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 seek original and unpublished research papers based on theoretical or experimental works for the publication globally.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 solicit original and unpublished research papers, based on theoretical or experimental works.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

Journal of Computational Design and Engineering Impact Factor 2024-2025 - ResearchHelpDesk - Journal of Computational Design and Engineering is an international journal that aims to provide academia and industry with a venue for rapid publication of research papers reporting innovative computational methods and applications to achieve a major breakthrough, practical improvements, and bold new research directions within a wide range of design and engineering: Theory and its progress in computational advancement for design and engineering Development of computational framework to support large scale design and engineering Interaction issues among human, designed artifacts, and systems Knowledge-intensive technologies for intelligent and sustainable systems Emerging technology and convergence of technology fields presented with convincing design examples Educational issues for academia, practitioners, and future generation Proposal on new research directions as well as survey and retrospectives on mature field. Examples of relevant topics include traditional and emerging issues in design and engineering but are not limited to: Field specific issues in mechanical, aerospace, shipbuilding, industrial, architectural, plant, and civil engineering as well as industrial design Geometric modeling and processing, solid and heterogeneous modeling, computational geometry, features, and virtual prototyping Computer graphics, virtual and augmented reality, and scientific visualization Human modeling and engineering, user interaction and experience, HCI, HMI, human-vehicle interaction(HVI), cognitive engineering, and human factors and ergonomics with computers Knowledge-based engineering, intelligent CAD, AI and machine learning in design, and ontology Product data exchange and management, PDM/PLM/CPC, PDX/PDQ, interoperability, data mining, and database issues Design theory and methodology, sustainable design and engineering, concurrent engineering, and collaborative engineering Digital/virtual manufacturing, rapid prototyping and tooling, and CNC machining Computer aided inspection, geometric and engineering tolerancing, and reverse engineering Finite element analysis, optimization, meshes and discretization, and virtual engineering Bio-CAD, Nano-CAD, and medical applications Industrial design, aesthetic design, new media, and design education Survey and benchmark reports

International Journal of Engineering and Advanced Technology Publication fee - 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

The Artificial Intelligence (AI) Engineering market is experiencing robust growth, driven by increasing adoption of AI across various sectors and continuous advancements in AI technologies. While precise market size figures for 2025 are not provided, considering the significant investment and rapid innovation in this field, a reasonable estimation for the market size in 2025 would be $50 billion USD. This substantial figure reflects the expanding use of AI in diverse applications such as predictive maintenance, fraud detection, and personalized medicine. The market's Compound Annual Growth Rate (CAGR) for the forecast period (2025-2033) is projected to be approximately 25%, indicating a substantial expansion in the coming years. This growth is fueled by several key drivers, including the growing availability of large datasets, the development of more sophisticated algorithms, and increased cloud computing capabilities that enable scalable AI solutions. Furthermore, the rise of edge computing and the development of more powerful and energy-efficient AI chips are contributing factors. Leading companies like Microsoft, Google, and Amazon, along with specialized AI firms, are heavily invested in this space, driving innovation and expanding the market's scope. Several trends are shaping the future of AI engineering. The increasing demand for explainable AI (XAI) is pushing for more transparent and understandable AI models, addressing concerns about bias and accountability. The convergence of AI and other emerging technologies like the Internet of Things (IoT) and blockchain is creating new opportunities for innovation. However, the market faces certain restraints. The high cost of developing and implementing AI solutions, the scarcity of skilled AI engineers, and ethical concerns surrounding AI's societal impact are significant hurdles. Segmentation within the market includes AI software, AI hardware, AI services, and AI consulting, each segment exhibiting different growth trajectories based on factors like technological advancements and industry-specific demands. The regional breakdown is likely to show strong growth across North America, Europe, and Asia-Pacific, with regional variations reflecting differences in technological maturity, regulatory landscapes, and market adoption rates. The projected market size in 2033, considering the estimated 25% CAGR, is projected to exceed $300 Billion USD, demonstrating the substantial growth potential of the AI Engineering market.

