, Hybrid vehicles, HVDC transmission, Power quality, Renewal energy generation, Switched-mode power supply, Solid-state control of motor drives. The IETE Journal of Research is indexed in: British Library CLOCKSS CrossRef EBSCO - Applied Science & Technology Source EBSCO - Academic Search Complete EBSCO - STM Source EI Compendex/ Engineering Village (Elsevier) Google Scholar Microsoft Academic Portico ProQuest - ProQuest Central ProQuest - Research Library ProQuest - SciTech Premium Collection ProQuest - Technology Collection Science Citation Index Expanded (Thomson Reuters) SCImago (Elsevier) Scopus (Elsevier) Ulrich's Periodicals Directory Web of Science (Thomson Reuters) WorldCat Local (OCLC) Zetoc RG Journal Impact: 0.59 * *This value is calculated using ResearchGate data and is based on average citation counts from work published in this journal. The data used in the calculation may not be exhaustive. RG Journal impact history 2020 Available summer 2021 2018 / 2019 0.59 2017 0.39 2016 0.33 2015 0.49 2014 0.49 2013 0.41 2012 0.61 2011 0.90 2010 0.43 2009 0.22 2008 0.19 2007 0.23 2006 0.09 2005 0.11 2004 0.23 2003 0.38 IETE Journal of Research more details H Index - 20 Subject Area and Category: Computer Science, Computer Science Applications, Engineering, Electrical, and Electronic Engineering, Mathematics, Theoretical Computer Science Publisher: Taylor & Francis Publication Type: Journals Coverage : 1979-1989, 1993-ongoing

The dataset contains 5 saved predictive models (.h5 files) which were trained using labeled JPL GIMs to predict the number of HDRs in a map and lists of GIM maps used for training and testing.The data can be found in JPL database of Global Ionospheric Maps (GIMs): https://sideshow.jpl.nasa.gov/pub/iono_daily/gim_for_research/jpli/

According to Cognitive Market Research, the global Neural Network market size will be USD 15214.20 million in 2024. It will expand at a compound annual growth rate (CAGR) of 27.20% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 6085.68 million in 2024 and will grow at a compound annual growth rate (CAGR) of 25.4% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 4564.26 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 3499.27 million in 2024 and will grow at a compound annual growth rate (CAGR) of 29.2% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 760.71 million in 2024 and will grow at a compound annual growth rate (CAGR) of 26.6% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 304.28 million in 2024 and will grow at a compound annual growth rate (CAGR) of 26.9% from 2024 to 2031.
The Software category is the fastest growing segment of the Neural Network industry

Market Dynamics of Neural Network Market

Key Drivers for Neural Network Market

Rising Investments in AI Research and Development to Boost Market Growth

Rising investments in AI research and development are significantly driving the neural network market by accelerating advancements in technology and expanding applications. Increased funding from both public and private sectors fuels innovation, enabling the development of more sophisticated and efficient neural network models. This investment supports breakthroughs in areas such as deep learning, natural language processing, and computer vision. Enhanced research efforts lead to improved algorithms, reduced training times, and greater accuracy in neural networks. Additionally, increased R&D funding helps address current limitations, such as interpretability and scalability, further boosting market growth. As more resources are allocated to AI research, the capabilities and adoption of neural networks continue to expand, driving the overall market forward. For instance, Google AI has introduced GraphWorld, a tool designed to enhance performance benchmarking for graph neural networks (GNNs). This tool enables AI engineers and researchers to evaluate new GNN architectures using larger graph datasets, facilitating innovative approaches to testing and designing GNN architectures.

