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检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

共 7120 条记录，以下是31-40 订阅

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Bridging Healthcare and Telecommunications A Unified Model for Multi-Task Image Classification 11

Bridging Healthcare and Telecommunications A Unified Model f...

引用

11th international conference on Wireless Networks and Mobile Communications (WINCOM) - Inclusive and intelligent Connectivity

作者： Rabbah, Jalal Ridouani, Mohammed Hassouni, Larbi Hassan II Univ RITM Lab CED Engn Sci Casablanca Morocco

ISBN: (纸本)9798350377873;9798350377866

this study introduces a novel multi-task learning model that seamlessly integrates the classification of medical X-ray images with churn prediction in telecommunications, utilizing both radar and deep insight images. the model leverages cross-domain data to enhance decision-making processes, demonstrating the versatility and efficiency of machine learning in diverse applications. this approach not only bridges the gap between healthcare diagnostics and customer retention strategies but also sets a new benchmark for multi-task learning models in handling complex, multidimensional datasets.

关键词： Multi-Task learning Image Classification Healthcare Diagnostics Telecommunications Churn Prediction Cross-Domain Modeling Machine learning Deep learning X-Ray Analysis Radar Image Analysis Deep Insight Images.

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intelligent Approach to Massive data Analysis and Its Impact on Leaf Disease Detection DecisionMaking 11

Intelligent Approach to Massive Data Analysis and Its Impact...

引用

11th international conference on Wireless Networks and Mobile Communications (WINCOM) - Inclusive and intelligent Connectivity

作者： Oubadriss, Adib Laassiri, Jalal El Makrani, Adil Ibn Tofail Univ Fac Sci Lab Res Informat Kenitra Morocco

ISBN: (纸本)9798350377873;9798350377866

Food production and economic growth depend heavily on the agricultural sector, yet plant diseases present serious risks. Maintaining food quality and avoiding losses need early detection. through image classification, deep learning-more specifically, convolutional neural networks (CNNs)-has demonstrated potential in the identification of plant diseases. In this work, we construct a dependable leaf disease detection system using CNNs and pre-trained models, such as VGG-16 and Resnet50. Our tests produced impressive results, using a dataset of 54,201 images from different classifications. the performance evaluation includes important performance measures including F1 score and classification accuracy. With a remarkable classification accuracy of 97.30%, VGG-16 outperforms Resnet50 with 93.04%. the results demonstrate the efficacy of deep learning models in the identification of leaf diseases, with VGG16 demonstrating the highest performance. this strategy presents practical methods to advance sustainable crop production and agriculture.

关键词： deep learning transfer learning CNN leaf disease VGG-16 Resnet-50

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Exploring AI-Driven Drug Repurposing Strategies Targeting ErbB Signalling Pathway for Brain Cancer therapy 11

Exploring AI-Driven Drug Repurposing Strategies Targeting Er...

引用

11th IEEE international conference on Cybernetics and intelligent Systems (CIS) / 11th IEEE international conference on Robotics, Automation and Mechatronics (RAM)

作者： Ng, Chi Kit Long, Jianqiao Tang, Muran Li, Jichun Quan, Mingming Newcastle Univ Sch Comp Newcastle Upon Tyne Tyne & Wear England

ISBN: (纸本)9798350364200;9798350364194

this study explores the application of machine learning methodologies in drug repurposing for brain cancer therapy, focusing on targeting the epidermal growth factor receptor (EGFR). Our approach involved the development of a predictive model to estimate the half-maximal inhibitory concentration (IC50) values of compounds against EGFR, leveraging existing biological activity data of known EGFR inhibitors and molecular structure descriptors. the constructed model exhibited efficacy in predicting the inhibitory activity of compounds against EGFR. Subsequent screening of a library of known drugs using the predictive model led to the identification of several compounds with low predicted IC50 values, indicating their potential as drug candidates for further investigation. this study underscores the utility of integrating machine learning techniques into drug repurposing endeavours, offering a pragmatic approach to identifying potential therapeutic options for brain cancer treatment.

关键词： Adversarial machine learning

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Padel Two-Dimensional Tracking Extraction from Monocular Video Recordings 25th

Padel Two-Dimensional Tracking Extraction from Monocular Vid...

