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Quadratic Weighted Kappa Score Exploration in Diabetic Retinopathy Severity Classification Using EfficientNet 1

Quadratic Weighted Kappa Score Exploration in Diabetic Retin...

引用

1st Zimbabwe Conference of Information and Communication Technologies, ZCICT 2022

作者： Chivinge, Lincoln Nyandoro, Leslie Kudzai Zvarevashe, Kudakwashe University of Zimbabwe Faculty of Computer Engineering Informatics and Communication Analytics and Informatics Department Harare Zimbabwe

ISBN: (纸本)9781665475761

Diabetic Retinopathy (DR), chronic progressive disease of the eye, may give rise to permanent sight loss. Clinicians use fundus pictures to check if DR is present and rely on physicians to diagnose the stage or severity by visual inspection of the images. In relying on a clinician's subjective prognosis, this is deemed a procedure that takes a lot of time and susceptible to misjudgements. In discovering DR, poor Quadratic Weighted Kappa (QWK) scores have resulted from poor quality of pictures and imbalanced distribution of classes. Even though studies have shown high accuracy, sensitivity, specificity and ROC metrics, their limitation is that they do not consider the level of disparity across the classified labels. The QWK score demonstrates that even if an algorithm presents high accuracy, it is still not best fit to classify DR into its 5 classes. Many researchers have tried fine-tuning the neural network to create noise-resistant deep learning and recorded high accuracy and sensitivity but low QWK scores. The problem with the other methods is mainly pre-processing of the images and model building patterns. Most of the studied literature lacks the image augmentation step which might lead to an erroneous result. This research aims to create an algorithm from deep learning models with a data augmentation step and demonstrate how important it is for attaining better QWK scores for all stages of diabetic retinopathy. The model met study objectives and obtained an accuracy of 93% and a QWK score of 0.961. The outcomes show that the method can make accurate predictions without need for human feature extraction and that it may be used as an early DR diagnostic and staging screening tool. © 2022 IEEE.

关键词： Diagnosis

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Generating Chest Radiology Report Findings Using a Multimodal Method 28th

Generating Chest Radiology Report Findings Using a Multimod...

引用

28th Annual Conference on Medical Image Understanding and Analysis, MIUA 2024

作者： Wang, Chenyu Janjic, Vladimir McKenna, Stephen Computer Science and Informatics School of Science and Engineering University of Dundee DundeeDD1 4HN United Kingdom

ISBN: (纸本)9783031669545

Automatic report generation from chest x-ray imaging (CXR) could potentially alleviate the workload of radiologists and improve clinical efficacy. We introduce a multimodal approach, that integrates radiology images with text describing patients’ indications, to generate the findings section in radiology reports. We instantiate this approach by building on two existing methods, R2Gen and CvT2DistilGPT2. We report experiments on two public datasets, MIMIC-CXR and IU X-ray, using evaluation metrics for natural language generation and clinical efficacy assessment. Results show that improvements across all metrics are obtained through the incorporation of indications text. For example, we obtain 35% and 8% increases in BLEU-4 and F1 scores, respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Radiology

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A computer Vision System to Classify the Quality of Central Sulawesi Cocoa Beans Using Convolutional Neural Networks (CNN) 3

A Computer Vision System to Classify the Quality of Central ...

引用

3rd International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2023

作者： Angreni, Dwi Shinta Anshori, Yusuf Wirdayanti Arifin, Suci Ramadhani Humaira, Fitrah Maharani Universitas Tadulako Faculty of Engineering Departement of Information Technology Palu Indonesia Universitas Dipa Makassar Departement of Informatics Engineering Makassar Indonesia Politeknik Elektronika Negeri Surabaya Departemen of Informatics and Computer Engineering Surabaya Indonesia

ISBN: (纸本)9798350394559

The cocoa industry serves as a vital economic pillar, holding a pivotal role in the daily lives of farmers in Central Sulawesi. However, a prevailing challenge for cocoa cultivators in this region resides in the inherently subjective nature of cocoa bean quality assessment, often necessitating the involvement of a substantial human workforce. Addressing this challenge requires the implementation of computer vision technology capable of autonomously grading cocoa bean quality. Such a system not only expedites the evaluation process but also enhances the precision and consistency in determining the quality of the beans. The study employed Convolutional Neural Networks (CNN) methodology to analyze images of cocoa beans sourced from Central Sulawesi farmers. The research demonstrated a remarkable accuracy of 94.66%, accompanied by a minimal loss of 0.1195. The model demonstrates strong performance on both training and validation sets, indicating effective learning of relevant patterns. Consistent accuracy and loss trends across epochs signify stable training without notable overfitting or underfitting. The minimal gap between training and validation metrics reflects the model's effective generalization to unseen data. This technological integration promises to revolutionize cacao quality assessment, offering a sophisticated and efficient solution for the benefit of Central Sulawesi's cacao farming community. © 2023 IEEE.

关键词： computer vision

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Application of Machine Learning Models to Predict e-Learning Engagement Using EEG Data 20

Application of Machine Learning Models to Predict e-Learning...