Growing Interest in Artificial Intelligence to Drive Market Growth

The growing interest in artificial intelligence (AI) is driving the neural network market as organizations across various sectors recognize the transformative potential of AI technologies. Neural networks, a core component of AI, offer powerful solutions for complex data analysis, pattern recognition, and decision-making. The increasing demand for AI-driven innovations in fields such as healthcare, finance, and autonomous systems fuels the need for advanced neural network applications. As businesses and governments invest in AI to gain competitive advantages, enhance efficiency, and create personalized experiences, the adoption of neural networks rises. This heightened focus on AI encourages continuous development and refinement of neural network technologies, contributing to market growth and expanding their applications in solving real-world challenges.

Restraint Factor for the Neural Network Market

High Computational Costs, will Limit Market Growth

High computational costs are a significant restraint on the neural network market due to the substantial resources required for training and deploying complex models. Neural networks, especially deep learning models, demand powerful hardware such as GPUs and TPUs, which incurs high expenses. The energy consumption associated with running these models also adds to operational costs. For many organizations, particularly startups and small enterprises, these costs can be prohibitive, limiting their ability to invest in advanced neural network technologies. Additionally, the need for specialized infrastructure and maintenance further escalates expenses. As a result, high computational costs can hinder the widespread adoption and development of neural networks, impacting the overall growth of the market.

Impact of ...

Dataset consisting of neural network models trained on low-pass filtered and intact images.

Related to our paper: Jinsi* O, Henderson* MM, Tarr MJ (2023) Early experience with low-pass filtered images facilitates visual category learning in a neural network model. PLoS ONE 18(1): e0280145. https://doi.org/10.1371/journal.pone.0280145

Each .tar file contains .csv and .pt files resulting from one method of model training (grayscale or colored images, training from-scratch on images from ecoset or fine-tuning models on imagenet). Numbered folders correspond to models initialized with different random seeds. Different files in each folder correspond to different blur conditions. See paper for more details.

For all experiment code, see our github repository at: https://github.com/tarrlab/startingblurry

Contact mmhender@cmu.edu or mt01@andrew.cmu.edu with any questions or concerns.

International journal of engineering research and applications Impact Factor 2024-2025 - ResearchHelpDesk - International Journal of Engineering Research and Applications - IJERA is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Plastic Engineering, Software Engineering, Mechanical Engineering, Electrical Engineering, Chemical Engineering, Food Technology, Textile Engineering, Information Technology, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc. International Journal of Engineering Research and Applications (IJERA) aims to cover the latest outstanding developments in the field of all Engineering Technologies & science. International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientist and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access. IJERA journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science.

Dataset of noisy input and output measurements collected from a simulator of a pH reactor. The system is single input single output.

The dataset consists of the following files:

'PH_U_Train.csv' and 'PH_Y_Train.csv': training dataset. The training dataset consists of 15 experiments, each one with 2000 (u, y) samples. The i-th column corresponds to the input (or output) variable of the i-th experiment.

'PH_U_Val.csv' and 'PH_Y_Val.csv': validation dataset. The validation dataset consists of 4 experiments, each one with 2000 (u, y) samples. The i-th column corresponds to the input (or output) variable of the i-th experiment.

'PH_U_Test.csv' and 'PH_Y_Test.csv': test dataset. The test dataset consists of 1 experiment of 2000 (u, y) samples.

'PH_U.csv' and 'PH_Y.csv': concatenation of the training, validation, and test datasets.

Moreover, the parameters of an Incrementally Input-to-State Stable LSTM neural network trained to learn the dynamical system are reported in the file 'LSTM_parameters.mat'.

If you use this data please cite the related paper

Terzi, E., Bonassi, F., Farina, M., & Scattolini, R. (2021). Learning model predictive control with long short‐term memory networks. International Journal of Robust and Nonlinear Control.

Quantum Photonic Neural Network Market Outlook

According to our latest research, the global Quantum Photonic Neural Network market size reached USD 1.24 billion in 2024 and is experiencing robust expansion driven by technological breakthroughs and enterprise adoption. The market is forecasted to grow at a CAGR of 34.7% from 2025 to 2033, reaching a projected value of USD 16.24 billion by 2033. This remarkable growth is primarily attributed to increasing investments in quantum computing, the proliferation of artificial intelligence applications, and the demand for ultra-fast, energy-efficient neural network processing capabilities across key industries.

