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Automatic detection of Covid-19 based on lung CT images using Deep Convolutional Neural Networks (CNN) 1

Automatic detection of Covid-19 based on lung CT images usin...

引用

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

作者： Mahachi, Shawn Zvarevashe, Kudakwashe Nyandoro, Leslie Kudzai University of Zimbabwe Faculty of Computer Engineering Informatics and Communications Analytics and Informatics Department Harare Zimbabwe

ISBN: (纸本)9781665475761

In recent years, the COVID-19 pandemic has spread all over the world. Due to its rapid transmission, techniques that automatically detect COVID-19 infections and distinguish it from other forms of pneumonia are crucial. The scientific community has embarked on finding solutions to quick detection of COVID-19 through implementation of deep learning(DL) techniques that can diagnose COVID-19 using computed tomography (CT) lung scans. The use of CT images has been widely accepted in medical imaging and it is a pertinent screening tool due to its higher sensitivity in recognizing early pneumonic changes. Also, most developed DL models developed have been end-to-end from feature extraction to categorization of the COVID19 infected images. The proposed model results showed high accuracy rates on both training and testing of the model in COVID-19 classification. A customised ResNet-50 architecture has the best results in classifying the images and achieved state of art accuracy of 97% on training and testing using the COVID dataset with 200 epochs. This work presented a computationally efficient and highly accurate model for multi-class classification of normal and infected individuals. The model can help in effective early screening of COVID-19 cases hence reducing the burden on healthcare systems. © 2022 IEEE.

关键词： COVID-19

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Smart Victims Detection in Natural Disaster using Deep Learning 24

Smart Victims Detection in Natural Disaster using Deep Learn...

引用

24th International Electronics Symposium, IES 2022

作者： Adamu Islam Mashuri, M. Hadi, Moch Zen Samsono Sudibyo, Rahardhita Widyatra Kristalina, Prima Pratiarso, Aries Department of Informatics and Computer Engineering Electronic Engineering Jawa Timur Surabaya Indonesia

ISBN: (数字)9781665489713

ISBN: (纸本)9781665489713

Nowadays, artificial intelligence has been widely implemented in agriculture, animal husbandry, education, and many more. The most widely developed model at this time is the Deep Learning model. To get maximum results, the best Deep Learning architecture is needed. In this paper, we will compare methods for detecting victims of natural disasters based on the Deep Learning model. The models that we will compare are the You Only Look Once (YOLO) algorithm, namely YOLOv5, and Convolutional Neural Network (CNN) with the MobileNet model and the VGG-16 model. We train each algorithm through a natural disaster victim training data set and analyze performance to determine what model is the most optimal. To identify the types of victims of natural disasters. From our results, the MobileNet model is superior to other models with 98% accuracy. © 2022 IEEE.

关键词： Agriculture

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Late Breaking Results: Language-level QoR modeling for High-Level Synthesis 24

Late Breaking Results: Language-level QoR modeling for High-...

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61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Masouros, Dimosthenis Ferikoglou, Aggelos Zervakis, Georgios Xydis, Sotirios Soudris, Dimitrios Microprocessors and Digital Systems Laboratory Ece National Technical University of Athens Greece Department of Computer Engineering and Informatics University of Patras Greece

ISBN: (纸本)9798400706011

This paper proposes a language-level modeling approach for HighLevel Synthesis based on the state-of-the-art Transformer architecture. Our approach estimates the performance and required resources of HLS applications directly from the source code when different synthesis directives, in terms of HLS #pragmas, are applied. Results show that the proposed architecture achieves 96.02% accuracy for predicting the feasibility class of applications and an average of 0.95 and 0.91 R2 scores for predicting the actual performance and required resources, respectively. © 2024 Copyright held by the owner/author(s).

关键词： High level synthesis

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Advancing Color Perception: An IoT-driven Pedagogical System for Visually Impaired Children 1

Advancing Color Perception: An IoT-driven Pedagogical System...

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1st IEEE International Conference on Advances in Signal Processing, Power, Communication, and Computing, ASPCC 2024

作者： Balage, Prasandika Jayawardene, Nuwan University of Westminster School of Computer Science and Engineering London United Kingdom Informatics Institute of Technology Department of Computing Colombo Sri Lanka

ISBN: (纸本)9798350355093

Visual impairment is characterized by the loss of vision, encompassing both complete blindness and partial vision loss. Studies reveal a notable prevalence of visual impairment among school-aged children. The significance of colors in acquiring and discerning visual information is crucial, aiding children in effectively perceiving and comprehending their surroundings. This study presents an Internet of Things (IoT) system designed for teaching colors to visually impaired children. The system achieved an accuracy rate surpassing 77.8%, and it is operated effectively regardless of surrounding lighting conditions. Given that the target users of the system are visually impaired children, this research was conducted in close alignment with Human-computer Interaction (HCI) principles. © 2024 IEEE.

