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International Conference on Computational Intelligence and Knowledge Economy (ICCIKE)

作者： Ahmed Taha Ameen Ahmed M. Fouad Emil Mourad Matta Rawan Abdelkhalek Elkhishen Shorouk Abdelfattah Alalem Aya Mohamed Safan Islam Tharwat Abdel Halim School of Engineering and Applied Sciences Nile University Giza Egypt School of Information Technology and Computer Science Nile University Giza Egypt Center of Informatics (CIS) Nile University Giza Egypt

Shortest Path Algorithms are an important set of algorithms in today’s world. It has many applications like Traffic Consultation, Route Finding, and Network Design. It is essential for these applications to be fast and efficient as they mostly require real-time execution. Sequential execution of shortest path algorithms for large graphs with many nodes is time-consuming. On the other hand, parallel execution can make these applications faster. In this paper, three popular shortest path algorithms - Dijkstra, Bellman-Ford, and Floyd Warshall - are both implemented as serial and parallel programs and tested on various problem sizes. The performance of these algorithms is evaluated by comparing their execution times. To achieve parallelization, the OpenMP (Open multiprocessing) framework is employed.

关键词： Real-time systems Computational intelligence

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Stock Price Forecasting in Finance with Artificial Neural Networks

Stock Price Forecasting in Finance with Artificial Neural Ne...

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Information engineering (ACIE), IEEE Asia Conference on

作者： Turgut Daloğlu Garibe Osi Murat Aslan Mehmet Beşkirli Cizre District Directorate of National Education Şırnak University Şırnak Türkiye Department of Computer Engineering Şırnak University Şırnak Türkiye Departmen of Medical Education and Informatics Faculty of Medicine Karamanoğlu Mehmetbey University Karaman Türkiye

ISBN: (数字)9798331528409

ISBN: (纸本)9798331528416

This paper examines the applications of Artificial Neural Networks (ANNs) in the field of finance, particularly in stock price forecasting. Artificial neural networks are mathematical models that mimic biological nervous systems and are used to solve complex problems such as financial data analysis. Stock price forecasting is an important topic for investors and finance professionals. Artificial neural networks have been widely used in this field due to their ability to process large amounts of financial data, learn complex relationships and predict future price movements. This study deals with stock price forecasting with artificial neural networks using historical stock prices, economic indicators and other financial data. Neural networks attempt to predict future price movements by learning the complex relationships between these data. Stock price forecasting with artificial neural networks can offer a more flexible and adaptive approach than traditional statistical methods. However, the success of the model depends on factors such as the quality of the data set used, network architecture and training parameters. This study was conducted to understand the potential of artificial neural networks in finance and to evaluate their effectiveness in stock price forecasting. The purpose of this study is to explore how artificial neural networks can be used in financial data analysis, and in particular their potential in stock price forecasting. The study is designed to understand the advantages of applying ANN in finance, to evaluate the stock price forecasting performance of this technology and to provide a new perspective that can influence future investment strategies.

关键词： Training Accuracy Statistical analysis Finance Training data Predictive models Data models Forecasting Long short term memory Testing

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Wide-range Angle-sensitive Plasmonic Color Printing with a Lossy Resonator

Wide-range Angle-sensitive Plasmonic Color Printing with a L...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Chowdhury, Sarah N. Simon, Jeffrey Nowak, Michal P. Prokopeva, Ludmila J. Pagadala, Karthik Fruhling, Colton Nyga, Piotr Bravo, Esteban Garcia Kildishev, Alexander V. Shalaev, Vladimir M. School of Electrical and Computer Engineering Birck Nanotechnology Center Purdue University 1205 W State St West LafayetteIN47906 United States Institute of Optoelectronics Military University of Technology 2 Kaliskiego St Warsaw00-908 Poland Institute of Physics Faculty of Physics Astronomy and Informatics Nicolaus Copernicus University Toruń87-100 Poland Department of Computer Graphics Technology Purdue University 401 N. Grant St Knoy Hall West LafayetteIN47907 United States

We demonstrate a wide gamut of color generation by large-scale, lithography-free, and environment-friendly plasmonic structures with a resolution of 100 urn for macroscopic color printing by utilizing femtosecond lase... 详细信息

ISBN: (纸本)9781957171258

关键词： Color

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Deep learning-based real time detection for cardiac objects with fetal ultrasound video

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informatics in Medicine Unlocked 2023年 36卷

