The diamond is 58 times harder than any other mineral in the world, and its elegance as a jewel has long been appreciated. Forecasting diamond prices is challenging due to nonlinearity in important features such as carat, cut, clarity, table, and depth. Against this backdrop, the study conducted a comparative analysis of the performance of multiple supervised machine learning models (regressors and classifiers) in predicting diamond prices. Eight supervised machine learning algorithms were evaluated in this work including Multiple Linear Regression, Linear Discriminant Analysis, eXtreme Gradient Boosting, Random Forest, k-Nearest Neighbors, Support Vector Machines, Boosted Regression and Classification Trees, and Multi-Layer Perceptron. The analysis is based on data preprocessing, exploratory data analysis (EDA), training the aforementioned models, assessing their accuracy, and interpreting their results. Based on the performance metrics values and analysis, it was discovered that eXtreme Gradient Boosting was the most optimal algorithm in both classification and regression, with a R2 score of 97.45% and an Accuracy value of 74.28%. As a result, eXtreme Gradient Boosting was recommended as the optimal regressor and classifier for forecasting the price of a diamond specimen. Methods Kaggle, a data repository with thousands of datasets, was used in the investigation. It is an online community for machine learning practitioners and data scientists, as well as a robust, well-researched, and sufficient resource for analyzing various data sources. On Kaggle, users can search for and publish various datasets. In a web-based data-science environment, they can study datasets and construct models.

BackgroundBiomarkers are a key component of precision medicine. However, full clinical integration of biomarkers has been met with challenges, partly attributed to analytical difficulties. It has been shown that biomarker reproducibility is susceptible to data preprocessing approaches. Here, we systematically evaluated machine-learning ensembles of preprocessing methods as a general strategy to improve biomarker performance for prediction of survival from early breast cancer.ResultsWe risk stratified breast cancer patients into either low-risk or high-risk groups based on four published hypoxia signatures (Buffa, Winter, Hu, and Sorensen), using 24 different preprocessing approaches for microarray normalization. The 24 binary risk profiles determined for each hypoxia signature were combined using a random forest to evaluate the efficacy of a preprocessing ensemble classifier. We demonstrate that the best way of merging preprocessing methods varies from signature to signature, and that there is likely no ‘best’ preprocessing pipeline that is universal across datasets, highlighting the need to evaluate ensembles of preprocessing algorithms. Further, we developed novel signatures for each preprocessing method and the risk classifications from each were incorporated in a meta-random forest model. Interestingly, the classification of these biomarkers and its ensemble show striking consistency, demonstrating that similar intrinsic biological information are being faithfully represented. As such, these classification patterns further confirm that there is a subset of patients whose prognosis is consistently challenging to predict.ConclusionsPerformance of different prognostic signatures varies with pre-processing method. A simple classifier by unanimous voting of classifications is a reliable way of improving on single preprocessing methods. Future signatures will likely require integration of intrinsic and extrinsic clinico-pathological variables to better predict disease-related outcomes.

Can machine learning effectively lower the effort necessary to extract important information from raw data for hydrological research questions? On the example of a typical water-management task, the extraction of direct runoff flood events from continuous hydrographs, we demonstrate how machine learning can be used to automate the application of expert knowledge to big data sets and extract the relevant information. In particular, we tested seven different algorithms to detect event beginning and end solely from a given excerpt from the continuous hydrograph. First, the number of required data points within the excerpts as well as the amount of training data has been determined. In a local application, we were able to show that all applied Machine learning algorithms were capable to reproduce manually defined event boundaries. Automatically delineated events were afflicted with a relative duration error of 20 and 5% event volume. Moreover, we could show that hydrograph separation patterns could easily be learned by the algorithms and are regionally and trans-regionally transferable without significant performance loss. Hence, the training data sets can be very small and trained algorithms can be applied to new catchments lacking training data. The results showed the great potential of machine learning to extract relevant information efficiently and, hence, lower the effort for data preprocessing for water management studies. Moreover, the transferability of trained algorithms to other catchments is a clear advantage to common methods.