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20th International Conference on Web Information Systems and Technologies, WEBIST 2024

作者： Dritsas, Elias Trigka, Maria Mylonas, Phivos Industrial Systems Institute Athena Research and Innovation Center Patras Greece Department of Informatics and Computer Engineering University of West Attica Greece

ISBN: (纸本)9789897587184

The rapid evolution of e-learning platforms necessitates the development of innovative methods to enhance learner engagement. This study leverages machine learning (ML) techniques and models to predict e-learning engagement with the aid of Electroencephalography (EEG). Various ML models, including Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Machine (GBM), and Neural Networks (NN), were applied to a dataset comprising EEG signals collected during e-learning sessions. Among these models, NN demonstrated the highest accuracy (90%), with precision and F1-score of 88%, a recall of 89%, and an Area Under the Curve (AUC) of 0.92 for predicting engagement levels. The results underscore the potential of EEG-based analysis combined with advanced ML techniques to optimize e-learning environments by accurately monitoring and responding to learner engagement. Copyright © 2024 by SCITEPRESS.

关键词： Adversarial machine learning

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YOLOv8 vs. YOLOv9: Evaluating Object Detection Algorithms for Marine Waste Recognition 1

YOLOv8 vs. YOLOv9: Evaluating Object Detection Algorithms fo...

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1st IEEE International Symposium on Consumer Technology, ISCT 2024

作者： Wajdi, Achmad Zahir Wibowo, Prasetyo Sasaki, Shiori Politeknik Elektronika Negeri Surabaya Department of Informatics and Computer Engineering Surabaya Indonesia Faculty of Data Science Musashino University Tokyo Japan

ISBN: (纸本)9798350365191

Marine debris, the majority of which is composed of plastic (61 % to 87 %), is a significant environmental issue facing the world. Between 4.8 million and 12.7 million metric tons of plastic are thought to have entered the ocean in 2010 alone, increasing the estimated total amount of particles in the water to at least 5 trillion. Global plastic production, which reached 348 million tons in 2017, is expected to double in the next 20 years. To overcome this challenge, effective waste detection methods are needed, one of which is by utilizing computer vision, specifically the You Only Look Once (YOLO) algorithm. YOLOv9, as the latest version, offers improved object detection performance compared to its predecessor, YOLOv8. This research aims to evaluate the performance and efficiency of YOLOv9 and YOLOv8 in detecting marine waste. Using the Trash Annotations in Context dataset (TACO), We train both models and conduct tests to evaluate their accuracy and efficiency. Experimental results show that YOLOv9 has higher precision at 45% but lower recall at 6%, compared to YOLOv8, which has a precision of 33 % and a recall of 15 %. Overall, YOLOv8 shows better performance with higher mAP50 at 13% and mAP50-95 values at 10%, compared to YOLOv9, which has mAP50 at 4% and mAP50-95 values at 3%. © 2024 IEEE.

关键词： Marine Waste Neural Net-work Object Detection TACO Dataset YOLO

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Exploring Energy Culture and Efficiency Awareness: Insights from Private and Public Sectors in Romania 3

Exploring Energy Culture and Efficiency Awareness: Insights ...

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3rd International Conference on Energy Transition in the Mediterranean Area, SyNERGY MED 2024

作者： Muresan, Claudia Stet, Denisa Cirstea, Stefan Czumbil, Levente Polycarpou, Alexis Micu, Dan D. Technical University of Cluj-Napoca Energy Transition Research Center Cluj-Napoca Romania Frederick University Computer Engineering and Informatics Department of Electrical Engineering Nicosia Cyprus

ISBN: (纸本)9798350375923

This paper focuses on the energy transition as a social transition and highlights the importance of understanding individual and collective behaviors, aspirations and perceptions regarding energy consumption. The conceptual framework of energy cultures serves as a basic tool for investigating energy behavior, facilitating a deeper understanding of the factors influencing energy consumption. The study, carried out within the ENERGEIA project, focuses on assessing energy culture and energy efficiency awareness in both private entities and public institutions in Romania. Using a comprehensive questionnaire tool, the research aims to uncover the main challenges and barriers faced by organizations in implementing energy efficiency measures. The paper presents a quantitative analysis of the survey data aiming to gather information about the energy culture of companies and public bodies to identify common opportunities for action to improve energy culture and awareness levels. © 2024 IEEE.