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,

CBeamXP: Continuous Beam Cross-section Predictors datasetThe CBeamXP (Continuous Beam Cross-section (X) Predictors) is a dataset containing 1,000,000 data-points to be used for machine learning research. Each data-point represents an Ultimate Limit State (ULS) compliant beam from a continuous system consisting out of 11 members with utilisation ratios between 0.97 to 1.00. The predictors include span and uniformly distributed loads (UDLs) which can be used to predict the cross-sectional properties of each beam contained within the dataset. This dataset is publicly available on a CC-BY-4.0 licence and was used within the Gallet et al. (2024) journal article "Machine learning for structural design models of continuous beam systems via influence zones" (doi.org/10.1088/1361-6420/ad3334). Publications making use of the CBeamXP dataset are requested to cite the aforementioned journal article.In addition to the dataset, a training script, environment YAML file and a collection of saved models developed in the Gallet et al. (2024) study are available. These can be used to quickly generate user defined neural networks, compare performances and verify the results achieved by the Gallet et al. (2024) investigation.There are 5 files in this directory:CBeamXP_dataset.csvGallet_2024_training_script.pyGallet_2024_environment.ymlREADME.txtsaved_models.zipClick "Download all" (button at the top) to download the files and and look at the README.txt file for further details on the dataset and how to use the training script.

Context

Coursera is an online platform for higher education. The Coursera Global Skills Index (GSI) draws upon this rich data to benchmark 60 countries and 10 industries across Business, Technology, and Data Science skills to reveal skills development trends around the world.

Content

Cousera measured the skill proficiency of countries in AI overall and in the related skills of math, machine learning, statistics, statistical programming, and software engineering. These related skills cover the breadth of knowledge needed to build and deploy AI-powered technologies within organizations and society: • Math: the theoretical background necessary to conduct and apply AI research •**Statistics**: empirical skills needed to fit and measure the impact of AI models •**Machine Learning**: skills needed to build self-learning models like deep learning and other supervised models that power most AI applications today •**Statistical Programming**: programming skills needed to implement AI models such as in python and related packages like sci-kit learn and pandas •**Software Engineering**: programming skills needed to design and scale AI-powered applications

Acknowledgements

• Artificial Intelligence Index Report 2019
• Global Skills Index 2019

AI-Based Menu Engineering Airport Market Outlook

According to our latest research, the AI-Based Menu Engineering Airport market size reached USD 1.21 billion in 2024, reflecting a robust surge in adoption across global airport ecosystems. The market is currently advancing at a CAGR of 14.7%, driven by increasing demand for personalized passenger experiences and operational efficiency. By 2033, the market is forecasted to attain a value of USD 4.08 billion, underscoring the transformative impact of artificial intelligence on airport food and beverage, retail, and lounge services. The primary growth factor fueling this expansion is the integration of AI-driven analytics and automation, which enables airports and their partners to optimize menu offerings, streamline operations, and enhance customer satisfaction.

One of the key growth factors in the AI-Based Menu Engineering Airport market is the rising emphasis on passenger-centric services. As airports worldwide strive to differentiate themselves in an increasingly competitive landscape, the focus has shifted toward delivering tailored experiences that cater to diverse traveler preferences. AI-powered menu engineering empowers food and beverage outlets, lounges, and in-flight catering providers to analyze passenger demographics, travel patterns, and feedback in real time. This data-driven approach facilitates the creation of dynamic menus that adapt to changing tastes, dietary restrictions, and regional trends, ultimately contributing to higher customer satisfaction and increased revenue per passenger. The ability to personalize offerings not only enhances the airport experience but also establishes a competitive edge for airports and concessionaires seeking to boost non-aeronautical revenues.

Another significant driver is the operational efficiency achieved through AI-based menu engineering solutions. The deployment of advanced software and hardware enables seamless inventory management, demand forecasting, and automated supply chain coordination. By leveraging machine learning algorithms, airport food service providers can minimize food waste, optimize procurement cycles, and ensure the availability of popular menu items during peak travel periods. Additionally, AI-powered analytics provide actionable insights into sales trends and customer preferences, allowing for continuous menu refinement and promotional strategies. These operational enhancements are particularly vital in large international airports, where the complexity of managing multiple outlets and diverse culinary offerings can be challenging without intelligent automation.

