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A Comparison of Machine Learning Methods for best Accuracy COVID-19 Diagnosis Using Chest X-Ray Images 9

A Comparison of Machine Learning Methods for best Accuracy C...

9th IEEE International Conference on sciences of Electronics, Technologies of Information and Telecommunications, SETIT 2022

作者： Ahmed Ali, Hussein Hariri, Walid Smaoui Zghal, Nadia Ben Aissa, Dalenda University Tunis El-Manar Microwave Electronics Research Laboratory Faculty of Sciences of Tunis Tunis El-Manar Tunis Tunisia Badji MokhtarAnnaba University LABGED Laboratory Computer Science Department Annaba Algeria ENIS Sfax Tunisia

ISBN: (数字)9781728184425

ISBN: (纸本)9781728184425

Coronavirus (COVID-19) changed the view of people towards life in all the countries of the world in December 2019. The virus has made chaos that cannot be predicted. This problem requires using a variety of technologies to aid in the identification of COVID-19 patients and to control the disease spread. For suspected instances of COVID-19 disease, chest X-ray (CXR) imaging is a standard with fewer costs, but it does not need a COVID-19 examination approach without using technology to help for a suitable diagnosis. In response to this issue, a big dataset of CXR images was divided into four classes found on the website Kaggle. Dealing with large data of the images needs dataset reprocessing through choosing the optimal method for getting speed and best accuracy. Dataset reprocessing converts into gray level then adjust image intensity, resize and extract the best features then apply Machine Learning ML models. The use of different prediction models, ML algorithms, and their performances are calculated with evaluation on the dataset after reprocessing. Decision Tree (DT), Random Forest (RF), Stochastic Gradient Descent (SGD), Logistic Regression (LR), Gaussian Naive Bayes (GNB), and K-Nearest Neighbors (KNN) are models used to foretell the specialized who would be diagnosed with COVID-19 quickly by using CXR images classification. The KNN has revealed the best accuracy compared with the others such as GNB, DT, SGD, LR, and RF. Also, KNN has the best-weighted average for all parameters, which are precision, sensitivity, and F1-score compared with the other models. © 2022 IEEE.

关键词： COVID-19

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Computing the Domain of Attraction using Numerical Techniques 4

Computing the Domain of Attraction using Numerical Technique...

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4th International Conference on Applied Automation and Industrial Diagnostics, ICAAID 2022

作者： Jerbi, Houssem Dabbagui, Boudour Hamidi, Faical Aoun, Mohamad Bouazzi, Yassine Aoun, Sondess Ben University of Hail College of Engineering Department of Industrial Engineering Hail1234 Saudi Arabia University of Gabes Laboratory Modelisation Analyse et Commande des Systemes LR16ES22 Gabes Tunisia University of ha'Il College of Computer Science and Engineering Department of Computer Engineering Hail1234 Saudi Arabia

ISBN: (纸本)9781728189390

Stability analysis of controlled nonlinear systems is a problem of fundamental importance in system engineering. This paper elaborates an explicit numerical technique to maximize a quadratic Lyapunov function for the class of polynomial nonlinear models. Using the computed Lyapunov function an enlarged subsets of the basin of attraction of an asymptotically stable equilibrium can be computed in an iterative analytical way. We mainly use the Carleman linearization technique that converts a nonlinear autonomous system of finite dimension into an equivalent linear infinite dimension one. We implement the sampling technique as a numerical tool allowing the maximization of estimated regions of attraction. An example is given to demonstrate the efficiency of the proposed approach. The numerical study analysis of the designed scheme is led using the Matlab software environment. © 2022 IEEE.

