This project aims to use artificial intelligence to identify potential risk factors for damaged asphalt pavements under the road, explore the pre-processing procedures and steps of ground penetrating radar data, and propose initial solutions or recommendations for difficulties and problems encountered in the pre-processing process.

Improving the accuracy of prediction on future values based on the past and current observations has been pursued by enhancing the prediction's methods, combining those methods or performing data pre-processing. In this paper, another approach is taken, namely by increasing the number of input in the dataset. This approach would be useful especially for a shorter time series data. By filling the in-between values in the time series, the number of training set can be increased, thus increasing the generalization capability of the predictor. The algorithm used to make prediction is Neural Network as it is widely used in literature for time series tasks. For comparison, Support Vector Regression is also employed. The dataset used in the experiment is the frequency of USPTO's patents and PubMed's scientific publications on the field of health, namely on Apnea, Arrhythmia, and Sleep Stages. Another time series data designated for NN3 Competition in the field of transportation is also used for benchmarking. The experimental result shows that the prediction performance can be significantly increased by filling in-between data in the time series. Furthermore, the use of detrend and deseasonalization which separates the data into trend, seasonal and stationary time series also improve the prediction performance both on original and filled dataset. The optimal number of increase on the dataset in this experiment is about five times of the length of original dataset.

HelpSteer: AI Alignment Dataset

Real-World Helpfulness Annotated for AI Alignment

By Huggingface Hub [source]

About this dataset

HelpSteer is an Open-Source dataset designed to empower AI Alignment through the support of fair, team-oriented annotation. The dataset provides 37,120 samples each containing a prompt and response along with five human-annotated attributes ranging between 0 and 4; with higher results indicating better quality. Using cutting-edge methods in machine learning and natural language processing in combination with the annotation of data experts, HelpSteer strives to create a set of standardized values that can be used to measure alignment between human and machine interactions. With comprehensive datasets providing responses rated for correctness, coherence, complexity, helpfulness and verbosity, HelpSteer sets out to assist organizations in fostering reliable AI models which ensure more accurate results thereby leading towards improved user experience at all levels

More Datasets

For more datasets, click here.

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How to use the dataset

How to Use HelpSteer: An Open-Source AI Alignment Dataset

HelpSteer is an open-source dataset designed to help researchers create models with AI Alignment. The dataset consists of 37,120 different samples each containing a prompt, a response and five human-annotated attributes used to measure these responses. This guide will give you a step-by-step introduction on how to leverage HelpSteer for your own projects.

Step 1 - Choosing the Data File

Helpsteer contains two data files – one for training and one for validation. To start exploring the dataset, first select the file you would like to use by downloading both train.csv and validation.csv from the Kaggle page linked above or getting them from the Google Drive repository attached here: [link]. All the samples in each file consist of 7 columns with information about a single response: prompt (given), response (submitted), helpfulness, correctness, coherence, complexity and verbosity; all sporting values between 0 and 4 where higher means better in respective category.

## Step 2—Exploratory Data Analysis (EDA) Once you have your file loaded into your workspace or favorite software environment (e.g suggested libraries like Pandas/Numpy or even Microsoft Excel), it’s time explore it further by running some basic EDA commands that summarize each feature's distribution within our data set as well as note potential trends or points of interests throughout it - e.g what are some traits that are polarizing these responses more? Are there any outliers that might signal something interesting happening? Plotting these results often provides great insights into pattern recognition across datasets which can be used later on during modeling phase also known as “Feature Engineering”

## Step 3—Data Preprocessing After your interpretation of raw data while doing EDA should form some hypotheses around what features matter most when trying to estimate attribute scores of unknown responses accurately so proceeding with preprocessing such as cleaning up missing entries or handling outliers accordingly becomes highly recommended before starting any modelling efforts with this data set - kindly refer also back at Kaggle page description section if unsure about specific attributes domain ranges allowed values explicitly for extra confidence during this step because having correct numerical suggestions ready can make modelling workload lighter later on while building predictive models . It’s important not rushing over this stage otherwise poor results may occur later when aiming high accuracy too quickly upon model deployment due low quality

Research Ideas

• Designating and measuring conversational AI engagement goals: Researchers can utilize the HelpSteer dataset to design evaluation metrics for AI engagement systems.
• Identifying conversational trends: By analyzing the annotations and data in HelpSteer, organizations can gain insights into what makes conversations more helpful, cohesive, complex or consistent across datasets or audiences.
• Training Virtual Assistants: Train artificial intelligence algorithms on this dataset to develop virtual assistants that respond effectively to customer queries with helpful answers

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

**License: [CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication](https://creativecommons.org/pu...

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US Deep Learning Market Size 2025-2029

The deep learning market size in US is forecast to increase by USD 5.02 billion at a CAGR of 30.1% between 2024 and 2029.

