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

共 7123 条记录，以下是261-270 订阅

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Advances in Machine learning and Deep learning for automated Banana Disease Detection: A Comprehensive Survey 4

Advances in Machine Learning and Deep Learning for Automated...

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4th international conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2024

作者： Kanimalar, C. Karthikeyan, M. Annamalai university Department of Computer and Information Science Annamalai Nagar India Annamalai University Dept. Of Computer & Information Science Annamalai Nagar India

ISBN: (纸本)9798331529635

Banana plants are a crucial agricultural crop worldwide, yet they are vulnerable to various diseases that can drastically impact yield and quality. Traditional methods of disease detection in banana cultivation rely heavily on manual inspection, which is labor-intensive, error-prone, and lacks scalability. In recent years, machine learning (ML) and deep learning (DL) models have shown significant potential in automating and enhancing disease detection in agriculture. this survey explores the application of ML and DL models for banana disease detection, reviewing techniques such as supervised learning, feature engineering, convolutional neural networks (CNNs), and transfer learning. A comparative analysis of these approaches reveals differences in performance metrics such as accuracy, precision, and scalability. Additionally, we address challenges in data acquisition, model generalization across varied environmental conditions, and computational limitations. this survey aims to provide insights into the current state of ML and DL in agricultural disease detection, highlighting opportunities for future research to improve model robustness and practical deployment in banana farming. © 2024 IEEE.

关键词： Adversarial machine learning

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the Combination between Deep learning and Ensemble Stacking for a Fast and Accurate Detection of Diabetic Retinopathy Using Fundus Images 6

The Combination between Deep Learning and Ensemble Stacking ...

引用

6th international conference on intelligent Computing in data Sciences, ICDS 2024

作者： Tibari, Anass Ridouani, Mohammed CED Engineering Sciences Hassan II University RITM Laboratory Casablanca Morocco

ISBN: (纸本)9798350351200

Diabetic retinopathy (DR) is the leading cause of human vision loss in the world. To slow the progression of the disease we need early detection and diagnosis. Hence, rapid detection and accurate classification of DR is crucial in patient care. the use of Deep learning (DL) alone turns out to be slow and expensive, also the use of a single Machine learning (ML) algorithm for classification leaves a significant margin of error. the detection of the disease also proves to be more difficult, especially in its early stages, this is due to the characteristics of DR less clear and visible on images. therefore, the combination of Deep learning and ensemble stacking was a relevant solution that we chose to enhance the ability to detect the disease in its early stages. In our research, we introduce an innovative method utilizing an automated model to identify and classify the existence and stage of severity of DR directly from fundus images. We utilized both of Machine learning algorithms and a Deep learning model;to achieve an accurate and fast detection we used the transfer learning and ensemble stacking as techniques. the proposed model uses a dataset published on Kaggle;our solution achieves an accuracy of 99.50%. © 2024 IEEE.

关键词： Contrastive learning

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Medical Image-to-Image Translation with Spatial Self-Attention for Radiotherapy in Federated learning 5

Medical Image-to-Image Translation with Spatial Self-Attenti...

引用

5th international conference on intelligent data Science Technologies and Applications, IDSTA 2024

作者： Bdair, Tariq Saadeh, Hiba Qaqish, Ban Sulaq, Aya Rawashdeh, Majdi Princess Sumaya University for Technology Data Science Department Amman Jordan Al Yamamah University Software Engineering Department Riyadh Saudi Arabia Princess Sumaya University for Technology Business Information Technology Department Amman Jordan

ISBN: (纸本)9798350354751

Globally, cancer remains a leading cause of death, affecting millions of people each year. Accurate medical imaging is crucial for the effective planning of radiotherapy. However, repeated exposure to radiation from Computed Tomography (CT) scans during treatment planning can put patients at more risk. Fortunately, the recent improvement in automated image-to-image translation using deep learning methods has reached a superior performance. However, this might be challenged by data limitation of requiring a large amount of annotated data assembled in one location, privacy, and motion artifacts in medical imaging. Yet, finding such conditions usually is not feasible. To address this, we propose RadiaSync. RadiaSync is a federated learning framework proposed to train decentralized models in a privacy-preserved fashion. In our method, we use CycleGAN architecture for image translation within an FL environment, ensuring patient privacy and collaborative learning across different clients. Further, we propose a spatial attention mechanism that enhances the translated image quality with more than 55% improvement over the baseline. © 2024 IEEE.

关键词： Federated learning

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FLBC-DSS: Federated learning-Enabled Blockchain-Based data Sharing Scheme for IIoT 11

FLBC-DSS: Federated Learning-Enabled Blockchain-Based Data S...

