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 seek original and unpublished research papers based on theoretical or experimental works for the publication globally.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 solicit original and unpublished research papers, based on theoretical or experimental works.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

Open Source Data Labelling Tool Market Outlook

The global market size for Open Source Data Labelling Tools was valued at USD 1.5 billion in 2023 and is projected to reach USD 4.6 billion by 2032, growing at a compound annual growth rate (CAGR) of 13.2% during the forecast period. This significant growth can be attributed to the increasing adoption of artificial intelligence (AI) and machine learning (ML) across various industries, which drives the need for accurately labelled data to train these technologies effectively.

The rapid advancement and integration of AI and ML in numerous sectors serve as a primary growth factor for the Open Source Data Labelling Tool market. With the proliferation of big data, organizations are increasingly recognizing the importance of high-quality, annotated data sets to enhance the accuracy and efficiency of their AI models. The open-source nature of these tools offers flexibility and cost-effectiveness, making them an attractive choice for businesses of all sizes, especially startups and SMEs, which further fuels market growth.

Another key driver is the rising demand for automated data labelling solutions. Manual data labelling is a time-consuming and error-prone task, leading many organizations to seek automated tools that can swiftly and accurately label large datasets. Open source data labelling tools, often augmented with advanced features like natural language processing (NLP) and computer vision, provide a scalable solution to this challenge. This trend is particularly pronounced in data-intensive industries such as healthcare, automotive, and finance, where the precision of data labelling can significantly impact operational outcomes.

Additionally, the collaborative nature of open-source communities contributes to the market's growth. Continuous improvements and updates are driven by a global community of developers and researchers, ensuring that these tools remain at the cutting edge of technology. This ongoing innovation not only boosts the functionality and reliability of open-source data labelling tools but also fosters a sense of community and shared knowledge, encouraging more organizations to adopt these solutions.

In the realm of data labelling, Premium Annotation Tools have emerged as a significant player, offering advanced features that cater to the needs of enterprises seeking high-quality data annotation. These tools often come equipped with enhanced functionalities such as collaborative interfaces, real-time updates, and integration capabilities with existing AI systems. The premium nature of these tools ensures that they are designed to handle complex datasets with precision, thereby reducing the margin of error in data labelling processes. As businesses increasingly prioritize accuracy and efficiency, the demand for premium solutions is on the rise, providing a competitive edge in sectors where data quality is paramount.

From a regional perspective, North America holds a significant share of the market due to the robust presence of tech giants and a well-established IT infrastructure. The region's strong focus on AI research and development, coupled with substantial investments in technology, drives the demand for data labelling tools. Meanwhile, the Asia Pacific region is expected to exhibit the highest growth rate during the forecast period, attributed to the rapid digital transformation and increasing AI adoption across countries like China, India, and Japan.

Component Analysis

When dissecting the Open Source Data Labelling Tool market by component, it is evident that the segment is bifurcated into software and services. The software segment dominates the market, primarily due to the extensive range of features and functionalities that open-source data labelling software offers. These tools are customizable and can be tailored to meet specific needs, making them highly versatile and efficient. The software segment is expected to continue its dominance as more organizations seek comprehensive solutions that integrate seamlessly with their existing systems.

The services segment, while smaller in comparison, plays a crucial role in the overall market landscape. Services include support, training, and consulting, which are vital for organizations to effectively implement and utilize open-source data labelling tools. As the adoption of these tools grows, so does the demand for professional services that can aid in deployment, customization

AI-Generated Email Subject Line Market Outlook

According to our latest research, the global AI-Generated Email Subject Line market size reached USD 1.12 billion in 2024, with a robust compound annual growth rate (CAGR) of 22.6% projected from 2025 to 2033. By the end of 2033, the market is expected to attain a value of USD 8.97 billion. This rapid expansion is primarily driven by the increasing adoption of artificial intelligence solutions to optimize email marketing campaigns, enhance open rates, and deliver personalized content at scale. The rise in digital marketing budgets and the pressing need for higher engagement rates across industries are further propelling market growth, as organizations seek innovative tools to gain a competitive edge.

