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检索条件"任意字段=International Conference on Advances in Science, Engineering and Robotics Technology"

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A Unified Deep Learning Approach for Integrating Retinal Image Diagnosis and Surgical Tool Classification 1

A Unified Deep Learning Approach for Integrating Retinal Ima...

引用

1st IEEE international conference on Cognitive robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Jesica, J. Anitha, J. Sujitha Juliet, D. Belfin, R.V. Karunya Institute of Technology and Sciences Dept. of Data Science and Cyber Security Coimbatore India Karunya Institute of Technology and Sciences Dept. of Computer Science and Engineering Coimbatore India University of North Carolina Dept. of Neurology Chapel Hill United States

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372748

Early and accurate diagnosis by using retinal image processing is critical for enabling optimized patient care. Existing techniques for the diagnosis in medical image processing often face limitations. This research study leverages the comparative analysis of advanced Convolutional Neural Networks (CNNs), including AlexNet, InceptionV3, Xception, and EfficientNetB3;out of which EfficientNetB3 gives the comparatively higher accuracy of 84%. This research work presents a novel deep learning-based approach for detecting diseases along with the integration of Lenscraft AI. Lenscraft AI helps to segment surgical masks and identify the surgical tools used in surgical recordings using YOLOv8. The developed tool and tracking method offers a higher precision value of 0.8863 and efficiency in identifying the surgical instruments and potentially improving surgical technique for the trainee surgeons and patient safety. © 2024 IEEE.

关键词： Convolutional neural networks

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A Control Algorithm for an Aerial Co-Manipulation System 4

A Control Algorithm for an Aerial Co-Manipulation System

引用

4th international conference on Industrial Automation, robotics and Control engineering, IARCE 2024

作者： Chen, Fan Zhang, Xinyue Li, Yuxin Wang, Yixin Southwest University of Science and Technology Mianyang China Southwest University of Science and Technology Mianyang China Southwest University of Science and Technology Mianyang China

ISBN: (纸本)9798350380323

During aerial co-manipulation, addressing the disturbances caused by the motion of the lower UAV on the aerial dexterous manipulation platform and the perturbations induced by the manipulation platform's own movement, as well as the issue of the inadequate number of existing simulation platforms, this study introduces an adaptive sliding mode control approach that leverages radial basis function neural networks and establishes a simulation platform for aerial comanipulation within the Gazebo environment. In the control method, the Lagrangian equation is employed for standardized modeling, and the model is simplified through projection. The universal approximation property of neural networks is utilized for adaptive rate optimization. Additionally, a sliding mode algorithm grounded on the exponential reaching law is implemented to bolster robustness. To further mitigate chattering, the hyperbolic tangent function is employed. Finally, the results were verified through trajectory tracking in Matlab/Simulink, ensuring that the system converged in about 1 second and achieved micron-level multi-channel tracking error accuracy, effectively reducing chatter. The Gazebo simulation platform sets up three aerial dexterous manipulation platforms capable of performing basic aerial tasks, and control stability is verified using PID algorithms. © 2024 IEEE.

关键词： Radial basis function networks

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Developing a Digital Twin Framework for Monitoring the Trajectory of the UR10 Robot Using an Extended Kalman Filter 6

Developing a Digital Twin Framework for Monitoring the Traje...

引用

6th international conference on robotics, Control and Automation engineering, RCAE 2023

作者： Alham, Ramy Hammadi, Moncef Université de Toulon La Garde France Norwegian University of Science and Technology Trondheim Norway QUARTZ Lab EA7393 ISAE-SUPMECA Saint-Ouen93400 France

ISBN: (纸本)9798350315301

In the era of modern robotics and Industry 4.0, integrating advanced state estimation techniques with digital twin technology is crucial for enhancing robotic system performance and safety. This study presents a pioneering Digital Twin framework to monitor the trajectory of Universal Robots' UR10 robotic arm. We address challenges such as system uncertainties, sensor noise, and environmental factors by combining a Kalman Filter-based state estimation with a Digital Twin approach. By creating a virtual replica that accurately mimics the UR10's physical properties and dynamics, our method enables realtime tracking and prediction of the robot's states, improving control and operational efficiency. The Digital Twin incorporates the robot's kinematics and dynamics to compute the Jacobian matrix for EKF estimation. Through extensive simulations, we demonstrate a significant increase in accuracy in position (approximately 90.14%) and velocity (approximately 85.10%) estimations after introducing noise to the UR10 robot simulation. This approach paves the way for advanced control strategies, fault detection systems, and virtual testing platforms in industries like manufacturing, healthcare, and research, where precision and performance are critical. © 2023 IEEE.

