Hotel customer dataset with 31 variables describing a total of 83,590 instances (customers). It comprehends three full years of customer behavioral data. In addition to personal and behavioral information, the dataset also contains demographic and geographical information. This dataset contributes to reducing the lack of real-world business data that can be used for educational and research purposes. The dataset can be used in data mining, machine learning, and other analytical field problems in the scope of data science. Due to its unit of analysis, it is a dataset especially suitable for building customer segmentation models, including clustering and RFM (Recency, Frequency, and Monetary value) models, but also be used in classification and regression problems.

The purpose of data mining analysis is always to find patterns of the data using certain kind of techiques such as classification or regression. It is not always feasible to apply classification algorithms directly to dataset. Before doing any work on the data, the data has to be pre-processed and this process normally involves feature selection and dimensionality reduction. We tried to use clustering as a way to reduce the dimension of the data and create new features. Based on our project, after using clustering prior to classification, the performance has not improved much. The reason why it has not improved could be the features we selected to perform clustering are not well suited for it. Because of the nature of the data, classification tasks are going to provide more information to work with in terms of improving knowledge and overall performance metrics. From the dimensionality reduction perspective: It is different from Principle Component Analysis which guarantees finding the best linear transformation that reduces the number of dimensions with a minimum loss of information. Using clusters as a technique of reducing the data dimension will lose a lot of information since clustering techniques are based a metric of 'distance'. At high dimensions euclidean distance loses pretty much all meaning. Therefore using clustering as a "Reducing" dimensionality by mapping data points to cluster numbers is not always good since you may lose almost all the information. From the creating new features perspective: Clustering analysis creates labels based on the patterns of the data, it brings uncertainties into the data. By using clustering prior to classification, the decision on the number of clusters will highly affect the performance of the clustering, then affect the performance of classification. If the part of features we use clustering techniques on is very suited for it, it might increase the overall performance on classification. For example, if the features we use k-means on are numerical and the dimension is small, the overall classification performance may be better. We did not lock in the clustering outputs using a random_state in the effort to see if they were stable. Our assumption was that if the results vary highly from run to run which they definitely did, maybe the data just does not cluster well with the methods selected at all. Basically, the ramification we saw was that our results are not much better than random when applying clustering to the data preprocessing. Finally, it is important to ensure a feedback loop is in place to continuously collect the same data in the same format from which the models were created. This feedback loop can be used to measure the model real world effectiveness and also to continue to revise the models from time to time as things change.

This thesis lays the ground work for enabling scalable data mining in massively parallel dataflow systems, using large datasets. Such datasets have become ubiquitous. We illustrate common fallacies with respect to scalable data mining: It is in no way sufficient to naively implement textbook algorithms on parallel systems; bottlenecks on all layers of the stack prevent the scalability of such naive implementations. We argue that scalability in data mining is a multi-leveled problem and must therefore be approached on the interplay of algorithms, systems, and applications. We therefore discuss a selection of scalability problems on these different levels. We investigate algorithm-specific scalability aspects of collaborative filtering algorithms for computing recommendations, a popular data mining use case with many industry deployments. We show how to efficiently execute the two most common approaches, namely neighborhood methods and latent factor models on MapReduce, and describe a specialized architecture for scaling collaborative filtering to extremely large datasets which we implemented at Twitter. We turn to system-specific scalability aspects, where we improve system performance during the distributed execution of a special class of iterative algorithms by drastically reducing the overhead required for guaranteeing fault tolerance. Therefore we propose a novel optimistic approach to fault-tolerance which exploits the robust convergence properties of a large class of fixpoint algorithms and does not incur measurable overhead in failure-free cases. Finally, we present work on an application-specific scalability aspect of scalable data mining. A common problem when deploying machine learning applications in real-world scenarios is that the prediction quality of ML models heavily depends on hyperparameters that have to be chosen in advance. We propose an algorithmic framework for an important subproblem occuring during hyperparameter search at scale: efficiently generating samples from block-partitioned matrices in a shared-nothing environment. For every selected problem, we show how to execute the resulting computation automatically in a parallel and scalable manner, and evaluate our proposed solution on large datasets with billions of datapoints.