关键词： awareness energy culture energy culture framework energy efficiency sustainability

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Detecting Human Walking Direction Using Wi-Fi Signals 1

引用

International Conference on Applied Soft Computing and Communication Networks, ACN 2023

作者： Ali, Hanan Awad Hassan Seytnazarov, Shinnazar Faculty of Computer Science and Engineering Innopolis University Innopolis420500 Russia Faculty of Computers and Informatics Suez Canal University Ismailia Egypt

ISBN: (数字)9789819720040

ISBN: (纸本)9789819720033

Using wireless signals, it is possible to map some patterns in the received signal to human activities in the vicinity of the wireless receiver. One of the potential applications of this is to detect the direction of human movement in a user device-free manner. To achieve it, we propose to use the channel state information (CSI) of received Wi-Fi signals and process the raw CSI using calibration, Hampel filter, and discrete wavelet transform to minimize the noise and retrieve the useful features of phase and amplitude components of the CSI. Then machine learning algorithms are applied to processed CSI to classify the direction of human walk. Our experimental study with off-the-shelf commodity Wi-Fi hardware and diverse users showed that the proposed system can produce 92.9%, 95.1%, and 89% accuracy for data from two different environments, combined data, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Machine learning

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Atomic Lifecycle for Cyber Threat Intelligence 17

Atomic Lifecycle for Cyber Threat Intelligence

引用

17th International Conference on Information Security and Cryptology, ISCTurkiye 2024

作者： Arikan, Suleyman Muhammed Kocak, Aynur Alkan, Mustafa Computer Forensics Institute of Informatics Gazi University Ankara Turkey Electrical and Electronics Engineering Faculty of Technology Gazi University Ankara Turkey

ISBN: (纸本)9798331533649

The utilisation of cyber threat intelligence provides a multitude of advantages in the assurance of security. A growing body of research is being published on the subject of cyber threat intelligence. However, the precise sequence of steps involved in the cyber threat intelligence life cycle remains unclear. The life cycle presented in each study is not consistent. Moreover, the transition from data to intelligence throughout the life cycle is not sufficiently delineated. The intelligence produced in many studies can be of different types. In our study, all existing lifecycles were examined and their naming approaches were analysed. The objective was to present a cyber threat intelligence life cycle that is as atomic as possible, with each step clearly expressed. This should facilitate a more rigorous comparison of studies in the existing literature on cyber threat intelligence. Furthermore, the life cycle can be utilised in new cyber threat intelligence studies. Researchers who aspire to implement best practices in their study are able to run more effective processes by following the life cycle. © 2024 IEEE.

关键词： Phishing

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Non-stationary Domain Generalization: Theory and Algorithm 40

Non-stationary Domain Generalization: Theory and Algorithm

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40th Conference on Uncertainty in Artificial Intelligence, UAI 2024

作者： Pham, Thai-Hoang Zhang, Xueru Zhang, Ping Department of Computer Science and Engineering The Ohio State University United States Department of Biomedical Informatics The Ohio State University United States

Although recent advances in machine learning have shown its success to learn from independent and identically distributed (IID) data, it is vulnerable to out-of-distribution (OOD) data in an open world. Domain generalization (DG) deals with such an issue and it aims to learn a model from multiple source domains that can be generalized to unseen target domains. Existing studies on DG have largely focused on stationary settings with homogeneous source domains. However, in many applications, domains may evolve along a specific direction (e.g., time, space). Without accounting for such non-stationary patterns, models trained with existing methods may fail to generalize on OOD data. In this paper, we study domain generalization in non-stationary environment. We first examine the impact of environmental non-stationarity on model performance and establish the theoretical upper bounds for the model error at target domains. Then, we propose a novel algorithm based on adaptive invariant representation learning, which leverages the non-stationary pattern to train a model that attains good performance on target domains. Experiments on both synthetic and real data validate the proposed algorithm. © 2024 Proceedings of Machine Learning Research. All rights reserved.

关键词： Adversarial machine learning

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Driver drowsiness detection using Convolutional Neural Networks-inspired features and Principal component analysis with K-Nearest Neighbors 1

Driver drowsiness detection using Convolutional Neural Netwo...

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1st Zimbabwe Conference of Information and Communication Technologies, ZCICT 2022

作者： Panganai, Marvelous Alexander Nyandoro, Leslie Kudzai Zvarevashe, Kudakwashe University of Zimbabwe Faculty of Computer Engineering Informatics and Communication Analytics and Informatics Department Harare Zimbabwe

ISBN: (纸本)9781665475761

Drowsiness and fatigue are the major reasons for triggering serious and severe road crashes in Zimbabwe and the whole world at large. The developments in technology in recent years brought support and backing to drivers using intelligent automobile systems. Several research studies used datasets of a single driver for training and testing. Some as well used mainly day time images for training and testing the models. Therefore, fatigue and drowsiness is a key possible field of study to prevent numerous number of sleep induced road crashes. In this paper, two methods of feature extraction were proposed which are the MLP (Multilayer - Perceptron), the CNN (Convolutional Neural Network). The PCA (Principal Component Analysis) method was used for dimensionality reduction. Based on these methods, five classifiers where used to detect drowsiness on the driver. The five classifiers used where the LDA, XGBoost, LR, Decision Tree and the K-Nearest neighbors. Experiments were done in order to examine the capacity and usefulness of the approaches contrasted with other techniques. Experimental outcomes demonstrate that the feature extraction technique of CNN provided high accuracy on the five classifiers. The KNN was the average best classifier with a 100% accuracy. Experimental results indicated that the PCA improved the classifiers. This study delivers important and significant answers in practice to curb motor vehicle crashes due to drowsiness. © 2022 IEEE.

关键词： Feature extraction

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