The deep learning market is experiencing robust growth, driven by the increasing adoption of artificial intelligence (AI) in various industries for advanced solutioning. This trend is fueled by the availability of vast amounts of data, which is a key requirement for deep learning algorithms to function effectively. Industry-specific solutions are gaining traction, as businesses seek to leverage deep learning for specific use cases such as image and speech recognition, fraud detection, and predictive maintenance. Alongside, intuitive data visualization tools are simplifying complex neural network outputs, helping stakeholders understand and validate insights. 


However, challenges remain, including the need for powerful computing resources, data privacy concerns, and the high cost of implementing and maintaining deep learning systems. Despite these hurdles, the market's potential for innovation and disruption is immense, making it an exciting space for businesses to explore further. Semi-supervised learning, data labeling, and data cleaning facilitate efficient training of deep learning models. Cloud analytics is another significant trend, as companies seek to leverage cloud computing for cost savings and scalability. 

What will be the Size of the market During the Forecast Period?

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Deep learning, a subset of machine learning, continues to shape industries by enabling advanced applications such as image and speech recognition, text generation, and pattern recognition. Reinforcement learning, a type of deep learning, gains traction, with deep reinforcement learning leading the charge. Anomaly detection, a crucial application of unsupervised learning, safeguards systems against security vulnerabilities. Ethical implications and fairness considerations are increasingly important in deep learning, with emphasis on explainable AI and model interpretability. Graph neural networks and attention mechanisms enhance data preprocessing for sequential data modeling and object detection. Time series forecasting and dataset creation further expand deep learning's reach, while privacy preservation and bias mitigation ensure responsible use.

In summary, deep learning's market dynamics reflect a constant pursuit of innovation, efficiency, and ethical considerations. The Deep Learning Market in the US is flourishing as organizations embrace intelligent systems powered by supervised learning and emerging self-supervised learning techniques. These methods refine predictive capabilities and reduce reliance on labeled data, boosting scalability. BFSI firms utilize AI image recognition for various applications, including personalizing customer communication, maintaining a competitive edge, and automating repetitive tasks to boost productivity. Sophisticated feature extraction algorithms now enable models to isolate patterns with high precision, particularly in applications such as image classification for healthcare, security, and retail.

How is this market segmented and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Application

  Image recognition
  Voice recognition
  Video surveillance and diagnostics
  Data mining


Type

  Software
  Services
  Hardware


End-user

  Security
  Automotive
  Healthcare
  Retail and commerce
  Others


Geography

  North America

    US

By Application Insights

The Image recognition segment is estimated to witness significant growth during the forecast period. In the realm of artificial intelligence (AI) and machine learning, image recognition, a subset of computer vision, is gaining significant traction. This technology utilizes neural networks, deep learning models, and various machine learning algorithms to decipher visual data from images and videos. Image recognition is instrumental in numerous applications, including visual search, product recommendations, and inventory management. Consumers can take photographs of products to discover similar items, enhancing the online shopping experience. In the automotive sector, image recognition is indispensable for advanced driver assistance systems (ADAS) and autonomous vehicles, enabling the identification of pedestrians, other vehicles, road signs, and lane markings.

Furthermore, image recognition plays a pivotal role in augmented reality (AR) and virtual reality (VR) applications, where it tracks physical objects and overlays digital content onto real-world scenarios. The model training process involves the backpropagation algorithm, which calculates the loss fu

🎯 Dataset Overview

A intentionally messy synthetic personal finance dataset designed for practicing real-world data preprocessing challenges before building AI-based expense forecasting models.

💡 Context & Inspiration

Created for BudgetWise - an AI expense forecasting tool. This dataset simulates real-world financial transaction data with all the messiness data scientists encounter in production: inconsistent formats, typos, duplicates, outliers, and missing values.

🔍 What Makes This Dataset Special?

• Realistic Data Quality Issues: ~30% of data contains intentional errors
• Class Imbalance: 85% expenses vs 15% income (perfect for SMOTE practice)
• Multi-format Dates: 4 different date formats mixed throughout
• Currency Chaos: Mixed symbols (₹, $, Rs.) in amounts
• Text Inconsistencies: Typos, case variations, and duplicates

📊 Key Statistics

• 15,000+ transactions
• 150 unique users
• 4-year period (2021-2024)
• 9 feature columns
• ~6% duplicate rows
• ~5% missing values per column

🎓 Learning Opportunities

Perfect for practicing: - Data cleaning & normalization - Handling missing values - Date parsing & time-series analysis - Currency extraction & conversion - Outlier detection - Feature engineering - Class balancing (SMOTE) - Text standardization - Duplicate detection

Question Paper Solutions of chapter Data pre-processing and clean-up of Data Mining, 6th Semester , B.Tech in Computer Science & Engineering (Artificial Intelligence and Machine Learning)

Data Balance Optimization AI Market Outlook

According to our latest research, the global Data Balance Optimization AI market size in 2024 stands at USD 2.18 billion, with a robust compound annual growth rate (CAGR) of 23.7% projected from 2025 to 2033. By the end of 2033, the market is forecasted to reach an impressive USD 17.3 billion. This substantial growth is driven by the surging demand for AI-powered analytics and increasing adoption of data-intensive applications across industries, establishing Data Balance Optimization AI as a critical enabler for enterprise digital transformation.

