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检索条件"任意字段=5th International Conference on Machine Learning and Human-Computer Interaction, MLHMI 2024"

共 558 条记录，以下是181-190 订阅

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Identification of Different Medicinal Plants Using machine learning and Image Processing 5th

Identification of Different Medicinal Plants Using Machine L...

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5th EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Pokkuluri, Kiran Sree Phaneendra Varma, Ch Gurujukota, Ramesh Babu Raja Rao, P.B.V. Usha Devi N, S.S.S.N. Prasad, M. Pamarthi, Nagaraju Shalem Raju, P.J.R. Department of Computer Science and Engineering Shri Vishnu Engineering College for Women Bhimavaram India Department of Computer Science and Engineering University College of Engineering JNTU Kakinada Kakinada India

ISBN: (纸本)9783031770746

Medicinal plants have been an essential source of remedies and treatments for various ailments throughout human history. this paper presents an innovative approach that combines machine learning and image processing to identify various medicinal plants and extract information about their traditional and modern uses. this method offers efficiency and dependability by automating the identification procedure, which lessens the reliance on human expertise and may hasten the discovery and application of medicinal plants for therapeutic purposes. In this work identification of the medical plats is done thought the shape of the plant leave, text features using digital image processing techniques, color using Convolutional Neural Works (CNN). We have achieved an accuracy of 98.6% in identifying the medical plants and we have compared our work with the existing literature with various parameters like precision, accuracy and f1 score. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Adversarial machine learning

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Sign Detection Using an N-Gram Language Model and MobileNet 5th

Sign Detection Using an N-Gram Language Model and MobileNe...

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5th international conference on Advances in Distributed Computing and machine learning, ICADCML 2024

作者： Gauns, Kaivalya Lawande, Aniruddh Panigrahi, Trilochan National Institute of Technology Goa Farmagudi Goa Ponda403401 India

ISBN: (纸本)9789819718405

Typically a human being has no trouble communicating with each other through speech, gestures, body language, reading, and writing. However, people with speech impairments rely solely on sign language, which makes it harder for them to interact with the majority. this creates a need for sign language recognition systems that can understand and convert sign language into spoken or written language, and vice versa so that others can understand. Existing systems for this purpose are limited, expensive, and difficult to use and much work has not been done for Indian sign language. As a result, the main objective of this paper is to increase the accuracy of sign detection using language models for Indian sign language. For this N-gram sign model is used in MobileNet machine learning system. the simulation results showing the improvement of sign detection with accuracy of nearly 91% from 85% with the prediction model followed by machine learning method. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： computer vision

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A methodology to guide companies in using Explainable AI-driven interfaces in manufacturing contexts 5

A methodology to guide companies in using Explainable AI-dri...

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5th international conference on Industry 4.0 and Smart Manufacturing (ISM)

作者： Grandi, Fabio Zanatto, Debora Capaccioli, Andrea Napoletano, Linda Cavallaro, Sara Peruzzini, Margherita Univ Modena & Reggio Emilia Dept Engn Enzo Ferrari Via Pietro Vivarelli 10 I-41125 Modena Italy Deep Blue Srl Via Daniele Manin 53 I-00185 Rome Italy CNH Ind SpA Viale Nazioni 55 I-41122 Modena Italy

Nowadays, the increasing integration of artificial intelligence (AI) technologies in manufacturing processes is raising the need of users to understand and interpret the decision-making processes of complex AI systems. Traditional black-box AI models often lack transparency, making it challenging for users to comprehend the reasoning behind their outputs. In contrast, Explainable Artificial Intelligence (XAI) techniques provide interpretability by revealing the internal mechanisms of AI models, making them more trustworthy and facilitating human-AI collaboration. In order to promote XAI models' dissemination, this paper proposes a matrix-based methodology to design XAI-driven user interfaces in manufacturing contexts. It helps in mapping the users' needs and identifying the "explainability visualization types" that best fits the end users' requirements for the specific context of use. the proposed methodology was applied in the XMANAI European Project (https://***), aimed at creating a novel AI platform to support XAI-supported decision making in manufacturing plants. Results showed that the proposed methodology is able to guide companies in the correct implementation of XAI models, realizing the full potential of AI while ensuring human oversight and control. (c) 2023 the Authors. Published by ELSEVIER B.V.

关键词： explainable AI (XAI) artificial intelligence (AI) manufacturing human-machine interaction user interface

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Simplifying Robot Grasping in Manufacturing with a Teaching Approach based on a Novel User Grasp Metric 5

Simplifying Robot Grasping in Manufacturing with a Teaching ...

