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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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SIRR: Semantically Infused Recipe Recommendation Model Using Ontology Focused Machine Intelligence

SIRR: Semantically Infused Recipe Recommendation Model Using...

引用

international conference on robotics, Control, Automation and Artificial Intelligence, RCAAI 2022

作者： Anand, Mrinal Deepak, Gerard Santhanavijayan, A. R.V College of Engineering Bangalore India Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering National Institute of Technology Tiruchirappalli India

ISBN: (纸本)9789819946334

In the current era, recipe recommendation is of extreme significance as there is no expert intelligence system specifically for recommendation. In the era of Web 3.0, semantically driven frameworks are mostly absent. As a result, semantically infused recipe recommendation model is highly important. This study proposes a semantically infused intelligent approach to recommend recipes using Ontology Focused Machine Intelligence to classify the documents. The semantic similarity was computed by hybridizing Normalized Compression Distance (NCD) and KL divergence under a cultural algorithm. The model uses both Recurrent Neural Network (RNN) and XGBoost at different locations to sort the data and compute semantic similarity. The model yielded an FDR of 0.04, and an accuracy of 97.09%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Ontology

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Automated Tweet Sentiment Analysis Using Machine Learning models

Automated Tweet Sentiment Analysis Using Machine Learning mo...

引用

2023 international conference in advances in Power, Signal, and Information technology, APSIT 2023

作者： Akhouri, Shivam Nigam, Meher Shrishti Shah, Suraj Pandey, Trilok Nath School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350339369

The work adds towards sentiment analysis by introducing an understanding lexicon and BERT based NLP model XLNet. Based on fundamental research and systematic practice in Design Phase, the lexicon describes the benefits and drawbacks of products as a result of the interaction between artifacts and users. The study collects data from Twitter API that represent customer interactions about the items in various domains as per filters given by the consumers. It compares the findings of a typical sentiment analysis with the results filtered using the lexicon and delivers visual statistics for the same. The initial product and brand comparison results show the value of sentiment analysis and text analysis on publicly available data, and the utilization of deep learning classification models for forecasting sentiment polarity gives consumers, product manufacturers and distributors, regulatory and monitoring agencies a great tool for tracking brand or product perception trends and taking action in the event of a sudden or significant upsurge in negative sentiment. © 2023 IEEE.

关键词： Sentiment analysis

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Facial Expression Based User Concentration Alert System

Facial Expression Based User Concentration Alert System

引用

2023 international conference on advances in Computation, Communication and Information technology, ICAICCIT 2023

作者： Arora, Ginni Adlakha, Garima Joshi, Rashmi Batra, Mohit Sarkar, Tanisha Luthra, Meghna Manav Rachna International Institute of Research and Studies Department of Computer Science and Engineering Faridabad India

ISBN: (纸本)9798350344387

Nowadays, due to more social platform usage, the user concentration on major activities has been declined. It can be seen in the education sector as the students are unable to pay attention in online sessions. Therefore, there is a need of an automatic alert system to make a student engaged. This paper focus on implementing face detection, face recognition and face emotion recognition in the education sector. With the concepts of Machine Learning and Neural Networks such as ResNet, AlexNet, VGG-16 as pretrained network helps in recognizing the human face. The emotions of the recognized face are then analysed through the machine learning model i.e. face emotion detector which detects facial emotions like happy, sad, confused, focused, angry, etc. This facial expression system help to send alert to the parents and students for evaluation and for preventing them from getting distracted during sessions. © 2023 IEEE.

关键词： Deep Neural Network Emotion detection Face Expression Machine learning

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Estimation of Hip Joint Torque By Using Parallel Fusion Neural Dynamics Model

Estimation of Hip Joint Torque By Using Parallel Fusion Neur...

