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Fine-Tuning Pre-Trained Models for Automated Analysis of Ophthalmic Imaging in Diagnosing Eye Diseases 24

Fine-Tuning Pre-Trained Models for Automated Analysis of Oph...

引用

24th International Arab Conference on Information Technology, ACIT 2023

作者： Al Mamlook, Rabia Emhamed Nasayreh, Ahmad Gharaibeh, Hasan Aljaidi, Mohammad Al-Na'amneh, Qais Alzu'bi, Dalia Bzizi, Hanin Abdeltawab, Abdelrahman Trine University Dep. of Business Administration Indiana United States University Zawia Department of Industrial Engineering Tripoli Libya Yarmouk University Dep. Computer Science Irbid Jordan Zarqa University Dep. of Computer Science Zarqa Jordan Applied Science Private University Dep. of Cyber Security Amman Jordan Concordia University Dep. Information and Systems Engineering Montreal Canada Western Michigan University Dep of Biomedical Science KalamazooMI49008 United States Bani Sweif University Faculty of Pharmacy Bani Suwayf Egypt

ISBN: (纸本)9798350384307

This paper searches into the convergence of such ad-vanced techniques with architectures like DenseNet201, VGG16, InceptionResNetV2, and NasNetMobile. Our focus centers on harnessing deep learning capabilities for the precise detection of eye ailments. Preliminary findings spotlight a notable uplift in diagnostic precision. Among the tested algorithms, the Nas-NetMobile architecture emerged superior, boasting an impressive accuracy rate of 92% on the chosen dataset. Through this work, we extend a significant advancement in the realm of automated eye disease diagnosis, potentially equipping medical professionals with sharper, swifter diagnostic tools. The outcomes presented herein could play a seminal role in sculpting more sophisticated and precise diagnostic approaches within ophthalmology. © 2023 IEEE.

关键词： Deep Learning Eye Disease Fine Tuning Transfer Learning

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RF-Based Lightweight Machine Learning for Comprehensive Drone Activity Classification 2

RF-Based Lightweight Machine Learning for Comprehensive Dron...

引用

2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2024

作者： Alqodah, Mohammed A. Tahsin, Mahdia Omari, Mohammad H. Matalgah, Mustafa M. Harrison, David University of Mississippi Dep. of Electrical & Computer Engineering UniversityMS United States University of Mississippi Dept. of Computer and Information Science UniversityMS United States

ISBN: (纸本)9798331529529

The rapid rise of commercially available drones, driven by miniaturization and affordability, has introduced potential security risks alongside numerous societal benefits. While drones serve various industries, their unregulated use necessitates robust detection and classification methods. This paper proposes a novel, efficient model for classifying unwanted drone activity. We leverage statistical and time-domain features extracted from radio frequency (RF) signals and machine learning (ML) classifiers. This approach aims to not only detect drones but also distinguish their models and operational modes. Utilizing the DroneRF dataset, our feature selection strategy prioritizes informative signals in the low-band spectrum, achieving {100%} accuracy in both drone detection and drone model identification tasks. Furthermore, our hybrid model, combining time-domain and frequency-domain features, achieves an accuracy of {97.66%} in classifying different flight modes. This demonstrates the effectiveness of this comprehensive feature extraction approach for drone activity classification. © 2024 IEEE.

关键词： Drones

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MPI Impact in C+L+S Multiband Transmission Reach

MPI Impact in C+L+S Multiband Transmission Reach

引用

2024 Photonic Networks and Devices, NETWORKS 2024

作者： Cancela, Luís G. Pires, João O. Instituto de Telecomunicações Dep. Information Science and Technology Iscte Instituto Universitário de Lisboa Portugal Instituto de Telecomunicações Dep. Electrical and Computer Engineering Instituto Superior Técnico Portugal

Multipath interference (MPI) impact is assessed in a C+L+S multiband transmission scenario. For a typical -34 dB/span MPI, transmission reach in the L-band suffers a 20% reach penalty considering the QPSK modulation f... 详细信息

关键词： C++ (programming language)

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Maximize Leader Election Algorithm Performance in Bi-Directional Ring Networks With Both Links Down Between Two Nodes 25

Maximize Leader Election Algorithm Performance in Bi-Directi...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Al Refai, Mohammed N. Fawareh, Hamed Aljawawdeh, Hamzeh Alshraiedeh, Fuad Sameh Al-Quraan, Mohammad Al-Odat, Hisham Ali Otoom, Ahmed Jamhawi, Zeyad M. Kanan, Mohammad Zarqa University Software Engineering Dep. Zarqa Jordan Zarqa University Zarqa Jordan Applied Science Private University Department of Cyber Security Amman Jordan Applied Science Private University Department of Computer Science Amman Jordan University of Business and Technelogy Dep. of Industry Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

The process of selecting a leader to avoid instability in distributed system network after leader failure is called Leader Election Algorithm (LEA). This process becomes more complicated when it faces intermittent or complete links disconnected. the network becomes a one-way behavior if both lines of communication between two points are disrupted and thus the network becomes very slow, especially in systems where time is a critical issue, and the process of communication between points becomes much slower if the network leader is next to the fault or close to it, so the criteria will be established to ensures that the system leader in the Bidirectional ring communication network is in a place that allows next leader to communicate with all points as quickly as possible, and this will be during the election *** research presents a solution for leader failure problem in bidirectional ring network. This research takes into account the existence of intermittent or disconnection links between nodes. It will produce three algorithms to determine a new leader in the network with maximum network performance. It uses the least probability of time steps and the number of messages needed to elect the new leader in bidirectional ring network with N communicated nodes. so that the system remains able to work and communicate with its best capacity during this problem with the number of message complexity O(N) in the worst case. The third scenario minimizes the value of proper leader ID IDCurrent/IDCurrent-idStepCount', and maximizes the value of proper leader place N - (N - nStepCount). © 2024 IEEE.

