his dataset is designed for research and analysis of load balancing in distributed systems. It includes key features such as task size, CPU and memory demand, network latency, I/O operations, disk usage, number of connections, and priority level, along with a target variable for classification or optimization. Timestamp data is also provided for temporal analysis. It is suitable for machine learning, simulation studies, and performance optimization research.

Code smells can compromise software quality in the long term by inducing technical debt.For this reason, in the last decade many approaches aimed at identifying these design flaws have been proposed.Most of them are based on heuristics in which a set of metrics (e.g., code metrics, process metrics) is used to detect smelly code components.However, these techniques suffer of subjective interpretation, low agreement between detectors, and threshold dependability.To overcome the limitations, previous work applied Machine Learning techniques that can learn from previous datasets without needing any threshold definition.However, more recent work has shown that Machine Learning is not always suitable for code smell detection due to the highly unbalanced nature of the problem.In this study we investigate several approaches able to mitigate data unbalancing issues to understand their impact on ML-based code smells detection algorithms.Our findings highlight a number of limitations and open issues with respect to the usage of data balancing for ML-based code smell detection.

Data Balance Optimization AI Market Outlook

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

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

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

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

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

Component Analysis

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

The balance sheets show the Government's assets, liabilities, and net position. When combined with stewardship information, this information presents a more comprehensive understanding of the Government's financial position. The net position for earmarked funds is shown separately.

Price of energy activated to maintain a balance in Elia’s control area to ensure adequacy in the Elia control area. The prices per quart-hour are indicated for every product category (if the product was actually used). Only regulation-related measures requested by Elia with a view to offsetting imbalances in the control area are included.Contains historical data since April 2018 and is refreshed daily.This dataset contains data until 21/05/2024 (before MARI local go-live).

Since the late 1950s, the USGS has maintained a long-term glacier mass-balance program at three North American glaciers. Measurements began on South Cascade Glacier, WA in 1958, expanding to Gulkana and Wolverine glaciers, AK in 1966, and later Sperry Glacier, MT in 2005. Additional measurements have been made on Lemon Creek Glacier, AK to compliment data collected by the Juneau Icefield Research Program (JIRP; Pelto and others, 2013). Direct field measurements of point glaciological data are combined with weather and geodetic data to estimate the seasonal and annual mass balance at each glacier in both a conventional and reference surface format (Cogley and others, 2011). The analysis framework (O'Neel, 2019; prior to v 3.0 van Beusekom and others, 2010) is identical at each glacier to enable cross-comparison between output time series. Vocabulary used follows Cogley and others (2011) Glossary of Glacier Mass Balance.

This layer shows radiation balance in Armenia. Radiation balance of underlying terrain calculated by equation: R=(Q+q)(1-Ao)-E, where R – radiation balance value; Q and q – direct and diffused radiation; Ao - underlying terrain albedo; E – effective-terrestrial radiation. Refer to the Features section in the metadata for copyright information.