BackgroundThis study proposes machine learning-driven data preparation (MLDP) for optimal data preparation (DP) prior to building prediction models for cancer cohorts.MethodsA collection of well-established DP methods were incorporated for building the DP pipelines for various clinical cohorts prior to machine learning. Evolutionary algorithm principles combined with hyperparameter optimization were employed to iteratively select the best fitting subset of data preparation algorithms for the given dataset. The proposed method was validated for glioma and prostate single center cohorts by 100-fold Monte Carlo (MC) cross-validation scheme with 80-20% training-validation split ratio. In addition, a dual-center diffuse large B-cell lymphoma (DLBCL) cohort was utilized with Center 1 as training and Center 2 as independent validation datasets to predict cohort-specific clinical endpoints. Five machine learning (ML) classifiers were employed for building prediction models across all analyzed cohorts. Predictive performance was estimated by confusion matrix analytics over the validation sets of each cohort. The performance of each model with and without MLDP, as well as with manually-defined DP were compared in each of the four cohorts.ResultsSixteen of twenty established predictive models demonstrated area under the receiver operator characteristics curve (AUC) performance increase utilizing the MLDP. The MLDP resulted in the highest performance increase for random forest (RF) (+0.16 AUC) and support vector machine (SVM) (+0.13 AUC) model schemes for predicting 36-months survival in the glioma cohort. Single center cohorts resulted in complex (6-7 DP steps) DP pipelines, with a high occurrence of outlier detection, feature selection and synthetic majority oversampling technique (SMOTE). In contrast, the optimal DP pipeline for the dual-center DLBCL cohort only included outlier detection and SMOTE DP steps.ConclusionsThis study demonstrates that data preparation prior to ML prediction model building in cancer cohorts shall be ML-driven itself, yielding optimal prediction models in both single and multi-centric settings.

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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

Multiplexed imaging technologies provide insights into complex tissue architectures. However, challenges arise due to software fragmentation with cumbersome data handoffs, inefficiencies in processing large images (8 to 40 gigabytes per image), and limited spatial analysis capabilities. To efficiently analyze multiplexed imaging data, we developed SPACEc, a scalable end-to-end Python solution, that handles image extraction, cell segmentation, and data preprocessing and incorporates machine-learning-enabled, multi-scaled, spatial analysis, operated through a user-friendly and interactive interface. The demonstration dataset was derived from a previous analysis and contains TMA cores from a human tonsil and tonsillitis sample that were acquired with the Akoya PhenocyclerFusion platform. The dataset can be used to test the workflow and establish it on a user’s system or to familiarize oneself with the pipeline. Methods Tissue samples: Tonsil cores were extracted from a larger multi-tumor tissue microarray (TMA), which included a total of 66 unique tissues (51 malignant and semi-malignant tissues, as well as 15 non-malignant tissues). Representative tissue regions were annotated on corresponding hematoxylin and eosin (H&E)-stained sections by a board-certified surgical pathologist (S.Z.). Annotations were used to generate the 66 cores each with cores of 1mm diameter. FFPE tissue blocks were retrieved from the tissue archives of the Institute of Pathology, University Medical Center Mainz, Germany, and the Department of Dermatology, University Medical Center Mainz, Germany. The multi-tumor-TMA block was sectioned at 3µm thickness onto SuperFrost Plus microscopy slides before being processed for CODEX multiplex imaging as previously described. CODEX multiplexed imaging and processing To run the CODEX machine, the slide was taken from the storage buffer and placed in PBS for 10 minutes to equilibrate. After drying the PBS with a tissue, a flow cell was sealed onto the tissue slide. The assembled slide and flow cell were then placed in a PhenoCycler Buffer made from 10X PhenoCycler Buffer & Additive for at least 10 minutes before starting the experiment. A 96-well reporter plate was prepared with each reporter corresponding to the correct barcoded antibody for each cycle, with up to 3 reporters per cycle per well. The fluorescence reporters were mixed with 1X PhenoCycler Buffer, Additive, nuclear-staining reagent, and assay reagent according to the manufacturer's instructions. With the reporter plate and assembled slide and flow cell placed into the CODEX machine, the automated multiplexed imaging experiment was initiated. Each imaging cycle included steps for reporter binding, imaging of three fluorescent channels, and reporter stripping to prepare for the next cycle and set of markers. This was repeated until all markers were imaged. After the experiment, a .qptiff image file containing individual antibody channels and the DAPI channel was obtained. Image stitching, drift compensation, deconvolution, and cycle concatenation are performed within the Akoya PhenoCycler software. The raw imaging data output (tiff, 377.442nm per pixel for 20x CODEX) is first examined with QuPath software (https://qupath.github.io/) for inspection of staining quality. Any markers that produce unexpected patterns or low signal-to-noise ratios should be excluded from the ensuing analysis. The qptiff files must be converted into tiff files for input into SPACEc. Data preprocessing includes image stitching, drift compensation, deconvolution, and cycle concatenation performed using the Akoya Phenocycler software. The raw imaging data (qptiff, 377.442 nm/pixel for 20x CODEX) files from the Akoya PhenoCycler technology were first examined with QuPath software (https://qupath.github.io/) to inspect staining qualities. Markers with untenable patterns or low signal-to-noise ratios were excluded from further analysis. A custom CODEX analysis pipeline was used to process all acquired CODEX data (scripts available upon request). The qptiff files were converted into tiff files for tissue detection (watershed algorithm) and cell segmentation.

