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共 71769 条记录，以下是551-560 订阅

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Detection of Primary Open Angle Glaucoma Based on Deep CNN Using Fundus Images 26

Detection of Primary Open Angle Glaucoma Based on Deep CNN U...

引用

26th International Conference on computer and Information Technology, ICCIT 2023

作者： Ratul, Md. Toyaha Rahman Afroge, Shyla Peyal, Hasibul Islam Zafrin, Dia Ahmed, Kazi Fardeen Abdal, Md. Nazmul Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Bangladesh Uttara University Dept. of Computer Science & Engineering Bangladesh Khulna University Dept. of Computer Science & Engineering Discipline Bangladesh

ISBN: (纸本)9798350359015

The early and accurate diagnosis of glaucoma, a primary cause of permanent blindness, is critical for efficient treatment and prevention of vision loss. Although the exact causes of glaucoma are not yet fully understood, it is thought to be a result of several factors, including raised pressure inside the eye and decreased blood supply to the optic nerve. We have developed a convolutional neural network model for accurate detection of glaucoma. Methods based on deep learning have been effective at classifying diseases in retinal fundus images, facilitating in the evaluation of the growing number of images. The goal of this work is to create and train a unique deep CNN model that makes use of the connections between related eye-fundus tasks and metrics used to identify glaucoma. We have meticulously selected two distinct datasets to underpin this research endeavor: the ACRIMA dataset and the LAG dataset. Notably, our model attains a remarkable accuracy score of 99.29% on the ACRIMA dataset and an equally commendable accuracy score of 97.22% on the LAG dataset. This performance eclipses that of the majority of contemporary deep CNN models, underscoring the prowess and sophistication of our approach. © 2023 IEEE.

关键词： CNN deep learning fundus image glaucoma image classification

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SECURITY INTEGRATION IN AGILE METHODOLOGY

SECURITY INTEGRATION IN AGILE METHODOLOGY

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2024 IEEE SmartBlock4Africa, SmartBlock4Africa 2024

作者： Shokunbi, Opeyemi Uche, Obinna Akinwunmi, Damilare Akinwumi, Hannah Awodele, Oludele Ayankoya, Folasade Babcock University Dept. of Computer Science Ilishan-Remo Nigeria

ISBN: (纸本)9798350376838

This systematic review explores the integration of security practices into Agile software development. Through a comprehensive analysis of academic papers, industry reports, and case studies, the review identifies key challenges, solutions, and best practices for enhancing security within Agile methodologies. Key findings reveal the importance of incorporating security activities at all stages of Agile processes, providing a framework for achieving secure software development. The review concludes with actionable recommendations, including the early involvement of security experts, continuous integration of security practices, and fostering a security-centric culture within Agile teams. © 2024 IEEE.

关键词： Agile methodology Agile security practices Risk management Secure coding practices Secure software development Security integration Threat modeling Vulnerability management

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Machine Learning Techniques in Epileptic Seizure Detection: A Comprehensive Review 15

Machine Learning Techniques in Epileptic Seizure Detection: ...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Thakare, Vishal Ranawat, Rekha Sage University Dept. of computer Science and Engineering Indore India

ISBN: (纸本)9798350370249

The neurological disorder known as epilepsy is defined by its recurrent, spontaneous episodes of seizures impacting millions of people globally. The timely and accurate detection of epileptic seizures is necessary for a successful administration and ensuring patient safety. In recent times, techniques using machine learning have emerged as promising solutions to automate the detection of seizures. This review paper presents a thorough overview of the latest advancements in the identification of epileptic seizures through the application of machine learning, encompassing data sources, extraction of features, application of classification algorithms, use of performance evaluation metrics, addressing various challenges, and future directions. In addition, we put forth a novel methodology in order to enhance seizure detection accuracy by integrating the models of deep learning. Our approach uses multiple modalities for seizure prediction with cross-model training to mitigate data scarcity. Through a rigorous analysis of existing literature and our proposed methodology, this paper provides insightful information for practitioners and researchers looking into the detection of epileptic seizures. © 2024 IEEE.

关键词： deep learning Epilepsy epileptic seizures Machine learning neurological Seizures

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Enhanced Level Brain Tumor Identification Using CNN, VGG16 and ResNet Models 2

Enhanced Level Brain Tumor Identification Using CNN, VGG16 a...

