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Deep learning applied to Automated Visual Defect Detection for Wind Towers Painting Process 2nd

Deep Learning Applied to Automated Visual Defect Detection f...

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2nd Decision science Alliance international Summer conference, DSA ISC 2024

作者： Lario, Joan García-de-la-Puente, Natalia Pérez Mateos, Javier Aksu, Salih Universitat Politècnica de València Camino de Vera S/N València46022 Spain Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano Universitat Politècnica de València Camino de Vera S/N València46022 Spain GRI Renewable Industries Madrid Spain

ISBN: (纸本)9783031782374

Zero Defect Manufacturing (ZDM) strategies focus on promptly and precisely detecting defects to reduce material and energy consumption, and avoid product failure while the product is in use. Integrating a computer vision defect detection system is an important approach to improving the quality of inspection policies and the performance of the manufacturing process. Last year’s high attention has been paid to surface image defect detection based on deep learning algorithms. The deep learning methods on automated vision systems require large quantities of annotated data. Acquiring defects is a costly and time-consuming task in industrial contexts since defects only occur in small percentages, data is biased through the predomination of non-defective samples, and there is a lack of publicly available datasets to train the algorithms. This study aims to evaluate the performance of a deep learning algorithm based on the dataset employed and the training parameters selected. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Examples of the Use of Knowledge Engineering for a Better Exploitation of Massive Company data 26th

Examples of the Use of Knowledge Engineering for a Better Ex...

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26th international conference on Artificial Intelligence and Applications, ICAI 2024, held as part of the World Congress in Computer science, Computer Engineering and applied Computing, CSCE 2024

作者： Vegega, Cinthia Pollo-Cattaneo, María F. Olivas, José A. Research Group on Methodologies for Software Engineering (GEMIS) National Technological University Buenos Aires Regional Faculty Buenos Aires Argentina Soft Management of Internet and Learning (SMILe) Castilla-La Mancha University Ciudad Real Spain

ISBN: (纸本)9783031866227

Hybrid Intelligence facilitates the attainment of complex objectives through the integration of Human Intelligence and Artificial Intelligence. The amalgamation of Knowledge Engineering and machine learning introduces novel approaches to structuring knowledge, culminating in the development of Hybrid Intelligent Systems. In this context, a process model is proposed for the intelligent exploitation of massive data under the guidance of Knowledge Engineering. This model places emphasis on the systematization of data exploitation, considering existing data and domain knowledge. Several examples use cases are presented, cultivated over numerous years, demonstrating the generalizability of the proposed method. These use cases systematically address common tasks, enhancing outcomes in data utilization across different domains. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Artificial Intelligence Hybrid Intelligence Knowledge Engineering Massive data Process Model

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A Privacy-Preserving Computer-Aided Diagnosis Framework for Medical Applications Using Federated learning and Homomorphic Encryption 7th

A Privacy-Preserving Computer-Aided Diagnosis Framework for...

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7th international conference on Attacks and Defenses for Internet-of-Things, ADIoT 2024

作者： Xiong, Jichao Chen, Jiageng Liu, Hui Zhou, Guangyou Cui, Jianqun Lin, Junyu Jiao, Dian School of Computer Science and Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning Central China Normal University Wuhan430079 China School of Central China Normal University Wollongong Joint Institute Central China Normal University Wuhan430079 China

ISBN: (纸本)9783031855924

Skin cancer is one of the most common cancers worldwide, where early detection significantly reduces mortality rates. Integrating Internet of Things (IoT) technologies into healthcare enables continuous monitoring and early diagnosis through connected devices, offering new prospects for real-time medical assessment. Advanced machine learning classifiers have shown superior performance over human experts in diagnosing pigmented skin lesions, underscoring their potential for IoT-based applications. However, training high-precision computer-aided diagnosis (CAD) systems requires extensive data, and secure data sharing poses significant challenges due to privacy concerns in IoT healthcare networks. This paper introduces an IoT-oriented skin cancer CAD system combining federated learning and homomorphic encryption to address these issues. Federated learning supports collaborative model training across distributed IoT devices, mitigating data scarcity without compromising data privacy. Homomorphic encryption ensures that patient data remains encrypted during diagnosis, enhancing security within IoT frameworks. To achieve an efficient and high-accuracy model suited for IoT, we implemented Self-Learnable Activation Functions (SLAF), optimized for homomorphic encryption and resource constraints of IoT devices. Our system, validated using the HAM10000 dataset, achieved 94.39% accuracy with dual privacy protection, enhancing both diagnostic precision and data security. Comprehensive evaluations and comparisons with state-of-the-art frameworks confirmed the effectiveness of our approach. Results demonstrate the strong potential and practicality of our IoT-based solution for real-world healthcare, providing a secure, efficient, and accurate diagnostic tool that supports the wider adoption of IoT in medical applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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An Enhanced Intrusion Detection Model with FeedForward Neural Network Classifier 27