Data Science And ML Platforms Market Outlook

The global market size for Data Science and ML Platforms was estimated to be approximately USD 78.9 billion in 2023, and it is projected to reach around USD 307.6 billion by 2032, growing at a Compound Annual Growth Rate (CAGR) of 16.4% during the forecast period. This remarkable growth can be largely attributed to the increasing adoption of artificial intelligence (AI) and machine learning (ML) across various industries to enhance operational efficiency, predictive analytics, and decision-making processes.

The surge in big data and the necessity to make sense of unstructured data is a substantial growth driver for the Data Science and ML Platforms market. Organizations are increasingly leveraging data science and machine learning to gain insights that can help them stay competitive. This is especially true in sectors like retail and e-commerce where customer behavior analytics can lead to more targeted marketing strategies, personalized shopping experiences, and improved customer retention rates. Additionally, the proliferation of IoT devices is generating massive amounts of data, which further fuels the need for advanced data analytics platforms.

Another significant growth factor is the increasing adoption of cloud-based solutions. Cloud platforms offer scalable resources, flexibility, and substantial cost savings, making them attractive for enterprises of all sizes. Cloud-based data science and machine learning platforms also facilitate collaboration among distributed teams, enabling more efficient workflows and faster time-to-market for new products and services. Furthermore, advancements in cloud technologies, such as serverless computing and containerization, are making it easier for organizations to deploy and manage their data science models.

Investment in AI and ML by key industry players also plays a crucial role in market growth. Tech giants like Google, Amazon, Microsoft, and IBM are making substantial investments in developing advanced AI and ML tools and platforms. These investments are not only driving innovation but also making these technologies more accessible to smaller enterprises. Additionally, mergers and acquisitions in this space are leading to more integrated and comprehensive solutions, which are further accelerating market growth.

Machine Learning Tools are at the heart of this technological evolution, providing the necessary frameworks and libraries that empower developers and data scientists to create sophisticated models and algorithms. These tools, such as TensorFlow, PyTorch, and Scikit-learn, offer a range of functionalities from data preprocessing to model deployment, catering to both beginners and experts. The accessibility and versatility of these tools have democratized machine learning, enabling a wider audience to harness the power of AI. As organizations continue to embrace digital transformation, the demand for robust machine learning tools is expected to grow, driving further innovation and development in this space.

From a regional perspective, North America is expected to hold the largest market share due to the early adoption of advanced technologies and the presence of major market players. However, the Asia Pacific region is anticipated to exhibit the highest growth rate during the forecast period. This is driven by increasing investments in AI and ML, a burgeoning start-up ecosystem, and supportive government policies aimed at digital transformation. Countries like China, India, and Japan are at the forefront of this growth, making significant strides in AI research and application.

Component Analysis

When analyzing the Data Science and ML Platforms market by component, it's essential to differentiate between software and services. The software segment includes platforms and tools designed for data ingestion, processing, visualization, and model building. These software solutions are crucial for organizations looking to harness the power of big data and machine learning. They provide the necessary infrastructure for data scientists to develop, test, and deploy ML models. The software segment is expected to grow significantly due to ongoing advancements in AI algorithms and the increasing need for more sophisticated data analysis tools.

