The dataset consists of feature vectors belonging to 12,330 sessions. The dataset was formed so that each session would belong to a different user in a 1-year period to avoid any tendency to a specific campaign, special day, user profile, or period. Of the 12,330 sessions in the dataset, 84.5% (10,422) were negative class samples that did not end with shopping, and the rest (1908) were positive class samples ending with shopping.The dataset consists of 10 numerical and 8 categorical attributes. The 'Revenue' attribute can be used as the class label.The dataset contains 18 columns, each representing specific attributes of online shopping behavior:Administrative and Administrative_Duration: Number of pages visited and time spent on administrative pages.Informational and Informational_Duration: Number of pages visited and time spent on informational pages.ProductRelated and ProductRelated_Duration: Number of pages visited and time spent on product-related pages.BounceRates and ExitRates: Metrics indicating user behavior during the session.PageValues: Value of the page based on e-commerce metrics.SpecialDay: Likelihood of shopping based on special days.Month: Month of the session.OperatingSystems, Browser, Region, TrafficType: Technical and geographical attributes.VisitorType: Categorizes users as returning, new, or others.Weekend: Indicates if the session occurred on a weekend.Revenue: Target variable indicating whether a transaction was completed (True or False).The original dataset has been picked up from the UCI Machine Learning Repository, the link to which is as follows :https://archive.ics.uci.edu/dataset/468/online+shoppers+purchasing+intention+datasetAdditional Variable InformationThe dataset consists of 10 numerical and 8 categorical attributes. The 'Revenue' attribute can be used as the class label. "Administrative", "Administrative Duration", "Informational", "Informational Duration", "Product Related" and "Product Related Duration" represent the number of different types of pages visited by the visitor in that session and total time spent in each of these page categories. The values of these features are derived from the URL information of the pages visited by the user and updated in real time when a user takes an action, e.g. moving from one page to another. The "Bounce Rate", "Exit Rate" and "Page Value" features represent the metrics measured by "Google Analytics" for each page in the e-commerce site. The value of "Bounce Rate" feature for a web page refers to the percentage of visitors who enter the site from that page and then leave ("bounce") without triggering any other requests to the analytics server during that session. The value of "Exit Rate" feature for a specific web page is calculated as for all pageviews to the page, the percentage that were the last in the session. The "Page Value" feature represents the average value for a web page that a user visited before completing an e-commerce transaction. The "Special Day" feature indicates the closeness of the site visiting time to a specific special day (e.g. Mother’s Day, Valentine's Day) in which the sessions are more likely to be finalized with transaction. The value of this attribute is determined by considering the dynamics of e-commerce such as the duration between the order date and delivery date. For example, for Valentina’s day, this value takes a nonzero value between February 2 and February 12, zero before and after this date unless it is close to another special day, and its maximum value of 1 on February 8. The dataset also includes operating system, browser, region, traffic type, visitor type as returning or new visitor, a Boolean value indicating whether the date of the visit is weekend, and month of the year.

Signals (y=1) events from the first one million events in the HIGGS machine learning dataset at: http://archive.ics.uci.edu/ml/datasets/HIGGS The data has been produced using Monte Carlo simulations. The first 21 features (columns 2-22) are kinematic properties measured by the particle detectors in the accelerator. The last seven features are functions of the first 21 features; these are high-level features derived by physicists to help discriminate between the two classes. There is an interest in using deep learning methods to obviate the need for physicists to manually develop such features. Benchmark results using Bayesian Decision Trees from a standard physics package and 5-layer neural networks are presented in a paper here: http://rdcu.be/cb58. In the Nature paper the full dataset was used, not just hte first 1million which were uploaded here.

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What is the Motor Driver ICs Market Size?

The motor driver ICs market size is forecast to increase by USD 1.81 billion and is estimated to grow at a CAGR of 6.6% between 2024 and 2029. The market is experiencing significant growth due to several key factors. The increasing adoption of electric vehicles (EVs) is driving market demand, as motor driver ICs are essential components in EV powertrains. Vendors are introducing new products to cater to this trend, leading to innovation and competition in the market. They are also instrumental in the design and development of intelligent systems, enabling features such as machine learning, deep learning, and remote control. Regulatory compliance and safety are also major growth factors, as stricter regulations are being implemented to ensure the safety and efficiency of motor driver ICs in various applications. These trends are expected to continue shaping the market dynamics in the coming years.