This dataset contains water balance data for each year when soybean [Glycine max (L.) Merr.] was grown at the USDA-ARS Conservation and Production Laboratory (CPRL), Soil and Water Management Research Unit (SWMRU), Bushland, Texas (Lat. 35.186714°, Long. -102.094189°, elevation 1170 m above MSL). Soybean [Glycine max (L.) Merr.] was grown on two large, precision weighing lysimeters, each in the center of a 4.44 ha square field in 1995, 2003, 2004 and 2010. Soybean was grown on four large, precision weighing lysimeters and their surrounding 4.4-ha fields in 2019. Irrigation in 1995, 2003, 2004, and 2010 was by linear move sprinkler system. Irrigation in 2019 was by subsurface drip irrigation (SDI) system on the northeast (NE) and southeast (SE) weighing lysimeters an fields, while irrigation was by linear move sprinkler system on the northwest (NW) and southwest (SW) lysimeters and fields. Full irrigations were managed to replenish soil water used by the crop on a weekly or more frequent basis as determined by soil profile water content readings made with a neutron probe to 2.4-m depth in the field. Deficit irrigations were less than full - see crop calendars and irrigation data in these files for details. The weighing lysimeters were used to measure relative soil water storage to 0.05 mm accuracy at 5-minute intervals, and the 5-minute change in soil water storage was used along with precipitation and irrigation amounts to calculate crop evapotranspiration (ET), which is reported at 15-minute intervals. Because the large (3 m by 3 m surface area) weighing lysimeters are better rain gages than are tipping bucket gages, the 15-minute precipitation data are derived for each lysimeter from changes in lysimeter mass. The land slope is <0.3% and flat. The water balance data consist of 15-minute and daily amounts of evapotranspiration (ET), dew/frost fall, precipitation (rain/snow), irrigation, scale counterweight adjustment, and emptying of drainage tanks, all in mm. The values are the result of a rigorous quality control process involving algorithms for detecting dew/frost accumulations, and precipitation (rain and snow). Changes in lysimeter mass due to emptying of drainage tanks, counterweight adjustment, maintenance activity, and harvest are accounted for such that ET values are minimally affected. The ET data should be considered to be the best values offered in these datasets. Even though ET data are also presented in the "lysimeter" datasets, the values herein are the result of a more rigorous quality control process. Dew and frost accumulation varies from year to year and seasonally within a year, and it is affected by lysimeter surface condition [bare soil, tillage condition, residue amount and orientation (flat or standing), etc.]. Particularly during winter and depending on humidity and cloud cover, dew and frost accumulation sometimes accounts for an appreciable percentage of total daily ET. These datasets originate from research aimed at determining crop water use (ET), crop coefficients for use in ET-based irrigation scheduling based on a reference ET, crop growth, yield, harvest index, and crop water productivity as affected by irrigation method, timing, amount (full or some degree of deficit), agronomic practices, cultivar, and weather. Prior publications have focused on crop ET, crop coefficients, and crop water productivity. Crop coefficients have been used by ET networks. The data have utility for testing simulation models of crop ET, growth, and yield. See the README for descriptions of each data file. Resources in this dataset:Resource Title: 1995 Bushland, TX, West Soybean Evapotranspiration, Irrigation, and Water Balance Data. File Name: 1995_W_Soybean_water_balance.xlsxResource Title: 2003 Bushland, TX, East Soybean Evapotranspiration, Irrigation, and Water Balance Data. File Name: 2003_E_Soybean_water_balance.xlsxResource Title: 2004 Bushland, TX, East Soybean Evapotranspiration, Irrigation, and Water Balance Data. File Name: 2004_E_Soybean_water_balance.xlsxResource Title: 2010 Bushland, TX, West Soybean Evapotranspiration, Irrigation, and Water Balance Data. File Name: 2010_W_Soybean_water_balance.xlsxResource Title: 2019 Bushland, TX, East Soybean Evapotranspiration, Irrigation, and Water Balance Data. File Name: 2019_E_Soybean_water_balance.xlsxResource Title: 2019 Bushland, TX, West Soybean Evapotranspiration, Irrigation, and Water Balance Data. File Name: 2019_W_Soybean_water_balance.xlsxResource Title: README. File Name: README_Soybean_Water_Balance.txt

Map of the eight balancing authority areas in California:

A balancing authority is responsible for operating a transmission control area. It matches generation with load and maintains consistent electric frequency of the grid, even during extreme weather conditions or natural disasters. The California Independent System Operator (CAISO) is the largest of about 38 balancing authorities in the western interconnection coordinating council (WECC), handling an estimated 35 percent of the electric load in the west. CAISO manages 80 percent of the load in California and a small portion of Nevada.The main balancing authorities in California are:CAISO - California Independent System OperatorBANC - Balancing Authority of Northern CaliforniaLADWP - Los Angeles Department of Water & PowerIID - Imperial Irrigation DistrictTID - Turlock Irrigation DistrictWALC - Western Area Power Administration, Lower ColoradoPacifiCorp WestNV Energy

We collect the data from the electricity balancing authorities (BAs) that operate the grid. A balancing authority ensures, in real time, that power system demand and supply are finely balanced. This balance is needed to maintain the safe and reliable operation of the power system.