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11th IEEE international conference on Behavioural and Social Computing, BESC 2024

作者： Yao, Yan Li, Guangshun Zhang, Huihui Feng, Shanshan Yan, Biwei Cao, Jian School of Computer Science Qufu Normal University Rizhao China Rizhao-Qufu Normal University Joint Technology Transfer Center Rizhao China School of Computer Engineering Weifang University Weifang China Qilu University of Technology Shandong Academy of Sciences Jinan China School of Information Science and Engineering Shandong Normal University Jinan China School of Computer Scienceand Technology Shandong University Qingdao China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China

ISBN: (纸本)9798331531904

this paper introduces the Federated learning-Empowered Blockchain-based data Sharing Scheme (FLBC-DSS), a novel framework designed to enhance data security and efficiency in Industrial Internet of things (IIoT) environments. By integrating federated learning with blockchain technology, FLBC-DSS addresses the critical challenges of data privacy and unauthorized access, promoting secure, decentralized data sharing across various industrial sectors. Utilizing zk-SNARKs, the scheme provides a mechanism for anonymous data verification without compromising data integrity. Performance evaluations conducted using Hyperledger Fabric and SecureBoost demonstrate significant improvements in prediction accuracy and computational efficiency compared to traditional approaches. the implementation of non-cooperative game theory further illustrates the economic benefits of FLBC-DSS, highlighting its potential to increase profitability through strategic data sharing. the results affirm the effectiveness of FLBC-DSS in leveraging advanced cryptographic and machine learning techniques to foster a secure, transparent, and efficient digital ecosystem for IIoT. © 2024 IEEE.

关键词： Federated learning

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An Exploration on Big data Analytical Techniques: A Review 18

An Exploration on Big Data Analytical Techniques: A Review

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18th INDIAcom;11th international conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Prajapati, Abhinav Gupta, Ayush Mishra, Sanskar Raj, Dharm Singh, Murari Kumar Goyal, Mayank Kumar Sharda School of Engineering and Technology Department of Computer Science and Engineering UP Greater Noida India

ISBN: (纸本)9789380544519

the exponential growth of large, complex, and dynamic datasets, known as big data, required the development of robust analytical techniques beyond conventional tools. this article examines the landscape of big data processing techniques like LSTM, CNN, and more identifying their suitability for different application domains like social media, finance, healthcare, and agriculture. Recognizing the frequent time-sensitive nature of big data analysis, we emphasize the importance of data quality considerations within the processing pipeline. through a structured analysis of existing literature, we present a comprehensive overview of diverse big data analytical techniques, challenges, technologies, and applications, including statistical methods, machine learning algorithms, and distributed computing frameworks. Our key findings reveal the strengths and limitations of each approach, highlighting the need for hybrid solutions and domain-specific adaptations. Furthermore, we discuss the broader implications of big data processing on knowledge extraction, decision-making, and ethical considerations for responsible data analysis in various fields. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Big data Big data Analysis data mining

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Using Contrastive learning to Map Stylistic Similarities in Narrative Writers 25th

Using Contrastive Learning to Map Stylistic Similarities in ...

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25th international conference on intelligent data engineering and automated learning

作者： Valero-Redondo, Maria Huertas-Tato, Javier D'Antonio Maceiras, Sergio Martin, Alejandro Camacho, David Univ Cordoba Cordoba Spain Univ Politecn Madrid Madrid Spain

ISBN: (纸本)9783031777301;9783031777318

Exploring how literary authors influence each other and how intertextual connections can be found among them is a complex problem that is handled manually by experts. this research field, intertextuality, seeks to understand how writers relate to each other and how the works of one writer have reflected on the work of another. this analysis usually provides insight into the historical, cultural, biographical, and literary contexts that shape literary works. From a computational point of view, the approaches related to this problem have mainly dealt with problems of style classification, known as authorship attribution, but none have so far dealt with the problem of intertextuality. this paper proposes a novel approach based on contrastive learning to generate authorship embeddings that encapsulate the stylistic signatures of narrative writers and that can be projected into graphical representations to discern similarities between a dataset of literary writers. the embeddings generated with this approach aim to represent authorship styles are created and evaluated using a dataset of books from Project Gutemberg. During the evaluation, we perform a book and chunk-level evaluation, showing good performance in both cases. Finally, we present different graphical representations and provide a deep analysis of the relations that arise between the writers.

关键词： Contrastive learning Intertextual analysis Language Models Transformer

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Optimizing Pedestrian Paths to Minimize Exposure to Urban Pollution through Traffic data Analysis 25th

Optimizing Pedestrian Paths to Minimize Exposure to Urban Po...

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25th international conference on intelligent data engineering and automated learning

作者： Nadal, Silvia Jordan, Jaume Sanchez-Anguix, Victor Alberola, Juan M. Julian, Vicente Botti, Vicent Univ Politecn Valencia Valencian Res Inst Artificial Intelligence Cami Vera S-N Valencia 46022 Spain Univ Politecn Valencia Grp Sistemas Optimizac Aplicada Cami Vera S-N Valencia 46022 Spain

ISBN: (纸本)9783031777370;9783031777387

the increase in urban population translates into denser urban traffic and, in turn, more environmental pollution. By taking advantage of the high level of sensorisation in smart cities, pollution levels can be monitored in real time, thus helping citizens to make informed decisions. In this research, we propose a healthy routing system. For this purpose, we estimate the pollution dispersion in the urban area from traffic intensity data. then, the fastest walking route for a given trip is calculated. this route is evaluated with respect to the pollution that a pedestrian would be exposed to by completing it at a certain time. Finally, the system proposes alternative routes to avoid streets with a higher accumulation of harmful particles. the results demonstrate the feasibility of our proposal for the creation of healthy routes and, furthermore, the usefulness of the system as a real-time pollution estimator.

