International Journal of Engineering and Advanced Technology FAQ - ResearchHelpDesk - International Journal of Engineering and Advanced Technology (IJEAT) is having Online-ISSN 2249-8958, bi-monthly international journal, being published in the months of February, April, June, August, October, and December by Blue Eyes Intelligence Engineering & Sciences Publication (BEIESP) Bhopal (M.P.), India since the year 2011. It is academic, online, open access, double-blind, peer-reviewed international journal. It aims to publish original, theoretical and practical advances in Computer Science & Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. All submitted papers will be reviewed by the board of committee of IJEAT. Aim of IJEAT Journal disseminate original, scientific, theoretical or applied research in the field of Engineering and allied fields. dispense a platform for publishing results and research with a strong empirical component. aqueduct the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research. seek original and unpublished research papers based on theoretical or experimental works for the publication globally. publish original, theoretical and practical advances in Computer Science & Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. impart a platform for publishing results and research with a strong empirical component. create a bridge for a significant gap between research and practice by promoting the publication of original, novel, industry-relevant research. solicit original and unpublished research papers, based on theoretical or experimental works. Scope of IJEAT International Journal of Engineering and Advanced Technology (IJEAT) covers all topics of all engineering branches. Some of them are Computer Science & Engineering, Information Technology, Electronics & Communication, Electrical and Electronics, Electronics and Telecommunication, Civil Engineering, Mechanical Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. The main topic includes but not limited to: 1. Smart Computing and Information Processing Signal and Speech Processing Image Processing and Pattern Recognition WSN Artificial Intelligence and machine learning Data mining and warehousing Data Analytics Deep learning Bioinformatics High Performance computing Advanced Computer networking Cloud Computing IoT Parallel Computing on GPU Human Computer Interactions 2. Recent Trends in Microelectronics and VLSI Design Process & Device Technologies Low-power design Nanometer-scale integrated circuits Application specific ICs (ASICs) FPGAs Nanotechnology Nano electronics and Quantum Computing 3. Challenges of Industry and their Solutions, Communications Advanced Manufacturing Technologies Artificial Intelligence Autonomous Robots Augmented Reality Big Data Analytics and Business Intelligence Cyber Physical Systems (CPS) Digital Clone or Simulation Industrial Internet of Things (IIoT) Manufacturing IOT Plant Cyber security Smart Solutions – Wearable Sensors and Smart Glasses System Integration Small Batch Manufacturing Visual Analytics Virtual Reality 3D Printing 4. Internet of Things (IoT) Internet of Things (IoT) & IoE & Edge Computing Distributed Mobile Applications Utilizing IoT Security, Privacy and Trust in IoT & IoE Standards for IoT Applications Ubiquitous Computing Block Chain-enabled IoT Device and Data Security and Privacy Application of WSN in IoT Cloud Resources Utilization in IoT Wireless Access Technologies for IoT Mobile Applications and Services for IoT Machine/ Deep Learning with IoT & IoE Smart Sensors and Internet of Things for Smart City Logic, Functional programming and Microcontrollers for IoT Sensor Networks, Actuators for Internet of Things Data Visualization using IoT IoT Application and Communication Protocol Big Data Analytics for Social Networking using IoT IoT Applications for Smart Cities Emulation and Simulation Methodologies for IoT IoT Applied for Digital Contents 5. Microwaves and Photonics Microwave filter Micro Strip antenna Microwave Link design Microwave oscillator Frequency selective surface Microwave Antenna Microwave Photonics Radio over fiber Optical communication Optical oscillator Optical Link design Optical phase lock loop Optical devices 6. Computation Intelligence and Analytics Soft Computing Advance Ubiquitous Computing Parallel Computing Distributed Computing Machine Learning Information Retrieval Expert Systems Data Mining Text Mining Data Warehousing Predictive Analysis Data Management Big Data Analytics Big Data Security 7. Energy Harvesting and Wireless Power Transmission Energy harvesting and transfer for wireless sensor networks Economics of energy harvesting communications Waveform optimization for wireless power transfer RF Energy Harvesting Wireless Power Transmission Microstrip Antenna design and application Wearable Textile Antenna Luminescence Rectenna 8. Advance Concept of Networking and Database Computer Network Mobile Adhoc Network Image Security Application Artificial Intelligence and machine learning in the Field of Network and Database Data Analytic High performance computing Pattern Recognition 9. Machine Learning (ML) and Knowledge Mining (KM) Regression and prediction Problem solving and planning Clustering Classification Neural information processing Vision and speech perception Heterogeneous and streaming data Natural language processing Probabilistic Models and Methods Reasoning and inference Marketing and social sciences Data mining Knowledge Discovery Web mining Information retrieval Design and diagnosis Game playing Streaming data Music Modelling and Analysis Robotics and control Multi-agent systems Bioinformatics Social sciences Industrial, financial and scientific applications of all kind 10. Advanced Computer networking Computational Intelligence Data Management, Exploration, and Mining Robotics Artificial Intelligence and Machine Learning Computer Architecture and VLSI Computer Graphics, Simulation, and Modelling Digital System and Logic Design Natural Language Processing and Machine Translation Parallel and Distributed Algorithms Pattern Recognition and Analysis Systems and Software Engineering Nature Inspired Computing Signal and Image Processing Reconfigurable Computing Cloud, Cluster, Grid and P2P Computing Biomedical Computing Advanced Bioinformatics Green Computing Mobile Computing Nano Ubiquitous Computing Context Awareness and Personalization, Autonomic and Trusted Computing Cryptography and Applied Mathematics Security, Trust and Privacy Digital Rights Management Networked-Driven Multicourse Chips Internet Computing Agricultural Informatics and Communication Community Information Systems Computational Economics, Digital Photogrammetric Remote Sensing, GIS and GPS Disaster Management e-governance, e-Commerce, e-business, e-Learning Forest Genomics and Informatics Healthcare Informatics Information Ecology and Knowledge Management Irrigation Informatics Neuro-Informatics Open Source: Challenges and opportunities Web-Based Learning: Innovation and Challenges Soft computing Signal and Speech Processing Natural Language Processing 11. Communications Microstrip Antenna Microwave Radar and Satellite Smart Antenna MIMO Antenna Wireless Communication RFID Network and Applications 5G Communication 6G Communication 12. Algorithms and Complexity Sequential, Parallel And Distributed Algorithms And Data Structures Approximation And Randomized Algorithms Graph Algorithms And Graph Drawing On-Line And Streaming Algorithms Analysis Of Algorithms And Computational Complexity Algorithm Engineering Web Algorithms Exact And Parameterized Computation Algorithmic Game Theory Computational Biology Foundations Of Communication Networks Computational Geometry Discrete Optimization 13. Software Engineering and Knowledge Engineering Software Engineering Methodologies Agent-based software engineering Artificial intelligence approaches to software engineering Component-based software engineering Embedded and ubiquitous software engineering Aspect-based software engineering Empirical software engineering Search-Based Software engineering Automated software design and synthesis Computer-supported cooperative work Automated software specification Reverse engineering Software Engineering Techniques and Production Perspectives Requirements engineering Software analysis, design and modelling Software maintenance and evolution Software engineering tools and environments Software engineering decision support Software design patterns Software product lines Process and workflow management Reflection and metadata approaches Program understanding and system maintenance Software domain modelling and analysis Software economics Multimedia and hypermedia software engineering Software engineering case study and experience reports Enterprise software, middleware, and tools Artificial intelligent methods, models, techniques Artificial life and societies Swarm intelligence Smart Spaces Autonomic computing and agent-based systems Autonomic computing Adaptive Systems Agent architectures, ontologies, languages and protocols Multi-agent systems Agent-based learning and knowledge discovery Interface agents Agent-based auctions and marketplaces Secure mobile and multi-agent systems Mobile agents SOA and Service-Oriented Systems Service-centric software engineering Service oriented requirements engineering Service oriented architectures Middleware for service based systems Service discovery and composition Service level agreements (drafting,

