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检索条件"任意字段=8th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2007"

共 205 条记录，以下是81-90 订阅

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Sustainable On-Street Parking Mapping with Deep learning and Airborne Imagery 24th

Sustainable On-Street Parking Mapping with Deep Learning an...

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24th international conference on intelligent data engineering and automated learning, ideal 2023

作者： Mahaarachchi, Bashini K. Cohen, Sarel Bookhagen, Bodo Doskoč, Vanja Friedrich, Tobias Institute of Computer Science University of Potsdam Potsdam Germany The Academic College of Tel Aviv-Yaffo Tel Aviv Israel Institute of Geosciences University of Potsdam Potsdam Germany Hasso Plattner Institute University of Potsdam Potsdam Germany

ISBN: (纸本)9783031482311

In large cities, a considerable amount of traffic congestion is caused by drivers actively looking for unoccupied parking spots, leading to reduced average velocities on the streets, reduced air quality, and an increase in sound pollution. A parking space map helps drivers to find vacant parking spaces more easily and helps minimising search time, fuel and energy consumption. We introduce a machine learning framework to detect occupied and empty parking spaces using airborne images. Our approach is based on three steps. First, we present novel ways to include observations from outside the visible spectrum (RGB) to delineate vegetation in urban environments. We remove roads on the images to minimise interference from moving vehicles and use a mask-RCNN to detect vacant parking spaces on the processed images. third, we propose to use a logistic regression model (LRM) as a post-processing step in order to better filter true positives. From our experiments we conclude that Red-Green-NIR (RG-NIR) spectrum contains the most important information for training Faster-Region-based Convolutional Neural Network with Feature Pyramid Network (RCNN FPN) for vehicle detection. Our empirical studies on aerial images of Berlin, Germany, show that our framework can be employed successfully to create trustworthy parking space maps. Our best model obtain an accuracy of 97.7% and an F1-score of 0.645 for detecting both occupied and vacant on-street packing. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Object detection

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Measuring the Relationship Between the Use of Typical Manosphere Discourse and the Engagement of a User with the Pick-Up Artist Community 24th

Measuring the Relationship Between the Use of Typical Man...

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24th international conference on intelligent data engineering and automated learning, ideal 2023

作者： Torregrosa, Javier Panizo-LLedot, Ángel D’Antonio-Maceiras, Sergio Camacho, David Department of Computer System Engineering Universidad Politécnica de Madrid Calle de Alan Turing Madrid28031 Spain

ISBN: (纸本)9783031482311

the Manosphere movement and its subgroups have garnered the attention of researchers seeking deeper insights into their dynamics. this study specifically focuses on a particular subgroup within the Manosphere called Pick Up Artists (PUAs). PUAs concentrate on teaching heterosexual men sexual seduction techniques to attract women, often promoting a clear objectifying and sexist perspective. the primary goal of this research is to explore whether users who engage more with the PUA community tend to adopt the typical Manosphere discourse. To achieve this objective, we aim to measure the relationship between linguistic features and the number of relevant Pick-Up Artist accounts followed by a user. Four linguistic features were considered: sexist language, misogyny, use of slang, and Manosphere topics. To assess these linguistic features, we employ relevant state-of-the-art tools, including various classifiers to identify misogynistic and sexist content, topic extraction techniques like BERTopic, and dictionaries containing typical Manosphere slang. the results of our analysis indicate a moderate correlation between the presence of misogynistic/sexist content and the number of accounts followed. However, we did not find a similar correlation with the other variables. this could indicate that the amount of relevant accounts followed does not influence the discourse of an account. Nevertheless, further research is necessary to gain a better understanding of the typical discourse within the PUAs subculture of the Manosphere as the tools used in this study do not seem to clearly identify the expected discourse. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Pickups

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Low-rank Fusion Network for Multi-modality Person Re-identification

Low-rank Fusion Network for Multi-modality Person Re-identif...

