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共 24667 条记录，以下是241-250 订阅

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Deep Learning Meets Traditional Machine Learning: CNN-SVM Hybrid models for Parkinson's Diagnosis 3

Deep Learning Meets Traditional Machine Learning: CNN-SVM Hy...

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3rd IEEE international conference on automation and Computation, AUTOCOM 2025

作者： Mehta, Shiva Bhardwaj, Rishabh Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact & Outcome Punjab Rajpura140401 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India

ISBN: (纸本)9798331542375

Parkinson's disease (PD) is a neurodegenerative disorder that needs early and accurate diagnosis for good management. Our approach in this study is to have a hybrid diagnostic framework which uses Convolutional Neural Networks (CNNs) for the feature extraction, and Support Vector Machines (SVMs) for the classification. By leveraging on the complementary strengths of the above-mentioned approaches, the model reports improved performance in detection of PD by using medical imaging data. We evaluated proposed CNN-SVM hybrid model on a dataset consist of 5,000 MRI images (2,500 with PD cases and 2,500 with healthy control) with achieved accuracy of 94.8%, sensitivity of 93.7%, and specificity of 95.5%. These results demonstrate a significant improvement over baseline methods, including standalone CNNs (accuracy: 91. The performance of the proposed methods is experimentally demonstrated to be better than SVMs with handcrafted features (accuracy: 84.5%, sensitivity: 83.2%, specificity: 85.0%) and even SVMs using Fisher vectors (accuracy: 81.4%, sensitivity: 86.1%, specificity: 81.1%) for certain classes (e.g. 2%, sensitivity: 89.5%, specificity: 92.3%). The class separability of the hybrid model was excellent, with an Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of 0.96. These findings were further confirmed under confusion matrix analysis with 2,343 true positives and 2,388 true negatives and with only 157 false negatives (sensitivity: 0.017%) and 112 false positives (specificity: 0.970%). The model demonstrated generalizability by the convergence of training and validation curves throughout 50 epochs with a final loss of 0.06 and validation error stabilizing at 0.04. The CNN-SVM hybrid model also can demonstrate the ability to become a reliable diagnostic tool for PD detection, with robust classification performance and few errors. Multi-modal data integration and scalability for use over a broader range of clinical applications is the subject of fut

关键词： Diseases

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Modelling of Shear Walls with Opening Using Machine Learning Techniques

Modelling of Shear Walls with Opening Using Machine Learning...

引用

international conference on Autonomous Robots and Agents, ICARA

作者： Yogesh Aggarwal Department of Civil Engineering NIT Kurukshetra Kurukshetra Haryana India

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

A Shear wall is the most appropriate structure shape in reinforced concrete buildings. Thus, examining the Shear Wall in structural systems is vital because it effectively resists lateral loads such as wind and seismic forces. The behaviour of such reinforced concrete building with Shear Wall differs from that of other reinforced concrete constructions. Thus, structural analysis of Shear Walls with openings for natural light, ventilation, entry, escape etc is required This factor has been analysed in terms of maximum storey displacement and base shear for model with Shear Walls and openings, using Equivalent static method to analyse the seismic performance of the structures. A comprehensive dataset is prepared using Staad Pro v8i software. The models are developed for the number of stories from 5 to 40, with different sizes of openings in the shear wall. A total of 240 models were analysed in III, IV and V zones of the earthquake, as defined by Indian code. The study involves the data set preparation and training, testing and validation using different machine learning techniques like deep neural networks, artificial neural networks and random forests. so that it will be helpful in predicting the seismic response of any similar multi-storey building in terms of Maximum storey displacement and Base shear.

关键词： Training Radio frequency Analytical models Buildings Artificial neural networks Predictive models Ventilation Concrete Random forests Testing

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Weighted Feature Ranking Merging for Supervised Machine Learning 19th

Weighted Feature Ranking Merging for Supervised Machine Lea...