The services segment in the Data Science and ML Platforms market encompasses consulting, system integration, and support services. Consulting services help organizatio

This dataset, named hate_speech_offensive, is a carefully assembled collection of annotated tweets designed for the purpose of detecting hate speech and offensive language. It consists primarily of English tweets and serves as a vital resource for training machine learning models and algorithms in this domain. Researchers and developers can utilise this dataset to build effective systems for identifying and classifying hateful or offensive content, contributing to safer online environments. The dataset is presented in a CSV file format, specifically 'train.csv', and includes detailed annotations for each tweet.

Columns

• count: The total number of annotations provided for each individual tweet. (Integer)
• hate_speech_count: The number of annotations that classified a particular tweet as hate speech. (Integer)
• offensive_language_count: The number of annotations that categorised a tweet as containing offensive language. (Integer)
• neither_count: The number of annotations that identified a tweet as neither hate speech nor offensive language. (Integer)
• class: The classification label for the tweet.
• tweet: The actual tweet content.

Distribution

The dataset is provided in a CSV file format, specifically 'train.csv'. It is structured with each row representing an individual tweet along with its corresponding annotations. The dataset currently comprises a single training split. There are approximately 24,783 unique tweets within the dataset.

Usage

This dataset is ideal for various applications and use cases, including: * Training machine learning models or algorithms for automated hate speech and offensive language detection. * Conducting Sentiment Analysis on Twitter data to understand the sentiment behind tweets and identify patterns of negative or offensive language. * Developing and evaluating Hate Speech Detection systems that can identify and flag hate speech in real-time. * Improving Content Moderation systems for social media platforms by automatically detecting and removing offensive or hateful content. * Performing Exploratory Data Analysis (EDA) to gain insights into the distribution of tweet classifications, identify common words associated with each class, and analyse co-occurrences of hate speech and offensive language.

Coverage

The dataset primarily consists of English tweets. Its scope is global in potential application, aiming to address social issues and advocacy related to online discourse. While no specific time range for data collection is provided, the dataset focuses on general English tweet content.

License

CCO

Who Can Use It

This dataset is intended for: * Researchers and developers seeking to create and improve machine learning models for detecting hate speech and offensive language on social media platforms like Twitter. * Data scientists and analysts interested in understanding patterns of online discourse and sentiment. * Social media platforms and their moderation teams aiming to enhance automated content moderation systems.

Dataset Name Suggestions

• Twitter Hate Speech and Offensive Language Dataset
• Annotated Tweet Toxicity Data
• Social Media Content Moderation Tweets
• English Tweet Hate Speech Classifier Data
• Online Language Offensiveness Dataset

Attributes

Original Data Source: Hate Speech and Offensive Language Detection

An optimal alarm system is simply an optimal level-crossing predictor that can be designed to elicit the fewest false alarms for a fixed detection probability. It currently use Kalman filtering for dynamic systems to provide a layer of predictive capability for the forecasting of adverse events. Predicted Kalman filter future process values and a fixed critical threshold can be used to construct a candidate level-crossing event over a predetermined prediction window. Due to the fact that the alarm regions for an optimal level-crossing predictor cannot be expressed in closed form, one of our aims has been to investigate approximations for the design of an optimal alarm system. Approximations to this sort of alarm region are required for the most computationally efficient generation of a ROC curve or other similar alarm system design metrics. Algorithms based upon the optimal alarm system concept also require models that appeal to a variety of data mining and machine learning techniques. As such, we have investigated a serial architecture which was used to preprocess a full feature space by using SVR (Support Vector Regression), implicitly reducing it to a univariate signal while retaining salient dynamic characteristics (see AIAA attachment below). This step was required due to current technical constraints, and is performed by using the residual generated by SVR (or potentially any regression algorithm) that has properties which are favorable for use as training data to learn the parameters of a linear dynamical system. Future development will lift these restrictions so as to allow for exposure to a broader class of models such as a switched multi-input/output linear dynamical system in isolation based upon heterogeneous (both discrete and continuous) data, obviating the need for the use of a preprocessing regression algorithm in serial. However, the use of a preprocessing multi-input/output nonlinear regression algorithm in serial with a multi-input/output linear dynamical system will allow for the characterization of underlying static nonlinearities to be investigated as well. We will even investigate the use of non-parametric methods such as Gaussian process regression and particle filtering in isolation to lift the linear and Gaussian assumptions which may be invalid for many applications. Future work will also involve improvement of approximations inherent in use of the optimal alarm system of optimal level-crossing predictor. We will also perform more rigorous testing and validation of the alarm systems discussed by using standard machine learning techniques and consider more complex, yet practically meaningful critical level-crossing events. Finally, a more detailed investigation of model fidelity with respect to available data and metrics has been conducted (see attachment below). As such, future work on modeling will involve the investigation of necessary improvements in initialization techniques and data transformations for a more feasible fit to the assumed model structure. Additionally, we will explore the integration of physics-based and data-driven methods in a Bayesian context, by using a more informative prior.