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

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Market Segmentation

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

Type

  AC motor drive IC
  DC motor drive IC
  Stepper motor drive IC
  Others


End-user

  Automotive
  Industrial automation
  Consumer electronics
  Healthcare
  Others


Geography

  APAC

    China
    India
    Japan
    South Korea


  North America

    Canada
    US


  Europe

    Germany
    UK
    France


  Middle East and Africa



  South America

    Brazil

Which is the largest segment driving market growth?

The AC motor drive IC segment is estimated to witness significant growth during the forecast period. AC motor drive Integrated Circuits (ICs) are essential components in managing the speed, direction, and torque of alternating current motors for diverse applications. These ICs are offered in single-phase and three-phase versions, catering to extensive industrial and consumer needs. By transforming fixed-frequency AC power into variable-frequency outputs, AC motor drive ICs ensure precise motor control, leading to enhanced overall performance.

Get a glance at the market share of various regions. Download the PDF Sample

The AC motor drive IC segment was the largest segment and was valued at USD 1.57 billion in 2019. The growing adoption of these ICs is fueled by their energy efficiency, cost-effectiveness, and compatibility with Industry 4.0 technologies. Single-phase AC motor drive ICs predominantly serve low-power applications, such as fans, pumps, and household appliances, due to their compact size, affordability, and ease of implementation. These advantages make them a popular choice for consumer electronics. Hence, such factors are fuelling the growth of this segment during the forecast period.

Which region is leading the market?

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APAC is estimated to contribute 50% 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. The Asia-Pacific (APAC) region is a significant contributor to the global market due to its thriving automotive and electronics industries in countries like China, India, and Japan. China, in particular, leads the world in automotive production, with an anticipated output of over 30 million units by 2025. This massive production capacity fuels the demand for motor driver ICs, which are essential for various automotive applications, including electric vehicles (EVs), advanced driver-assistance systems (ADAS), and power steering systems. The APAC region's electronics manufacturing sector also plays a crucial role in the market, as motor driver ICs are integral to robotics and other industrial applications.

How do company ranking index and market positioning come to your aid?

Companies are implementing various strategies, such as strategic alliances, partnerships, mergers and acquisitions, geographical expansion, and product/service launches, to enhance their presence in the market.

Allegro MicroSystems Inc: This company offers motor driver ICs for brushless DC, brush DC, and stepper motors.

Technavio provides the ranking index for the top 20 companies along with insights on the market positioning of:

AMETEK Inc.
Analog Devices Inc.
Delta Electronics Inc.
Diodes Inc.
Infineon Technologies AG
Microchip Technology Inc.
Mitsubishi Electric Corp.
Monolithic Power Systems Inc.
NXP Semiconductors NV
ON Semiconductor Corp.
Panasonic Holdings Corp.
Power Integrations Inc.
Qualcomm Inc.
Renesas Electronics Corp.
ROHM Co. Ltd.
STMicroelectronics NV
Texas Instruments Inc.

This dataset consists of imagery, imagery footprints, associated ice seal detections and homography files associated with the KAMERA Test Flights conducted in 2019. This dataset was subset to include relevant data for detection algorithm development. This dataset is limited to data collected during flights 4, 5, 6 and 7 from our 2019 surveys.

The Industrial Control Systems (ICS) Security market is experiencing robust growth, projected to reach $25.40 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 8.16% from 2025 to 2033. This expansion is driven by the increasing digitalization of industrial operations, escalating cyber threats targeting critical infrastructure, and stringent government regulations mandating enhanced cybersecurity measures across various sectors. Key drivers include the rising adoption of cloud-based security solutions, the growing prevalence of Internet of Things (IoT) devices within industrial environments, and the increasing need for robust network security to protect against sophisticated cyberattacks like ransomware and data breaches. The market is segmented by security type (network and cloud security) and end-user industry (oil and gas, power, chemicals and mining, automotive, and others). North America currently holds a significant market share due to early adoption of advanced technologies and robust cybersecurity infrastructure. However, Asia-Pacific, particularly China and Japan, is witnessing rapid growth fueled by significant industrial expansion and increasing government investment in cybersecurity initiatives. Europe also plays a significant role, driven by robust regulatory frameworks and a mature industrial sector. The competitive landscape is highly fragmented, with numerous established players like ABB, Cisco, and Honeywell competing alongside emerging cybersecurity specialists. Strategic alliances, acquisitions, and technological innovations are defining the competitive dynamics. The continued growth of the ICS security market hinges on the sustained adoption of advanced technologies like artificial intelligence (AI) and machine learning (ML) for threat detection and response. Furthermore, effective strategies to address the skills gap in cybersecurity professionals and the need for robust incident response capabilities are critical factors. Despite robust growth, challenges remain, including the complexity of ICS environments, legacy systems that are difficult to secure, and the potential for supply chain vulnerabilities. The market will see an increasing focus on proactive security measures, risk-based approaches, and integrated security solutions that address the unique requirements of various industrial sectors. The long-term outlook for the ICS security market remains positive, driven by ongoing digital transformation initiatives within critical infrastructure sectors globally.

