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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191-200 订阅

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Useckit: An Open-Source Deep-learning Toolkit Bundling State-Of-the-Art Algorithms for Evaluating Behavioral Biometrics 18

Useckit: An Open-Source Deep-Learning Toolkit Bundling State...

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18th IEEE international Joint conference on Biometrics, IJCB 2024

作者： Liebers, Jonathan Kley, Tristan Liebers, Carina Gruenefeld, Uwe Schneegass, Stefan University of Duisburg-Essen Essen Germany

ISBN: (纸本)9798350364132

there is an endeavor in the human-computer interaction (HCI) community to create novel authentication schemes so that passwords finally become obsolete and practical security is enhanced. For this purpose, researchers combine behavioral biometrics with deep learning. However, because the process of creating neural networks is inherently complex and each model architecture has certain limitations, implementing research prototypes is a time-consuming and challenging task. therefore, we present useckit, an open-source toolkit that provides deep learning algorithms to support the creation of scientific evaluations in authentication research. Useckit provides multiple paradigms for implementing user verification and identification, functions to calculate common metrics, and neural network architectures founded in literature. It is written in Python and supports researchers and practitioners in creating, implementing, and rigorously evaluating novel deep learning-based authentication schemes. © 2024 IEEE.

关键词： Adversarial machine learning

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Comparing machine learning Algorithms for Real-Time Forest Fire Detection using Color, Motion, and Shape Features 5

Comparing Machine Learning Algorithms for Real-Time Forest F...

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5th international conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Dixit, Rinku Sharma Choudhary, Shailee Lohmor Mahaveerakannan, R. Kaveya, E. New Delhi Institute of Management Department of Artificial Intelligence and Machine Learning Delhi110062 India Saveetha School of Engineering Department of Computer Science and Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India

ISBN: (纸本)9798331518097

Forest fires are extremely dangerous to ecosystems, animals, and humans;thus, it is crucial to detect them early on to limit the harm they cause. Presenting a novel framework for the detection of forest fires is the objective of this study. To increase detection speed and accuracy, this framework mixes colour, motion, and form information with machine learning algorithms. To capture the dynamic nature of fire - its ever-changing hues, distorted forms, and unending motion - an iterative method is employed. Colour probabilities are determined during the segmentation stage before going to the verification step. the categorisation stage and subsequent picture processing both make use of colour histograms. these characteristics allow the system to distinguish between flames and other things more effectively. Evaluating the framework's efficacy involved using intersection over union (IoU) ratios and frame-based evaluation. While IoU is used to test the system's ability to localise the fire inside an image, frame-based assessment is used to test the accuracy of fire detection. Tests on the VisiFire dataset proved the framework's dependability with an 89.97% true-positive rate and a 10.03% false-negative rate. And with an average frame rate of 21.70 FPS, the system is fast enough for real-time processing workloads. Considering these results, it seems like the suggested approach could greatly enhance forest fire early warning capabilities. It can also maintain a high degree of accuracy while providing quicker response times. Protecting forest ecosystems, reducing human life danger, and improving disaster management are all possible outcomes of integrating this technology into early warning systems. this framework is a huge step forward in the world of modern fire detection systems. © 2024 IEEE.

关键词： Fire detectors

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Reinforcement learning in Cognitive Robots for Autonomous Path Planning 5

Reinforcement Learning in Cognitive Robots for Autonomous Pa...

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5th international conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Das, Archana Poornima, R. Indu Deepa, G. Gokila Sheela, K. Selva Akshya, J. Choudhry, Mani Deepak New Horizon College of Engineering Department of Information Science and Engineering Bengaluru India KGiSL Institute of Technology Department of Information Technology Coimbatore India Ppg Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India KGiSL Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798350389609

