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检索条件"机构=Artificial Intelligence and Machine Learning School of Computer Science and Mathematics"

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Intelligent Posture Detection System for Improved Ergonomics

Intelligent Posture Detection System for Improved Ergonomics

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Patil, Jay Joshi, Suvarna Mulla, Rahesha Yadav, Kushal Mit School of Computing Mit Adt University Department of Computer Science and Eng. Pune India University Department of Artificial Intelligence and Machine Learning Pune India Indian Institute of Technology Madras Department of Data Science India

ISBN: (纸本)9798331527495

This paper introduces a cost-effective smart posture detection system aimed at promoting ergonomic sitting habits and preventing musculoskeletal disorders. The system integrates ultrasonic sensors for backrest proximity measurement and load cells for seat weight distribution analysis. By analyzing these inputs, it identifies posture irregularities and provides real-time feedback for corrective action. Testing results demonstrate the system's reliability, achieving a detection accuracy of 99% across users with varying body types and postures. The proposed hybrid approach ensures simplicity, affordability, and scalability, making it a viable solution for homes, workplaces, and schools. This research contributes to the growing field of smart health monitoring systems, offering a practical alternative to traditional posture correction methods. © 2025 IEEE.

关键词： Ultrasonic testing

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Disentangling Long-Short Term State Under Unknown Interventions for Online Time Series Forecasting 39

Disentangling Long-Short Term State Under Unknown Interventi...

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39th Annual AAAI Conference on artificial intelligence, AAAI 2025

作者： Cai, Ruichu Huang, Haiqin Jiang, Zhifan Li, Zijian Zhou, Changze Liu, Yuequn Liu, Yuming Hao, Zhifeng School of Computer Science Guangdong University of Technology China Peng Cheng Laboratory Shenzhen China Machine Learning Department Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates Shantou University China

ISBN: (纸本)157735897X

Current methods for time series forecasting struggle in the online scenario, since it is difficult to preserve long-term dependency while adapting short-term changes when data are arriving sequentially. Although some recent methods solve this problem by controlling the updates of latent states, they cannot disentangle the long/short-term states, leading to the inability to effectively adapt to nonstationary. To tackle this challenge, we propose a general framework to disentangle long/short-term states for online time series forecasting. Our idea is inspired by the observations where short-term changes can be led by unknown interventions like abrupt policies in the stock market. Based on this insight, we formalize a data generation process with unknown interventions on short-term states. Under mild assumptions, we further leverage the independence of short-term states led by unknown interventions to establish the identification theory to achieve the disentanglement of long/short-term states. Built on this theory, we develop a Long Short-Term Disentanglement model (LSTD) to extract the long/short-term states with long/short term encoders, respectively. Furthermore, the LSTD model incorporates a smooth constraint to preserve the long-term dependencies and an interrupted dependency constraint to enforce the forgetting of short-term dependencies, together boosting the disentanglement of long/short-term states. Experimental results on several benchmark datasets show that our LSTD model outperforms existing methods for online time series forecasting, validating its efficacy in real-world applications. Copyright © 2025, Association for the Advancement of artificial intelligence (***). All rights reserved.

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Pneumonia Detection in Chest X-Rays Using Combined Approach of Deep learning 2

Pneumonia Detection in Chest X-Rays Using Combined Approach ...

引用

2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Aditya Pai, H. Pareek, Piyush Kumar Guruprasad, M.S. Chandrappa, S. Bhargava, Krishna Mit Art Design and Technology University Mit School of Computing Pune India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Department of Computer Science and Engineering Dehradun India School of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350372892

A respiratory infection like pneumonia faces a serious health challenge across the globe, especially in the poor and underdeveloped countries. The pneumonia is affected by various factors like bacteria or viruses. The reason for the affection is due to higher level of pollution, poor air quality, excess of population, poor infrastructure etc. To reduce the threat from pneumonia, there is a need for early and accurate detection of the infection by using chest X-ray images as the most effective diagnostic method. The paper proposes an automated chest X-ray analysis system by using VGG16 architecture and a weighted average ensemble method to enhance diagnostic accuracy. By integrating deep transfer learning, the system overcomes data scarcity issues, achieving a remarkable 99% accuracy on a publicly available dataset. The research underscores the potential of automated systems in improving pneumonia diagnosis, highlighting the urgent need for advanced diagnostics in resource-limited settings. © 2024 IEEE.