The Ultra Large-Scale ICs (ULSI) market is experiencing robust growth, projected to reach a market size of $97.5 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 11.09% from 2025 to 2033. This expansion is driven primarily by the increasing demand for high-performance computing across diverse sectors. The proliferation of smartphones, advanced driver-assistance systems (ADAS) in automobiles, and the rise of data centers fueled by cloud computing and AI are key factors propelling this growth. Technological advancements in miniaturization, leading to denser and more powerful chips, further contribute to market expansion. The segment breakdown reveals significant contributions from both thick-film and thin-film ICs, with consumer electronics and automotive applications currently dominating. However, the healthcare and data center sectors are poised for substantial growth in the coming years, driven by the increasing adoption of sophisticated medical devices and the expanding capacity of cloud infrastructure. Competition within the ULSI market is fierce, with established players like Intel, Samsung, and Qualcomm vying for market share alongside emerging innovators. Regional analysis suggests a significant presence in North America and Asia, with Europe and other regions exhibiting strong growth potential. The forecast period (2025-2033) presents considerable opportunities for growth within the ULSI market, particularly within specialized application segments. Strategic partnerships, acquisitions, and technological innovations will be crucial for companies to maintain competitiveness. The market's steady growth trajectory is expected to continue, fuelled by ongoing technological progress and the sustained demand for powerful, energy-efficient computing solutions across various industries. The increasing adoption of 5G technology and the expanding Internet of Things (IoT) ecosystem further contribute to the positive outlook for the ULSI market. However, potential challenges include the high cost of research and development, the complexities of advanced manufacturing processes, and the potential for supply chain disruptions. Recent developments include: September 2024: The National Science Foundation granted approximately USD 600 thousand to Zhuo Feng, a professor in the Department of Electrical and Computer Engineering at the Stevens Institute of Technology. The project aims to simplify the modeling, design, and verification of intricate computer chips. This endeavor is crucial, as simulating a sizable circuit block can span days or weeks. Moreover, optimizing and verifying the complete chip may necessitate hundreds or even thousands of such simulations.March 2024: Cerebras Systems, a leading force in the generative AI landscape, intensified its focus on its fastest AI chip by unveiling the Wafer-Scale Engine 3 (WSE-3). The WSE-3 is meticulously designed to train the industry's most expansive AI models with double the performance of its predecessor. Built on a 5nm architecture and housing 4 trillion transistors, the WSE-3 fuels the Cerebras CS-3 AI supercomputer, achieving a remarkable 125 petaflops of peak AI performance via its 900,000 AI-optimized compute cores.. Key drivers for this market are: Advancements in AI and Machine Learning, Growth in 5G and IoT Technologies. Potential restraints include: Advancements in AI and Machine Learning, Growth in 5G and IoT Technologies. Notable trends are: The Telecommunication Segment is Expected to Witness an Increase in Demand.

Consisting of six multi-label datasets from the UCI Machine Learning repository.

Each dataset contains missing values which have been artificially added at the following rates: 5, 10, 15, 20, 25, and
30%. The “amputation” was performed using the “Missing Completely at Random” mechanism.

File names are represented as follows:

amp_DB_MR.arff

where:

DB = original dataset;

MR = missing rate.

