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Articoli di riviste sul tema "Multi-view architectures"

Autore: Grafiati

Pubblicato: 16 novembre 2024

Ultima modifica: 31 luglio 2025

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1

Tehrani, Mehrdad Panahpour, Michael Droese, Toshiaki Fujii, and Masayuki Tanimoto. "Distributed Source Coding Architectures for Multi-view Images." Journal of the Institute of Image Information and Television Engineers 58, no. 10 (2004): 1461–64. http://dx.doi.org/10.3169/itej.58.1461.

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Wang, Liang Hao, Ming Xi, Dong Xiao Li, and Ming Zhang. "A Network-Friendly Architecture for Multi-View Video Coding (MVC)." Advanced Materials Research 121-122 (June 2010): 678–81. http://dx.doi.org/10.4028/www.scientific.net/amr.121-122.678.

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Multi-view Video Coding (MVC) is very promising in applicable field for its 3D effect and interactive functions (multi viewpoint). In this paper, a network-friendly architecture for MVC is proposed. To exploit temporal as well as inter-view dependencies between adjacent cameras, two main features of the coder are used: hierarchical B picture and FGS (fine granularity scalable). Coding results are shown for the proposed multi-view coder and compared to the traditional coding architectures to show that our presented coding scheme outperforms the other approaches for the tested sequence.

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Koutris, Aristotelis, Theodoros Siozos, Yannis Kopsinis, et al. "Deep Learning-Based Indoor Localization Using Multi-View BLE Signal." Sensors 22, no. 7 (2022): 2759. http://dx.doi.org/10.3390/s22072759.

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In this paper, we present a novel Deep Neural Network-based indoor localization method that estimates the position of a Bluetooth Low Energy (BLE) transmitter (tag) by using the received signals’ characteristics at multiple Anchor Points (APs). We use the received signal strength indicator (RSSI) value and the in-phase and quadrature-phase (IQ) components of the received BLE signals at a single time instance to simultaneously estimate the angle of arrival (AoA) at all APs. Through supervised learning on simulated data, various machine learning (ML) architectures are trained to perform AoA esti

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Goktug Gurler, C., Anil Aksay, Gozde Bozdagi Akar, and A. Murat Tekalp. "Architectures for multi-threaded MVC-compliant multi-view video decoding and benchmark tests." Signal Processing: Image Communication 25, no. 5 (2010): 325–34. http://dx.doi.org/10.1016/j.image.2010.01.002.

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Ahn, Jun Hyong, Heung Cheol Kim, Jong Kook Rhim, et al. "Multi-View Convolutional Neural Networks in Rupture Risk Assessment of Small, Unruptured Intracranial Aneurysms." Journal of Personalized Medicine 11, no. 4 (2021): 239. http://dx.doi.org/10.3390/jpm11040239.

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Auto-detection of cerebral aneurysms via convolutional neural network (CNN) is being increasingly reported. However, few studies to date have accurately predicted the risk, but not the diagnosis itself. We developed a multi-view CNN for the prediction of rupture risk involving small unruptured intracranial aneurysms (UIAs) based on three-dimensional (3D) digital subtraction angiography (DSA). The performance of a multi-view CNN-ResNet50 in accurately predicting the rupture risk (high vs. non-high) of UIAs in the anterior circulation measuring less than 7 mm in size was compared with various CN

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Bajraktari, Flakë, and Peter P. Pott. "Multi-view surgical phase recognition during laparoscopic cholecystectomy." Current Directions in Biomedical Engineering 10, no. 4 (2024): 45–48. https://doi.org/10.1515/cdbme-2024-2011.

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Abstract In the realm of laparoscopic procedures, intelligent context-aware assistance systems hold promise for enhancing surgical workflows and patient safety. This study employs a multi-view approach to recognize surgical phases, combining data from a laparoscopic camera and an in-room camera simultaneously. The study aimed to improve phase recognition accuracy using a Transformer-based model with late sensor fusion, which yielded mixed results. The data poses significant challenges, as self-recorded videos are insufficient for extracting relevant information, necessitating real-world data.

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Zhao, Haimei, Qiming Zhang, Shanshan Zhao, Zhe Chen, Jing Zhang, and Dacheng Tao. "SimDistill: Simulated Multi-Modal Distillation for BEV 3D Object Detection." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 7 (2024): 7460–68. http://dx.doi.org/10.1609/aaai.v38i7.28577.