One of the most significant growth factors for the AI-Generated Email Subject Line market is the proven impact of AI-driven subject lines on email open rates and overall campaign performance. Marketers are increasingly leveraging machine learning algorithms to analyze vast datasets and generate subject lines that resonate with specific audience segments. These AI-powered solutions can test, refine, and personalize subject lines in real time, resulting in higher engagement and improved return on investment (ROI) for email campaigns. As brands compete for consumer attention in overcrowded inboxes, the ability to craft compelling and contextually relevant subject lines has become a critical differentiator, fueling the demand for advanced AI-based tools.

Another driving force is the integration of AI-generated subject line solutions within broader marketing automation platforms. Many organizations are seeking seamless workflows that allow them to automate not only the creation of subject lines but also the delivery, timing, and segmentation of email campaigns. The interoperability of AI solutions with existing customer relationship management (CRM) and marketing technology stacks is accelerating adoption, particularly among enterprises aiming to streamline operations and scale their outreach efforts. Moreover, the increasing sophistication of natural language processing (NLP) and deep learning models is enabling more nuanced and emotionally intelligent subject line generation, further enhancing campaign effectiveness and customer satisfaction.

The democratization of AI technology is also playing a pivotal role in market expansion. As AI-generated email subject line tools become more user-friendly and accessible, small and medium enterprises (SMEs) are increasingly able to leverage these capabilities without the need for extensive technical expertise or significant upfront investment. Cloud-based deployment models, subscription pricing, and integration with popular email service providers have lowered barriers to entry, making advanced AI-driven marketing accessible to a broader range of businesses. This widespread adoption across diverse sectors—ranging from retail and e-commerce to BFSI, healthcare, and media—continues to drive the upward trajectory of the market.

Regionally, North America remains the dominant market for AI-Generated Email Subject Line solutions, accounting for the largest revenue share in 2024. The region's leadership is attributed to the presence of major technology providers, high digital marketing maturity, and early adoption of AI-powered tools by enterprises. However, Asia Pacific is emerging as the fastest-growing region, with organizations in China, India, and Southeast Asia rapidly embracing AI to enhance digital engagement and customer experience. Europe is also witnessing steady growth, fueled by stringent data privacy regulations and a strong focus on personalized marketing. Meanwhile, Latin America and the Middle East & Africa are gradually catching up, supported by increasing digital transformation initiatives and rising internet penetration.

Component Analysis

The AI-Generated Email Subject Line market by component is segmented into software and services. The software segment com

Edge AI Vision Sensor Market Outlook

According to our latest research, the global Edge AI Vision Sensor market size in 2024 stands at USD 2.18 billion, reflecting robust adoption across multiple industries. The market is expected to grow at a CAGR of 18.7% from 2025 to 2033, reaching a projected value of USD 10.87 billion by 2033. This impressive growth is driven by the increasing demand for real-time data processing, intelligent automation, and the proliferation of AI-powered edge devices across sectors such as automotive, industrial automation, healthcare, and smart cities. As per the latest research, the convergence of artificial intelligence with edge computing and advanced vision sensors is fundamentally transforming the landscape of machine perception and decision-making at the edge.

A significant growth factor propelling the Edge AI Vision Sensor market is the surge in demand for real-time, on-device data processing capabilities. Traditional cloud-based vision systems often suffer from latency, bandwidth constraints, and privacy concerns, making them less suitable for time-sensitive or mission-critical applications. Edge AI Vision Sensors address these challenges by enabling rapid image analysis and decision-making directly at the point of data capture. This capability is particularly vital in sectors such as autonomous vehicles, industrial robotics, and security surveillance, where split-second responses can impact safety, efficiency, and operational continuity. The integration of AI algorithms within vision sensors is further enhancing their ability to detect, classify, and interpret complex visual data, thereby unlocking new levels of automation and intelligence.