关键词： Nonlinear systems

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ICERS:Intelligent Collaborative Emergency Response System 23

ICERS:Intelligent Collaborative Emergency Response System

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23rd International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2024

作者： Yang, Guoyin Zhang, Haotian Yao, Junfeng Cai, Chengyou Song, Jun Xu, Yuelin Deng, Bin School of Computer Engineer and Science Shanghai University Shanghai China The 32nd Research Institute of China Electronics Technology Group Corporation China CSSC Seago System Technology Co. Ltd. Shanghai200010 China Department of Geography Hong Kong Baptist University Hong Kong Science and Technology on Near-Surface Detection Laboratory Wuxi China

ISBN: (纸本)9781643685380

Advances in computer science and technology, coupled with the growing societal demand for enhanced safety, have paved the way for innovative smart home environments. While current smart home systems can send alarms and notify emergency contacts during a crisis, they do not provide timely and convenient on-site emergency assistance, potentially delaying critical aid and compromising user safety. This paper introduces the Intelligent Collaborative Emergency System (ICERS), which addresses this gap through three integrated subsystems: Fall Detection, Vital Signs Monitoring, and Central Dispatch. The Fall Detection module leverages camera video data to classify human positions and estimate potential falls. The Vital Signs Monitoring module employs millimeter-wave radar technology to provide non-contact, real-time health monitoring. The Central Dispatch system receives alert signals from on-edge devices and relays them to medical facilities and volunteer organizations for a coordinated emergency response. By utilizing multimodal data for collaborative efforts, the ICERS ensures that individuals receive timely notifications and coordinated emergency assistance during unforeseen situations. © 2024 IOS Press. All rights reserved.

关键词： Fall detection

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A new online field feature selection algorithm based on streaming data

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Journal of Ambient Intelligence and Humanized Computing 2024年第2期15卷 1365-1377页

作者： Zhang, Zhenjiang Song, Fuxing Zhang, Peng Chao, Han-Chieh Zhao, Yingsi School of Software Engineering Beijing Jiaotong University Beijing100044 China Department of Electronic and Information Engineering Key Laboratory of Communication and Information Systems Beijing Municipal Commission of Education Beijing Jiaotong University Beijing100044 China School of Information Science and Engineering Fujian University of Technology Fuzhou350118 China School of Mathematics and Computer Science Wuhan Polytechnic University Wuhan430023 China Department of Electrical Engineering National Dong Hwa University Hualien97401 Taiwan School of Economics and Management Beijing Jiaotong University Beijing100044 China

The rapid development of Internet technology derived out a massive network text data. Therefore, how to classify the massive text data efficiently has important theoretical significance and application value. In order to acquire accurate classification results, the process has been divided into two parts. In terms of text representation, this paper proposes an online field feature selection algorithm (OFFS algorithm) based on streaming data, which solves the problems of low efficiency and memory consumption of traditional feature selection algorithms. With improvements in the vector space model, the new algorithm can select the real-time feature of the data and quickly generate text vector. In the aspect of classifier design, an OFFS-BP neural network text classifier based on BP neutral network and OFFS algorithm is designed. It adapts to the distributed parallel computing, reduces the training time and balances the computation efficiency and classification accuracy. Finally based on the Spark platform, the OFFS-BP neural network classifier is implemented. The experimental results show that the OFFS-BP neural network classifier is more suitable for big data environment with less computation time and higher classification efficiency. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： Neural networks

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Joint Sparsification and Quantization for Wireless Federated Learning under Communication Constraints 24

Joint Sparsification and Quantization for Wireless Federated...

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24th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2023

作者： Su, Junshen Wang, Xijun Chen, Xiang Cheng, Ray-Guang Sun Yat-Sen University School of Electronics and Information Technology Guangzhou China The Chinese University of HongKong Guangdong Provincial Key Laboratory of Big Data Computing Shenzhen China National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei Taiwan

ISBN: (纸本)9781665496261

Communication efficiency is one of the key bottlenecks in Federated Learning (FL). Compression techniques, such as sparsification and quantization, are used to reduce communication overhead. However, joint designs of these techniques under communication constraints are not well-explored. This paper investigates the joint uplink compression problem in communication-constrained FL systems. We propose a Block-TopK sparsification scheme to reduce the proportion of bits used for locating entries of a sparsified vector. Considering the communication constraints, an optimization formulation is proposed to minimize the compression error. By solving the optimization problem, our joint compression method provides a better trade-off between sparsity budget and bit width. Numerical results demonstrate that our approach achieves 99.96% of baseline accuracy with only 1.56% of the baseline communication overhead when training ResNet-18 on CIFAR-10. © 2023 IEEE.