One of the primary growth factors for the Quantum Photonic Neural Network market is the accelerating pace of research and development in quantum computing and photonic technologies. As traditional electronic neural networks approach their physical and energy efficiency limits, photonic neural networks offer a compelling alternative, leveraging the inherent advantages of photons for high-speed, low-latency data processing. The convergence of quantum computing with photonics enables the execution of complex computations at unprecedented scales, which is particularly advantageous for applications in machine learning, optimization, and big data analytics. With global tech giants and governments making significant investments in quantum research, the commercialization of quantum photonic neural networks is becoming increasingly viable, spurring market growth and innovation.

Another key driver is the widespread adoption of artificial intelligence and machine learning across diverse sectors such as healthcare, finance, telecommunications, and defense. Quantum photonic neural networks deliver significant performance improvements for deep learning models, enabling faster training times, reduced energy consumption, and enhanced scalability. These benefits are crucial for industries handling massive datasets and requiring real-time insights, such as medical diagnostics, fraud detection, and autonomous systems. The growing need for advanced computing infrastructure to power next-generation AI applications is prompting organizations to explore quantum photonic solutions, further catalyzing market expansion. Additionally, the emergence of cloud-based quantum computing platforms is democratizing access to quantum resources, allowing enterprises of all sizes to experiment and innovate with photonic neural networks.

Strategic collaborations between academia, industry, and government agencies are also playing a pivotal role in shaping the Quantum Photonic Neural Network market landscape. Public-private partnerships, research consortia, and innovation hubs are fostering the development of open standards, interoperability frameworks, and scalable hardware architectures. These collaborative initiatives are accelerating the transition from laboratory prototypes to commercial products, reducing time-to-market and lowering entry barriers for new players. Furthermore, increasing awareness of the environmental impact of data centers and high-performance computing is driving demand for energy-efficient photonic solutions, aligning with global sustainability goals and regulatory mandates.

From a regional perspective, North America currently dominates the Quantum Photonic Neural Network market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The United States leads in terms of research funding, patent filings, and the presence of key industry players, while Europe is making significant strides through collaborative research programs and government-backed initiatives. Asia Pacific is emerging as a high-growth region, fueled by rapid digital transformation, robust semiconductor manufacturing capabilities, and increasing investments in quantum technologies by countries such as China, Japan, and South Korea. The regional dynamics are expected to evolve as more countries ramp up their quantum research efforts and establish supportive regulatory frameworks.

Component Analysis

The Quantum Photonic Neural Network market by component is segmented into Hardware, Software, and Services. Hardware represents the foundational layer, encompassing photonic chips, quantum processors, optical interconnects, and supporting infrastructure. Recent advancements in integrated photonics, such as silicon photonics and quantum dot technology, have significantly improved the scalabi

FDO-MLP

This data is a matlab coding. It is an implementation of a reasearch work using Fitness Dependent Optimizer (FDO) algorithm for training a Multilayer Perceptron Neural Network (MLP), which is in the process of submitting to a journal.

Cite the following articles:

J. M. Abdullah and T. A. Rashid (2019). Fitness Dependent Optimizer: Inspired by the Bee Swarming Reproductive Process," in IEEE Access, vol. 7, pp. 43473-43486. DOI:https://doi.org/10.1109/ACCESS.2019.2907012

Rashid TA, Abbas DK, Turel YK (2019) A multi hidden recurrent neural network with a modified grey wolf optimizer. PLoS ONE 14(3): e0213237. https://doi.org/10.1371/journal.pone.0213237

Tarik A. Rashid and Nian Kh. Aziz (2016) Student Academic Performance Using Artificial Intelligence. ZANCO Journal of Pure and Applied Sciences, The official scientific journal of Salahaddin University-Erbil, ZJPAS, 28 (2); 56-69.https://doi.org/10.21271/zjpas.v28i2.544

