International Research Award on Computer Vision: June 2025

Sunday, June 29, 2025

How AI Spots Estrus in Dairy Cows—Fast! #Sciencefather #researchawards

Efficient reproduction in dairy cows is crucial for the economic viability of dairy farms. Estrus detection is a key component of dairy cow reproductive management, and traditional estrus detection methods primarily rely on human visual observation, which is time-consuming, labor-intensive, and has low accuracy and efficiency. With the development of computer technology, sensor technology, and artificial intelligence, automatic estrus detection technology for dairy cows has received increasing attention.

Early studies mainly focused on using a single sensor or detection method. Xu et al. (1998) compared a radio telemetry system (HeatWatch) and visual observation combined with tail painting for detecting estrus in grazing dairy cows, finding that the efficiency and accuracy of visual observation were 98.4% and 97.6%, respectively, while the efficiency and accuracy of the HeatWatch system were 91.7% and 100%, respectively. Rae et al. (1999) evaluated the effect of visual observation and a pressure-sensitive detection device on estrus detection in beef cattle, finding that the pregnancy rate within 25 days was 60.5% in the identified cows in the visual observation group, which was higher than the 45.8% in the pressure-sensitive detection device group. Roelofs et al. (2005) explored the feasibility of using pedometer readings as an indicator for estrus detection and ovulation time prediction in dairy cows, and achieved estrus detection efficiencies ranging from 51% to 87%. Peralta et al. (2005) compared the performance of the HeatWatch device, an ALPRO activity sensor, and visual observation three times daily for estrus detection in hot summer conditions, with the highest estrus detection efficiency of 80.2% achieved when the three systems were used in combination. Palmer et al. (2010) found that, compared to indoor housing, the efficiency of all three detection methods (visual observation, tail painting, and HeatWatch) was higher under grazing conditions, but there was no difference in accuracy. Løvendahl and Chagunda (2010) constructed an algorithm for detecting and describing behavioral estrus in dairy cows using hourly recorded activity data and exponential smoothing deviations.

As research progressed, multi-sensor data fusion and machine learning methods began to be applied to estrus detection in dairy cows. Mayo et al. (2019) evaluated the effect of using multiple commercial precision dairy cow monitoring technologies in combination for estrus detection, finding that they could achieve at least the same detection effect as visual observation, with four technologies having a detection efficiency 15% to 35% higher than visual observation. Fricke et al. (2014) analyzed data from 2,661 artificial inseminations and determined that the optimal insemination time for dairy cows using a radio telemetry system for estrus detection was within 4–12 h after the first standing activity. Aungier et al. (2012) used a neck-mounted activity monitor to explore the influence of cow-related factors on the relationship between activity and estrous behavior, and improved the accuracy of estrus detection to 87.5% by adjusting the activity duration threshold. Chanvallon et al. (2014) compared the performance of pedometers and two activity monitors, finding that the sensitivity of pedometers was higher than that of the activity monitors, but the latter had a higher positive predictive value. Rutten et al. (2014) demonstrated through modeling analysis that investing in activity monitors for automatic estrus detection was economically feasible.

International Conference on Computer Vision

The International Research Awards on Computer Vision recognize groundbreaking contributions in the field of computer vision, honoring researchers, scientists and innovators whose work has significantly advanced the domain. This prestigious award highlights excellence in fundamental theories, novel algorithms and real-world applications, fostering progress in artificial intelligence, image processing and deep learning.

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Thursday, June 26, 2025

How Avatar Detection Works in the Metaverse! 🚀 #Sciencefather #researchawards

The metaverse, a growing digital trend since 2022, offers immersive 3D environments where users, represented by avatars, can interact socially and economically. It has gained popularity due to global shifts like the COVID pandemic and climate change, which emphasized virtual collaboration. Major platforms like Second Life, Decentraland, Roblox, Fortnite, and Meta Horizon Worlds have shown how metaverse spaces are becoming more mainstream, with increasing user engagement and corporate investment.

A key part of the metaverse experience is the avatar—digital representations of users that interact within virtual worlds. These avatars can be recorded in metaverse recordings (MVRs), producing multimedia content like images or videos. MVRs have several applications, including VR training, experience sharing, and industrial simulations. However, to make use of these recordings effectively, especially in multimedia information retrieval (MMIR), there is a need to detect and classify avatars within the content.

