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Clinically Significant Prostate Cancer Detection and Diagnosis in Bi-Parametric MRI with Deep Learning Models 9

Clinically Significant Prostate Cancer Detection and Diagnos...

引用

9th International conference on Computer Science and engineering, UBMK 2024

作者： Asoh-Itambi, Clinton Binda Yüzkat, Mecit Varli, Songül Yildiz Technical University Faculty of Electrical and Electronics Department of Computer Engineering Istanbul Turkey Muş Alparslan University Health Institutes of Türkiye Faculty of Engineering and Architecture Department of Software Engineering Istanbul Turkey Yildiz Technical University Health Institutes of Türkiye Faculty of Electrical and Electronics Department of Computer Engineering Istanbul Turkey

ISBN: (纸本)9798350365887

this study presents a significant improvement in the detection and diagnosis of clinically significant prostate cancer (csPCa) in bi-parametric magnetic resonance imaging (bpMRI) by adapting the nnU-Net framework. We address the inherent limitations of traditional imaging analysis techniques by modifying the loss function used in nnU-Net, replacing the default combination of Cross-Entropy and soft Dice loss with a novel integration of Cross-Entropy loss and Focal loss. this modification targets class imbalance and enhances the detection sensitivity for less represented, clinically significant lesions, which are crucial for effective csPCa management while minimizing false diagnosis. Employing a semi-supervised learning approach, the modified nnU-Net was trained and validated on the PI-CAI (Prostate Imaging: Cancer AI) Public Training dataset (1500 cases), the current benchmark dataset for csPCa detection and diagnosis. It was also tested on the PI-CAI Hidden Testing cohort dataset consisting of 100 unseen cases. these datasets offer a comprehensive and diverse collection of prostate MRI exams, providing a robust foundation for model training and testing. We conducted a rigorous 5-fold cross-validation to ensure the robustness and reproducibility of our findings. the model's performance was evaluated with Average Precision (AP) at the lesion level and Area Under the Receiver Operating Characteristics curve (AUROC) at the patient level. Our model with AUROC and AP of 0.824 and 0.603 respectively on the Hidden Tuning cohort, outperformed the state-of-the-art U-Net, nnDetection models, and other nnU-Net variants. this work contributes to ongoing efforts to refine diagnostic tools in medical imaging, offering the potential for more accurate and timely prostate cancer screenings. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Anomaly Detection based on Deep Learning: Insights and Opportunities 10

Anomaly Detection Based on Deep Learning: Insights and Oppor...

引用

10th IEEE International conference on Cyber Security and Cloud Computing and 9th IEEE International conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Zhang, Huan Xie, Rui Li, Kuanching Huang, Weihong Yang, Chaoyi Liu, Jingnian Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan China

With the advent of the 5G/6G and Big Data, the network has become indispensable in people9;s lives, and Cyber security has turned a relevant topic that people pay attention to. For Cyber security, anomaly detection... 详细信息

ISBN: (纸本)9798350312461

With the advent of the 5G/6G and Big Data, the network has become indispensable in people's lives, and Cyber security has turned a relevant topic that people pay attention to. For Cyber security, anomaly detection, a.k.a. outlier detection or novelty detection, is one of the key points widely used in financial fraud detection, medical diagnosis, network security, and other aspects. As a hot topic, deep learning-based anomaly detection has been studied by more and more researchers. For such an objective, this article aims to classify anomaly detection based on deep learning, pointing out the problem and the principle, advantages, disadvantages, and application scenarios of each method, and describe possible future opportunities to address challenges. © 2023 IEEE.

关键词： Anomaly detection

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Yoga Pose Detection and Correction Using 3D Pose Estimation and Machine Learning 9

Yoga Pose Detection and Correction Using 3D Pose Estimation ...

引用

9th International conference on Communication and Electronics Systems, ICCES 2024

作者： Anusha, N. Prabhu, Shreya S. Poojari, Shruthi Shekhar Department of Information Science and Engineering India

ISBN: (纸本)9798350377972

Yoga is an ancient Indian practice that nurtures and enhances both physical and mental well-being. However, performing yoga poses incorrectly can lead to adverse health effects. Many practitioners lack sufficient knowledge of the poses or fail to follow proper instructions, creating a need for a system that can accurately classify yoga poses and offer corrective guidance. this research aims to integrate technology with yoga to assist individuals in performing poses correctly, thus reducing the risk of injury and enhancing the effectiveness of their practice. Specifically, the objective is to detect live, performed yoga poses and provide real-time assistance using the Mediapipe library and deep learning techniques, such as the multilayer perceptron model. Recent advancements in computer vision and deep learning, including pose estimation algorithms and convolutional neural networks (CNNs), have shown promise in yoga pose recognition. However, challenges persist, such as accurately identifying complex and dynamic poses, dealing with variations in body shapes and postures, and ensuring real-time feedback. the proposed system seeks to overcome these challenges by leveraging robust pose detection methods and providing corrective suggestions to improve performance. this research marks a significant advancement in the fusion of technology with wellness, offering innovative solutions in the evolving field of mind-body fitness. © 2024 IEEE.