MetaMath QA

Mathematical Questions for Large Language Models

By Huggingface Hub [source]

About this dataset

This dataset contains meta-mathematics questions and answers collected from the Mistral-7B question-answering system. The responses, types, and queries are all provided in order to help boost the performance of MetaMathQA while maintaining high accuracy. With its well-structured design, this dataset provides users with an efficient way to investigate various aspects of question answering models and further understand how they function. Whether you are a professional or beginner, this dataset is sure to offer invaluable insights into the development of more powerful QA systems!

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How to use the dataset

Data Dictionary

The MetaMathQA dataset contains three columns: response, type, and query. - Response: the response to the query given by the question answering system. (String) - Type: the type of query provided as input to the system. (String) - Query:the question posed to the system for which a response is required. (String)

Preparing data for analysis

It’s important that before you dive into analysis, you first familiarize yourself with what kind data values are present in each column and also check if any preprocessing needs to be done on them such as removing unwanted characters or filling in missing values etc., so that it can be used without any issue while training or testing your model further down in your process flow.

##### Training Models using Mistral 7B

Mistral 7B is an open source framework designed for building machine learning models quickly and easily from tabular (csv) datasets such as those found in this dataset 'MetaMathQA ' . After collecting and preprocessing your dataset accordingly Mistral 7B provides with support for various Machine Learning algorithms like Support Vector Machines (SVM), Logistic Regression , Decision trees etc , allowing one to select from various popular libraries these offered algorithms with powerful overall hyperparameter optimization techniques so soon after selecting algorithm configuration its good practice that one use GridSearchCV & RandomSearchCV methods further tune both optimizations during model building stages . Post selection process one can then go ahead validate performances of selected models through metrics like accuracy score , F1 Metric , Precision Score & Recall Scores .

##### Testing phosphors :

After successful completion building phase right way would be robustly testing phosphors on different evaluation metrics mentioned above Model infusion stage helps here immediately make predictions based on earlier trained model OK auto back new test cases presented by domain experts could hey run quality assurance check again base score metrics mentioned above know asses confidence value post execution HHO updating baseline scores running experiments better preferred methodology AI workflows because Core advantage finally being have relevancy inexactness induced errors altogether impact low

Research Ideas

• Generating natural language processing (NLP) models to better identify patterns and connections between questions, answers, and types.
• Developing understandings on the efficiency of certain language features in producing successful question-answering results for different types of queries.
• Optimizing search algorithms that surface relevant answer results based on types of queries

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 No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: train.csv | Column name | Description | |:--------------|:------------------------------------| | response | The response to the query. (String) | | type | The type of query. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Huggingface Hub.

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.