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Multi-view camera-based 3D object detection has become popular due to its low cost, but accurately inferring 3D geometry solely from camera data remains challenging and may lead to inferior performance. Although distilling precise 3D geometry knowledge from LiDAR data could help tackle this challenge, the benefits of LiDAR information could be greatly hindered by the significant modality gap between different sensory modalities. To address this issue, we propose a Simulated multi-modal Distillation (SimDistill) method by carefully crafting the model architecture and distillation strategy. Spec

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Suwarningsih, Wiwin, Ana Heryana, Dianadewi Riswantini, Ekasari Nugraheni, and Dikdik Krisnandi. "The multi-tenancy queueing system “QuAntri” for public service mall." Bulletin of Electrical Engineering and Informatics 11, no. 5 (2022): 2663–71. http://dx.doi.org/10.11591/eei.v11i5.4348.

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In the new-normal era, public services must make various adjustments to keep the community safe during the COVID-19 pandemic. The Public Service Mall is an initiative to put several public services offices in a centralized location. However, it will create a crowd of people who want access to public service. This paper evaluates multi-tenant models with the rapid adaptation of cloud computing technology for all organizations' shapes and sizes, focusing on multi-tenants and multi-services, where each tenant might have multiple services to offer. We also proposed a multi-tenant architecture that

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Debats, Oscar A., Geert J. S. Litjens, and Henkjan J. Huisman. "Lymph node detection in MR Lymphography: false positive reduction using multi-view convolutional neural networks." PeerJ 7 (November 22, 2019): e8052. http://dx.doi.org/10.7717/peerj.8052.

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Purpose To investigate whether multi-view convolutional neural networks can improve a fully automated lymph node detection system for pelvic MR Lymphography (MRL) images of patients with prostate cancer. Methods A fully automated computer-aided detection (CAD) system had been previously developed to detect lymph nodes in MRL studies. The CAD system was extended with three types of 2D multi-view convolutional neural networks (CNN) aiming to reduce false positives (FP). A 2D multi-view CNN is an efficient approximation of a 3D CNN, and three types were evaluated: a 1-view, 3-view, and 9-view 2D

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t M.D.Shelar, t. M. D. Shelar, Aishwarya C. Ayare Aishwarya.C.Ayare, Trushna S. Bagade Trushna.S.Bagade, Shivashree P. Nimbalkar Shivashree.P.Nimbalkar, and Muskan A. Mujawar Muskan.A.Mujawar. "Multi-View Feature Fusion for Effective Malware Classification Using Deep Learning." International Journal of Pharmaceutical Research and Applications 10, no. 3 (2025): 1070–76. https://doi.org/10.35629/4494-100310701076.

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The rapid increase in global malware infections has necessitated the development of robust malware detection systems to mitigate threats, such as ransomware and crypto-miners, that aim for financial gain. Deep learning-based Convolutional Neural Network (CNN) model for classifying malware in Portable Executable (PE) binary files using a fusion feature set approach. An extensive evaluation of various deep learning architectures and machine learning classifiers, including Support Vector Machines (SVM), was conducted across multi-aspect feature sets encompassing static, dynamic, and image-based f

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M.D.Shelar, M. D. Shelar, Aishwarya C. Ayare Aishwarya.C.Ayare, Trushna S. Bagade Trushna.S.Bagade, Shivashree P. Nimbalkar Shivashree.P.Nimbalkar, and Muskan A. Mujawar Muskan.A.Mujawar. "Multi-View Feature Fusion for Effective Malware Classification Using Deep Learning." International Journal of Advances in Engineering and Management 7, no. 6 (2025): 01–09. https://doi.org/10.35629/5252-07060109.

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The rapid increase in global malware infections has necessitated the development of robust malware detection systems to mitigate threats, such as ransomware and cryptominers, that aim for financial gain. Deep learningbased Convolutional Neural Network (CNN) model for classifying malware in Portable Executable (PE) binary files using a fusion feature set approach. An extensive evaluation of various deep learning architectures and machine learning classifiers, including Support Vector Machines (SVM), was conducted across multi-aspect feature sets encompassing static, dynamic, and image-based fea

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Abdikenov, Beibit, Tomiris Zhaksylyk, Aruzhan Imasheva, Yerzhan Orazayev, and Temirlan Karibekov. "Innovative Multi-View Strategies for AI-Assisted Breast Cancer Detection in Mammography." Journal of Imaging 11, no. 8 (2025): 247. https://doi.org/10.3390/jimaging11080247.

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Mammography is the main method for early detection of breast cancer, which is still a major global health concern. However, inter-reader variability and the inherent difficulty of interpreting subtle radiographic features frequently limit the accuracy of diagnosis. A thorough assessment of deep convolutional neural networks (CNNs) for automated mammogram classification is presented in this work, along with the introduction of two innovative multi-view integration techniques: Dual-Branch Ensemble (DBE) and Merged Dual-View (MDV). By setting aside two datasets for out-of-sample testing, we evalu

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Fuentes Reyes, M., P. d’Angelo, and F. Fraundorfer. "AN EVALUATION OF STEREO AND MULTIVIEW ALGORITHMS FOR 3D RECONSTRUCTION WITH SYNTHETIC DATA." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1/W2-2023 (December 13, 2023): 1021–28. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-w2-2023-1021-2023.