This leads to the introduction of Avatar Detection, a specialized object detection task focused on identifying avatars in images and videos. While some platforms could provide metadata (Scene Raw Data) during live use, such data is often unavailable in recordings. Accurate avatar detection helps in organizing and retrieving content from large datasets, enabling semantic search, interaction analysis, and even identity recognition. As avatars reflect user actions and interactions, their detection becomes crucial for improving searchability and understanding content in the metaverse.

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How AI is Revolutionizing Ocean Life Analysis! 🌊🤖 #Sciencefather #researchawards #deeplearning

Over the past decade, the use of Remotely Operated Vehicles (ROVs) in marine research has grown significantly due to advances in computational power and robotics. These tools now allow marine biologists to gather high-quality underwater video footage for analyzing marine life. However, challenges such as low visibility, light scattering, and colour distortion hinder accurate object detection and classification in these environments. As a result, researchers have turned to computer vision methods—particularly deep learning models like YOLO—for real-time detection of underwater objects such as fish and corals.

While YOLO-based models have shown strong performance in underwater fish detection, current research remains limited in scope. Most existing datasets and models, including FishNet, FishInTurbidWater, and FishDETECT, are fish-centric and do not account for the broader ecological diversity, particularly marine vegetation. There is a noticeable lack of well-defined datasets and ontologies for identifying and classifying underwater plants, which are essential for comprehensive marine ecosystem monitoring. Efforts like CoralNet and CATNet's MSID dataset have broadened species categories, yet marine vegetation remains underrepresented.

To bridge this gap, we introduce FjordVision, a hierarchical deep learning framework designed for detecting and classifying both marine vegetation and fauna in Esefjorden, Norway. FjordVision includes the Esefjorden Marine Vegetation Segmentation Dataset (EMVSD), featuring over 17,000 annotated images with more than 30,000 labelled marine objects. Leveraging YOLOv8 for instance segmentation and enhanced with a taxonomically structured classification model, FjordVision improves on traditional flat classification by categorizing objects into binary, class, genus, and species levels. This approach delivers more ecologically relevant insights, making FjordVision a vital tool for biodiversity monitoring and marine conservation.

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Wednesday, June 25, 2025

How AI Tracks Fish in 3D Underwater! 🐟 #Sciencefather #researchawards #artificialintelligence

Indoor recirculating aquaculture systems (RAS) are advanced setups designed to improve aquaculture productivity by integrating components such as water circulation, filtration, oxygen supply, and microbial filters. These systems support high-density fish farming while maintaining water quality. Given their complexity, automated monitoring technologies like target detection and tracking are essential for observing fish behavior. Behavior such as reduced swimming or surface gathering can indicate stress, illness, or environmental issues like low dissolved oxygen, highlighting the need for continuous monitoring.

Among monitoring techniques, 3D target tracking stands out for its ability to accurately capture fish movements and behavior in three-dimensional space. This enables more detailed behavioral metrics such as swimming speed, spatial distribution, and depth. While 2D tracking is limited by the lack of depth data and is commonly used for animals on flat surfaces, 3D tracking is more suitable for fish that swim freely in all directions. Of the available 3D tracking systems, underwater parallel stereo vision offers the most promise for aquaculture due to its cost-effectiveness, single imaging medium, and accurate depth perception without the complications of air-water refraction.

To address the limitations of current 3D tracking methods—such as high computational costs and accuracy loss in noisy underwater environments—a two-stage 3D multi-fish tracking (TMT) model has been proposed. In the first stage, it uses YOLOv8x and DeepSORT to extract fish patches from stereo images. In the second stage, it applies patch-based stereo matching, improved Semi-global Matching (SGM), and point cloud filtering to calculate 3D positions. By focusing only on fish-containing patches, the TMT model improves tracking accuracy, reduces computational load, and streamlines the 3D fish behavior monitoring process in RAS environments.

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Monday, June 23, 2025

Counting Rice Grains with AI: Fast & Accurate! #Sciencefather #researchawards #artificialintelligence

Rice (Oryza sativa) is a key global staple, accounting for around 25% of total grain production with about 800 million tons harvested yearly. As cultivated land declines, it's essential to develop high-yielding rice varieties. One critical factor in determining yield is the number of grains per panicle. Traditionally, measuring this involves labor-intensive steps like manual threshing and counting, which are time-consuming and inefficient. Moreover, due to grain occlusion—where grains overlap each other—existing image-based methods struggle to maintain both speed and accuracy in grain counting.