For more details, please read:

IEEE Access article (in review process)

The global disk array controller ICs market is anticipated to reach a valuation of USD 388.3 million by 2033, expanding at a CAGR of 4.2% from 2025 to 2033. The increasing demand for high-performance and reliable storage solutions for data centers and cloud computing infrastructures is driving market growth. Additionally, the adoption of solid-state drives (SSDs) and the proliferation of edge computing are expected to fuel demand for disk array controller ICs. North America is projected to account for a significant market share due to the presence of leading data center operators and cloud service providers. Additionally, the increasing demand for AI and machine learning applications in this region is expected to drive the adoption of disk array controller ICs. The Asia Pacific region is also expected to witness significant growth, driven by the rapid expansion of data centers and the increasing uptake of cloud services in emerging economies such as China and India.

In the digital era of the Industrial Internet of Things (IIoT), the conventional Critical Infrastructures (CIs) are transformed into smart environments with multiple benefits, such as pervasive control, self-monitoring and self-healing. However, this evolution is characterised by several cyberthreats due to the necessary presence of insecure technologies. DNP3 is an industrial communication protocol which is widely adopted in the CIs of the US. In particular, DNP3 allows the remote communication between Industrial Control Systems (ICS) and Supervisory Control and Data Acquisition (SCADA). It can support various topologies, such as Master-Slave, Multi-Drop, Hierarchical and Multiple-Server. Initially, the architectural model of DNP3 consists of three layers: (a) Application Layer, (b) Transport Layer and (c) Data Link Layer. However, DNP3 can be now incorporated into the Transmission Control Protocol/Internet Protocol (TCP/IP) stack as an application-layer protocol. However, similarly to other industrial protocols (e.g., Modbus and IEC 60870-5-104), DNP3 is characterised by severe security issues since it does not include any authentication or authorisation mechanisms. This dataset contains labelled Transmission Control Protocol (TCP) / Internet Protocol (IP) network flow statistics (Common-Separated Values - CSV format) and DNP3 flow statistics (CSV format) related to 9 DNP3 cyberattacks. These cyberattacks are focused on DNP3 unauthorised commands and Denial of Service (DoS). The network traffic data are provided through Packet Capture (PCAP) files. Consequently, this dataset can be used to implement Artificial Intelligence (AI)-powered Intrusion Detection and Prevention (IDPS) systems that rely on Machine Learning (ML) and Deep Learning (DL) techniques

The Analog IC market is projected to grow from $72.73 billion in 2025 to $173.38 billion by 2033, exhibiting a CAGR of 7.78% during the forecast period. The market is anticipated to witness significant growth due to the increasing demand for analog ICs across various end-use industries, such as consumer electronics, automotive, and industrial. The growing popularity of IoT devices, electric vehicles, and smart cities is expected to further drive the market's growth. The rising adoption of advanced technologies, such as machine learning and artificial intelligence, is also expected to drive the market. These technologies rely heavily on analog ICs for signal conditioning, power management, and interfacing with real-world sensors and actuators. Furthermore, the increasing focus on energy efficiency and power reduction is expected to boost the demand for low-power and high-performance analog ICs. The market is expected to be dominated by Asia-Pacific, followed by North America and Europe. The presence of a large number of manufacturing companies and the growing demand for analog ICs from emerging economies are expected to drive the growth in these regions. The global Analog IC market is projected to reach USD 142.8 billion by 2032, exhibiting a CAGR of 7.78% during the forecast period (2023-2032). The increasing demand for analog ICs in automotive, consumer electronics, industrial, and healthcare applications drives this growth. Recent developments include: The Analog IC Market is projected to grow from an estimated USD 72.73 billion in 2023 to USD 142.8 billion by 2032, exhibiting a CAGR of 7.78% during the forecast period. This growth is attributed to increasing demand for analog ICs in various end-use industries, such as automotive, consumer electronics, industrial, and healthcare. The automotive industry is a major driver of growth, as analog ICs are used in a wide range of automotive applications, including engine control, power management, and safety systems. The consumer electronics segment is also expected to contribute significantly to market growth, driven by the increasing demand for smartphones, tablets, and other portable devices.Recent developments in the Analog IC Market include the growing adoption of advanced technologies such as artificial intelligence (AI) and machine learning (ML). These technologies are enabling the development of more sophisticated and efficient analog ICs that can meet the demands of emerging applications. Additionally, the increasing use of analog ICs in power management and energy-efficient systems is driving market growth.. Key drivers for this market are: Increased demand for cloud computing growth in automotive electronics expanding deployment in smartphones and wearables surge in Internet of Things IoT adoptions. Potential restraints include: Increasing demand for analog ICs in automotive and industrial applications Growing adoption of analog ICs in consumer electronics Rise of IoT and connected devices Technological advancements in analog IC design Increasing focus on energy efficiency.