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Abstract. The reconstruction of 3D scenes from images has usually been addressed with two different strategies, namely stereo and multiview. The former requires rectified images and generates a disparity map, while the latter relies on the camera parameters and directly retrieves a depth map. For both cases, deep learning architectures have shown an outstanding performance. However, due to the differences between input and output data, the two strategies are difficult to compare on a common scene. Moreover, for remote sensing applications multi-view data is hard to acquire and the ground truth

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Joshi, Pratibha T., Gurpreet Singh Saini, and Shivaji D. Pawara. "Application Of Densenet Architecture And Its Variants Towards Breast Cancer Detection: A Multi-View Analysis." International Journal of Environmental Sciences 11, no. 9s (2025): 835–54. https://doi.org/10.64252/azskbm54.

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Artificial Intelligence has made giant strides in medical image classification using the development of Convolutional Neural Networks (CNNs) in the past decade. Different CNN architectures like Dense-Net Res-Net, etc., are used in the medical industry to identify patterns and features leading to a faster diagnosis. The fundamental motivation behind this research article is to study the application of different variants of Dense-Net architecture (DenseNet121, 169, and 201) towards breast cancer detection and provide a comparative analysis of Dense-net variants to the intended area of research w

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Zhang, Yueping, Ao-Jan Su, and Guofei Jiang. "Understanding data center network architectures in virtualized environments: A view from multi-tier applications." Computer Networks 55, no. 9 (2011): 2196–208. http://dx.doi.org/10.1016/j.comnet.2011.03.001.

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Mao, Wenju, Zhijie Liu, Heng Liu, Fuzeng Yang, and Meirong Wang. "Research Progress on Synergistic Technologies of Agricultural Multi-Robots." Applied Sciences 11, no. 4 (2021): 1448. http://dx.doi.org/10.3390/app11041448.

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Multi-robots have shown good application prospects in agricultural production. Studying the synergistic technologies of agricultural multi-robots can not only improve the efficiency of the overall robot system and meet the needs of precision farming but also solve the problems of decreasing effective labor supply and increasing labor costs in agriculture. Therefore, starting from the point of view of an agricultural multiple robot system architectures, this paper reviews the representative research results of five synergistic technologies of agricultural multi-robots in recent years, namely, e

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Al Farid, Fahmid, Ahsanul Bari, Abu Saleh Musa Miah, Sarina Mansor, Jia Uddin, and S. Prabha Kumaresan. "A Structured and Methodological Review on Multi-View Human Activity Recognition for Ambient Assisted Living." Journal of Imaging 11, no. 6 (2025): 182. https://doi.org/10.3390/jimaging11060182.

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Ambient Assisted Living (AAL) leverages technology to support the elderly and individuals with disabilities. A key challenge in these systems is efficient Human Activity Recognition (HAR). However, no study has systematically compared single-view (SV) and multi-view (MV) Human Activity Recognition approaches. This review addresses this gap by analyzing the evolution from single-view to multi-view recognition systems, covering benchmark datasets, feature extraction methods, and classification techniques. We examine how activity recognition systems have transitioned to multi-view architectures u

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Yan, Fengyu, Xiaobao Wang, Dongxiao He, Longbiao Wang, Jianwu Dang, and Di Jin. "HeterGP: Bridging Heterogeneity in Graph Neural Networks with Multi-View Prompting." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 20 (2025): 21895–903. https://doi.org/10.1609/aaai.v39i20.35496.

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The challenges tied to unstructured graph data are manifold, primarily falling into node, edge, and graph-level problem categories. Graph Neural Networks (GNNs) serve as effective tools to tackle these issues. However, individual tasks often demand distinct model architectures, and training these models typically requires abundant labeled data, a luxury often unavailable in practical settings. Recently, various "prompt tuning" methodologies have emerged to empower GNNs to adapt to multi-task learning with limited labels. The crux of these methods lies in bridging the gap between pre-training t

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Zhu, Binglin, Fusang Liu, Ziwen Xie, Yan Guo, Baoguo Li, and Yuntao Ma. "Quantification of light interception within image-based 3-D reconstruction of sole and intercropped canopies over the entire growth season." Annals of Botany 126, no. 4 (2020): 701–12. http://dx.doi.org/10.1093/aob/mcaa046.