Another critical driver for the Edge AI Vision Sensor market is the exponential growth of smart devices and the Internet of Things (IoT) ecosystem. As billions of connected devices generate vast amounts of visual data, centralized processing becomes impractical and costly. Edge AI Vision Sensors provide a scalable solution by distributing intelligence closer to the source, reducing the need for constant data transmission to the cloud. This distributed architecture not only enhances system responsiveness but also improves energy efficiency and data security. The growing adoption of smart cameras, wearable devices, and intelligent home appliances is accelerating the deployment of edge vision technologies, making them indispensable in the modern digital infrastructure.

Technological advancements in sensor hardware, AI chipsets, and embedded software are further fueling the expansion of the Edge AI Vision Sensor market. Innovations such as low-power CMOS sensors, advanced neural processing units (NPUs), and optimized AI frameworks are enabling the development of compact, energy-efficient, and high-performance vision solutions. These advancements are lowering barriers to entry for manufacturers and end-users alike, fostering greater innovation and customization. Moreover, the emergence of open-source AI libraries and standardized development platforms is catalyzing the rapid prototyping and deployment of edge vision applications across diverse industries, from healthcare diagnostics to retail analytics.

Regionally, Asia Pacific is emerging as the dominant market for Edge AI Vision Sensors, driven by rapid industrialization, urbanization, and significant investments in smart infrastructure. North America and Europe are also experiencing substantial growth, fueled by strong R&D capabilities, early adoption of AI technologies, and a mature industrial base. The Middle East & Africa and Latin America are gradually catching up, propelled by increasing digital transformation initiatives and government support for AI-driven innovation. This global momentum underscores the universal relevance and transformative potential of edge AI vision solutions across geographic and economic boundaries.

Component Analysis

The Edge AI Vision Sensor market is segmented by component into hardware, software, and services&

Responsible-AI Bias Audit Platform Market Outlook

According to our latest research, the global Responsible-AI Bias Audit Platform market size reached USD 1.47 billion in 2024 and is projected to grow at a robust CAGR of 31.2% through the forecast period, resulting in a forecasted market size of USD 17.42 billion by 2033. This strong expansion is driven by the increasing regulatory focus on ethical AI, rising enterprise adoption of AI technologies, and the growing demand for transparency and fairness in automated decision-making systems.

A primary growth factor for the Responsible-AI Bias Audit Platform market is the intensifying regulatory landscape surrounding artificial intelligence across major economies. Governments and regulatory bodies in North America, Europe, and Asia Pacific are introducing stringent guidelines and frameworks to ensure AI systems operate transparently and without discriminatory biases. The European Union’s AI Act and similar initiatives in the United States are compelling organizations to adopt bias audit solutions as a compliance measure. This regulatory push is not only raising awareness but also mandating the integration of Responsible-AI Bias Audit Platforms across sectors such as finance, healthcare, and government, thereby fueling market growth. Organizations are increasingly recognizing that failing to proactively address bias in AI can result in severe reputational, legal, and financial repercussions, making bias audit platforms an essential component of their AI governance strategy.

Another significant driver is the accelerating adoption of AI technologies by enterprises of all sizes. As AI and machine learning models become ubiquitous in decision-making processes, the risk of embedded biases leading to unfair outcomes has become a major concern for both businesses and consumers. Enterprises are investing in Responsible-AI Bias Audit Platforms to detect, mitigate, and monitor algorithmic bias throughout the AI lifecycle. This trend is particularly pronounced in sectors like healthcare, banking, and retail, where biased AI outcomes can directly impact human lives or financial equity. The integration of bias audit solutions is also being propelled by competitive pressures, as organizations strive to demonstrate their commitment to ethical AI to customers, investors, and partners. As a result, the Responsible-AI Bias Audit Platform market is witnessing a surge in demand for both software and services that enable comprehensive, scalable, and automated bias detection and remediation.

