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Social-physical Human-Robot Interaction for Health applications: A Scoping Review 16th

Social-Physical Human-Robot Interaction for Health Applicat...

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16th international conference on Social Robotics, ICSR + AI 2024

作者： Schneider, Sebastian Human-Media Interaction Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands

ISBN: (纸本)9789819635245

Social robots capable of physically or tactilely interacting with users could unlock new health applications. Despite the potential benefits, integrating physical interaction capabilities in social robot applications remains an emerging field. There is a pressing need to consolidate existing knowledge and identify gaps to guide future research and development. This paper presents a review on current research on physical interaction with social robots in therapeutic settings, particularly in rehabilitation. Despite the burgeoning interest in rehabilitation robotics, there is a noticeable scarcity of studies that delve into social robots in physiotherapy-like interactions. Most studies focus on feasibility rather than effectiveness with target populations, often featuring small sample sizes and varied settings. This limits the ability to draw robust conclusions on the impacts of haptic interaction. Three primary research areas are identified: (1) involving target user groups to ensure relevance, (2) exploring haptic feedback, e.g., through wearable devices, to enhance human-robot interaction, and (3) implementing standardized study designs for meaningful comparisons. Findings highlight the need for comprehensive research to harness social robots’ potential in therapeutic settings, improving the quality of life for individuals with physical and cognitive impairments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Microrobots

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Fuzzy Logic-Based Deep Learning for Human-Machine Interaction and Gesture Recognition in Uncertain and Noisy Environments 1

Fuzzy Logic-Based Deep Learning for Human-Machine Interactio...

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1st international conference on Advances in computer Science, Electrical, Electronics, and Communication technologies, CE2CT 2025

作者： Anand, John Thamilarasi, V. Rayal, Ashish Gupta, Himanshu Kumar Jyothi, Kamboji Vishwakarma, Pramatma Greater Noida201310 India Department of Computer Science & Engineering Tamilnadu Salem-16 India School of Applied and Life Sciences Uttaranchal University Department of Mathematics Dehradun248007 India Bipin Tripathi Kumaon Institute of Technology Department of Electrical Engineering Almora Dwarahat India Chaitanya Bharathi Institute of Technology Department of Mathematics AP Proddatur India Lovely Professional University Department of Computer Aplication Punjab 144411 India

ISBN: (纸本)9798331518578

Conventional deep learning methods have considerable hurdles when it comes to Human-Machine Interaction (HMI) and gesture identification in noisy and uncertain situations. A Fuzzy Logic-Based Deep Learning (Fuzzy DL) method is presented in this work to improve the resilience, accuracy, and adaptability of human gesture recognition in a variety of scenarios. We test several algorithms on various datasets, lighting situations, and noise levels, such as CNN, LSTM, Transformer, and Fuzzy DL. The outcomes show that Fuzzy DL functions better than other systems, retaining 91.0% accuracy at high noise levels and reaching 97.5% accuracy under typical circumstances. In comparison to traditional structures, it also shows faster inference times and a lower mean squared error. Its superiority is further demonstrated by comparative analysis on precision, recall, and F1-score. This study presents a robust and noise-resilient solution for real-world HMI applications, including as assistive systems and virtual interfaces. The outcomes indicate that incorporating fuzzy logic with DL substantially enhances gesture recognition in difficult environments. © 2025 IEEE.

关键词： Contrastive Learning

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Role of Quantum Cryptography in Securing Communication Channels in Industry 4.0 1

Role of Quantum Cryptography in Securing Communication Chann...