引用

25th international conference on intelligent data engineering and automated learning

作者： Novillo, Alvaro Acena, Victor Lancho, Carmen Cuesta, Marina De Diego, Isaac Martin Rey Juan Carlos Univ Data Sci Lab C Tulipan S-N Mostoles 28933 Spain

ISBN: (纸本)9783031777301;9783031777318

this study introduces a novel framework for the automatic two-dimensional tracking of padel games using monocular recordings. By integrating advanced Computer Vision and Deep learning techniques, our algorithm detects and tracks players, the court, and the ball. through homography, we accurately project detected player positions onto a two-dimensional court, enabling comprehensive tracking throughout the game. We tested the proposed algorithm using amateur video recordings of padel games found in literature. this approach remains user-friendly, cost-effective, and adaptable to various camera angles and lighting conditions. this makes it accessible to both amateur and professional players and coaches, providing a valuable tool for performance analysis. Additionally, the proposed framework holds potential for adaptation to other sports with minimal modifications, further broadening its applicability.

关键词： Computer Vision Padel Deep learning Court Detection Homography Applied Intelligence Tracking data

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10th international conference on Innovation and New Trends in Information Technology, INTIS 2022 and 11th international conference on Innovation and New Trends in Information Technology, INTIS 2023

10th International Conference on Innovation and New Trends i...

引用

10th international conference on Innovation and New Trends in Information Technology, INTIS 2022 and 11th international conference on Innovation and New Trends in Information Technology, INTIS 2023

ISBN: (纸本)9783031473654

the proceedings contain 15 papers. the special focus in this conference is on Innovation and New Trends in Information Technology. the topics include: intelligent Multi-agent Distributed System for Improving and Secure Travel Procedures: Al-Karama-King Hussein Bridge Study Case;a New Approach for the Analysis of Resistance to Change in the Digital Transformation Context;augmented data Warehouses for Value Capture;a Parallel Processing Architecture for Querying Distributed and Heterogeneous data Sources;comprehensive data Life Cycle Security in Cloud Computing: Current Mastery and Major Challenges;systematic Mapping Study on Applications of Deep learning in Stock Market Prediction;exploring the Knowledge Distillation;intelligent Traffic Congestion and Collision Avoidance Using Multi-agent System Based on Reinforcement learning;BERT for Arabic NLP Applications: Pretraining and Finetuning MSA and Arabic Dialects;VacDist MAS for Covid-19 Vaccination Distribution: Palestine as a Case Study;Smart E-Waste Management System Utilizing IoT and DL Approaches;towards a Platform for Higher Education in Virtual Reality of engineering Sciences;Robust intelligent Control for Two Links Robot Based ACO Technique.

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Towards Reliable Drift Detection and Explanation in Text data 25th

Towards Reliable Drift Detection and Explanation in Text Dat...

引用

25th international conference on intelligent data engineering and automated learning

作者： Feldhans, Robert Hammer, Barbara Bielefeld Univ Bielefeld Germany

ISBN: (纸本)9783031777301;9783031777318

When delivered to the market, machine learning models face new data which are possibly subject to novel characteristics - a phenomenon known as concept drift. As this might lead to performance degradation, it is necessary to detect such drift and, if required, adapt the model accordingly. While a variety of drift detection and adaptation methods exists for standard vectorial data, a suitable treatment of text data is less researched. In this work we present a novel approach which detects and explains drift in text data based on their representation via transformer embeddings. In a nutshell, the method generates suitable statistical features from the original distribution and the possibly shifted variation. Based on these representations, drift scores can be assigned to individual data points, allowing a visualization and human-readable characterization of the type of drift. We demonstrate the approach's effectiveness in reliably detecting drift in several experiments.

关键词： Drift Explanation Text data Transformer Visualization

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Comparative Study of Federated learning Algorithms Based on SPADE Agents 25th

Comparative Study of Federated Learning Algorithms Based on ...