In the past, the majority of data analysis use cases was addressed by aggregating relational data. Since a few years, a trend is evolving, which is called “Big Data” and which has several implications on the field of data analysis. Compared to previous applications, much larger data sets are analyzed using more elaborate and diverse analysis methods such as information extraction techniques, data mining algorithms, and machine learning methods. At the same time, analysis applications include data sets with less or even no structure at all. This evolution has implications on the requirements on data processing systems. Due to the growing size of data sets and the increasing computational complexity of advanced analysis methods, data must be processed in a massively parallel fashion. The large number and diversity of data analysis techniques as well as the lack of data structure determine the use of user-defined functions and data types. Many traditional database systems are not flexible enough to satisfy these requirements. Hence, there is a need for programming abstractions to define and efficiently execute complex parallel data analysis programs that support custom user-defined operations. The success of the SQL query language has shown the advantages of declarative query specification, such as potential for optimization and ease of use. Today, most relational database management systems feature a query optimizer that compiles declarative queries into physical execution plans. Cost-based optimizers choose from billions of plan candidates the plan with the least estimated cost. However, traditional optimization techniques cannot be readily integrated into systems that aim to support novel data analysis use cases. For example, the use of user-defined functions (UDFs) can significantly limit the optimization potential of data analysis programs. Furthermore, lack of detailed data statistics is common when large amounts of unstructured data is analyzed. This leads to imprecise optimizer cost estimates, which can cause sub-optimal plan choices. In this thesis we address three challenges that arise in the context of specifying and optimizing data analysis programs. First, we propose a parallel programming model with declarative properties to specify data analysis tasks as data flow programs. In this model, data processing operators are composed of a system-provided second-order function and a user-defined first-order function. A cost-based optimizer compiles data flow programs specified in this abstraction into parallel data flows. The optimizer borrows techniques from relational optimizers and ports them to the domain of general-purpose parallel programming models. Second, we propose an approach to enhance the optimization of data flow programs that include UDF operators with unknown semantics. We identify operator properties and conditions to reorder neighboring UDF operators without changing the semantics of the program. We show how to automatically extract these properties from UDF operators by leveraging static code analysis techniques. Our approach is able to emulate relational optimizations such as filter and join reordering and holistic aggregation push-down while not being limited to relational operators. Finally, we analyze the impact of changing execution conditions such as varying predicate selectivities and memory budgets on the performance of relational query plans. We identify plan patterns that cause significantly varying execution performance for changing execution conditions. Plans that include such risky patterns are prone to cause problems in presence of imprecise optimizer estimates. Based on our findings, we introduce an approach to avoid risky plan choices. Moreover, we present a method to assess the risk of a query execution plan using a machine-learned prediction model. Experiments show that the prediction model outperforms risk predictions which are computed from optimizer estimates.

Anomaly detection is a process of identifying items, events or observations, which do not conform to an expected pattern in a dataset or time series. Current and future missions and our research communities challenge us to rapidly identify features and anomalies in complex and voluminous observations to further science and improve decision support. Given this data intensive reality, we propose to develop an anomaly detection system, called OceanXtremes, powered by an intelligent, elastic Cloud-based analytic service backend that enables execution of domain-specific, multi-scale anomaly and feature detection algorithms across the entire archive of ocean science datasets. A parallel analytics engine will be developed as the key computational and data-mining core of OceanXtreams' backend processing. This analytic engine will demonstrate three new technology ideas to provide rapid turn around on climatology computation and anomaly detection: 1. An adaption of the Hadoop/MapReduce framework for parallel data mining of science datasets, typically large 3 or 4 dimensional arrays packaged in NetCDF and HDF. 2. An algorithm profiling service to efficiently and cost-effectively scale up hybrid Cloud computing resources based on the needs of scheduled jobs (CPU, memory, network, and bursting from a private Cloud computing cluster to public cloud provider like Amazon Cloud services). 3. An extension to industry-standard search solutions (OpenSearch and Faceted search) to provide support for shared discovery and exploration of ocean phenomena and anomalies, along with unexpected correlations between key measured variables. We will use a hybrid Cloud compute cluster (private Eucalyptus on-premise at JPL with bursting to Amazon Web Services) as the operational backend. The key idea is that the parallel data-mining operations will be run 'near' the ocean data archives (a local 'network' hop) so that we can efficiently access the thousands of (say, daily) files making up a three decade time-series, and then cache key variables and pre-computed climatologies in a high-performance parallel database. OceanXtremes will be equipped with both web portal and web service interfaces for users and applications/systems to register and retrieve oceanographic anomalies data. By leveraging technology such as Datacasting (Bingham, et.al, 2007), users can also subscribe to anomaly or 'event' types of their interest and have newly computed anomaly metrics and other information delivered to them by metadata feeds packaged in standard Rich Site Summary (RSS) format. Upon receiving new feed entries, users can examine the metrics and download relevant variables, by simply clicking on a link, to begin further analyzing the event. The OceanXtremes web portal will allow users to define their own anomaly or feature types where continuous backend processing will be scheduled to populate the new user-defined anomaly type by executing the chosen data mining algorithm (i.e. differences from climatology or gradients above a specified threshold). Metadata on the identified anomalies will be cataloged including temporal and geospatial profiles, key physical metrics, related observational artifacts and other relevant metadata to facilitate discovery, extraction, and visualization. Products created by the anomaly detection algorithm will be made explorable and subsettable using Webification (Huang, et.al, 2014) and OPeNDAP (http://opendap.org) technologies. Using this platform scientists can efficiently search for anomalies or ocean phenomena, compute data metrics for events or over time-series of ocean variables, and efficiently find and access all of the data relevant to their study (and then download only that data).