Furthermore, the market is benefiting from rapid advancements in AI explainability and transparency tools. Modern Responsible-AI Bias Audit Platforms are leveraging cutting-edge technologies such as explainable AI (XAI), natural language processing, and advanced analytics to provide actionable insights into model behavior and bias sources. This technological evolution is making bias audits more accessible, efficient, and effective, even for complex deep learning models. Additionally, the growing ecosystem of AI ethics frameworks, open-source bias detection libraries, and industry collaborations is fostering innovation and interoperability among bias audit platforms. These developments are lowering adoption barriers for both large enterprises and small and medium-sized businesses, further expanding the market’s addressable base.

Regionally, North America currently dominates the Responsible-AI Bias Audit Platform market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The United States, in particular, is at the forefront due to its advanced AI ecosystem, early adoption of AI governance practices, and strong regulatory momentum. Europe is rapidly catching up, driven by the EU’s comprehensive regulatory approach and widespread enterprise adoption. Meanwhile, Asia Pacific is emerging as a high-growth region, fueled by significant investments in digital transformation and AI across China, Japan, and India. Latin America and the Middle East & Africa are also witnessing increased adoption, albeit at a slower pace, as awareness of AI bias and regulatory requirements continues to rise.

AI-Generated Personalized Greeting Video Market Outlook

According to our latest research, the AI-Generated Personalized Greeting Video market size reached USD 1.37 billion in 2024, reflecting robust momentum driven by rapid digitalization and increasing adoption of artificial intelligence in content creation. The market is projected to expand at a CAGR of 22.6% from 2025 to 2033, with the global market anticipated to reach USD 9.05 billion by 2033. This surge is primarily attributed to the growing demand for hyper-personalized digital experiences, the proliferation of AI-powered software platforms, and the expanding use of video content in both personal and business communications.

A significant growth factor for the AI-Generated Personalized Greeting Video market is the increasing consumer preference for personalized digital interactions. As individuals and organizations strive to create meaningful connections in an increasingly virtual world, AI-generated greeting videos offer a scalable and efficient solution for delivering custom messages. This trend is especially pronounced in the corporate sector, where businesses are leveraging AI-driven videos for customer engagement, employee recognition, and marketing campaigns. The ability of AI to analyze recipient data and tailor video content accordingly enhances engagement rates and emotional resonance, making these solutions highly attractive across diverse application areas.

Another key driver is the technological advancements in natural language processing, deep learning, and video synthesis. These innovations have significantly improved the realism, quality, and customization capabilities of AI-generated videos, allowing for seamless integration of personalized elements such as names, images, and contextual messaging. The proliferation of cloud-based platforms has further democratized access to these technologies, enabling even small businesses and individual users to create high-quality personalized video greetings without requiring extensive technical expertise. This democratization is fueling widespread adoption and expanding the addressable market.

The integration of AI-generated personalized greeting videos into marketing and advertising strategies is also accelerating market growth. Brands are increasingly incorporating personalized video content in their outreach efforts to capture audience attention, drive conversions, and foster brand loyalty. The measurable impact of personalized video on open rates, click-through rates, and customer retention is prompting marketers to allocate greater resources to these solutions. Moreover, the versatility of AI-generated videos in various contexts—such as birthdays, anniversaries, corporate milestones, and event invitations—broadens their appeal and application scope, further propelling market expansion.

From a regional perspective, North America currently dominates the AI-Generated Personalized Greeting Video market, accounting for the largest share due to its advanced digital infrastructure, high adoption of AI technologies, and presence of leading market players. Europe follows closely, benefiting from strong demand in both corporate and personal segments, while Asia Pacific is emerging as the fastest-growing region, fueled by rapid digital transformation, rising internet penetration, and increasing consumer awareness. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as local businesses and individuals begin to embrace the benefits of AI-driven personalized video content.