Advancements in deep learning have shown great promise for automating crop analysis. Object detection algorithms like Faster R-CNN and YOLO have been successfully applied to count seeds and grains in crops like wheat and rice. For example, researchers achieved over 99% accuracy in counting threshed rice grains by combining feature pyramid networks with convolutional neural networks. However, these methods often depend on manual threshing, which is not ideal for large-scale or real-time applications. Detecting grains in their natural form—still attached and possibly overlapping—remains a major challenge.

Direct counting of rice grains in their natural form is difficult due to dense distribution, overlapping grains, and differences in shape and color across varieties. Current approaches that rely on deep learning sometimes require threshing or image preprocessing to overcome occlusion. To improve accuracy and reduce labor, researchers have begun integrating multiple deep learning models—such as object detection, image classification, and segmentation networks. For instance, combining classification models to first identify panicle morphology before detection has shown promise in enhancing accuracy. There is an urgent need for a method that quickly and accurately counts rice grains in natural conditions with minimal manual effort.

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Sunday, June 22, 2025

How Deep Transfer Learning is Revolutionizing Online Civil Dispute Consultations! #Sciencefather #researchawards #deeplearning

The rapid increase in civil disputes and the limited capacity of legal systems have challenged the effectiveness of traditional dispute resolution methods. Online Dispute Resolution (ODR) platforms—such as China’s Internet Court, British Columbia’s Civil Resolution Tribunal, and the UK’s Online Court—have emerged as promising solutions. A core component of these platforms is the Classification of Online Consultation (COC), which helps route civil legal questions to the appropriate departments. However, manual classification is inefficient and error-prone, especially as civil disputes become more diverse and complex.

COC tasks rely heavily on text classification, but several issues hinder accurate results. Many platforms lack sufficient and balanced training data, while the short, colloquial, and vague nature of users’ input makes it difficult for traditional machine learning models to perform well. Additionally, the use of Chinese text introduces further complexity due to limited labeled data and grammatical challenges. These factors collectively result in poor classification accuracy and hinder the effectiveness of civil dispute resolution services online.

To address these challenges, the study introduces a deep transfer learning-based classification method called CMDTL (Cross-platform Mapping with Deep Transfer Learning). By transferring knowledge from richer data sources and applying advanced techniques like joint distribution adaptation and improved marginal Fisher analysis, this method significantly improves accuracy despite limited and unbalanced data. It also uses ontology modeling to clarify legal concepts, ensuring a more accurate understanding of the user’s legal queries. This approach ultimately aims to enhance the efficiency and precision of online civil dispute consultations.

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Saturday, June 21, 2025

Deep Learning Magic: Modeling Threshold Curves #Sciencefather #researchawards #deeplearning

Neurons are specialized cells responsible for transmitting electrical signals throughout the body, enabling communication between the brain, muscles, and other tissues. This signal transmission is possible due to their excitability — the ability to generate short-lived electrical impulses in response to external stimuli. Interestingly, the concept of excitability is not unique to neurons; it applies broadly to systems like cardiac tissue, calcium signaling in cells, and even predator–prey dynamics. These systems, known as excitable media, are typically modeled using nonlinear reaction–diffusion equations, which describe how activity spreads and interacts within a medium.

A key feature of excitable media is the existence of a threshold — a stimulus must surpass a certain critical value to trigger sustained wave propagation. This study focuses on a one-component bistable reaction–diffusion system described by the Zeldovich–Frank–Kamenetsky (ZFK) or Nagumo equation. By setting a rectangular initial stimulus and applying no-flux boundary conditions, we investigate whether the system’s response decays or leads to a propagating wavefront. The outcome depends on both the spatial extent and amplitude of the stimulus, and we aim to map the critical strength-extent curve that separates these two regimes.

Solving nonlinear partial differential equations in excitable systems is challenging, especially under complex conditions. Traditional methods like spectral collocation or meshfree schemes have provided numerical solutions, but recent advances in scientific machine learning, such as Physics-Informed Neural Networks (PINNs), offer a new paradigm. PINNs embed physical laws into the learning process, enabling accurate, data-efficient modeling of complex systems. In this work, we apply PINNs and transfer learning techniques to predict the strength-extent curve, improving computational efficiency and allowing precise identification of critical thresholds in excitable media dynamics.