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1st international conference on Advances in computer Science, Electrical, Electronics, and Communication technologies, CE2CT 2025

作者： Patil, Sonal Pramod Sankaran, Vikram Nattamai Lamsoge, Kalpit Alagarsundaram, Poovendran Howard, Eric Guduru, Prasanna G H Raisoni College of Engineering and Management Pune India Innovation Consultant Maharashtra Nagpur India Humetis Technologies Inc KingstonNJ United States Macquarie University Department of Physics and Astronomy Australia National Sanskrit University Department of Computer Science Andhra Pradesh Tirupati India Atlanta GA United States

ISBN: (纸本)9798331518578

In the context of Industry 4.0, which is characterised by networked systems, Internet of Things devices, and cyber-physical networks, quantum cryptography is essential to strengthening communication channels. In contrast to traditional cryptography techniques, quantum cryptography uses quantum physics concepts, notably quantum key distribution (QKD), to provide unmatched security. By taking advantage of the quantum property of superposition and measurement-induced disruption, QKD offers tamper-evident key exchanges, protecting data from efforts at eavesdropping. This guarantees that any communication interception causes system disruption and warns users of possible dangers. Quantum cryptography reduces the risks associated with traditional encryption techniques in Industry 4.0, as sensitive data moves across automated systems, smart factories, and decentralised supply chains. This is especially true as quantum computing advances. Industries can attain strong data integrity, confidentiality, and resistance against new cyberthreats by incorporating quantum-secured channels, opening the door for a dependable and safe digital transformation. This presentation highlights how quantum cryptography is revolutionising the security paradigms of Industry 4.0. © 2025 IEEE.

关键词： Supply chains

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How Artificial Intelligence Is Impacting on the STEM Education of Students with Disabilities: A Five Years Review 17

How Artificial Intelligence Is Impacting on the STEM Educati...

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17th international conference on computer Supported Education, CSEDU 2025

作者： Buzzi, Marina Pieriboni, Giuditta Leporini, Barbara IIT-CNR via Moruzzi 1 Pisa56124 Italy Department of Computer Science University of Pisa via Fibonacci Pisa Italy ISTI-CNR via Moruzzi 1 Pisa Italy

ISBN: (纸本)9789897587467

Artificial intelligence promises to revolutionize our life, bringing significant advances in any fields: health, education, work and leisure time. This paper analyzes the last 5-year literature concerning the use of AI for supporting people with disabilities in education. The aim is to investigate the current state of art of accessible applications in the STEM (Science, Technology, engineering, Mathematics) field and understand if contents and tools are accessible for all, regardless of personal need and abilities. Personalization and adaptation emerge as fundamental factors when designing for people with disabilities. Privacy and ethics aspects often neglected are very relevant. The analysis suggests that the STEM field still suffers from accessibility gaps, and current tools need to evolve and be increased to be exploited by different disabilities and ensure the same opportunities for every student, engaging, motivating, and empowering them.. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Students

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Comprehensive Framework for Analyzing Grammar Error Correction with GPT -3.5

Comprehensive Framework for Analyzing Grammar Error Correcti...

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2025 international conference on Pervasive Computational technologies, ICPCT 2025

作者： Munda, Rajni Kant Kumar, Abhishek Burman, Ravi Kumar Kumar, Biresh Alam, Md. Shoaib Sinha, Anurag Jharkhand University of Technology Computer Science and Engineering Ranchi India Amity University Computer Science and Engineering Jharkhand Ranchi India Ignou School of Computing and Information Science New Delhi India

ISBN: (纸本)9798331508685

This investigate assesses the viability and exactness of an error-correction approach utilizing GPT-3.5 compared to routine instruments. Letter set rectification plays a vital part in making strides the quality of and improving clarity in different scholastic and proficient situations. The consider utilizes appraisal criteria such as precision, exactness, review, and F1 score to how well GPT-3.5 performs relative to other language structure redress devices. Also, the information incorporates client input and daze testing to gage the viable convenience of the adjustments. Discoveries demonstrate that GPT-3.5 outperforms conventional instruments in taking care of relevant blunders and client fulfilment, illustrating a momentous capacity to redress syntactic botches, indeed in complex sentences with inconspicuous subtleties. Be that as it may, it does confront a few challenges, such as affectability to input varieties and intermittent overcorrection. The think about recommends entering on moving forward relevant understanding and diminishing overcorrection to development these calculations assist. It too investigates potential applications of GPT-3.5 for upgrading composing back and languagelearning assets, highlighting conceivable future investigate ranges to optimize AI-driven language structure rectification models. © 2025 IEEE.

