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

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GACNNXAI: Employing Genetic Algorithm-Enhanced Convolutional Neural Networks and Explainable Artificial intelligence and its Applications 3

GACNNXAI: Employing Genetic Algorithm-Enhanced Convolutional...

引用

3rd IEEE international conference on Power Electronics, Intelligent Control and Energy Systems, ICPEICES 2024

作者： Khanna, Sakshi Taaresh Khatri, Sunil Kumar Sharma, Neeraj Kumar Amity Institute of Information Technology Amity University Uttar Pradesh Noida201313 India Ram Lal Anand College Department of Computer Science New Delhi110021 India

ISBN: (纸本)9798350387193

We have proposed a Genetic Algorithm Enhanced Convolutional Neural Network coupled with Explainable Artificial intelligence for oral cancer detection in medical imaging. Our approach associated CNN with an explanation method to make CNN-based diagnostic decisions more understandable for a clinician. A high resolution of oral imaging is a challenge due to downsampling in several CNN networks. By optimizing the hyperparameters of CNN using Genetic Algorithms, we compensated for downsampling. Our model's hyperparameters, such as the number of layers, filter size, and learning rate, were adjusted based on performance in the validation set. Advancing further in the interpretability, we have used Layer-wise Relevance Propagation, a Local Interpretable Model-agnostic Explanation technique to indicate CNN's decision-making rationale by selecting influential regions of input images. Our proposed GA-CNN model applied to a comprehensive oral image dataset resulted in a classification accuracy of 93.7%. Our sensitivity and specificity rates were 92.5% and 94.9% improvement over other CNN models available in the literature. We have demonstrated that our GA-based optimization mechanism performs better for parameter searching in the CNN. Our proposed AI tool with an explanation for oral cancer screening will assist clinicians in the early identification of disease, establishing a complementary relationship between human health service providers and machine learning technologies for improved patient outcomes. © 2024 IEEE.

关键词： Convolutional neural networks

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Teaching Embodied Artificial intelligence to Children (Teach E-AI 2C): An Educational Proposal for Young Learners 3

Teaching Embodied Artificial Intelligence to Children (Teach...

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3rd IEEE international conference on Metrology for eXtended Reality, Artificial intelligence and Neural Engineering, MetroXRAINE 2024

作者： Nobile, Clara Marocco, Davide Ponticorvo, Michela Gigliotta, Onofrio University of Naples 'Federico II' Department of Humanistic Studies Naples Italy

ISBN: (纸本)9798350378009

This paper presents the Teach E-AI 2C (Teaching Embodied Artificial intelligence to Children) project, which aims to educate children and early adolescents about Embodied Artificial intelligence using the 4C/ID instructional design model. This project is intended to bridge the gap between scientific progress and users' knowledge of AI, by providing personalized and hands-on learning experiences. The implementation involves classroom trials, where students engage in both virtual and physical AI learning activities, experimenting the integration of theoretical concepts, through learning units, with practical tasks, using a purpose-built software. The paper also introduces the methodology for evaluating learning units and software, as well as usability and effects of the whole implementations. © 2024 IEEE.

关键词： Adversarial machine learning

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A Survey on Indian Sign Language Translation Using Artificial intelligence 3rd

A Survey on Indian Sign Language Translation Using Artifici...

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3rd international conference on "Emerging Trends and Technologies on Intelligent Systems" (ETTIS-2023)

作者： Kumar, Santosh Sharma, Amarjeet Dhote, Ritesh R. Patna India

ISBN: (纸本)9789819939626

This survey paper presents a summary of current work on the Indian Sign Language Translation in real time. Various approaches to this translation and the use of artificial intelligence in Indian Sign Language recognition and translation systems have been covered. The paper also discusses Indian Sign Language datasets and artificial intelligence models, including their results and comparisons to global approaches. The paper concludes by discussing techniques that have contributed to the advancement of real time, continuous translation of Indian Sign Language. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Artificial intelligence

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Constructing the Evaluation Index System of Chinese-Portuguese machine Translation using the Delphi and Analytic Hierarchy Process Methods 3

Constructing the Evaluation Index System of Chinese-Portugue...

