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Conference on Digital Avionics systems (DASC)

作者： Felipe Rodrigo Evangelista Matilde Johnny Marques Software and Information Systems Department Aeronautics Institute of Technology São José dos Campos Brazil

ISBN: (数字)9798350349610

ISBN: (纸本)9798350349627

Operating an airplane is not an easy task. The pilots must check for weight and balance, communicate with towers, flight crew and passengers, fly the airplane, navigate from origin to destination, sometimes deal with system failures, and perform many procedures. Electronic Flight Bags (EFB) are applications pilots use inside the airplane cabin to make operating an airplane easier. This type of software application has been growing in the last twenty years. Most of these applications developed for tablets help pilots considerably operate airplanes by reducing workloads, optimizing airplane performance, and removing paper manuals from airplanes. In 2021, EUROCAE published the standard ED-273 for developing EFBs and introduced some requirements related to software development assurance, among other relevant points. The EUROCAE ED-273, as expected, made EFB manufacturers concerned about how to follow the new requirements. On the other hand, a 30-year-old software standard, the RTCA DO-178C, establishes considerations for developers, installers, and users when designing embedded equipment using software. Several aircraft manufacturers have well-established RTCA DO-178C software processes. We believe it would be very beneficial for organizations with an RTCA DO-178C compliant process to take advantage of it as a means of compliance for the new ED-273. Our work proposes to create an EFB development approach compliant with ED-273 guidelines but using the original RTCA DO-178C processes defined by the aircraft manufacturer. A mapping between the two standards was performed. The development approach was thoroughly assessed by a panel of highly experienced experts in aeronautical software. The main contributions of this paper are (i) the mapping between EUROCAE ED-273 and RTCA DO-178C, (ii) the development approach, and (iii) the evaluation processes defined and executed with the participation of 5 experts in RTCA DO-178C or EFB software development.

关键词： Airplanes Standards organizations Poles and towers Manuals Companies software software standards Aircraft manufacture software development management Guidelines

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Performance Optimization for Vehicular Cooperative Sensing: A Graph Attention Based Reinforcement Learning Approach

Performance Optimization for Vehicular Cooperative Sensing: ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Li, Xuefei Chen, Mingzhe Liu, Danpu Zhang, Zhilong Quek, Tony Q. S. Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing100876 China University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL33146 United States Information Systems Technology and Design Pillar Singapore University of Technology and Design 487372 Singapore

ISBN: (纸本)9798350351255

In this paper, the problem of collaborative vehicle sensing is investigated. In the considered model, a set of cooperative vehicles provide sensing information to sensing request vehicles with limited sensing and communication resources. A base station (BS) determines the subset of sensing request vehicles that each cooperative vehicle will serve and the sub-regions that each cooperative vehicle will detect. We formulate an optimization problem aiming to maximize the number of successfully detected sub-regions of sensing request vehicles while satisfying the cooperative sensing energy requirement by jointly determining the cooperative vehicle association and the sensing sub-region selection. To solve this problem, we propose a graph attention based reinforcement learning (RL) algorithm that can generate the graph information vectors based on the correlation between each cooperative vehicle and each sensing request vehicle. Using the learned graph information, the joint cooperative vehicle association and sensing sub-region selection strategy will be determined. Simulation results show that the proposed scheme can improve the number of successfully detected sub-regions of sensing request vehicles by up to 12.5% compared to the conventional RL algorithm without using graph attention networks (GANs). © 2024 IEEE.

关键词： Reinforcement learning

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Ensemble-Based Approach for Efficient Intrusion Detection in Network Traffic

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Intelligent Automation & Soft computing 2023年第8期37卷 2499-2517页

作者： Ammar Almomani Iman Akour Ahmed M.Manasrah Omar Almomani Mohammad Alauthman Esra’a Abdullah Amaal Al Shwait Razan Al Sharaa School of Computing Skyline University CollegeUniversity City of SharjahP.O.Box 1797SharjahUnited Arab Emirates IT-Department-Al-Huson University College Al-Balqa Applied UniversityP.O.Box 50IrbidJordan Information Systems Department College of Computing&InformaticsUniversity of SharjahUnited Arab Emirates Comp.Info Sciences(CIS)Division Higher Colleges of TechnologySharjahUnited Arab Emirates Computer Sciences Department Yarmouk UniversityIrbidJordan Computer Network and Information Systems Department The World Islamic Sciences and Education UniversityAmman11947Jordan Department of Information Security Faculty of Information TechnologyUniversity of PetraAmmanJordan

