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Using AI for Detection, Prediction and Classification of Retinal Detachment

Using AI for Detection, Prediction and Classification of Ret...

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21st international conference on Informatics, Management, and Technology in Healthcare, ICIMTH 2023

作者： Zaky, Hesham Salem, Ahmed Alzubaidi, Mahmoud Shah, Hurmat Ali Alam, Tanvir Shah, Zubair Househ, Mowafa College of Science and Engineering Hamad Bin Khalifa University Doha Qatar

ISBN: (纸本)9781643684000

The current state of machine learning (ML) and deep learning (DL) algorithms used to detect, classify and predict the onset of retinal detachment (RD) were examined in this scoping review. This severe eye condition can cause vision loss if left untreated. By analyzing the medical imaging modalities such as fundus photography, AI could help to detect peripheral detachment at an earlier stage. We have searched five databases: PubMed, Google Scholar, ScienceDirect, Scopus, and IEEE. Two reviewers independently carried out the selection of the studies and their data extractions. 32 studies fulfilled our eligibility criteria from the 666 references collected. In particular, based on the performance metrics employed in these studies, this scoping review provides a general overview of emerging trends and practices concerning using ML and DL algorithms for detecting, classifying, and predicting RD. © 2023 The authors and IOS Press.

关键词： Ophthalmology

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Personalized Federated learning with Feature Alignment via Knowledge Distillation 21st

Personalized Federated Learning with Feature Alignment via...

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21st Pacific Rim international conference on Artificial Intelligence, PRICAI 2024

作者： Qi, Guangfei Qu, Zhihao Lyu, Shen-Huan Jia, Ninghui Ye, Baoliu Key Laboratory of Water Big Data Technology of Ministry of Water Resources College of Computer Science and Software Engineering Hohai University Nanjing China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)9789819601189

Personalized Federated learning (PFL) has gained significant attention for its ability to handle heterogeneous data effectively. Parameter decoupling is a typical approach to PFL. It decouples the model into a feature extractor and a classifier head, where the feature extractor is trained collaboratively to learn a common representation and the classifier head is personalized for local data. Since local training only learns personalized feature information and ignores global information, the generalization ability of the feature extractor is limited. To improve the performance of the local model, a feasible approach is to make the local feature extractor more generalized. However, prior work requires the transmission of additional feature data beyond the transmission of model parameters, which leads to privacy leakage and higher communication overhead. To address these shortcomings, we propose a PFL algorithm with feature alignment via knowledge distillation, named PFAKD. PFAKD enhances the training of local feature extractors by explicitly aligning each sample’s local features with global features, providing more detailed guidance. Meanwhile, it avoids additional communication overhead and the risk of privacy leakage. We conduct extensive experiments in heterogeneous data scenarios. PFAKD outperforms other state-of-the-art methods by up to 4.35% in terms of model accuracy. Our code is available at https://***/fei0829/PFAKD. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Learners' non-cognitive skills and behavioral patterns of programming: A sequential analysis 21

Learners' non-cognitive skills and behavioral patterns of pr...

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21st IEEE international conference on Advanced learning Technologies (ICALT)

作者： Zhao, Xi Zhang, Jingjing Li, Wenshuo Kahn, Ken Lu, Yu Winters, Niall Beijing Normal Univ Fac Educ Beijing Peoples R China Univ Oxford Depatment Educa Oxford England

ISBN: (纸本)9781665441063

The interest in artificial intelligence (AI) education is growing exponentially;nevertheless, how to learn about AI, particularly Natural Language Processing (NLP), has been a challenging problem for educators and researchers worldwide. This study used a graphical programming platform Snap! to facilitate learning by allowing learners to explore AI and its NLP techniques in class. data from 18,452 logged events were collected and Lag Sequential Analysis (LSA) was used to examine how learners behaved and learned sequentially. Non-cognitive factors were used to group learners as detailed and subtle behavior sequences that did not occur by chance could be uncovered. The results showed that five groups of learners, that is Passive Learners, Performers, Adaptive Learners, Interested Learners, and Dedicated Learners. They presented varied learning behavior patterns, which should be considered further in designing personalized and intelligent learning platforms to support AI education.

关键词： Graphical programming non-cognitive factors online learning behavioral sequences K-means Cluster Lag Sequential Analysis

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SMATE: Semi-Supervised Spatio-Temporal Representation learning on Multivariate Time Series 21

SMATE: Semi-Supervised Spatio-Temporal Representation Learni...

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21st IEEE international conference on data Mining (IEEE ICDM)

作者： Zuo, Jingwei Zeitouni, Karine Taher, Yehia Univ Paris Saclay Univ Versailles DAVID Lab Versailles France

ISBN: (纸本)9781665423984

learning from Multivariate Time Series (MTS) has attracted widespread attention in recent years. In particular, label shortage is a real challenge for the classification task on MTS, considering its complex dimensional and sequential data structure. Unlike self-training and positive unlabeled learning that rely on distance-based classifiers, in this paper, we propose SMATE, a novel semi-supervised model for learning the interpretable Spatio-Temporal representation from weakly labeled MTS. We validate empirically the learned representation on 30 public datasets from the UEA MTS archive. We compare it with 13 state-of-the-art baseline methods for fully supervised tasks and four baselines for semi-supervised tasks. The results show the reliability and efficiency of our proposed method.

