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检索条件"任意字段=3rd International Conference on Pattern Recognition and Machine Intelligence"

共 7139 条记录，以下是41-50 订阅

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Modeling and Performance Analysis of PEM Fuel Cells Using machine Learning Techniques 3

Modeling and Performance Analysis of PEM Fuel Cells Using Ma...

引用

3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Gaddam, Shamitha Reddy, P. Srihith Joshi, Shruti V. Bnm Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Bnm Institute of Technology Department of Electrical Engineering Bengaluru India

ISBN: (纸本)9798331515911

Proton Exchange Membrane Fuel Cells (PEMFCs) play a vital role in the transition to clean energy technologies, requiring accurate and efficient models for their control, optimization, and real-time monitoring. This research focuses on creating machine learning models as Support Vector Regression (SVR), Gradient Boosting (GBR), XGBoost, and Random Forest (RFR) and MLPRegressor to predict PEMFC key performance attributes. Using a dataset from Hamidi et al [1]., which focuses on Nafion 112 membranes and MEA activation tests. Input features include current density, voltage, power density, gas pressure, relative humidity, and impedance components (real and imaginary). The primary output is cell voltage, a key performance indicator. Among the models, Random Forest Regressor achieved the best results with an accuracy of 94.715, R2 score of 0.8049, MSE of 0.001036, and RMSE of 0.03219. Feature importance analysis revealed relative humidity, current density, and voltage as the most influential factors. This research illustrates the use of machine learning techniques in modeling the functioning of attributes of PEMFCs, providing insights for optimizing fuel cell performance and accelerating the inculcation of clean energy technologies. © 2025 IEEE.

关键词： Support vector regression

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The application of artificial intelligence seismic inversion techniques in sandstone and shale reservoir studies 3

The application of artificial intelligence seismic inversion...

引用

3rd international conference on Geographic Information and Remote Sensing Technology, GIRST 2024

作者： Lin, Yaoting Liang, Lin Zhou, Wei School of Computer Science and Engineering Guangdong Ocean University Yangjiang529500 China Kunlun Digital Technology Co. Ltd. Beijing Beijing100043 China

ISBN: (纸本)9781510689077

Artificial intelligence Probabilistic Neural Networks are a type of neural network based on the theory of probability density functions, widely applicable in areas such as pattern recognition. Addressing the current issue of low prediction accuracy in seismic lithology inversion, the authors propose a specific probabilistic neural network method, including the construction of network models and steps for prediction and identification. The primary target layer in the study area is the lower part of the Sha-1 Member of the Shaximiao Formation, characterized by lakeshore and river channel sands. The reservoirs are thin-layered with significant lateral lithological variations, making conventional reservoir prediction using seismic data challenging. Therefore, leveraging the correlation between well log responses, seismic attributes, and geological lithological characteristics of the reservoirs in the area, the probabilistic neural network method is employed to transform seismic attribute data for predicting and identifying stratigraphic features. This approach has achieved good results in practical data processing. © 2025 SPIE.

关键词： Stratigraphy

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Artificial intelligence, Intelligent Manufacturing, and Sustainable Futures in Next-Gen Textiles: A Comprehensive Approach 3

Artificial Intelligence, Intelligent Manufacturing, and Sust...

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

作者： Shabi, Sathika Begum Mohammad Bhuvaneshwari, M. Kanimozhi, S. Pavithra, S. Department of Computer Science and Engineering Chennai Institute of Technology Tamilnadu Chennai600069 India

ISBN: (纸本)9798331527549

The next revolutionary innovation in the textile industry is artificially driven by intelligent quality control and next-generation textile materials. This review, based on AI-driven automation and sustainable practices in textiles, provides a comprehensive overview of how emerging technologies are reshaping manufacturing processes, quality control, and functionalities in textiles. It examines the use of machine learning in defect detection, the integration of smart materials, and the adoption of recycled and bioengineered fabrics, thus helping producers to overcome issues associated with fabric production, environmental impact, quality control, and scalability. The synthesis of recent advances enables this study to critically review literature and case studies on AI, smart materials, and sustainable practices in textiles. It lays a pathway for future developments and advancements in the textile industry. © 2025 IEEE.

关键词： Smart manufacturing

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Enhancing Gastrointestinal Disease Detection Through ResNet50 and Comparative Analysis of Deep Learning Architectures for Medical Image Classification 3

Enhancing Gastrointestinal Disease Detection Through ResNet5...