关键词： Artificial intelligence

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Fault-Tolerant FPGA-Based System for Mitigating SEUs in Configuration and User Bits

Fault-Tolerant FPGA-Based System for Mitigating SEUs in Conf...

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2024 International Conference on Microelectronics, ICM 2024

作者： Amer, O.T. Alkady, G.I. Mohamed, F.Y. Mahmoud, A.W. Amer, H.H. Daoud, R.M. Cairo University Communication & Computer Eng. Dep. Faculty of Engineering Cairo Egypt American University in Cairo Electronics & Communications Engineering Department Sead Group Cairo Egypt

ISBN: (纸本)9798350379396

This paper addresses two issues. First, in the context of factory automation, a low-cost fault-tolerant sensor system is proposed along with its FPGA-based voter. Second, the voter itself is designed to recover from failures affecting its own circuitry. The sensor system consists of one high-quality sensor used for measurement along with two functionally identical, but lower cost sensors used for monitoring purposes. A biased voter circuit is designed and implemented on the same Field Programmable Gate Array (FPGA) used to house the controller. It is shown that the voter can detect and recover from Single Event Upsets (SEUs) which often occur in the harsh factory environments. Both FPGA configuration and user bits are protected. It was shown that the proposed design requires less resources and power than some other designs presented in the literature. Focusing next on recovering from SEUs in both user and configuration FPGA bits, another use case is investigated, namely an I2C master;it is shown that the proposed design again requires less resources than some other commonly used techniques. Both proposed designs were implemented on the Xilinx Zynq-7000 (xc7z020clg400-1). © 2024 IEEE.

关键词： Field programmable gate arrays (FPGA)

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A New Implementation of SHA-3 Hash Function Using Seed Value 5

A New Implementation of SHA-3 Hash Function Using Seed Value

引用

5th International Conference on Information Technology, Applied Mathematics and Statistics, ICITAMS 2023

作者： Kashmar, Ali H. Shihab, Ammar I. Jawad, Wafaa S. University Of Baghdad Dept. Of Computer Science College Of Science Baghdad Iraq Islamic University Of Lebanon Faculty Of Engineering Dep. Of Computer And Communication Beirut Lebanon

ISBN: (纸本)9798350335118

This paper aimed to suggest a new Secured Hash Algorithm 3 (SHA-3) with seed value implementation. Seed worth ensuring that there are fewer expectations collisions, despite an attacker selecting the data. Furthermore, attackers who combine seed values with hash functions are prevented from exploiting the properties of a hash function to construct many security attacks, such as denial of service attacks and dictionary attacks. A novel approach to enhanced SHA-3 diffusion was proposed here by XORing the seed value with θ step result after transforming the seed bits into a 5 × 5 × 64 matrix and discarding the rest. © 2023 IEEE.

关键词： Hash functions

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Enhancing Freezing of Gait Detection in Parkinson’s Through Fine-Tuned Deep Learning Models

Enhancing Freezing of Gait Detection in Parkinson’s Through...

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Digital Health (ICDH), IEEE International Conference on

作者： Michele Tebaldi Graziano Pravadelli Florenc Demrozi Rosalba Giugno Cristian Turetta Dep. of Computer Science University of Verona Italy Dep. of Engineering for Innovation Medicine University of Verona Italy Dep. of Electrical Engineering and Computer Science University of Stavanger Norway

ISBN: (数字)9798350368574

ISBN: (纸本)9798350368581

Freezing of Gait (FoG) is a common and disabling symptom in Parkinson’s Disease (PD), characterized by a sudden and temporary inability to initiate or continue walking. FoG arises from various factors such as environmental triggers, or physiological status of people with Parkinson’s. Traditional methods for preventing or alleviating FoG have limitations, prompting exploration into new technologies, such as the combination of sensing technologies and Deep Learning (DL) and Machine Learning (ML) algorithms. However, recognizing FoG with sensors and ML/DL poses challenges, such as the generalizability of the FoG recognition models over different individuals. Moreover, current approaches often require extensive time and effort to personalize the FoG recognition models. To mitigate these challenges, we propose a system that reduces the workload for creating personalized models through a fine-tuning approach. Our methodology has undergone rigorous testing in a subject-indep.ndent setup on a self-collected dataset of 22 subjects. Through the fine-tuning phase, we observed a remarkable average increase of up to $20.9 \%$ in F1-score performance compared to the training and testing approach without fine-tuning.