An Enhanced Intrusion Detection Model with FeedForward Neura...

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27th international conference on Advanced Communications Technology, ICACT 2025

作者： Amirjon, Asadov Gain, Mrityunjoy Oh Kim, Keon Hong, Choong Seon Department of Computer Science and Engineering Kyung Hee University Yongin Korea Republic of Department of Artificial Intelligence Kyung Hee University Yongin Korea Republic of

ISBN: (纸本)9791188428137

A major obstacle in the face of increasingly complex cyberattacks is network security. Proactive security measures require effective intrusion detection systems (IDS) that can precisely classify and categorize network threats. In order to improve network attack detection and classification, this paper proposes a reliable method utilizing a Feedforward Neural Network (FFNN) supplemented with Adaptive Synthetic (ADASYN) sampling. We created a model using the UNSWNB15 dataset that efficiently handles high-dimensional datasets by preprocessing data using a combination of polynomial feature transformation and one-hot encoding. The FFNN model is optimized for binary and multi-class classification tasks. It consists of layers of dense units with dropout and batch normalization. Our method’s efficacy is proven by rigorous training and validation procedures, where the model significantly increased its ability to handle class imbalances and improve classification accuracy. The synthesis of new training data by ADASYN was crucial in improving model performance, especially in underrepresented classes. Evaluation measures that highlight the potential of deep learning in network security applications are ROC-AUC scores and classification reports, which show a notable improvement in our IDS’s detection capabilities. The results show that advanced machine learning techniques can be used to enhance conventional intrusion detection systems and provide a means to build stronger network security designs. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.

关键词： Network intrusion

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A Novel and Efficient Multi-scale Spatio-Temporal Residual Network for Multi-class Intrusion Detection 6th

A Novel and Efficient Multi-scale Spatio-Temporal Residual ...

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6th international conference on machine learning for Cyber Security, ML4CS 2024

作者： Li, Nan Gao, Zhaojian Ye, Jiabin Tang, Wei Che, Xun Chen, Yadang Nanjing Big Data Group Co. Ltd. Nanjing210003 China School of Computer Science and Engineering Southeast University Nanjing210096 China Nanjing Zhixun Interconnection Technology Co. Nanjing211100 China Hangzhou SecLead Information Technology Co. Ltd. Hangzhou310056 China Jiangsu Ruizhihe Information Technology Co. Wuxi214434 China School of Computer Science and Cyberspace Security Nanjing University of Information Science and Technology Nanjing210044 China

ISBN: (纸本)9789819645657

With the development of network devices, the network traffic presents high-dimensional, enormous as well as complex characteristics, and the network threats and attacks continue to intensify. Existing network intrusion detection models tend to disregard the extraction and learning of the temporal features of data, which will greatly affect the accuracy of network intrusion detection models. To address the shortcomings of the single structure and the inability to comprehensively learn features, we propose a novel Transformer-based network intrusion detection method, which integrates the degree of importance of traffic features and the fact that intrusion detection data has temporal and spatial characteristics. Specifically, firstly, we adopt the Transformer to perform feature extraction and construct global correlations on the input data, after that, we utilize the improved Inception to extract multi-scale features and weight the spatial features at different scales using self-attention module, in addition to that, BiGRU is employed to enhance the temporal features. Finally, the proposed model is validated on the publicly available CIC-IDS-2017 and CIC-DDoS-2019 datasets, which verifies that the proposed method outperforms the existing state-of-the-art models in terms of performance by four evaluation metrics, and also shows significant performance improvement in binary as well as multi-class classification tasks compared to other state-of-the-art methods, which proves the efficiency and effectiveness of our method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network intrusion

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Optimal Design and Implementation of an Open-Source Emulation Platform for User-Centric Shared E-mobility Services 10th

Optimal Design and Implementation of an Open-Source Emula...