关键词： Neural Networks Multivariate Time Series Semi-supervised learning Representation learning

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Anti-DDoS Attacks strategy of SDN data Plane with data Augmentation Based on P4 25

Anti-DDoS Attacks Strategy of SDN Data Plane with Data Augme...

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25th IEEE international conferences on High Performance Computing and Communications, 9th international conference on data Science and Systems, 21st IEEE international conference on Smart City and 9th IEEE international conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Zhang, Wenxiu Jing, Shan Zhao, Chuan School of Information Science and Engineering University of Jinan Jinan China Quan Cheng Laboratory Jinan China

ISBN: (纸本)9798350330014

As Software Defined Networking (SDN) becomes increasingly prevalent, the risk of Distributed Denial of Service (DDoS) attacks targeting SDN also grows. Specifically, the SDN data plane, consisting of simple forwarding devices, is more susceptible to DDoS attacks launched by malicious actors. These attacks can impose tremendous load on the SDN, potentially leading to its complete collapse in severe cases. The traditional SDN architecture based on the OpenFlow Protocol falls short in meeting the requirements for programming the data plane. To address this challenge and improve the flexibility of SDN architectures, the Programming Protocol-Independent Packet Processors (P4) has emerged. However, existing solutions, primarily based on statistical learning, Machine learning, and Deep learning, require further enhancement in terms of accuracy, complexity, and latency. In this study, we utilize the P4 language to implement a programmable data plane and propose the P4-CACNN model for detecting and defending against DDoS attacks on the data plane. Firstly, we employ an appropriate attention mechanism to enhance the processing of incoming traffic. Subsequently, the data is fed into a discriminator consisting of a Convolutional Neural Network (CNN) to classify whether it constitutes attack traffic. Experimental results demonstrate that our proposed model achieves a remarkable 98.99% accuracy in detecting DDoS attacks on the SDN data plane, while maintaining low latency. © 2023 IEEE.

关键词： Denial-of-service attack

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Medical Device Safety Training by Using Quick Response Codes and Extended Reality-Based Technologies

Medical Device Safety Training by Using Quick Response Codes...

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21st international conference on Informatics, Management, and Technology in Healthcare, ICIMTH 2023

作者： Saurio, Riika Jutila, Merja Tienari, Marjo Pirhonen, Jukka-Pekka Laukkavirta, Minna Ikonen, Tuija S. Ostrobothnia Wellbeing Services County Finnish Centre for Client and Patient Safety Vaasa Finland Lappeenranta-Lahti University of Technology LUT School of Engineering Science Lahti Finland Tampere University of applied sciences Tampere Finland Kanta-Häme Central Hospital Hämeenlinna Finland University of Turku Department of Public Health Turku Finland

ISBN: (纸本)9781643684000

This article describes the results of three different study settings where the use of QR codes and XR technologies were tested for medical device training among 132 social and health care professionals. data from three types of learning events and different contents of training indicated that the professionals considered these novel technologies both beneficial for learning and useful in their working environments for training safe use of medical devices. Based on the collected data, these technologies can be considered prospective options for medical device training. © 2023 The authors and IOS Press.

关键词： Health care

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Investigation of Inspection Methods in Acoustic Analysis Using Pronunciation Feature Extraction 21

Investigation of Inspection Methods in Acoustic Analysis Usi...

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21st international conference on Machine learning and Cybernetics, ICMLC 2022

作者： Yagi, Naomi Hata, Yutaka Sakai, Yoshitada Advanced Medical Engineering Research Institute University of Hyogo Himeji670-0836 Japan University of Hyogo Graduate School of Information Science Kobe Japan Kobe University Graduate School of Medicine Division of Rehabilitation Medicine Kobe650-0017 Japan

ISBN: (数字)9781665488327

ISBN: (纸本)9781665488327

Since the structure of speech is wide-ranging such as prosody, articulation, vocalization, and breathing, there is no screening test for speech disorder unlike the areas of aphasia and dysphagia. Speech intelligibility in speech-language pathology is evaluated by a Speech-Language-Hearing Therapist (st), however the evaluation time per person is long and the evaluation criteria are ambiguous. So, the evaluation results will differ depending on the st. Therefore, in this study, we proposed a system to easily inspect the normality of pronunciation by using 8 characteristics of data divided into single notes. As the results, this system enabled to identify whether the pronunciation is normal or abnormal with high accuracy of 93.3 %. © 2022 IEEE.

关键词： Audition

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Role of Machine learning in Climate Change Prediction and Mitigation

Role of Machine Learning in Climate Change Prediction and Mi...