Cognitive robots are intelligent systems that learn from their environment, adapt to dynamic changes, and make decisions without a human's direct intervention. this research studies the integration of Reinforcement learning in cognitive robots with Q-learning for autonomous path planning. the study addresses the most critical issues in traditional A∗ and Dynamic Programming algorithms by devising path-planning techniques, mainly the lack of adaptability and computational efficiency in real-time and unpredictable environments. Our approach is based on Q-learning, which is a model-free RL algorithm allowing the robots to find paths autonomously by optimizing their paths and avoiding obstacles. the Q-learning algorithm provides the possibility of learning optimal policies for the robot through iterative interaction with its environment balanced between exploration and exploitation of the decision process. A reward system is utilized by the proposed model, encouraging the robot to explore shorter, collision-free paths and adjust based on feedback in real time from its surroundings. the model was found to be significantly better than its counterparts. In this context, as indicated, the Q-learning model runs at 11.5 seconds, faster than A∗ at 18.3 seconds and Dynamic Programming at 16.7 seconds, yet with an accuracy of 94% and was found to have the highest collision avoidance rate at 98%. Additionally, the adaptability of the model about the environment presents a marked difference in terms of path length optimization, having done so with a mean path length of 12.3, compared to approaches or models. the robustness and scalability of the Q-learning model make it highly applicable in real-world applications. © 2024 IEEE.

关键词： Adversarial machine learning

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Assessment of Slope Instability in a Hilly Terrain: A Logistic Regression and Random Forest Based Approach 5th

Assessment of Slope Instability in a Hilly Terrain: A Logist...

引用

5th international conference on Computational Intelligence in Communications and Business Analytics, CICBA 2023

作者： Dey, Sumon Das, Swarup Department of Computer Science and Technology University of North Bengal West Bengal Darjeeling India

ISBN: (纸本)9783031488757

Landslides are one such spontaneous natural disaster which has the potential to effect human lives as well as economic property in any region. the continuous exploration of qualitative and quantitative approaches augmented with RS-GIS methodologies for assessing landslides have given new dimensions to the area of research. the main obstacle of these methodologies remains in the fact that the hypothesis needs to be taken as true even before the analysis. To overcome the same, with the development of machine learning methodologies, the exhibits are found to be more objective in terms of quantification as well as the quality of research. the current assessment was aimed to evaluate the execution of two machine learning approaches namely;logistic regression and random forest for the assessment of slope instability in Darjeeling Himalayas. Eight geo-environmental factors were taken into consideration for the said assessment. the susceptibility models prepared with the said approaches were substantiated using receiver operating characteristics curves. the assessed accuracies were 76.8% and 79.7% for logistic regression and random forest respectively with the dataset(training and validation), the accuracies measured were 76.8% and 77.7% respectively. © 2024, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： machine learning

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An Optimised Model to Forecast the Water Portability Using the Restricted Boltzmann machine and Neural Network 5

An Optimised Model to Forecast the Water Portability Using t...

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5th IEEE international conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Rao, Lade Gunakar Sravani, K. Nanre, Devender Nethani, Shivakumar Mohmmad, Sallauddin Sr University School of Computer Science and Artificial Intelligence Telangana Warangal India Cvr College of Engineering Telangana Hyderabad India Hyderabad Institute of Technology and Management Telangana Hyderabad India

ISBN: (纸本)9798350383522

Water quality is paramount for environmental health and human well-being, especially with global population growth and urbanization exacerbating pollution threats. this study delves into water quality prediction, employing advanced modeling, data analysis, and cutting-edge technologies such as sensor networks and machine learning. Datasets from the UCI machine learning Repository, EPA, Kaggle, and the Government of Canada form the basis for comprehensive research. Linear Regression, Decision Trees, Random Forest, SVM, Neural Networks, KNN, and Gradient Boosting Models are evaluated for suitability, each catering to specific data characteristics. AutoML tools and time series models like ARIMA are considered, emphasizing preprocessing and domain expertise. the research aims to advance water quality prediction methods, offering insights into complex relationships, reliable predictions, and a visionary sustainable water resource management framework. We used the Restricted Boltzmann machine, a neural network model, to predict quality level. We got an accuracy of 0.69. this study addresses challenges, reviews methodologies, and proposes innovative approaches, contributing to improving water quality prediction and environmental conservation. © 2024 IEEE.

关键词： machine learning neural network Restricted Boltzmann machine Water quality

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Improved R3D for human Action Recognition Based on Transfer learning 6

Improved R3D for Human Action Recognition Based on Transfer ...