关键词： Air quality

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Asymmetric Visual Semantic Embedding Framework for Efficient Vision-Language Alignment 39

Asymmetric Visual Semantic Embedding Framework for Efficient...

引用

39th Annual AAAI Conference on artificial intelligence, AAAI 2025

作者： Liu, Yang Liu, Mengyuan Huang, Shudong Lv, Jiancheng College of Computer Science Sichuan University Chengdu610065 China Engineering Research Center of Machine Learning and Industry Intelligence Ministry of Education Chengdu China State Key Laboratory of General Artificial Intelligence Peking University Shenzhen Graduate School Shenzhen China

ISBN: (纸本)157735897X

learning visual semantic similarity is a critical challenge in bridging the gap between images and texts. However, there exist inherent variations between vision and language data, such as information density, i.e., images can contain textual information from multiple different views, which makes it difficult to compute the similarity between these two modalities accurately and efficiently. In this paper, we propose a novel framework called Asymmetric Visual Semantic Embedding (AVSE) to dynamically select features from various regions of images tailored to different textual inputs for similarity calculation. To capture information from different views in the image, we design a radial bias sampling module to sample image patches and obtain image features from various views, Furthermore, AVSE introduces a novel module for efficient computation of visual semantic similarity between asymmetric image and text embeddings. Central to this module is the presumption of foundational semantic units within the embeddings, denoted as "meta-semantic embeddings." It segments all embeddings into meta-semantic embeddings with the same dimension and calculates visual semantic similarity by finding the optimal match of meta-semantic embeddings of two modalities. Our proposed AVSE model is extensively evaluated on the large-scale MS-COCO and Flickr30K datasets, demonstrating its superiority over recent state-of-the-art methods. Copyright © 2025, Association for the Advancement of artificial intelligence (***). All rights reserved.

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An Optimized Hybrid Deep learning and IoT-Enabled Health System in Retina Image Analysis for Blindness Detection 2

An Optimized Hybrid Deep Learning and IoT-Enabled Health Sys...

引用

2nd International Conference on Self Sustainable artificial intelligence Systems, ICSSAS 2024

作者： Yamini, J. Kanimozhi, T. Jenefa, A. Bagyalakshmi, K. Aarthi, K. Dhivya, P. Kalaignar Karunanidhi Institute of Technology Department of Artificial Intelligence and Machine learning Coimbatore India Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Coimbatore India Ppg Institute of Technology Department of Computer Science and Engineering Coimbatore India Sns College of Technology Department of Mathematics Coimbatore India Kalaignar Karunanidhi Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India Bannari Amman Institute of Technology Department of Computer Science and Engineering Erode India

ISBN: (纸本)9798350368413

Early signs of blind identification and retina disorder in retina image has become prominent. Deep learning techniques are used to improve the accuracy in detecting pathological conditions from the retina images. However, the models have some challenges such as suboptimal feature extraction and inefficient filtering methods. Most of the traditional methods often result in overfitting and under fitting, thus decreasing the generalizability and robustness of the model. The proposed hybrid approach combines bilateral filtering with Convolutional Neural Networks (CNN). In this approach, bilateral filtering acts as a pre-processing technique to enhance image quality by reducing noise and preserving edges for the correct extraction of features. The CNN is applied for feature extraction and it is used to self-learn complex patterns from filtered images. The proposed approach gives an accuracy of 95.65% with a loss of 0.18 without overfitting and underfitting. © 2024 IEEE.