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6th international conference on intelligent Computing and Signal Processing (ICSP)

作者： Ziling He Hanqin Shi Youqing Wu Zhengzheng Tu School of Computer Science and Technology Anhui University Hefei China School of Information Engineering (School of Big Data) Xuzhou University of Technology Xuzhou China School of Computer Science and Technology Hefei Normal University Hefei China

ideally, multi-modality person re-identification (Re-ID) models can make use of the complementary information contained in different modality images to obtain richer target features under ideal conditions, thus making up for the shortcomings of traditional visible person re-identification in complex environments. However, the advantages of different modalities may not be fully exploited due to incomplete feature learning. therefore, we propose a low-rank fusion network for multi-modality person Re-ID, which can extract multi-scale features of each modality under the guidance of efficient channel attention (ECA) to capture different details of the target image, and then apply the low-rank feature fusion module to achieve feature complementation between multiple modalities and learn recognition features with strong representational ability. Extensive experiments on multi-modality person Re-ID dataset RGBNT201 comparing to other advanced methods verify the effectiveness of our method in complex environments.

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Exploring Machine learning in IoT Smart Home Automation

Exploring Machine Learning in IoT Smart Home Automation

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international conference on Software engineering and Computer Systems (ICSECS)

作者： Quadri Waseem Wan Isni Sofiah Wan Din Azamuddin Bin Ab Rahman Kashif Nisar Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) Pekan Malaysia AnalytiCray Kuala Lumpur Malaysia Professional Computing & Data Science Swinburne University of Technology Sydney Parramatta New South Wales Australia

the Internet of things (IoT) has evolved in these years. Various types of organizations, industries, research domains and almost all types of intelligent future applications are utilizing the advantages of IoT. these applications include smart homes, smart cities, smart infrastructure smart communities, smart healthcare, smart agriculture and many more. “Smart Homes” has emerged as one the latest Internet of things (IoT) applications known to automate household equipment's using remote or automated functioning from remote locations to improve the quality of life for its inhabitants. For a smart home system to function effectively, the machine learning (ML) implementation must go beyond basic remote control and simple automation. To fully realize its potential and provide homeowners with tremendous and unexpected benefits, more research and development in the fields of machine intelligence and smart home automation are required. In this research work, we aim to traverse ML in IoT smart home automation by classifying the home automation applications. We propose a taxonomy of machine learning (ML) for smart homes based on its application. this research also includes related surveys and literature reviews along with open challenges and issues as well as future directions in detail.

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Causal Graph Discovery for Explainable Insights on Marine Biotoxin Shellfish Contamination 24th

Causal Graph Discovery for Explainable Insights on Marine ...

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24th international conference on intelligent data engineering and automated learning, ideal 2023

作者： Ribeiro, Diogo Ferraz, Filipe Lopes, Marta B. Rodrigues, Susana Costa, Pedro Reis Vinga, Susana Carvalho, Alexandra M. Lisbon1000-049 Portugal Lisbon1000-049 Portugal INESC-ID Instituto Superior Técnico Universidade de Lisboa Lisbon1000-029 Portugal Department of Mathematics NOVA SST Caparica2829-516 Portugal UNIDEMI Department of Mechanical and Industrial Engineering NOVA SST Caparica2829-516 Portugal IPMA - Instituto Portuguës do Mar e da Atmosfera Algés1495-165 Portugal Lisbon Unit for Learning and Intelligent Systems Lisbon Portugal

ISBN: (纸本)9783031482311

Harmful algal blooms are natural phenomena that cause shellfish contamination due to the rapid accumulation of marine biotoxins. To prevent public health risks, the Portuguese Institute of the Ocean and the Atmosphere (IPMA) regularly monitors toxic phytoplankton in shellfish production areas and temporarily closes shellfish production when biotoxins concentration exceeds safety limits. However, this reactive response does not allow shellfish producers to anticipate toxic events and reduce economic losses. Causality techniques applied to multivariate time series data can identify the variables that most influence marine biotoxin contamination and, based on these causal relationships, can help forecast shellfish contamination, providing a proactive approach to mitigate economic losses. this study used causality discovery algorithms to analyze biotoxin concentration in mussels Mytilus galloprovincialis and environmental data from IPMA and Copernicus Marine Environment Monitoring Service. We concluded that the toxins that cause diarrhetic and paralytic shellfish poisoning had more predictors than the toxins that cause amnesic poisoning. Moreover, maximum atmospheric temperature, DSP toxins-producing phytoplankton and wind intensity showed causal relationships with toxicity in mussels with shorter lags, while chlorophyll a (chl-a), mean sea surface temperature and rainfall showed causal associations over longer periods. Causal relationships were also found between toxins in nearby production areas, indicating a spread of biotoxins contamination. this study proposes a novel approach to infer the relationships between environmental variables to enhance decision-making and public health safety regarding shellfish consumption in Portugal. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Surface waters

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An automated Detection and Segmentation of thyroid Nodules using Res-UNet 8

An Automated Detection and Segmentation of Thyroid Nodules u...