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Friday, June 20, 2025

Laser Ultrasonic Detection: Next-Gen LAM Defect Finder! #Sciencefather #researchawards

Laser Additive Manufacturing (LAM) is an advanced technique that uses high-energy lasers to build complex metal parts layer by layer with high precision, efficiency, and minimal material waste. It has wide applications in industries such as aerospace, medical, and automotive. However, the LAM process faces challenges due to non-equilibrium thermodynamics, which often cause metallurgical defects like cracks and pores. If not detected during printing, these flaws can grow and affect the final part's quality and structural integrity, limiting the broader adoption of LAM.

To ensure quality and reliability, several online nondestructive testing (NDT) methods are used, including X-ray computed tomography, infrared thermography, optical photography, structured light imaging, and ultrasonic detection. Among them, laser ultrasonic testing stands out due to its non-contact, high-temperature resistance, and ability to generate multiple wave modes in one pulse, which helps identify both surface and internal defects. Recent studies have shown the potential of laser ultrasonics in real-time monitoring of mechanical properties and defect detection during LAM processes.

Despite advancements, challenges such as surface roughness and environmental noise reduce the clarity of ultrasonic signals. Post-processing methods like SAFT and TFM improve resolution but are time-consuming and require heavy data storage. To address these issues, this study introduces a novel ultrasonic imaging method—Variable Time Window Intensity Mapping (VTWIM) with adaptive 2σ thresholds. This approach adapts to changing noise levels and enables rapid, accurate detection of submillimeter defects in real time, demonstrating significant promise for improving LAM quality control.

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Wednesday, June 18, 2025

AR & VR: The Future of Learning #Sciencefather #researchawards #augmentedreality #virtualreality

Augmented Reality (AR) and Virtual Reality (VR) are transforming the way education is delivered by offering immersive and interactive learning experiences. These technologies go beyond traditional lectures and textbooks by allowing students to engage deeply with complex topics through virtual simulations, 3D models, and digital overlays. They also empower educators to customize content to suit individual learning styles, enhancing both comprehension and retention. Virtual field trips, science experiments, and historical recreations are now possible without physical boundaries, enriching the overall learning experience.

While AR and VR are often mentioned together, they serve different functions. AR overlays digital content onto the real-world environment, enhancing what we see and interact with, whereas VR creates a fully digital environment that immerses users through the use of headsets or glasses. These technologies have found practical applications in sectors like education, healthcare, manufacturing, and retail. In education, they are especially valuable for simulating real-life scenarios, allowing students to gain practical skills and professional experience in a safe and controlled digital space.

Recent studies show a significant rise in research on AR and VR in education over the past twelve years. This trend highlights their growing importance and effectiveness in online, mobile, and hybrid learning environments. Research has explored both the benefits—such as increased student engagement and interactive content—and the challenges, including cost and accessibility. By analyzing past developments and identifying gaps in the literature, ongoing research aims to guide future innovations and expand the use of AR and VR in modern educational systems.

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How AR Makes Geometry Fun for Kids! 🚀📐 #Sciencefather #researchawards #augmentedreality

Learning geometry is essential for children’s cognitive development of spatial abilities. Clements and Battista noted in their study that geometry offers an effective way to interpret and reflect on the physical environment, while also serving as a tool for learning other mathematical or scientific concepts. Learning geometry is recognized as an effective activity for improving visual–spatial cognition, which profoundly impacts the development of crucial abilities and skills in many STEM fields

In elementary geometry instruction, the lower and middle grades primarily focus on plane geometry, emphasizing the understanding and manipulation of geometric shapes. In the upper grades, the focus shifts to solid geometry, with prisms and pyramids as the primary content, which focuses on calculating and reasoning about geometric quantities through the segmentation and manipulation of shapes, aiming to develop visual–spatial cognitive skills by observing solid geometric forms. However, the process of transforming a two-dimensional view into a three-dimensional form through imagination may be challenging for students at this stage, leading to misconceptions when they struggle to effectively and correctly connect different representations.

Although traditional teaching materials and tools, such as geometric blocks, aim to help students overcome challenges with form conversion, they are often inconvenient to carry and store. Moreover, these teaching tools can be difficult to visualize effectively and often require extensive time for assembly and preparation before being used in educational activities. The additional tasks not only increase students’ cognitive load but also reduce the available time for instructional activities within the curriculum.