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19th international conference on Soft Computing models in Industrial and Environmental Applications, SOCO 2024

作者： Coto-Palacio, Jessica Ortiz-Tandazo, Daniel Alejandro Bautista-Juárez, Alejandro Grangetto, Agustina Cabello-Solorzano, Kelsy León-Castro, Diana Santana-Morales, Paola Tallón-Ballesteros, Antonio J. International University of Andalusia Palos de la Frontera Huelva Spain University of Huelva Huelva Spain Department of Electronic Computer Systems and Automation Engineering University of Huelva Huelva Spain

ISBN: (纸本)9783031750090

This paper introduces a novel approach to ranking-based feature selection. Numerous metrics exist to assess univariate filters. This contribution identifies the top best features by employing two distinct methods, computes the global weights of each feature, and sorts these weights in decreasing order for those which are common to both procedures. According to the first part, this work is a feature selection proposal and concerning the last, it is besides a feature sorting approach. The benchmark data sets originate from NIPS 2003. This approach has been tested on problems with up to 20000 features and up to 7000 samples. The test results are very promising since, in some cases the outcomes significantly outperform the raw scenario, and in others, they surpass those of an approach which may be considered to be related to the current contribution. Finally, this study opens new avenues for research, particularly in examining the importance of specific feature sorting methods for certain supervised machine learning algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Hybrid FM-GCN-Attention Model for Personalized Recommendation

Hybrid FM-GCN-Attention Model for Personalized Recommendatio...

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Electrical automation and Artificial Intelligence (ICEAAI), international conference on

作者： Erfan Wang Rice University Dallas TX USA

ISBN: (数字)9798331506797

ISBN: (纸本)9798331506803

Personalized recommendation systems play a crucial role in enhancing user engagement and decision-making across various domains. Traditional approaches, such as collaborative filtering and matrix factorization, have shown effectiveness but suffer from data sparsity and cold-start problems. Recent advances in deep learning, graph-based models, and attention mechanisms have significantly improved recommendation performance. This paper proposes a novel hybrid recommendation model that integrates Factorization Machines (FM), Graph Convolutional Networks (GCN), and Multi-Layer Attention Networks (MLAN) to optimize feature representations and enhance prediction accuracy. Experimental results demonstrate the superiority of the proposed approach over baseline methods in key performance metrics.

关键词： Measurement Frequency modulation Attention mechanisms Accuracy Graph convolutional networks Scalability Predictive models Robustness Recommender systems Optimization

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CORA: A Chain of Robotic Actions Reasoning Model for Autonomous Robotic Arm Manipulation

CORA: A Chain of Robotic Actions Reasoning Model for Autonom...

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international conference on Autonomous Robots and Agents, ICARA

作者： Yu Hao Fan Yang Nicholas Fang Embodied AI and Robotics (AIR) Lab NYU Tandon School of Engineering NYU Abu Dhabi Cranleigh Abu Dhabi

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

The integration of large language models, combined with pretrained skills, has emerged as a powerful tool for proposing natural language actions that are both feasible and contextually appropriate to perform real-world tasks. In this paper, to further enhance both the feasibility and contextual appropriateness of actions within physical environments, we introduce CORA (Chain Of Robotic Actions), a model that leverages the chain of thought methodology within multimodal foundation models for robust robotic arm manipulation. CORA initiates the task-grounding process through the chain of thought approach, adeptly identifying action chains from high-level hu-man instructions. This method decomposes complex tasks into transparent, intermediate steps, boosting the model's precision and clarity in problem-solving. The chain of thought methodology not only provides a granular and explicit breakdown of the task-solving process but also improves the model's capacity to address complex, multi-step, and implicit instructions with enhanced interpretability. In the subsequent phase, CORA utilizes a multimodal foundation model for world-grounding, assisting the robot in completing the action plan within the physical environment. In a rigorous experimental setup, we evaluate our method in multiple contexts, including simulated and real-world environments. Ablation studies are conducted to evaluate the effectiveness of the Chain of Robotic Action Reasoning strategies in comprehending action chains explicitly and implicitly stated in human instructions. A comparative analysis with existing approaches further substantiates its superior performance. The results conclusively demonstrate the method's effectiveness and efficiency across various components.