作者： Iriani Sapitri, Ade Nurmaini, Siti Naufal Rachmatullah, Muhammad Tutuko, Bambang Darmawahyuni, Annisa Firdaus, Firdaus Rini, Dian Palupi Islami, Anggun Intelligent System Research Group Faculty of Computer Science Universitas Sriwijaya Palembang 30139 Indonesia Doctoral Program Faculty of Engineering Universitas Sriwijaya Palembang 30139 Indonesia Department of Informatics Engineering Faculty of Computer Science Universitas Sriwijaya Palembang 30139 Indonesia

Fetal cardiac anatomical structure interpretation by ultrasound (US) is a key part of prenatal assessment. Unfortunately, the numerous speckles in US video, the small size of fetal cardiac structures, and unfixed fetal positions make manual detection processes difficult. To alleviate such problems, a deep learning model was developed to fully automate the processing of fetal echocardiographs with 2-dimensional cross-sectional images. The weakness of such an approach is that a physician immediately interprets US video with 3-dimensional objects to diagnose patients in clinical practice. To improve fetal cardiac anatomical structure interpretation via US for accurate and real time diagnosis, this paper proposes a real-time fetal cardiac substructure detection using US video with the You Only Look Once (YOLO) framework. In YOLO, an end-to-end neural network makes predictions for cardiac substructure objects, boxes, and class probabilities simultaneously. To achieve reliable performance, 40 fetal echocardiography videos were trained with the new YOLOv7 architecture and fine-tuned to work optimally and run efficiently. We conducted with nine fetal cardiac substructure objects such as the left atrium, right atrium, left ventricle, right ventricle, tricuspid valve, pulmonary valve, mitral valve, aortic valve, and aorta. The results yielded the highest mean average precision of 82.10%, reaching 17 frames per second (FPS) for nine cardiac substructure objects in 0.3 ms. The main finding of our study is that with even a limited number of US videos, YOLOv7 can detect fetal cardiac substructure objects in real-time. Such a network can work efficiently to detect small fetal cardiac objects automatically in a rapid phase with the help of proper fine-tuning. This work mainly assists medical experts in the fetal cardiac anatomy diagnostic process. © 2022 The Authors

关键词： computer vision Fetal echocardiography Object detection YOLOv7

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Action Localization and Recognition through Unsupervised I3D and TSN

Action Localization and Recognition through Unsupervised I3D...

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Computing and Information Technology (ICCIT-1441), 2020 International Conference on

作者： Munzir Umran Kahlil Muchtar Taufik Fuadi Abidin Fitri Arnia Dept. of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia Dept. of Electrical and Computer Engineering Telematics Research Center (TRC) Universitas Syiah Kuala Banda Aceh Indonesia Department of Informatics Faculty of Mathematics and Natural Sciences Universitas Syiah Kuala Banda Aceh Indonesia

Since CCTV technology was widely introduced as a video surveillance system, it has become very popular due to increased security concerns. However, detecting suspicious behavior is still done manually by humans, which is inefficient in terms of accuracy and manpower (prone to distraction and fatigue). Many studies use the entire frame of a video to recognize human action, whereas in this study first localize the area of object movement using the unsupervised inflated 3D (I3D) network which produces several frames that have been determined as the region of interest (ROI) from the total frames in the video. Then the collection of ROI frames is generated into a video which will then be classified to recognize human action through the Temporal Segment Networks (TSN). For efficiency purposes, the proposed pipeline can be run separately in the edge (for localization), and cloud environment (for recognition), respectively. The results show that there are 54 classes of ROI video data sets that have an average human action recognition prediction similarity rate above 84.6% which is the average of the prediction similarity rate of the entire ROI video data. In terms of prediction accuracy, 37 classes of data (36.63%) are better than the original class, including 25 classes of data (24.75%) above the threshold, and the processing speed of classification with ROI video data is on average 5 times faster than full-frame video data.