One of the primary growth factors fueling the Data Balance Optimization AI market is the exponential surge in data generation across various sectors. Organizations are increasingly leveraging digital technologies, IoT devices, and cloud platforms, resulting in vast, complex, and often imbalanced datasets. The need for advanced AI solutions that can optimize, balance, and manage these datasets has become paramount to ensure high-quality analytics, accurate machine learning models, and improved business decision-making. Enterprises recognize that imbalanced data can severely skew AI outcomes, leading to biases and reduced operational efficiency. Consequently, the demand for Data Balance Optimization AI tools is accelerating as businesses strive to extract actionable insights from diverse and voluminous data sources.

Another critical driver is the rapid evolution of AI and machine learning algorithms, which require balanced and high-integrity datasets for optimal performance. As industries such as healthcare, finance, and retail increasingly rely on predictive analytics and automation, the integrity of underlying data becomes a focal point. Data Balance Optimization AI technologies are being integrated into data pipelines to automatically detect and correct imbalances, ensuring that AI models are trained on representative and unbiased data. This not only enhances model accuracy but also helps organizations comply with stringent regulatory requirements related to data fairness and transparency, further reinforcing the market’s upward trajectory.

The proliferation of cloud computing and the shift toward hybrid IT infrastructures are also significant contributors to market growth. Cloud-based Data Balance Optimization AI solutions offer scalability, flexibility, and cost-effectiveness, making them attractive to both large enterprises and small and medium-sized businesses. These solutions facilitate seamless integration with existing data management systems, enabling real-time optimization and balancing of data across distributed environments. Furthermore, the rise of data-centric business models in sectors such as e-commerce, telecommunications, and manufacturing is amplifying the need for robust data optimization frameworks, propelling further adoption of Data Balance Optimization AI technologies globally.

From a regional perspective, North America currently dominates the Data Balance Optimization AI market, accounting for the largest share due to its advanced technological infrastructure, high investment in AI research, and the presence of leading technology firms. However, the Asia Pacific region is poised to experience the fastest growth during the forecast period, driven by rapid digitalization, expanding IT ecosystems, and increasing adoption of AI-powered solutions in emerging economies such as China, India, and Southeast Asia. Europe also presents significant opportunities, particularly in regulated industries such as finance and healthcare, where data integrity and compliance are paramount. Collectively, these regional trends underscore the global momentum behind Data Balance Optimization AI adoption.

Component Analysis

The Data Balance Optimization AI market by component is segmented into software, hardware, and services, each playing a pivotal role in the overall ecosystem. The software segment commands the largest market share, driven by the continuous evolution of AI algorithms, data preprocessing tools, and machine learning frameworks designed to address data imbalance challenges. Organizations are increasingly investing in advanced software solutions that automate data balancing, cleansing, and augmentation processes, ensuring the reliability of AI-driven analytics. These software platforms often integrate seamlessly with existing data management systems, providing us

This article examines the opportunities and benefits of artificial intelligence (AI)–enabled social media listening (SML) in assisting successful patient-focused drug development (PFDD). PFDD aims to incorporate the patient perspective to improve the quality, relevance, safety, and efficiency of drug development and evaluation. Gathering patient perspectives to support PFDD is aided by the participation of patient groups in communicating their treatment experiences, needs, preferences, and priorities through online platforms. SML is a method of gathering feedback directly from patients; however, distilling the quantity of data into actionable insights is challenging. AI–enabled methods, such as natural language processing (NLP), can facilitate data processing from SML studies. Herein, we describe a novel, trainable, AI-enabled, SML workflow that classifies posts made by patients or caregivers and uses NLP to provide data on their experiences. Our approach is an iterative process that balances human expert–led milestones and AI-enabled processes to support data preprocessing, patient and caregiver classification, and NLP methods to produce qualitative data. We explored the applicability of this workflow in 2 studies: 1 in patients with head and neck cancers and another in patients with esophageal cancer. Continuous refinement of AI-enabled algorithms was essential for collecting accurate and valuable results. This approach and workflow contribute to the establishment of well-defined standards of SML studies and advance the methodologic quality and rigor of researchers contributing to, conducting, and evaluating SML studies in a PFDD context.