The increasing adoption of cloud-based deployment models further accelerates market growth by offering scalability, flexibility, and cost-effectiveness. Cloud platforms facilitate real-time data sharing and centralized control across dispersed airport locations, enabling standardized menu engineering practices and rapid implementation of AI-driven updates. This is especially beneficial for airport chains and multinational concessionaires seeking to maintain consistency and quality across their global operations. Moreover, the integration of AI with emerging technologies such as IoT and mobile applications is opening new avenues for interactive menu experiences, contactless ordering, and personalized promotions, further enhancing the value proposition of AI-based menu engineering solutions.

From a regional perspective, North America currently leads the AI-Based Menu Engineering Airport market, accounting for the largest market share in 2024, followed closely by Europe and Asia Pacific. The strong presence of major international airports, early adoption of digital transformation strategies, and a mature ecosystem of technology providers contribute to North America's dominance. Europe is witnessing rapid growth, fueled by regulatory support and a strong focus on sustainability and passenger experience. Meanwhile, Asia Pacific is emerging as the fastest-growing region, driven by significant investments in airport infrastructure, rising air travel demand, and the proliferation of smart airport initiatives. Latin America and the Middle East & Africa are also showing promising potential, albeit at a relatively nascent stage, as airports in these regions increasingly recognize the benefits of AI-driven menu engineering in enhancing operational efficiency and customer engagement.

International Journal of Engineering and Advanced Technology CiteScore 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

PurposeTo analyze the global publications on artificial intelligence (AI) in strabismus using a bibliometric approach.MethodsThe Web of Science Core Collection (WoSCC) database was used to retrieve all of the publications on AI in strabismus from 2002 to 2023. We analyzed the publication and citation trend and identified highly-cited articles, prolific countries, institutions, authors and journals, relevant research domains and keywords. VOSviewer (software) and Bibliometrix (package) were used for data analysis and visualization.ResultsBy analyzing a total of 146 relevant publications, this study found an overall increasing trend in the number of annual publications and citations in the last decade. USA was the most productive country with the closest international cooperation. The top 3 research domains were Ophthalmology, Engineering Biomedical and Optics. Journal of AAPOS was the most productive journal in this field. The keywords analysis showed that “deep learning” and “machine learning” may be the hotspots in the future.ConclusionIn recent years, research on the application of AI in strabismus has made remarkable progress. The future trends will be toward optimized technology and algorithms. Our findings help researchers better understand the development of this field and provide valuable clues for future research directions.

The Computer-aided engineering software market was valued at USD 7.65 billion in 2022 and will reach USD 18.94 billion by 2030, registering a CAGR of 12% for the forecast period 2023-2030. Increasing demand for IoT and 3D printing is driving the Computer-aided Engineering software market

The increased use of digital technologies in product design, including the Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), automation, cloud, and others, helps to improve the quality and longevity of the finished goods. Users are also adopting computer-aided engineering solutions, which are predicted to boost market expansion, in order to reduce the time and cost associated with product development. The CAE trend is anticipated to be influenced by expanding engineering techniques like building information modeling (BIM), 3D printing, and concurrent engineering. Users can print any product as a three-dimensional picture using additive manufacturing techniques known as 3D printing. The use of this technology lowers the cost of production and supports the creation of innovative production techniques.

Lack of technical expertise and high installation costs hinder the growth of the Computer Aided Engineering Software market

CAE software is simple to access online. However, CAE solution providers have significant support, customization, and maintenance costs, which are likely to encourage more people to use open-source platforms while slowing the market's expansion. Additionally, it is anticipated that a shortage of technical expertise regarding the adoption of CAE software will impede market expansion. Users' perceptions make it difficult to push the creation of more composite elements since they think exact results only show later in the product design cycle. Therefore, the high cost of the software hinders the growth of the computer-aided engineering software market.

Impact of the COVID-19 Pandemic on the CAE Software Market:

The COVID-19 outbreak interrupted daily life and forced individuals and organizations to reevaluate their plans and objectives. It has been demonstrated that these advancements fuel technical development and innovation. Complete and integrated computer-aided engineering solutions were developed, promoted, sold, and supported by businesses using time and money. Although sales have been consistent, the COVID-19 pandemic caused a modest reduction in the rate of sales growth in 2020. In the early stages of the pandemic, market expansion was hampered by the declining demand from end-use verticals for CAE software. Vendors experienced a tight deadline for new orders and CAE software purchases as a result of the shutdown of factories and industrial plants. Investing in this software enables businesses to recognize problems and opportunities as they appear.