The exponential growth of Internet and network usage has neces-sitated heightened security measures to protect against data and network ***,executed through network packets,pose a significant challenge for firewalls to detect and prevent due to the similarity between legit-imate and intrusion *** vast network traffic volume also complicates most network monitoring systems and *** intrusion detection methods have been proposed,with machine learning techniques regarded as promising for dealing with these *** study presents an Intrusion Detection System Based on Stacking Ensemble Learning base(Random For-est,Decision Tree,and k-Nearest-Neighbors).The proposed system employs pre-processing techniques to enhance classification efficiency and integrates seven machine learning *** stacking ensemble technique increases performance by incorporating three base models(Random Forest,Decision Tree,and k-Nearest-Neighbors)and a meta-model represented by the Logistic Regression *** using the UNSW-NB15 dataset,the pro-posed IDS gained an accuracy of 96.16%in the training phase and 97.95%in the testing phase,with precision of 97.78%,and 98.40%for taring and testing,*** obtained results demonstrate improvements in other measurement criteria.

关键词： Intrusion detection system(IDS) machine learning techniques stacking ensemble random forest decision tree k-nearest-neighbor

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Deep transfer learning technique to detect white blood cell classification in regular clinical practice using histopathological images

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Multimedia Tools and Applications 2025年第9期84卷 5699-5723页

作者： Davamani, K. Anita Jawahar, Malathy Anbarasi, L. Jani Ravi, Vinayakumar Al Mazroa, Alanoud Robin, C. R. Rene Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Leather Process Technology Division CSIR-Central Leather Research Institute Chennai India School of Computing Science and Engineering Vellore Institute of Technology Vellore India Computer Science and Engineering Sri Sairam Engineering College Sirukalathur India Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia

Leukemia, a malignant disease characterized by the rapid proliferation of specific types of white blood cells (WBC), has prompted increased interest in leveraging automatic WBC classification system. This study presents the design of a robust WBC classification system, integrating deep learning with microscopic image analysis. The proposed classification model is designed to categorize blood cells into four distinct groups: eosinophil, lymphocyte, monocyte, and neutrophil. The study employs a Kaggle image dataset for comprehensive learning. The dataset comprises 12,500 microscopic images used to categorize the four blood cell groups. To enhance performance and reduce the likelihood of overfitting, transfer learning techniques are employed. Six deep transfer learning models namely VGG16, ResNet50, AlexNet, MobileNet, GoogLeNet, and EfficientNetBO are used for feature extraction and classification. The results of the experiments show that VGG16 and MobileNet demonstrated enhancements compared to other pre-trained models when evaluated on both augmented and original datasets. A diverse set of evaluation criteria was employed to thoroughly assess the effectiveness of medical image classification. Notably, the VGG16 and MobileNet models accurately predicted Lymphocytes and Monocytes with a 100% accuracy rate, and Eosinophil and Neutrophil with rates of 99.35% and 99.81%, respectively. This research work empower medical practitioners, particularly in the field of pathology and hematology by providing advanced tools for accurate blood cell classification. The proposed VGG16 and MobileNet models hold promise for simplifying WBC classification, improving decision-making processes, and ultimately advancing patient care in the medical domain. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

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Switched-Capacitor ADC Error and Performance Analysis 14

Switched-Capacitor ADC Error and Performance Analysis

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14th International Conference on Advanced Computer information Technologies, ACIT 2024

作者： Mychuda, Lesya Mychuda, Zynowij Korobeinikova, Tetiana Zhuravel, Ihor Romanyuk, Olexandr Romanyuk, Oksana Lviv Polytechnic National University Departament of Information Technology Security Lviv Ukraine Lviv Polytechnic National University Departament of Computerized Automatic Systems Lviv Ukraine Vinnytsia National Technical University Department of Software Engineering Vinnytsia Ukraine

ISBN: (纸本)9798350350036

This article presents the findings from a simulation study on an interpolating analog-to-digital converter (ADC), which is classified as a high-precision device. The study reveals that the proposed interpolating ADC significantly enhances both accuracy and speed of conversion, outperforming known analogs by at least twice. For certain sub-ranges, improvements in accuracy and speed are observed to be more than double. The combined output code error of the interpolating ADC is found to be less than the set value at the 2nd stage of conversion by 4-5 times, with errors not exceeding 0.022% for a set 0.1 %, 0.01 % for a set 0.05%, and 0.0022% for a set 0.01%. The conversion duration increases twofold in the millivolt range when the set error values are halved, while in the range above 1V, the duration remains under 300 μ s for both cases. For the highest set error of 0.001%, the duration can reach up to 2 ms. The optimally chosen interpolating converter with an increased accuracy to 0.05% demonstrates typical performance in the 1 mV-1V range and showcases a conversion speed twice as fast in the 1V-10V range with enhanced accuracy. The proposed interpolating converter is thus considered a superior high-precision device, offering significant improvements in both accuracy and speed for various operating sub-ranges. © 2024 IEEE.