Today, deep neural networks are widely used in many computer vision problems, also for geographic information systems (GIS) data. This type of data is commonly used for urban analyzes and spatial planning. We used orthophotographic images of two residential districts from Kielce, Poland for research including urban sprawl automatic analysis with Transformer-based neural network application.Orthophotomaps were obtained from Kielce GIS portal. Then, the map was manually masked into building and building surroundings classes. Finally, the ortophotomap and corresponding classification mask were simultaneously divided into small tiles. This approach is common in image data preprocessing for machine learning algorithms learning phase. Data contains two original orthophotomaps from Wietrznia and Pod Telegrafem residential districts with corresponding masks and also their tiled version, ready to provide as a training data for machine learning models.Transformed-based neural network has undergone a training process on the Wietrznia dataset, targeted for semantic segmentation of the tiles into buildings and surroundings classes. After that, inference of the models was used to test model's generalization ability on the Pod Telegrafem dataset. The efficiency of the model was satisfying, so it can be used in automatic semantic building segmentation. Then, the process of dividing the images can be reversed and complete classification mask retrieved. This mask can be used for area of the buildings calculations and urban sprawl monitoring, if the research would be repeated for GIS data from wider time horizon.Since the dataset was collected from Kielce GIS portal, as the part of the Polish Main Office of Geodesy and Cartography data resource, it may be used only for non-profit and non-commertial purposes, in private or scientific applications, under the law "Ustawa z dnia 4 lutego 1994 r. o prawie autorskim i prawach pokrewnych (Dz.U. z 2006 r. nr 90 poz 631 z późn. zm.)". There are no other legal or ethical considerations in reuse potential.Data information is presented below.wietrznia_2019.jpg - orthophotomap of Wietrznia districtmodel's - used for training, as an explanatory imagewietrznia_2019.png - classification mask of Wietrznia district - used for model's training, as a target imagewietrznia_2019_validation.jpg - one image from Wietrznia district - used for model's validation during training phasepod_telegrafem_2019.jpg - orthophotomap of Pod Telegrafem district - used for model's evaluation after training phasewietrznia_2019 - folder with wietrznia_2019.jpg (image) and wietrznia_2019.png (annotation) images, divided into 810 tiles (512 x 512 pixels each), tiles with no information were manually removed, so the training data would contain only informative tilestiles presented - used for the model during training (images and annotations for fitting the model to the data)wietrznia_2019_vaidation - folder with wietrznia_2019_validation.jpg image divided into 16 tiles (256 x 256 pixels each) - tiles were presented to the model during training (images for validation model's efficiency); it was not the part of the training datapod_telegrafem_2019 - folder with pod_telegrafem.jpg image divided into 196 tiles (256 x 265 pixels each) - tiles were presented to the model during inference (images for evaluation model's robustness)Dataset was created as described below.Firstly, the orthophotomaps were collected from Kielce Geoportal (https://gis.kielce.eu). Kielce Geoportal offers a .pst recent map from April 2019. It is an orthophotomap with a resolution of 5 x 5 pixels, constructed from a plane flight at 700 meters over ground height, taken with a camera for vertical photos. Downloading was done by WMS in open-source QGIS software (https://www.qgis.org), as a 1:500 scale map, then converted to a 1200 dpi PNG image.Secondly, the map from Wietrznia residential district was manually labelled, also in QGIS, in the same scope, as the orthophotomap. Annotation based on land cover map information was also obtained from Kielce Geoportal. There are two classes - residential building and surrounding. Second map, from Pod Telegrafem district was not annotated, since it was used in the testing phase and imitates situation, where there is no annotation for the new data presented to the model.Next, the images was converted to an RGB JPG images, and the annotation map was converted to 8-bit GRAY PNG image.Finally, Wietrznia data files were tiled to 512 x 512 pixels tiles, in Python PIL library. Tiles with no information or a relatively small amount of information (only white background or mostly white background) were manually removed. So, from the 29113 x 15938 pixels orthophotomap, only 810 tiles with corresponding annotations were left, ready to train the machine learning model for the semantic segmentation task. Pod Telegrafem orthophotomap was tiled with no manual removing, so from the 7168 x 7168 pixels ortophotomap were created 197 tiles with 256 x 256 pixels resolution. There was also image of one residential building, used for model's validation during training phase, it was not the part of the training data, but was a part of Wietrznia residential area. It was 2048 x 2048 pixel ortophotomap, tiled to 16 tiles 256 x 265 pixels each.