Opportunity for Computer-aided engineering software The Machine learning and Artificial intelligence present key market growth opportunity. The growth of artificial intelligence in Computer-aided engineering software Market is enabling reductions in downtime and enhancing personalization providing user specific tailored software. The artificial intelligence and Machine learning have been on the forefront of the development of the market and present key market growth opportunity. These changes contributing significantly for enhancing product quality and performance and provide a better horizon of user for the market by integration of cloud computing and platforms like PML and ERP streamlining data exchange and enhance collaborations within organization. AI powered tools integrated in the software helps for automation of task and enable real time data processing which is a crucial revenue generating factor with modern application. The digital transformation has been significantly contributing for improvement in the market of software and it is optimizing designs before physical prototypes. Factors like the improved product quality, increasing efficiency and data driven decision contributing significantly for market development and present key growth opportunity for Computer-aided engineering software Market. What is Computer-aided engineering software?

The practice of using computer software to mimic performance in order to enhance product designs or help engineers solve engineering difficulties for a variety of sectors is known as computer-aided engineering (CAE). This covers the modeling, veri...

Civil Engineering Design Software Market Outlook

The civil engineering design software market size is poised to witness a significant growth trajectory, expanding from an estimated USD 9.5 billion in 2023 to a projected USD 16.8 billion by 2032, reflecting a robust CAGR of 6.4%. The market's growth is primarily driven by the increasing demand for sophisticated design solutions in infrastructure development, urban planning, and sustainability initiatives. As global urbanization intensifies, the need for advanced tools that enhance the efficiency and accuracy of civil engineering projects has become paramount, propelling the expansion of the market.

A primary growth factor in the civil engineering design software market is the escalating infrastructure development across the globe. Governments and private sector players are significantly investing in construction and infrastructure projects such as smart cities, highways, and bridges to accommodate growing urban populations and to advance economic development. The sophistication and precision offered by design software enable engineers to create more efficient, sustainable, and cost-effective structures. Moreover, these tools facilitate better visualization and simulation of designs, which is pivotal in minimizing errors and optimizing resource use, thereby marrying technological advancement with economic prudence.

Another substantial driver is the increasing focus on sustainable development and environmental conservation. As environmental regulations become more stringent, civil engineering projects now require more thorough environmental impact assessments and sustainable design practices. Design software helps engineers incorporate eco-friendly materials and efficient energy consumption patterns into their projects, reducing the carbon footprint and adhering to compliance standards. Additionally, the software aids in water resources management and environmental engineering, playing a critical role in mitigating the adverse effects of construction on natural ecosystems.

Technological advancements in data analytics and cloud computing also spur market growth. The integration of artificial intelligence and machine learning within civil engineering design software enhances predictive analytics capabilities, allowing for more intelligent and adaptive design processes. Cloud-based deployment facilitates greater collaboration among teams, offering real-time data sharing and project updates, which is crucial for large-scale projects spread across different geographies. This technological evolution not only bolsters the efficiency and accuracy of engineering projects but also reduces operational costs, making such software more accessible and appealing to a broader range of users.

Bridge Analysis Software plays a pivotal role in the realm of civil engineering, particularly in the design and analysis of bridge structures. As infrastructure projects become increasingly complex, the need for specialized software that can handle the intricate calculations and simulations required for bridge design has grown. Bridge Analysis Software provides engineers with the tools to model various load scenarios, assess structural integrity, and optimize design for safety and efficiency. This software is essential for ensuring that bridges are not only structurally sound but also cost-effective and sustainable, aligning with the broader goals of modern civil engineering projects.

Regionally, the market outlook is bright, with Asia Pacific expected to lead the charge due to rapid urbanization and infrastructure development in countries like China and India. North America, with its mature construction sector and focus on smart infrastructure, remains a substantial contributor to market growth. Europe's market is driven by stringent environmental regulations and the pursuit of sustainable construction practices, while Latin America and the Middle East & Africa regions offer untapped potential, with increasing investments in infrastructure aimed at boosting their economic landscapes. Each region presents unique opportunities and challenges, but collectively, they contribute to a dynamic global market.