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2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Sambandam, Rakoth Kandan Jyoshna, Mallela Christ University Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350383652

The comprehension of brain growths is significantly improved through the identification and categorization of these disorders. Still, their discovery is relatively grueling due to their variability in terms of position, shape, and size. Fortunately, deep literacy has revolutionized the field and significantly improved recognition, prediction, and opinion in various healthcare areas, including brain excrescences. The main goal of this study is to thoroughly review exploration that utilizes CNN, VGG16, and RESNET infrastructures to classify brain excrescences using MRI images. The performance of these models varied significantly, with CNN, VGG16, and RESNET achieving an emotional delicacy of 99.6. Additionally, ResNet and VGG16 achieved rigor of 92.4 and 89.7 independently. Likewise, the visualization of the decision-making processes of these models has provided valuable insight into the features they prioritize. By incorporating these models into their practice, healthcare professionals have the opportunity to enhance their individual capabilities, eventually leading to improved patient outcomes. © 2024 IEEE.

关键词： Deep learning

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FedAssist: Federated Learning in AI-Powered Prosthetics for Sustainable and Collaborative Learning 46

FedAssist: Federated Learning in AI-Powered Prosthetics for ...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Lee, Hunmin Jiang, Ming Zhao, Qi University of Minnesota Dept. of Computer Science Minneapolis United States

ISBN: (纸本)9798350371499

This paper explores the integration of federated learning in developing deep learning-powered surface electromyography decoding methods for AI-controlled prosthetics. Our proposed FL framework, FedAssist, aims to preserve data ownership while fostering decentralized collaborative modeling. Specifically, it focuses on mitigating the non-independent and identically distributed (non-IID) nature of sEMG datasets. Through collaborative local-level and global-level warm-start strategies, FedAssist achieves superior performance in non-IID scenarios compared to conventional learning paradigms. This research contributes to advancing decentralized machine learning approaches in the context of sEMG, with potential applications to improve prosthetic precision and rehabilitation effectiveness. © 2024 IEEE.

关键词： Federated learning

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EMGCipher: Decoding Electromyography for Upper-limb Gesture Classification with Explainable AI for Resource Optimization 46

EMGCipher: Decoding Electromyography for Upper-limb Gesture ...

引用

46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Lee, Hunmin Jiang, Ming Zhao, Qi University of Minnesota Dept. of Computer Science Minneapolis United States

ISBN: (纸本)9798350371499

Assistive limb devices often employ surface electromyography (sEMG) and deep learning (DL) models for gesture classification. While DL models effectively classify diverse upper-limb gestures, their decision-making mechanisms often lack transparency. To address this, we introduce EMGCipher, an interpretable DL framework for upper-limb gesture classification using sEMG. It aims to bridge the gap between interpretability and performance by combining low-level sEMG feature representations with DL model-derived knowledge, quantitatively assessing the probabilistic significance of input sensors and features in gesture classification. Experiments on the Ninapro DB5 dataset demonstrate EMGCipher's effectiveness in sensor-wise and feature-wise interpretation, demonstrating its potential to optimize the usage of sensors and features for improved gesture classification performance and efficiency. © 2024 IEEE.

关键词： Electromyography

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From EHR to Machine Learning: A Preliminary Report on an Ingestion Pipeline Based on JSON-LD

From EHR to Machine Learning: A Preliminary Report on an Ing...

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34th Medical Informatics Europe Conference, MIE 2024

作者： Baroni, Giulia Lucrezia della Mea, Vincenzo Foresti, Gian Luca Dept. of Mathematics Computer Science and Physics University of Udine Italy

In this paper, we present the preliminary experiments for the development of an ingestion mechanism to move data from Electronic Health Records to machine learning processes, based on the concept of Linked data and th... 详细信息

ISBN: (纸本)9781643685335

关键词： Adversarial machine learning

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Explainable AI for Enhanced Intrusion Detection Systems 3

Explainable AI for Enhanced Intrusion Detection Systems

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Ananya, G. Siri, N. Kodipalli, Ashwini Rao, Trupthi Global Academy of Technology Dept. of Computer Science and Engineering Karnataka Bengaluru India Global Academy of Technology Dept. of Electronic and Communication Engineering Karnataka Bengaluru India Global Academy of Technology Dept. of Artificial Intelligence & Data Science Karnataka Bengaluru India