This real-life event log contains events of sepsis cases from a hospital. Sepsis is a life threatening condition typically caused by an infection. One case represents the pathway through the hospital. The events were recorded by the ERP (Enterprise Resource Planning) system of the hospital. There are about 1000 cases with in total 15,000 events that were recorded for 16 different activities. Moreover, 39 data attributes are recorded, e.g., the group responsible for the activity, the results of tests and information from checklists. Events and attribute values have been anonymized. The time stamps of events have been randomized, but the time between events within a trace has not been altered.

BackgroundTivozanib, a vascular endothelial growth factor tyrosine kinase inhibitor, has demonstrated efficacy in a phase III clinical trials for the treatment of renal cell carcinoma. However, comprehensive evaluation of its long-term safety profile in a large sample population remains elusive. The current study assessed Tivozanib-related adverse events of real-world through data mining of the US Food and Drug Administration Adverse Event Reporting System FDA Adverse Event Reporting System.MethodsDisproportionality analyses, utilizing reporting odds ratio proportional reporting ratio Bayesian confidence propagation neural network and multi-item gamma Poisson shrinker (MGPS) algorithms, were conducted to quantify signals of Tivozanib-related AEs. Weibull distribution was used to predict the varying risk incidence of AEs over time.ResultsOut of 5,361,420 reports collected from the FAERS database, 1,366 reports of Tivozanib-associated AEs were identified. A total of 94 significant disproportionality preferred terms (PTs) conforming to the four algorithms simultaneously were retained. The most common AEs included fatigue, diarrhea, nausea, blood pressure increased, decreased appetite, and dysphonia, consistent with prior specifications and clinical trials. Unexpected significant AEs such as dyspnea, constipation, pain in extremity, stomatitis, and palmar-plantar erythrodysaesthesia syndrome was observed. The median onset time of Tivozanib-related AEs was 37 days (interquartile range [IQR] 11.75–91 days), with a majority (n = 127, 46.35%) occurring within the initial month following Tivozanib initiation.ConclusionOur observations align with clinical assertions regarding Tivozanib’s safety profile. Additionally, we unveil potential novel and unexpected AE signatures associated with Tivozanib administration, highlighting the imperative for prospective clinical studies to validate these findings and elucidate their causal relationships. These results furnish valuable evidence to steer future clinical inquiries aimed at elucidating the safety profile of Tivozanib.

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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. 

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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

Real-World Edge Case Mining Platform Market Outlook

According to our latest research, the global Real-World Edge Case Mining Platform market size reached USD 1.47 billion in 2024, with a robust CAGR of 18.9% projected through the forecast period. By 2033, the market is expected to surge to USD 7.50 billion, driven by the increasing need for advanced AI systems to identify and manage rare and complex edge cases in real-world scenarios. This rapid expansion is primarily fueled by the proliferation of autonomous technologies, rising adoption across industries, and the critical demand for improved operational safety and efficiency.

One of the primary growth factors propelling the Real-World Edge Case Mining Platform market is the exponential increase in data generated by connected devices and autonomous systems. As industries such as automotive, manufacturing, and healthcare integrate AI-driven automation, the complexity of operational environments grows. Edge case mining platforms are becoming indispensable for identifying rare and unexpected scenarios that traditional testing and validation processes often overlook. This capability not only enhances safety and reliability but also accelerates the deployment of next-generation autonomous solutions. The integration of advanced analytics and machine learning algorithms enables these platforms to continuously learn from new data, ensuring that AI models remain robust and adaptive in dynamic real-world conditions. As a result, organizations are increasingly investing in edge case mining solutions to mitigate risks, reduce downtime, and gain a competitive edge in their respective markets.

Another significant driver is the growing regulatory scrutiny and industry standards around the deployment of AI and autonomous systems. Governments and industry bodies are imposing stringent requirements to ensure the safety, transparency, and accountability of AI models, particularly in critical sectors like automotive and healthcare. Real-World Edge Case Mining Platforms play a pivotal role in meeting these compliance mandates by providing comprehensive testing, validation, and documentation of edge cases encountered in real-world operations. This not only helps organizations avoid regulatory penalties but also builds trust with end-users and stakeholders. Furthermore, the ability to proactively address potential failure points and edge cases enhances product reliability and brand reputation, creating a virtuous cycle of adoption and innovation.

The market's growth is also fueled by the increasing convergence of cloud computing, edge computing, and AI technologies. The deployment of edge case mining platforms on cloud and hybrid infrastructures enables organizations to scale their operations, process vast volumes of data in real time, and leverage global collaboration. This flexibility is particularly valuable for multinational enterprises and industries with geographically distributed operations. Moreover, advancements in hardware acceleration, such as GPUs and TPUs, are enhancing the performance and efficiency of edge case mining solutions, making them accessible to a broader range of organizations, including small and medium enterprises. As the ecosystem matures, strategic partnerships and investments in R&D are expected to further drive innovation and market growth.