关键词： Color vision

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Insurance Fraud Detection using Machine Learning 1

Insurance Fraud Detection using Machine Learning

引用

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

作者： Tongesai, MacHinya Mbizo, Godfrey Zvarevashe, Kudakwashe University of Zimbabwe Faculty of Computer Engineering Informatics and Communication Analytics and Informatics Department Harare Zimbabwe

ISBN: (纸本)9781665475761

Many insurance companies today deal with the issue of fraudulent insurance claims, which results in significant yearly financial loss. Since the losses are covered by raising policyholders' premium costs, these frauds have a negative impact on society. The traditional claim investigation procedure has also been blamed for producing unreliable conclusions because it is time-consuming and laborious. Therefore, using machine learning and the XGBoost method, we construct an automated fraud detection application framework in this study. Accurately identifying fraud claims in a shorter amount of time is the goal. Data analysis is utilized throughout the process to validate, sanitize, and extract the pertinent data. As a result, the insurance firm can retain its reputation outside by employing this structure and has a reliable relationship with clients that they can share. © 2022 IEEE.

关键词： Insurance

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Data Driven Methods to Predict Vertical Profiles of the Rails: A Case Study 10

Data Driven Methods to Predict Vertical Profiles of the Rail...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Nugraha, Ade Chandra Supangkat, Suhono Harso Nugraha, Igb Baskara Handoko, Yunendar Aryo Purwadinata, Awan Hermawan Saputro, Dimas Wisnu Bandung State of Polytechnic School of Electrical Engineering and Informatics Bandung Institute of Technology Department of Computer Engineering and Informatics Bandung Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Smart City and Community Innovation Center Bandung Indonesia Bandung Institute of Technology Faculty of Mechanical and Aerospace Engineering Bandung Indonesia PT Kereta Api Indonesia Managing Director of Infrastructure Bandung Indonesia Bandung State of Polytechnic Department of Computer Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9798350339543

Predicting the value of railroad track geometry is needed to reduce the cost and time of rail condition measurements that have been carried out periodically every three months by railroad operators in Indonesia (PT Kereta Api Indonesia/PT KAI). Four track geometry parameters - vertical profile, horizontal alignment, twist, and gauge - are the reference measurements that will be mapped with alternative measurement results using accelerometers installed on regular trains (in-service trains). In this paper, eight prediction algorithms from the machine learning model are applied to map the vertical profile with 73 kilometers of measurement data in the Jakarta to Padalarang railroad segment, precisely on the Jatinegara-Cikampek measuring trip conducted from April 2019 to June 2022. The evaluation performance of the prediction results has been measured with the GadientBoosting Regressor algorithm is the best algorithm with MSE = 0.791, R2 = 0.507 and feature importance on the Train speed and Az attributes. The research can be continued to detect the correlation of three other track geometry parameters referring to the dataset of rail geometry condition measurement results through accelerometers on regular trains (in-service trains). © 2023 IEEE.

关键词： Forecasting

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Advancing Weather Image Classification Using Deep Convolutional Neural Networks 18

Advancing Weather Image Classification Using Deep Convolutio...

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18th International Workshop on Semantic and Social Media Adaptation and Personalization, SMAP 2023

作者： Papadimitriou, Orestis Kanavos, Athanasios Mylonas, Phivos Maragoudakis, Manolis University of the Aegean Department of Information and Communication Systems Engineering Samos Greece University of West Attica Department of Informatics and Computer Engineering Athens Greece Ionian University Department of Informatics Corfu Greece

ISBN: (纸本)9798350327717

Automatic classification of weather images is a crucial task in the field of meteorology. Recent advancements in Convolutional Neural Networks (CNNs) have demonstrated their effectiveness in image classification. In this paper, we propose an innovative CNN-based approach for weather image classification. Building upon prior research, our model capitalizes on the exceptional feature extraction capabilities of CNNs to accurately classify weather images across various categories. To enhance the generalization performance, we train the model using a combination of data augmentation techniques, including rotation, scaling and flipping. This paper introduces a CNN model specifically made for weather image classification, encompassing categories such as cloudy, sunny, rainy and snowy conditions. Through extensive training and evaluation on a substantial weather image dataset, our proposed model achieves an impressive accuracy rate of 98%. Our experimental results highlight the superior performance of our CNN model when compared to various state-of-The-Art methods in weather image classification. © 2023 IEEE.