ISBN: (纸本)9798350354171

As everything is turning digital there is a hike in the invasion of digital space, because of which advanced tools are needed. One such tool is the Intrusion Detection System (IDS). In this work, we have tried to detect and classify suspicious connections accurately. This is done by incorporating advanced computing into Intrusion Detection Systems. To achieve this, we have used the Coburg Intrusion Detection dataset. To achieve this, we used the Coburg intrusion detection dataset. We investigate the effectiveness of Single Classifiers and Bagging and Boosting in this context. We proposed a four-stage Ensemble model to classify the connections using its attributes. The proposed model uses four Single Classification models: Logistic Regression, KNN, Decision Tree, and SVM. Before applying the algorithms, the data was Preprocessed. Later, the models were hyper-tuned to increase the accuracy. The results showed that Random Forest outperforms other single classifiers and machine learning algorithms, with a mean accuracy of 99.99%, a precision of 99.99%, and an F1-score of 99.99%. Furthermore, as models are usually hard to understand by using Explainable AI we can increase the transparency of the model. Explainable AI techniques are used to make the advanced computing models reliable and more understandable. This study aims to develop advanced and dependable intrusion detection systems. © 2024 IEEE.

关键词： Network intrusion

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Detection of Cardiovascular Disease from ECG Image Using Optimized Weighted Average Ensemble Machine Learning Technique 26

Detection of Cardiovascular Disease from ECG Image Using Opt...

引用

26th International Conference on computer and Information Technology, ICCIT 2023

作者： Hasan, Md. Nahid Hossain, Md. Ali Akash, Ahsan Sadek Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Bangladesh Pabna University of Science & Technology Dept. of Computer Science & Engineering Bangladesh

ISBN: (纸本)9798350359015

cardiovascular diseases (CVD) stand as a prominent global cause of death in worldwide. Timely identification and intervention are paramount in dealing with heart disease, as its failure to detect and treated promptly can lead to life-threatening outcomes. The Electrocardiogram (ECG) Signals are mainly used as diagnostic tools that routinely assess the electrical and muscular function of the heart. Until recently, ECG records were predominantly maintained in paper form;thus, manually examining and re-examining the ECG paper records are often proved time-consuming and laborious tasks. Therefore, this research work utilizes, the power of machine learning and deep learning in predicting four different cardiac abnormalities named Abnormal Heartbeats (AHB), History of Myocardial Infarction (HMI), Myocardial Infarction (MI), and Normal Heartbeats (NHB) from paper-based ECG images. Different image pre-processing methods have been employed to improve the quality of individual ECG images and a novel lightweight deep neural model is proposed for extracting prominent features from each ECG images. Finally, we used an optimized weighted average ensemble technique for the final prediction. The grid search technique is used to find the optimal parameters and weights of each machine-learning model. Our proposed model shows state-of-the-art results in accuracy, precision, recall, and f1-score of 99.29%, outperforming the existing work. The model has attained an Area Under Curve (AUC) score of 99.96%. © 2023 IEEE.

关键词： Cardiovascular disease convolution neural network (CNN) deep learning ECG image feature extraction image classification machine learning weighted average ensemble

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An Effective Deep Learning Approach for the Estimation of Proton Energy by Using Artificial Neural Network 21

An Effective Deep Learning Approach for the Estimation of Pr...

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21st International Multi-Conference on Systems, Signals and Devices, SSD 2024

作者： Al-Mahdawi, Manar Kareem, Aythem Khairi Nafea, Ahmed Adil Shaban, Ali Muwafaq Aliesawi, Salah A. S. Al-Ani, Mohammed M College of Science AL-Nahrain University Baghdad Dept. Solid State Physics Iraq Ministry of Education Dept. of Heet Education General Anbar Heet31007 Iraq College of Computer Science and IT University of Anbar Ramadi Dept. Artificail Inteligence Iraq College of Computer Science and IT University of Anbar Ramadi Dept. Information System Iraq College of Computer Science and Information Technology University of Anbar Ramadi Dept. Computer Science Iraq Selangor Bangi Malaysia

ISBN: (纸本)9798350374131

The prediction of proton energy shows a key part in various scientific and technological studies including particle physics, medical imaging, and radiation therapy. In the last years the regression techniques study a lot trying to discover the difficult relationships among proton properties energy, needing to be studying of additional advanced techniques. This study proposed Artificial Neural Networks (ANN) as a tool for predicting types of proton energy (Max, Total and A vg). This paper proposed a comprehensive methodology for developing an ANN model for proton energy predection, covering data preparation, model architecture design, evaluation, and prediction. This proposed can ability to learn difficult models and non-linear relationships from large datasets, making them well-suited for this task. The ANN proposed achieved the highest R2 on the Total of proton energy predation of 0.96 % while the best mean_squared_error on 0.03668 for testing dataset. The results displayed the efficiency and accuracy of the proposed ANN model in predicted proton energy. The findings highlight the ability of ANNs as a tool for proton energy prediction. © 2024 IEEE.

关键词： Forecasting

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