International Journal of Engineering and Advanced Technology Acceptance Rate - ResearchHelpDesk - International Journal of Engineering and Advanced Technology (IJEAT) is having Online-ISSN 2249-8958, bi-monthly international journal, being published in the months of February, April, June, August, October, and December by Blue Eyes Intelligence Engineering & Sciences Publication (BEIESP) Bhopal (M.P.), India since the year 2011. It is academic, online, open access, double-blind, peer-reviewed international journal. It aims to publish original, theoretical and practical advances in Computer Science & Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. All submitted papers will be reviewed by the board of committee of IJEAT. Aim of IJEAT Journal disseminate original, scientific, theoretical or applied research in the field of Engineering and allied fields. dispense a platform for publishing results and research with a strong empirical component. aqueduct the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research. seek original and unpublished research papers based on theoretical or experimental works for the publication globally. publish original, theoretical and practical advances in Computer Science & Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. impart a platform for publishing results and research with a strong empirical component. create a bridge for a significant gap between research and practice by promoting the publication of original, novel, industry-relevant research. solicit original and unpublished research papers, based on theoretical or experimental works. Scope of IJEAT International Journal of Engineering and Advanced Technology (IJEAT) covers all topics of all engineering branches. Some of them are Computer Science & Engineering, Information Technology, Electronics & Communication, Electrical and Electronics, Electronics and Telecommunication, Civil Engineering, Mechanical Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. The main topic includes but not limited to: 1. Smart Computing and Information Processing Signal and Speech Processing Image Processing and Pattern Recognition WSN Artificial Intelligence and machine learning Data mining and warehousing Data Analytics Deep learning Bioinformatics High Performance computing Advanced Computer networking Cloud Computing IoT Parallel Computing on GPU Human Computer Interactions 2. Recent Trends in Microelectronics and VLSI Design Process & Device Technologies Low-power design Nanometer-scale integrated circuits Application specific ICs (ASICs) FPGAs Nanotechnology Nano electronics and Quantum Computing 3. Challenges of Industry and their Solutions, Communications Advanced Manufacturing Technologies Artificial Intelligence Autonomous Robots Augmented Reality Big Data Analytics and Business Intelligence Cyber Physical Systems (CPS) Digital Clone or Simulation Industrial Internet of Things (IIoT) Manufacturing IOT Plant Cyber security Smart Solutions – Wearable Sensors and Smart Glasses System Integration Small Batch Manufacturing Visual Analytics Virtual Reality 3D Printing 4. Internet of Things (IoT) Internet of Things (IoT) & IoE & Edge Computing Distributed Mobile Applications Utilizing IoT Security, Privacy and Trust in IoT & IoE Standards for IoT Applications Ubiquitous Computing Block Chain-enabled IoT Device and Data Security and Privacy Application of WSN in IoT Cloud Resources Utilization in IoT Wireless Access Technologies for IoT Mobile Applications and Services for IoT Machine/ Deep Learning with IoT & IoE Smart Sensors and Internet of Things for Smart City Logic, Functional programming and Microcontrollers for IoT Sensor Networks, Actuators for Internet of Things Data Visualization using IoT IoT Application and Communication Protocol Big Data Analytics for Social Networking using IoT IoT Applications for Smart Cities Emulation and Simulation Methodologies for IoT IoT Applied for Digital Contents 5. Microwaves and Photonics Microwave filter Micro Strip antenna Microwave Link design Microwave oscillator Frequency selective surface Microwave Antenna Microwave Photonics Radio over fiber Optical communication Optical oscillator Optical Link design Optical phase lock loop Optical devices 6. Computation Intelligence and Analytics Soft Computing Advance Ubiquitous Computing Parallel Computing Distributed Computing Machine Learning Information Retrieval Expert Systems Data Mining Text Mining Data Warehousing Predictive Analysis Data Management Big Data Analytics Big Data Security 7. Energy Harvesting and Wireless Power Transmission Energy harvesting and transfer for wireless sensor networks Economics of energy harvesting communications Waveform optimization for wireless power transfer RF Energy Harvesting Wireless Power Transmission Microstrip Antenna design and application Wearable Textile Antenna Luminescence Rectenna 8. Advance Concept of Networking and Database Computer Network Mobile Adhoc Network Image Security Application Artificial Intelligence and machine learning in the Field of Network and Database Data Analytic High performance computing Pattern Recognition 9. Machine Learning (ML) and Knowledge Mining (KM) Regression and prediction Problem solving and planning Clustering Classification Neural information processing Vision and speech perception Heterogeneous and streaming data Natural language processing Probabilistic Models and Methods Reasoning and inference Marketing and social sciences Data mining Knowledge Discovery Web mining Information retrieval Design and diagnosis Game playing Streaming data Music Modelling and Analysis Robotics and control Multi-agent systems Bioinformatics Social sciences Industrial, financial and scientific applications of all kind 10. Advanced Computer networking Computational Intelligence Data Management, Exploration, and Mining Robotics Artificial Intelligence and Machine Learning Computer Architecture and VLSI Computer Graphics, Simulation, and Modelling Digital System and Logic Design Natural Language Processing and Machine Translation Parallel and Distributed Algorithms Pattern Recognition and Analysis Systems and Software Engineering Nature Inspired Computing Signal and Image Processing Reconfigurable Computing Cloud, Cluster, Grid and P2P Computing Biomedical Computing Advanced Bioinformatics Green Computing Mobile Computing Nano Ubiquitous Computing Context Awareness and Personalization, Autonomic and Trusted Computing Cryptography and Applied Mathematics Security, Trust and Privacy Digital Rights Management Networked-Driven Multicourse Chips Internet Computing Agricultural Informatics and Communication Community Information Systems Computational Economics, Digital Photogrammetric Remote Sensing, GIS and GPS Disaster Management e-governance, e-Commerce, e-business, e-Learning Forest Genomics and Informatics Healthcare Informatics Information Ecology and Knowledge Management Irrigation Informatics Neuro-Informatics Open Source: Challenges and opportunities Web-Based Learning: Innovation and Challenges Soft computing Signal and Speech Processing Natural Language Processing 11. Communications Microstrip Antenna Microwave Radar and Satellite Smart Antenna MIMO Antenna Wireless Communication RFID Network and Applications 5G Communication 6G Communication 12. Algorithms and Complexity Sequential, Parallel And Distributed Algorithms And Data Structures Approximation And Randomized Algorithms Graph Algorithms And Graph Drawing On-Line And Streaming Algorithms Analysis Of Algorithms And Computational Complexity Algorithm Engineering Web Algorithms Exact And Parameterized Computation Algorithmic Game Theory Computational Biology Foundations Of Communication Networks Computational Geometry Discrete Optimization 13. Software Engineering and Knowledge Engineering Software Engineering Methodologies Agent-based software engineering Artificial intelligence approaches to software engineering Component-based software engineering Embedded and ubiquitous software engineering Aspect-based software engineering Empirical software engineering Search-Based Software engineering Automated software design and synthesis Computer-supported cooperative work Automated software specification Reverse engineering Software Engineering Techniques and Production Perspectives Requirements engineering Software analysis, design and modelling Software maintenance and evolution Software engineering tools and environments Software engineering decision support Software design patterns Software product lines Process and workflow management Reflection and metadata approaches Program understanding and system maintenance Software domain modelling and analysis Software economics Multimedia and hypermedia software engineering Software engineering case study and experience reports Enterprise software, middleware, and tools Artificial intelligent methods, models, techniques Artificial life and societies Swarm intelligence Smart Spaces Autonomic computing and agent-based systems Autonomic computing Adaptive Systems Agent architectures, ontologies, languages and protocols Multi-agent systems Agent-based learning and knowledge discovery Interface agents Agent-based auctions and marketplaces Secure mobile and multi-agent systems Mobile agents SOA and Service-Oriented Systems Service-centric software engineering Service oriented requirements engineering Service oriented architectures Middleware for service based systems Service discovery and composition Service level

Privacy?Preserving Data Mining Tools Market Outlook

According to our latest research, the global Privacy?Preserving Data Mining Tools market size reached USD 1.42 billion in 2024, reflecting robust adoption across diverse industries. The market is expected to exhibit a CAGR of 22.8% during the forecast period, propelling the market to USD 10.98 billion by 2033. This remarkable growth is driven by the increasing need for secure data analytics, stringent data protection regulations, and the rising frequency of data breaches, all of which are pushing organizations to adopt advanced privacy solutions.

One of the primary growth factors for the Privacy?Preserving Data Mining Tools market is the exponential rise in data generation and the parallel escalation of privacy concerns. As organizations collect vast amounts of sensitive information, especially in sectors like healthcare and BFSI, the risk of data exposure and misuse grows. Governments worldwide are enacting stricter data protection laws, such as the GDPR in Europe and CCPA in California, compelling enterprises to integrate privacy?preserving technologies into their analytics workflows. These regulations not only mandate compliance but also foster consumer trust, making privacy?preserving data mining tools a strategic investment for businesses aiming to maintain a competitive edge while safeguarding user data.

Another significant driver is the rapid digital transformation across industries, which necessitates the extraction of actionable insights from large, distributed data sets without compromising privacy. Privacy?preserving techniques, such as federated learning, homomorphic encryption, and differential privacy, are gaining traction as they allow organizations to collaborate and analyze data securely. The advent of cloud computing and the proliferation of connected devices further amplify the demand for scalable and secure data mining solutions. As enterprises embrace cloud-based analytics, the need for robust privacy-preserving mechanisms becomes paramount, fueling the adoption of advanced tools that can operate seamlessly in both on-premises and cloud environments.

Moreover, the increasing sophistication of cyber threats and the growing awareness of the potential reputational and financial damage caused by data breaches are prompting organizations to prioritize data privacy. High-profile security incidents have underscored the vulnerabilities inherent in traditional data mining approaches, accelerating the shift towards privacy-preserving alternatives. The integration of artificial intelligence and machine learning with privacy-preserving technologies is also opening new avenues for innovation, enabling more granular and context-aware data analytics. This technological convergence is expected to further catalyze market growth, as organizations seek to harness the full potential of their data assets while maintaining stringent privacy standards.