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Abstract Background and Aims Light interception is closely related to canopy architecture. Few studies based on multi-view photography have been conducted in a field environment, particularly studies that link 3-D plant architecture with a radiation model to quantify the dynamic canopy light interception. In this study, we combined realistic 3-D plant architecture with a radiation model to quantify and evaluate the effect of differences in planting patterns and row orientations on canopy light interception. Methods The 3-D architectures of maize and soybean plants were reconstructed for sole c

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O’Connell, Thomas P., Tyler Bonnen, Yoni Friedman, et al. "Approximating Human-Level 3D Visual Inferences With Deep Neural Networks." Open Mind 9 (2025): 305–24. https://doi.org/10.1162/opmi_a_00189.

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Abstract Humans make rich inferences about the geometry of the visual world. While deep neural networks (DNNs) achieve human-level performance on some psychophysical tasks (e.g., rapid classification of object or scene categories), they often fail in tasks requiring inferences about the underlying shape of objects or scenes. Here, we ask whether and how this gap in 3D shape representation between DNNs and humans can be closed. First, we define the problem space: after generating a stimulus set to evaluate 3D shape inferences using a match-to-sample task, we confirm that standard DNNs are unabl

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Wu, Min, Sirui Xu, Ziwei Wang, et al. "ICT-Net: A Framework for Multi-Domain Cross-View Geo-Localization with Multi-Source Remote Sensing Fusion." Remote Sensing 17, no. 12 (2025): 1988. https://doi.org/10.3390/rs17121988.

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Traditional single neural network-based geo-localization methods for cross-view imagery primarily rely on polar coordinate transformations while suffering from limited global correlation modeling capabilities. To address these fundamental challenges of weak feature correlation and poor scene adaptation, we present a novel framework termed ICT-Net (Integrated CNN-Transformer Network) that synergistically combines convolutional neural networks with Transformer architectures. Our approach harnesses the complementary strengths of CNNs in capturing local geometric details and Transformers in establ

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Chidera Ogeawuchi, Jeffrey, Abel Chukwuemeke Uzoka, Abraham Ayodeji Abayomi, Oluwademilade Aderemi Agboola, Toluwase Peter Gbenle, and Samuel Owoade. "Advancements in Scalable Data Modeling and Reporting for SaaS Applications and Cloud Business Intelligence." International Journal of Advanced Multidisciplinary Research and Studies 4, no. 6 (2024): 2155–62. https://doi.org/10.62225/2583049x.2024.4.6.4267.

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As Software-as-a-Service (SaaS) applications and cloud-based Business Intelligence (BI) platforms proliferate across industries, the demand for scalable, responsive, and intelligent data modeling and reporting solutions has become paramount. This paper presents a comprehensive conceptual and technical exploration of scalable data architectures and reporting mechanisms tailored for SaaS ecosystems and cloud-native BI environments. It begins by contextualizing the rise of multi-tenant cloud systems and outlines the need for resilient data modeling practices that balance extensibility with operat

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Núñez, Lorena, Jesús Savage, Miguel Moctezuma-Flores, Luis Contreras, Marco Negrete, and Hiroyuki Okada. "Multi-View Object Recognition and Pose Sequence Estimation Using HMMs." Journal of Robotics and Mechatronics 37, no. 3 (2025): 579–93. https://doi.org/10.20965/jrm.2025.p0579.

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This work proposes an integration of a vision system for a service robot when its gripper holds an object. Based on the particular conditions of the problem, the solution is modular and allows one to use various options to extract features and classify data. Since the robot can move the object and has information about its position, the proposed solution takes advantage of this by applying preprocessing techniques to improve the performance of classifiers that can be considered weak. In addition to being able to classify the object, it is possible to infer the sequence of movements that it car

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Efimov, A. O., I. I. Livshits, M. O. Meshcheryakov, E. A. Rogozin, and V. R. Romanova. "On certain aspects of standardization and operating conditions of automated systems." Herald of Dagestan State Technical University. Technical Sciences 50, no. 4 (2024): 101–8. http://dx.doi.org/10.21822/2073-6185-2023-50-4-101-108.

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Objective. In this paper, the main aspects of the operating conditions of the AS are considered, as well as the issues of standardization of the stages of the life cycle of the AS (creation, commissioning, maintenance, etc.) at the state level. In this subject area, the technological features of building an AS based on various technical architectures are briefly considered, since both foreign processors based on x86-64 architectures and processors of domestic development based on the Advanced RISC Machine architecture are currently applicable. The use of various components of the AS requires a

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Ugile, Tukaram, and Dr Nilesh Uke. "TRANSFORMER ARCHITECTURES FOR COMPUTER VISION: A COMPREHENSIVE REVIEW AND FUTURE RESEARCH DIRECTIONS." Journal of Dynamics and Control 9, no. 3 (2025): 70–79. https://doi.org/10.71058/jodac.v9i3005.