Component Analysis

The civil engineering design software market is segmented by component into software and services, each playing a distinct role in the market's landscape. The software component dominates the market, as it encompasses a wide range of solutions that cater to various aspects of civil

Arabic Speech Commands Dataset

This dataset is designed to help train simple machine learning models that serve educational and research purposes in the speech recognition domain, mainly for keyword spotting tasks.

Dataset Description

Our dataset is a list of pairs (x, y), where x is the input speech signal, and y is the corresponding keyword. The final dataset consists of 12000 such pairs, comprising 40 keywords. Each audio file is one-second in length sampled at 16 kHz. We have 30 participants, each of them recorded 10 utterances for each keyword. Therefore, we have 300 audio files for each keyword in total (30 * 10 * 40 = 12000), and the total size of all the recorded keywords is ~384 MB. The dataset also contains several background noise recordings we obtained from various natural sources of noise. We saved these audio files in a separate folder with the name background_noise and a total size of ~49 MB.

Dataset Structure

There are 40 folders, each of which represents one keyword and contains 300 files. The first eight digits of each file name identify the contributor, while the last two digits identify the round number. For example, the file path rotate/00000021_NO_06.wav indicates that the contributor with the ID 00000021 pronounced the keyword rotate for the 6^th time.

Data Split

We recommend using the provided CSV files in your experiments. We kept 60% of the dataset for training, 20% for validation, and the remaining 20% for testing. In our split method, we guarantee that all recordings of a certain contributor are within the same subset.

License

This dataset is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. For more details, see the LICENSE file in this folder.

Citations

If you want to use the Arabic Speech Commands dataset in your work, please cite it as:

@article{arabicspeechcommandsv1,
   author = {Ghandoura, Abdulkader and Hjabo, Farouk and Al Dakkak, Oumayma},
   title = {Building and Benchmarking an Arabic Speech Commands Dataset for Small-Footprint Keyword Spotting},
   journal = {Engineering Applications of Artificial Intelligence},
   year = {2021},
   publisher={Elsevier}
}

Industrial Plant Engineering Software Tools Market Outlook

The global market size for Industrial Plant Engineering Software Tools was valued at approximately USD 5.9 billion in 2023 and is forecasted to reach USD 10.2 billion by 2032, growing at a Compound Annual Growth Rate (CAGR) of 6.3% during the forecast period. This growth is primarily fueled by the increasing demand for digital transformation in industrial operations and the need for efficient management of complex engineering projects.

One of the primary growth factors driving the market is the escalating complexity of industrial plants. Modern industrial plants involve intricate designs, stringent regulations, and the integration of various advanced technologies. Engineering software tools enable plant engineers to create detailed models, simulate scenarios, and optimize designs, ultimately improving efficiency and reducing costs. The growing emphasis on automation and digitalization in industrial processes is further propelling the demand for these software tools. Companies are increasingly adopting these tools to streamline workflows, enhance productivity, and stay competitive in a rapidly evolving market.

Another significant growth driver is the rising awareness and implementation of Industry 4.0 principles. With the advent of the fourth industrial revolution, there is a heightened focus on integrating Internet of Things (IoT), Artificial Intelligence (AI), and Big Data analytics into industrial operations. Engineering software tools play a crucial role in collecting, processing, and analyzing data from various sources, providing actionable insights and facilitating predictive maintenance. This not only enhances operational efficiency but also minimizes downtime and maintenance costs, thereby boosting the overall plant performance.

The increasing regulatory pressures and the need for compliance with environmental and safety standards are also contributing to market growth. Industrial plants are subject to a myriad of regulations aimed at ensuring safety, reducing environmental impact, and maintaining operational standards. Engineering software tools assist in adhering to these regulations by offering features such as compliance tracking, documentation management, and risk assessment. This compliance capability is particularly vital in industries such as oil & gas, chemical, and power & energy, where stringent regulations are prevalent.

From a regional perspective, North America and Europe are poised to lead the market due to the early adoption of advanced technologies and robust industrial infrastructure. Asia Pacific is expected to witness the highest growth rate due to rapid industrialization, increasing investments in infrastructure development, and the growing emphasis on digital transformation in emerging economies such as China and India. Latin America and the Middle East & Africa are also anticipated to experience significant growth, driven by the expanding oil & gas, power, and energy sectors.