关键词： Foundation models Electric breakdown Large language models Natural languages Grasping Manipulators Boosting Cognition Problem-solving Context modeling

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Object Localization Based on a Single RGB Camera for a 4-DOF Robotic Arm

Object Localization Based on a Single RGB Camera for a 4-DOF...

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international conference on Autonomous Robots and Agents, ICARA

作者： Elvira Chebotareva Adel Mukhamedshin Niyaz Imamov Evgeni Magid Institute of Information Technology and Intelligent Systems Kazan Federal University Kazan Russia Russia HSE University Moscow Russia

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

This study presents a method for constructing metric depth maps based on relative depth maps for a 4-DOF manipulator with a single RGB camera, which is attached to an end effector. To create a relative depth map of a scene, we utilize the Depth Anything V2 model along with a system of twelve ArUco markers positioned at varying distances from one another. Unlike traditional methods for determining distances to objects using ArUco markers, the proposed approach does not require a continuous presence of markers within a camera's field of view; it only necessitates measuring a distance to a few markers in an initial frame. The proposed method enables the robot to perform object localization using a relative depth map concurrently with an object search process.

关键词： Location awareness automation Depth measurement Robot vision systems Grasping Cameras Search problems End effectors Calibration

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Development of an IoT-Integrated Automated Air Exchanger for Indoor Air Management in Computer Laboratories

Development of an IoT-Integrated Automated Air Exchanger for...

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robotics and Technologies for Industrial automation (ROBOTHIA), IEEE international conference on

作者： Leonard Nathaniel S. Majaducon Jose Ryandale D. Gonzaga Joost C. Besonia Glenda S. Guanzon Adha Theza Naputol Jopearlson Icy Ituriaga Department of Computer Engineering University of San Agustin Iloilo City Iloilo Philippines

ISBN: (数字)9798350356755

ISBN: (纸本)9798350356762

The study develops an IoT-integrated automated air exchange system, addressing the issue on poor IAQ within computer laboratories. The developed system addresses gaps in previous solutions by introducing IoT integration and automated interventions to improve IAQ management within computer laboratories. This system is equipped with sensors capable of real-time monitoring of key indoor pollutants, such as carbon dioxide, particulate matter 2.5, and volatile organic compounds. Also, it comprises of an air exchange mechanism that automatically triggers when the pollutants' thresholds exceeded, ensuring timely and efficient way of reducing pollutants to maintain the optimal level of environment conditions. The air exchange mechanism comprises two fans, one for air intake and the other for air exhaustion to provide proper air circulation. Upon simulating real-world conditions in a prototype room model, results show that the system is effective in reducing pollutants' concentrations by measuring the levels of air parameters before and after operation of the system. With IoT-driven and automated intervention capabilities, the system's adaptable design and scalability provide a promising solution for improving the air quality of high-occupancy indoor spaces, allowing for a foundation to support broader real-world applications in industrial settings.

关键词： Volatile organic compounds Adaptation models Service robots Atmospheric modeling Scalability Laboratories Prototypes Real-time systems Energy efficiency Sensors

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ViCo: A Multitask Video-enhanced and Cognition-preserving Modality Alignment Training Framework

ViCo: A Multitask Video-enhanced and Cognition-preserving Mo...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Yu, Zhenda Chen, Jin Shen, Jiayu Zhou, Lanxiang Fang, Han Zang, Xianghao Ban, Chao Chen, Jingfeng He, Zhongjiang Sun, Hao Li, Zerui Kang, Yanmei Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China TeleAI Beijing China School of Mathematics and Statistics Xi'an Jiaotong University Xi'an China Duke Kunshan University Suzhou China The Department of Automation University of Science and Technology of China Hefei China School of Artificial Intelligence Anhui University Hefei China