Regionally, North America currently leads the Real-World Edge Case Mining Platform market, accounting for the largest share due to the presence of major technology companies, robust R&D infrastructure, and early adoption of AI and autonomous technologies. However, Asia Pacific is expected to exhibit the fastest growth over the forecast period, supported by rapid industrialization, increasing investments in smart manufacturing, and government initiatives promoting digital transformation. Europe remains a significant market, driven by stringent safety regulations and a strong automotive sector. Meanwhile, Latin America and the Middle East & Africa are emerging as promising markets, benefiting from growing awareness and gradual adoption of AI-driven solutions across various industries.

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Life Sciences Analytics Market Size 2025-2029

The life sciences analytics market size is valued to increase USD 26.37 billion, at a CAGR of 20.6% from 2024 to 2029. Growing integration of big data with healthcare analytics will drive the life sciences analytics market.

Major Market Trends & Insights

Asia dominated the market and accounted for a 37% growth during the forecast period.
By Deployment - Cloud segment was valued at USD 7.18 billion in 2023
By End-user - Pharmaceutical companies segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 277.25 million
Market Future Opportunities: USD 26365.00 million
CAGR from 2024 to 2029 : 20.6%

Market Summary

The market represents a dynamic and continually evolving landscape, driven by the increasing integration of big data with healthcare analytics. This market encompasses core technologies such as machine learning, artificial intelligence, and data mining, which are revolutionizing the way life sciences companies analyze and interpret complex data. Applications of life sciences analytics span various sectors, including drug discovery, clinical research, and population health management. Despite its transformative potential, the high implementation cost of life sciences analytics poses a significant challenge for market growth. However, the growing emphasis on value-based medicine and the increasing regulatory focus on data-driven decision-making present substantial opportunities for market expansion. For instance, according to a recent report, the global market for life sciences analytics is projected to account for over 30% of the total healthcare analytics market by 2025. This underscores the immense potential of this market and the ongoing efforts to harness its power to drive innovation and improve patient outcomes.

What will be the Size of the Life Sciences Analytics Market during the forecast period?

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How is the Life Sciences Analytics Market Segmented ?

The life sciences analytics 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. DeploymentCloudOn-premisesEnd-userPharmaceutical companiesBiotechnology companiesOthersTypeDescriptive analyticsPredictive analyticsPrescriptive analyticsDiagnostic analyticsGeographyNorth AmericaUSCanadaEuropeFranceGermanyItalyUKAPACChinaIndiaJapanSouth KoreaRest of World (ROW)

By Deployment Insights

The cloud segment is estimated to witness significant growth during the forecast period.

In the dynamic and evolving landscape of life sciences analytics, cloud-based solutions have emerged as a game-changer, revolutionizing data management and analysis in the healthcare sector. According to recent reports, the number of biotech and pharmaceutical companies adopting cloud analytics has increased by 18%, enabling real-world evidence synthesis and disease pathway mapping for improved patient care. Furthermore, the integration of genomic data, proteomic data processing, and systems biology approaches has led to a 21% rise in target identification validation and clinical outcome assessment. Data security measures are paramount in this industry, with regulatory compliance software ensuring pharmacovigilance signal detection and biostatistical modeling to maintain the highest standards. Advanced analytics techniques, such as machine learning algorithms and predictive modeling, have driven a 25% surge in drug development informatics and precision medicine insights. Toxicogenomics applications and network biology analysis have also gained significant traction, contributing to a 27% increase in drug metabolism prediction and AI-driven drug discovery. The integration of high-throughput screening data, patient stratification methods, and translational bioinformatics has further enhanced the value of cloud-based life sciences analytics. Pharmacokinetics modeling and biomarker discovery platforms have seen a 29% growth in usage, providing valuable insights for drug repurposing identification and regulatory compliance. The ongoing unfolding of these trends underscores the importance of cloud computing infrastructure, next-generation sequencing, and omics data integration in the life sciences sector.

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The Cloud segment was valued at USD 7.18 billion in 2019 and showed a gradual increase during the forecast period.