AI Dataset Search Platform Market Outlook

According to our latest research, the global AI Dataset Search Platform market size is valued at USD 1.18 billion in 2024, with a robust year-over-year expansion driven by the escalating demand for high-quality datasets to fuel artificial intelligence and machine learning initiatives across industries. The market is expected to grow at a CAGR of 22.6% from 2025 to 2033, reaching an estimated USD 9.62 billion by 2033. This exponential growth is primarily attributed to the increasing recognition of data as a strategic asset, the proliferation of AI applications across sectors, and the need for efficient, scalable, and secure platforms to discover, curate, and manage diverse datasets.

One of the primary growth factors propelling the AI Dataset Search Platform market is the exponential surge in AI adoption across both public and private sectors. Businesses and institutions are increasingly leveraging AI to gain competitive advantages, enhance operational efficiencies, and deliver personalized experiences. However, the effectiveness of AI models is fundamentally reliant on the quality and diversity of training datasets. As organizations strive to accelerate their AI initiatives, the need for platforms that can efficiently search, aggregate, and validate datasets from disparate sources has become paramount. This has led to a significant uptick in investments in AI dataset search platforms, as they enable faster data discovery, reduce development cycles, and ensure compliance with data governance standards.

Another key driver for the market is the growing complexity and volume of data generated from emerging technologies such as IoT, edge computing, and connected devices. The sheer scale and heterogeneity of data sources necessitate advanced search platforms equipped with intelligent indexing, semantic search, and metadata management capabilities. These platforms not only facilitate the identification of relevant datasets but also support data annotation, labeling, and preprocessing, which are critical for building robust AI models. Furthermore, the integration of AI-powered search algorithms within these platforms enhances the accuracy and relevance of search results, thereby improving the overall efficiency of data scientists and AI practitioners.

Additionally, regulatory pressures and the increasing emphasis on ethical AI have underscored the importance of transparent and auditable data sourcing. Organizations are compelled to demonstrate the provenance and integrity of the datasets used in their AI models to mitigate risks related to bias, privacy, and compliance. AI dataset search platforms address these challenges by providing traceability, version control, and access management features, ensuring that only authorized and compliant datasets are utilized. This not only reduces legal and reputational risks but also fosters trust among stakeholders, further accelerating market adoption.

From a regional perspective, North America dominates the AI Dataset Search Platform market in 2024, accounting for over 38% of the global revenue. This leadership is driven by the presence of major technology providers, a mature AI ecosystem, and substantial investments in research and development. Europe follows closely, benefiting from stringent data privacy regulations and strong government support for AI innovation. The Asia Pacific region is experiencing the fastest growth, propelled by rapid digital transformation, expanding AI research communities, and increasing government initiatives to foster AI adoption. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as organizations in these regions gradually embrace AI-driven solutions.

Component Analysis

The AI Dataset Search Platform market by component is segmented into platforms and services, each playing a pivotal role in the ecosystem. The platform segment encompasses the core software infrastructure that enables users to search, index, curate, and manage datasets. This segmen

AI Data Versioning Platform Market Outlook

According to our latest research, the AI Data Versioning Platform market size reached USD 1.42 billion in 2024 globally, demonstrating robust expansion driven by the surging adoption of artificial intelligence and machine learning initiatives across industries. The market is exhibiting a strong compound annual growth rate (CAGR) of 22.8% from 2025 to 2033. By the end of 2033, the global AI Data Versioning Platform market is forecasted to attain a value of USD 11.84 billion. This remarkable growth is primarily fueled by the increasing complexity and scale of AI projects, necessitating advanced data management solutions that ensure data integrity, reproducibility, and collaborative workflows in enterprise environments.

The primary growth factor propelling the AI Data Versioning Platform market is the exponential increase in data generated by organizations leveraging artificial intelligence and machine learning. As enterprises deploy more sophisticated AI models, the need to track, manage, and reproduce datasets and model versions becomes critical. This has led to a surge in demand for platforms that can provide granular version control, ensuring that data scientists and engineers can collaborate efficiently without risking data inconsistencies or loss. Additionally, regulatory compliance requirements across sectors such as healthcare, BFSI, and manufacturing are pushing organizations to adopt robust data versioning practices, further bolstering market growth.

Another significant driver is the rising complexity of AI model development and deployment pipelines. Modern AI workflows often involve multiple teams working on various aspects of data preprocessing, feature engineering, model training, and validation. This complexity necessitates seamless collaboration and traceability, which AI Data Versioning Platforms offer by enabling users to track changes, roll back to previous versions, and maintain a comprehensive audit trail. The integration capabilities of these platforms with popular machine learning frameworks and DevOps tools have also made them indispensable in enterprise AI strategies, accelerating their adoption across industries.

