Real-Time Defect Detection Model in Industrial Environment Based on Lightweight Deep Learning Network by Jiaqi Lu, Soo-Hong Lee

“…Moreover, the complexity of general detection network architectures relies on high-tech hardware, making it difficult to deploy on devices without GPUs or on edge computing and mobile devices. …”
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Performance Analysis of Wavelength Division Multiplexing-Based Passive Optical Network Protection Schemes by Means of the Network Availability Evaluator by Rastislav Róka

“…First, a short basic classification of passive optical network architectures utilizing advanced wavelength division multiplexing techniques is introduced. …”
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PF-ClusterCache: Popularity and Freshness-Aware Collaborative Cache Clustering for Named Data Networking of Things by Samar Alduayji, Abdelfettah Belghith, Achraf Gazdar, Saad Al-Ahmadi

“…Named Data Networking (NDN) has been recognized as the most promising information-centric networking architecture that fits the application model of IoT systems. …”
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Remote-Sensing Image Change Detection Based on Adjacent-Level Feature Fusion and Dense Skip Connections by Bin Huang, Yichen Xu, Feng Zhang

“…On the other hand, large network architectures limit their actual deployment in resource-limited environments. …”
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Toward the Protection of IoT Networks: Introducing the LATAM-DDoS-IoT Dataset by Josue Genaro Almaraz-Rivera, Jesus Arturo Perez-Diaz, Jose Antonio Cantoral-Ceballos, Juan Felipe Botero, Luis A. Trejo

“…Furthermore, we build a smart anomaly-based Intrusion Detection System from our new dataset, training Decision Tree and Multi-layer Perceptron models, for later deployment and evaluation on a Software Defined Networking architecture with physical and virtual components. …”
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PyNCS: a microkernel for high-level definition and configuration of neuromorphic electronic systems by Fabio eStefanini, Emre O Neftci, Sadique eSheik, Giacomo eIndiveri

“…Neuromorphic hardware offers an electronic substrate for the realization of asynchronousevent-based sensory-motor systems and large-scale spiking neural network architectures. Inorder to characterize these systems, configure them, and carry out modeling experiments, it isoften necessary to interface them to workstations. …”
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APPROACH TO IMAGE ANALYSIS FOR COMPUTER VISION SYSTEMS by N. A. Iskra

“…In addition, the use of this model allows partial cleansing and normalization of data for training neural network architectures, which are widely used in image analysis among others. …”
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Identifying the Key Components in ResNet-50 for Diabetic Retinopathy Grading from Fundus Images: A Systematic Investigation by Yijin Huang, Li Lin, Pujin Cheng, Junyan Lyu, Roger Tam, Xiaoying Tang

“…We also examine the proposed training practices on other fundus datasets and other network architectures to evaluate their generalizability. …”
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A Comparative Study of Software Defined Networking Controllers Using Mininet by Neelam Gupta, Mashael S. Maashi, Sarvesh Tanwar, Sumit Badotra, Mohammed Aljebreen, Salil Bharany

“…Software Defined Networking (SDN) is a relatively new networking architecture that has become the most widely discussed networking technology in recent years and the latest development in the field of developing digital networks, which aims to break down the traditional connection in the middle of the control surface and the infrastructure surface. …”
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Enhancement of handwritten text recognition using AI-based hybrid approach by Supriya Mahadevkar, Shruti Patil, Ketan Kotecha

“…This underscores the potential and efficacy of the consecutive use of these advanced neural network architectures in enhancing handwritten text recognition accuracy. • The proposed method introduces a hybrid approach for handwritten text recognition, employing CNN and BiLSTM with CTC decoder. • Results showcase a remarkable accuracy improvement of 98.50% and 98.80% on IAM and RIMES datasets, emphasizing the potential of this model for enhanced accuracy in recognizing handwritten text from images.…”
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A continuous learning approach to brain tumor segmentation: integrating multi-scale spatial distillation and pseudo-labeling strategies by Ruipeng Li, Jianming Ye, Yueqi Huang, Wei Jin, Peng Xu, Lilin Guo

“…This framework addresses common challenges in brain tumour segmentation, such as computational complexity, limited generalisability, and the extensive need for manual annotation.MethodsOur approach uniquely combines multi-scale spatial distillation with pseudo-labelling strategies, exploiting the coordinated capabilities of the ResNet18 and DeepLabV3+ network architectures. This integration enhances feature extraction and efficiently manages model size, promoting accurate and fast segmentation. …”
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Frequency-Based Enhancement Network for Efficient Super-Resolution by Parichehr Behjati, Pau Rodriguez, Carles Fernandez Tena, Armin Mehri, F. Xavier Roca, Seiichi Ozawa, Jordi Gonzalez

“…Our FEB design is simple and generic and can be used as a direct replacement of commonly used SR blocks with no need to change network architectures. We experimentally show that when replacing SR blocks with FEB we consistently improve the reconstruction error, while reducing the number of parameters in the model. …”
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PTEENet: Post-Trained Early-Exit Neural Networks Augmentation for Inference Cost Optimization by Assaf Lahiany, Yehudit Aperstein

“…For many practical applications, a high computational cost of inference over deep network architectures might be unacceptable. A small degradation in the overall inference accuracy might be a reasonable price to pay for a significant reduction in the required computational resources. …”
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Secure internet of medical things based electronic health records scheme in trust decentralized loop federated learning consensus blockchain by Megha Kuliha, Sunita Verma

“…The proposed classification model demonstrates high flexibility and scalability, making it applicable to a wide range of network architectures for various computer vision tasks. …”
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A Novel Building Extraction Network via Multi-Scale Foreground Modeling and Gated Boundary Refinement by Junlin Liu, Ying Xia, Jiangfan Feng, Peng Bai

“…Second, buildings have complex boundary information, while conventional network architectures fail to capture fine boundaries. In this paper, we designed a multi-task U-shaped network (BFL-Net) to solve these problems. …”
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Segmentation of Aorta 3D CT Images Based on 2D Convolutional Neural Networks by Simone Bonechi, Paolo Andreini, Alessandro Mecocci, Nicola Giannelli, Franco Scarselli, Eugenio Neri, Monica Bianchini, Giovanna Maria Dimitri

“…Two different network architectures, U-Net and LinkNet, were used and compared. …”
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Characterizations of how neural networks learn by Boix-Adsera, Enric

“…Training neural network architectures on Internet-scale datasets has led to many recent advances in machine learning. …”
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A high-throughput screening approach to discovering good forms of inspired visual representation by Pinto, Nicolas, Doukhan, David, DiCarlo, James, Cox, David D.

“…In analogy to high-throughput screening approaches in molecular biology and genetics, we explored thousands of potential network architectures and parameter instantiations, screening those that show promising object recognition performance for further analysis. …”
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A High-Throughput Screening Approach to Discovering Good Forms of Biologically Inspired Visual Representation by Cox, David D., Pinto, Nicolas, Doukhan, David, DiCarlo, James

“…In analogy to highthroughput screening approaches in molecular biology and genetics, we explored thousands of potential network architectures and parameter instantiations, screening those that show promising object recognition performance for further analysis. …”
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Visual localization at NTU campus by Abhinaya, Kesarimangalam Srinivasan

“…Soft assignment to clusters makes NetVLAD readily pluggable into Convolutional Neural Network architectures for end - to - end training. Instead of uniform pooling as in the case of NetVLAD, Attention Pyramid Pooling of Salient Visual Residuals (APPSVR) uses attention, generated based on semantic segmentation, to de-prioritize task irrelevant features. …”
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