Malaria is the leading cause of death in the African region. Data mining can help extract valuable knowledge from available data in the healthcare sector. This makes it possible to train models to predict patient health faster than in clinical trials. Implementations of various machine learning algorithms such as K-Nearest Neighbors, Bayes Theorem, Logistic Regression, Support Vector Machines, and Multinomial Naïve Bayes (MNB), etc., has been applied to malaria datasets in public hospitals, but there are still limitations in modeling using the Naive Bayes multinomial algorithm. This study applies the MNB model to explore the relationship between 15 relevant attributes of public hospitals data. The goal is to examine how the dependency between attributes affects the performance of the classifier. MNB creates transparent and reliable graphical representation between attributes with the ability to predict new situations. The model (MNB) has 97% accuracy. It is concluded that this model outperforms the GNB classifier which has 100% accuracy and the RF which also has 100% accuracy. Methods Prior to collection of data, the researcher was be guided by all ethical training certification on data collection, right to confidentiality and privacy reserved called Institutional Review Board (IRB). Data was be collected from the manual archive of the Hospitals purposively selected using stratified sampling technique, transform the data to electronic form and store in MYSQL database called malaria. Each patient file was extracted and review for signs and symptoms of malaria then check for laboratory confirmation result from diagnosis. The data was be divided into two tables: the first table was called data1 which contain data for use in phase 1 of the classification, while the second table data2 which contains data for use in phase 2 of the classification. Data Source Collection Malaria incidence data set is obtained from Public hospitals from 2017 to 2021. These are the data used for modeling and analysis. Also, putting in mind the geographical location and socio-economic factors inclusive which are available for patients inhabiting those areas. Naive Bayes (Multinomial) is the model used to analyze the collected data for malaria disease prediction and grading accordingly. Data Preprocessing: Data preprocessing shall be done to remove noise and outlier. Transformation: The data shall be transformed from analog to electronic record. Data Partitioning The data which shall be collected will be divided into two portions; one portion of the data shall be extracted as a training set, while the other portion will be used for testing. The training portion shall be taken from a table stored in a database and will be called data which is training set1, while the training portion taking from another table store in a database is shall be called data which is training set2. The dataset was split into two parts: a sample containing 70% of the training data and 30% for the purpose of this research. Then, using MNB classification algorithms implemented in Python, the models were trained on the training sample. On the 30% remaining data, the resulting models were tested, and the results were compared with the other Machine Learning models using the standard metrics. Classification and prediction: Base on the nature of variable in the dataset, this study will use Naïve Bayes (Multinomial) classification techniques; Classification phase 1 and Classification phase 2. The operation of the framework is illustrated as follows: i. Data collection and preprocessing shall be done. ii. Preprocess data shall be stored in a training set 1 and training set 2. These datasets shall be used during classification. iii. Test data set is shall be stored in database test data set. iv. Part of the test data set must be compared for classification using classifier 1 and the remaining part must be classified with classifier 2 as follows: Classifier phase 1: It classify into positive or negative classes. If the patient is having malaria, then the patient is classified as positive (P), while a patient is classified as negative (N) if the patient does not have malaria.
Classifier phase 2: It classify only data set that has been classified as positive by classifier 1, and then further classify them into complicated and uncomplicated class label. The classifier will also capture data on environmental factors, genetics, gender and age, cultural and socio-economic variables. The system will be designed such that the core parameters as a determining factor should supply their value.

Early detection of Diabetic Retinopathy is a key challenge to prevent a patient from potential vision loss. The task of DR detection often requires special expertise from ophthalmologists. In remote places of the world such facilities may not be available, so In an attempt to automate the detection of DR, machine learning and deep learning techniques can be adopted. Some of the recent papers have proven such success on various publicly available dataset.

Another challenge of deep learning techniques is the availability of rightly processed standardized data. Cleaning and preprocessing the data often takes much longer time than the model training. As a part of my research work, I had to preprocess the images taken from APTOS and Messidor before training the model. I applied circle-crop and Graham Ben's preprocessing technique and scaled all the images to 512X512 format. Also, I applied the data augmentation technique and increased the number of samples from 3662 data of APTOS to 18310, and 400 messidor samples to 3600 samples. I divided the images into two classes class 0 (NO DR) and class 1 (DR). The large number of data is essential for transfer learning. This process is very cumbersome and time-consuming. So I thought to upload the newly generated dataset in Kaggle so that some people might find it useful for their work. I hope this will help many people. Feel free to use the data.