Tarik A. Rashid (2015). Improvement on Classification Models of Multiple Classes through Effectual Processes. International Journal of Advanced Computer Science and Applications(IJACSA), 6(7). http://dx.doi.org/10.14569/IJACSA.2015.060709

S. Mirjalili, How effective is the GreyWolf optimizer in training multi-layer perceptrons, Applied Intelligence, In press, 2015, DOI: http://dx.doi.org/10.1007/s10489-014-0645-7

Identifying land-use drivers of changes in river condition is complicated by spatial scale, geomorphological context, land management, and correlations among responding variables such as nutrients and sediments. Furthermore, variations in standard metrics, such as substratum composition, do not necessarily relate causally to ecological impacts. Consequently, the absence of a significant relationship between a hypothesised driver and a dependent variable does not necessarily indicate the absence of a causal relationship. We conducted a gradient survey to identify impacts of catchment-scale grazing by domestic livestock on river macroinvertebrate communities. A standard correlative approach showed that community structure was strongly related to the upstream catchment area under grazing. We then used data from a stream mesocosm experiment that independently quantified the impacts of nutrients and fine sediments on macroinvertebrate communities to train artificial neural networks (ANNs) to assess the relative influence of nutrients and fine sediments on the survey sites from their community composition. The ANNs developed to predict nutrient impacts did not find a relationship between nutrients and catchment area under grazing, suggesting that nutrients were not an important factor mediating grazing impacts on community composition, or that these ANNs had no generality or insufficient power at the landscape-scale. In contrast, ANNs trained to predict the impacts of fine sediments indicated a significant relationship between fine sediments and catchment area under grazing. Macroinvertebrate communities at sites with a high proportion of land under grazing were thus more similar to those resulting from high fine sediments in a mesocosm experiment than to those resulting from high nutrients. Our study confirms that 1) fine sediment is an important mediator of land-use impacts on river macroinvertebrate communities, 2) ANNs can successfully identify subtle effects and separate the effects of correlated variables, and 3) data from small-scale experiments can generate relationships that help explain landscape-scale patterns.

Recognition results of 10 experiments with fusion neural network.

International Journal of Artificial Intelligence Acceptance Rate - ResearchHelpDesk - The main aim of the International Journal of Artificial Intelligence™ (ISSN 0974-0635) is to publish refereed, well-written original research articles, and studies that describe the latest research and developments in the area of Artificial Intelligence. This is a broad-based journal covering all branches of Artificial Intelligence and its application in the following topics: Technology & Computing; Fuzzy Logic; Neural Networks; Reasoning and Evolution; Automatic Control; Mechatronics; Robotics; Parallel Processing; Programming Languages; Software & Hardware Architectures; CAD Design & Testing; Web Intelligence Applications; Computer Vision and Speech Understanding; Multimedia & Cognitive Informatics, Data Mining and Machine Learning Tools, Heuristic and AI Planning Strategies and Tools, Computational Theories of Learning; Signal, Image & Speech Processing; Intelligent System Architectures; Knowledge Representation; Bioinformatics; Natural Language Processing; Mathematics & Physics. The International Journal of Artificial Intelligence (IJAI) is a peer-reviewed online journal and is published in Spring and Autumn i.e. two times in a year. The International Journal of Artificial Intelligence (ISSN 0974-0635) was reviewed, abstracted and indexed in the past by the INSPEC The IET, SCOPUS (Elsevier Bibliographic Databases), Zentralblatt MATH (io-port.net) of European Mathematical Society, Indian Science Abstracts, getCITED, SCImago Journal & Country Rank, Newjour, JournalSeek, Math-jobs.com’s Journal Index, Academic keys, Ulrich's Periodicals Directory, IndexCopernicus, and International Statistical Institute (ISI, Netherlands)Journal Index. The IJAI is already in request process to get reviewed, abstracted and indexed by the Clarivate Analytics Web of Science (Also known as Thomson ISI Web of Knowledge SCI), Mathematical Reviews and MathSciNet of American Mathematical Society, and by other agencies.