关键词： Smart cities Optimization Traffic data Air pollution

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automated Traffic Signal Recognition in Distributed Acoustic Sensing data via Deep learning 11

Automated Traffic Signal Recognition in Distributed Acoustic...

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11th international conference on Environmental and engineering Geophysics, ICEEG 2024

作者： Zhang, Zhiyu Li, Jing Xiong, Hongqiang Sun, Jiaxin College of Geo-Exploration Science and Technology Jilin University Changchun China

Distributed Acoustic Sensing (DAS), an advanced vibration-sensing technology, shows immense promise for data-centric urban surveillance, notably in tracking vehicle speeds. the extraction of vehicle information from DAS data demands real-time processing. Nonetheless, current techniques face challenges in the precise and automated interpretation of vehicle signals. this study presents an integrated two-phase deep learning framework meticulously designed to facilitate the real-time and automated detection of vehicle speeds and directions using Distributed Acoustic Sensing (DAS) data. Initially, the approach employs a contrastive learning-based model to process DAS signals, eliminating disturbances within the profile. the YOLOv8 detection model subsequently accurately detects the processed DAS signals. the vehicle's speed and direction are ultimately ascertained by harnessing the positional data derived from the bounding box's coordinates. Within real-world test scenarios, the method introduced can precisely identify vehicles originating from various directions and sources. the strategy demonstrates robust generalizability even in complex scenarios characterized by intense interference and the presence of multiple vehicles. From a quantitative assessment perspective, the system processes DAS data for 60-second intervals in an average of 4.89 seconds, achieving an accuracy rate of 90.07%, which satisfies the demands for real-time vehicle signal detection. the approach presented in this article offers crucial guidance for the real-time, automatic detection of vehicle signals in DAS systems. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Vehicle actuated signals

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IoT-Based Smart Green Campus Leveraging Machine learning 11

IoT-Based Smart Green Campus Leveraging Machine Learning

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11th international conference on Information and Communication Technology, ICoICT 2023

作者： Mootien, Kasarajen Nagowah, Soulakshmee Devi University of Mauritius Software and Information Systems Department Reduit Mauritius

ISBN: (纸本)9798350321982

For the well-being of all inhabitants of the planet and future generations, it is important to foster a green mindset. In order to increase the likelihood of a successful transition, the utilization of recent technological tools is fundamental. Green campus is about adopting practices that prioritize the protection and preservation of the ecosystems in universities. Smart green campus makes use of the recent technologies in an intelligent way in order to enhance effectiveness of the universities' sustainability initiatives. Smart involves the interconnection of technological devices, which is a feature of the Internet of things (IoT) that allows real-time data acquisition via various sensors. this paper proposes a system that gathers data from the sensors via an IoT network in the university premises. these data can be used to assist the management of the university in the decision-making process. Prediction using Machine learning (ML) is used for analyzing data and to gain further insights of the data. thus, preventive actions can be formulated to, in turn, protect the environment based on the predictions. With the help of Streamlit, the real-time and prediction data are presented on a web-application. this system aims to improve the usage of resources in an efficient and sustainable way for the day-to-day activities carried out in the campus. © 2023 IEEE.

关键词： Internet of things

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Scrutinization of threats from a Cloud-based Network Traffic Environment 18

Scrutinization of Threats from a Cloud-based Network Traffic...

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18th INDIAcom;11th international conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Chopra, Shraddha Sreedevi, A.G. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9789380544519

In this age of digitalization, securing computer networks is of utmost importance to protect sensitive data and critical infrastructure. threat scrutinization plays an integral role in identifying and thwarting cyber risks. this study focuses on a pioneering strategy to bolster the network's reliability by harnessing Artificial Intelligence (AI) techniques in conjunction with cloud computing infrastructure, particularly AWS SageMaker. the focus lies in leveraging a comprehensive and realistic cyber dataset, CSE-IDS-2018, to assess the efficacy of the proposed model. the study commences by architecting a threat scrutinization mechanism that harnesses the feasibility of machine learning algorithms, deep learning models, and cloud computing resources. Specifically, the Random Forest algorithm is employed for the multi-class classification of seven types of attacks within the CSE-CICIDS2018 dataset, facilitated by AWS SageMaker's machine-learning capabilities. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Artificial Intelligence (AI) in cybersecurity AWS SageMaker Cloud Computing Infrastructure CSE-CIC-IDS-2018 Machine learning Network Security Random Forest threat Traffic

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