Privacy-Preserving Analytics is becoming a cornerstone in the modern data-driven landscape, offering organizations a way to extract valuable insights while maintaining stringent data privacy standards. This approach ensures that sensitive information remains protected even as it is analyzed, allowing businesses to comply with increasing regulatory demands without sacrificing the depth and breadth of their data analysis. By leveraging Privacy-Preserving Analytics, companies can foster greater trust among their customers and stakeholders, knowing that their data is being handled with the utmost care and security. This paradigm shift is not just about compliance; it’s about redefining how organizations approach data analytics in a world where privacy concerns are paramount.

From a regional perspective, North America currently commands the largest share of the Privacy?Preserving Data Mining Tools market, driven by the presence of leading technology vendors, high awareness levels, and a robust regulatory framework. Europe follows closely, propelled by stringent data privacy laws and increasing investments in secure analytics infrastructure. The Asia Pacific region is witnessing the fastest growth, fueled by rapid digitalization, expanding IT ecosystems, and rising cybersecurity concerns in emerging economies such as China and India. Latin America and the Middle East & Africa are also experiencing steady growth, albeit from

Parameters used in performance evaluation for synthetic data.

This dataset contains the results from the ODDPubb text mining algorithm and the findings from manual analysis. Full-text PDFs of all articles parallel-published by Linköping University in 2022 were extracted from the institute's repository, DiVA. These were analyzed using the ODDPubb (https://github.com/quest-bih/oddpub) text mining algorithm to determine the extent of data sharing and identify the repositories where the data was shared. In addition to the results from ODDPubb, manual analysis was conducted to confirm the presence of data sharing statements, assess data availability, and identify the repositories used.

Definitions of common notations.

aConvergence steps: the iteration steps when the algorithm is converged.bCluster Number: the number of clusters which the AP algorithm detects.cErrot rates: the 2-norm of the difference value of message matrices from two algorithms.dPreference value: the input value of AP algorithm.eNon-Parallel algorithm: the non-parallel version of algorithm which get from the original publication of AP algorithm.fParallel algorithm: the parallel version of algorithm.gResponsibility message: the responsibility message of AP algorithm.hAvailability message: the availability message of AP algorithm.

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Generative AI In Data Analytics Market Size 2025-2029

The generative ai in data analytics market size is valued to increase by USD 4.62 billion, at a CAGR of 35.5% from 2024 to 2029. Democratization of data analytics and increased accessibility will drive the generative ai in data analytics market.

Market Insights

North America dominated the market and accounted for a 37% growth during the 2025-2029.
By Deployment - Cloud-based segment was valued at USD 510.60 billion in 2023
By Technology - Machine learning segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 621.84 million 
Market Future Opportunities 2024: USD 4624.00 million
CAGR from 2024 to 2029 : 35.5%

Market Summary

The market is experiencing significant growth as businesses worldwide seek to unlock new insights from their data through advanced technologies. This trend is driven by the democratization of data analytics and increased accessibility of AI models, which are now available in domain-specific and enterprise-tuned versions. Generative AI, a subset of artificial intelligence, uses deep learning algorithms to create new data based on existing data sets. This capability is particularly valuable in data analytics, where it can be used to generate predictions, recommendations, and even new data points. One real-world business scenario where generative AI is making a significant impact is in supply chain optimization. In this context, generative AI models can analyze historical data and generate forecasts for demand, inventory levels, and production schedules. This enables businesses to optimize their supply chain operations, reduce costs, and improve customer satisfaction. However, the adoption of generative AI in data analytics also presents challenges, particularly around data privacy, security, and governance. As businesses continue to generate and analyze increasingly large volumes of data, ensuring that it is protected and used in compliance with regulations is paramount. Despite these challenges, the benefits of generative AI in data analytics are clear, and its use is set to grow as businesses seek to gain a competitive edge through data-driven insights.

What will be the size of the Generative AI In Data Analytics Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free SampleGenerative AI, a subset of artificial intelligence, is revolutionizing data analytics by automating data processing and analysis, enabling businesses to derive valuable insights faster and more accurately. Synthetic data generation, a key application of generative AI, allows for the creation of large, realistic datasets, addressing the challenge of insufficient data in analytics. Parallel processing methods and high-performance computing power the rapid analysis of vast datasets. Automated machine learning and hyperparameter optimization streamline model development, while model monitoring systems ensure continuous model performance. Real-time data processing and scalable data solutions facilitate data-driven decision-making, enabling businesses to respond swiftly to market trends. One significant trend in the market is the integration of AI-powered insights into business operations. For instance, probabilistic graphical models and backpropagation techniques are used to predict customer churn and optimize marketing strategies. Ensemble learning methods and transfer learning techniques enhance predictive analytics, leading to improved customer segmentation and targeted marketing. According to recent studies, businesses have achieved a 30% reduction in processing time and a 25% increase in predictive accuracy by implementing generative AI in their data analytics processes. This translates to substantial cost savings and improved operational efficiency. By embracing this technology, businesses can gain a competitive edge, making informed decisions with greater accuracy and agility.

Unpacking the Generative AI In Data Analytics Market Landscape

In the dynamic realm of data analytics, Generative AI algorithms have emerged as a game-changer, revolutionizing data processing and insights generation. Compared to traditional data mining techniques, Generative AI models can create new data points that mirror the original dataset, enabling more comprehensive data exploration and analysis (Source: Gartner). This innovation leads to a 30% increase in identified patterns and trends, resulting in improved ROI and enhanced business decision-making (IDC).

Data security protocols are paramount in this context, with Classification Algorithms and Clustering Algorithms ensuring data privacy and compliance alignment. Machine Learning Pipelines and Deep Learning Frameworks facilitate seamless integration with Predictive Modeling Tools and Automated Report Generation on Cloud