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Regional Analysis

Asia is estimated to contribute 37% to the growth of the global market during the forecast period.Technavio’s analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

In response to NASA SBIR topic A1.05, "Data Mining for Integrated Vehicle Health Management", Michigan Aerospace Corporation (MAC) asserts that our unique SPADE (Sparse Processing Applied to Data Exploitation) technology meets a significant fraction of the stated criteria and has functionality that enables it to handle many applications within the aircraft lifecycle. SPADE distills input data into highly quantized features and uses MAC's novel techniques for constructing Ensembles of Decision Trees to develop extremely accurate diagnostic/prognostic models for classification, regression, clustering, anomaly detection and semi-supervised learning tasks. These techniques are currently being employed to do Threat Assessment for satellites in conjunction with researchers at the Air Force Research Lab. Significant advantages to this approach include: 1) completely data driven; 2) training and evaluation are faster than conventional methods; 3) operates effectively on huge datasets (> billion samples X > million features), 4) proven to be as accurate as state-of-the-art techniques in many significant real-world applications. The specific goals for Phase 1 will be to work with domain experts at NASA and with our partners Boeing, SpaceX and GMV Space Systems to delineate a subset of problems that are particularly well-suited to this approach and to determine requirements for deploying algorithms on platforms of opportunity.

📱 Global Mobile Reviews Dataset (2025 Edition)

🌍 Research-Based, Web-Scraped Global Review Collection

This dataset presents a curated collection of over 50,000 mobile phone reviews gathered through web scraping, market analysis, and content aggregation from multiple e-commerce and tech review platforms.
It covers eight countries and includes detailed user opinions, ratings, sentiment polarity, and pricing data across leading smartphone brands.

Each record captures customer experience holistically — spanning demographics, verified purchase details, multi-aspect ratings, and currency-adjusted pricing — making this dataset a powerful asset for research, NLP, and analytics.

🎯 Ideal For

• 🧠 Sentiment Analysis & NLP Modeling
• 💬 Text Classification & Review Mining
• 💰 Market Research & Pricing Analytics
• 📊 Consumer Behavior Studies
• 🤖 AI Model Training & Data Science Projects

🧩 Key Highlights

• 50,000+ mobile reviews scraped from top global sources
• Reviews across 8 major countries and multiple platforms
• Demographic data (customer name, age, location)
• Verified purchase flags for reliability
• Detailed product-level sub-ratings
• Pricing in both USD and local currencies
• Multilingual data support and country-specific sentiment distribution
• Professionally cleaned and normalized for research applications

📦 Brands Covered

🌐 Countries Represented

🧾 Example Record

📈 Research & Analytical Applications

• Sentiment Mining: Detect sentiment polarity in real-world review text
• Cross-Country Analysis: Compare satisfaction trends by region and currency
• Price–Rating Studies: Explore pricing elasticity and value perception
• Demographic Insights: Link sentiment to user age and verified purchase behavior
• Market Comparison: Understand brand trust and perception across regions

🧪 Data Collection & Research Approach

This dataset was compiled through an extensive research process combining web scraping, content aggregation, and analytical validation from multiple open and public review sources including:

• E-commerce platforms (e.g., Amazon, Flipkart, BestBuy, eBay)
• Tech review forums and discussion threads
• Mobile product feedback portals and blogs

Data was then: - Filtered for quality and consistency
- Mapped with real-world pricing and currency exchange rates
- Manually validated for sentiment balance and linguistic variation

⚠️ Note: All data is collected from publicly available review information and anonymized for research and educational use only.
No private or personally identifiable data was used or retained.

🧩 Research Summary

The dataset provides a multi-dimensional representation of the modern mobile ecosystem — integrating global pricing, sentiment trends, and demographic diversity to aid data scientists, researchers, and AI practitioners in building better understanding of customer perspectives.

Daratumumab is widely used in multiple myeloma (MM) and light chain amyloidosis (AL amyloidosis). The purpose of this study was to identify adverse event (AE) signals for daratumumab through the FDA Adverse Event Reporting System (FAERS) database to assess its safety in a large sample of people. Based on data from the FAERS database, three disproportionality analysis methods were used to mine AE signals for daratumumab, including reporting odd ratio (ROR), proportional reporting ratio (PRR), and bayesian configuration promotion neural network (BCPNN). A total of 9220 AE reports with daratumumab as the primary suspect drug were collected, containing 23,946 AEs. Within these reports, 252 preferred terms (PT) levels, 73 high level term (HLT) levels and 11 system organ class (SOC) levels of AE signals were detected, along with some new AEs. Most AEs occurred within the first month after drug administration. Our findings were consistent with the results of established studies that daratumumab has a good safety profile. The newly identified AEs are of concern and prospective clinical studies are needed to confirm whether they are causally related to daratumumab. This study provided an early warning for the safe use of daratumumab and also provided guidance for further safety studies.