The global Memory IC market size was valued at USD 674.52 million in 2025 and is projected to grow at a CAGR of 6.11% from 2025 to 2033, reaching USD 1080.6 million by 2033. The growth of the market is attributed to the increasing demand for memory ICs in various applications, such as computer systems, mobile devices, consumer electronics, industrial automation, and healthcare. The rising adoption of cloud computing, artificial intelligence (AI), and machine learning (ML) is also driving the demand for memory ICs. Key drivers for the growth of the Memory IC market include the increasing demand for faster and more efficient memory solutions, the proliferation of connected devices, and the growing popularity of cloud computing and data analytics. Additionally, the rising adoption of advanced technologies such as 5G and the Internet of Things (IoT) is expected to fuel the demand for memory ICs in the coming years. The market is also witnessing the emergence of new trends, such as the adoption of emerging memory technologies like MRAM and ReRAM, which are expected to offer higher performance and lower power consumption compared to traditional memory ICs. Key drivers for this market are: 5G network development Cloud and data center expansion Growing demand for mobile devices Artificial intelligence and machine learning applications Automotive electronics advancements. Potential restraints include: Increasing demand for AI 5G cloud computing penetration rising consumer electronics sales.

Snapshot img

Mixed-signal Integrated Circuits (IC) Market Size 2024-2028

The Mixed-signal Integrated Circuits (IC) Market size is estimated to grow by USD 43.69 billion at a CAGR of 6.96% between 2023 and 2028. The growth trajectory of the market is influenced by several key factors. Firstly, the perpetual evolution of both digital and analogue technologies propels innovation, fostering the development of advanced solutions. Secondly, there's a rising demand for energy-efficient and high-performance mixed-signal ICs, driven by the need for optimized performance across various industries. Lastly, the surge in demand for consumer electronics applications serves as a significant driver, highlighting the importance of ICs in powering modern devices. These factors collectively shape the market landscape, driving research, development, and adoption of cutting-edge technologies to meet evolving consumer needs and industry requirements.

What will be the size of the Market During the Forecast Period?

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Market Dynamics and Customer Landscape

The market witnessing significant growth driven by advancements in analog and digital circuits, especially with the proliferation of 5G technology. Key segments such as Mixed-signal SoC and microcontrollers are experiencing heightened demand, particularly in telecommunication and automotive systems. These ICs enable the seamless integration of digital signals with analog signals, facilitating the operation of various electronic devices across different sectors. The emergence of Mixed Signal System-on-Chip (SoC) further enhances efficiency and functionality, catering to the evolving needs of telecommunications and automotive industries. With a focus on data converters and microcontroller segments, the market continues to innovate, delivering solutions that optimize performance and reliability in mixed-signal applications across diverse electronic systems.

Key Market Driver

The continuous evolution of digital and analog technologies is the key factor driving market growth. The ongoing improvement of digital and analog technologies, which permit for the integration of numerous functions into a single chip, is driving the growth of the mixed-signal integrated circuits (IC) market. Mixed-signal ICs are currently necessary for modern electronics due to the increasing demands for functionality and efficiency.

Furthermore, the demand for mixed signal ICs is further highlighted by the growth of 5G networks and IoT devices, which is propelling innovation and growth in the market. Thus, the continuous evolution of digital and analog technologies is expected to present significant opportunities to the companies of mixed signal ICs and thereby drive the growth of the market during the forecast period.

Significant Market Trends

The integration of artificial intelligence (AI) and machine learning into mixed-signal ICs is one of the major market trends. In the market, the integration of AI and machine learning is a significant and developing trend. These technologies are being included in ICs more and more to facilitate decision-making, pattern recognition, and real-time data processing. Furthermore, mixed signal ICs with AI in the healthcare industry can provide predictive diagnostics, interpret medical imaging, and monitor vital signs.

In addition, AI is being used by mixed signal ICs in IoT devices to minimize power consumption, prolong battery life, and enhance network efficiency. The expansion of AI and machine learning applications, together with their integration into mixed signal ICs, will stimulate innovation, which in turn will drive the growth of the market during the forecast period.