引用

2023 IEEE international conference on robotics and Biomimetics, ROBIO 2023

作者： Liu, Lei Wang, Jiaxin Xiang, Qian Zhao, Zhendong Liu, Yong Guo, Shijie Hebei University of Technology Eng. Res. Ctr. of the Min. of Educ. for Intelligent Rehab. Equipment and Detection Technologies School of Mechanical Engineering Tianjin300132 China Hebei University of Technology School of Artificial Intelligence Tianjin300132 China

ISBN: (纸本)9798350325706

Estimation of hip joint torque is of great significance for exoskeleton assist torque planning. However, traditional single neural network models are difficult to reliably estimate human joint torque and the dynamic models based on physical theorem are limited by the measurement technology of ground reaction force. Therefore, this paper proposes a parallel fusion neural dynamic model that incorporates LSTM, NTM, and Newton-Euler dynamical equation. The model only needs human kinematic parameters as inputs to complete the estimation of human hip joint torque. To evaluate the estimation performance, this paper introduces relative accuracy as an evaluation standard. The experimental result shows that the estimation performance of the fusion model is greatly improved compared with the traditional single neural network models. The fusion model proposed in this study can be used to estimate the torque of the hip joint. It can be integrated into the exoskeleton control system and used as the basis for planning the assisting torque of the exoskeleton. © 2023 IEEE.

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Mechanical Properties of Fiber Metal Laminate Hybrid Composite Materials 1st

Mechanical Properties of Fiber Metal Laminate Hybrid Composi...

引用

Proceedings of the 1st international conference on Sustainable Technologies and advances in Automation, Aerospace and robotics, STAAAR 2022

作者： Karthik, K. Rajamani, D. Balaguru, S. Ramesh, V. Bishwakarma, Sunil Kumara, M. Uma Naga Aruna Department of Mechanical Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Tamil Nadu Chennai600062 India School of Mechanical Engineering VIT Bhopal University Kothrikalan India

ISBN: (纸本)9789819923489

The fiber metal laminate (FML) hybrid composite has superior properties in structural and thermal applications. The stacking sequence of FML composite is arranged alternatively for fibers with metals fabricated by vacuum bag infusion methods. Epoxy with LY 59 hardener is used as a matrix material. The fibers were arranged in different stacking sequences and were controlled by a vacuum pump. Pressure was applied with a roller till set and so the composite was left for 24–48 h till it absolutely was hardened. Also, metal laminates were enclosed between the fiber layers. Kevlar/ copper sheet/ glass fibers are used as reinforcement materials. The novelty of the work is to change the sequence of metals between the fibers. After the fabricated samples as per the ASTM Stands experimental was conducted, the mechanical properties of FML. Finally, mechanical properties are improved when compared to neat fiber laminates. Fiber metal laminates are suggested for the structural and automotive industry. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fibers

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REVIEW OF INTERVENTIONAL ROBOTIC SYSTEMS 18

REVIEW OF INTERVENTIONAL ROBOTIC SYSTEMS

引用

ASME 18th Int Mfg Sci and Engn Conf held jointly with the 51st N Amer Res Conf / Japan-Soc-of-Mech-Engineers Int Conf on Leading Edge Mfg/Mat and Proc

作者： Liu, Yang Li, Dian-Ru Shanghai Jiao Tong Univ Global Inst Future Technol Shanghai Peoples R China Shanghai Jiao Tong Univ UM SJTU Joint Inst Shanghai Peoples R China Natl Taiwan Univ Dept Mech Engn Taipei Taiwan

ISBN: (纸本)9780791887233

The success of the da Vinci system has shown the feasibility and a great potential of using robotic systems to improve healthcare. With a trend of replacing more and more invasive surgeries with minimally invasive interventional procedures, developing robotic systems for interventions has seen a boost of interest from both the industry and the academia in the past two decades. Using robotics to conduct intervention procedures is expected to better protect the clinicians by reducing their radiation exposure and to create better patient outcome in cases where high positional accuracy is needed, such as placing a stent or a coil. However, adoption of interventional robotics has been slow due to the limitations of current interventional robotic systems such as a steep learning curve, lack of haptic feedback, and safety concern. This narrative review first introduces the current clinical applications of interventional robotic systems and their limitations. The major technological features of interventional robotics, including the master-slave manipulator, soft continuum robot, and end effector, are then described. Finally, several manufacturing research topics are proposed for future interventional robotic systems.