关键词： Convolutional neural networks

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Optimization Of Electric Vehicle (EV) Routing to Facilitate EV Hub 9

Optimization Of Electric Vehicle (EV) Routing to Facilitate ...

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9th International conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Raghavendran, P.S. Sheela, A. Joseph, J. Alwin Ragul, S. Kongu Engineering College Department of EEE Erode India Kongunadu College of Engineering and Technology Department of EEE Trichy India

ISBN: (纸本)9798350397376

An Electric Vehicle Routing (EVR) Methodology is the developed system that guides the Electric Vehicle Owner to reach out to the nearest Electric Vehicle Hub (EV Hub) either to swap or recharge the Electric Vehicle Battery (EVB). EVR Methodology is developed based on the Euclidean distance. Coordinates which is the latitudinal and longitudinal combination of the four EV Hubs, as well as the Current location of the Electric Vehicle utilized. Finally, the Euclidean distances obtained are compared to fetch the nearest Hub. Execution of EVR Methodology in EV hub could enhance EV utility. An increase in EV utility reduces carbon footprint. the proposed work has been simulated in MATLAB software and is also expected in the ARDUINO UNO embedded system. © 2023 IEEE.

关键词： Vehicle routing

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Temporal Fusion Transformer based Temporal Convolutional Network Model for Sleep Apnea Detection Using Single-Lead ECG Signals 9

Temporal Fusion Transformer Based Temporal Convolutional Net...

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9th International conference on Communication and Electronics Systems, ICCES 2024

作者： Devashania, Ielin Daisy Paulraj, Getzi Jeba Leelipushpam Jebadurai, Immanuel Johnraja Santhiya, P. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798350377972

Sleep apnea (SA) is a sleep disorder which is characterized by intermittent interruptions in breathing and airflow throughout a sleep cycle. Traditional techniques such as polysomnography (PSG), remain costly, labor-intensive, and require overnight monitoring in specialized labs, which limits its accessibility and scalability. For continuous sleep apnea diagnosis, deep learning models offer a scalable, non-invasive, and economical alternative to multi-sensor PSG monitoring which paves the way for integration into wearable IoT devices for real-time health monitoring. To address the limitations posed by traditional multi-sensor techniques, a novel framework based on Deep Learning techniques is proposed: Temporal Fusion Transformer-based Temporal Convolutional Network (TFT-TCN), designed for the detection of obstructive sleep apnea (OSA) with the use of single-lead ECG signals. the TCN module uses dilated convolutions to extract temporal features efficiently whereas the TFT module employs Gated Residual Network (GRN), Adaptive Fusion and Multi-Head Attention to compute attention scores and fuse the feature vectors. Overall, the proposed TCN-TFT model achieved a noteworthy Accuracy of 89.55 %, FI-Score of 89.3% and AUC score of 0.95. © 2024 IEEE.

关键词： Sleep research

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A Model-based PID Control of Turning Maneuver for Catamaran Autonomous Surface Vessel 9

A Model-Based PID Control of Turning Maneuver for Catamaran ...

引用

9th International conference on engineering and Emerging Technology, ICEET 2023

作者： Astrov, Igor Tallinn University of Technology Department of Software Science Tallinn Estonia

ISBN: (纸本)9798350316926

the effective automatic control of aerial, water, and ground autonomous vehicles become one of the high-priority tasks in the areas of Cyber-Physical Systems (CPS), Industry 4.0, and the world economy in general. Besides extensive research on Unmanned Aerial Vehicles (UAVs), the study of Autonomous Surface Vessels (ASVs) has been more than 10 times less intensive. the paper discusses the control mathematics of a realistic ASV nonlinear model of the real autonomous boat 'Nymo' developed at the Tallinn University of Technology. the paper offers and simulates in Simulink/MATLAB environment a specially designed control system using a PID controller. the control for the desired tracking has been presented for such maneuvers as the turn of the catamaran at different angles. the proposed control system showed good performance in tracking the desired heading angles. © 2023 IEEE.