关键词： Capacitor bank

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Large area crystalline Weyl semimetal with nano Au film based micro-fold line array for THz detector

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Science China(Technological Sciences) 2023年第11期66卷 3267-3275页

作者： SONG Qi ZHOU Yu JIA ErSe WANG JiaTong ZHANG Min ZHANG BingYuan Shandong Key Laboratory of Optical Communication Science and Technology School of Physics Science and Information TechnologyLiaocheng UniversityLiaocheng 252059China North China Institute of Computing Technology Beijing 100083China Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province College of Physics and Optoelectronic EngineeringShenzhen UniversityShenzhen 518060China School of Precision Instrument and Opto-electronics Engineering Tianjin UniversityTianjin 300072China

The advancement of 6G technology relies on the development of high-performance terahertz detectors that can operate at room *** detectors are crucial for Internet of Things(Io T)applications,which require sensitive environmental sensing and efficient reception of 6G *** significant research focus is on detection technology with high responsiveness and low equivalent noise power for 6G signals,which experience high losses in the *** meet the demand for ultra-sensitive detectors in 6G technology,we have employed several ***,we prepared a large area of Weyl-semimetal layer through magnetron ***,we obtained a high-quality Weyl-semimetal active layer by carefully controlling the annealing ***,a thin nano-Au layer was introduced as a micro-cavity reflection layer to enhance the device's detection ***,we incorporated an electromagnetic induction well to improve carrier confinement and enhance the detection *** proposed high-performance terahertz detector,with its potential for industrial production,offers a valuable technical solution for the advancement of 6G technology.

关键词： micro-cavity Weyl semimetal crystalline thin film THz device

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Temporal and Heterogeneous Graph Neural Network for Remaining Useful Life Prediction

arXiv

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arXiv 2024年

作者： Wen, Zhihao Fang, Yuan Wei, Pengcheng Liu, Fayao Chen, Zhenghua Wu, Min School of Computing and Information Systems Singapore Management University Singapore188065 Singapore Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore Institute for Infocomm Research A*STAR Singapore

Predicting Remaining Useful Life (RUL) plays a crucial role in the prognostics and health management of industrial systems that involve a variety of interrelated sensors. Given a constant stream of time series sensory data from such systems, deep learning models have risen to prominence at identifying complex, nonlinear temporal dependencies in these data. In addition to the temporal dependencies of individual sensors, spatial dependencies emerge as important correlations among these sensors, which can be naturally modelled by a temporal graph that describes time-varying spatial relationships. However, the majority of existing studies have relied on capturing discrete snapshots of this temporal graph, a coarse-grained approach that leads to loss of temporal information. Moreover, given the variety of heterogeneous sensors, it becomes vital that such inherent heterogeneity is leveraged for RUL prediction in temporal sensor graphs. To capture the nuances of the temporal and spatial relationships and heterogeneous characteristics in an interconnected graph of sensors, we introduce a novel model named Temporal and Heterogeneous Graph Neural Networks (THGNN). Specifically, THGNN aggregates historical data from neighboring nodes to accurately capture the temporal dynamics and spatial correlations within the stream of sensor data in a fine-grained manner. Moreover, the model leverages Feature-wise Linear Modulation (FiLM) to address the diversity of sensor types, significantly improving the model’s capacity to learn the heterogeneity in the data sources. Finally, we have validated the effectiveness of our approach through comprehensive experiments. Our empirical findings demonstrate significant advancements on the N-CMAPSS dataset, achieving improvements of up to 19.2% and 31.6% in terms of two different evaluation metrics over state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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Diagnosing Breast Cancer Accurately Based on Weighting of HeterogeneousClassification Sub-Models

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Computer systems Science & Engineering 2022年第9期42卷 1257-1272页

作者： Majdy Mohamed Eltayeb Eltahir Tarig Mohammed Ahmed Department of Information Systems College of Science&Arts at MahayilKing Khalid UniversityMuhayel Aseer62529Kingdom of Saudi Arabia Department of Information Technology Faculty of Computing and Information TechnologyKing Abdul-Aziz UniversityJeddah21589Kingdom of Saudi Arabia Department of Computer Sciences Faculty of Mathematical Sciences and InformaticsUniversity of KhartoumKhartoum11115Sudan