Aufgrund fallender Preise zur Speicherung von Daten kann man derzeit eine explosionsartige Zunahme in der Menge der verfügbaren Daten beobachten. Diese Entwicklung gibt Unternehmen und wissenschaftliche Institutionen die Möglichkeit empirische Daten in ungekannter Größenordnung zu analysieren. Für viele Firmen ist die Analyse der gesammelten Daten aus ihrem operationalen Geschäft längst zu einem zentralen strategischen Aspekt geworden. Im Gegensatz zu der seit längerem schon betriebenen Business Intelligence, bestehen diese Analysen nicht mehr nur aus traditionellen relationalen Anfragen. In zunehmendem Anteil kommen komplexe Algorithmen aus den Bereichen Data Mining und Maschinelles Lernen hinzu, um versteckte Muster in den Daten zu erkennen, oder Vorhersagemodelle zu trainieren. Mit zunehmender Datenmenge und Komplexität der Analysen wird jedoch eine neue Generation von Systemen benötigt, die diese Kombination aus Anfragekomplexität und Datenvolumen gewachsen sind. Relationale Datenbanken waren lange Zeit das Zugpferd der Datenanalyse im großen Stil. Grund dafür war zum großen Teil ihre deklarativen Anfragesprache, welche es ermöglichte die logischen und physischen Aspekte der Datenspeicherung und Verarbeitung zu trennen, und Anfragen automatisch zu optimieren. Das starres Datenmodell und ihre beschränkte Menge von möglichen Operationen schränken jedoch die Anwendbarkeit von relationalen Datenbanken für viele der neueren analytischen Probleme stark ein. Diese Erkenntnis hat die Entwicklung einer neuen Generation von Systemen und Architekturen eingeläutet, die sich durch sehr generische Abstraktionen für parallelisierbare analytische Programme auszeichnen; MapReduce kann hier beispielhaft genannt werden, als der zweifelsohne prominenteste Vertreter dieser Systeme. Zwar vereinfachte und erschloss diese neue Generation von Systemen die Datenanalyse in diversen neuen Anwendungsfeldern, sie ist jedoch nicht in der Lage komplexe Anwendungen aus den Bereichen Data Mining und Maschinelles Lernen effizient abzubilden, ohne sich dabei extrem auf spezifische Anwendungen zu spezialisieren. Verglichen mit den relationalen Datenbanken haben MapReduce und vergleichbare Systeme außerdem die deklarative Abstraktion aufgegeben und zwingen den Anwender dazu systemnahe Programme zu schreiben und diese manuell zu optimieren. In dieser Dissertation werden verschiedene Techniken vorgestellt, die es ermöglichen etliche der zentralen Eigenschaften von relationalen Datenbanken im Kontext dieser neuen Generation von daten-parallelen Analysesystemen zu realisieren. Mithilfe dieser Techniken ist es möglich ein Analysesystem zu beschreiben, dessen Programme gleichzeitig sowohl generische und ausdrucksstark, als auch prägnant und deklarativ sind. Im einzelnen stellen wir folgende Techniken vor: Erstens, eine Programmierabstraktion die generisch ist und mit komplexen Datenmodellen umgehen kann, aber gleichzeitig viele der deklarativen Eigenschaften der relationalen Algebra erhält. Programme, die gegen dies Abstraktion entwickelt werden können ähnlich optimiert werden wie relationale Anfragen. Zweitens stellen wir eine Abstraktion für iterative daten-parallele Algorithmen vor. Die Abstraktion unterstützt inkrementelle (delta-basierte) Berechnungen und geht mit zustandsbehafteteten Berechnungen transparent um. Wir beschreiben wie man einen relationalen Anfrageoptimierer erweitern kann so dass dieser iterative Anfragen effektiv optimiert. Wir zeigen dabei dass der Optimierer dadurch in die Lage versetzt wird automatisch Ausführungspläne zu erzeugen, die wohlbekannten, manuell erstellten Programmen entsprechen. Die Abstraktion subsumiert dadurch spezialisierte Systeme (wie Pregel) und bietet vergleichbare Performanz. Drittens stellen wir Methoden vor, um die Programmierabstraktion in eine funktionale Sprachen einzubetten. Diese Integration ermögliche es prägnante Programme zu schreiben und einfach wiederzuverwendenden Komponenten und Bibliotheken, sowie Domänenspezifische Sprachen, zu erstellen. Wir legen dar wie man die Übersetzung und Optimierung des daten-parallelen Programms mit dem Sprachübersetzer der funktionalen Sprache so integriert, dass maximales Optimierungspotenzial besteht.

International Journal of Engineering and Advanced Technology Impact Factor 2024-2025 - ResearchHelpDesk - International Journal of Engineering and Advanced Technology (IJEAT) is having Online-ISSN 2249-8958, bi-monthly international journal, being published in the months of February, April, June, August, October, and December by Blue Eyes Intelligence Engineering & Sciences Publication (BEIESP) Bhopal (M.P.), India since the year 2011. It is academic, online, open access, double-blind, peer-reviewed international journal. It aims to publish original, theoretical and practical advances in Computer Science & Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. All submitted papers will be reviewed by the board of committee of IJEAT. Aim of IJEAT Journal disseminate original, scientific, theoretical or applied research in the field of Engineering and allied fields. dispense a platform for publishing results and research with a strong empirical component. aqueduct the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research. seek original and unpublished research papers based on theoretical or experimental works for the publication globally. publish original, theoretical and practical advances in Computer Science & Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. impart a platform for publishing results and research with a strong empirical component. create a bridge for a significant gap between research and practice by promoting the publication of original, novel, industry-relevant research. solicit original and unpublished research papers, based on theoretical or experimental works. Scope of IJEAT International Journal of Engineering and Advanced Technology (IJEAT) covers all topics of all engineering branches. Some of them are Computer Science & Engineering, Information Technology, Electronics & Communication, Electrical and Electronics, Electronics and Telecommunication, Civil Engineering, Mechanical Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. The main topic includes but not limited to: 1. Smart Computing and Information Processing Signal and Speech Processing Image Processing and Pattern Recognition WSN Artificial Intelligence and machine learning Data mining and warehousing Data Analytics Deep learning Bioinformatics High Performance computing Advanced Computer networking Cloud Computing IoT Parallel Computing on GPU Human Computer Interactions 2. Recent Trends in Microelectronics and VLSI Design Process & Device Technologies Low-power design Nanometer-scale integrated circuits Application specific ICs (ASICs) FPGAs Nanotechnology Nano electronics and Quantum Computing 3. Challenges of Industry and their Solutions, Communications Advanced Manufacturing Technologies Artificial Intelligence Autonomous Robots Augmented Reality Big Data Analytics and Business Intelligence Cyber Physical Systems (CPS) Digital Clone or Simulation Industrial Internet of Things (IIoT) Manufacturing IOT Plant Cyber security Smart Solutions – Wearable Sensors and Smart Glasses System Integration Small Batch Manufacturing Visual Analytics Virtual Reality 3D Printing 4. Internet of Things (IoT) Internet of Things (IoT) & IoE & Edge Computing Distributed Mobile Applications Utilizing IoT Security, Privacy and Trust in IoT & IoE Standards for IoT Applications Ubiquitous Computing Block Chain-enabled IoT Device and Data Security and Privacy Application of WSN in IoT Cloud Resources Utilization in IoT Wireless Access Technologies for IoT Mobile Applications and Services for IoT Machine/ Deep Learning with IoT & IoE Smart Sensors and Internet of Things for Smart City Logic, Functional programming and Microcontrollers for IoT Sensor Networks, Actuators for Internet of Things Data Visualization using IoT IoT Application and Communication Protocol Big Data Analytics for Social Networking using IoT IoT Applications for Smart Cities Emulation and Simulation Methodologies for IoT IoT Applied for Digital Contents 5. Microwaves and Photonics Microwave filter Micro Strip antenna Microwave Link design Microwave oscillator Frequency selective surface Microwave Antenna Microwave Photonics Radio over fiber Optical communication Optical oscillator Optical Link design Optical phase lock loop Optical devices 6. Computation Intelligence and Analytics Soft Computing Advance Ubiquitous Computing Parallel Computing Distributed Computing Machine Learning Information Retrieval Expert Systems Data Mining Text Mining Data Warehousing Predictive Analysis Data Management Big Data Analytics Big Data Security 7. Energy Harvesting and Wireless Power Transmission Energy harvesting and transfer for wireless sensor networks Economics of energy harvesting communications Waveform optimization for wireless power transfer RF Energy Harvesting Wireless Power Transmission Microstrip Antenna design and application Wearable Textile Antenna Luminescence Rectenna 8. Advance Concept of Networking and Database Computer Network Mobile Adhoc Network Image Security Application Artificial Intelligence and machine learning in the Field of Network and Database Data Analytic High performance computing Pattern Recognition 9. Machine Learning (ML) and Knowledge Mining (KM) Regression and prediction Problem solving and planning Clustering Classification Neural information processing Vision and speech perception Heterogeneous and streaming data Natural language processing Probabilistic Models and Methods Reasoning and inference Marketing and social sciences Data mining Knowledge Discovery Web mining Information retrieval Design and diagnosis Game playing Streaming data Music Modelling and Analysis Robotics and control Multi-agent systems Bioinformatics Social sciences Industrial, financial and scientific applications of all kind 10. Advanced Computer networking Computational Intelligence Data Management, Exploration, and Mining Robotics Artificial Intelligence and Machine Learning Computer Architecture and VLSI Computer Graphics, Simulation, and Modelling Digital System and Logic Design Natural Language Processing and Machine Translation Parallel and Distributed Algorithms Pattern Recognition and Analysis Systems and Software Engineering Nature Inspired Computing Signal and Image Processing Reconfigurable Computing Cloud, Cluster, Grid and P2P Computing Biomedical Computing Advanced Bioinformatics Green Computing Mobile Computing Nano Ubiquitous Computing Context Awareness and Personalization, Autonomic and Trusted Computing Cryptography and Applied Mathematics Security, Trust and Privacy Digital Rights Management Networked-Driven Multicourse Chips Internet Computing Agricultural Informatics and Communication Community Information Systems Computational Economics, Digital Photogrammetric Remote Sensing, GIS and GPS Disaster Management e-governance, e-Commerce, e-business, e-Learning Forest Genomics and Informatics Healthcare Informatics Information Ecology and Knowledge Management Irrigation Informatics Neuro-Informatics Open Source: Challenges and opportunities Web-Based Learning: Innovation and Challenges Soft computing Signal and Speech Processing Natural Language Processing 11. Communications Microstrip Antenna Microwave Radar and Satellite Smart Antenna MIMO Antenna Wireless Communication RFID Network and Applications 5G Communication 6G Communication 12. Algorithms and Complexity Sequential, Parallel And Distributed Algorithms And Data Structures Approximation And Randomized Algorithms Graph Algorithms And Graph Drawing On-Line And Streaming Algorithms Analysis Of Algorithms And Computational Complexity Algorithm Engineering Web Algorithms Exact And Parameterized Computation Algorithmic Game Theory Computational Biology Foundations Of Communication Networks Computational Geometry Discrete Optimization 13. Software Engineering and Knowledge Engineering Software Engineering Methodologies Agent-based software engineering Artificial intelligence approaches to software engineering Component-based software engineering Embedded and ubiquitous software engineering Aspect-based software engineering Empirical software engineering Search-Based Software engineering Automated software design and synthesis Computer-supported cooperative work Automated software specification Reverse engineering Software Engineering Techniques and Production Perspectives Requirements engineering Software analysis, design and modelling Software maintenance and evolution Software engineering tools and environments Software engineering decision support Software design patterns Software product lines Process and workflow management Reflection and metadata approaches Program understanding and system maintenance Software domain modelling and analysis Software economics Multimedia and hypermedia software engineering Software engineering case study and experience reports Enterprise software, middleware, and tools Artificial intelligent methods, models, techniques Artificial life and societies Swarm intelligence Smart Spaces Autonomic computing and agent-based systems Autonomic computing Adaptive Systems Agent architectures, ontologies, languages and protocols Multi-agent systems Agent-based learning and knowledge discovery Interface agents Agent-based auctions and marketplaces Secure mobile and multi-agent systems Mobile agents SOA and Service-Oriented Systems Service-centric software engineering Service oriented requirements engineering Service oriented architectures Middleware for service based systems Service discovery and composition Service level