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Radar Probing Optimization for Joint Beamforming and UAV Trajectory Design in UAV-Enabled Integrated Sensing and Communication

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IEEE Transactions on Communications 2024年

作者： Liu, Yaxi Mao, Wencan He, Boxin Huangfu, Wei Huang, Tianyao Zhang, Haijun Long, Keping University Of Science And Technology Beijing Beijing Advanced Innovation Center For Materials Genome Engineering Beijing Engineering And Technology Research Center For Convergence Networks And Ubiquitous Services School Of Computer And Communication Engineering Beijing100083 China University Of Science And Technology Shunde Innovation School Beijing China National Institute Of Informatics Information Systems Architecture Science Research Division Tokyo101-8430 Japan

Unmanned aerial vehicle (UAV)-enabled massive multiple-input-multiple-output (MIMO) integrated sensing and communication (ISAC) is an emerging platform to perform communication and sensing efficiently and flexibly. However, the existing works barely consider the radar probing tasks and neglect the benefits of the dedicated sensing signal. In this paper, we focus on joint optimizations in radar probing tasks, and a novel indicator is introduced, namely radar probing error. Two optimizations in radar probing tasks are established: i) joint transmit beamforming design for large-scale regional radar probing and communication task;ii) joint transmit beamforming and UAV trajectory design for communication enhancement and radar probing task. For the former task, we adopt both communication and novel sensing precoders to further support the MIMO radar. A semidefinite relaxation is utilized to relax the original non-convex problem, which is proven to be tight. For the latter task, we adopt block coordinate descent to alternately optimize the precoders and UAV trajectory where the fractional programming approach and successive convex approximation are further adopted. Experiment results testify the validation of the proposed methods for radar probing tasks in UAV-enabled MIMO ISAC. Moreover, results show the fundamental trade-off between the dual functions and reveal the effectiveness of the introduced sensing precoder. © 1972-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Research Trends, Challenges, and Future Directions in Vehicular Networks

Research Trends, Challenges, and Future Directions in Vehicu...

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ACS/IEEE International Conference on computer Systems and Applications

作者： Islam Tharwat Abdel Halim Hossam M.A. Fahmy Center for Informatics Science (CIS) School of Information Technology and Computer Science (ITCS) Nile University (NU) Giza Egypt Department of Computers and Systems Engineering Ain Shams University Cairo Egypt

nowadays, vehicular networks constitute the core of the Intelligent Transportation Systems (ITSs) with the main objective is to improve the road safety by taking advantage of the emerging communication technology and the lower cost of wireless devices. Indeed, thanks to sensors installed in vehicles or at the edge of roads and control centers. Vehicle communications will enable drivers to be warned early enough, in case of possible dangers. In addition, these networks are no longer content to improve road safety only, but they also will offer new services to road users making the road more pleasant. The major objective of this article is to provide a brief review of vehicular networks evolution, architecture, characteristics, standards, constraints, and challenges that face designers in the design of protocols dedicated to this type of networks. Also, this paper provides perspectives for future research and developments.

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Towards Unified Field-Testing and Monitoring for Safe and Secure Robotic Applications

Towards Unified Field-Testing and Monitoring for Safe and Se...

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Workshop on Robotics Software engineering (RoSE)

作者： Marco Stadler Stefan Biffl Michael Vierhauser Johannes Sametinger LIT SCSL / Inst. of Business Informatics - Software Eng. Johannes Kepler University Linz Austria CDL-SQI Institute of Information Systems Engineering TU Wien and CDP Vienna Austria Department of Computer Science University of Innsbruck Austria

ISBN: (数字)9798331537951

ISBN: (纸本)9798331537968

Problems and failures that emerge in Cyber-Physical Systems (CPSs), particularly in robotic applications, may originate from various sources, including software bugs, security incidents, hardware malfunctions, and human errors. As robotic systems are deployed in various domains and application contexts, such as manufacturing sites, shop floors, agriculture, and autonomous vehicles, ensuring their safe and secure operation is a crucial aspect. While high-fidelity simulations are frequently used to validate system behavior and to perform tests, the “simulator-to-reality gap” presents significant challenges, requiring additional field testing to validate a system under realistic conditions. As simulations alone are insufficient for performing comprehensive testing and for ensuring adherence to both functional and non-functional requirements, real-world field testing helps to alleviate these issues. However, compared to wellestablished unit testing approaches, field testing typically is still a rather ad hoc process, with insufficient support from tools and frameworks. Field tests often heavily rely on human observations, hence risking overlooking critical issues. There is a pressing need for structured, guided field-testing processes combined with adaptive runtime monitoring to capture the data required for effective error diagnosis and analysis. This paper introduces initial concepts for the Smart Unified Runtime Monitoring Infrastructure for Guided Field-Testing (SMURF) framework designed for robotic applications, combining structured test execution with automated, adaptable monitors, to ensure the efficient collection of data required for post-test analysis. Building on prior efforts in drone field-testing frameworks, we extend our scope to identify essential features for testing and monitoring ROS-based systems. Future work shall further refine this process and implement a practical framework to support developers and testers in achieving reliable, safe, and secure