Component Analysis

The AI-Generated Personalized Greeting Video market is segmented by component into Software and Services. The software segment encompasses AI-powered platforms and applications that enable users to create, customize, and distribute personalized greeting videos. This segment is witnessing substantial growth due to continuous improvements in AI algorithms, user-friendly interfaces, and integration capabilities with other digital tools. The rise of no-code and low-code platforms has further reduced barriers to entry, allowing users with limited technical skills to generate high-quality personalized videos efficiently. As businesses and individuals seek scalable solutions for mass personalization, the demand for robust, feature-rich software is expected to remain strong.

Within the software segment, cloud-based solutions are gaining si

EI Machine Learning Workload: Achieving Workload Reduction in Employment Insurance Recalculation Processes Recalculation within the context of Employment Insurance (EI) typically occurs when changes in circumstances or new information emerge that could impact the accuracy of benefit calculations. Recalculation falls under a specialized category of EI claims aimed at correcting previously determined benefits. During the recalculation process, the program implements specific measures based on the outcomes: - In instances of underpayment, where the initial benefit rate or weeks of entitlement were underestimated, the claim is adjusted to compensate for the financial shortfall. - Conversely, in cases of overpayment, where the initial benefit rate or weeks of entitlement were excessive, the claim is reduced to recover the excess amount. - When no changes are identified, indicating that the initial benefit rate and weeks of entitlement were accurate, the claim remains unchanged. The primary objective of the EI Machine Learning Workload is to reduce the time spent by officers on claim reviews by identifying cases where a recalculation will not result in any change. This approach allows officers to focus on more intricate reviews that require intervention and precision to ensure clients receive the correct benefit rate and entitlement. This initiative has been implemented in accordance with the guidelines delineated in the Treasury Board of Canada Secretariat (TBS) Directive on Automated Decision Making (ADM). These regulations guarantee that the integration of Artificial Intelligence in government programs and services is guided by transparent values, ethics, and legal standards. In alignment with these principles, numerous approvals have been secured, and a wide array of stakeholders, including the Chief Data Office, Privacy Management Division, IT Security, Legal Services, Accessibility, Architecture IT Systems, and the Unions, have been consulted. The EI program will continue with the utilization and testing of the EI Machine Learning workload to systematically decrease inventories in the coming years. This strategic approach not only facilitates inventory management but also empowers EI officers to redirect their focus toward more substantive tasks. A Random Forest model is employed for these runs, but other approaches may be considered in the future, in which case this page will be updated.

IntroductionSurveys conducted internationally have found widespread interest in artificial intelligence (AI) amongst medical students. No similar surveys have been conducted in Western Australia (WA) and it is not known how medical students in WA feel about the use of AI in healthcare or their understanding of AI. We aim to assess WA medical students’ attitudes towards AI in general, AI in healthcare, and the inclusion of AI education in the medical curriculum.MethodsA digital survey instrument was developed based on a review of available literature and consultation with subject matter experts. The survey was piloted with a group of medical students and refined based on their feedback. We then sent this anonymous digital survey to all medical students in WA (approximately 1539 students). Responses were open from the 7th of September 2021 to the 7th of November 2021. Students’ categorical responses were qualitatively analysed, and free text comments from the survey were qualitatively analysed using open coding techniques.ResultsOverall, 134 students answered one or more questions (8.9% response rate). The majority of students (82.0%) were 20–29 years old, studying medicine as a postgraduate degree (77.6%), and had started clinical rotations (62.7%). Students were interested in AI (82.6%), self-reported having a basic understanding of AI (84.8%), but few agreed that they had an understanding of the basic computational principles of AI (33.3%) or the limitations of AI (46.2%). Most students (87.5%) had not received teaching in AI. The majority of students (58.6%) agreed that AI should be part of medical training and most (72.7%) wanted more teaching focusing on AI in medicine. Medical students appeared optimistic regarding the role of AI in medicine, with most (74.4%) agreeing with the statement that AI will improve medicine in general. The majority (56.6%) of medical students were not concerned about the impact of AI on their job security as a doctor. Students selected radiology (72.6%), pathology (58.2%), and medical administration (44.8%) as the specialties most likely to be impacted by AI, and psychiatry (61.2%), palliative care (48.5%), and obstetrics and gynaecology (41.0%) as the specialties least likely to be impacted by AI. Qualitative analysis of free text comments identified the use of AI as a tool, and that doctors will not be replaced as common themes.ConclusionMedical students in WA appear to be interested in AI. However, they have not received education about AI and do not feel they understand its basic computational principles or limitations. AI appears to be a current deficit in the medical curriculum in WA, and most students surveyed were supportive of its introduction. These results are consistent with previous surveys conducted internationally.