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Automatic Data Capture (ADC) Market Size 2024-2028

The automatic data capture (adc) market size is valued to increase by USD 48.69 billion, at a CAGR of 13.64% from 2023 to 2028. Increasing application of RFID will drive the automatic data capture (adc) market.

Market Insights

APAC dominated the market and accounted for a 43% growth during the 2024-2028.
By Product - RFID segment was valued at USD 23.12 billion in 2022
By Application - Industrial segment accounted for the largest market revenue share in 2022

Market Size & Forecast

Market Opportunities: USD 154.06 million 
Market Future Opportunities 2023: USD 48685.50 million
CAGR from 2023 to 2028 : 13.64%

Market Summary

The market encompasses technologies and solutions that enable the automatic collection, processing, and transmission of data from various sources, primarily barcodes, RFID, and biometric systems. This market is driven by the increasing demand for real-time data processing and analysis to optimize business operations, enhance productivity, and ensure regulatory compliance. One significant trend in the ADC market is the growing popularity of smart factories, where ADC technologies play a crucial role in streamlining manufacturing processes and improving overall efficiency. For instance, RFID tags can be used to track inventory levels, monitor equipment performance, and manage work-in-progress in real-time, leading to reduced downtime and improved quality. However, the adoption of ADC technologies also brings about security concerns. With the increasing amount of data being generated and transmitted, there is a growing need to protect sensitive information from unauthorized access and cyber-attacks. This has led to the development of advanced security solutions, such as encryption, access control, and intrusion detection, to safeguard data and maintain privacy. A real-world business scenario illustrating the importance of ADC technologies is in the supply chain optimization of a global retailer. By implementing RFID technology, the retailer can monitor inventory levels in real-time, reducing the need for manual stock checks and minimizing stockouts. Furthermore, RFID tags can be used to track the movement of goods throughout the supply chain, enabling better visibility and control over the entire process, ultimately leading to improved customer satisfaction and operational efficiency.

What will be the size of the Automatic Data Capture (ADC) Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free SampleThe market continues to evolve, driven by advancements in technology and increasing business demands. Parallel processing and distributed computing are key trends transforming the ADC landscape, enabling real-time data capture and analysis. These innovations can significantly impact boardroom-level decisions, such as compliance and budgeting. For instance, data privacy regulations like GDPR and HIPAA mandate strict data handling procedures, making real-time data capture and analysis crucial for companies to ensure compliance. Furthermore, distributed computing can help organizations save on IT infrastructure costs by optimizing resource utilization. According to recent research, companies have achieved a 30% reduction in processing time by implementing parallel processing techniques. Data capture workflows, API integration methods, and data preprocessing steps are essential components of successful ADC implementations. System reliability analysis, algorithm optimization, and data transformation methods are also crucial for ensuring data accuracy and efficiency. By embracing these trends, businesses can streamline their data capture processes, enhance operational efficiency, and make informed decisions based on real-time data insights.

Unpacking the Automatic Data Capture (ADC) Market Landscape

The market encompasses technologies and solutions that facilitate real-time data processing from various sources, including barcode scanning and RFID tagging systems. ADC solutions streamline data acquisition systems, enabling businesses to improve data quality metrics by up to 30% through real-time data validation techniques and data integrity checks. Furthermore, real-time analytics and predictive modeling techniques enhance operational efficiency by up to 25%, ensuring data-driven decision-making and compliance alignment with regulatory standards. Wireless data transmission and cloud data storage enable scalable data architecture and high-volume data handling, while machine learning algorithms and data mining strategies uncover valuable insights from the data stream. Data visualization tools and error correction algorithms further enhance system performance benchmarks, ensuring data security measures remain effective. Pattern recognition systems and anomaly detection methods further bolster data g

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Generative AI In Chemical Market Size 2025-2029

The generative AI in chemical market size is forecast to increase by USD 1.93 billion, at a CAGR of 36.5% between 2024 and 2029.

In the market, the imperative to accelerate research and development cycles and reduce innovation costs is a key driver. The adoption of generative AI is transforming the chemical industry by enabling the creation of new molecules and optimizing existing ones, leading to significant time and cost savings. Another trend is the rise of platform-based solutions and AI-as-a-service, which offer flexibility and scalability to companies, allowing them to leverage AI technologies without the need for extensive in-house resources. However, the market also faces challenges. Data scarcity, quality, and accessibility remain significant obstacles. The market is experiencing a rise in the adoption of generative and conversational AI technologies.
The chemical industry generates massive data sets, but much of it is unstructured and difficult to access. Addressing these challenges will require collaboration between industry players, academia, and technology providers to develop standardized data formats and open access platforms. Companies that successfully navigate these challenges and harness the power of generative AI will be well-positioned to innovate and compete in the rapidly evolving chemical market. Generative AI models require vast amounts of high-quality data to function effectively. AI-driven quality control, material science, and deep learning applications are enhancing safety risk assessments and molecular dynamics simulations.

What will be the Size of the Generative AI In Chemical Market during the forecast period?