关键词： Runtime Pressing Software Safety Manufacturing Security Reliability Robots Monitoring Testing

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Promoting Accurate Image Reconstruction via Synthetic Noise for Unsupervised Screw Anomaly Detection and Location

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Chen, Jian Wen Ju Lin, Wan Li, Kuan Ming Mrozek, Dariusz Hung, Che Lun National Tsing Hua University Department of Computer Science Hsinchu300044 Taiwan National Kaohsiung University of Science and Technology Department of Mold and Die Kaohsiung80778 Taiwan National Taiwan University Department of Mechanical Engineering Taipei106319 Taiwan Silesian University of Technology Department of Applied Informatics Gliwice44-100 Poland National Yang Ming Chiao Tung University Institute of Biomedical Informatics Taipei11221 Taiwan

With residuals-based unsupervised models, defects can be detected using only normal samples, boosting both efficiency and practicality in industrial anomaly detection (AD) applications. While these approaches offer benefits, they also present challenges in detecting anomalies in cylindrical objects, such as imprecise image reconstruction and camera blind spots. As a result, this article introduces ScrewNet, an unsupervised AD framework designed specifically for cylindrical objects like screws. To improve the accuracy of reconstruction in the AD network, the synthetic noise module and pair training approach are employed during the training stage. The synthetic noise module, utilizing one of the CycleGAN generators, produces defects that match real-world distributions, thereby enabling the AD network to extract valuable features while preserving image structure. The pair training approach is designed to enable the AD network to achieve accurate reconstruction using similar background input-output pairs. To tackle the inspection blind spots problem, an efficient, cost-effective inspection system using multiple mirrors is developed for the inference stage. This approach enables effective AD across the entire 360° surface of the screw. In experiments, three different types of screw datasets containing both damaged and undamaged screws are provided. Numerous experiments demonstrate the superiority of our approach over several state-of-the-art (SOTA) unsupervised AD networks. For instance, ScrewNet outperforms baselines by 4 dB in average peak signal-to-noise ratio (PSNR) for image reconstruction quality and by 32% in average area under the receiver operating characteristic curve (AUROC) for pixel-level anomaly segmentation. In addition, image-level detection achieves 98.3% accuracy across the three screw datasets. The code is available at https://***/vitowen9580/ScrewNet. © 2025 IEEE.

关键词： Image reconstruction

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ENHANCING LOW DOSE COMPUTED TOMOGRAPHY IMAGES USING CONSISTENCY TRAINING TECHNIQUES

arXiv

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arXiv 2024年

作者： Gokmen, Mahmut S. Bumgardner, Cody Zhang, Jie Wang, Ge Chen, Jin Department of Computer Science University of Kentucky United States Department of Internal Medicine Institute for Biomedical Informatics University of Kentucky United States Department of Radiology University of Kentucky United States Department of Biomedical Engineering Rensselaer Polytechnic Institute United States Department of Medicine Department of Biomedical Informatics and Data Science University of Alabama Birmingham United States

Diffusion models have significant impact on wide range of generative tasks, especially on image inpainting and restoration. Although the improvements on aiming for decreasing number of function evaluations (NFE), the iterative results are still computationally expensive. Consistency models are as a new family of generative models, enable single-step sampling of high quality data without the need for adversarial training. In this paper, we introduce the beta noise distribution, which provides flexibility in adjusting noise levels. This is combined with a sinusoidal curriculum that enhances the learning of the trajectory between the noise distribution and the posterior distribution of interest, allowing High Noise Improved Consistency Training (HN-iCT) to be trained in a supervised fashion. Additionally, High Noise Improved Consistency Training with Image Condition (HN-iCT-CN) architecture is introduced, enables to take Low Dose images as a condition for extracting significant features by Weighted Attention Gates (WAG). Our results indicate that unconditional image generation using HN-iCT significantly outperforms basic CT and iCT training techniques with NFE=1 on the CIFAR10 and CelebA datasets. Moreover, our image-conditioned model demonstrates exceptional performance in enhancing low-dose (LD) CT scans. Copyright © 2024, The Authors. All rights reserved.

关键词： computerized tomography

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