Annotated notebook used to define and train the unstained/stained CNN.

The initial value is the measured RE (left) UER (right) at the start of the period. The difference is the model performances calculated as measured value at the end of the period (i.e. as a result of policymaker) minus the one predicted by implementing the model’s recommended policies (higher is better, where positive values show the policy intervention of the RLA model improved the RE or UER). (XLSX)

CBeamXP: Continuous Beam Cross-section Predictors datasetThe CBeamXP (Continuous Beam Cross-section (X) Predictors) is a dataset containing 1,000,000 data-points to be used for machine learning research. Each data-point represents an Ultimate Limit State (ULS) compliant beam from a continuous system consisting out of 11 members with utilisation ratios between 0.97 to 1.00. The predictors include span and uniformly distributed loads (UDLs) which can be used to predict the cross-sectional properties of each beam contained within the dataset. This dataset is publicly available on a CC-BY-4.0 licence and was used within the Gallet et al. (2024) journal article "Machine learning for structural design models of continuous beam systems via influence zones" (doi.org/10.1088/1361-6420/ad3334). Publications making use of the CBeamXP dataset are requested to cite the aforementioned journal article.In addition to the dataset, a training script, environment YAML file and a collection of saved models developed in the Gallet et al. (2024) study are available. These can be used to quickly generate user defined neural networks, compare performances and verify the results achieved by the Gallet et al. (2024) investigation.There are 5 files in this directory:CBeamXP_dataset.csvGallet_2024_training_script.pyGallet_2024_environment.ymlREADME.txtsaved_models.zipClick "Download all" (button at the top) to download the files and and look at the README.txt file for further details on the dataset and how to use the training script.

OSCAR, the Occluded Stereo dataset for Convolutional Architectures with Recurrence. Version: 2.0 (dataset as presented in our JOV 2021 journal publication "Recurrent Processing Improves Occluded Object Recognition and Gives Rise to Perceptual Hysteresis")

If you make use of the dataset, please cite as follows:

Ernst, M. R., Burwick, T., & Triesch, J. (2021). Recurrent Processing Improves Occluded Object Recognition and Gives Rise to Perceptual Hysteresis. In Journal of Vision

Contents

readme.md - detailed description and sample pictures

img.zip - folder that contains images for the readme file

licence.md - licence agreement for using the datasets

os-fmnist2c.zip - compressed archive of the occluded stereo FashionMNIST dataset (centered, ~1.1GB)

os-fmnist2r.zip - compressed archive of the occluded stereo FashionMNIST dataset (random, ~1.2GB)

os-mnist2c.zip - compressed archive of the occluded stereo MNIST dataset (centered, ~865MB)

os-mnist2r.zip - compressed archive of the occluded stereo MNIST dataset (random, ~851MB)

os-ycb2.zip - compressed archive of the occluded stereo ycb-object dataset (~1.1GB)

os-ycb2_highres.zip - compressed archive of the occluded stereo ycb-object dataset (high resolution, ~9.8GB)

OSCARv2_dataset.py - python script to directly load image data from folder, pytorch dataset

This data contains the code from SolProp the repository that was used for the training of the paper: https://gitlab.kuleuven.be/creas/vermeiregroup/solprop. Additionally, a digitized version of the Phase transition enthalpy compendium from Acree, W. and Chickos, J. S. is added. Please cite the following sources if you use it in your work: [1] Acree, W. and Chickos, J. S. Phase Transition En-thalpy Measurements of Organic and Organometallic Compounds. Sublimation, Vaporization and Fusion En-thalpies From 1880 to 2015. Part 1. C1-C10. Journal of Physical and Chemical Reference Data 2016, 45 [2] Acree, W. and Chickos, J. S. Phase transition en-thalpy measurements of organic and organometallic compounds and ionic liquids. sublimation, vaporization, and fusion enthalpies from 1880 to 2015. part 2. C11-C192. Journal of Physical and Chemical Reference Data 2017, 46 [3] Acree, W. and Chickos, J. S. Phase Transition En-thalpy Measurements of Organic Compounds. An Up-date of Sublimation, Vaporization, and Fusion En-thalpies from 2016 to 2021. Journal of Physical and Chemical Reference Data 2022, 51