引用

3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Vardhini, Harsha Sophia Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Tamil Nadu Coimbatore India

ISBN: (纸本)9798331509675

Accurate and timely detection of gastrointestinal diseases is critical to an effective treatment and the best outcomes for patients. Manual examination of endoscopic images during an endoscopy by trained specialists still constitutes the traditional and most reliable method of diagnostic of diseases in their early stages. This study presents a deep learning-based approach of automating the detection of gastrointestinal diseases using the ResNet50 convolutional neural network (CNN) architecture. This research evaluates the performance of ResNet50 on the task of classifying gastrointestinal conditions from medical images against other established models, VGG16 and MobileNetv2, by means of a comparative analysis. The results of experiments show that ResNet50 has better accuracy, precision, recall and computational efficiency compared the other models. The results suggest the application of ResNet50 in improving diagnostic accuracy as well as a shortening of the detection process, hence paving the way for clinical practices. © 2025 IEEE.

关键词： Endoscopy

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Enhanced Tuberculosis Detection using Deep Learning and GAN Data Augmentation Techniques for Chest X-ray Analysis 3

Enhanced Tuberculosis Detection using Deep Learning and GAN ...

引用

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

作者： Chaykam, Varun Reddy Koduru, Avinash Reddy Karthick, S. Arun, C. Department of Computational Intelligence SRM Institute of Science and Technology KTR campus Chennai India

ISBN: (纸本)9798331527549

With around 1.6 million deaths yearly, primarily in low-resource environments, abstract-tuberculosis (TB) is a serious worldwide health issue. While human analysis can be error-prone and labor-intensive, early discovery using chest X-rays is vital. We used advanced machine learning methods including a mix of Histogram of Oriented Gradients (HOG) and Convolutional Neural Networks (CNN), alongside Deep Convolutional Generative Adversarial Networks (DCGANs) to create synthetic TB images, so helping to balance the dataset and reduce class imbalance. We applied explainable artificial intelligence (XAI) methods including LIME and Grad-CAM for greater interpretability in order to improve model transparency. The outcomes showed notable progress;our hybrid model obtained a stunning 99.5% diagnosis accuracy. With respective accuracy of 96.87% and 94.67%, models such as Xception and MobileNetV2 also fared rather well. Especially in resource-limited healthcare settings, this study provides a consistent and interpretable strategy for tuberculosis screening. © 2025 IEEE.

关键词： Generative adversarial networks

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Development of an Automatic Speech recognition System for Irula Language: A machine Learning Approach 3

Development of an Automatic Speech Recognition System for Ir...

引用

3rd international conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Krishnaveni, M. Subashini, P. Janani, R. Komalavalli, R. P.prabhusundhar Prabavathi, G.T. Avinashilingam University for Home Science and Higher Education for Women Centre for Machine Learning and Intelligence Tamil Nadu Coimbatore India Gobi Arts & Science College Tamil Nadu India

ISBN: (纸本)9798331523893

The Irula language, which is predominantly spoken by the Irula tribes in southern India, offers specific challenges because of its linguistic features and limited resources. To address these challenges, this research study presents the development of an Automatic Speech recognition (ASR) system for Irula, which accelerates the conversion of spoken language into English text. In the proposed system, the data is collected and pre-processed, features are extracted, and acoustic and language models are developed for efficient translation. The spectral subtraction noise removal method is used to reduce the background noise from collected speech data during the data pre-processing. The Mel Frequency Cepstral Coefficients (MFCC) are then used to extract the features which represent the characteristics of speech signals. Acoustic modelling is implemented using Hidden Markov Models (HMMs) and their fine-tuned version. A pre-trained language model such as GPT is used for accurate translation. Finally, the web application is developed for translating Irula language into English text to increase the accessibility for Irula speakers. © 2025 IEEE.

关键词： Automatic Speech recognition Hidden Markov Model Irula language machine Learning Pre-trained language model Streamlit

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An Enhanced Compact Convolution Transformer for Age, Gender and Emotion Detection in Egyptian Arabic Speech 26th

An Enhanced Compact Convolution Transformer for Age, Gender ...

引用

26th international conference on Speech and Computer

作者： Abdalla, Aya Sharaf, Nada Sabty, Caroline German Int Univ Reg Ring Rd Cairo Egypt