关键词： Training Deep learning Radio frequency Measurement Machine learning algorithms Refining Physiology

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Parallel and hierarchically-distributed Shoreline Alert Model (SAM) 31

Parallel and hierarchically-distributed Shoreline Alert Mode...

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31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing, PDP 2023

作者： De Vita, Ciro Giuseppe Mellone, Gennaro Florio, Aniello Charles, Catherine Alessandra Torres Di Luccio, Diana Lapegna, Marco Benassai, Guido Budillon, Giorgio Montella, Raffaele University of Naples 'Parthenope' Dep. of Science and Technologies Naples Italy University of Madrid Carlos Iii Dep. of Computer Science and Engineering Madrid Spain University of Naples 'Federico II' Dept. of Mathematics and Applications Naples Italy

ISBN: (纸本)9798350337631

In this paper, the Shoreline Alert Model (SAM) is presented as a component of a computation platform based on workflows dedicated to extreme weather/marine event simulation. The model aims to mitigate the effects of global change by providing decision-makers, scientists, and engineers with a novel, next-generation tool set for facing extreme weather events and implementing related management or emergency responses. SAM uses a parallelization schema, allowing users to run it on heterogeneous parallel architectures. As a result, SAM produces approximately 24 times faster results than the baseline when using shared memory with distributed memory and dealing with about 20,000 transects along the Campania coastline. The system is based on the algorithms of the open-source numerical models WRF (Weather Research and Forecasting) and WW3 (Wave-watch III) implemented with refraction and shoaling routines together with run-up equations to form the modeling chain used for coastal flooding assessment. © 2023 IEEE.

关键词： Parallel architectures

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Exploring Genetic Programming Models in computer-Aided Diagnosis of Skin Cancer Images 13

Exploring Genetic Programming Models in Computer-Aided Diagn...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Ain, Qurrat Ui Al-Sahaf, Harith Xue, Bing Zhang, Mengjie School of Engineering and Computer Science Victoria University of Wellington PO Box 600 Wellington New Zealand College of Engineering and Info. Technology AlShaab University Dep. of Cybersecurity Engineering Baghdad Iraq

ISBN: (纸本)9798350308365

Extracting important information from complex skin lesion images is vital to effectively distinguish between different types of skin cancer images. In addition to providing high classification performance, such computer-aided diagnostic methods are needed where the models are interpretable and can provide knowledge about the discriminative features in skin lesion images. This underlying information can significantly assist dermatologists in identifying a particular stage or type of cancer. With its flexible representation and global search abilities, Genetic Programming (GP) is an ideal learning al-gorithm to evolve interpretable models and identify important features with significant information to discriminate between skin cancer classes. This paper provides an in-dep.h analysis of a recent GP-based feature learning method where different well-developed feature descriptors are integrated into the learning algorithms to extract high-level features for skin cancer image classification. The study explores the effectiveness of utilizing feature learning for this complex task and designing program structure to suit the problem domain as it has shown promising results compared to commonly used feature descriptors and an existing GP-based feature learning method developed for general image classification. This study analyzes the GP-evolved models to identify the prominent features and most effective feature descriptors important for the classification of these skin cancer images. The evolved models are interpretable, they provide knowledge that can assist dermatologists in making diagnoses in real-time clinical situations by identifying prominent skin cancer characteristics captured by the feature descriptors and learned during the evolutionary process. © 2024 IEEE.

关键词： Genetic programming

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Deep Learning Technique for Real-Time Forest Fire Detection from Video Streams 11

Deep Learning Technique for Real-Time Forest Fire Detection ...

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11th International Conference on Intelligent Systems and Embedded Design, ISED 2023

作者： Gupta, Nidhi Chakravarty, Sujata Dash, Satyabrata Centurion University of Technology & Management Dept. of Computer Science & Engineering Odisha India Gandhi Institute of Technology & Management Gitam Deemed to Be University Dep. of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9798350318159

Forest fires are currently the most common issue that we are dealing with. The resources that have been lost are irreplaceable. Forest fires are uncontrollable fires that occur in nature as a result of human involvement or any other natural event. Forest fires can be so large that putting them out takes a long time. Contrasting with previous years, there has been a striking surge in the frequency of forest fires. Changes in climatic conditions are the key factor affecting the scope of forest fires. Long dry spells in the spring and summer make the woodlands particularly vulnerable. The extent of forest fires is mostly determined by weather conditions such as precipitation and wind, as well as the terrain's architecture. Long dry spells in the spring and summer make the woodlands particularly vulnerable. The extent of forest fires is mostly determined by weather conditions such as precipitation and wind, as well as the terrain's architecture. The incorporation of a Video Fire Detection model significantly enhances the efficiency of fire detection systems, particularly in large spaces. Establishing a video fire detection system involves focusing on spatial, spectral, and temporal indicators, as well as flame picture segmentation, recognition, tracking, and prediction. Analytical techniques, specifically utilizing ResNet50 and VGG16 models, can be applied to identify pool fire images through these segmentation processes, contributing to more robust fire detection capabilities. © 2023 IEEE.

关键词： Fires

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