Overview

This dataset contains a collection of over 2,000 company documents, categorized into four main types: invoices, inventory reports, purchase orders, and shipping orders. Each document is provided in PDF format, accompanied by a CSV file that includes the text extracted from these documents, their respective labels, and the word count of each document. This dataset is ideal for various natural language processing (NLP) tasks, including text classification, information extraction, and document clustering.

Dataset Content

PDF Documents: The dataset includes 2,677 PDF files, each representing a unique company document. These documents are derived from the Northwind dataset, which is commonly used for demonstrating database functionalities.

The document types are:

• Invoices: Detailed records of transactions between a buyer and a seller.
• Inventory Reports: Records of inventory levels, including items in stock and units sold.
• Purchase Orders: Requests made by a buyer to a seller to purchase products or services.
• Shipping Orders: Instructions for the delivery of goods to specified recipients.

Example Entries

Here are a few example entries from the CSV file:

Shipping Order:

• Order ID: 10718
• Shipping Details: "Ship Name: Königlich Essen, Ship Address: Maubelstr. 90, Ship City: ..."
• Word Count: 120

Invoice:

• Order ID: 10707
• Customer Details: "Customer ID: Arout, Order Date: 2017-10-16, Contact Name: Th..."
• Word Count: 66

Purchase Order:

• Order ID: 10892
• Order Details: "Order Date: 2018-02-17, Customer Name: Catherine Dewey, Products: Product ..."
• Word Count: 26

Applications

This dataset can be used for:

• Text Classification: Train models to classify documents into their respective categories.
• Information Extraction: Extract specific fields and details from the documents.
• Document Clustering: Group similar documents together based on their content.
• OCR and Text Mining: Improve OCR (Optical Character Recognition) models and text mining techniques using real-world data.

Advances in computing power and expansion of the Internet have led to increasing optimism that big data will lead to new insights. However, in the life sciences, relevant data is not only "big"; it is also highly decentralized across thousands of online databases. Wringing value from it depends on the discipline of data science and on the humble bricks and mortar that make it possible -- identifiers. However, our collective handling of identifiers has lagged behind these advances. Diverse identifier problems (for instance broken links and ‘content drift’) make it difficult to integrate data and derive new knowledge from it. This is a snapshot of a living document intended to show real-world examples of identifier problems representative of those encountered by data integrators. It is not meant to be exhaustive.

This is the dataset for the paper "Fast and Accurate Data-Driven Goal Recognition Using Process Mining Techniques." Including a running example, evaluation dataset for synthetic domains, and real-world business logs.

Here are a few use cases for this project:

1. Mining Safety Monitoring: The "MineCarOutTrack" model can be used for ensuring safety by monitoring mining carts. By quickly identifying any abnormal situations or the presence of people on the tracks, it would be able to alert supervisors or control systems to prevent potential accidents.

2. Mining Process Optimization: The model could be used for optimizing mining processes by identifying normal and abnormal carts. Insights on the frequently detected abnormalities could assist in proactive maintenance or modification of the mining transport systems.

3. Human Presence Detection: The model could be used to enforce safety regulations by identifying instances where people are improperly located near or on the tracks and triggering automated warnings or alerts.

4. Autonomous Vehicle Control in Mines: This model could be applied in the development of autonomous mining machines. These machines, equipped with real-time object detection, can navigate through intricate mining tunnels, identify abnormal obstacles, or recognize the presence of people, enabling them to operate safely.

5. Training Simulations: The model could be used to generate data for training simulations, providing real-world examples of normal and abnormal scenarios that might be encountered in mining tunnels. This would be useful in preparing mine workers for various situations.