关键词： Image classification

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A Systematic Literature Review of XAI-based Approaches on Brain Disease Detection using Brain MRI Images 4

A Systematic Literature Review of XAI-based Approaches on Br...

引用

4th International Conference on Advanced Research in Computing, ICARC 2024

作者： Madapatha, Savin Fernando, Pumudu University of Westminster Department of Computer Science and Engineering London United Kingdom School of Computing Informatics Institute of Technology Colombo Sri Lanka

ISBN: (纸本)9798350384864

Brain Magnetic Resonance Imaging (MRI) analysis is a widely used medical procedure for the early diagnosis of various brain diseases. Accurate pathology identification during the brain MRI analysis procedure is crucial as misdiagnoses or missed findings can greatly affect a patient's treatment and long-term prediction. With the recent advancement of Artificial Intelligence (AI) in the medical field, researchers have approached various techniques to detect brain diseases using AI. Although AI models exhibit high accuracy, they suffer from a lack of transparency and interpretability, paving the way for the development of eXplainable Artificial Intelligence (XAI) methods in brain disease diagnosis. Image segmentation, machine learning, deep learning and XAI are important for assisting the diagnostic procedure. In this paper, a comprehensive overview of various existing techniques in brain disease detection using MRI is presented, starting with image segmentation techniques, followed by classification techniques, and finally, XAI techniques. In conclusion, the paper identifies a critical need for further research on XAI integration to advance brain disease detection. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Concierge: Towards Accuracy-Driven Bandwidth Allocation for Video Analytics Applications in Edge Network 8

Concierge: Towards Accuracy-Driven Bandwidth Allocation for ...

引用

8th IEEE International Conference on Edge Computing and Communications, EDGE 2024

作者： Huang, Yuyang Zharfan, Faishal Hendrawan, Hendrawan Gunawi, Haryadi S. Jiang, Junchen University of Chicago Department of Computer Science Chicago United States Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9798350368499

When performing inference on sensor data, edge video analytics applications may not always need high-fidelity data, since important information may not appear all the time. Consequently, each edge AI application's bandwidth demand is highly dynamic. Thus, a shared edge system should dynamically allocate more bandwidth to the applications in need to reach high accuracy at each moment. However, previous bandwidth allocators are ill-suited because they are agnostic to the timevarying impact of bandwidth on each application's *** short paper explores a new accuracy-driven approach to bandwidth allocation, which periodically re-allocates bandwidth across edge AI applications based on the sensitivity of each application's accuracy to its bandwidth share. To examine its practical benefit and technical challenges, we present a concrete accuracy-driven bandwidth allocator called ConciERGE, which exposes a simple yet efficient interface to estimate each application's sensitivity to a small change in its bandwidth *** run CONCIERGE on state-of-the-art video-analytics applications with real video streams and show its early promise in greatly improving the inference accuracy of video analytics. © 2024 IEEE.

关键词： Bandwidth

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Enhancing Solar Farm Operations: Machine Learning for Equipment Fault Detection and Classification

Enhancing Solar Farm Operations: Machine Learning for Equipm...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Hamza, Ali Ali, Zunaib Dudley, Sandra Saleem, Komal Christofides, Nicholas School of Engineering LondonSE1 0AA United Kingdom Department of Engineering and Construction London United Kingdom Frederick University Department of Electrical Engineering Computer Engineering and Informatics Cyprus

ISBN: (纸本)9798350376067

Photovoltaic (PV) energy is considered one of the most widespread renewable sources. In this study, the vulnerability of solar PV systems to various faults, leading to potential performance degradation, has been addressed. A robust fault detection and classification strategy is proposed, employing Gradient Boosting Machine Learning (ML) algorithms (Light Gradient Boosting Method (LGBM), Categorical Boosting (Cat-boost), and AdaBoost). The methodology involved formulating a comprehensive PV system to create a synthetic fault database, utilizing diverse features. To simulate normal conditions and various fault scenarios, a MATLAB/Simulink-based PV System is developed. The optimization of hyperparameters of ML algorithms has been achieved by grid search optimization technique, resulting in enhanced performance, and reduced computational cost/time. The study involved multiple independent runs on ML algorithms and the application of Principal Component Analysis (PCA) for dimensionality reduction in the context of fault classification to assess the accuracy and consistency. Cross-validation is implemented to ensure the generalization capability of the ML algorithms to unseen data. Comparison has been established with Random Forest (RF) algorithms to show the performance accuracy of ML in fault diagnosis of PV systems. © 2024 IEEE.

关键词： Dimensionality reduction

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