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Transformers have made revolutionary impacts in Natural Language Processing (NLP) area and started making significant contributions in Computer Vision problems. This paper provides a comprehensive review of the Transformer Architectures in Computer Vision, providing a detailed view about their evolution from Vision Transformers (ViTs) to more advanced variants of transformers like Swin Transformer, Transformer-XL, and Hybrid CNN-Transformer models. We have tried to make the study of the advantages of the Transformers over the traditional Convolutional Neural Networks (CNNs), their applications

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Aung, Aye Nyein, Che-Wei Liao, and Jeih-Weih Hung. "Effective Monoaural Speech Separation through Convolutional Top-Down Multi-View Network." Future Internet 16, no. 5 (2024): 151. http://dx.doi.org/10.3390/fi16050151.

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Speech separation, sometimes known as the “cocktail party problem”, is the process of separating individual speech signals from an audio mixture that includes ambient noises and several speakers. The goal is to extract the target speech in this complicated sound scenario and either make it easier to understand or increase its quality so that it may be used in subsequent processing. Speech separation on overlapping audio data is important for many speech-processing tasks, including natural language processing, automatic speech recognition, and intelligent personal assistants. New speech separat

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Xu, Kele, Kang You, Ming Feng, and Boqing Zhu. "Trust-worth multi-representation learning for audio classification with uncertainty estimation." Journal of the Acoustical Society of America 153, no. 3_supplement (2023): A125. http://dx.doi.org/10.1121/10.0018383.

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Multi-view learning has been explored for audio classification tasks, exploiting different representations of audio signals, ranging from MFCC, CQT, to raw signals. The quality of each view may vary for different audio signals, and the appropriate uncertainty quantification for each view has not been fully explored. In this work, we explore a trusted multi-view learning framework for classification tasks in order to fully incorporate different views. Our framework consists of three parallel branches of Transformer architectures (Gammatone spectrogram, log-Mel and CQT) and they are combined usi

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Wang, Jinglu, Bo Sun, and Yan Lu. "MVPNet: Multi-View Point Regression Networks for 3D Object Reconstruction from A Single Image." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8949–56. http://dx.doi.org/10.1609/aaai.v33i01.33018949.

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In this paper, we address the problem of reconstructing an object’s surface from a single image using generative networks. First, we represent a 3D surface with an aggregation of dense point clouds from multiple views. Each point cloud is embedded in a regular 2D grid aligned on an image plane of a viewpoint, making the point cloud convolution-favored and ordered so as to fit into deep network architectures. The point clouds can be easily triangulated by exploiting connectivities of the 2D grids to form mesh-based surfaces. Second, we propose an encoder-decoder network that generates such kind

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Yalcin, Ilyas, Recep Can, Candan Gokceoglu, and Sultan Kocaman. "A Novel Rock Mass Discontinuity Detection Approach with CNNs and Multi-View Image Augmentation." ISPRS International Journal of Geo-Information 13, no. 6 (2024): 185. http://dx.doi.org/10.3390/ijgi13060185.

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Discontinuity is a key element used by geoscientists and civil engineers to characterize rock masses. The traditional approach to detecting and measuring rock discontinuity relies on fieldwork, which poses dangers to human life. Photogrammetric pattern recognition and 3D measurement techniques offer new possibilities without direct contact with rock masses. This study proposes a new approach to detect discontinuities using close-range photogrammetric techniques and convolutional neural networks (CNNs) trained on a small amount of data. Investigations were conducted on basalts in Bala, Ankara,

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Ganeshkumar Palanisamy. "From Data Lakes to Data Fabric/Mesh: The Future of Enterprise Data Platforms in a Multi-Cloud World." Journal of Computer Science and Technology Studies 7, no. 5 (2025): 23–34. https://doi.org/10.32996/jcsts.2025.7.5.4.

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The evolution of enterprise data management has seen a shift from traditional data warehouses to data lakes, and now towards data fabrics and data meshes. This article explores this progression, particularly in the context of multi-cloud environments. Data fabrics provide a unified, virtualized view of data across disparate sources, while data meshes decentralize data ownership and governance, aligning with modern, agile development practices. The article discusses how these architectures can be implemented in multi-cloud setups, ensuring data consistency, security, and performance. It also ad

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Mrozek, Mirosław. "Multi-Agent Control System for the Movement of Uniaxial Objects." Solid State Phenomena 237 (August 2015): 183–88. http://dx.doi.org/10.4028/www.scientific.net/ssp.237.183.