ObjectiveEfgartigimod alfa, approved for treating generalized myasthenia gravis (gMG) in adult patients who are anti-acetylcholine receptor (AChR) antibody positive, has uncertain long-term safety in large populations This study analyzed adverse events (AEs) linked to efgartigimod alfa using data from the FDA Adverse Event Reporting System (FAERS).MethodsWe collected and analyzed efgartigimod alfa-related reports from the FAERS database from the first quarter of 2022 through the second quarter of 2024. Disproportionality analysis was used in data mining to quantify efgartigimod alfa-related AE signals.ResultsA total of 3,040 reports with efgartigimod alfa as the primary suspect and 12,487 AEs were retrieved from FAERS. The most frequently reported serious outcome was hospitalization (53.22%), and death occurred in 270 cases (8.88%). Disproportionality analysis detected 137 AE signals, with the most common in nervous system disorders (22.69%), general disorders and administration site conditions (16.90%), and infections and infestations (14.05%). Notably, in addition to infection-related AEs identified during clinical trials, this study detected unexpected signals, including inappropriate schedule of product administration (ROR 2.60, PRR 2.53, IC 1.34, EBGM 2.53) and nephrolithiasis (ROR 8.13, PRR 7.99, IC 2.99, EBGM 7.95). The median onset time of AEs was 81.0 days.ConclusionOur study provides a comprehensive assessment of the post-marketing safety of efgartigimod alfa and highlights the need for continued vigilance regarding infection-related adverse events. Additionally, the detection of inappropriate schedules of product administration underscores the importance of enhanced training and pharmacist involvement in medication management. Further research is warranted to explore the potential association between efgartigimod alfa and nephrolithiasis.

🧑‍💼 Employee Performance and Salary Dataset

This synthetic dataset simulates employee information in a medium-sized organization, designed specifically for data preprocessing and exploratory data analysis (EDA) tasks in Data Mining and Machine Learning labs.

It includes over 1,000 employee records with realistic variations in age, gender, department, experience, performance score, and salary — along with missing values, duplicates, and outliers to mimic real-world data quality issues.

📊 Columns Description

🧠 Example Lab Tasks

• Identify and impute missing values using mean or median.
• Detect and remove duplicate employee records.
• Detect outliers in Salary using IQR or Z-score.
• Normalize Salary and Performance_Score using Min-Max scaling.
• Encode categorical columns (Gender, Department) for model training.
• Ideal for Regression

🎯 Possible Regression Targets (Dependent Variables)

Salary → Predict salary based on experience, performance, department, and age. Performance_Score → Predict employee performance based on age, experience, and department.

🧩 Example Regression Problem

Predict the employee's salary based on their experience, performance score, and department.

🧠 Sample Features:

X = ['Age', 'Experience_Years', 'Performance_Score', 'Department', 'Gender'] y = ['Salary']

You can apply:

• Linear Regression
• Ridge/Lasso Regression
• Random Forest Regressor
• XGBoost Regressor
• SVR (Support Vector Regression)
• and evaluate with metrics like:

R², MAE, MSE, RMSE, and residual plots.

Continuous Road Edge Case Mining Market Outlook

According to our latest research, the global Continuous Road Edge Case Mining market size reached USD 1.16 billion in 2024, driven by the accelerating adoption of advanced analytics and artificial intelligence in automotive and transportation sectors. The market is expected to grow at a robust CAGR of 17.8% during the forecast period, reaching an estimated USD 5.18 billion by 2033. This significant growth is underpinned by the rising demand for enhanced road safety, the proliferation of autonomous vehicles, and the increasing integration of real-time data analytics in traffic management systems.

One of the primary growth factors for the Continuous Road Edge Case Mining market is the rapid advancement in autonomous vehicle technologies. As automotive OEMs and technology companies race to develop fully autonomous vehicles, the need for comprehensive edge case mining solutions becomes paramount. Edge cases—rare or unusual scenarios encountered on the road—pose significant challenges for the safe deployment of autonomous vehicles. Continuous road edge case mining leverages machine learning and big data analytics to identify, catalog, and address these scenarios, ensuring that vehicles can safely navigate even the most unpredictable conditions. This not only enhances the safety and reliability of autonomous vehicles but also accelerates their path to commercial deployment.

Another critical driver is the increasing emphasis on road safety and regulatory compliance. Governments and transportation agencies worldwide are mandating stricter safety standards for both autonomous and human-driven vehicles. Continuous road edge case mining enables organizations to proactively detect potential hazards and anomalies in real-world driving environments, facilitating timely interventions and policy adjustments. By systematically analyzing vast amounts of driving data, these solutions help stakeholders reduce accident rates, improve traffic flow, and ensure compliance with evolving safety regulations. The growing collaboration between public agencies and private sector innovators is further fueling the adoption of these technologies.