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8th international conference on Electrical engineering, Computer Science and Informatics (EECSI)

作者： Nugroho, Hanung Adi Frannita, Eka Legya Nurfauzi, Rizki Univ Gadjah Mada Fac Engn Dept Elect & Informat Engn Yogyakarta Indonesia

ISBN: (纸本)9786236264201;9786236264195

Recently, some countries have been distressing with the increasing number of thyroid cancer cases. the number of cases is increased every year. Practically, one of the causes of the increase in the number of patients was due to manual examination. Recently, some researchers have involved in the development of CAD to solve this problem. However, CAD itself still has some limitations. One of the major limitations is that the nodules segmentation process was not well-conducted. thus, to overcome that problem, we proposed a scheme for detecting and segmenting the thyroid nodules. Our scheme consisted of four major steps which were data augmentation process, normalization process, segmentation and evaluation process. the proposed scheme was tested in 480 thyroid ultrasound images. the proposed scheme successfully achieved more than 90% in all evaluation metrics in both detection and segmentation process. According to this achievement, we concluded that our proposed method had potential to be integrated as part of the intelligent system for detecting and segmenting thyroid cancer.

关键词： deep learning detection segmentation thyroid nodules thyroid cancer

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intelligent threat Detection and Response System for Secure Cloud Environments

Intelligent Threat Detection and Response System for Secure ...

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international conference on Trends in Electronics and Informatics (ICEI)

作者： Manju C Nair Ch Praharsha Kumar Ch Gnana Naga Sri Vignesh Lalitha R Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

this project is aimed at addressing increasing security issues in cloud environments through the use of soft computing methods. It performs real-time detection and classification of security incidents through dynamic analysis of network traffic, user activity logs, and other public data sources. the system uses adaptive learning models that are constantly updated to identify new threats while reducing false alarms and providing means for automated responses. the architecture proposed consistently monitors and classifies security events as per risk levels and industry best practices, paving the way for proactive defense strategies. An optimized feedback loop enables the system to improve its performance according to past security events, improving its resistance to new-generation cyber threats. By incorporating robust encryption and access control mechanisms, the framework protects cloud resources without impacting performance or availability. this proactive solution enhances organizational cybersecurity by neutralizing threats, enhancing structural protections, and averting unauthorized access to confidential information. the solution is a notable innovation in cloud security that meets the dynamic challenges of a rapidly interconnected digital environment.

关键词： Resistance Technological innovation Cloud computing security Soft sensors Telecommunication traffic threat assessment Real-time systems Encryption Protection Monitoring

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Shoppers Interaction Classification Based on An Improved DenseNet Model Using RGB-D data

Shoppers Interaction Classification Based on An Improved Den...

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international conference on Systems and Informatics (ICSAI)

作者： Almustafa Abed Belhassen Akrout Ikram Amous Miracl Laboratory Enet’com University of sfax Sfax Tunisia Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj Saudi Arabia

ISBN: (纸本)9781665474498

this study aims to present a deep learning approach utilizing transfer learning and an RGB-D dataset termed HADA (Hands dataset) acquired by a depth sensor from a top-view configuration capable of monitoring customers and classifying their interaction in intelligent retail settings. With the intention of developing an automated RGB-D approach for video analysis, we provide an innovative, intelligent technology that can comprehend customer behavior, in particular their interactions with items on the shelves. the camera system identifies the presence of humans and classifies their interactions with products accurately. through the RGB and depth frames, the system determines consumer interactions with shelf objects and identifies if a product is picked up, taken and subsequently returned, or if there is no touch at all. Our approach obtained good accuracy, precision, and recall, demonstrating the efficiency of the proposed model, and testing findings have proved that its performance in real-world conditions is adequate.