The proliferation of cloud computing and the growing trend towards hybrid and multi-cloud environments have further augmented the adoption of AI Data Versioning Platforms. Cloud-based solutions offer scalability, flexibility, and cost-effectiveness, allowing organizations to manage vast volumes of data and model artifacts efficiently. Moreover, the increasing focus on data governance, security, and privacy in the wake of stringent data protection regulations worldwide has underscored the importance of data versioning as a foundational element of enterprise AI infrastructure. As organizations strive to derive actionable insights from their data assets while maintaining compliance, the AI Data Versioning Platform market is poised for sustained growth.

Regionally, North America continues to dominate the AI Data Versioning Platform market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The presence of leading technology companies, advanced research institutions, and a mature AI ecosystem in North America has fostered early adoption of data versioning solutions. However, Asia Pacific is expected to witness the highest growth rate during the forecast period, driven by rapid digital transformation, increased investments in AI research, and the emergence of technology startups. Europe, with its strong regulatory framework and focus on data privacy, also represents a significant market, particularly in sectors such as healthcare and BFSI. Latin America and the Middle East & Africa are gradually catching up, supported by growing awareness and digitalization initiatives across industries.

Component Analysis

The AI Data Versioning Platform market is segmented by component into software and services, each playing a crucial role in enabling organizations to manage their data assets effectively. Software solutions constitute the backbone of this market, offering comprehensive functionalities such as data tracking, version control, metadata management, and integration with popular machine learning frameworks. These platforms are designed to cater to the diverse needs of data scientists, engineers, and business analysts, providing intuitive interfaces and automation capabilities that streamline the data lifecycle.

DATASET NAME: DAILYDIALOG PREPROCESSED(PROCESSED FOR CONVERSATIONAL AI)🤖🎯

DESCRIPTION:

This dataset is a cleaned version of the DailyDialog dataset, optimized for conversational AI training. The modifications focus on text preprocessing to improve dialogue coherence while preserving natural language flow.

SOURCE AND COLLECTION:

• ### Original dataset:https://www.kaggle.com/datasets/thedevastator/dailydialog-unlock-the-conversation-potential-in
• ### Modifications:The dataset has been cleaned and preprocessed using Polars and NLTK in Google Colab to enhance text quality.

FEATURES AND COLUMNS:

My dataset has following columns dialog,cleaned_text,lemmatized_text,question,response ,act,emotion, the first three columns will be in cleaned_train,test,valid files. The cleaned_text column is a cleaned text format of dialog column,the lemmatized_column has lemmatized text format of cleaned_text(i processed according to my needs).The conversational files has lemmatized text in a question and response pairs for all three i,e train,test and valid

.

PREPROCESSING AND CLEANING:

I applied the following text normalization techniques:

✅ Contraction Expansion (e.g., "can't" → "cannot") ✅ Lemmatization for Verbs, Adverbs, and Adjectives (except preserved words like "going") ✅ Apostrophe Space Fixes (e.g., "don 't" → "don't") ✅ Preserving Important Words (e.g., "us", "they", "there") ✅ Plural Preservation (e.g., "beers" remains "beers") ✅ Handling Informal Language & Slang (e.g., "gonna" → "going to") ✅ Light Grammar Correction

hello! this dataset is complete_*preprocessing*_completed dataset and easily understand

All artificial intelligence models today require preprocessed and cleaned data to work properly. This crucial step depends on the quality of the data analysis being done. The Space Weather community increased its use of AI in the past few years, but a thorough data analysis addressing all the potential issues is not always performed beforehand. Here is an analysis of a largely used dataset: Level-2 Advanced Composition Explorer’s SWEPAM and MAG measurements from 1998 to 2021 by the ACE Science Center. This work contains guidelines and highlights issues in the ACE data that are likely to be found in other space weather datasets: missing values, inconsistency in distributions, hidden information in statistics, etc. Amongst all specificities of this data, the following can seriously impact the use of algorithms: Histograms are not uniform distributions at all, but sometime Gaussian or Laplacian. Algorithms will be inconsistent in the learning samples as some rare cases will be underrepresented. Gaussian distributions could be overly brought by Gaussian noise from measurements and the signal-to-noise ratio is difficult to estimate. Models will not be reproducible from year to year due to high changes in histograms over time. This high dependence on the solar cycle suggests that one should have at least 11 consecutive years of data to train the algorithm. Rounding of ion temperatures values to different orders of magnitude throughout the data, (probably due to a fixed number of bits on which measurements are coded) will bias the model by wrongly over-representing or under-representing some values. There is an extensive number of missing values (e.g., 41.59% for ion density) that cannot be implemented without pre-processing. Each possible pre-processing is different and subjective depending on one’s underlying objectives A linear model will not be able to accurately model the data. Our linear analysis (e.g., PCA), struggles to explain the data and their relationships. However, non-linear relationships between data seem to exist. Data seem cyclic: we witness the apparition of the solar cycle and the synodic rotation period of the Sun when looking at autocorrelations.Some suggestions are given to address the issues described to enable usage of the dataset despite these challenges.