Explore in-depth regional segment analysis with market size data with forecasts 2025-2029 - in the full report.
Request Free Sample

The market for generative AI in the chemical industry continues to evolve, driven by advancements in molecular simulation packages, statistical modeling methods, and chemical process simulation. These technologies enable reaction mechanism elucidation, thermodynamic property prediction, and reaction kinetics modeling, among other applications. For instance, computational chemistry software and NMR data interpretation have led to a 15% increase in efficiency in the development of new pharmaceuticals. Moreover, the integration of graph neural networks, natural language processing, and knowledge graph databases facilitates the automation of complex chemical processes.
Chromatographic data processing and spectral data analysis are also benefiting from AI-driven solutions, leading to improved accuracy and productivity. Quantum mechanics methods, deep learning frameworks, semi-empirical calculations, and chemical reaction databases are further expanding the scope of AI in the chemical industry. Data mining techniques and parallel computing clusters enable the extraction of valuable insights from vast amounts of data, while process safety management systems ensure the safe and efficient operation of chemical processes. The industry is expected to grow at a rate of over 12% annually, underpinned by the continuous adoption of these advanced technologies.

How is this Generative AI In Chemical Market segmented?

The generative AI in chemical market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, for the following segments.

Technology

  Machine learning
  Deep learning
  Generative models
  Molecular docking
  Others


Application

  Molecular design
  Materials discovery
  Product optimization
  Reaction prediction
  Others


End-user

  Base chemicals and petrochemicals
  Specialty chemicals
  Consumer chemicals
  Agrochemicals


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Technology Insights

The Machine learning segment is estimated to witness significant growth during the forecast period. Machine learning (ML) is a key technological driver in the generative AI chemical market, powering algorithms and statistical models that enable computers to learn from data and make predictions without explicit programming. In the chemical industry, ML plays a pivotal role, extending beyond generation to predictive and analytical functions. One primary application is the creation of Quantitative Structure-Activity Relationship (QSAR) and Quantitative Structure-Property Relationship (QSPR) models. These models, trained on extensive datasets of known chemical compounds and their properties, accurately forecast the characteristics of new, untested molecules. Cloud computing platforms, high-performance computing, big data analytics, and process control syste

please cite this dataset by :

Nicolas Turenne, Ziwei Chen, Guitao Fan, Jianlong Li, Yiwen Li, Siyuan Wang, Jiaqi Zhou (2021) Mining an English-Chinese parallel Corpus of Financial News, BNU HKBU UIC, technical report

The dataset comes from Financial Times news website (https://www.ft.com/)

news are written in both languages Chinese and English.

FTIE.zip contains all documents in a file individually

FT-en-zh.rar contains all documents in one file

Below is a sample document in the dataset defined by these fields and syntax :

id;time;english_title;chinese_title;integer;english_body;chinese_body

1021892;2008-09-10T00:00:00Z;FLAW IN TWIN TOWERS REVEALED;科学家发现纽约双子塔倒塌的根本原因;1;Scientists have discovered the fundamental reason the Twin Towers collapsed on September 11 2001. The steel used in the buildings softened fatally at 500?C – far below its melting point – as a result of a magnetic change in the metal. @ The finding, announced at the BA Festival of Science in Liverpool yesterday, should lead to a new generation of steels capable of retaining strength at much higher temperatures.;科学家发现了纽约世贸双子大厦(Twin Towers)在2001年9月11日倒塌的根本原因。由于磁性变化,大厦使用的钢在500摄氏度——远远低于其熔点——时变软,从而产生致命后果。 @ 这一发现在昨日利物浦举行的BA科学节(BA Festival of Science)上公布。这应会推动能够在更高温度下保持强度的新一代钢铁的问世。

The dataset contains 60,473 bilingual documents.

Time range is from 2007 and 2020.

This dataset has been used for parallel bilingual news mining in Finance domain.

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AI In Genomics Market Size 2025-2029

The ai in genomics market size is valued to increase by USD 1.73 billion, at a CAGR of 32.6% from 2024 to 2029. Precipitous decline in sequencing costs and subsequent genomic data will drive the ai in genomics market.

Market Insights

Europe dominated the market and accounted for a 32% growth during the 2025-2029.
By Component - Software segment was valued at USD 87.00 billion in 2023
By Technology - Machine learning segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 1.00 million 
Market Future Opportunities 2024: USD 1729.20 million
CAGR from 2024 to 2029 : 32.6%

Market Summary

The market is experiencing significant growth due to the precipitous decline in sequencing costs and subsequent genomic data proliferation. This data deluge is driving the need for advanced analytical tools to make sense of the complex genetic information. Enter generative AI and foundation models, which are increasingly being adopted in the biological domain to analyze and interpret genomic data. These models can identify patterns, make predictions, and even generate new sequences, revolutionizing research and development in genomics. However, the implementation of AI in genomics is not without challenges. The labyrinth of data privacy, security, and complex regulatory frameworks presents significant hurdles. For instance, in a pharmaceutical company, AI is used to optimize the supply chain by predicting demand for specific genetic therapies. This involves analyzing vast amounts of patient data, raising concerns around data security and privacy. Additionally, regulatory compliance adds another layer of complexity, requiring stringent data handling protocols. Despite these challenges, the potential benefits of AI in genomics are immense, from accelerating drug discovery to improving patient outcomes. The future of genomics lies in harnessing the power of AI to unlock the secrets of the human genome.

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

Get Key Insights on Market Forecast (PDF) Request Free SampleThe market continues to evolve, revolutionizing various sectors such as comparative genomics, population genetics studies, and infectious disease genomics. Big data analytics plays a pivotal role in processing vast genomic data, enabling faster and more accurate discoveries. Microbial genomics, cancer genomics, and structural genomics are among the fields benefiting from advanced algorithm optimization and high-performance computing. In the realm of human genomics, data mining methods and statistical genetics methods uncover hidden patterns and correlations, while explainable AI methods ensure transparency and interpretability. Parallel computing and predictive modeling enable real-time analysis and model validation techniques ensure accuracy. Variant annotation databases facilitate quicker identification of genetic mutations, contributing to personalized medicine and diagnostics. Cloud computing platforms provide scalable and cost-effective genomic data storage solutions, ensuring easy access to data for researchers and clinicians. Synthetic biology and plant genomics also gain from AI, with applications ranging from gene editing to crop improvement. Data sharing initiatives foster collaboration and accelerate research progress. In the boardroom, AI in Genomics translates to significant improvements in research efficiency and accuracy. For instance, companies have reported a substantial reduction in processing time, enabling them to bring products to market faster and stay competitive. The integration of AI in genomics is a strategic investment, offering potential cost savings, increased productivity, and improved patient outcomes.

Unpacking the AI In Genomics Market Landscape

In the dynamic realm of genomics, Artificial Intelligence (AI) is revolutionizing various applications, including genotype-phenotype association and therapeutic target validation. AI-driven solutions enable a 30% increase in efficiency compared to traditional methods, resulting in accelerated research and development. CRISPR gene editing benefits from AI integration, achieving a 25% improvement in precision and accuracy. Data security measures are reinforced through AI's ability to monitor and analyze access patterns, reducing potential breaches by 40%. Bioinformatics pipelines, diagnostics test development, and machine learning algorithms leverage AI for enhanced performance and accuracy. Protein-protein interactions, epigenetic modifications, and systems biology modeling gain new insights through AI-powered analysis. Personalized medicine approaches, gene expression profiling, and protein structure prediction are transformed by AI, leading to improved ROI and compliance alignment with data privacy regulatio

Runtime of the parallel affinity propagation algorithm.

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Game Feed Market Size 2024-2028

The game feed market size is valued to increase by USD 11.71 billion, at a CAGR of 4.2% from 2023 to 2028. Growing popularity of animal sports will drive the game feed market.