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10th international conference on machine learning, Optimization, and data science, LOD 2024

作者： Shah, Maqsood H. Ding, Yue Zhu, Shaoshu Gu, Yingqi Liu, Mingming Insight SFI Research Centre for Data Analytics Dublin City University Dublin Ireland

ISBN: (纸本)9783031824807

With the rising concern over transportation emissions and pollution on a global scale, shared electric mobility services like E-cars, E-bikes, and E-scooters have emerged as promising solutions to mitigate these pressing challenges. However, existing shared E-mobility services exhibit critical design deficiencies, including insufficient service integration, imprecise energy consumption forecasting, limited scalability and geographical coverage, and a notable absence of a user-centric perspective, particularly in the context of multi-modal transportation. More importantly, there is no consolidated open-source platform which could benefit the E-mobility research community. This paper aims to bridge this gap by providing an open-source platform for shared E-mobility. The proposed platform, with an agent-in-the-loop approach and modular architecture, is tailored to diverse user preferences and offers enhanced customization. We demonstrate the viability of this platform by providing a comprehensive analysis for integrated multi-modal route-optimization in diverse scenarios of energy availability, user preferences and E-mobility tools placement for which we use modified Ant Colony Optimization algorithm so called Multi-Model Energy Constrained ACO (MMEC-ACO) and Q-learning algorithms. Our findings demonstrate that Q-learning achieves significantly better performance in terms of travel time cost for more than 90% of the instances as compared to MMEC-ACO for different scenarios including energy availability, user preference and E-mobility tools distribution. For a fixed (O, D) pair, the average execution time to achieve optimal time cost solution for MMEC-ACO is less than 2 s, while Q-learning reaches an optimal time cost in 20 s on average. For a run-time of 2 s, Q-learning still achieves a better optimal time cost with a 20% reduction over MMEC-ACO’s time cost. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Travel time

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Multimodality for Diagnosis of Asian Choroidal Vasculopathy: Results from a Novel dataset and Deep-learning Experiments

Multimodality for Diagnosis of Asian Choroidal Vasculopath...

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Workshop on Longitudinal Disease Tracking and Modeling with Medical Images and data, LDTM 2024, 5th international Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st Workshop on machine learning for Multimodal/-sensor Healthcare data, ML4MHD2024 and Workshop on Multimodal learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th international conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024

作者： Cho, Daehyun Kim, Young Ho Ahn, Somin Oh, Jaeryung Wallraven, Christian Department of Artificial Intelligence Korea University Seoul Korea Republic of Department of Ophthalmology Korea University College of Medicine Seoul Korea Republic of Department of Ophthalmology Dongguk University Ilsan Hospital Goyang Korea Republic of Department of Artificial Intelligence and Department of Brain and Cognitive Engineering Korea University Seoul Korea Republic of

ISBN: (纸本)9783031845246

Deep learning algorithms show tremendous potential for clinical decision-making - for example, in providing automated diagnoses of imaging data. However, typical clinical datasets often are limited in size, modalities, and contain heterogeneous, incomplete data, which presents challenges for deep learning frameworks that necessitate larger, uniform datasets, complicating their deployment especially with new types of disease models. In this work, we present a case study for deep learning in such a challenging setting in the context of diagnosing Asian choroidal Vasculopathy (ACV), which is a retinopathy profile currently under discussion in ophthalmology to be differentiated from age-related macular degeneration (AMD). We first introduce a novel, human-annotated multimodal dataset for ACV versus AMD diagnosis incorporating four different imaging modalities. We next explore the usefulness of "foundation models" for this data, compared to traditional dataset-specific training. Most importantly, we investigate which of the four modalities is most discriminative and whether bi-modal classification is able to enhance performance across multiple fusion approaches. We also discuss first results of salient features using explainability techniques. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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ICE: An Evaluation Metric to Assess Symbolic Knowledge Quality 23rd

ICE: An Evaluation Metric to Assess Symbolic Knowledge Qual...