关键词： Extended Kalman filters

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Deep Learning Based Digital Image Encryption 1

Deep Learning Based Digital Image Encryption

引用

1st IEEE international conference on advances in Computing, Communication and Networking, ICAC2N 2024

作者： Faisal, Shah Agrawal, Amrit Kumar Verma, Abhishek Singh Sharda School of Engineering and Technology Sharda University Department of Computer Science and Engineering Utter Pradesh Greater Noida India Sharda School of Engineering and Technology Sharda University Department of Computer Science and Engineering Utter Pradesh Greater Noida India

ISBN: (纸本)9798350356816

As technology has evolved, digital images have become increasingly popular. However, these images often exhibit a high degree of correlation and redundancy among pixels, which creates specific requirements for applications like real-time communication and assessment. Traditional encryption approaches for digital images are inefficient, and the algorithms are difficult. A thorough analysis can highlight existing patterns and provide light on unstudied issues in deep learning and chaotic image encryption. This paper, provides a schematic review of digital image encryption using deep learning architecture. We also proposed a framework that merges the strengths of deep learning and chaotic systems, tackling both computational efficiency and the need for secure encryption. The combination is crucial for pulling intricate patterns from large sets of data and using the chaotic, unpredictable nature of the map to create highly secure encryption keys. The complex behavior of the multi-dimensional chaotic map, along with deep learning model's ability to extract features from vast data, ensures our generated keys are both random and unpredictable which makes them highly resistant to attempts at pattern finding and reverse engineering, safeguarding the integrity of the encryption. © 2024 IEEE.

关键词： Convolutional neural networks

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Hybrid Approach for Accurate Vocal Anomaly Detection Using State-of-the-Art

Hybrid Approach for Accurate Vocal Anomaly Detection Using S...

引用

2023 international conference on Recent advances in science and engineering technology, ICRASET 2023

作者： Manikandan, M. Rengarajan, A. Naval, Preeti Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Department of Computer Science and Information Technology Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India

ISBN: (纸本)9798350306927

Accurate voice anomaly detection is achieved by the suggested method's combination of signal processing for feature extraction and improvement with deep learning. Preprocessing, feature extraction, and anomaly detection are the three pillars upon which this approach rests. The raw vocal signal is improved during preprocessing by denoising and filtering. From the preprocessed signal, the feature extraction stage derives Mel-Frequency Cepstral Coefficients (MFCCs) and their derivatives. Finally, an ensemble of three algorithms, including Support Vector Machines (SVM), Long Short-Term Memory (LSTM) Networks, and Convolutional Neural Networks (CNN), is used in the anomaly identification stage to conduct an in-depth evaluation of the detection of voice anomalies. The suggested hybrid method is meant to be more flexible and efficient in practical settings. © 2023 IEEE.

关键词： Long short-term memory

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Stacked Bi-LSTM for Advanced Toxicity Detection in Comment Classification 6

Stacked Bi-LSTM for Advanced Toxicity Detection in Comment C...

引用

6th IEEE international conference on Artificial Intelligence in engineering and technology, IICAIET 2024

作者： Kumar, N. Naveen Abraham, Prabhakaran Kaliba, Syed Ibrahim Diniesh, V.C. Maruthamuthu, R. Balamurugan Seshu Kumar, P. Vekkudu, Naveen Madanapalle Institute of Technology & Science Department of Computer Applications Andhra Pradesh Madanapalle India Mai-nefhi College of Engineering and Technology Department of Computer Science and Engineering Mainefhi Eritrea Kongu Engineering College Department of Electronics & Communication Engineering Tamil Nadu Erode India Jain University Department of Information Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798350389692

The surge in Internet usage has revolutionized online forums, providing dynamic forums for active participation and meaningful debates. However, this has also exposed users to the risk of harassment. Efforts to address toxic comments in online forums have faced challenges, with current solutions unveiling unreliability. advances in hardware, cloud computing, and natural language processing (NLP) enable the development of more robust approaches, predominantly NLP-based deep learning models. Pre-trained transformer models are gradually utilized for accurate toxic comment classification, bigger traditional methods. Stacked LSTM models, emphasizing hierarchical feature learning and improved contextual understanding, outperform single layer counterparts, offering an effective solution for online content moderation and sentiment analysis. This research indicates a significant stride towards more dependable and sophisticated toxic comment detection, addressing the challenges of social media effectively. © 2024 IEEE.

关键词： Sentiment analysis

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An Assembly Sequence Planning Method Using Ant Colony Algorithm for Arbitrary Placed Workpieces

An Assembly Sequence Planning Method Using Ant Colony Algori...

