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检索条件"任意字段=IEEE Conference on Technologies for Practical Robot Applications"

共 5247 条记录，以下是41-50 订阅

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Machine Learning-Enhanced Voice Interaction: Revolutionizing Windows Desktop applications 1

Machine Learning-Enhanced Voice Interaction: Revolutionizing...

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1st International conference on Advances in Computer Science, Electrical, Electronics, and Communication technologies, CE2CT 2025

作者： Vora, Raj Chakrawarti, Rajesh Kumar Raghuwanshi, Namrata Sushila Devi Bansal College of Technology Computer Science and Engineering Indore India

ISBN: (纸本)9798331518578

The use of voice interaction is changing the way human being communicate with devices as it offers a simple and effective way of interaction. To implement voice interaction within desktop applications and make it more effective as it is integrated with the operating system, there is a various need models, to particularly improve Convolutional its neural quality. Networks This (CNNs), paper Recurrent considers Neural how Networks (RNNs), and transformers, can enhance the effectiveness of voice interaction in the context of Windows desktop platforms. The research focuses on several major issues: enhancing the speech recognition performance in the presence of background noise, handling the differences in user's accent and speech manner, and real-time operation with reduced delay. It also examines issues of privacy relating to handling of sensitive voice information and ways of reducing these vulnerabilities. Using and enhanced user machine intent learning and results operating encompass connecting detection strategies, the indicate system the in this user's and that voice possibility developing voice research experience provides the interaction the of recognition in is an application with next increasing systems. Different designed better of other these generation the practical user to machine current models of multilingual applications experience. Improve learning and are desktop capabilities, including the enhances future assessed apps. Implementing productivity current The AI from the tools, limited paper effectiveness solutions, the ability accessibility capabilities also and thus perspective to features of provides quality stating of recognize and contextual recommendations of that performance, users' intelligent understanding for voice flexibility emotions, assistants. Future control is and the research, commands the which on key them to Windows. © 2025 ieee.

关键词： Speech recognition

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Aquaponic Farming with Advanced Decision Support System: practical Implementation and Evaluation 6

Aquaponic Farming with Advanced Decision Support System: Pra...

引用

6th ieee International conference on Image Processing, applications and Systems, IPAS 2025

作者： Pechlivani, Eleftheria Maria Gkogkos, Georgios Christakakis, Panagiotis Kapetas, Dimitrios Hadjigeorgiou, Ioannis Ioannidis, Dimosthenis Tzovaras, Dimitrios Centre for Research and Technology Hellas Information Technologies Institute Thessaloniki Greece Sidroco Holdings Ltd. Nicosia Cyprus

ISBN: (纸本)9798331506520

The adoption of Decision Support Systems (DSS) by farmers faces significant challenges, including the need for user-friendly interfaces, comprehensive training, reliable support, and affordable pricing. Addressing these challenges is critical to advancing sustainable agriculture and meeting global food production demands. This paper introduces an advanced DSS tailored for aquaponics, leveraging cutting-edge technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), and Blockchain. These technologies are integrated into the DSS to optimize resource management, ensure cybersecurity, and simplify complex processes through an intuitive interface. Real-time data is presented in an accessible format, enabling farmers of all skill levels to adopt the system with ease. The DSS features AI-driven robotic traps with 75% accuracy in real-time insect detection, autonomous robots achieving 94% precision in 3D spot spraying, and nutrient analyzers with over 92% accuracy in monitoring critical levels. A blockchain layer ensures secure data verification, traceability, and distributed AI model verification. Advanced hierarchical clustering algorithms analyze pest and nutrient dynamics, providing actionable insights for farm management. The system emphasizes interoperability and scalability, supporting seamless integration with various aquaponic setups. Field evaluations demonstrate the DSS's capacity to reduce pesticide use by 50%, enhance crop yields, and lower sample analysis costs by 70%, highlighting its efficiency and sustainability. Data visualization latency remains below 430ms, enabling real-time responsiveness, while predictive models achieve 91% accuracy in forecasting pest population trends. These results solidify the DSS's role as a transformative tool in precision agriculture. By improving productivity, plant health, and the utilization of biopesticides and biofertilizers, this work bridges the gap between theoretical models and real-world applicat

关键词： Fertilizers

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Navigating Obstacles with Monocular Image Inputs: A Comparative Analysis of Path Planning Algorithms

Navigating Obstacles with Monocular Image Inputs: A Comparat...