Advancements in augmented reality (AR) technology are revolutionizing the way geometry is taught and learned, offering new pathways for interactive and immersive education. AR overlays virtual elements on the real world, allowing students to manipulate and examine geometric shapes in three-dimensional (3D) space. This hands-on experience can enhance spatial understanding and reduce the cognitive load associated with abstract visualization, which has traditionally been a barrier in geometry education. A study by Atit et al. [4] found that spatial skills and motivation interact to significantly predict students’ mathematics performance, suggesting that AR can bridge the gap between theoretical and practical understanding of geometric concepts.

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Monday, June 16, 2025

How Optical Microwaves Measure Gas-Liquid Flow #Sciencefather #researchawards #computervision

Multiphase flow refers to the simultaneous movement of two or more distinct phases, such as gas, liquid, or solid, with clearly defined interfaces. Among them, gas-liquid two-phase flow is one of the most common and significant types, widely present in industries like power generation, petroleum, chemical processing, refrigeration, and aerospace. Applications include steam-water flow in turbines, gas-liquid mixing in heat exchangers, and oil-gas transportation in pipelines. To ensure safe and efficient operation, accurate real-time measurement of gas-liquid flow rates is essential. Traditional methods such as the separation method, dual parameter method, and cross-correlation technique have been used for this purpose. While these methods provide some level of accuracy, they often suffer from drawbacks like complex equipment, sensitivity to flow patterns, limited measurement range, and inability to provide real-time results.

To address these limitations, the Optical Carrier Microwave Interferometry (OCMI) technique has emerged as a promising alternative. OCMI combines the advantages of optics and microwaves, offering features like distributed sensing, high positioning accuracy, and insensitivity to optical polarization. It has been successfully used for measuring physical parameters like temperature, strain, pressure, and vibration. However, conventional demodulation methods used with OCMI—such as dip frequency tracking and phase demodulation—are typically limited to detecting one parameter at a time and may struggle with low sensitivity or resolution, especially when applied to complex systems like gas-liquid two-phase flow. These limitations highlight the need for an improved approach that can handle the simultaneous measurement of multiple flow parameters under dynamic industrial conditions.

In response, this study proposes a new sensing method that integrates OCMI technology with machine learning, specifically artificial neural networks (ANN). By learning from data patterns, ANN models can simultaneously predict gas and liquid flow rates, overcoming the limitations of traditional OCMI demodulation. This approach not only enhances measurement accuracy and adaptability to different flow patterns but also provides a foundation for real-time monitoring in complex flow environments. The effectiveness of different input schemes to the neural network is also investigated, demonstrating the potential of combining advanced sensing techniques with intelligent data processing to revolutionize multiphase flow measurement in industrial applications.

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Sunday, June 15, 2025

Machine Learning vs. AI #Sciencefather #researchawards

Machine learning differ in AI

The terms "artificial intelligence" and "machine learning" are often used interchangeably, but one is more specific than the other.

Artificial intelligence (AI) is the broader of the two terms. It originated in the 1950s and can be used to describe any application or machine that mimics human intelligence. This includes both simple programs, such as a virtual checkers player, and sophisticated machines, such as self-driving cars. Some in the field distinguish between AI tools that exist today and general artificial intelligence—thinking, autonomous agents—that do not yet exist.

Machine learning describes a subset of artificial intelligence. This term arose in the 1970s. Machine learning is distinguished by a machine or program that is fed and trained on existing data and then is able to find patterns, make predictions, or perform tasks when it encounters data it has never seen before.

Machine learning can be thought of as the process of converting data and experience into new knowledge, usually in the form of a mathematical model. Once it is created, this model can then be used to perform other tasks. This allows for the design of applications that would be too complex or time consuming to develop without computer assistance. For example, a machine learning system may be trained on millions of examples of labeled tumors in MRI images. On the basis of these examples, the system recognizes patterns of characteristics that constitute a tumor. This serves as a model that can then determine if tumors are present in new MRI images. These systems are often able to outperform experts.