In developed and developing countries, breast cancer is one of the leading forms of cancer affecting women alike. As a consequence of growing lifeexpectancy, increasing urbanization and embracing Western lifestyles, the highprevalence of this cancer is noted in the developed world. This paper aims todevelop a novel model that diagnoses Breast Cancer by using heterogeneous datasets. The model can work as a strong decision support system to help doctors tomake the right decision in diagnosing breast cancer patients. The proposed modelis based on three datasets to develop three sub-models. Each sub-model worksindependently. The final diagnosis decision is taken by the three sub-models independently. The power of the model comes from the diversity checks of patientsand this reduces the risk of wrong diagnosing. The model has been developedby conducting intensive experiments. Several classification algorithms were usedto select the best one in each sub-model. As the final results, the sub-modelaccuracies were 72%, 74% and 97%.

关键词： Breast cancer data mining classification

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The Role of AI in Enhancing Decision-Making in Small and Medium Enterprises (SMEs) 2

The Role of AI in Enhancing Decision-Making in Small and Med...

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2nd International Conference on computing and Data Analytics, ICCDA 2024

作者： Ines, Joefrelin C. Tolentino, Marilou Q. Cruz, Jay C. Dela Estabillo, Rolando Madlambayan, Anthony M. Mirano, Cirilo E. Vergara, Juvy A. College of Economics and Business Administration University of Technology and Applied Sciences Department of Business Admnistration Shinas Oman College of Business Systems Plus College Foundation Angeles City Philippines College of Computing and Information Sciences University of Technology and Applied Sciences Department of It Shinas Oman

ISBN: (纸本)9798331542573

This study investigates the transformative role of artificial intelligence (AI) in enhancing decision-making processes within small and medium enterprises (SMEs). By focusing on AI applications such as predictive analytics, the research highlights how these tools improve operational efficiency, optimize resource management, and facilitate real-time decision-making. Employing a quantitative methodology, data was collected from 100 SMEs in the Angeles City, Philippines through structured surveys, which were analyzed using descriptive statistics, regression, and correlation techniques. The findings reveal that AI significantly enhances decision-making processes, leading to improved business outcomes, including increased operational efficiency and profitability. Notably, 75% of the surveyed SMEs reported using predictive analytics, resulting in heightened decision- aking speed and accuracy. This study underscores the critical importance of AI in empowering SMEs to navigate market challenges effectively and sustain competitive advantages in an increasingly data-driven business landscape. © 2024 IEEE.

关键词： Artificial Intelligence Business Performance Decision-Making Operational Efficiency Predictive Analytics Resource Optimization Small and Medium Enterprises

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MRIAD: A Pre-clinical Prevalence Study on Alzheimer’s Disease Prediction Through Machine Learning Classifiers 4th

MRIAD: A Pre-clinical Prevalence Study on Alzheimer’s Dise...

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4th Joint International Conference on Deep Learning, Big Data and Blockchain, DBB 2023

作者： Loba, Jannatul Mia, Md. Rajib Mahmud, Imran Mahi, Md. Julkar Nayeen Whaiduzzaman, Md. Ahmed, Kawsar Department of Software Engineering Daffodil International University Dhaka Bangladesh School of Information Systems Queensland University of Technology Brisbane Australia Department of Electrical and Computer Engineering University of Saskatchewan Saskatoon Canada

ISBN: (纸本)9783031423161

Alzheimer’s disease (AD) is a neurological illness that worsens with time. The aged population has expanded in recent years, as has the prevalence of geriatric illnesses. There is no cure, but early detection and proper treatment allow sufferers to live normal lives. Furthermore, people with this disease’s immune systems steadily degenerate, resulting in a wide range of severe disorders. Neuroimaging Data from magnetic resonance imaging (MRI) is utilized to identify and detect the disease as early as possible. The data is derived from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) collection of 266 people with 177 structural brain MRI imaging, DTI, and PET data for intermediate disease diagnosis. When neuropsychological and cognitive data are integrated, the study found that ML can aid in the identification of preclinical Alzheimer’s disease. Our primary objective is to develop a model that is reliable, simple, and rapid for diagnosing preclinical Alzheimer’s disease. According to our findings (MRIAD), the Logistic Regression (LR) model has the best accuracy and classification prediction of about 98%. The ML model is also developed in the paper. This article profoundly, describes the possibility to getting into Alzheimer’s disease (AD) information from the pre-clinical or non-preclinical trial datasets using Machine Learning Classifier (ML) approaches. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Magnetic resonance imaging

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