The proliferation of connected infrastructure and the rise of smart cities are also propelling the growth of the Continuous Road Edge Case Mining market. With the deployment of IoT sensors, high-definition cameras, and connected traffic management systems, unprecedented volumes of real-time data are being generated. Continuous edge case mining systems can harness this data to provide actionable insights for urban planners, traffic authorities, and automotive manufacturers. The integration of these solutions into smart city initiatives is enabling more efficient traffic management, reducing congestion, and enhancing overall urban mobility. This trend is particularly pronounced in regions with significant investments in digital infrastructure, such as North America, Europe, and Asia Pacific.

From a regional perspective, North America currently leads the global market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The region’s dominance is attributed to the early adoption of autonomous vehicle technologies, a robust ecosystem of technology providers, and supportive regulatory frameworks. Meanwhile, Asia Pacific is emerging as the fastest-growing market, driven by rapid urbanization, increasing investments in smart transportation, and the presence of leading automotive manufacturers. Europe continues to make significant strides, propelled by stringent safety regulations and a strong focus on innovation in mobility solutions.

Component Analysis

The Component segment of the Continuous Road Edge Case Mining market is broadly categorized into Software, Hardware, and Services. Each component plays a vital role in the overall ecosystem, contributing to the efficiency and effectiveness of edge case mining solutions. Software solutions form the backbone of the market, encompassing advanced analytics platforms, machine learning algorithms, and data visualization tools. These software solutions enable the automated identification and classification of edge cases from vast datasets, facilitating continuous improvement in vehicle safety and performance. The demand for customizable and scalable software platforms is on the rise, as organizations seek to tailor solutions to their specific operational needs.

Hardwar

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AI Consulting Market Size 2025-2029

The AI consulting market size is valued to increase by USD 38.16 billion, at a CAGR of 28.8% from 2024 to 2029. Proliferation of generative AI as strategic imperative will drive the AI consulting market.

Market Insights

North America dominated the market and accounted for a 36% growth during the 2025-2029.
By Service Type - IT consulting segment was valued at USD 411.00 billion in 2023
By End-user - BFSI segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 5.00 million 
Market Future Opportunities 2024: USD 38157.50 million
CAGR from 2024 to 2029 : 28.8%

Market Summary

The market is experiencing significant growth as businesses increasingly recognize the strategic value of generative AI in optimizing operations, enhancing decision-making, and driving innovation. This trend is driven by the proliferation of AI technologies and the rise of vertical-specific solutions and domain-specific models that cater to industry-specific needs. One real-world business scenario illustrating the benefits of AI consulting is supply chain optimization. A global manufacturing company sought to improve its supply chain efficiency and reduce costs. By implementing AI-powered predictive analytics, the company was able to forecast demand accurately, optimize inventory levels, and streamline logistics operations. This resulted in a significant reduction in lead times, improved customer satisfaction, and increased operational efficiency.
AI consulting firms play a crucial role in helping businesses navigate the complex landscape of AI technologies and applications. They provide expert guidance on AI strategy, implementation, and management, ensuring that clients achieve measurable returns on investment and manage client expectations effectively. As AI continues to transform industries and businesses, the demand for AI consulting services is expected to remain strong.

What will be the size of the AI Consulting Market during the forecast period?

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The market continues to evolve, driven by the increasing adoption of advanced technologies such as machine learning, deep learning, and natural language processing. One significant trend is the integration of AI in business operations, particularly in areas like compliance and budgeting. For instance, companies have reported a 25% increase in compliance efficiency through AI-powered solutions. These technologies enable automated monitoring and analysis of vast amounts of data, reducing errors and ensuring regulatory compliance. Moreover, AI's predictive capabilities can help organizations optimize their budgets by forecasting future trends and resource requirements. These advancements underscore the strategic importance of AI in today's business landscape.
In the realm of AI, various techniques are employed, including image recognition algorithms, statistical modeling, transfer learning approaches, and knowledge graph technology. Computer vision systems, neural network architectures, time series forecasting, online learning algorithms, and feature engineering techniques are some of the essential components of AI applications. Furthermore, AI consulting firms provide expertise in implementing these technologies, ensuring optimal performance and integration with existing systems. API integration services, incremental learning methods, chatbot development kits, supervised and unsupervised learning models, algorithm accuracy metrics, and generative adversarial networks are all integral parts of the market. 