1) Data Introduction • The Fruit Classification Dataset is a beginner classification dataset configured to classify fruit types based on fruit name, color, and weight information.

2) Data Utilization (1) Fruit Classification Dataset has characteristics that: • This dataset consists of a total of three columns: categorical variable Color, continuous variable Weight, and target class Fruit, allowing you to pre-process categorical and numerical variables when learning classification models. (2) Fruit Classification Dataset can be used to: • Model learning and evaluation: It can be used as educational and research experimental data to compare and evaluate the performance of various machine learning classification algorithms using color and weight characteristics. • Data preprocessing practice: can be used as hands-on data to learn basic data preprocessing and feature engineering courses such as categorical variable encoding and continuous variable scaling.

The SalmonScan dataset is a collection of images of salmon fish, including healthy fish and infected fish. The dataset consists of two classes of images:

Fresh salmon 🐟 Infected Salmon 🐠

This dataset is ideal for various computer vision tasks in machine learning and deep learning applications. Whether you are a researcher, developer, or student, the SalmonScan dataset offers a rich and diverse data source to support your projects and experiments.

So, dive in and explore the fascinating world of salmon health and disease!

The SalmonScan dataset (raw) consists of 24 fresh fish and 91 infected fish. [Due to server cleaning in the past, some raw datasets have been deleted]

The SalmonScan dataset (augmented) consists of approximately 1,208 images of salmon fish, classified into two classes:

• Fresh salmon (healthy fish with no visible signs of disease), 456 images
• Infected Salmon containing disease, 752 images

Each class contains a representative and diverse collection of images, capturing a range of different perspectives, scales, and lighting conditions. The images have been carefully curated to ensure that they are of high quality and suitable for use in a variety of computer vision tasks.

Data Preprocessing

The input images were preprocessed to enhance their quality and suitability for further analysis. The following steps were taken:

Resizing 📏: All the images were resized to a uniform size of 600 pixels in width and 250 pixels in height to ensure compatibility with the learning algorithm. Image Augmentation 📸: To overcome the small amount of images, various image augmentation techniques were applied to the input images. These included: Horizontal Flip ↩️: The images were horizontally flipped to create additional samples. Vertical Flip ⬆️: The images were vertically flipped to create additional samples. Rotation 🔄: The images were rotated to create additional samples. Cropping 🪓: A portion of the image was randomly cropped to create additional samples. Gaussian Noise 🌌: Gaussian noise was added to the images to create additional samples. Shearing 🌆: The images were sheared to create additional samples. Contrast Adjustment (Gamma) ⚖️: The gamma correction was applied to the images to adjust their contrast. Contrast Adjustment (Sigmoid) ⚖️: The sigmoid function was applied to the images to adjust their contrast.

Usage

To use the salmon scan dataset in your ML and DL projects, follow these steps:

• Clone or download the salmon scan dataset repository from GitHub.
• Use standard libraries such as numpy or pandas to convert the images into arrays, which can be input into a machine learning or deep learning model.
• Split the dataset into training, validation, and test sets as per your requirement.
• Preprocess the data as needed, such as resizing and normalizing the images.
• Train your ML/DL model using the preprocessed training data.
• Evaluate the model on the test set and make predictions on new, unseen data.

Snapshot img { margin: 10px !important; } AI In Pharma And Biotech Market Size 2025-2029

The ai in pharma and biotech market size is forecast to increase by USD 3.6 billion, at a CAGR of 20.0% between 2024 and 2029.

The global AI in pharma and biotech market is driven by the need to resolve diminishing R&D productivity. This addresses the high costs and failure rates of traditional drug development, leveraging artificial intelligence in drug discovery. The shift toward integrated, industrial-scale R&D platforms marks a significant trend, moving from isolated AI projects to end-to-end systems for therapeutic innovation. This industrialization aims to make drug discovery a more predictable and scalable process through continuous learning and prediction. Such platforms use interconnected AI models for hypothesis generation, target identification, and de novo molecule design. These systems are central to AI in genomics and AI in precision medicine.However, the market is constrained by significant data-related issues. The utility of AI models is limited by poor data quality, fragmented data silos, and a lack of standardization in biomedical information. This problem of 'garbage in, garbage out' requires extensive data preprocessing, cleaning, and annotation before AI can be effectively applied in areas like AI in pathology or applied AI in healthcare. These data wrangling activities represent a substantial portion of the time and cost of any AI project, creating a foundational barrier to unlocking the full potential of AI in chemicals and drug development.