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Multi-agent systems are used mainly in IT solutions and control groups of robots. From the point of view of classical control architectures, they are a kind of distributed systems in which nodes perform advanced algorithms, usually associated with the technology of artificial intelligence, and they can be considered as agents. The article describes the multi-agents control system of objects of uniaxial movements. An example of such a system to control a repository with movable racks with electric motors is presented. Each rack acts as an agent through the implemented control of the resources o

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Griffiths, David, and Jan Boehm. "A Review on Deep Learning Techniques for 3D Sensed Data Classification." Remote Sensing 11, no. 12 (2019): 1499. http://dx.doi.org/10.3390/rs11121499.

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Over the past decade deep learning has driven progress in 2D image understanding. Despite these advancements, techniques for automatic 3D sensed data understanding, such as point clouds, is comparatively immature. However, with a range of important applications from indoor robotics navigation to national scale remote sensing there is a high demand for algorithms that can learn to automatically understand and classify 3D sensed data. In this paper we review the current state-of-the-art deep learning architectures for processing unstructured Euclidean data. We begin by addressing the background

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PARIS, NICOLAS. "POMPC: A C LANGUAGE FOR DATA PARALLELISM." International Journal of Modern Physics C 04, no. 01 (1993): 85–96. http://dx.doi.org/10.1142/s0129183193000094.

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POMPC is a parallel language dedicated to the programming of Massively Parallel Computers according to a synchronous Data Parallel model. It is an extension of the ANSI C language and follows its philosophy. Parallelism is explicitly handled by the definition of collections of parallel variables and the definition of communication primitives. A methodology is presented in order to easily port the language on different target architectures. Virtualization is introduced to handle simultaneously several collections of different sizes and shapes. Virtualization management is a key point of the com

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Alouane-Ksouri, Sonia, and Minyar Sassi Hidri. "Fuzzy Learning of Co-Similarities from Large-Scale Documents." International Journal of Fuzzy System Applications 4, no. 4 (2015): 70–86. http://dx.doi.org/10.4018/ijfsa.2015100104.

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To analyze and explore large textual corpus, we are generally limited by the available main memory. This may lead to a proliferation of processor load due to greedy computing. The authors propose to deal with this problem to compute co-similarities from large-scale documents. The authors propose to enhance co-similarity learning by upstream and downstream parallel computing. The first deploys the fuzzy linear model in a Grid environment. The second deals with multi-view datasets while introducing different architectures by using several instances of a fuzzy triadic similarity algorithm.

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Naseer Ahamed Mohammed. "The convergence horizon: Cloud-native technologies reshaping society and infrastructure." World Journal of Advanced Engineering Technology and Sciences 15, no. 1 (2025): 538–46. https://doi.org/10.30574/wjaets.2025.15.1.0212.

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This article explores the transformative impact of cloud-native technologies on society and infrastructure beyond their technical implementations. Beginning with an overview of market growth and architectural principles, the discussion explores how these technologies drive environmental sustainability through optimized resource utilization and reduced carbon emissions. The article continues by examining workforce evolution, including emerging specializations, skills requirements, and the shift toward distributed work models. The article further follows how cloud-native platforms democratize te

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Yoshida, Naoto. "Homeostatic Agent for General Environment." Journal of Artificial General Intelligence 8, no. 1 (2018): 1–22. http://dx.doi.org/10.1515/jagi-2017-0001.

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AbstractOne of the essential aspect in biological agents is dynamic stability. This aspect, called homeostasis, is widely discussed in ethology, neuroscience and during the early stages of artificial intelligence. Ashby’s homeostats are general-purpose learning machines for stabilizing essential variables of the agent in the face of general environments. However, despite their generality, the original homeostats couldn’t be scaled because they searched their parameters randomly. In this paper, first we re-define the objective of homeostats as the maximization of a multi-step survival probabili

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Casellas, Ramon, Ricardo Martínez, Ricard Vilalta, and Raül Muñoz. "Abstraction and Control of Multi-Domain Disaggregated Optical Networks with OpenROADM Device Models." IEEE Journal of Lightwave Technology 38, no. 9 (2020): 2606–15. https://doi.org/10.1109/JLT.2020.2969248.

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Network operators are evolving their optical transport networks in order to make them cost effective. In some scenarios, this means considering adopting software-defined networking principles along with open and standard interfaces, leveraging the underlying hardware programmability while, at the same time, considering the benefits of (partial) disaggregation, in view of the potential benefits of decoupling terminal devices from the line systems or of separating the hardware from the controlling software. In this evolution, operators often segment their networks into domains. Reasons include t

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Agarla, Mirko, Paolo Napoletano, and Raimondo Schettini. "Quasi Real-Time Apple Defect Segmentation Using Deep Learning." Sensors 23, no. 18 (2023): 7893. http://dx.doi.org/10.3390/s23187893.