关键词： Nonlinear systems

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Cloud-based AIOT Soil Monitoring System for Earthworms 9

Cloud-Based AIOT Soil Monitoring System for Earthworms

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9th International conference on Communication and Electronics Systems, ICCES 2024

作者： Varma, S. Asha Mangamuri, Meghana Kalagara, Anusha Kurukanti, Kezia Velagapudi Ramakrishna Siddhartha Engineering College Department of Artificial Intelligence and Data Science India

ISBN: (纸本)9798350377972

Determining the soil conditions that are favourable to earthworm species is vital for maintaining soil health and improving agricultural yield. Timely reactions are constrained by the work-intensive nature of conventional soil monitoring techniques and their lack of real-time insights into characteristics of the soil. In order to address these issues, we created an Internet of things (IoT) cloud-based soil monitoring system that tracks pH and moisture levels, two factors that are essential for earthworm survival, while utilizing clustering algorithms for categorizing soil conditions and Random Forest machine learning algorithms for predicting optimal habitats. this state-of-the-device enables more informed soil management decisions by offering cloud-based trend analysis visuals and fast feedback via an LCD display. the method effectively improves the detection of suitable circumstances for earthworm habitats by combining innovative sensor technology with real-time data transmission and processing. Compared to conventional techniques, our system reveals significant gains in graphical representation and data collection as well as automated choices. Collectively, these features promote a more proactive approach to soil management, improving the fertility of the soil and creating optimal conditions for earthworms. With potential uses across various precision farming domains, our methodology provides a significant instrument for sustainable agriculture. © 2024 IEEE.

关键词： Soil surveys

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Automotive Lightweight Design Modeling And Intelligent Optimization Learn Key Technologies 10

Automotive Lightweight Design Modeling And Intelligent Optim...

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10th IEEE International conference on Cyber Security and Cloud Computing and 9th IEEE International conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Xu, Gejing Liang, Wei Cai, Jiahong Xiao, Jiahong Chen, Xingyu Gong, Yinyan QuanZhou University Information Engineering Software College QuanZhou362000 China Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China

ISBN: (纸本)9798350312461

the automotive industry has always been seeking innovative solutions to improve car performance, safety, and cost savings. Lightweight design technology has become one of the solutions. this article summarizes the modeling and optimization methods of automotive lightweight design, as well as key technologies based on intelligent optimization learning. First, this article outlines the basic concepts of automotive lightweight design, as well as the needs and challenges of the industry for lightweight design. then, the modeling methods of lightweight design are introduced in detail, including geometric modeling, topology optimization, structural optimization, and multidisciplinary optimization. At the same time, commonly used materials, manufacturing processes, and testing methods in lightweight design are introduced, as well as relevant design guidelines and standards. this article also introduces some algorithms and their applicable scenarios. Additionally, this article summarizes the application prospects and future development directions of key technologies for automotive lightweight design modeling and intelligent optimization learning. We emphasize the opportunities and challenges in this field and propose how to continue promoting the development of lightweight design technology and responding to increasingly complex market demands. this article provides a systematic review of key technologies for automotive lightweight design modeling and intelligent optimization learning, which helps researchers and practitioners to deepen their understanding of the technical development and application trends in this field. © 2023 IEEE.

关键词： Automotive industry

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Design of an IP core for motion blur detection in fundus images using an FPGA-based accelerator 9

Design of an IP core for motion blur detection in fundus ima...

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9th International conference on Biosignals, Images, and Instrumentation, ICBSII 2023

作者： George, Rohit Jacob Charaan, S. Swathi, R. Joy Vasantha Rani, S.P. Madras Institute of Technology Department of Electronics Engineering Chennai India

ISBN: (纸本)9798350338171

this paper focuses on applying an algorithm for real-time blur detection in fundus images via hardware acceleration. Blur in fundus images is caused due to many factors, but most of the time, with a reasonable degree of accuracy, they could be classified as motion blur. A motion blur could be modelled as an image convolved with a blur transfer function. Blur metrics are identified via techniques such as Haar DWT as it gives reasonable accuracy for most types of linear blur. First, a hardware architecture using Verilog HDL is created that computes the edge maps of images. this architecture is based on a novel algorithm that encompasses a series of Haar DWT Units. the simplicity and flexibility in this proposed architecture allow any kind of software or hardware platform to integrate the proposed model with very little to no modification, onto them. Subsequently, the IP core for the proposed architecture is developed, which can be further extended into an SoC, which can then be programmed onto a suitable FPGA system, which could then be uploaded with images that get classified as blurred and clear images. the on-chip processing system of the FPGA-SoC reads the image data and sends it to the Blur Detector IP via the DMA IP in the SoC. the whole process uses a double-buffered design in order to reduce IP stall time and increase efficiency. © 2023 IEEE.

关键词： System-on-chip

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knowledge Graph Completion with Probabilistic Logic Programming

Knowledge Graph Completion with Probabilistic Logic Programm...

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2023 International conference of the Italian Association for Artificial Intelligence Doctoral Consortium, AIxIA-DC 2023

作者： Gentili, Elisabetta Department of Engineering University of Ferrara Via Saragat 1 Ferrara44124 Italy

knowledge Graphs have gained popularity in the last decade, given their ability to represent huge structured knowledge bases. However, they are often incomplete and thus knowledge Graph Completion (KGC) is currently a hot topic. In this paper we present our idea of performing KGC by learning liftable probabilistic logic programs via regularization, using LIFTCOVER+, with the aim of obtaining more accurate results while learning a smaller set of rules. © 2022 Copyright for this paper by its authors.

关键词： Inductive logic programming (ILP)

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