The dataset used in this study is the Wisconsin Diagnostic Breast Cancer (WDBC) dataset, originally provided by the University of Wisconsin and obtained via Kaggle. It consists of 569 observations, each corresponding to a digitized image of a fine needle aspirate (FNA) of a breast mass. The dataset contains 32 attributes: one identifier column (discarded during preprocessing), one diagnosis label (malignant or benign), and 30 continuous real-valued features that describe the morphology of cell nuclei. These features are grouped into three statistical descriptors—mean, standard error (SE), and worst (mean of the three largest values)—for ten morphological properties including radius, perimeter, area, concavity, and fractal dimension. All feature values were normalized using z-score standardization to ensure uniform scale across models sensitive to input ranges. No missing values were present in the original dataset. Label encoding was applied to the diagnosis column, assigning 1 to malignant and 0 to benign cases. The dataset was split into training (80%) and testing (20%) sets while preserving class balance via stratified sampling. The accompanying Python source code (breast_cancer_classification_models.py) performs data loading, preprocessing, model training, evaluation, and result visualization. Four lightweight classifiers—Decision Tree, Naïve Bayes, Perceptron, and K-Nearest Neighbors (KNN)—were implemented using the scikit-learn library (version 1.2 or later). Performance metrics including Accuracy, Precision, Recall, F1-score, and ROC-AUC were calculated for each model. Confusion matrices and ROC curves were generated and saved as PNG files for interpretability. All results are saved in a structured CSV file (classification_results.csv) that contains the performance metrics for each model. Supplementary visualizations include all_feature_histograms.png (distribution plots for all standardized features), model_comparison.png (metric-wise bar plot), and feature_correlation_heatmap.png (Pearson correlation matrix of all 30 features). The data files are in standard CSV and PNG formats and can be opened using any spreadsheet or image viewer, respectively. No rare file types are used, and all scripts are compatible with any Python 3.x environment. This data package enables reproducibility and offers a transparent overview of how baseline machine learning models perform in the domain of breast cancer diagnosis using a clinically-relevant dataset.

We have revised the dataset, ensuring that it is thoroughly processed and ready for analysis. The attached second dataset has undergone comprehensive preprocessing algorithms. This preprocessing includes steps such as data cleaning, normalization, and feature extraction to enhance the quality and usability of the data. These steps are crucial to ensure that the dataset is free from inconsistencies, missing values, and irrelevant information, thereby improving the accuracy and reliability of the subsequent machine learning models.

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 original Brazilian COVID-19 dataset (from the 26 Brazilian states and the Federal District) included information about tested patients, containing early-stage symptoms, comorbidities, demographics information, and symptoms description. The patients were tested by applying viral or antibody tests.

We preprocessed the dataset by selecting only completed tests, being marked as positive or negative, applied string matching algorithms to correct some inconsistencies, and removed rows with duplicated instances and asymptomatic patients. We also focused on the most frequent and relevant demographics information and reported early-stage symptoms to select features, and balanced the data considering positive and negative cases by random undersampling using the NearMiss algorithm. The preprocessing resulted in a dataset with 2,674 patients. The reduction in the number of patients from 55,676 to 2,674 occurred due to the asymptomatic patients, duplicated data, few reported symptoms by some patients, and the need for information about the dates of symptoms onset and testing.

Using this dataset, we implemented and evaluated supervised machine learning models for COVID-19 detection in Brazil based on early-stage symptoms and basic personal information.

These are the datasets used for the study titled: A Comparison framework for deep learning RFI detection algorithms in radio astronomy. These files are made publicly available as an additional resource to the submission of the author's Masters degree at Stellenbosch University. The detection is done in the field of radio astronomy. Each dataset consists of images/spectrograms/waterfall plots for baselines, and the corresponding binary mask for each image. The datasets can be used to train machine learning models, or for the case of this study, supervised fully convolutional neural networks.

The LOFAR datasets consists of real observations and was slightly modified from https://zenodo.org/record/6724065. See this resource regarding the observational parameters used to retrieve the data from the LOFAR Long Term Archive.The HERA dataset consists of simulated observations generated with hera_sim (https://readthedocs.org/projects/hera-sim/). The 28 March dataset consists of accurate pixel-perfect binary masks for each image. The 20 July dataset is identical to the first, except the binary masks are generated with AOFlagger. All three datasets have a test set stored with pixel-perfected simulation masks (HERA) or expert hand labeled masks (LOFAR).

The csv file contains the results of all trained models and and has fields for: model class, #filters, #FLOPS, #weights, preprocessing methods, train, validation and test accuracy scores as well as list of (threshold, FPR, TPR) values to generate receiver operating characteristic curves. See https://github.com/CharlDuToit/RFI-NLN to visualize the results, to train new models.

Dataset card for SVHN

The Street View House Numbers (SVHN) dataset is a real-world image dataset developed and designed for machine learning and object recognition algorithms, and is characterized by low data preprocessing and formatting requirements. Similar to MNIST, SVHN contains images of small cropped numbers, but in terms of labeled data, SVHN is an order of magnitude larger than MNIST, comprising over 600,000 digital images. Unlike MNIST, SVHN deals with a much more… See the full description on the dataset page: https://huggingface.co/datasets/Genius-Society/svhn.