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Periodica polytechnica Electrical engineering and computer science 2023年第4期67卷 384-393页

作者： Gyenes, Zoltán Bölöni, Ladislau Szádeczky-Kardoss, Emese Gincsainé Department of Control Engineering and Information Technology Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Muegyetem rkp. 3. BudapestH-1111 Hungary Department of Computer Science University of Central Florida 4328 Scorpius St OrlandoFL32816-2362 United States

The present study aims to explore the adaptation of estimation methodologies, specifically Particle filters and Kalman filters, for the purpose of determining the position and velocity vector of obstacles within the operational workspace of mobile robots. These algorithms are commonly employed in the motion planning tasks of mobile robots for the estimation of their own position. The proposed methodology utilizes LiDAR sensor data to estimate the position vectors and calculate the velocity vectors of obstacles. Additionally, an uncertainty parameter can be determined using the introduced perception method. The performance of the newly adapted algorithms is evaluated through comparison of the absolute error in position and velocity vector estimations. © 2023 Budapest University of Technology and Economics. All rights reserved.

关键词： Mobile robots

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Macular Holes Detection Using Deep Learning on Optical Coherence Tomography Images

Macular Holes Detection Using Deep Learning on Optical Coher...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Hung, Che-Lun Lin, Keng-Hung Lee, Yu-Kai Lin, ChunHsien Tsai, Yin-Te Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taiwan Taichung Veterans General Hospital Department of Ophthalmology Taiwan Providence University Department of Computer Science and Information Engineering Taiwan Providence University Department of Computer Science and Communication Engineering Taiwan

ISBN: (纸本)9798350337488

Macular holes (MHs) can be either idiopathic or secondary to a result of concurrent or previous pathology such as ocular inflammation, trauma or surgery. Idiopathic macular holes may eventually impair the life quality and self-care capability of patients. In clinical, the optical coherence tomography (OCT) images of macular holes can be divided into 4 stages based upon the size of macular hole. The size of macular hole is inversely influencing surgical success rate and visual outcomes. Minimizing human judgment errors in the classification of MHs and enhancing the overall quality of classification are critical objectives. In this paper, we develop a deep learning algorithm to detect MHs on OCT images and also propose an automatic algorithm to measure the size of MH. The MH detection algorithm demonstrates exceptional accuracy, while the measurement algorithm offers superior efficiency when compared to the conventional caliper-based method utilized with spectral-domain OCT devices - a time-consuming procedure for ophthalmologists. These advancements promise to expedite the diagnostic process and facilitate to rapidly recognize the stage 2 MHs from stage 3 or 4 cases. © 2023 IEEE.

关键词： Deep Learning Macular Holes OCT images

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Advancing Breast Cancer Diagnosis:The Development and Validation of the HERA-Net Model for Thermographic Analysis

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computers, Materials & Continua 2024年第12期81卷 3731-3760页

作者： S.Ramacharan Martin Margala Amjan Shaik Prasun Chakrabarti Tulika Chakrabarti School of Computing and Informatics University of LouisianaLafayetteLA 70504USA Computer Science and Engineering St.Peter’s Engineering CollegeHyderabad500100India Director Research and Dean International Affairs Sir Padampat Singhania UniversityUdaipur313601RajasthanIndia Department of Basic Sciences Sir Padampat Singhania UniversityUdaipur313601RajasthanIndia

Breast cancer remains a significant global health concern,with early detection being crucial for effective treatment and improved survival *** study introduces HERA-Net(Hybrid Extraction and Recognition Architec-ture),an advanced hybrid model designed to enhance the diagnostic accuracy of breast cancer detection by leveraging both thermographic and ultrasound imaging *** HERA-Net model integrates powerful deep learning architectures,including VGG19,U-Net,GRU(Gated Recurrent Units),and ResNet-50,to capture multi-dimensional features that support robust image segmentation,feature extraction,and temporal *** thermographic imaging,a comprehensive dataset of 3534 infrared(IR)images from the DMR(Database for Mastology Research)was utilized,with images captured by the high-resolution FLIR SC-620 *** dataset was partitioned with 70%of images allocated to training,15%to validation,and 15%to testing,ensuring a balanced approach for model development and *** prepare the images,preprocessing steps included resizing,Contrast-Limited Adaptive Histogram Equalization(CLAHE)for enhanced contrast,bilateral filtering for noise reduction,and Non-Local Means(NLMS)filtering to refine structural *** metrics such as mean,variance,standard deviation,entropy,kurtosis,and skewness were extracted to provide a detailed analysis of thermal distribution across ***,the ultrasound dataset was processed to extract detailed anatomical features relevant to breast cancer *** involved grayscale conversion,bilateral filtering,and Multipurpose Beta Optimized Bihistogram Equalization(MBOBHE)for contrast enhancement,followed by segmentation using Geodesic Active *** ultrasound and thermographic datasets were subsequently fed into HERA-Net,where VGG19 and U-Net were applied for feature extraction and segmentation,GRU for temporal pattern recognition,and ResNet-50 for *** performance assessme