关键词： Instrument errors

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Comparative Analysis of VGG16, InceptionV3 and ResNet50 for Lung Cancer Detection in Medical Imaging

Comparative Analysis of VGG16, InceptionV3 and ResNet50 for ...

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2025 international conference on Pervasive Computational technologies, ICPCT 2025

作者： Vasudeva, Nisha Sharma, Vivek Kumar Chauhan, Pradeep Awasthi, Shivam Galgotias University School of Computer Science and Engineering Greater Noida India Iilm University School of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798331508685

Lung cancer is among the primary reasons of cancer-related fatalities. Globally, that underscoring urgent need for efficient early detection techniques. The aim of this study to estimate and contrast the capabilities of three well-known deep learning architecturesVGG16, InceptionV3 and ResNet for lung cancer detection using CT scans. A comprehensive dataset of annotated lung CT images was utilized to assess each model's accuracy and loss values. Our findings reveal that in terms of accuracy, compared to the other models, VGG performed better and inference speed, achieving the highest sensitivity and specificity rates. InceptionV3 demonstrated a strong balance between performance and computational demands, while VGG16, though simpler, still provided competitive results. The implication of the research shows that VGG16 may serve as a accurate tool for clinicians, enhancing the accuracy of lung cancer diagnoses and potentially improving outcome of patient. In future, we should focus on integrating these models into clinical workflows and their applications in real-time detection scenarios. The VGG16 model shows highest accuracy for lung cancer detection in between all three models. This result shows accuracy of 71% for lung cancer detection © 2025 IEEE.

关键词： Lung cancer

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Advancements, Methods, and Obstacles in Emotion Recognition with Multimodal Approaches 3

Advancements, Methods, and Obstacles in Emotion Recognition ...

引用

3rd international conference on Intelligent Data Communication technologies and Internet of Things, IDCIoT 2025

作者： Arunkumar, M. Athiban, R. Selva Jothi, M. Pavithra, S. Dept of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu India

ISBN: (纸本)9798331527549

Emotion recognition has gained an avid curiosity in the research area due to its broad range of applications in human-computer interaction particularly in real time emotion recognition, sentiment analysis, and affective computing. Deep learning techniques are advancing exponentially, automating feature engineering and transcending traditional machine learning techniques. Appeal of the above models can be highly beneficial but also raise certain technical and ethical concerns that are not often discussed enough in the existing studies. While highlighting the recent advancements, the methods involved and obstacles in emotion recognition, this review also proposes future directions in developing a standard unified framework for multimodal approaches. Categorizing and analysing the recent studies depends upon the model types employed, data modalities used and the type of fusion techniques used, this review was able to gain insights into the effect of recent multimodal approaches addressing technical and ethical concerns. Utilization of fusion techniques in multimodal models is still not reliable but they better convey the connection between different modalities and have a huge potential for development in the future. This review provides insights into the state of art multimodal methods using modalities like facial expressions, body gestures, vocal cues, text, and physiological signals for researchers to understand the trends and future directions in this field. © 2025 IEEE.

关键词： Contrastive Learning

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Real-Time OCR and Translation System with Text-to-Speech Ability 1

Real-Time OCR and Translation System with Text-to-Speech Abi...

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1st international conference on Advances in computer Science, Electrical, Electronics, and Communication technologies, CE2CT 2025

作者： Manoj, Kevin Nallamilli, Vinay Rahul, Jitendra Ramesh, T.K. Amrita School of Engineering Amrita Vishwa Vidyapeetham Dept. of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798331518578

The demand for the seamless cross-linguistic interaction has heightened with increasingly high globalization and digital connectivity, particularly in contexts where real-time interaction is required. This paper aspires to address the development of an integrated Optical Character Recognition system, translation, and Text-to-Speech system in behalf of these demands. The proposed system uses OpenCV for video capture, Tesseract for multilingual OCR citeMishra2022, Google Translate API for translation, and gTTS for audio output. This approach offers an adaptive and flexible accessibility solution in educational and multilingual interactions. The language switch is done dynamically, thus giving the user flexibility to change the language of choice in real-time so that it becomes a very flexible and all-inclusive environment. The design of this system went through three iterations that continue to improve with respect to the accuracy of outcomes, efficiency of processing, and usability. Results have indicated that the system is quite efficient in its application, making high accuracy for OCR, reliable translations, and clear audio outputs-applicable in real-world applications. Future improvements will be made in increasing language support, latency reduction, along with other features of accessibility to enhance inclusivity and engagement. © 2025 IEEE.