Major Market Challenge

The design complexity in developing mixed signal ICs is a challenge that affects market growth. Producing mixed signal IC with high precision and low power consumption can be difficult to achieve precision and low power consumption at the same time since precision frequently necessitates ongoing processing and monitoring, which can be energy-intensive. It can be difficult to maintain low power consumption without sacrificing precision in medical devices, such as blood glucose monitors or ECG sensors, where exact results are crucial.

Furthermore, the necessity for creative solutions is further highlighted by the need for ICs across a range of industries, which puts semiconductor businesses globally in a competitive market. Owing to such factors, the market is expected to face production challenges, which may impede the growth of the global market during the forecast period.

Customer Landscape

The market growth analysis report includes the adoption lifecycle of the market, covering from the innovator’s stage to the laggard’s stage. It focuses on adoption rates in different regions based on penetration. Furthermore, the market growth and fore

Context

This database contains 76 attributes, but all published experiments refer to using a subset of 14 of them. In particular, the Cleveland database is the only one that has been used by Machine Learning researchers to this date. The "goal" field refers to the presence of heart disease in the patient. It is integer valued from 0 (no presence) to 4. Experiments with the Cleveland database have concentrated on simply attempting to distinguish presence (values 1,2,3,4) from absence (value 0).

Source: https://archive.ics.uci.edu/ml/datasets/heart+disease

This dataset contains data describing the relationship between global climate modes of variability and regional Arctic sea ice concentration anomalies lagged by 2-20 years. Data originates from global climate models from the Coupled Climate Model Intercomparison Project - Phase 6 (CMIP6), which are freely available from the Earth System Grid Federation (ESGF). This data from CMIP6 focuses on the historical simulation period of 1920-2014, but also leverages pre-industrial control simulations. Climate mode of variability data is obtained using the Climate Variability Diagnostics Package datasets (doi:10.1002/2014EO490002). Observational Arctic sea ice concentration data are obtained from the Hadley Centre Global Sea Ice and Sea Surface Temperature (HadISST1), doi:10.1029/2002JD002670. Firstly, this dataset firstly provides coefficients for the linear model relating standardized climate modes of variability with regional Arctic sea ice concentration (SIC) anomalies for 42 individual large ensembles and multi-model large ensembles, and is recorded in the data file "Linear_model_weights.nc". These linearly modeled relationships (found via machine learning) are able to be compared against a different method of linking these phenomena using instances of extreme SIC anomalies correlated with standardized climate modes of variability, as is recorded in the file "Binary_correlations_SIC_anomalies.nc". The performance of the linear model (and three other neural network configurations) is evaluated by correlating the predicted SIC anomalies using climate mode of variability inputs into the linear model against the known SIC anomalies and is recorded in the files "ML_validation_correlation_coefficients.nc" for the separated 15% validation data, "LE_test_ensemble_member_correlations.nc" for the remaining 10% of the data, and "MMLE_3_test_ensemble_member_correlations.nc" for all remaining unseen large ensemble members. The correlations using the linear model can be compared with the correlation obtained from SIC anomaly persistence, as a measure of the linear model's skill, as recorded in the file "Persistence_Pearson_correlations.nc". Finally, the skillful linear model can be used to make predictions into the future based on observed climate modes of variability, as recorded in the file "Linear_model_predictions_using_observations.nc". The historical performance of the linear model predicting SIC anomalies, based solely on observed climate modes of variability, is evaluated by correlating the observed SIC anomalies with those predicted by the linear model, as recorded in the file "Linear_model_prediction_vs_observations_correlations.nc". This dataset was created to record the influence of large-scale climate modes of variability on regional Arctic sea ice concentration anomalies and is used in the article C. Wyburn-Powell & Jahn A. (2024), Large-Scale Climate Modes Drive Low-Frequency Regional Arctic Sea Ice Variability, Journal of Climate, https://doi.org/10.1175/JCLI-D-23-0326.1. This work was conducted at the University of Colorado Boulder from 2022-2024. The figures from the Journal of Climate article can be reproduced from the datasets provided here. The code used to create the datasets is archived with Zenodo and can be located at https://doi.org/10.5281/zenodo.12580233