关键词： Cancer breast cancer detection sensitivity deep learning accuracy

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Enhancing Voxel-Based Human Pose Classification Using CNN with Modified VGG16 Method 8

Enhancing Voxel-Based Human Pose Classification Using CNN wi...

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8th International Conference on Information Technology and Digital Applications, ICITDA 2023

作者： Rahmanti, Farah Zakiyah Pradnyana, Gede Aditra Priyadi, Ardyono Yuniarno, Eko Mulyanto Purnomo, Mauridhi Hery Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Surabaya60111 Indonesia Universitas Pendidikan Ganesha Department of Informatics Bali81116 Indonesia Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Department of Computer Engineering Surabaya60111 Indonesia

ISBN: (纸本)9798350344691

Human pose classification based on 3D point cloud is a challenging problem in pattern recognition and computer vision. The human pose prediction based on a 3D point cloud is a first step in human monitoring because the advantages are robustness to light and having an accurate location in 3D space. This research proposed a novel method for human pose classification based on a 3D point cloud to overcome that condition. However, with the proposed method of voxel-based feature extraction and Convolutional Neural Network (CNN) modified VGG16 achieved great success in classifying human poses with 3D point cloud inputs. This research proposes a CNN with modified VGG16 network to classify 3D point cloud human poses by present voxel-based feature extraction. This work uses our primary 3D point cloud data from LiDAR 32-channel. Before 3D point cloud learning, the first step is pre-processing data with normalization and extracting features with voxelization. Our experiment uses two types of classification cases, namely the classification for binary-class and multi-class 3D point cloud human poses. Experimental results show that our proposed method performs well and excellently, obtaining an accuracy value of 90% for the binary-class case and outperforming other existing methods. With our proposed method, it will be possible to recognize human poses better. © 2023 IEEE.

关键词： Feature extraction

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Optimized Image Dehazing using Dark Channel Prior with Minimum Fusion and Improved Atmospheric Light Handling

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IEEE Access 2025年 13卷 100720-100730页

作者： Hussain, Ahmad Iqbal, Mansoor Aslam, Sheraz Alahmari, Saad Alkharashi, Abdulwahab University of Wah Department of Computer Science Cantt Wah Pakistan CTL eurocollege Department of Computer Science Limassol 3077 Cyprus Cyprus University of Technology Department of Electrical Engineering Computer Engineering and Informatics Limassol 3036 Cyprus Northern Border University Applied College Department of Computer Science Arar Saudi Arabia Saudi Electronic University College of Computing and Informatics Department of Computer Science Saudi Arabia

Haze-induced image degradation in outdoor scenes, caused by atmospheric scattering and absorption, poses significant challenges for applications such as surveillance, autonomous navigation, and remote sensing. This research focuses on improving image dehazing by enhancing two key components: Dark Channel Prior (DCP) and Atmospheric Light Estimation (ALE). DCP exploits the statistical property that haze-free regions exhibit low minimum intensity values, aiding in accurate haze detection. ALE estimates atmospheric light from distant, less-scattered sources, ensuring precise haze removal. To further enhance dehazing performance, this study introduces a Minimum Fusion Technique, which selects the minimum intensity value for each pixel across multiple dehazed outputs, effectively preserving fine details and reducing artifacts. The proposed method is evaluated on the SOTS Outdoor dataset from the RESIDE benchmark, comprising 500 pairs of real-world hazy and clear images. By integrating optimized DCP, ALE, and the Minimum Fusion Technique, the approach achieves superior performance with a PSNR of 35.45 dB and an SSIM of 0.97, outperforming existing methods such as Scene-Specific DCP, TON, and FFA_NET. Additionally, it demonstrates reduced execution time and enhanced visual quality, making it a more efficient and reliable solution for real-world dehazing applications. These improvements establish the proposed method as a state-of-the-art approach for restoring clarity in outdoor images, contributing to more effective and accurate image reconstruction in challenging environments. © 2013 IEEE.