引用

2025 International conference on Computer, Electrical and Communication Engineering, ICCECE 2025

作者： Vaishnav, Ram Singh, Shivendra Goswami, Pooja Central Research Laboratory Bharat Electronics Limited Ghaziabad India

ISBN: (纸本)9798331513894

Effective path planning in obstacle-filled environments is essential for the deployment of autonomous mobile robots. This paper provides an extensive comparative analysis of different path planning algorithms that utilize monocular image inputs for navigating obstacles. We evaluate the performance of popular graph based algorithms such as BFS, DFS, Dijkstra, A∗. examining their efficiency and effectiveness in complex and dynamic settings. Using a standardized dataset, we rigorously test each algorithm's ability to interpret monocular image data, determine viable paths, and avoid obstacles. Key metrics such as computational efficiency, accuracy of obstacle avoidance, and resilience to environmental variations are analyzed to identify the strengths and limitations of each method. Our findings underscore the trade-offs between traditional algorithms and deep learning approaches, providing valuable insights for selecting the most suitable path planning techniques for applications in robotics and autonomous vehicles. This study aims to bridge the gap between theoretical research and practical application, enhancing the development of more adaptive and dependable autonomous navigation systems. Through rigorous experimentation and statistical analysis, this research identifies the strengths and weaknesses of each algorithm in handling different environmental complexities. Results demonstrates that A∗ path planning method performs better than the other methods. © 2025 ieee.

关键词： robot programming

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GlobalDoc: A Cross-Modal Vision-Language Framework for Real-World Document Image Retrieval and Classification

GlobalDoc: A Cross-Modal Vision-Language Framework for Real-...

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2025 ieee/CVF Winter conference on applications of Computer Vision, WACV 2025

作者： Bakkali, Souhail Biswas, Sanket Ming, Zuheng Coustaty, Mickaël Rusiñol, Marçal Terrades, Oriol Ramos Lladós, Josep La Rochelle Université L3i France Universitat Autònoma de Barcelona Computer Vision Center Spain Université Sorbonne Paris Nord L2TI France AllRead Machine Learning Technologies Spain

ISBN: (纸本)9798331510831

Visual document understanding (VDU) has rapidly advanced with the development of powerful multi-modal language models. However, these models typically require extensive document pre-training data to learn intermediate representations and often suffer a significant performance drop in real-world online industrial settings. A primary issue is their heavy reliance on OCR engines to extract local positional information within document pages, which limits the models' ability to capture global information and hinders their generalizability, flexibility, and robustness. In this paper, we introduce GlobalDoc, a cross modal transformer-based architecture pre-trained in a self supervised manner using three novel pretext objective tasks. GlobalDoc improves the learning of richer semantic concepts by unifying language and visual representations, resulting in more transferable models. For proper evaluation, we also propose two novel document-level downstream VDU tasks, Few-Shot Document Image Classification (DIC) and Content-based Document Image Retrieval (DIR), designed to simulate industrial scenarios more closely. Extensive experimentation has been conducted to demonstrate GlobalDoc's effectiveness in practical settings. © 2025 ieee.

关键词： Content based retrieval

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Deep Learning Techniques for Intrusion Detection Systems in Healthcare Environments 14

Deep Learning Techniques for Intrusion Detection Systems in ...

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14th ieee International conference on Communication Systems and Network technologies, CSNT 2025

作者： Alam, Mahfooz Ashraf, Zubair Singh, Priya Pandey, Bishwajeet Rehman, Kaleemur Aldasheva, Laura Department of Master of Computer Applications G. L. Bajaj Institute of Technology and Management Greater Noida201306 India College of Computing and Information Technology University of Bisha Bisha67714 Saudi Arabia Department of Computer Science Institute of Engineering & Technology Dr. B. R. Ambedkar University Khandri Agra India Department of CSE-AI G. L. Bajaj Institute of Technology and Management Greater Noida201306 India Department of IS and CS Astana IT University Astana Kazakhstan

ISBN: (纸本)9798331531935

Nowadays, healthcare systems face ever-increasing cyber security threats due to the sensitive nature of patient data and the proliferation of IoT-enabled medical devices. Traditional Intrusion Detection Systems (IDS) struggle to adapt to healthcare environments' dynamic and heterogeneous data. Deep learning techniques, with their ability to analyze complex and high-dimensional data, have emerged as a promising approach to enhancing IDS performance. This paper explores the application of deep learning methods for intrusion detection in healthcare, focusing on their advantages, challenges, and practical implementations. Topics include neural network architectures, feature engineering for medical and network data, and real-time intrusion detection in resource-constrained environments. The goal is to provide a roadmap for leveraging deep learning to ensure robust and scalable security for healthcare systems. © 2025 ieee.