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5th international conference on Industry 4.0 and Smart Manufacturing (ISM)

作者： Pantano, Matteo Klass, Vladislav Yang, Qiaoyue Sathuluri, Akhil Regulin, Daniel Janisch, Lucas Zimmermann, Markus Lee, Dongheui Siemens Aktiengesell Technol Dept D-81739 Munich Germany Tech Univ Munich Human Ctr Assist Robot D-80333 Munich Germany Tech Univ Munich Lab Prod Dev & Lightweight Design D-85748 Garching Germany Tech Univ Wien Autonomous Syst AT-1040 Vienna Austria German Aerosp Ctr Inst Robot & Mechatron D-82234 Wessling Germany

the manufacturing industry is undergoing rapid evolution, necessitating flexible and adaptable robots. However, configuring such machines requires technical experts, which are hard to find, especially for small and medium enterprises. therefore, the process needs to be simplified by allowing non-experts to configure robots. During such configuration, one key aspect is the definition of objects' grasping poses. the literature proposes deep learning techniques to compute grasping poses automatically and facilitate the process. Nevertheless, practical implementation for inexperienced factory operators can be challenging, especially if task-specific knowledge and constraints should be considered. To overcome this barrier, we propose an approach that facilitates teaching such poses. Our method, employing a novel user grasp metric, combines the operator's initial grasp guess given by a 3D spatial device with a state-of-the-art deep learning algorithm, thus returning reliable grasping poses but simultaneously close to the operator's initial guess. We compare this approach against commercial grasping pose definition interfaces through a user test involving 28 participants and against state-of-the-art deep learning grasp estimators. the results demonstrate a significant improvement in system usability (+24%) and a reduced workload (-16%). Furthermore, our experiments reveal an increased grasp success rate when utilizing the user grasp metric, surpassing state-of-the-art deep learning grasping estimators.

关键词： operator empowerment spatial interaction robotic grasping usability small medium enterprises cobots

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Enhancing Acoustic-based Speech Emotion Recognition Using CNN 5

Enhancing Acoustic-based Speech Emotion Recognition Using CN...

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5th international conference on Advanced Information Technologies, ICAIT 2024

作者： Hlaing, Hnin Oo thet Zin, thet Wai, thinn thinn Nway Oo, Aung University of Information Technology Yangon Myanmar University of Information Technology Faculty of Computer Science Yangon Myanmar

ISBN: (纸本)9798331510046

Speech Emotion Recognition (SER) is a crucial field aiming to enhance machine understanding of human emotions. Among all the variety of data sources, we choose the acoustic features of the speech to explore both positive and negative aspects in feature extraction, aggressive data augmentation, and complex model architecture, highlighting its potential to inflate false confidence (overfitting) and hallucinations if not wielded with caution. Furthermore, our investigation explores the complex link between accuracy and feature set size, highlighting the need to extract rich emotional characteristics from speech. An intriguing finding of our analysis is: downscaling the filter size and learning rate of the neural network works better with small training dataset and the model can differentiate diverse emotions more accurately. this finding paves the way for improving the classification model, potentially leading to more robust emotion recognition systems. this multifaceted analysis goes beyond the conventional focus on accuracy, challenging assumptions and pinpointing areas for future exploration in the ever-evolving field of speech-based emotion recognition. © 2024 IEEE.

关键词： Convolutional Neural Feature Extraction Network (CNN)Data Augmentation Speech Emotion Recognition (SER)

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Integrating Social Platforms with IoT: Security Challenges, Applications, and Trust Issues 5

Integrating Social Platforms with IoT: Security Challenges, ...

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5th IEEE India Council international Subsections conference, INDISCON 2024

作者： Mohanta, Bhabendu Kumar Dehury, Mohan Kumar Tulasi, Desetti Das, Sunayana United Arab Emirates University College of Information Technology Al AinP.O. Box 15551 United Arab Emirates Amity Institute of Information Technology Amity University Jharkhand Ranchi India Koneru Lakshmaiah Education Foundation Dept. of Computer Science and Engineering Vaddeswaram Guntur India Driems University Tangi Dept. of Computer Scinece and Engineering Odisha Cuttack754 022 India

ISBN: (纸本)9798350376753

the term Social Internet of things (Social IoT) refers to an expansion of the term Internet of things (IoT) that integrates social networking capabilities and concepts into IoT applications. the number of connected devices has already crossed the total world population. With the development of other technologies like Blockchain, Artificial Intelligence, machine learning, and Data Science, IoT become more usable in many applications. With the rapid use of digital devices, social media like Facebook and others are part of the human lifestyle. the integration of IoT with a social platform for different services become an emerging research field in recent times. In this paper, the authors investigated the impact of social services in IoT and its corresponding decentralized architecture. the paper also identified the security challenges associated with the Social IoT and trust issues. Finally, the paper addresses some of the emerging technologies which are capable of addressing the security challenges in Social IoT. © 2024 IEEE.

关键词： Decentralization IoT Privacy Secure Communication Security Social IoT Trust Management

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Towards a human-Centric Industry 5.0: Exploring Team Roles to Improve human-machine Collaboration 5th

Towards a Human-Centric Industry 5.0: Exploring Team Roles t...