What will be the Size of the AI In Pharma And Biotech Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019 - 2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe global AI in pharma and biotech market is shaped by the application of AI-based target validation and federated learning for data privacy. Advances in AI-powered pathology analysis and computational drug repurposing are redefining diagnostic and therapeutic strategies. High-performance computing for AI is essential for processing complex datasets, while AI in biologics manufacturing streamlines production.Development of AI-driven companion diagnostics and AI models for ADMET prediction is critical for advancing personalized treatments. The use of generative AI for de novo molecule design and AI in multi-omics data integration enables the creation of novel therapeutic candidates. This progress is supported by efforts to improve AI model interpretability and explainability, ensuring trust in computational outcomes.The modernization of clinical trials is advanced through AI-driven clinical trial modernization and AI in patient monitoring. These technologies leverage AI-generated synthetic patient data and real-world evidence analysis to create more efficient and representative studies. Furthermore, AI-enhanced supply chain management optimizes logistics, ensuring that innovative treatments reach patients effectively and contributing to the growth of AI in oncology and AI in genomics.

How is this AI In Pharma And Biotech Industry segmented?

The ai in pharma and biotech industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in "USD million" for the period 2025-2029, as well as historical data from 2019 - 2023 for the following segments. TypeSmall moleculesLarge moleculesVaccinesCell and gene therapiesTechnologyMachine learningDeep learningNLPComputer visionGenerative AIApplicationDrug discoveryPreclinical and clinical trialsRegulatory compliance and pharmacovigilanceOthersGeographyNorth AmericaUSCanadaMexicoEuropeGermanyUKFranceThe NetherlandsItalySpainAPACChinaJapanIndiaSouth KoreaAustraliaIndonesiaSouth AmericaBrazilArgentinaColombiaMiddle East and AfricaUAESouth AfricaTurkeyRest of World (ROW)

By Type Insights

The small molecules segment is estimated to witness significant growth during the forecast period.The discovery and development of small molecules is the most mature segment, where AI is transforming a process plagued by high attrition rates and immense costs. AI addresses these issues by shifting from serendipitous screening to rational, predictive design. Generative AI algorithms are at the forefront, enabling the de novo design of novel chemical entities optimized for specific disease targets. This in-silico creation is significantly faster than traditional high-throughput screening. This rational design approach is critical, as more than 2.96% of the market's opportunities are linked to improving early-stage discovery efficiency through computational methods.Beyond generation, predictive machine learning models are indispensable for early-stage de-risking and ADMET prediction. These algorithms forecast a compound's absorption, distribution, metabolism, excretion, and toxicity properties, allo

Streaming Feature Engineering AI Market Outlook

According to our latest research, the global Streaming Feature Engineering AI market size reached USD 1.86 billion in 2024, reflecting the growing adoption of real-time data analytics and AI-driven automation across industries. The market is experiencing robust momentum, registering a CAGR of 28.2% from 2025 to 2033. By the end of 2033, the Streaming Feature Engineering AI market is forecasted to hit USD 16.45 billion, driven by advancements in artificial intelligence, the proliferation of IoT devices, and increasing demand for real-time decision-making capabilities. This growth trajectory is underpinned by the rising need for scalable, agile, and intelligent data processing frameworks that empower organizations to extract actionable insights from continuous data streams.

A primary driver behind the expansion of the Streaming Feature Engineering AI market is the exponential increase in data generated by connected devices and digital platforms. As enterprises transition to digital-first models, the volume, velocity, and variety of data have surged, necessitating advanced AI-powered feature engineering solutions capable of processing and analyzing information in real time. This necessity has led to the rapid integration of streaming feature engineering AI into sectors such as BFSI, healthcare, manufacturing, and retail, where real-time insights are critical for fraud detection, predictive maintenance, customer analytics, and operational optimization. The ability of these AI solutions to automate complex data preprocessing tasks and generate high-quality features on the fly significantly accelerates machine learning model development and deployment, thereby enhancing business agility and competitiveness.

Another significant growth factor is the increasing adoption of cloud-based deployment models, which offer scalability, flexibility, and cost efficiency. Cloud platforms facilitate seamless integration of streaming feature engineering AI tools with existing enterprise data architectures, allowing organizations to process massive data streams without the limitations of on-premises infrastructure. The shift towards cloud-native solutions is particularly pronounced among small and medium enterprises (SMEs) that seek to leverage AI-driven analytics without incurring substantial capital expenditures. Furthermore, advancements in edge computing and the convergence of AI with IoT are enabling real-time feature engineering at the data source, further expanding the addressable market and unlocking new use cases in areas such as smart manufacturing, autonomous vehicles, and intelligent healthcare monitoring.