关键词： Network intrusion

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Integrating Big Data Analytics in Soil Quality Assessment: Techniques, Challenges, and applications 3

Integrating Big Data Analytics in Soil Quality Assessment: T...

引用

3rd International conference on Intelligent and Innovative technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Mohammed, Shehu Singh, Arun Hanif, Md Abu Lovely Professional University Department of Computer Applications Phagwara India Lovely Professional University Department of Computer Science and Engineering Phagwara India Department of Computer Science 365 5th Ave New YorkNY110016 United States

ISBN: (纸本)9798331515911

This review paper investigates the application of Big Data analytics, focusing on soil quality assessment, with an emphasis on the innovative benefits offered by modern data-driven techniques for improved soil management. With world food demands rising, the importance for real-time and exact soil quality monitoring has increased. Although still useful, traditional approaches to modeling are limited in their ability to capture the spatial and temporal variability of soil properties. Big Data analytics sits as the answer to the above by facilitating extensive and large volume data processing resulting in better, on-demand and scalable soil quality assessment. In the paper, we describe machine learning algorithms, remote sensing and geospatial analysis as key techniques to mine soil data at scales and resolutions unprecedented so far. We also critically evaluate the challenge of data integration (heterogeneity, scalability and multi-disciplinarity). These analytics are scalable for assessment and decision-making in precision agriculture, sustainable land management and environmental conservation. Thus, this paper synthesizes current research and practical implications in support of the need for Big Data analytics within soils and soil sciences to cover also future challenges. Future research directions are also highlighted, including the importance of improved data acquisition and algorithmic advances as well greater synergy between Big Data and conventional soil science practices.. © 2025 ieee.

关键词： Data quality

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Surgical robotics: Recent Development Trends and Challenges

Surgical Robotics: Recent Development Trends and Challenges

引用

robotics and technologies for Industrial Automation (robotHIA), ieee International conference on

作者： Jingheng Shan School of Electrical Engineering and Artificial Intelligence Xiamen University Malaysia Sepang Selangor Malaysia

ISBN: (数字)9798350356755

ISBN: (纸本)9798350356762

This paper discusses the advantages and disadvantages of robots as medical devices for clinical applications. Since the 1980s, robots have been used in vivo (in vivo refers to use related to medical purposes). Unlike robots in other industries, surgical robots are held to strict standards and, like other medical devices, must be precisely and effectively tuned. This paper provides a comprehensive survey of current advances in the field and identifies some of the current challenges faced by the field. In such systems, it is extremely important to maximize the effectiveness of medical procedures, addressing the limitations of human capabilities. Various models of medical robots have been put into clinical and practical applications, including microbots, capsule cameras, and da Vinci surgical robots. However, these robots still require human operation to complete medical tasks. More autonomous medical robots are currently under development. It is believed that in the future, more precise, effective, safe, and autonomous medical robots will be developed and put into use.

关键词： Surveys Industries In vivo Medical robotics Medical devices Service robots robot vision systems Surgery Market research Standards

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applications of Sustainable Electric Vehicle Infrastructures Using Stochastic Threshold Strategies in Green Transportation and Highway Networks 1

Applications of Sustainable Electric Vehicle Infrastructures...