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5th international conference on Innovative Intelligent Industrial Production and Logistics

作者： Monopoli, Domenico Caggiano, Mariateresa Semeraro, Concetta Dassisti, Michele Polytech Univ Bari Dept Mech Math & Management DMMM Bari Italy Univ Sharjah Dept Ind & Management Engn Sharjah 27272 U Arab Emirates

ISBN: (纸本)9783031807749;9783031807756

Industry 5.0 aims to enhance the strengths of the current Industry 4.0 paradigm and broaden its scope by emphasizing the human operators within the working environment through harmonious interactions with machines and other human collaborators. One of the three pillars of Industry 5.0 is the redefined role that human operators would assume within rapidly evolving production systems due to significant technological improvements descending from the I4.0 paradigm. this new role is becoming a major organizational and technological challenge to ensure maximum productivity while preserving the health and well-being of the operator. In this context, the primary objective of this study was to investigate the impact of various human-machine team configurations on process performance and worker well-being. To achieve this, a collaborative workstation was designed and implemented to simulate different configurations of collaborative scenarios, each involving distinct roles for humans and machines. Preliminary findings indicate that key factors such as experience and attitude toward technology necessitate different collaborative setups. Specifically, experienced operators prefer supervisory roles within the team, with the machine primarily used for final inspections. In contrast, less experienced operators tend to rely more on the machine's decisions, adopting a more apprentice-like role.

关键词： Industry 5.0 human-Centricity human-machine interaction

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Fine-grained Action learning for human-Object interaction Detection 5

Fine-grained Action Learning for Human-Object Interaction De...

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5th international conference on Control, Robotics, and Intelligent System, CCRIS 2024

作者： Lin, Xue Fan, Linwei Cui, Chaoran Li, Lei Zhao, Xiuyang Zou, Qi School of Computer Science and Technology Shandong University of Finance and Economics Jinan China University of Jinan Jinan China Beijing Jiaotong University Beijing China

ISBN: (数字)9781510685871

ISBN: (纸本)9781510685864

the detection of human-object interaction (HOI) is essential to promote various visual tasks. It is challenging because of tiny difference between fine-grained actions, and data imbalance of HOI classes. Most approaches tackle the problems by designing complex structures or resorting to extra knowledge. However, it still suffer from inferior detection on the fine-grained HOIs. In this paper, we propose a fine-grained action learning method to advance the detection of hard-to-distinguish HOIs. Specially, we introduce the action correlation to figure out the fine-grained actions that are easy to misclassified. Furthermore, we design an action discriminating loss based on the action correlation to increase the penalty on the hard-to-distinguish actions, enhancing the detection of fine-grained HOIs. We do some comprehensive experiments on HICO-DET dataset to validate the effectiveness of the proposed fine-grained action learning method compared with the existing approaches. © 2024 SPIE.

关键词： Contrastive learning

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Real-time automated fabric defect detection system 5th

Real-time automated fabric defect detection system

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5th international conference on Advances in AI for Biomedical Instrumentation, Electronics and Computing, ICABEC 2023

作者： Mandawkar, Umakant Shahade, Makarand Wadekar, Samruddhi Kachhava, Chetan Patil, Yash Mandwekar, Sakshi Department of Computer Engineering SVKM Institute of Technology Dhule India

ISBN: (纸本)9781032642987

Even today the Textile Industries are using manpower to detect and classify the fabric defect, the process of depending on human inspectors for fabric defect detection can be considered as not an optimized way since it is time-consuming as well as an error prone process. To tackle this issue, here we propose a groundbreaking development- A real-time, automated fabric defect detection system. Making the most use of all the available cutting-edge advanced image processing techniques and machine learning algorithms, the proposed system will identify, locate and categorize a wide range of fabric defect where precision and accuracy will be the hallmarks of the proposed methodology the system will operate in real time, capturing highresolution fabric surface images, leading to precise and swift defect identification. Its automated and real-time capabilities render it to be an essential instrument in textile manufacturing as it will enhance its quality by diminishing the requirement of human inspectors, and evolving overall inspection process efficiency. Comprehensive and thorough testing across various fabric materials will prove its high accuracy rates in defect detection. It will possess the power to revolutionize the textile industry by enhancing product quality, cutting costs and boosting productivity. © 2024 the Author(s).

关键词： Textile industry

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Temporal Fusion of Time-Distributed VGG-16 and LSTM for Precise Action Recognition in Video Sequences 5

Temporal Fusion of Time-Distributed VGG-16 and LSTM for Prec...

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5th international conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Athira, K.A. Divya Udayan, J. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

Autonomous systems play a vital role in creating an ambient environment, but for comprehensive human-robot interaction, the system must interpret and identify specific human behaviours. the demand for recognizing human activity from videos has significantly increased across diverse domains, such as medicine, intelligent video surveillance, human-robot interaction, and ambient-assisted environments. Deep learning has emerged as a pioneering technique for human activity recognition from videos due to its ability to learn features from input videos automatically. this research proposes a novel approach for recognizing activities in videos by leveraging the combined power of the VGG-16 convolutional neural network (CNN) and the Long Short-Term Memory (LSTM). To assess the efficiency of our suggested methodology, experiments are conducted on the widely used benchmark dataset UCF-50. the experimental results demonstrate that our VGG-16 and LSTM-based model outperforms state-of-the-art methods for activity recognition, achieving higher accuracy and robustness across various activity categories. © 2024 the Authors. Published by Elsevier B.V.

关键词： Long short-term memory

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