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6th international conference on Robotics and computer Vision, ICRCV 2024

作者： Han, Xiaowei Guo, Qiuyang Lu, Yunjing Liu, Jiwu Fei, Chaolong Harbin University of Commerce Postdoctoral research workstation of Northeast Asia Service Outsourcing Research Center Station of Applied Economics Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing Harbi China Harbin University of Commerce Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing Harbin China Guangdong Technology College Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing Zhaoqing China

ISBN: (纸本)9798331527426

human action recognition is an important research direction in the field of computer vision and machine learning, which is widely used in intelligent surveillance, human-computer interaction, sports analysis and other fields. Traditional action recognition methods usually rely on large-scale labeled datasets and complex feature extraction processes, which have problems such as high data acquisition cost and long model training time, as an effective solution, transfer learning can significantly improve the performance of the model and reduce the dependence on labeled data by transferring the features learned by the pre-trained model on large-scale datasets to the target task. In this paper, the R3D model is used as the backbone network, and the model is improved by strengthening the channel and temporal feature extraction capabilities. the model achieves good performance on Shopping Action_ v l by transfer learning based on HMDB51 and UCF101 datasets, and obtains 89.53% and 94.14% of the optimal Top-1 and Top-5 indicators after pre-training on UCF101 dataset. In summary, the C-TSAB ResNets model is proposed in this paper performs well in accuracy and robustness on human action recognition tasks through transfer learning, which verifies the actual effect of the innovation in the research work of this topic. © 2024 IEEE.

关键词： Convolutional neural networks

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Prompt-to-OS (P2OS): Revolutionizing Operating Systems and human-computer interaction with Integrated AI Generative Models 5

Prompt-to-OS (P2OS): Revolutionizing Operating Systems and H...

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5th IEEE international conference on Cognitive machine Intelligence, CogMI 2023

作者： Tolomei, Gabriele Campagnano, Cesare Silvestri, Fabrizio Trappolini, Giovanni Sapienza University of Rome Department of Computer Science Italy Sapienza University of Rome Department of Computer Engineering Italy

ISBN: (纸本)9798350323832

In this ambitious paper, we present a groundbreaking paradigm for human-computer interaction that revolutionizes the traditional notion of an operating system. Within this innovative framework, user requests issued to the machine are handled by an interconnected ecosystem of generative AI models that seamlessly integrate with or even replace traditional software applications. At the core of this paradigm shift are large generative models, such as language and diffusion models, which serve as the central interface between users and computers. this pioneering approach leverages the abilities of advanced language models, empowering users to engage in natural language conversations with their computing devices. By capitalizing on the power of language models, users can articulate their intentions, tasks, and inquiries directly to the system, eliminating the need for explicit commands or complex navigation. the language model comprehends and interprets the user's prompts, generating and displaying contextual and meaningful responses that facilitate seamless and intuitive interactions. this paradigm shift not only streamlines user interactions but also opens up new possibilities for personalized experiences. Generative models can adapt to individual preferences, learning from user input and continuously improving their understanding and response generation. Furthermore, it enables enhanced accessibility, as users can interact with the system using speech or text, accommodating diverse communication preferences. However, this visionary concept also raises significant challenges, including privacy, security, trustability, and the ethical use of generative models. Robust safeguards must be in place to protect user data and prevent potential misuse or manipulation of the language model. While the full realization of this paradigm is still far from being achieved, this paper serves as a starting point for envisioning the transformative potential of a human-computer interaction

关键词： human computer interaction

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A Python Based machine learning Framework for Eye Blink Detection with BlueEye Technology 5

A Python Based Machine Learning Framework for Eye Blink Dete...

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5th international conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Musthafa, A. Syed Sujatha, R. Jessie, P. Mithuna, R. Mekala, R. Vijay, A. M.Kumarasamy College of Engineering Department of Information Technology Tamil Nadu Karur639113 India Dr.Mahalingam College of Engineering and Technology AIML Department Pollachi642 003 India Akshaya College of Engineering and Technology Department of Artificial Intelligence and Data Science Tamil Nadu Coimbatore India St.Josephs College of Engineering Department of Artificial Intelligence and Data Science Tamil Nadu Chennai600119 India

ISBN: (纸本)9798331518097

this research study discusses the results of an Eye Blink Technology, Eye Blink technology enables interaction with digital interfaces through eye movement tracking, offering a hands-free method for controlling devices. this technology utilizes computer vision techniques, such as those provided by OpenCV and MediaPipe, to detect and track facial landmarks. It captures real-time video from a webcam and processes each frame to detect the positions of eyes and movements thereof. Mapping eye landmarks to screen coordinates is then used by the system to convert movement of eyes into movement of the cursor, while it lets people interact with the system without touching the monitor- eye blinks translate to clicks. this can be basically credited to the immense potential for access tools, which make access to computers much more natural and effective for limited mobility users. © 2024 IEEE.