Component Analysis

The Industrial Plant Engineering Software Tools market can be segmented by component into software and services. The software segment includes various types of software solutions that are used in different stages of plant engineering, such as design software, simulation tools, and asset management systems. The services segment encompasses a range of services offered by vendors, including installation, training, consultancy, and maintenance support.

The software segment is expected to hold the largest market share during the forecast period. This is primarily due to the continuous demand for advanced software solutions that can handle the growing complexity of industrial plants. The rapid advancements in software technologies, such as the incorporation of AI, machine learning algorithms, and cloud-based solutions, are further boosting the market. Additionally, the software provides essential functionalities like 3D modeling, real-time data analysis, and digital twin technology, which are crucial for efficient plant engineering.

On the other hand, the services segment is anticipated to witness substantial growth. As industrial plants become more complex and technology-driven, the need for professional services to implement and maintain these advanced software tools is increasing. Companies often require specialized consultancy services to integrate new software into their existing systems, train their employees, and ensure continuous support. This growing reliance on professional services is expected to significantly contribute to

Marine CAE Software Market Outlook

The Marine CAE (Computer-Aided Engineering) Software market size is projected to grow significantly from its valuation of approximately USD 1.2 billion in 2023 to an expected USD 2.8 billion by 2032, reflecting a robust compound annual growth rate (CAGR) of around 9.5%. This increase is driven by several factors, including technological advancements in computational capabilities and the rising demand for more efficient and precise marine engineering solutions. The market growth is also influenced by the increasing complexity of marine projects, which necessitates sophisticated software solutions to enhance design accuracy and operational efficiency.

One of the primary growth factors driving the Marine CAE Software market is the rising need for innovation in marine design and engineering. As maritime operations become more complex and ambitious due to global trade expansions and the pursuit of greener technologies, there is a surging demand for advanced software tools that can simulate and optimize designs before physical prototyping. CAE software provides engineers with the ability to conduct robust analyses of ship behavior under various environmental conditions, thereby reducing the risk of costly errors and ensuring compliance with stringent regulatory standards. Moreover, this demand is further fueled by the growing number of offshore projects, which require precise engineering calculations to guarantee safety and performance under challenging conditions.

Another pivotal factor contributing to market expansion is the integration of artificial intelligence and machine learning technologies within CAE software. These advancements allow for more sophisticated simulations and predictive analytics, which help in optimizing resource allocation and improving the overall efficiency of design processes. Moreover, the incorporation of AI-driven analytics facilitates real-time data processing and decision-making, enabling engineers to make informed choices swiftly. As the maritime industry continues to seek ways to reduce costs and improve efficiency, the introduction of AI-enhanced CAE tools offers significant advantages in terms of reducing design cycles and enhancing the reliability of engineering outcomes, thereby fostering market growth.

The increasing focus on sustainability and environmental protection is also a critical driver for the Marine CAE Software market. With international regulations becoming more stringent regarding emissions and environmental impacts, marine companies are under pressure to design vessels that are not only efficient but also environmentally friendly. CAE software plays an essential role in optimizing the design and performance of vessels to meet these green standards by enabling simulations that predict the environmental impact of various design choices. Consequently, the market for marine CAE software is expected to benefit significantly from these regulatory pressures as industries strive to meet and exceed these requirements.

Regionally, the Marine CAE Software market is expected to witness significant growth across various parts of the world. North America, with its advanced technological infrastructure and strong emphasis on R&D activities, remains a key contributor to market growth. The Asia Pacific region, however, is anticipated to experience the fastest growth, driven by its expanding shipbuilding industry, particularly in countries like China, South Korea, and Japan. Europe also presents substantial opportunities due to its focus on sustainable maritime solutions and strong governmental support for innovation in marine engineering. These regions are expected to remain at the forefront of market expansion, leveraging their respective strengths to enhance their competitive positioning in the global market.

Component Analysis

The Marine CAE Software market can be segmented based on components into software and services, both of which play vital roles in the seamless operation and implementation of marine engineering projects. The software segment constitutes the backbone of this market, encompassing a wide range of solutions that enable complex simulations and modeling of marine structures. These software tools offer comprehensive functionalities that cover various aspects of marine engineering, including fluid dynamics, structural analysis, and hydrodynamics. The demand for sophisticated software solutions is being driven by the need for precision in modeling and simulation, allowing engineers to predict and mitigate potential challenges in marine design and operations.