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3rd IEEE international conference on Power, Electronics and Computer Applications, ICPECA 2023

作者： Sun, Yuqi Hoi, Lap Man Kei Im, Sio Macao Polytechnic University Engineering Research Centre of Applied Technology on Machine Translation and Artificial Intelligence Ministry of Education Macao China Macao Polytechnic University Faculty of Applied Sciences Macao China Macao Polytechnic University Macao China

ISBN: (纸本)9781665472784

Quality assessment is an important part of ensuring the quality and long-term development of machine translation. There is still much room for research into the evaluation of machine translation from Chinese to Portuguese, and no specific evaluation metrics have yet been developed. This study uses the Delphi method and Analytic Hierarchy Process (AHP) to construct an index system of Chinese-Portuguese machine Translation and analyzes the relative index weight. The system includes four first-level indicators and nine second-level indicators. The weight of primary indicators, from high to low, is "Accuracy, fidelity"(52.5%), "Readability"(22.9%), "Fluency"(16.8%), and "Acceptability & Usability"(7.8%). The secondary indicators with the highest weight of each primary indicator are "Correction", "Lexicon", "Readability", and "Utility". AHP was used to transform the qualitative problems in evaluating Chinese-Portuguese machine Translation into the quantitative problem in order to provide a theoretical basis and practical guidance for the future manual evaluation of Chinese-Portuguese machine Translation, and for the eventual modification of automatic machine translation evaluation methods. © 2023 IEEE.

关键词： Analytic hierarchy process

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Efficient Chromosome Abnormal Detection using Yolo-V8 model 3

Efficient Chromosome Abnormal Detection using Yolo-V8 model

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

作者： Umarani, V. Isaac, Augustian Premnath, K. Kannan, G. Sanjay, A. Hari Haran, L.K. Saveetha Engineering College Dept of Computer Science and Engineering TamilNadu Chennai India Saveetha Engineering College Dept of Artificial Intelligence & Machine Learning TamilNadu Chennai India

ISBN: (纸本)9798331523893

Human chromosomes exhibit complex and intricate patterns, making their analysis and data interpretation challenging. Studying chromosomes is crucial for identifying genetic disorders, such as Down syndrome, and various types of cancer. Traditional karyotyping methods depend heavily on manual method, which causes human error and high cost. To address these challenges, a deep learning-based approach is proposed to automate the karyotyping process, improving both accuracy and efficiency while minimizing human involvement and errors. This research leverages advanced deep learning techniques, specifically the YOLOv8 object detection framework, to enhance chromosomal analysis. From experiments, the proposed YOLO V8 model achieves a 95.2% accuracy, surpassing other models such as DCNN, CNN, and VGG-19. This work represents a significant step toward developing a more reliable and efficient diagnostic tool for detecting genetic disorders. © 2025 IEEE.

关键词： Deep learning

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AraSentiment: Arabic Sentiment Analysis on Data using machine Learning, and Transformers 3

AraSentiment: Arabic Sentiment Analysis on Data using Machin...

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3rd international Mobile, Intelligent, and Ubiquitous Computing conference, MIUCC 2023

作者： Abdelminaam, Diaa Salama Shorim, Abdelrahman Antoun, Mina Mohamed, Habiba Misr International University Faculty of Computer Science Cairo Egypt Benha University Faculty of Computers and Artificial Intelligence Benha Egypt

ISBN: (纸本)9798350306231

A natural language processing method called sentiment analysis automatically detects and measures subjective information in text or audio data. It may be used in many different situations. However, due to the complexity of the Arabic language and the scarcity of labeled audio datasets, sentiment analysis on Arabic audio data poses particular difficulties. This study suggests a fresh method for analyzing Arabic sentiment in audio data utilizing machine learning and transformers. With the Count Vectorizer and n-gram techniques serving as the feature extractors, the objective is to assess the effectiveness of several machine learning algorithms, such as AraBERT, Linear Regression, Decision Tree, Random Forest, and Naive Bayes. The suggested method seeks to achieve high accuracy in sentiment categorization by overcoming the difficulties of Arabic audio sentiment analysis. The study evaluates the proposed approach on a newly created Arabic audio dataset and compares the results with existing approaches. The obtained results in terms of Accuracy (ACC), Precision (PREC), and Recall (REC) show excellent results, with the proposed approach outperforming existing approaches. The discussion section analyzes the impact of different feature extraction methods and machine learning algorithms on the performance of the sentiment analysis model. The results show that the AraBERT model achieves the highest accuracy. © 2023 IEEE.

关键词： Decision trees

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An Intelligent Security-Enabled Home Automation System using Internet of Things Environment 3

An Intelligent Security-Enabled Home Automation System using...