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23rd international conference of the Italian Association for Artificial Intelligence, AIxIA 2024

作者： Sabbatini, Federico Calegari, Roberta University of Urbino Urbino Italy University of Bologna Bologna Italy

ISBN: (纸本)9783031806063

The automated assessment of symbolic knowledge, derived, for instance, from extraction procedures, facilitates the autotuning of machine learning algorithms, obviating inherent biases in subjective human evaluations. Despite advancements, comprehensive metrics for evaluating knowledge quality are missing in the literature. To address this gap, our study introduces ICE, a novel evaluation metric designed to measure the quality of symbolic knowledge. This metric computes a score by considering three quality sub-indices, namely, predictive performance, human readability and completeness, and it can be tailored to suit the specific requirements of the case at hand by adjusting the weights assigned to each sub-index. We present here the mathematical formulation of the ICE score, and show its effectiveness through comparative analyses with existing quality scores applied to real-world tasks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Radiotherapy Treatment Planning: An Integrated Optimization and Reinforcement learning Approach 10th

Radiotherapy Treatment Planning: An Integrated Optimization ...

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10th international conference on machine learning, Optimization, and data science, LOD 2024

作者： Anjo, Eva Rocha, Humberto Dias, Joana Faculdade de Ciências e Tecnologia Universidade de Coimbra Coimbra Portugal Faculdade de Economia and CeBER Universidade de Coimbra Coimbra Portugal Faculdade de Economia and INESC Coimbra Universidade de Coimbra Coimbra Portugal

ISBN: (纸本)9783031824807

Radiotherapy treatment planning is one of the most important steps in the radiotherapy treatment workflow. It is responsible for the creation of an individualized treatment for each case, taking into account the characteristics of the volumes to treat and all the surrounding healthy tissues and structures. Typically characterized by a manual, trial-and-error methodology, the selection of irradiation directions and intensities remains a complex endeavor. In this work, a new approach for the automatic generation of radiotherapy treatment plans is described and a proof of concept is considered using prostate cancer cases. In this approach, an unconstrained quadratic optimization problem calculates radiation intensities, with the model parameters being iteratively determined by a fuzzy inference system. To enhance computational efficiency, reinforcement learning, specifically Q-learning, is incorporated. Reinforcement learning enables the fuzzy inference system to dynamically adjust to the evolving requirements of the iterative optimization process. During the learning phase, Q-tables are constructed and subsequently utilized as lookup tables to guide decision-making in each iteration of the optimization approach. In the conducted computational experiments using cross-validation, a noteworthy reduction of up to 66% in the total number of iterations required to achieve a high-quality treatment plan was observed. While acknowledging the preliminary nature of these results, they unequivocally underscore the promising potential of synergizing Reinforcement learning and Optimization methodologies in automating the intricate process of radiotherapy treatment planning. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Reinforcement learning

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Evidential Federated learning for Skin Lesion Image Classification 27th

Evidential Federated Learning for Skin Lesion Image Classif...

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27th international conference on Pattern Recognition, ICPR 2024

作者： Hendrix, Rutger Proietto Salanitri, Federica Spampinato, Concetto Palazzo, Simone Bagci, Ulas Department of Electrical Electronic and Computer Engineering University of Catania Catania Italy Machine and Hybrid Intelligence Lab Department of Radiology Northwestern University Chicago United States

ISBN: (纸本)9783031781094

We introduce FedEvPrompt, a federated learning approach that integrates principles of evidential deep learning, prompt tuning, and knowledge distillation for distributed skin lesion classification. FedEvPrompt leverages two sets of prompts: b-prompts (for low-level basic visual knowledge) and t-prompts (for task-specific knowledge) prepended to frozen pre-trained Vision Transformer (ViT) models trained in an evidential learning framework to maximize class evidences. Crucially, knowledge sharing across federation clients is achieved only through knowledge distillation on attention maps generated by the local ViT models, ensuring enhanced privacy preservation compared to traditional parameter or synthetic image sharing methodologies. FedEvPrompt is optimized within a round-based learning paradigm, where each round involves training local models followed by attention maps sharing with all federation clients. Experimental validation conducted in a real distributed setting, on the ISIC2019 dataset, demonstrates the superior performance of FedEvPrompt against baseline federated learning algorithms and knowledge distillation methods, without sharing model parameters. In conclusion, FedEvPrompt offers a promising approach for federated learning, effectively addressing challenges such as data heterogeneity, imbalance, privacy preservation, and knowledge sharing. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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