Market Insights

North America dominated the market and accounted for a 52% growth during the 2024-2028.
By Type - Protein segment was valued at USD 15.5 billion in 2022
By segment2 - segment2_1 segment accounted for the largest market revenue share in 2022

Market Size & Forecast

Market Opportunities: USD 46.74 million 
Market Future Opportunities 2023: USD 11713.70 million
CAGR from 2023 to 2028 : 4.2%

Market Summary

The market encompasses the production and distribution of specialized nutritional formulas for animals engaged in competitive sports and entertainment industries. This market's expansion is driven by the growing popularity of animal sports and the increasing demand for optimized animal performance. An essential trend shaping the market is the introduction of medicated feed, which addresses various health concerns and enhances animal well-being. An illustrative business scenario demonstrates the significance of game feed in operational efficiency and regulatory compliance. Consider a large-scale livestock farm that raises animals for exhibition and competition. The farm's success hinges on the health and performance of its animals. By implementing a game feed regimen, the farm can ensure its animals receive the optimal balance of nutrients, leading to improved growth rates, enhanced muscle development, and better overall health. Additionally, game feed formulations can help the farm comply with stringent animal welfare regulations, as they often contain essential additives that promote animal health and minimize stress. The market is influenced by various factors, including technological advancements, changing consumer preferences, and evolving regulatory requirements. As the industry continues to evolve, game feed manufacturers must stay abreast of these trends and challenges to maintain a competitive edge.

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

Get Key Insights on Market Forecast (PDF) Request Free SampleThe market represents a dynamic and ever-evolving landscape, driven by advancements in player behavior analysis, distributed computing, and parallel processing. These technologies power mobile game optimization, cross-platform compatibility, and churn prediction models, among other innovations. One significant trend in this domain is game server architecture, which has seen a 30% increase in demand due to the rise of live stream integration and social media engagement. This shift necessitates boardroom-level decisions around network infrastructure, data enrichment processes, and data mining techniques to ensure seamless user experiences. Additionally, performance optimization and monetization strategies are critical areas of focus, with customized dashboards, in-game advertising, and dynamic content delivery playing essential roles. Player engagement metrics, such as retention rate analysis and data compression techniques, are also vital in understanding and retaining user bases. Game telemetry and real-time feedback loops further enable companies to make data-driven decisions, while data validation rules ensure the accuracy and reliability of the information. By staying informed of these trends and implementing effective strategies, businesses can remain competitive in the ever-changing the market.

Unpacking the Game Feed Market Landscape

In the dynamic and competitive landscape of the market, businesses leverage advanced technologies to optimize their data management and gain a competitive edge. API integration strategies enable real-time data access, enhancing operational efficiency by a factor of three compared to traditional methods. Machine learning models and predictive modeling algorithms power data stream management, delivering accurate in-game event detection and match outcome prediction with 95% accuracy. Scalable architecture designs, including serverless computing and microservice architecture, ensure database scalability and data pipeline optimization. Data security protocols safeguard sensitive information, while low-latency streaming and real-time analytics platforms facilitate instant decision-making. Big data infrastructure and data governance frameworks ensure data quality assurance and compliance alignment, ultimately driving significant ROI improvements.

Key Market Drivers Fueling Growth

The surge in popularity of animal sports serves as the primary catalyst for market growth. The market exhibits a dynamic and expanding scope, catering to the nutritional requirements of animals involved in various sports sectors. In Europe, bullfighting and horse racing necessitate specialized feed to ensure the animals' opt

Graphics Card Market Outlook

According to our latest research, the global graphics card market size reached USD 49.2 billion in 2024, driven by surging demand in gaming, professional visualization, and data center applications. The market is experiencing robust expansion, registering a CAGR of 12.1% from 2025 to 2033. By 2033, the graphics card market is forecasted to attain an impressive USD 137.2 billion, propelled by advancements in GPU technology, the proliferation of AI workloads, and the growing adoption of high-performance computing solutions across various industries. The graphics card marketÂ’s dynamic growth is further supported by a rapidly expanding gaming community, increasing investments in data centers, and the ongoing digital transformation across enterprises worldwide.

One of the primary growth factors for the graphics card market is the exponential rise in the gaming industry. The proliferation of high-definition and immersive gaming experiences has necessitated the adoption of advanced GPUs with enhanced processing capabilities and memory. With the emergence of virtual reality (VR) and augmented reality (AR) technologies, gamers are demanding graphics cards that can deliver ultra-realistic visuals and seamless frame rates. The global gaming population, which now exceeds 3.2 billion, continues to drive the need for dedicated graphics solutions, particularly as eSports and streaming platforms gain mainstream traction. Furthermore, the increasing popularity of AAA game titles and the integration of ray tracing and AI-driven graphics enhancements have set new performance standards, compelling both casual and professional gamers to upgrade their systems regularly.

Another significant growth driver is the expanding application of graphics cards in professional visualization and data center environments. Industries such as architecture, engineering, media and entertainment, and scientific research rely heavily on GPU acceleration for rendering, simulation, and deep learning tasks. The demand for real-time visualization, 3D modeling, and high-resolution content creation is pushing organizations to invest in powerful graphics solutions. Additionally, the rise of artificial intelligence and machine learning workloads in data centers has fueled the adoption of high-performance GPUs, as these processors offer unparalleled parallel processing capabilities. As enterprises increasingly migrate to cloud-based infrastructures and embrace AI-driven analytics, the graphics card market is set to benefit from sustained investments in GPU-powered servers and workstations.

Cryptocurrency mining has also played a pivotal role in shaping the graphics card market landscape. The surge in popularity of digital currencies such as Bitcoin and Ethereum has triggered a massive demand for GPUs, which are essential for mining operations due to their superior parallel processing abilities. Although the volatility of the cryptocurrency market introduces some uncertainty, the periodic booms in mining activity have historically led to supply shortages and price surges for graphics cards. This cyclical demand, coupled with the ongoing development of blockchain technologies, ensures that the mining segment remains a significant contributor to the overall market growth. Manufacturers are responding by developing mining-specific GPUs and optimizing their product lines to cater to both consumer and enterprise mining requirements.

In the realm of gaming and high-performance computing, the demand for GPU Overclocking Accessory has seen a notable rise. These accessories are designed to enhance the performance of graphics cards by allowing users to push their GPUs beyond factory settings. Overclocking can lead to significant improvements in frame rates and processing speeds, making it a popular choice among gamers and tech enthusiasts. With the right accessory, users can fine-tune voltage, clock speeds, and cooling solutions to achieve optimal performance. As the gaming industry continues to expand, the market for GPU Overclocking Accessories is expected to grow, driven by the desire for enhanced gaming experiences and competitive advantages in eSports.

From a regional perspective, Asia Pacific continues to dominate the global graphics card market, accounting for the largest revenue share in 2024. The regionÂ’s leadersh

Frequent sequence pattern mining is an excellent tool to discover patterns in event chains. In complex systems, events from parallel processes are present, often without proper labelling. To identify the groups of events related to the subprocess, frequent sequential pattern mining can be applied. Since most algorithms provide too many frequent sequences that make it difficult to interpret the results, it is necessary to post-process the resulting frequent patterns. The available visualisation techniques do not allow easy access to multiple properties that support a faster and better understanding of the event scenarios. To answer this issue, our work proposes an intuitive and interactive solution to support this task, introducing three novel network-based sequence visualisation methods that can reduce the time of information processing from a cognitive perspective. The proposed visualisation methods offer a more information rich and easily understandable interpretation of sequential pattern mining results compared to the usual text-like outcome of pattern mining algorithms. The first uses the confidence values of the transitions to create a weighted network, while the second enriches the adjacency matrix based on the confidence values with similarities of the transitive nodes. The enriched matrix enables a similarity-based Multidimensional Scaling (MDS) projection of the sequences. The third method uses similarity measurement based on the overlap of the occurrences of the supporting events of the sequences. The applicability of the method is presented in an industrial alarm management problem and in the analysis of clickstreams of a website. The method was fully implemented in Python environment. The results show that the proposed methods are highly applicable for the interactive processing of frequent sequences, supporting the exploration of the inner mechanisms of complex systems.