引用

25th international conference on intelligent data engineering and automated learning

作者： Enguix, Francisco Peris, Saul Cerda Rincon, J. A. Carrascosa, C. Univ Politec Valencia UPV Valencian Res Inst Artificial Intelligence VRAIN Camino Vera S-N Valencia 46022 Spain Univ Burgos Dept Digitalizac Escuela Politec Super Ctra Oron 28 Burgos 09200 Spain

ISBN: (纸本)9783031777370;9783031777387

Federated learning (FL) is a prominent method in machine learning, that ensures privacy by enabling distributed devices to collaboratively learn a shared model without exchanging local data. this paper provides a comparative analysis of various FL algorithms implemented on the Smart Python Agent Development Environment (SPADE) framework. We focus on evaluating the performance, scalability, and resilience of these algorithms across different network setups and data distribution scenarios. Our results highlight the differential impacts of decentralized versus centralized approaches, particularly under non-IID data conditions, common in real-world applications. By leveraging SPADE agents and consensus algorithms, this study not only tests algorithmic efficiency and system robustness but also explores advanced strategies like asynchronous updates and coalition-based learning, which show promise in enhancing model accuracy and reducing communication overhead.

关键词： Federated learning Multi-agent systems Consensus

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Abnormal Behavior Detection of Needle Selector's Knife heads Based on Improved Generative Adversarial Networks 11

Abnormal Behavior Detection of Needle Selector's Knife heads...

引用

11th IEEE international conference on Cybernetics and intelligent Systems (CIS) / 11th IEEE international conference on Robotics, Automation and Mechatronics (RAM)

作者： Peng, Laihu Chen, Xiangbin Ru, Xin Zhang, Xianming Liu, Song Zhejiang Sci Tech Univ Sch Mech Engn Hangzhou Peoples R China

ISBN: (纸本)9798350364200;9798350364194

the needle selector is a key component of the circular weft knitting machine to realize the pattern knitting, to ensure that the jacquard pattern on the fabric is complete and smooth. Due to its fast movement frequency, conventional detection methods need to consume a lot of time and energy to process the data, and it is difficult to detect the movement of the knife heads effectively by human identification. In this study, GAN and convolutional neural network are mainly used to realize the detection of abnormal motion of the knife heads of the needle selector. the model not only improves the robustness and accelerates the convergence speed of the GAN model, but also realizes the data augmentation and solves the problem of insufficient abnormal data. the improved model achieves an average correct detection rate of 98.11% for three types of knife heads motion anomalies.

关键词： Needle Selector Abnormal Behavior Detection GAN Deep learning data Augmentation

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Enhancing Railway Obstacle Detection System Based on Incremental learning 11

Enhancing Railway Obstacle Detection System Based on Increme...

引用

11th international conference on Vehicle Technology and intelligent Transport Systems, VEHITS 2025

作者： Guo, Qiushi Cao, Bin Hao, Dehao Wang, Cheng Chen, Lijun Yan, Peng China

ISBN: (纸本)9789897587450

Obstacle detection systems face challenges related to the Catastrophic forgetting problem, where old obstacles may be misclassified when training new unseen obstacles. Re-training a model from scratch for every new obstacle is often impractical. In this work, we propose a continual learning-based approach to efficiently update the model without repeatedly retraining on previous data, while simultaneously mitigating catastrophic forgetting. Experimental results demonstrate the effectiveness of our proposed method. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： engineering education

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Smart Sign Language Decoder 25th

Smart Sign Language Decoder

引用

25th international conference on intelligent data engineering and automated learning

作者： Costescu, Constantin Marius Popescu, Paul Stefan Mihaescu, Marian Cristian Univ Craiova Fac Automat Comp Sci & Elect Craiova Romania

ISBN: (纸本)9783031777370;9783031777387

Sign language recognition and understanding are challenging tasks for many people who are not familiar with it, which limits communication between deaf-mute people and others. the system presented in this paper lowers the communication barrier, introducing an automatic translation layer that facilitates sign language understanding. the system uses a deep-learning model for sign language detection and a separate library for hand joint mapping. the application's architecture was designed to allow users to access the system from desktop and mobile devices. the model's results revealed an 82% accuracy, and after several tweaks on the activation function in our tests, we achieved perfect classification in our real word tests. the results of the system offered excellent accuracy, and its usability lowers the communication barrier between people, providing flexibility as the application is available for any device with a browser.

关键词： Sign Language Recognition deep learning dataset

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