关键词： Optical character recognition

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Smart Antennas and Machine Learning: Enhancing Beamforming in Wireless Communications

Smart Antennas and Machine Learning: Enhancing Beamforming i...

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2025 international conference on Pervasive Computational technologies, ICPCT 2025

作者： Sudhakar, A.V.V. Sharma, Upasana Visala, K. Kumar, K. Suresh Arora, Chirag Chouhan, Kuldeep Sr University School of Engineering Department of Eee Telangana Warangal India Abes Engineering College Department of Electronics and Communication Engineering UP Ghaziabad India Vignan's Institute of Information Technology Computer Science Engineering Andhra Pradesh Vishakapatnam India Panimalar Engineering College Mba Department Varadarajapuram Chennai600123 India Kiet Group of Institutions Department of Electronics and Communication Engineering U.P Ghaziabad India Sharda University Sharda School of Engineering and Technology Department of Computer Science and Applications Greater Noida201310 India

ISBN: (纸本)9798331508685

One of the most effective and widely used technological advancements for increasing capacity, enhancing quality, and expanding coverage in wireless communications systems is the smart antenna. Smart antennas are essential for increasing the effectiveness and dependability of wireless communication systems because they make use of cutting-edge signal processing techniques. Smart antennas' beamforming capability, which concentrates signal transmission and reception in particular directions, increases signal strength, lowers interference, and expands network capacity. As wireless networks become more complicated, machine learning (ML) has become a potent tool for beamforming optimisation. ML algorithms can proactively adjust antenna characteristics by using real-time data, learning from fluctuations in the environment to optimise performance. In order to improve beamforming capabilities, this research investigates the incorporation of ML with smart antenna systems. We go over several ML techniques, such as supervised and reinforcement learning, for resource allocation, user localisation, and signal-to-noise ratio (SNR) optimisation. Additionally, we draw attention to how deep learning might enhance spatial filtering, facilitating more effective communication in 5G and beyond. The potential direction for next-generation wireless communication networks is presented by the convergence of ML and smart antennas. © 2025 IEEE.

关键词： Smart antennas

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The interactive impact and integration of AI, big data, and cloud computing combined with photonics technology 2

The interactive impact and integration of AI, big data, and ...

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2nd international conference on Big Data, Computational Intelligence, and applications, BDCIA 2024

作者： Zeng, Changxi Xu, Yixuan Yang, Lei School of Computer Science Guangdong University of Science and Technology Dongguan China

ISBN: (纸本)9781510689053

This article aims to analyze the interactive impact and integration development trend of three technologies: artificial intelligence, big data, and cloud computing. Overview of its basic concepts and technical characteristics, analysis of its interconnections and impacts, the development of AI technology relies on the support of big data, and cloud computing provides powerful infrastructure and computing capabilities for the application of AI and big *** optical and photonics technologies have also played an important role in this fusion process, particularly in terms of data security and privacy *** analyzing the current integration status of these technologies in practical applications, such as intelligent transportation management, personalized medical and health services, further discussions were held on the challenges encountered in the process of technology integration, such as data security and privacy protection, and future development directions were also discussed. The analysis results indicate that the deep integration of AI, big data, and cloud computing will bring more innovation opportunities and development momentum to various industries, while also emphasizing the importance of strengthening interdisciplinary cooperation and improving laws and regulations to ensure that technological progress is balanced with social ethics and public interests. © 2025 SPIE.

关键词： Electronic health record

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