关键词： Budget control

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Gaussian Process-based Active Learning for Efficient Cardiovascular Disease Inference

Gaussian Process-based Active Learning for Efficient Cardiov...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Tassi, Stavroula C. Polyzos, Konstantinos D. Fotiadis, Dimitrios I. Sakellarios, Antonis I. Department of Material Science and Engineering Ioannina Greece University of Patras Department of Mechanical and Aeronautics Engineering Patras Greece University of Minnesota Department of Electrical and Computer Engineering MN United States Biomedical Reasearch Institute Ioannina Greece University of Patras Laboratory of Biomechanics and Biomedical Engineering Department of Mechanical and Aeronautics Engineering Patras Greece Ioannina Greece

ISBN: (纸本)9798350337488

Cardiovascular disease (CVD) poses a significant global health challenge, and accurate inference methods are vital for early detection and intervention. However, the quality of prediction relies heavily on the availability of labeled data, which are often limited in medical applications. To cope with the challenge of limited labeled data, we are the first to propose an active learning (AL) approach that leverages a weighted ensemble of Gaussian processes to effectively infer CVD by strategically selecting the few most informative data points to label. Through experiments conducted on the SMARTool dataset, we demonstrate the effectiveness of the advocated approach, achieving superior performance in CVD inference compared to baseline methods. Our findings highlight the potential impact of the proposed AL framework in CVD diagnosis and treatment clinical cases, particularly in scenarios where labeled data are scarce, due to data confidentiality concerns or high sampling costs. © 2023 IEEE.

关键词： CVD inference Ensemble of Gaussian Processes (EGPs) FFR index Gaussian Processes (GPs)

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Dr-Hydranet: Enhancing Diabetic Retinopathy Diagnosis by Integrating Hydra Attention and Removing Inefficient Heads

SSRN

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SSRN 2024年

作者： Ali, Muhammad Shafqat Azhar, Muhammad He, Yulin Wattoo, Waqas Ahmad Masood, Saba Ferzund, Javed Department of Computer Science Comsats University Islamabad Pakistan Department of Applied Data Science Hong Kong Shue Yan University Hong Kong Guangdong Laboratory of Artificial Intelligence and Digital Economy Shenzhen China

Diabetic Retinopathy (DR) is a serious complication of diabetes that can lead to blindness if not diagnosed and treated promptly. While conventional machine learning (ML) techniques and deep learning models, especially Convolutional Neural Networks (CNNs), have been applied to automated DR diagnosis, CNNs encounter challenges such as high computational costs and difficulties in capturing long-range pixel relationships. The Vision Transformer (ViT) presents a promising architecture for image classification but is hindered by inefficiencies and accuracy issues due to the absence of specialized local attention mechanisms. To address these problems, we propose a novel solution, DR-HydraNet, which integrates hydra attention and optimizes the ViT model by removing inefficient attention heads. This approach effectively balances local and global feature extraction, reducing computation time while enhancing diagnostic accuracy. Extensive experiments demonstrate that our model achieves an accuracy of 97\%, an F1 score of 91\%, precision of 93\%, and recall of 90\%, significantly outperforming state-of-the-art models. © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Distinctive Image Captioning via CLIP Guided Group Optimization 17th

Distinctive Image Captioning via CLIP Guided Group Optimiza...

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17th European Conference on computer Vision, ECCV 2022

作者： Zhang, Youyuan Wang, Jiuniu Wu, Hao Xu, Wenjia State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China McGill University Montreal Canada Department of Computer Science City University of Hong Kong Kowloon Tong Hong Kong South China University of Technology Guangzhou China