关键词： Convolutional neural networks

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Uncertainty-induced Incomplete Multi-Omics Integration Network for Cancer Diagnosis

Uncertainty-induced Incomplete Multi-Omics Integration Netwo...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Gao, Peipei Du, Ling Qiao, Sibo Yin, Nan Tiangong University School of Computer Science and Technology Tianjin China Tiangong University School of Software Tianjin China Mohamed Bin Zayed University of Artificial Intelligence Department of Machine Learning Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350386226

Cancer diagnosis with incomplete multi-omics data is inevitable since there are widely missing arbitrary omics in real-world applications. Currently, although much progress has been made in handling missing data, existing methods for cancer diagnosis still struggle to obtain credible predictions due to the relatively high uncertainty of missing omics data. In this paper, we propose a trusted and attention-based multi-omics integrated framework for cancer diagnosis using incomplete multi-omics data (TAIMONET). For the raw omics data, the framework first builds an attention-based shared encoder to pick out important features and align different omics data. Next, it utilizes generative adversarial learning to impute the missing omics features based on the extracted available omics features. Meanwhile, different reconstructed complete omics data are weighted and fused according to the attention mechanism. Moreover, it uses true-class-probability to calibrate the classification results and improve the reliability of the results. Extensive experiments on four real-world multimodal medical datasets are conducted. Compared to state-of-the-art methods, the superior performance and trustworthiness of our proposed model are clearly validated. © 2024 IEEE.

关键词： Lung cancer

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GroundingMate: Aiding Object Grounding for Goal-Oriented Vision-and-Language Navigation

GroundingMate: Aiding Object Grounding for Goal-Oriented Vis...

引用

2025 IEEE/CVF Winter Conference on Applications of computer Vision, WACV 2025

作者： Liu, Qianyi Zhang, Siqi Qiao, Yanyuan Zhu, Junyou Li, Xiang Guo, Longteng Wang, Qunbo He, Xingjian Wu, Qi Liu, Jing Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Tongji University Department of Computer Science and Technology China Australian Institute for Machine Learning The University of Adelaide Australia

ISBN: (纸本)9798331510831

Goal-Oriented Vision-and-Language Navigation (VLN) aims to enable agents to navigate to specified locations and identify designated target objects following natural language instruction. This approach has gained popularity due to its close alignment with real-world scenarios. However, existing studies have predominantly focused on enhancing navigation performance, neglecting the ability to locate objects at the navigation endpoint. This oversight has resulted in a significant discrepancy between the success rates of navigation and object grounding. The challenge is compounded by the complex reasoning required by the instructions and the necessity to synthesize multiperspective images of objects, which overwhelms traditional object grounding methods. We leverage the Multi-Modal Large Language Model (MLLM) to bridge this gap, allowing agents to seek assistance from these models when struggling to locate the target object. The agent conducts a multi-stage evaluation to discern the cause of its confusion and promptly extracts and updates the most relevant information for MLLM to assess. Our method is plug-and-play and model-agnostic, facilitating integration with numerous existing VLN strategies without the need for retraining. Implementing our approach across four distinct methods has improved performance on the REVERIE and SOON datasets, demonstrating the effectiveness and generalizability of our technique. © 2025 IEEE.

关键词： Electric grounding

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Sample Complexity of Posted Pricing for a Single Item 38

Sample Complexity of Posted Pricing for a Single Item

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Jin, Billy Kesselheim, Thomas Ma, Will Singla, Sahil Cornell University School of Operations Research and Information Engineering United States University of Bonn Institute of Computer Science Lamarr Institute for Machine Learning and Artificial Intelligence Germany Columbia University Graduate School of Business Data Science Institute United States Georgia Tech School of Computer Science United States

Selling a single item to n self-interested buyers is a fundamental problem in economics, where the two objectives typically considered are welfare maximization and revenue maximization. Since the optimal mechanisms are often impractical and do not work for sequential buyers, posted pricing mechanisms, where fixed prices are set for the item for different buyers, have emerged as a practical and effective alternative. This paper investigates how many samples are needed from buyers' value distributions to find near-optimal posted prices, considering both independent and correlated buyer distributions, and welfare versus revenue maximization. We obtain matching upper and lower bounds (up to logarithmic factors) on the sample complexity for all these settings. © 2024 Neural information processing systems foundation. All rights reserved.