Within the software

The images within this repository contains raw, unprocessed, microscopic images of soil fungi belonging to the following genera: Fusarium, Trichoderma, Verticillium, and Chromista of the Phytophthora genus that filenames contain name of the genus followed by underscore and number of an image for a given genera. Each image is labeled based on the specific microorganism genus it represents. The subimages images contains retrieved subimages, where each subimage corresponds to a single object that contains fragments of a microorganism. The subimages are generated from the original images and follow a specific naming convention. The image name indicates the microorganism genus, followed by information about the input image it was generated from, and finally the number of the retrieved subimage from that input image.

These subimages are utilized for training purposes using the Transfer Learning method with the CNN model called ResNet50.

This repository is provided as a Supplementary Material to the article "Automated Identification of Soil Fungi and Chromista through Convolutional Neural Networks" submitted to the Engineering Applications of Artificial Intelligence journal.

The attached dataset contains over 17.5 hours of experimental sensor data, including measurements from the following sensors:

• Front axle steering angle [°]
• Longitudinal acceleration [g]
• Lateral acceleration [g]
• Yaw rate [deg/s]
• Wheel speed (front left) [km/h]
• Wheel speed (front right) [km/h]
• Wheel speed (rear left) [km/h]
• Wheel speed (rear right) [km/h]

Data was sampled at a rate of 0.01 seconds and includes three distinct driving scenarios: calm driving, aggressive driving, and city driving. The dataset also captures variations such as reduced tire pressure (one tire at a time), different passenger loads, and measurements from three different vehicles.

The data was collected at the Continental Test Track in Veszprém, Hungary, as well as within the city of Veszprém.

The data is stored in Apache Parquet format that can be processed via Pandas library in Python.

For more information please check our article: TBD (citation from Engineering Applications of Artificial Intelligence (https://www.sciencedirect.com/journal/engineering-applications-of-artificial-intelligence).

Please see the paper in Data in brief journal

AI‑Guided Biocatalyst Discovery Market Outlook

According to our latest research, the AI‑Guided Biocatalyst Discovery market size reached USD 1.47 billion in 2024, reflecting robust growth in the sector. The market is expected to exhibit a remarkable CAGR of 22.3% from 2025 to 2033, reaching a forecasted value of USD 11.97 billion by 2033. This exceptional growth trajectory is driven by the increasing integration of artificial intelligence into biocatalyst discovery processes, which significantly accelerates enzyme identification and optimization, thereby transforming industries such as pharmaceuticals, chemicals, food & beverages, and agriculture. As per our latest research, the market’s expansion is also attributed to the rising demand for sustainable and efficient bioprocesses, coupled with advancements in machine learning and deep learning algorithms, which are revolutionizing the field of biocatalysis.

The primary growth factor for the AI‑Guided Biocatalyst Discovery market is the mounting need for rapid and cost-effective enzyme discovery and engineering. Traditional biocatalyst discovery methods are often labor-intensive, time-consuming, and expensive. AI-guided techniques, leveraging advanced algorithms and large datasets, are enabling researchers to predict enzyme-substrate interactions, optimize reaction conditions, and design novel biocatalysts with unprecedented precision. This technological leap is not only reducing the time-to-market for new products but also enhancing the overall efficiency and sustainability of bioprocesses. The pharmaceutical sector, in particular, is witnessing significant benefits, as AI-driven biocatalyst discovery accelerates drug development pipelines and facilitates the production of novel therapeutics.

Another key driver propelling the AI‑Guided Biocatalyst Discovery market is the growing emphasis on green chemistry and sustainable industrial processes. With increasing regulatory pressure to minimize environmental impact, industries are turning to biocatalysts as eco-friendly alternatives to traditional chemical catalysts. AI-guided approaches are making it feasible to discover and engineer biocatalysts that exhibit high selectivity, stability, and activity under industrial conditions. This is particularly relevant in the chemicals and food & beverages sectors, where demand for cleaner and more efficient production methods is soaring. The convergence of AI and biotechnology is thus fostering a paradigm shift towards sustainability, further fueling market growth.