ISBN: (纸本)9783031780134;9783031780141

Voice signals convey hidden, valuable information about speakers, such as age, gender, and emotional state. Extracting this kind of information from human speech is significant in human-computer interaction (HCI). It enables computers to understand human behaviors and develop interactive systems with customized responses, raising the significance of advancements in speech emotion recognition (SER), especially for languages with large numbers of speakers. Despite over 100 million people speak the Egyptian dialect, SER studies that address the Egyptian dialect are extremely scarce and predominantly rely on traditional machine learning models and convolutional neural networks (CNN) for classification. In this context, we proposed an enhanced compact convolution transformer (CCT) that detects the speaker's age, gender, and emotional state, leveraging the strengths of CNNs for capturing spatial features and transformers for modeling long-range dependencies. The proposed approach combines the best of both architectures, marking a novel architecture for the Egyptian emotion recognition task. To the best of our knowledge, this is the first work to address age detection from Egyptian speech, as well as the first to propose a unified model for the recognition of age, gender, and emotion from Egyptian speech. In the context of HCI improvements, the proposed model was applied in a real-world setting by integrating it into a custom-developed Egyptian chatbot to enhance the chatbot's ability to provide emotionally aware responses based on the user's emotional state.

关键词： Speech emotion recognition Compact convolution transformer Age and gender recognition from speech

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AI-Powered Advertisement Design: A PIL-Based Approach for Quality and Performance Analysis 3

AI-Powered Advertisement Design: A PIL-Based Approach for Qu...

引用

3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Chilupuri, Harshitha Ramesh, G. Praveen, J. Gude, Venkataramaiah Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Artificial Intelligence and Machine Learning Hyderabad India Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Computer Science and Engineering Hyderabad500090 India Gokaraju Rangaraju Institute of Engineering and Technology Department of Electrical and Electronics Engineering Telangana Hyderabad500 090 India Gp Technologies Llc United States

ISBN: (纸本)9798331515911

The paper presents the development of a Data Science solution that applies generative AI models in order to create high-quality images to be used in visual marketing and advertising. Using the Python library PIL (Pillow) along with machine learning frameworks, the solution combines predefined product images with the selected backgrounds, enhancing image synthesis techniques for more appealing and contextually relevant visuals. This leads to improved efficiency and effectiveness in the creation of content by automatically placing product images onto the background images used in marketing campaigns. The approach demonstrated a 98.06% SSIM (Structural similarity Index) score, indicating significant visual quality improvement. Initial results suggest a 15% enhancement in visual appeal compared to traditional image creation methods. © 2025 IEEE.

关键词： Data Science

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SSVEP EEG signal classification model based on EEMD-FastICA-TRCA 24

SSVEP EEG signal classification model based on EEMD-FastICA-...

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3rd international conference on Artificial intelligence and Intelligent Information Processing, AIIIP 2024

作者： Liu, Yunfeng Zhang, Xueru Wan, Sijia Zhan, Weijian Liu, Yongming Zhao, Zhuanzhe School of Mechanical Engineering Anhui Polytechnic University Anhui Wuhu China School of Artificial Intelligence Anhui Polytechnic University Anhui Wuhu China

ISBN: (纸本)9798400707308

In order to retain as many effective signals in EEG as possible and improve the accuracy of a signal classification and the performance of the brain-computer interface system, a steady-state visual evoked potential EEG classification model combining ensemble empirical mode decomposition, fast, independent component analysis and task-related component analysis was proposed to solve the problem that noise would reduce the accuracy of signal classification and recognition. Firstly, it is proved that the proposed EEMD-FastICA noise reduction model has certain advantages over the standard noise reduction methods, and the performance of the model is verified by using the Benchmark dataset of the brain-computer interface group of Tsinghua University. On this basis, the EEMD-FastICA noise reduction model is combined with the TRCA algorithm to classify EEG signals, and the classification results are compared with other classification models. The comparison results show that under different time Windows, the classification accuracy and information transmission rate of the proposed model are higher than those of other models. Within 1.5 S, the highest classification recognition accuracy was 85.83%, and the highest ITR was 238.34 bits/min. © 2024 Copyright held by the owner/author(s).

关键词： Empirical mode decomposition

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Computational intelligence to Progress in WMN 3rd

Computational Intelligence to Progress in WMN

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3rd international conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Revathy, G. Senthilkumar, C. Rajesh, A. Selvi, C. P. Thamil Department of CSE Srinivasa Ramanujam Centre SASTRA Deemed University Tamilnadu Kumbakonam India Department of CSE Erode Sengunthar Engineering College Erode India Associate Professor Department of Artificial Intelligence & Data Science Rathinam Technical Campus Coimbatore India

ISBN: (纸本)9789819783540

Wireless mesh networks are been prominently developing in the past two decades and have received a lot of accomplished tasks in various applications. machine learning is an art of computational intelligence that enhances development day by day in a rapid manner. The planned work deals with the combination of machine learning techniques to increase the Eminence of Amenities in wireless mesh networks. The prominent ideas of machine learning are to enhance the performance in Wireless Mesh Networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Wireless mesh networks (WMN)

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