Email Optimization Market Outlook

In 2023, the global email optimization market size was valued at approximately USD 2.5 billion and is anticipated to reach USD 6.9 billion by 2032, reflecting a compound annual growth rate (CAGR) of 11.7% during the forecast period. The increased focus on personalized customer engagement and the burgeoning demand for data-driven marketing strategies are some of the key factors propelling the growth of this market. As organizations worldwide continue to strive for enhanced customer relationship management, email optimization tools and services are becoming indispensable assets, driving market expansion at a substantial pace.

The primary growth factor for the email optimization market is the growing emphasis on personalized communication strategies within marketing frameworks. Businesses are investing heavily in tools that allow for dynamic content creation and personalized email campaigns to increase engagement rates. This personalized approach not only enhances the customer experience but also significantly boosts conversion rates and customer loyalty. As a result, companies across various sectors are increasingly adopting email optimization solutions to harness these benefits, creating a robust demand in the market.

Another prominent driver is the advancement in artificial intelligence and machine learning technologies, which are being integrated into email optimization solutions. These technologies enable more sophisticated analytics and automation, allowing businesses to predict customer behaviors and preferences more accurately. This capability helps in the creation of highly targeted email campaigns that can lead to improved open and click-through rates. As AI and machine learning technologies become more accessible and affordable, their integration into email marketing strategies is expected to drive significant market growth in the coming years.

The rise of data analytics in marketing is also a crucial factor contributing to the growth of the email optimization market. Companies are increasingly recognizing the value of data-driven insights in crafting their marketing strategies, which includes optimizing email campaigns. The ability to track and analyze customer interactions and behaviors provides businesses with the information needed to fine-tune their email marketing strategies for maximum impact. This trend is particularly evident in sectors such as retail and BFSI, where customer engagement is paramount, and is expected to continue driving the adoption of email optimization solutions.

Regionally, North America is expected to hold the largest share of the email optimization market, driven by the presence of major technology companies and a high adoption rate of advanced marketing solutions. The Asia Pacific region, however, is poised for the fastest growth, with a projected CAGR of over 13%, as businesses in this region increasingly adopt digital marketing technologies to cater to a rapidly expanding customer base. This growth is further fueled by the increasing penetration of the internet and smartphones, enabling more consumers to engage with digital content, including emails. Europe is also a significant market, with steady growth expected due to the increasing emphasis on data privacy and GDPR compliance, which necessitates the use of optimized and compliant email solutions.

The evolution of Email Template Builder Software is revolutionizing how businesses approach email marketing. These tools provide marketers with the ability to design visually appealing and responsive email templates without the need for extensive coding knowledge. By offering drag-and-drop interfaces and a wide range of customizable elements, email template builders empower marketers to create professional-grade emails that align with their brand identity. This capability is particularly valuable in today's competitive market, where the visual appeal and functionality of an email can significantly impact engagement rates. As businesses continue to prioritize personalized and aesthetically pleasing communication, the demand for sophisticated email template builder software is expected to grow, further driving the expansion of the email optimization market.

Component Analysis

In the component segment of the email optimization market, software solutions dominate due to their capability to provide comprehensive tools that enhance email marketing strategies. Software solutions encompass a variety of features such as