关键词： Medical robotics intervention manipulation mechanism soft robot end effector

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Location Protection Mechanism in Sparse Mobile CrowdSensing 4

Location Protection Mechanism in Sparse Mobile CrowdSensing

引用

4th international conference on Electronic Information engineering and Computer Communication, EIECC 2024

作者： Li, Lanxin Wang, En Liu, Wenbin Jilin University College of Computer Science and Technology Changchun China

ISBN: (纸本)9798331534622

As Internet of Things technology advances quickly, Sparse Mobile CrowdSensing has been used as a productive way to collect and process data. Leveraging the real time capabilities and wide coverage of Internet of Things communication, Sparse Mobile CrowdSensing utilizes distributed sensing nodes to collect and process real-time data, thereby supporting diverse intelligent services. In practical scenarios, users are often required to upload data carrying location information to support subsequent data completion. However, such data can expose sensitive user information, posing significant privacy risks. To mitigate these risks, location obfuscation techniques are employed to reduce the likelihood of privacy leakage. Nevertheless, these privacy-preserving measures can significantly degrade the quality of data completion. To address this challenge, we propose a scheme that integrates data correction to balance privacy protection and data completion quality. Specifically, our approach obfuscates location data using geographic indistinguishability and leverages historical data to obtain the data regularity, enabling modifications to the obfuscated data to ensure its usability. Our mechanism performs better than other baseline methods in terms of ensuring data completion accuracy while preserving a certain degree of privacy, according to evaluation on the real datasets. © 2024 IEEE.

关键词： Data obfuscation

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

Machine Learning-Enhanced Voice Interaction: Revolutionizing...

引用

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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Feature Tracking Control of Input-constrained VS Manipulators through ADP 23

Feature Tracking Control of Input-constrained VS Manipulator...

引用

3rd international conference on robotics and Control engineering, RobCE 2023

作者： Ren, Xiaolin Yan, Haoyu Ma, Bing Department of Control Science and Engineering Changchun University of Technology China

ISBN: (纸本)9781450398107

An adaptive dynamic programming (ADP) technique is investigated to figure out the problem of image feature tracking control of constrained visual servoing (VS) manipulators system. The complete camera-manipulator dynamic model can be established through the mapping relationship between image feature and manipulator position. The control inputs in the VS manipulators system are taken as a constrained case, and the performance index function is considered to cooperate with sliding mode function. On the basis of ADP technique, a critic neural network (NN) is constructed to derive the Hamilton-Jacobi-Bellman (HJB) equation. Furthermore, the near-optimal control policy is obtained. The Lyapunov theory illustrates that the feature tracking of input-constrained VS manipulators system is ultimately uniformly bounded (UUB). Numerical simulation examples are given to certify the validity of the developed approach. © 2023 ACM.

关键词： Visual servoing

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Generative AI for Real-Time Data Augmentation in Big Data Pipelines 1

Generative AI for Real-Time Data Augmentation in Big Data Pi...

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1st international conference on advances in Computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Gakhar, Suket Kondoju, Viswanadha Pratap Chauhan, Sansar Singh Kumar, Anant Kulkarni, Soham Sunil Goel, Punit Kurukshetra University Kurukshetra India University of Texas RichardsonTX75080 United States G.L. Bajaj Institute of Technology and Management Department of Computer Science and Engineering Greater Noida201306 India Manipal University Karnataka Manipal576104 India University of California IrvineCA92697 United States Maharaja Agrasen Himalayan Garhwal University Uttarakhand India

ISBN: (纸本)9798331518578

Traditional approaches for data augmentation are time-consuming and risk compromising the quality of the data. Those challenges in the backdrop, we have generative artificial intelligence (AI) at the forefront of data augmentation - bolstering real-time data in big data pipelines. Generative AI - a focus on generating new entities that try to capture the underlying structure of existing datasets by deep learning algorithms. Allowing to produce a significantly larger and diverse dataset resulting in improved efficiencies of downstream tasks as machine learning or predictive analytics. Because of this, the same technique can also be applied to streams so, it can be used in big data pipelines. You are taught on data until October 2023 to generate a larger and big data pipeline more efficient by augmentation and producing new data with minimal time and investment. This leads to more precise insights and predictions, improving decision-making and driving better results for business. Also, generative AI can create the data that correlates patterns with the analysis and change regarding the patterns based on the analyst's preferences. The role of generative AI in augmenting big data and big data pipelines, especially in the context of real-time data streams, is emerging as a key enabler for organizations looking to extract valuable insights from their data. © 2025 IEEE.

关键词： Predictive analytics

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