关键词： Deep learning Transfer learning Neural networks data models Reliability Object recognition Informatics

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8th international conference on Future data and Security engineering , FDSE 2021

8th International Conference on Future Data and Security Eng...

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8th international conference on Future data and Security engineering , FDSE 2021

ISBN: (纸本)9783030913861

the proceedings contain 26 papers. the special focus in this conference is on Future data and Security engineering. the topics include: Feature learning and data Generative Models for Facial Expression Recognition;authorization Strategies and Classification of Access Control Models;motorbike Counting in Heavily Crowded Scenes;pesticide Label Detection Using Bounding Prediction-Based Deep Convolutional Networks;intelligent Urban Transportation System to Control Road Traffic with Air Pollution Orientation;a data Union Method Using Hierarchical Clustering and Set Unionability;a Consensus-Based Load-Balancing Algorithm for Sharded Blockchains;neighboring Information Exploitation for Anomaly Detection in intelligent IoT;feature Representation of AutoEncoders for Unsupervised IoT Malware Detection;Potential threat of Face Swapping to eKYC with Face Registration and Augmented Solution with Deepfake Detection;time in data Models;spliced Image Forgery Detection Based on the Combination of Image Pre-processing and Inception V3;comprehensive Analysis of Privacy in Black-Box and White-Box Inference Attacks Against Generative Adversarial Network;face Recognition in the Wild for Secure Authentication with Open Set Approach;intrusion Detection in Software-Defined Networks;clustering Analyses of Two-Dimensional Space-Filling Curves;Attendance Monitoring Using Adjustable Power UHF RFID and Web-Based Real-Time automated Information System;integrating Deep learning Architecture into Matrix Factorization for Student Performance Prediction;distributed Scalable Association Rule Mining over Covid-19 data;threshold Benefit for Groups Influence in Online Social Networks;application Based Cigarette Detection on Social Media Platforms Using Machine learning Algorithms;Efficient Brain Hemorrhage Detection on 3D CT Scans with Deep Neural Network;multi-class Bagged Proximal Support Vector Machines for the ImageNet Challenging Problem;selective Combination and Management of Distributed Machine Le

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Myocarditis Diagnosis: A Method using Mutual learning-Based ABC and Reinforcement learning

Myocarditis Diagnosis: A Method using Mutual Learning-Based ...

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international Symposium on Computational Intelligence and Informatics

作者： Saba Danaei Arsam Bostani Seyed Vahid Moravvej Fardin Mohammadi Roohallah Alizadehsani Afshin Shoeibi Hamid Alinejad-Rokny Saeid Nahavandi Adiban Institute of Higher Education Semnan Iran Department of mechanical engineering of biosystems Urmia university Department of exercise physiology & health science University of tehran Internship in UNSW BioMedical Machine Learning Lab Sydney NSW Australia Institute for Intelligent Systems Research and Innovation (IISRI) Deakin University Waurn Ponds Victoria Australia UNSW Data Science Hub The University of New South Wales (UNSW Sydney) Sydney New South Wales Australia BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney Sydney NSW Australia

ISBN: (纸本)9798350398830

Myocarditis occurs when the heart muscle becomes inflamed and inflammation occurs when your body’s immune system responds to infections. It can be diagnosed using cardiac magnetic resonance image (MRI), a non-invasive imaging technique with the possibility of operator bias. this paper proposes a hybrid method of deep reinforcement learning-based algorithms and meta-heuristics algorithms. A mutual learning-based artificial bee colony (ML-ABC) is employed for initial weight, which adjusts the candidate food source generated with the higher fitness between two individuals determined by a mutual learning factor. Moreover, a sequential decision-making process investigates the imbalanced classification issue, in which a convolutional neural network (CNN) is used as the foundation for policy architecture. At first, initial weights are produced using the ML-ABC algorithm. After that, the agent receives a sample at each phase and classifies it, obtaining environmental rewards. the minority class receives more rewards than the majority class. Eventually, the agent discovers an ideal strategy with the aid of a specific reward function and a beneficial learning environment. We evaluate our proposed approach on the Z-Alizadeh Sani myocarditis dataset based on standard criteria and demonstrate that the proposed method gives superior myocarditis diagnosis performance.

关键词： Mechatronics Computational modeling Metaheuristics Reinforcement learning Myocardium Markov processes Classification algorithms

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