The IC OEM Programming Service market size was valued at USD 1.58 billion in 2025 and is projected to reach USD 2.92 billion by 2033, exhibiting a CAGR of 6.9% during the forecast period. The growing adoption of electronics in various industries, including consumer electronics, automotive, and communications, is driving the market's growth. The demand for IC OEM Programming Services is attributed to the increasing complexity of electronic devices, rising disposable income, and the growing popularity of connected devices and the Internet of Things (IoT). Moreover, the trend toward miniaturization and integration of electronic devices is also contributing to the market's growth. The adoption of advanced technologies such as artificial intelligence (AI) and machine learning (ML) is expected to further drive market growth. However, factors such as the high cost of programming equipment and the availability of open-source programming tools may restrain market growth to some extent.

Context

Egyptian patients who underwent treatment dosages for HCV about 18 months. Discretization should be applied based on expert recommendations; there is an attached file shows how.

About

This data was obtained from UCI Machine Learning Repository Citation: Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.

This dataset includes model inputs (specifically, weather and flags for predicted ice-cover) and is part of a larger data release of lake temperature model inputs and outputs for 68 lakes in the U.S. states of Minnesota and Wisconsin (http://dx.doi.org/10.5066/P9AQPIVD).

This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

The traffic data are collected with the loop sensor installed on ramp for the 101 North freeway in Los Angeles. This location is close to Dodgers Stadium; therefore the traffic is affected by volume of visitors to the stadium. Missing values are represented with NaN. - Class 1: Normal Day - Class 2: Game Day There is nothing to infer from the order of examples in the train and test set. Missing values are represented with NaN in the text file. Data created by Ihler, Alexander, Jon Hutchins, and Padhraic Smyth (see [1][2][3]). Data edited by Chin-Chia Michael Yeh.

[1] Ihler, Alexander, Jon Hutchins, and Padhraic Smyth. "Adaptive event detection with time-varying poisson processes." Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2006.

[2] “UCI Machine Learning Repository: Dodgers Loop Sensor Data Set.” UCI Machine Learning Repository, archive.ics.uci.edu/ml/datasets/dodgers+loop+sensor.

[3] “Caltrans PeMS.” Caltrans, pems.dot.ca.gov/.

Donator: C. Yeh

BackgroundThe etiology of interstitial cystitis/painful bladder syndrome (IC/BPS) remains elusive, presenting significant challenges in both diagnosis and treatment. To address these challenges, we employed a comprehensive approach aimed at identifying diagnostic biomarkers that could facilitate the assessment of immune status in individuals with IC/BPS.MethodsTranscriptome data from IC/BPS patients were sourced from the Gene Expression Omnibus (GEO) database. We identified differentially expressed genes (DEGs) crucial for gene set enrichment analysis. Key genes within the module were revealed using weighted gene co-expression network analysis (WGCNA). Hub genes in IC/BPS patients were identified through the application of three distinct machine-learning algorithms. Additionally, the inflammatory status and immune landscape of IC/BPS patients were evaluated using the ssGSEA algorithm. The expression and biological functions of key genes in IC/BPS were further validated through in vitro experiments.ResultsA total of 87 DEGs were identified, comprising 43 up-regulated and 44 down-regulated genes. The integration of predictions from the three machine-learning algorithms highlighted three pivotal genes: PLAC8 (AUC: 0.887), S100A8 (AUC: 0.818), and PPBP (AUC: 0.871). Analysis of IC/BPS tissue samples confirmed elevated PLAC8 expression and the presence of immune cell markers in the validation cohorts. Moreover, PLAC8 overexpression was found to promote the proliferation of urothelial cells without affecting their migratory ability by inhibiting the Akt/mTOR/PI3K signaling pathway.ConclusionsOur study identifies potential diagnostic candidate genes and reveals the complex immune landscape associated with IC/BPS. Among them, PLAC8 is a promising diagnostic biomarker that modulates the immune response in patients with IC/BPS, which provides new insights into the future diagnosis of IC/BPS.

This dataset refers to the ICELEARNING project - Detection of ice core particles via deep neural networks, by Maffezzoli N. et al., The Cryosphere, 10.5194/tc-17-539-2023, 2023.

The main folder contains all TRAINING data.

The TEST data are contained in the folder /test.

Please refer to the icelearning GitHub repository for instructions.