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1st International conference on Sustainable Energy technologies and Computational Intelligence, SETCOM 2025

作者： Nayi, Kuldeepkumar Ramabhai Varshney, Shreekant Srivastava, Anurag Kumar Priyadarshini, Deepti Patel, Om Pandit Deendayal Energy University School of Liberal Studies Department of Public Administration Gujarat Gandhinagar382426 India Pandit Deendayal Energy University School of Technology Department of Mathematics Gujarat Gandhinagar382426 India Swarnim Startup and Innovation University Department of Swarnim School of Computing and It Gujarat Gandhinagar382421 India Pandit Deendayal Energy University School of Technology Department of Electronics and Communication Engineering Gujarat Gandhinagar382426 India

ISBN: (纸本)9798331520540

This study presents a queueing-theoretic framework for single-server electric vehicle (EV) charging networks, utilizing threshold-based strategies to enhance EV network efficiency associated with green transportation. The approach incorporates stochastic modeling and Chapman-Kolmogorov equations to dynamically modify service rates when demand reaches predetermined thresholds, thereby minimizing waiting times and optimizing server utilization. Further, numerous system performance indicators are demonstrated to emphasize the efficiency and superior performance of EV charging infrastructures located on green highway networks. Further, numerical results illustrate the model's efficacy in addressing critical operational issues, providing a practical framework to enhance EV network performance and customer satisfaction. © 2025 ieee.

关键词： Stochastic systems

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Developing Cognitive Abilities in robots: A Bibliometric Overview of AI and ML applications

Developing Cognitive Abilities in Robots: A Bibliometric Ove...

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International IoT, Electronics and Mechatronics conference, IEMTRONICS 2024

作者： Obaideen, Khaled AlShabi, Mohammad Gadsden, Stephen Andrew Bio-Sensing and Bio-Sensors Group Smart Automation and Communication Technologies RISE P.O. Box 27272 Sharjah United Arab Emirates Department of Mechanical and Nuclear Engineering University of Sharjah Sharjah27272 United Arab Emirates Department of Mechanical Engineering McMaster University HamiltonONL8S 4L8 Canada

ISBN: (纸本)9789819747832

The domain of cognitive robotics has gone through many technological innovations, majorly enhanced by the adoption of Artificial Intelligence (AI) and Machine Learning (ML) concepts. This study performs comprehensive bibliometric analysis, using tools like VOSviewer and Biblioshiny, to map the development, the existing scene and the future prospects of cognitive robotics studies since 1953. We address how AI and ML expand robotic abilities, marking the shift from early development phase to the period of fast evolution and improvement. The remarkable results show an increasing trend starting from mid- 1980s with a peak in the late 1990s and an exponential growth in the last ten years which highlights the growing impact and the broadening usage in different fields. The paper shows cross functional aspects of cognitive robotics, which combines biological and cognitive approaches with progress of technology. The two main areas of robots cognitive robotics are human–robot interaction and autonomous decision-making. The ability of robots to acquire knowledge, self-adaptability, and robot operation in human-centered environments is highlighted, as well as the practical problems that may come from robot autonomy and superior cognitive capacities. From this bibliometric study, we get key insights concerning the past and present of cognitive robotics which form the basis of future studies. The study reinforces the necessity of a multi-pronged approach in robotics, with progress aligned with the needs of society, to ensure the responsible deployment of cognitive robots. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Security Key Generation in SRAM based PUF for Cryptographic applications 5

Security Key Generation in SRAM based PUF for Cryptographic ...

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5th ieee International conference on Advances in Electrical, Computing, Communication and Sustainable technologies, ICAECT 2025

作者： Raikwal, Pushpa Gupta, Nipun Agarwal, Nikhil Maheshwari, Mahima Dayal, Neelam Pdpm Indian Institute of Information Technology Design and Manufacturing Department of Electronics and Communication Engineering Madhya Pradesh Jabalpur482005 India Pdpm Indian Institute of Information Technology Design and Manufacturing Department of Computer Science and Engineering Madhya Pradesh Jabalpur482005 India

ISBN: (纸本)9798350356236

This work meticulously focusses on SRAM designing and to verify its functionality as a PUF. It exploits the inherent manufacturing variations present in SRAM cells to generate unique start-up values, then utilizes them to producea cryptographic key. The design process induced mismatches in the inverters to enhance variability in the SRAM cells. Further this work focused on generating start-up values of SRAM based PUF in three methods: ideal state, practical state and by analyzing PUF Static Noise Margin (PSNM). Through these simulations, it is confirmed that the start-up values of the SRAM cells could be effectively utilized to generate a unique cryptographic key. All design and simulation tasks were conducted using Cadence Virtuoso, a sophisticated tool for integrated circuit design and verification at 90nm CMOS technology node. © 2025 ieee.

关键词： CMOS integrated circuits

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