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Biologically-Informed Shallow Classification learning Integrating Pathway Knowledge

Biologically-Informed Shallow Classification Learning Integr...

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17th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2024

作者： Voigt, Julius Saralajew, Sascha Kaden, Marika Bohnsack, Katrin Sophie Reuss, Lynn Villmann, Thomas Saxon Institute for Computational Intelligence and Machine Learning University of Applied Sciences Mittweida Mittweida Germany NEC Laboratories Europe GmbH Heidelberg Germany Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Groningen Netherlands

We propose a biologically-informed shallow neural network as an alternative to the common knowledge-integrating deep neural network architecture used in bio-medical classification learning. In particular, we focus on the Generalized Matrix learning Vector Quantization (GMLVQ) model as a robust and interpretable shallow neural classifier based on class-dependent prototype learning and accompanying matrix adaptation for suitable data mapping. To incorporate the biological knowledge, we adjust the matrix structure in GMLVQ according to the pathway knowledge for the given problem. During model training both the mapping matrix and the class prototypes are optimized. Since GMLVQ is fully interpretable by design, the interpretation of the model is straightforward, taking explicit account of pathway knowledge. Furthermore, the robustness of the model is guaranteed by the implicit separation margin optimization realized by means of the stochastic gradient descent learning. We demonstrate the performance and the interpretability of the shallow network by reconsideration of a cancer research dataset, which was already investigated using a biologically-informed deep neural network. © 2024 by SCITEPRESS–science and Technology Publications, Lda.

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IoT Empowered AI: Transforming Object Recognition and NLP Summarization with Generative AI 2

IoT Empowered AI: Transforming Object Recognition and NLP Su...

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2nd IEEE International Conference on computer Vision and machine intelligence, CVMI 2023

作者： Joseph, Samuel Priya, Bhagavathi S. Poorvaja, R. Santhosh Kumaran, M. Shivaraj, S. Jeyanth, V. Shivesh, R.P. Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India Amrita School of Computing Amrita Vishwa Vidhyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350305142

In anticipation of the widespread adoption of augmented reality in the future, this paper introduces an advanced mobile application that seamlessly integrates AR and IoT technologies. The application aims to make these cutting-edge technologies more affordable and accessible to users while highlighting their immense benefits in assisting with household appliance control, as well as providing interactive and educational experiences. The app employs advanced algorithms such as object detection, Natural Language Processing (NLP), and Optical Character Recognition (OCR) to scan the smartphone's camera feed. Upon identification, AR controls for appliances, their power consumption, and electric bill tracking are displayed. Additionally, the application makes use of APIs to access the internet, retrieving relevant 3D generative models, 360-degree videos, 2D images, and textual information based on user interactions with detected objects. Users can effortlessly explore and interact with the 3D generative models using intuitive hand gestures, providing an immersive experience without the need for additional hardware or dedicated VR headsets. Beyond home automation, the app offers valuable educational benefits, serving as a unique learning tool for students to gain hands-on experience. Medical practitioners can quickly reference organ anatomy and utilize its feature-rich functionalities. Its cost-effectiveness, requiring only installation, ensures accessibility to a wide audience. The app's functionality is both intuitive and efficient, detecting objects in the camera feed and prompting user interactions. Users can select objects through simple hand gestures, choosing desired content like 3D generative models, 2D images, textual information, 360-degree videos, or shopping-related details. The app then retrieves and overlays the requested information onto the real-world view in AR. In conclusion, this groundbreaking AR and IoT -powered app revolutionizes home automation and le

关键词： Optical character recognition

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