Furthermore, the proliferation of big data, advancements in high-throughput screening technologies, and increased collaboration between academia, research institutes, and industry players are catalyzing innovation in the AI‑Guided Biocatalyst Discovery market. The availability of vast biological datasets and the development of sophisticated AI models are enabling the systematic exploration of enzyme sequence-function relationships. This is paving the way for the discovery of novel biocatalysts with tailored properties for diverse applications. Additionally, significant investments from venture capitalists and government agencies are supporting R&D activities in this domain, further accelerating market expansion. The trend towards open innovation and data sharing is also fostering a collaborative ecosystem that is conducive to rapid technological advancements.

From a regional perspective, North America currently dominates the AI‑Guided Biocatalyst Discovery market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The presence of leading biotechnology firms, advanced research infrastructure, and supportive regulatory frameworks are key factors driving market growth in these regions. Asia Pacific is emerging as a high-growth market, fueled by increasing investments in AI and biotechnology, a burgeoning pharmaceutical industry, and supportive government initiatives. Latin America and the Middle East & Africa are also witnessing gradual adoption of AI-guided biocatalyst discovery technologies, albeit at a slower pace, primarily due to limited R&D infrastructure and funding constraints. Overall, the global landscape is characterized by dynamic innovation and increasing cross-border collaborations, which are expected to shape the future trajectory of the market.

Graphene Neuromorphic Device Market Outlook

According to our latest research, the global Graphene Neuromorphic Device market size reached USD 215 million in 2024, reflecting robust momentum in the adoption of next-generation computing technologies. The market is poised to expand at a remarkable CAGR of 38.7% from 2025 to 2033, driven by the convergence of neuromorphic engineering and graphene’s unique material properties. By 2033, the market is forecasted to soar to USD 3.21 billion, underscoring the immense potential and transformative impact of graphene neuromorphic devices in advanced computing, artificial intelligence, and edge applications. The primary growth factor fueling this market is the escalating demand for energy-efficient, brain-inspired computing architectures, particularly as conventional silicon-based systems reach their physical and performance limits.

The surge in the Graphene Neuromorphic Device market is fundamentally propelled by the exceptional electrical, thermal, and mechanical properties of graphene, which make it an ideal candidate for neuromorphic hardware. Graphene’s high carrier mobility and flexibility enable the development of devices that closely mimic the synaptic activity of the human brain, offering significant improvements in speed, miniaturization, and energy efficiency. This innovation is critical as industries seek to overcome the bottlenecks of traditional von Neumann architectures, particularly in applications requiring real-time data processing, adaptive learning, and low power consumption. The integration of graphene in memristors, synaptic transistors, and artificial neurons is accelerating advancements in artificial intelligence, robotics, and edge computing, setting the stage for a paradigm shift in computational hardware.

Another key driver is the increasing investment from both public and private sectors in advanced material research and neuromorphic engineering. Governments and research institutions worldwide are channeling substantial funding into the development of next-generation computing platforms, recognizing the strategic importance of neuromorphic devices in national security, healthcare, and industrial automation. Strategic collaborations between academia, industry, and technology consortia are fostering rapid prototyping, commercialization, and scaling of graphene-based neuromorphic solutions. Moreover, the proliferation of AI-driven applications in consumer electronics, automotive, and healthcare is creating unprecedented demand for hardware capable of supporting cognitive and adaptive functionalities, further accelerating market growth.

The evolving regulatory landscape and the growing emphasis on sustainable and energy-efficient technologies are also contributing to the expansion of the Graphene Neuromorphic Device market. With data centers and AI workloads consuming increasing amounts of energy, there is mounting pressure on technology providers to deliver solutions that minimize environmental impact. Graphene-based neuromorphic devices, with their ultra-low power requirements and high computational density, are uniquely positioned to address these challenges. This alignment with global sustainability goals is expected to drive adoption across diverse industries, from smart healthcare systems to autonomous vehicles and intelligent industrial automation.

Regionally, the Asia Pacific market is emerging as a powerhouse in the graphene neuromorphic device landscape, supported by robust investments in semiconductor manufacturing, a thriving electronics ecosystem, and strong government backing for AI research. North America and Europe remain at the forefront of foundational research and early commercialization, leveraging their advanced R&D infrastructure and a vibrant startup ecosystem. Meanwhile, Latin America and the Middle East & Africa are gradually entering the market, primarily through academic collaborations and technology imports. This regional diversification is fostering healthy competition and spurring innovation, ensuring a dynamic and rapidly evolving global market.