Photonic Neural Network Compiler Market Outlook

According to our latest research, the global Photonic Neural Network Compiler market size reached USD 412 million in 2024, reflecting the sector’s rapid emergence as a critical enabler of next-generation artificial intelligence and high-performance computing. The market is projected to expand at a CAGR of 41.2% from 2025 to 2033, reaching an estimated USD 7.32 billion by 2033. This exceptional growth trajectory is being driven by the convergence of photonic hardware advancements, escalating demand for low-latency AI processing, and the need for energy-efficient computational models across diverse industries.

The primary growth factor propelling the Photonic Neural Network Compiler market is the exponential increase in data volumes and the corresponding demand for high-speed, low-power computation. Traditional electronic neural network accelerators are increasingly constrained by thermal and bandwidth limitations, prompting industry and academia to explore photonic solutions. Photonic neural networks, which leverage the unique properties of light for parallel data processing, offer orders-of-magnitude improvements in speed and energy efficiency. However, the true potential of these systems can only be realized with advanced compiler technologies that optimize neural network models for photonic hardware, translating complex AI algorithms into photonic-friendly instructions. This need for specialized compilers is fueling market expansion, as organizations seek to bridge the gap between software innovation and photonic hardware capabilities.

Another significant driver is the growing adoption of AI-powered applications in mission-critical domains such as healthcare diagnostics, autonomous vehicles, and telecommunications. In these sectors, the ability to process neural network workloads in real time with minimal latency can have transformative impacts, from accelerating medical image analysis to enhancing the responsiveness of autonomous driving systems. The Photonic Neural Network Compiler market is benefitting from strategic collaborations between hardware manufacturers, AI software vendors, and research institutions, all of whom are investing heavily in compiler development to unlock new performance thresholds. Furthermore, the increasing availability of open-source photonic design frameworks and standardization efforts is lowering barriers to entry, enabling a broader ecosystem of developers and end-users to leverage photonic neural network compilers.

The market is also being shaped by the rapid pace of innovation in photonic integrated circuits (PICs) and silicon photonics, which are making photonic neural networks more scalable and commercially viable. As photonic hardware becomes more accessible and affordable, the demand for robust, scalable, and user-friendly compilers is intensifying. These compilers are essential for translating high-level AI models into instructions optimized for unique photonic architectures, including analog, digital, and hybrid systems. The integration of machine learning techniques into compiler design is further enhancing performance, enabling dynamic optimization and adaptation to evolving workloads. This virtuous cycle of hardware and software co-evolution is expected to sustain market momentum well into the next decade.

Regionally, North America continues to dominate the Photonic Neural Network Compiler market, accounting for the largest share in 2024, driven by strong investments in AI research, robust venture capital activity, and the presence of leading photonics and semiconductor companies. However, Asia Pacific is emerging as the fastest-growing market, propelled by government-led initiatives in quantum and photonic computing, a thriving electronics manufacturing base, and increasing collaboration between academia and industry. Europe also plays a pivotal role, particularly in the areas of photonic hardware innovation and standardization, while the Middle East & Africa and Latin America are gradually entering the market through research partnerships and pilot projects. This dynamic regional landscape is fostering global competition and accelerating the pace of technological advancement.

Component Analysis

The Component segment of the Photonic Neural Network Compiler market is categorized into Software, Hardware, and Services, each playing a vital role in the ecosystem. Software forms the backbone o