关键词： Atmospheric Light Estimation Dark Channel Prior Image Dehazing Minimum Fusion Technique Visual Clarity

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Evaluating the Inclusiveness of Artificial Intelligence Software in Enhancing Project Management Efficiency - A review and examples of quantitative measurement methods

Evaluating the Inclusiveness of Artificial Intelligence Soft...

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2024 International Conference on Artificial Intelligence, computer, Data Sciences, and Applications, ACDSA 2024

作者： Alevizos, Vasileios Georgousis, Ilias Simasiku, Akebu Messinis, Antonis Karypidou, Sotiria Malliarou, Dimitra Karolinska Institute Department of Learning Informatics Managament and Ethics Stockholm Sweden International Hellenic University Mlv Research Group Department of Computer Science Kavala Greece Zambia University Department of Computer Software Engineering Ndola Zambia Greece International Hellenic University Mlv Research Group Department of Computer Science Greece IntelliSolutions Athens Greece

ISBN: (纸本)9798350394528

The escalating integration of Artificial Intelligence (AI) in various domains, especially Project Management (PM), has brought to light the imperative need for inclusivity in AI systems. This paper investigates the role of AI software in augmenting both the inclusiveness and efficiency within the realm of PM. The research pivots around specific criteria that define and measure the inclusiveness of AI in PM, highlighting how AI, when developed with inclusiveness in mind, can significantly enhance project outcomes. However, there are inherent challenges in achieving this inclusiveness, primarily due to biases embedded in AI learning databases and the design and development processes of AI systems. The study offers a comprehensive examination of AI's potential to revolutionize PM by enabling managers to concentrate more on people-centric aspects of their work. This is achieved through AI's ability to perform tasks such as data collection, reporting, and predictive analysis more consistently and efficiently than human counterparts. However, the incorporation of AI in PM extends beyond mere efficiency;it represents a paradigm shift in the epistemology of PM, calling for a deeper understanding of AI's role and impact on society. Despite these advantages, the adoption of AI comes with significant challenges, particularly in terms of bias and inclusiveness. Biased AI learning databases, which use shared and reusable datasets, often perpetuate initially discriminatory algorithms. Moreover, unconscious biases and stereotypes of AI designers, developers, and trainers can inadvertently influence the behavior of the AI systems they create. This necessitates a paradigmatic shift in how AI systems are developed and governed to ensure they do not replicate or exacerbate existing social inequalities. The research proposes a methodological approach involving the development of criteria for inclusion and exclusion, alongside data extraction, to evaluate the inclusiveness and efficienc

关键词： Project management

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Advanced CNN Architectures for Improved Garbage Image Classification: An In-depth Evaluation

Advanced CNN Architectures for Improved Garbage Image Classi...

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South Eastern European Design Automation, computer engineering, computer Networks and Social Media Conference (SEEDA-CECNSM)

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

ISBN: (数字)9798331504489

ISBN: (纸本)9798331504496

Convolutional Neural Networks (CNNs) have become instrumental in advancing image classification, particularly in the context of garbage image classification, a critical component for efficient waste management. This paper introduces a tailored CNN architecture that demonstrates enhanced accuracy in garbage classification tasks, even with constrained datasets. Our architecture incorporates multiple convolutional, max-pooling, and fully connected layers, with dropout regularization strategically applied to curb overfitting and improve model generalization across a varied waste image dataset. Comparative evaluations reveal that our model achieves a significant improvement in accuracy over existing CNN models. The results not only validate the robustness of our approach but also contribute valuable insights toward developing more precise and efficient systems for garbage image classification.

关键词： Waste management Accuracy Design automation Social networking (online) Instruments Europe computer architecture Robustness Convolutional neural networks Image classification

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Numerical Modelling of The System of Doubly Nonlinear Diffusion Equations with Convective Transfer and Damping

Numerical Modelling of The System of Doubly Nonlinear Diffus...