We developed an improved approach to the parameterization of the Operational Simplified Surface Energy Balance (SSEBop) model using the Forcing and Normalizing Operation (FANO). The FANO parameterization was implemented on two computing platforms using Landsat and gridded meteorological datasets: 1) Google Earth Engine (GEE) and 2) Earth Resources Observation and Science (EROS) Center Science Processing Architecture (ESPA). FANO brought substantial improvements in model accuracy and operational implementation.

This dataset contains Balance Sheets data from IDA?s published financial statements. It was compiled from data in our systems as well as by extracting the data from the published Financial Statements documents. The dataset goes as far back as the foundation of the association (1961). This data has been verified and validated for publication, but does not, in any capacity, replace the official published Financial Statements. Please note that this dataset includes certain rows that are calculated totals, summing up values from related individual records. These are included for completeness and ease of analysis. An archive for IDA?s annual Financial Statements is available at www.worldbank.org/financialresults

United States BOP: Current Account: Balance data was reported at -295.874 USD bn in Dec 2024. This records an increase from the previous number of -334.444 USD bn for Sep 2024. United States BOP: Current Account: Balance data is updated quarterly, averaging -29.574 USD bn from Mar 1960 (Median) to Dec 2024, with 260 observations. The data reached an all-time high of 17.741 USD bn in Mar 1991 and a record low of -334.444 USD bn in Sep 2024. United States BOP: Current Account: Balance data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s United States – Table US.JB001: BPM6: Balance of Payments.

Evaluation results of different models using various data balancing method trees.

Graph and download economic data for Trade Balance: Goods and Services, Balance of Payments Basis (BOPGSTB) from Jan 1992 to Aug 2025 about BOP, balance, headline figure, trade, goods, services, and USA.

We establish topological and parametric conditions under which phase angles across three identical impedances can be balanced with small-signal stability guarantees when served from three single-phase sources executing active-power frequency droop control. All standard topologies involving Delta and Wye interconnections of sources and loads are examined.

Abro Balancing Inc Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Concept: Balance on goods accounts transactions on items that are result of productive activities. Goods are physical, produced items over which ownership rights can be established and whose economic ownership can be passed from one institutional unit to another by engaging in transactions. The Balance on goods is divided in exports and imports. The exports registers the selling of goods from residents to nonresidents and the imports registers the purchases of goods by residents from nonresidents. The trade balance is compiled by the Ministry of Development, Industry and Foreign Trade (MDIC) based on custom records of the Integrated Foreign Trade System (Siscomex), and are adjusted by the Central Bank with the goal of an ampler coverage. Information published by the MDIC are therefore incorporated to electric energy purchases that are not registered on Siscomex. Additionally, exports and imports are adjusted by the inclusion of operations in which the product is traded with a nonresident but doesn’t cross the border of the original country. Lastly, the trade balance incorporates international postal delivery registration. Source: Central Bank of Brazil – Department of Economics 1293ca53-b3a0-46ee-bc63-f73ac31da4ac 22709-imports---balance-of-payments---monthly

United States Trade Balance: Goods data was reported at -151.629 USD bn in Mar 2025. This records a decrease from the previous number of -120.619 USD bn for Feb 2025. United States Trade Balance: Goods data is updated monthly, averaging -52.418 USD bn from Jan 1989 (Median) to Mar 2025, with 435 observations. The data reached an all-time high of -1.890 USD bn in Mar 1991 and a record low of -152.364 USD bn in Jan 2025. United States Trade Balance: Goods data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s United States – Table US.JA005: Trade Balance: Census Basis.