ISBN: (纸本)9798350368741

The rapid development of multimodal large language models (MLLMs) has brought significant breakthroughs to this field. However, current MLLMs typically rely on vision instruction tuning based on large language models (LLMs) to endow them with multimodal capabilities, which may lead to low video utilization and catastrophic forgetting due to the task-specific nature of vision instruction tuning. Take case of multiple-choice Video Question Answering (VideoQA) as an example, requiring only selecting correct answers makes it easier for LLMs to shortcut rather than fully comprehend video content. Moreover, the fixed task format of "predicting the correct option" risks catastrophic forgetting, which can cause LLMs to lose their origin cognitive abilities. To address this, we propose ViCo, a training framework that enables LLMs to perform a specific auxiliary tasks during vision instruction tuning, which helps the model leverage video information more thoroughly while mitigating catastrophic forgetting, thereby improving modality alignment and enhancing performance. We evaluate our method on several strong VideoQA benchmarks, where it outperforms all state-of-the-art methods, demonstrating its effectiveness and generality. Additionally, we also provide extensive analysis on the video information utilization and catastrophic forgetting resulting. The code will be made available at https://***/dunknsabsw/ViCo. © 2025 IEEE.

关键词： Auxiliary Task Catastrophic Forgetting LLM ViCo VideoQA

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SWEAformer: Sliding Window Energy Attention Based Transformer for Fault Detection in Distribution Network

SWEAformer: Sliding Window Energy Attention Based Transforme...

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international conference of Electrical, Electronic and Networked Energy Systems, EENES 2024

作者： Xiang, Zheng Li, Qiyue Luo, Huan Sun, Wei Li, Weitao Liu, Xin Zhang, Yang School of Electrical Engineering and Automation Hefei University of Technology Hefei230009 China State Grid Anhui Ultra High Voltage Company Hefei230041 China

ISBN: (纸本)9789819618552

Fault detection in power distribution networks is crucial for the stable operation of the grid and the continuous supply of power. However, faults in distribution networks exhibit significant randomness in their occurrence and duration. Due to their complexity and non-stationarity, existing methods frequently encounter issues with false positives and missed detections. To address these challenges, we introduce a novel fault detection model based on the Transformer architecture, incorporating a newly proposed Sliding Window Energy Attention (SWEA) mechanism and a time-frequency feature extraction structure. Within this model, each encoder’s time-domain channel utilizes sliding window techniques to extract energy features and calculate attention scores, while the frequency-domain channel employs Fast Fourier Transform (FFT) to extract the fault spectrum. The final fault detection results are outputted by a Multilayer Perceptron (MLP). Experimental validation on a real distribution network fault dataset demonstrates the significant advantages in detection performance of the proposed model over selected baseline models. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Frequency domain analysis

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Harnessing CNN and LSTM models for Enhanced Predictive Maintenance in Industrial IoT Systems

Harnessing CNN and LSTM Models for Enhanced Predictive Maint...

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automation and Computation (AUTOCOM), international conference on

作者： Rajeev Kumar Bhaskar Balasubramaniam Kumaraswamy Department of CS & IT Kalinga University Raipur India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

In this research, the problem of predictive maintenance using the Industrial Internet of Things (IIoT) systems is investigated with the use of hybrid Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) models. Depending on the temporal dependency in the sensor data we want to bring in the LSTM to capture the temporal relations in the data as well as to extract the spatial feature using the CNN. Using publicly available IIoT datasets, including NASA Prognostics Data Repository to train and evaluate the models. It results in accuracy of 94.5% vs traditional machine learning algorithms. Finally, edge computing was tested for real-time deployment, making these predictions within a second and maintenance interventions prompt. It is shown that the hybrid CNN-LSTM model drastically improves PdM accuracy over other models, thereby reducing downtime and maintenance costs. It is a promising solution for promoting predictive maintenance in industrial cases, based on the scalability and robustness of this approach. Finally, future work will understand data quality challenges and more advanced methods, such as reinforcement learning and digital twins, to further improve model performance.

关键词： Accuracy Computational modeling Reinforcement learning Predictive models Data models Real-time systems Convolutional neural networks Long short term memory Predictive maintenance Industrial Internet of Things

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