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Defect segmentation of apples is an important task in the agriculture industry for quality control and food safety. In this paper, we propose a deep learning approach for the automated segmentation of apple defects using convolutional neural networks (CNNs) based on a U-shaped architecture with skip-connections only within the noise reduction block. An ad-hoc data synthesis technique has been designed to increase the number of samples and at the same time to reduce neural network overfitting. We evaluate our model on a dataset of multi-spectral apple images with pixel-wise annotations for seve

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Thompson, Alison, Kelly Thorp, Matthew Conley, et al. "Comparing Nadir and Multi-Angle View Sensor Technologies for Measuring in-Field Plant Height of Upland Cotton." Remote Sensing 11, no. 6 (2019): 700. http://dx.doi.org/10.3390/rs11060700.

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Plant height is a morphological characteristic of plant growth that is a useful indicator of plant stress resulting from water and nutrient deficit. While height is a relatively simple trait, it can be difficult to measure accurately, especially in crops with complex canopy architectures like cotton. This paper describes the deployment of four nadir view ultrasonic transducers (UTs), two light detection and ranging (LiDAR) systems, and an unmanned aerial system (UAS) with a digital color camera to characterize plant height in an upland cotton breeding trial. The comparison of the UTs with manu

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Sharada, Gupta, and N. Eshwarappa Murundi. "Breast cancer detection through attention based feature integration model." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 2 (2024): 2254–64. https://doi.org/10.11591/ijai.v13.i2.pp2254-2264.

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Breast cancer is detected by screening mammography wherein X-rays are used to produce images of the breast. Mammograms for screening can detect breast cancer early. This research focuses on the challenges of using multi-view mammography to diagnose breast cancer. By examining numerous perspectives of an image, an attention-based feature-integration mechanism (AFIM) model that concentrates on local abnormal areas associated with cancer and displays the essential features considered for evaluation, analyzing cross-view data. This is segmented into two views the bi-lateral attention module (BAM)

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Sharifi, Ali Asghar, Ali Zoljodi, and Masoud Daneshtalab. "TrajectoryNAS: A Neural Architecture Search for Trajectory Prediction." Sensors 24, no. 17 (2024): 5696. http://dx.doi.org/10.3390/s24175696.

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Autonomous driving systems are a rapidly evolving technology. Trajectory prediction is a critical component of autonomous driving systems that enables safe navigation by anticipating the movement of surrounding objects. Lidar point-cloud data provide a 3D view of solid objects surrounding the ego-vehicle. Hence, trajectory prediction using Lidar point-cloud data performs better than 2D RGB cameras due to providing the distance between the target object and the ego-vehicle. However, processing point-cloud data is a costly and complicated process, and state-of-the-art 3D trajectory predictions u

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Zou, Yanmei, Hongshan Yu, Zhengeng Yang, Zechuan Li, and Naveed Akhtar. "Improved MLP Point Cloud Processing with High-Dimensional Positional Encoding." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 7 (2024): 7891–99. http://dx.doi.org/10.1609/aaai.v38i7.28625.

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Multi-Layer Perceptron (MLP) models are the bedrock of contemporary point cloud processing. However, their complex network architectures obscure the source of their strength. We first develop an “abstraction and refinement” (ABS-REF) view for the neural modeling of point clouds. This view elucidates that whereas the early models focused on the ABS stage, the more recent techniques devise sophisticated REF stages to attain performance advantage in point cloud processing. We then borrow the concept of “positional encoding” from transformer literature, and propose a High-dimensional Positional En

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Vinodkrishnan, Kulathumani, Nikhil Chandhok, Arjan Durresi, Raj Jain, Ramesh Jagannathan, and Srinivasan Seetharaman. "Survivability in IP over WDM networks." Journal of High Speed Networks 10, no. 2 (2001): 79–90. https://doi.org/10.3233/hsn-2001-200.

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The Internet is emerging as the new universal telecommunication medium. IP over WDM has been envisioned as one of the most attractive architectures for the new Internet. Consequently survivability is a crucial concern in designing IP over WDM networks. This paper presents a survey of the survivability mechanisms for IP over WDM networks and thus is intended to provide a summary of what has been done in this area and help further research. A number of optical layer protection techniques have been discussed. They are examined from the point of view of cost, complexity, and application. Survivabi

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Nathanael, Oliverio Theophilus, and Simeon Yuda Prasetyo. "Color and Attention for U : Modified Multi Attention U-Net for a Better Image Colorization." JOIV : International Journal on Informatics Visualization 8, no. 3 (2024): 1453. http://dx.doi.org/10.62527/joiv.8.3.1828.