ISBN: (纸本)9783031250682

Image captioning models are usually trained according to human annotated ground-truth captions, which could generate accurate but generic captions. In this paper, we focus on generating distinctive captions that can distinguish the target image from other similar images. To evaluate the distinctiveness of captions, we introduce a series of metrics that use large-scale vision-language pre-training model CLIP to quantify the distinctiveness. To further improve the distinctiveness of captioning models, we propose a simple and effective training strategy that trains the model by comparing target image with similar image group and optimizing the group embedding gap. Extensive experiments are conducted on various baseline models to demonstrate the wide applicability of our strategy and the consistency of metric results with human evaluation. By comparing the performance of our best model with existing state-of-the-art models, we claim that our model achieves new state-of-the-art towards distinctiveness objective. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Embeddings

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LES-Talker: Fine-Grained Emotion Editing for Talking Head Generation in Linear Emotion Space

arXiv

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arXiv 2024年

作者： Feng, Guanwen Qian, Zhihao Li, Yunan Jin, Siyu Miao, Qiguang Pun, Chi-Man School of Computer Science and Technology Xidian University Xi’an710071 China Xi’an Key Laboratory of Big Data and Intelligent Vision Shaanxi Xi’an710071 China Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xidian University Xi’an710071 China Department of Computer and Information Science University of Macau 999078 China

While existing one-shot talking head generation models have achieved progress in coarse-grained emotion editing, there is still a lack of fine-grained emotion editing models with high interpretability. We argue that for an approach to be considered fine-grained, it needs to provide clear definitions and sufficiently detailed differentiation. We present LES-Talker, a novel one-shot talking head generation model with high interpretability, to achieve fine-grained emotion editing across emotion types, emotion levels, and facial units. We propose a Linear Emotion Space (LES) definition based on Facial Action Units to characterize emotion transformations as vector transformations. We design the Cross-Dimension Attention Net (CDAN) to deeply mine the correlation between LES representation and 3D model representation. Through mining multiple relationships across different feature and structure dimensions, we enable LES representation to guide the controllable deformation of 3D model. In order to adapt the multimodal data with deviations to the LES and enhance visual quality, we utilize specialized network design and training strategies. Experiments show that our method provides high visual quality along with multilevel and interpretable fine-grained emotion editing, outperforming mainstream methods. Project page: https://***/LES-Talker/ Copyright © 2024, The Authors. All rights reserved.

关键词： 3D modeling

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What’s Behind This Water Table Depth Forecasting? RISE Application for Spatial, Temporal, and Spatio-Temporal Explanations 1

What’s Behind This Water Table Depth Forecasting? RISE Appl...

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1st International Workshop on Artificial Intelligence for Climate Change, 12th Italian Workshop on Planning and Scheduling, 31st RCRA Workshop on Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion, and SPIRIT Workshop on Strategies, Prediction, Interaction, and Reasoning in Italy, AI4CC-IPS-RCRA-SPIRIT 2024

作者： Salis, Matteo Sartor, Gabriele Pellegrino, Marco Ferraris, Stefano Atto, Abdourrahmane M. Meo, Rosa Computer Science Department University of Turin Corso Svizzera 185 Torino Italy Interuniversity Department of Regional and Urban Studies and Planning Politecnico di Torino University of Turin Viale Pier Andrea Mattioli 39 Torino Italy LISTIC Laboratory Université Savoie Mont Blanc 5 chemin de bellevue Annecy-le-vieux74 940 France

Over the past few years, the effects of climate change have significantly increased, directing our attention to our environmental resources. One of the most critical resources is fresh water, whose availability has been endangered by even more frequent extreme events of precipitations and droughts. Deep Learning (DL) has revealed a useful tool for accurate hydrological forecasting, but its main drawback is its black-box nature. To face this issue eXplainable Artificial Intelligence (XAI) has come out. In this work, Randomized Input Sampling for Explanation (RISE) is applied to a regression spatio-temporal model trained to predict the Water Table Depth (WTD) collected by a sensor in Vottignasco, in the northwest of Italy. An investigation of the model behaviour over spatial, temporal and spatio-temporal dimensions has been conducted formalizing S-RISE, T-RISE, and ST-RISE respectively. Results suggest the usefulness of a spatio-temporal approach (ST-RISE) and give interesting intuitions on the events that occurred over the area. © 2024 Copyright for this paper by its authors.

关键词： Explainable AI Model agnostic algorithms RISE Spatio-temporal explanations

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