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

作者： Karthikeyan, P. Dinesh, M. Akila, A. Veeramuthu, A. Giri, Jayant Al Sherideh, Ala'a Prathyusha Engineering College Department of Eee Tiruvallur Chennai India Saveetha School of Engineering Department of Vlsi Microelectronics Chennai India R.M.K. College of Engineering and Technology Department of Artificial Intelligence and Data Science Tamil Nadu Puduvoyal India Sathyabama Institute of Science and Technology Department of Information Technology Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India Zarqa University College of Information Technology Department of Cyber Security Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331509675

IoT technologies to bolster residential security, safety, and sustainability. By leveraging facial recognition for door access, enhancing fire safety monitoring, and integrating energy harvesting, it aims to create more resilient and eco-friendly residential environments. These innovations offer comprehensive solutions to address evolving challenges and optimize residential living. The proposed research introduces an innovative smart home automation system leveraging IoT technologies and Google Assistant for the voice-controlled operation of household devices. It integrates a comprehensive array of sensors for safety and security monitoring, facilitating real-time alerts for fire outbreaks, unauthorized entry, and environmental anomalies. MQTT enables smooth remote access and management of devices. The research evaluates IoT routing protocols, highlighting ISHAS-IoTs's benefits in efficiency, delivery ratio, and delay. These findings underscore the protocol's potential to enhance IoT network performance and efficiency, contributing to the design of more robust and reliable smart home systems. © 2025 IEEE.

关键词： Smart homes

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Third international conference on Image Processing, Object Detection, and Tracking, IPODT 2024

Third International Conference on Image Processing, Object D...

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2024 3rd international conference on Image Processing, Object Detection, and Tracking, IPODT 2024

ISBN: (纸本)9781510685512

The proceedings contain 35 papers. The topics discussed include: design of a book misplacement detection system based on image recognition;automatic detection of ultrasonic water meter LCD pattern elements based on ConvNeXt;deep intrinsic image popularity assessment by learning to rank;a late fusion framework for multi-vehicle collaborative perception;subpixel precision BGA chip localization method based on Lagrange interpolation;signal-to-noise ratio analysis and experimental verification of imaging laser detection system;comparison of cell image segmentation results based on U-Net and transformer;and deep learning based algorithm for classifying pedestrian behavior at crosswalks.

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Self-supervised Diffusion Model for Anomaly Segmentation in Medical Imaging 10th

Self-supervised Diffusion Model for Anomaly Segmentation in ...

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10th Biennial international conference on pattern recognition and machine intelligence (PReMI)

作者： Kumar, Komal Chakraborty, Snehashis Roy, Sudipta Jio Inst Artificial Intelligence & Data Sci Navi Mumbai 410206 India

ISBN: (纸本)9783031451690;9783031451706

A powerful mechanism for detecting anomalies in a self-supervised manner was demonstrated by model training on normal data, which can then be used as a baseline for scoring anomalies. Recent studies on diffusion models (DMs) have shown superiority over generative adversarial networks (GANs) and have achieved better-quality sampling over variational autoencoders (VAEs). Owing to the inherent complexity of the systems being modeled and the increased sampling times in the long sequence, DMs do not scale well to high-resolution imagery or a large amount of training data. Furthermore, in anomaly detection, DMs based on the Gaussian process do not control the target anomaly size and fail to repair the anomaly image, which led us to the development of a simplex diffusion and selective denoising ((SD)(2)) model. (SD)(2) does not require a full sequence of Markov chains in image reconstruction for anomaly detection, which reduces the time complexity, samples from the simplex noise diffusion process that have control over the anomaly size and are trained to reconstruct the selective features that help to repair the anomaly. (SD)(2) significantly outperformed the publicly available Brats2021 and Phenomena detection from X-ray image datasets compared to the self-supervised model. The source code is made publicly available at https://***/MAXNORM8650/SDSquare.

关键词： Anomaly detection Self-supervise learning MRI X-rays Diffusion models Simplex noise

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2022 3rd international conference on Computer Vision, Image and Deep Learning and international conference on Computer Engineering and Applications, CVIDL and ICCEA 2022

2022 3rd International Conference on Computer Vision, Image ...

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3rd international conference on Computer Vision, Image and Deep Learning and international conference on Computer Engineering and Applications, CVIDL and ICCEA 2022

ISBN: (纸本)9781665459112

The proceedings contain 253 papers. The topics discussed include: testing the feasibility of quantizing the progress of stroke patients’ rehabilitation with a computer vision method;recognition of surface defects of aluminum profiles based on convolutional neural network;a novel generative adversarial network simulating the complementary structure of DNA genetic information;an improved lightweight method of convolutional neural network for face recognition;a calculation method for line-of-sight stable attitude of networked optical transceiver based on depth feedforward neural network;a grapevine virus disease detection method based on convolution neural network;a predict method of measuring equipment operation performance based on improved local weighted partial least squares;research on multi-scale network computer modeling based on mobile vendor management;and identification method of dress pattern drawing based on machine vision algorithm.

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