Machine learning is a powerful tool that increasingly is incorporated into more computer applications. Its ubiquity makes it harder to spot AI applications that are not trained on data but that rely on human-written and readable rules and facts. Applications that use artificial intelligence but do not learn from or produce new results based on exposure to data are sometimes referred to as "good old-fashioned AI" or "GOFAI." And some are still in operation. For example, a simple chatbot may address questions solely by supplying pre-written answers that contain relevant keywords.

Finally, deep learning is a subset of machine learning. Deep learning uses machine learning algorithms but structures the algorithms in layers to create "artificial neural networks." These networks are modeled after the human brain and have been effective in many situations. Deep learning applications are most likely to provide an experience that feels like interacting with a real human.

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Friday, June 13, 2025

How Big Data Powers Digital Transformation! 🚀 #Sciencefather #researchawards #datascience

Digital transformation has become a vital driver of global economic growth, especially in the post-pandemic era. With global digitalization investments projected to surpass $6.8 trillion between 2022 and 2023, countries like China are playing a more prominent role. Leveraging its vast manufacturing base and leading in 5G infrastructure—with about 70% of global 5G base stations—China has rapidly advanced its digital capabilities. Government initiatives have been crucial in this transformation, with national and local policies prioritizing the integration of digital technologies across industries. These measures aim to promote high-quality development and sustainable growth by encouraging enterprises to adopt digital tools, upgrade business models, and build digital ecosystems.

Despite progress, measuring the extent of enterprise digital transformation remains a challenge. Most current methods rely on keyword analysis of company reports or limited-scale indicators. Addressing this gap, the study constructs a comprehensive measurement scale that captures various stages of transformation. Furthermore, it explores how digitalization policies influence enterprise transformation using a hierarchical regression model. By integrating institutional theory, resource-based theory, and strategic transformation theory, the study identifies key policy themes and reveals how factors like big data capabilities and network infrastructure mediate the impact of policies. This research not only enriches academic understanding but also offers actionable insights for policymakers and businesses aiming to foster effective digital transformation.

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Data Science #Sciencefather #researchawards

Data Science

Data science combines math and statistics, specialized programming, advanced analytics, artificial intelligence (AI) and machine learning with specific subject matter expertise to uncover actionable insights hidden in an organization’s data. These insights can b

e used to guide decision making and strategic planning.

The accelerating volume of data sources, and subsequently data, has made data science is one of the fastest growing field across every industry. As a result, it is no surprise that the role of the data scientist was dubbed the “sexiest job of the 21st century” by Harvard Business Review. Organizations are increasingly reliant on them to interpret data and provide actionable recommendations to improve business outcomes.

The data science lifecycle involves various roles, tools, and processes, which enables analysts to glean actionable insights. Typically, a data science project undergoes the following stages:

Data ingestion: The lifecycle begins with the data collection, both raw structured and unstructured data from all relevant sources using a variety of methods. These methods can include manual entry, web scraping, and real-time streaming data from systems and devices. Data sources can include structured data, such as customer data, along with unstructured data like log files, video, audio, pictures, the Internet of Things (IoT), social media, and more.

Data storage and data processing: Since data can have different formats and structures, companies need to consider different storage systems based on the type of data that needs to be captured. Data management teams help to set standards around data storage and structure, which facilitate workflows around analytics, machine learning and deep learning models. This stage includes cleaning data, deduplicating, transforming and combining the data using ETL (extract, transform, load) jobs or other data integration technologies. This data preparation is essential for promoting data quality before loading into a data warehouse, data lake, or other repository.

Data analysis: Here, data scientists conduct an exploratory data analysis to examine biases, patterns, ranges, and distributions of values within the data. This data analytics exploration drives hypothesis generation for a/b testing. It also allows analysts to determine the data’s relevance for use within modeling efforts for predictive analytics, machine learning, and/or deep learning. Depending on a model’s accuracy, organizations can become reliant on these insights for business decision making, allowing them to drive more scalability.

Communicate: Finally, insights are presented as reports and other data visualizations that make the insights and their impact on business easier for business analysts and other decision-makers to understand. A data science programming language such as R or Python includes components for generating visualizations; alternately, data scientists can use dedicated visualization tools.
International Conference on Computer Vision
The International Research Awards on Computer Vision recognize groundbreaking contributions in the field of computer vision, honoring researchers, scientists and innovators whose work has significantly advanced the domain. This prestigious award highlights excellence in fundamental theories, novel algorithms and real-world applications, fostering progress in artificial intelligence, image processing and deep learning.
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