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Image colorization is a tedious task that requires creativity and understanding of the image context and semantic information. Many models have been made by harnessing various deep learning architectures to learn the plausible colorization. With the rapid discovery of new architecture and image generation techniques, more powerful options can be explored and improved for image colorization tasks. This research explores a new architecture to colorize an image by using pre-trained embeddings on U-Net combined with several attention modules across the model. Using embeddings from a pre-trained cl

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Chen, Kun, Xin Li, and Huaiqing Wang. "On the model design of integrated intelligent big data analytics systems." Industrial Management & Data Systems 115, no. 9 (2015): 1666–82. http://dx.doi.org/10.1108/imds-03-2015-0086.

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Abstract (sommario):

Purpose – Although big data analytics has reaped great business rewards, big data system design and integration still face challenges resulting from the demanding environment, including challenges involving variety, uncertainty, and complexity. These characteristics in big data systems demand flexible and agile integration architectures. Furthermore, a formal model is needed to support design and verification. The purpose of this paper is to resolve the two problems with a collective intelligence (CI) model. Design/methodology/approach – In the conceptual CI framework as proposed by Schut (201

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Guptha, Sharada, and Murundi N. Eshwarappa. "Breast cancer detection through attention based feature integration model." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 2 (2024): 2254. http://dx.doi.org/10.11591/ijai.v13.i2.pp2254-2264.

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Abstract (sommario):

<span lang="EN-US">Breast cancer is detected by screening mammography wherein X-rays are used to produce images of the breast. Mammograms for screening can detect breast cancer early. This research focuses on the challenges of using multi-view mammography to diagnose breast cancer. By examining numerous perspectives of an image, an attention-based feature-integration mechanism (AFIM) model that concentrates on local abnormal areas associated with cancer and displays the essential features considered for evaluation, analyzing cross-view data. This is segmented into two views the bi-latera

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S, Janakiraman. "AI-POWERED DEPTH ESTIMATION USING DEEP LEARNING." International Scientific Journal of Engineering and Management 04, no. 06 (2025): 1–9. https://doi.org/10.55041/isjem04525.

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Abstract: Depth estimation is a crucial component in many computer vision domains, such as autonomous navigation, robotics, and augmented reality. This project investigates the use of deep learning to enhance depth prediction capabilities, aiming to deliver accurate and real-time 3D scene understanding. Utilizing advanced neural architectures like convolutional neural networks (CNNs), we introduce a novel approach for deriving depth information from either single-view or multi-view imagery. The proposed model effectively captures spatial context and depth indicators from extensive training dat

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Muhamad, Wardani, and Wawa Wikusna. "Software Architecture of E-assessment on Higher Education." IJAIT (International Journal of Applied Information Technology) 1, no. 02 (2017): 102. http://dx.doi.org/10.25124/ijait.v1i02.1030.

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Computer technology has been used to support the learning process at the university. Learning process, generally involved students and teachers in order to learn about materials on subject courses and also evaluate student competencies regularly. Teachers can evaluate student competencies or knowledge by e-assessment. E-assessment is one of the domains of e-learning which involves the use computer in assessment, includes: setting, delivery, marking and reporting of assessments. The Major benefit of the e - assessment system is its flexibility in term of global access and devices used to access

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Chen, Hanyue, Wenjiang Huang, Wang Li, Zheng Niu, Liming Zhang, and Shihe Xing. "Estimation of LAI in Winter Wheat from Multi-Angular Hyperspectral VNIR Data: Effects of View Angles and Plant Architecture." Remote Sensing 10, no. 10 (2018): 1630. http://dx.doi.org/10.3390/rs10101630.

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View angle effects present in crop canopy spectra are critical for the retrieval of the crop canopy leaf area index (LAI). In the past, the angular effects on spectral vegetation indices (VIs) for estimating LAI, especially in crops with different plant architectures, have not been carefully assessed. In this study, we assessed the effects of the view zenith angle (VZA) on relationships between the spectral VIs and LAI. We measured the multi-angular hyperspectral reflectance and LAI of two cultivars of winter wheat, erectophile (W411) and planophile (W9507), across different growing seasons. T

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Bhargava Pananthula, Manoj Kumar. "3D Image Reconstruction from Single 2D Image using Deep Learning." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44955.

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Abstract— Accurate 3D reconstruction from 2D images plays a critical role in various applications including medical imaging, robotics, autonomous navigation, and augmented reality. Traditional reconstruction techniques often require multiple viewpoints or sensor setups, limiting their feasibility in resource-constrained environments. In this work, we propose a deep learning-based monocular 3D reconstruction pipeline that generates high-quality 3D models from a single RGB image. The core of this framework lies in a custom U-Net++ architecture, designed and trained on the NYU Depth V2 dataset fo

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