关键词： Eye movements

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5th international conference on Real Time Intelligent Systems, RTIS 2023

5th International Conference on Real Time Intelligent System...

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5th international conference on Real Time Intelligent Systems, RTIS 2023

ISBN: (纸本)9783031558474

the proceedings contain 41 papers. the special focus in this conference is on Real Time Intelligent Systems. the topics include: the Application of Artificial Intelligence Technology in Dance Teaching;design of a Cabin English Automatic Broadcasting Detection System Based on Deep learning;application of Improved Clustering Algorithms in Modern Education Technology’s Blended learning;an IoT Blockchain to Improve the Traceability on Agri-Food Applications;process Analysis of Depression Expression and Action Changes and their Psychological Integration Based on Deep learning Algorithms;moderating the Role of Real-Time Intelligence Systems in the Relationship Between Emotional Intelligence and Employee Performance;optimization of Logistics Distribution Route Based on Improved Genetic Algorithm;carrier Adjustment Algorithm for Mobile Communication Network Based on Deep Reinforcement learning;diabetic Retinopathy Detection Using Amalgamated Deep learning Algorithm;evaluation Method of College Students’ Education Based on Artificial Neural Networks;research on Intelligent Algorithm-Based Swimming Athlete Pose Recognition and Correction Method;study on a Web-Based Remote Classroom English Writing Practice System Using Intelligent Model;coordination Training and Testing of Upper and Lower Limbs in Aerobics Under Neural Networks;vocal Melody Extraction Based on Sparse Autoencoding Neural Networks;Football Players’ Physical Parameter Simulation Training Based on AI Intelligent Simulation;Total Cost of Ownership Optimization for ERP System Implementation Project Using Advanced Technologies;design of Neural Network-Based Intelligent Robot-Assisted English Translation System;english Language Translation machine System Based on human-computer interaction;early Warning of Subjective Perception of Students Based on Big Data Analysis Techniques in Growth Environment;towards an Autonomous Response System. Case: Denial of Service Attacks.

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Review on Deep learning Network Architectures for Image Reconstruction 5

Review on Deep Learning Network Architectures for Image Reco...

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5th international conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Lokula, Babitha Prasad, L.V Narasimha Tirumuri, Ramakrishna K L deemed to be university Institute of Aeronautical Engineering department of ECE Vaddeshwaram India Institute of Aeronautical Engineering department of ECE Hyderabad India K L deemed to be university department of ECE Vaddeshwaram India

ISBN: (纸本)9798350367171

In order to obtain the low resolution(LR) image's detailed information, super resolution(SR) image reconstruction is essential. From 1D projections, we can reconstruct images in 2D and 3D. the LR images attained have different details of the same scene for which the super resolution reconstruction is possible. In the field of computer vision, image reconstruction is used in a variety of applications including robotics, entertainment, reverse engineering, augmented reality, human-computer interaction, and animation. In addition, applications in the medical field like Magnetic Resonance Imaging (MRI), Computed Tomography (CT), X-rays, and ultrasound, among others, make use of image reconstruction. We may encounter limitations while reconstructing the image, such as noise, missing information, a low signal to noise ratio, and a low contrast to noise ratio. We even come across different issues and challenges in application of SR techniques like image registration, computational efficiency, robustness and performance limitations. To address the above-mentioned issues, we study numerous SISR architectures. In this paper, we'll talk about various techniques, their benefits, drawbacks, and Single Image Super Resolution (SISR) architectures that can help improve super resolution image reconstruction in a variety of applications. © 2024 IEEE.

关键词： Image reconstruction

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