Unpacking the AI Consulting Market Landscape

In today's business landscape, Artificial Intelligence (AI) consulting has emerged as a critical driver of competitive advantage. Two-thirds of companies report that AI has increased operational efficiency by 10-20%, while 40% have seen a revenue uplift of over 5%. AI consulting services encompass a range of applications, including sales forecasting models, customer behavior prediction, and natural language processing. Ethical AI considerations are paramount, with 75% of organizations aligning their AI strategies with compliance regulations. Businesses leverage AI for cost reduction strategies through process optimization tools, resource allocation models, and project management software. Predictive analytics models and data mining techniques enable revenue generation models and customer segmentation. Model explainability techniques ensure transparency, while AI-driven decision support informs strategic planning. Bias detection methods and cybersecurity protocols maintain data privacy regulations, and performance monitoring metrics track operational efficiency gains. Supply chain optimization and risk assessment algorithms enhance business continuity, while deep learning algorithms and machine lear

Road Edge Case Mining from Fleet Data Market Outlook

According to our latest research, the global market size for Road Edge Case Mining from Fleet Data reached USD 1.12 billion in 2024, reflecting a robust adoption curve across automotive and mobility sectors. The market is experiencing a strong growth trajectory, with a compound annual growth rate (CAGR) of 18.7% projected from 2025 to 2033. By the end of 2033, the market is expected to attain a value of USD 5.48 billion. This impressive expansion is driven by the increasing integration of advanced analytics, AI-driven insights, and the rising need for road safety and operational efficiency in fleet operations globally.

The primary growth factor propelling the Road Edge Case Mining from Fleet Data market is the exponential increase in the deployment of connected vehicles and autonomous driving technologies. As fleets become more digitized, the volume of data generated by onboard sensors, telematics, and video analytics is surging, providing an unprecedented opportunity to mine for rare or critical road edge cases. These edge cases, which represent atypical or challenging driving scenarios, are vital for improving the robustness of autonomous driving algorithms and enhancing safety protocols. Automotive OEMs and technology providers are investing heavily in sophisticated software platforms that can efficiently process and analyze this data, ensuring that their autonomous systems are well-prepared to handle real-world complexities.

Another significant growth driver is the rising regulatory and societal emphasis on road safety and accident prevention. Governments and transportation agencies worldwide are mandating stricter safety standards and encouraging the adoption of advanced driver-assistance systems (ADAS). Edge case mining from fleet data enables stakeholders to uncover and address previously unidentified risk factors, contributing to the design of safer vehicles and more resilient infrastructure. The ability to analyze diverse data sources, such as GPS data and video analytics, empowers organizations to proactively mitigate hazards, enhance incident response, and optimize traffic management strategies, further fueling market growth.

The market is also benefiting from the increasing collaboration between automotive manufacturers, fleet operators, and research institutions. These partnerships are fostering innovation in data analytics, machine learning, and cloud computing, resulting in more scalable and efficient edge case mining solutions. The integration of hardware and software components, coupled with the availability of managed services, is making it easier for organizations to deploy and scale these solutions across large fleets. This collaborative ecosystem is accelerating the adoption of edge case mining technologies, enabling stakeholders to derive actionable insights from vast and varied datasets, and ultimately driving the market forward.

From a regional perspective, North America currently leads the Road Edge Case Mining from Fleet Data market, owing to its advanced automotive ecosystem, high penetration of connected vehicles, and strong regulatory support for road safety initiatives. Europe follows closely, with significant investment in smart mobility and autonomous vehicle research. The Asia Pacific region is emerging as a high-growth market, driven by rapid urbanization, expanding fleet operations, and increasing government focus on intelligent transportation systems. Latin America and the Middle East & Africa are also witnessing steady growth, albeit at a slower pace, as infrastructure and technology adoption continue to improve. This global landscape underscores the universal relevance and transformative potential of road edge case mining technologies.

Component Analysis

The Component segment of the Road Edge Case Mining from Fleet Data market is divided into software, hardware, and services, each playing a critical role in the overall ecosystem. Software solutions constitute the backbone of edge case mining, enabling the ingestion, processing, and analysis of vast datasets collected from various sources. These platforms leverage advanced algorithms, machine learning, and AI to identify rare or challenging driving scenarios, facilitating continuous improvement of autonomous driving systems and fleet safety protocols. The demand for customizable, scalable, and interoperable software is particularly high among au