Regulatory compliance and data privacy considerations are also shaping the growth trajectory of the Streaming Feature Engineering AI market. As governments and industry bodies implement stringent data protection regulations, enterprises are increasingly investing in AI solutions that ensure secure and compliant handling of sensitive information in real time. This trend is especially evident in highly regulated sectors like banking, healthcare, and telecommunications, where the ability to anonymize, encrypt, and audit data streams while maintaining analytical accuracy is paramount. The ongoing evolution of privacy-preserving AI techniques, coupled with the growing emphasis on explainable AI and model transparency, is fostering trust and accelerating the adoption of streaming feature engineering AI across diverse end-user segments.

From a regional perspective, North America currently dominates the Streaming Feature Engineering AI market, accounting for the largest revenue share in 2024. This leadership position is attributed to the presence of major technology vendors, a mature digital infrastructure, and early adoption of AI-driven analytics in key industries. However, Asia Pacific is emerging as the fastest-growing region, fueled by rapid digitalization, expanding industrial IoT deployments, and substantial investments in AI research and development. Europe also demonstrates significant growth potential, driven by strong regulatory frameworks, a focus on data sovereignty, and the proliferation of smart city initiatives. Collectively, these regional dynamics are contributing to a highly competitive and innovation-driven global market landscape.

Component Analysis

The Streaming Feature Engineering AI market is segmented by component into Software, Hardware, and Services

Codebase [Github] | Dataset [Zenodo]

Abstract

The advent of powerful neural classifiers has increased interest in problems that require both learning and reasoning. These problems are critical for understanding important properties of models, such as trustworthiness, generalization, interpretability, and compliance to safety and structural constraints. However, recent research observed that tasks requiring both learning and reasoning on background knowledge often suffer from reasoning shortcuts (RSs): predictors can solve the downstream reasoning task without associating the correct concepts to the high-dimensional data. To address this issue, we introduce rsbench, a comprehensive benchmark suite designed to systematically evaluate the impact of RSs on models by providing easy access to highly customizable tasks affected by RSs. Furthermore, rsbench implements common metrics for evaluating concept quality and introduces novel formal verification procedures for assessing the presence of RSs in learning tasks. Using rsbench, we highlight that obtaining high quality concepts in both purely neural and neuro-symbolic models is a far-from-solved problem. rsbench is available on Github.

Usage

We recommend visiting the official code website for instructions on how to use the dataset and accompaying software code.

License

All ready-made data sets and generated datasets are distributed under the CC-BY-SA 4.0 license, with the exception of Kand-Logic, which is derived from Kandinsky-patterns and as such is distributed under the GPL-3.0 license.

Datasets Overview

• CLIP-embeddings. This folder contains the saved activations from a pretrained CLIP model applied to the tested dataset. It includes embeddings that represent the dataset in a format suitable for further analysis and experimentation.
• BDD_OIA-original-dataset. This directory holds the original files from the X-OIA project by Xu et al. [1]. These datasets have been made publicly available for ease of access and further research. If you are going to use it, please consider citing the original authors.
• kand-logic-3k. This folder contains all images generated for the Kand-Logic project. Each image is accompanied by annotations for both concepts and labels.
• bbox-kand-logic-3k. In this directory, you will find images from the Kand-Logic project that have undergone a preprocessing step. These images are extracted based on bounding boxes, rescaled, and include annotations for concepts and labels.
• sdd-oia. This folder includes all images and labels generated using rsbench.
• sdd-oia-embeddings. This directory contains 512-dimensional embeddings extracted from a pretrained ResNet18 model on ImageNet. The embeddings are derived from the sdd-oia`dataset.
• BDD-OIA-preprocessed. Here you will find preprocessed data that follow the methodology outlined by Sawada and Nakamura [2]. The folder contains 2048-dimensional embeddings extracted from a pretrained Faster-RCNN model on the BDD-100k dataset.

The original BDD datasets can be downloaded from the following Google Drive link: [Download BDD Dataset].

References

[1] Xu et al., *Explainable Object-Induced Action Decision for Autonomous Vehicles*, CVPR 2020.

[2] Sawada and Nakamura, *Concept Bottleneck Model With Additional Unsupervised Concepts*, IEEE 2022.

AI And Machine Learning Operationalization Software Market size was estimated at USD 6.12 Billion in 2024 and is projected to reach USD 36.25 Billion by 2032, growing at a CAGR of 35.2% from 2026 to 2032.

Key Market Drivers

Surging Adoption of AI & ML: The widespread adoption of Artificial Intelligence (AI) and Machine Learning (ML) across various industries is driven primarily by the surge in demand. With AI and ML increasingly leveraged by organizations for tasks like automation, decision-making, and process optimization, there is a growing demand for MLOps software to effectively manage and operationalize these models.