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2024 Cognitive Models and Artificial Intelligence Conference, AICCONF 2024

作者： Sadullaeva, Shakhlo Azimbaevna Anarova, Shahzoda Amanbayevna Khaydarov, Abdugaffor Fayzullayeva, Zarnigor Nazirova, Dilorom Mirkhodzhaeva, Nazhiba departament Intellegent systems Joint Belarusian-Uzbek Intersectoral Institute of Applied Technical Qualifications Tashkent Uzbekistan Department of Convergence of Digital Technologies Computer Engineering Faculty Tashkent University of Information Technologies Tashkent Uzbekistan Department of Applied Mathematics and Computer Analysis National University of Uzbekistan Tashkent Uzbekistan Department of Informatics Computer Engineering Faculty Tashkent University of Information Technologies Tashkent Uzbekistan Department of Applied Mathematics and Computer Analysis NUUz Tashkent Uzbekistan

ISBN: (纸本)9798400716928

Currently, studies of double nonlinear equations and systems of double nonlinear equations representing many processes found in nature are considered relevant and necessary. These equations and systems, as a rule, are nonlinear, degenerate, and have properties that are absent in linear theory and therefore make them quite difficult for both analytical and numerical analysis of the solution. For this reason, great attention is paid to the research of nonlinear mathematical models, the construction of efficient numerical solution schemes and algorithms, and the creation of software using the capabilities of computer technologies. In this article, the mathematical model of the diffusion process in a two-component nonlinear medium under the influence of time-dependent convective transfer and damping is studied. The calculation scheme, algorithm, and software for nonlinear problems were developed. Based on a study of the qualitative properties of a nonlinear mathematical model of a reaction-diffusion system, computer modeling of reaction-diffusion processes was carried out. The developed computational schemes, algorithms, and software packages for solving systems of parabolic equations with double nonlinearity provide high productivity in studying the theory and processes of numerical solutions of such problems. © 2024 ACM.

关键词： Nonlinear equations

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Methodology for Identification of the Optimal Structure of Hybrid AC-DC Microgrids and Micro Energy Hubs 58

Methodology for Identification of the Optimal Structure of H...

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58th International Universities Power engineering Conference, UPEC 2023

作者： Charalambous, Chrysanthos Polycarpou, Alexis Efthymiou, Venizelos Georghiou, George E. Foss Research Centre for Sustainable Energy University of Cyprus Nicosia Cyprus Frederick University Department of Electrical Computer Engineering and Informatics Nicosia Cyprus

ISBN: (纸本)9798350316834

With the increasing availability of DC consumer appliances as well as the increasing penetration of direct current (DC) Distributed Energy Resources (DERs) such as photovoltaics (PV) and Energy Storage Systems (ESS), the concept of DC and hybrid AC-DC distribution systems and microgrids attract more and more attention. This paper presents the advantages of DC and hybrid AC-DC microgrids over the AC systems, based on the literature and proposes a methodology for identification of the optimal structure of a hybrid AC-DC microgrid and micro energy hub (mEH) to achieve high efficiency with lower losses and lower cost. In addition, a real test case was used as a case study in order to examine and validate the proposed methodology. The main outcome of this study is that the implementation of a hybrid AC-DC microgrid can bring a lot of benefits to consumers and system operators and play an important role in reducing losses and increasing the overall system performance in terms of energy efficiency and power quality. © 2023 IEEE.

关键词： Smart power grids

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On Combining Collective Entity Resolution and Repairing (Extended Abstract) 1

On Combining Collective Entity Resolution and Repairing (Ext...

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1st Workshop on AI-Driven Heterogeneous Data Management: Completing, Merging, Handling Inconsistencies and Query-Answering, ENIGMA 2023

作者： Bienvenu, Meghyn Cima, Gianluca Gutiérrez-Basulto, Víctor Univ. Bordeaux CNRS Bordeaux INP LaBRI UMR 5800 Department of Computer Control and Management Engineering Sapienza University of Rome School of Computer Science and Informatics Cardiff University

This work summarizes the salient aspects of our recent work [1], about combining collective entity resolution and repairing. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons Li... 详细信息

关键词： Repair

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