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Machine learning and Autonomous Systems (ICMLAS), international conference on

作者： Nayan Jikar Yash Tale Abhay Tale Aditya Barhate Prateek Verma Aman Jikar Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Computer Science and Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Climate change is one of the biggest challenges of the 21st century, with profound environmental, economic, and social impacts. Machine learning (ML) has emerged as a transformative tool to address climate challenges through data-driven solutions, offering predictive capabilities for extreme weather events and enhancing the efficiency of clean-energy systems. This technology benefits the optimization of renewable energy generation, intelligent grid management, and resource utilization towards reducing greenhouse gas emissions. Moreover, ML promotes climate justice through targeted region-specific intervention and inclusive solution-making for the most vulnerable. Several technical and ethical challenges associated with data quality, scalability, and model generalization must still be overcome for ML to realize its potential. This paper reviews the role of ML in climate change prediction and mitigation discusses ongoing real-world applications and outlines the future research agenda necessary to address existing challenges. Integrating ML with emerging technologies like the Internet of Things (IoT) and explainable AI, alongside global collaboration and a focus on climate justice, will be essential in advancing effective climate action.

关键词： Climate change Renewable energy sources Ethics Prevention and mitigation Scalability data integrity Collaboration data models Internet of Things Meteorology

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Integrating Deep and Handcrafted Features for Enhanced Remote Sensing Image Classification 21

Integrating Deep and Handcrafted Features for Enhanced Remot...

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2024 IEEE/ACS 21st international conference on Computer Systems and Applications, AICCSA 2024

作者： Ahmed, Vian Abdulmajeed Jouini, Khaled Tuama, Amel Korbaa, Ouajdi University of Sousse MARS Research Lab LR17ES05 ISITCom Sousse Tunisia Northern Technical University Computer Engineering Techniques Department Mosul Iraq MARS Research Lab LR17ES05 ISITCom University of Sousse Sousse Tunisia

ISBN: (纸本)9798331518240

Satellite imagery supports critical applications such as land cover mapping, environmental monitoring, disaster assessment, and urban planning. Despite significant advancements, challenges in analyzing satellite imagery persist, primarily due to data variability, atmospheric conditions, and complex land cover patterns. Traditional handcrafted descriptors like Scale-Invariant Feature Transform (SIFT) and encoding techniques such as Bag-of-Visual-Words (BoVW) are effective but often fall short in capturing global context and spatial relationships due to their inherent local nature. The advent of deep learning (DL), propelled by ample data and computational resources, has markedly improved satellite image analysis. However, the reliance on extensive annotated data constrains the wider applicability of DL methods. This study harnesses the strengths of both deep and hand-crafted features to enhance the classification accuracy of remote sensing images. Specifically, we synergize SIFT descriptors with pretrained MobileNetV2 and VGG16 deep features. While SIFT excels in capturing local features essential for identifying specific image characteristics, pretrained DL models provide enriched representations with global context, spatial relationships, and hierarchical features. This integration aims to overcome the individual limitations of each method, enabling the model to effectively handle perturbations, scale variations, and diverse landscapes. Extensive evaluations on the EuroSAT dataset demonstrate that our approach outperforms, not only SIFT, VGG16, and MobileNetV2 when used separately, but also surpasses state-of-the-art remote sensing image classification approaches. Another salient advantage of our approach is its robust applicability in scenarios with limited labeled data - a prevalent challenge in remote sensing image classification. © 2024 IEEE.

关键词： Satellite imagery

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Model-Based Artificial Intelligence Architecture for Digitizing Handwritten Medication Error Reports 21

Model-Based Artificial Intelligence Architecture for Digitiz...

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2024 IEEE/ACS 21st international conference on Computer Systems and Applications, AICCSA 2024

作者： Brini, Mohamed Ayachi Touati, Hanae Thabet, Rafika Fontanili, Franck Cleostrate, Marie-Helene Cufi, Marie-Noelle Pruski, Marc Lamine, Elyes Toulouse University CGI IMT Mines Albi Albi France Sousse University ISITCom Albi France MARS Laboratory LR17RS05 Tunisia Centre Hospitalier Intercommunal Castres-Mazamet France Atout Majeur Concept Toulouse France Toulouse University ISIS Engineering INUC Castres France Toulouse University CGI IMT Mines Albi Castres France

ISBN: (纸本)9798331518240

Optical Character Recognition (OCR) is extremely useful in various sectors for exploring massive archived data. This technology enables the digitization of printed and handwritten texts that are frequently present in the medical field. For instance, medication error (ME) reports were previously and still in some healthcare facilities written manually, this has led to the accumulation of numerous handwritten data that are unfortunately challenging to exploit. Their digitization through OCR allows extracting important data from these documents and using them to populate the database to implement future analysis techniques to optimize the medication error management process. This paper presents a transformer-based handwritten recognition architecture that employs the Transformer-Based Optical Character Recognition (TrOCR) model combined with image segmentation techniques. Although the TrOCR model provided by Microsoft performs reasonably well in handwritten recognition, it is limited to English text because its pretrained version was trained exclusively on English samples. This limitation is problematic for us, as our task involves digitizing French medication dictation errors. Additionally, its limitation to processing single-line text images impairs its ability to recognize paragraphs. To address these limitations, we will fine-Tune the model on French handwritten data and integrate a single-line level segmentation technique, thereby overcoming these constraints. Therefore, the preliminary results from implementing our proposed architecture are promising for the digitization of medication error reports. © 2024 IEEE.

关键词： Deep learning

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