引用

2023 IEEE international conference on Real-Time Computing and robotics, RCAR 2023

作者： Hou, Ruiming Xu, Sheng Yang, Chenguang Zhu, Bing Duan, Jianghua Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Shenzhen518055 China University of Chinese Academy of Science Beijing101408 China Shandong Institute of Advanced Technology Cas Shandong250102 China South China University of Technology College of Automation Science and Engineering Guangzhou510640 China Hong Kong University of Science and Technology Department of Mechanical and Aerospace Engineering Hong Kong Hong Kong

ISBN: (纸本)9798350327182

This paper proposes a particular and effective online sequence planning method to help manipulators complete complex assembly tasks. In many existing studies, the workpiece is constrained to be placed at a particular initial position. To overcome this drawback, we combine the ant colony algorithm with a target detection algorithm, which could get the positions of the workpieces in real time for online sequence planning. In addition, the constraint and obstacle avoidance of different workpieces in the assembly process is also considered. According to the experiment results, an observation is found that the new method is superior to the general way in terms of efficiency and cost function value. Furthermore, the proposed method effectively solves different practical and feasible assembly sequence planning problems. © 2023 IEEE.

关键词： Ant colony optimization

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Quantum Computing Enhancements in Deep Learning Models for Cybersecurity

Quantum Computing Enhancements in Deep Learning Models for C...

引用

2023 international conference on Recent advances in science and engineering technology, ICRASET 2023

作者： Yadav, Dhyan Chandra Bhagwat, Renuka Saha, Ananya Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350306927

The cybersecurity industry has undergone a sea change due to the combination of quantum computing and deep learning models. This study presents a novel approach that makes use of the unique features of quantum computing to improve the efficiency and safety of deep learning models used in cybersecurity. The suggested method improves upon previous methods in terms of both the precision and efficiency with which threats may be identified and categorized by making use of Quantum Neural Networks (QNNs), Quantum Support Vector Machines (QSVMs), and Quantum Key Distribution (QKD). Our findings show that the suggested method is superior to conventional approaches through a thorough examination of detection accuracy and computing efficiency. Combining quantum computing principles with deep learning techniques provides a game-changing answer to the growing complexity of today's cybersecurity threats. © 2023 IEEE.

关键词： Support vector machines

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Data-based Stabilization of Unknown Discrete-time 2-D Roesser Systems 23

Data-based Stabilization of Unknown Discrete-time 2-D Roesse...

引用

23rd international conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Yang, Runmin Yang, Rongni Ma, Renjie Zhu, Yanzheng Shi, Peng School of Control Science and Engineering Shandong University Jinan250061 China Harbin Institute of Technology State Key Laboratory of Robotics and Systems Harbin150001 China College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao266590 China School of Electrical and Mechanical Engineering University of Adelaide AdelaideSA5005 Australia

ISBN: (纸本)9798331528041

This paper studies the stabilization problem of discrete-time two-dimensional (2-D) systems represented by Roesser based on available data. First of all, based on the pre-collected input-state data, the original system is transformed into a data-based form. Then a set is established to represent all systems capable of generating the given data, thereby indirectly stabilizing the original system by stabilizing all systems within this set. Next, by utilizing the S-procedure, sufficient conditions are established for the resultant closed-loop systems that incorporate a state feedback controller based on collected data. Finally, an example is given to demonstrate the effectiveness of the proposed approach. © 2024 IEEE.

关键词： Discrete time control systems

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A Review on the Role of Blockchain technology for Decentralized Identity Management: A Future Without Passwords 4

A Review on the Role of Blockchain Technology for Decentrali...

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4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Raut, Rohini Gourshettiwar, Palash Thakre, Gayatri Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

Advanced identity mechanisms are essential for secure client-identifiable proof in the unambiguously computerized world. Because the board frameworks rely on aggregated databases, fraud, data breaches, and unauthorized access are significant security risks. By providing a decentralized, immutable structure, blockchain technology advances security and protection. Through Self-Sovereign Identity, blockchain gives people authority over their own data. While also improving confirmation and check mechanisms. The potential of blockchain-based Identity, this article examines the board frameworks, focusing on five essential components: authentication, privacy, trust, integrity, and simplicity. It examines how interoperability, flexibility, and collaboration with outdated frameworks prevent decentralized IDMS being more widely used. The study also covers customer satisfaction, legal concerns, and potential future developments, such as Web 3.0, security enhancements, and integration with decentralized money. By addressing these problems and looking into new possibilities, blockchain-based IDMS may be able to reform advanced identity across the entire board. © 2025 IEEE.

关键词： Decentralized finance

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