A Quantum-Inspired Ant Colony Optimization Approach for Exploring Routing Gateways in Mobile Ad Hoc Networks by Jamal Khudair Madhloom, Hussein Najm Abd Ali, Haifaa Ahmed Hasan, Oday Ali Hassen, Saad Mohamed Darwish

“…Current routing methods have been adapted and optimized for use with MANET through the use of both conventional routing techniques and tree-based network architectures. Exploring new or tacking-failure GWs also increases network overhead but is essential given that MANET is a dynamic and complicated network. …”
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Semantic segmentation of raw multispectral laser scanning data from urban environments with deep neural networks by Mikael Reichler, Josef Taher, Petri Manninen, Harri Kaartinen, Juha Hyyppä, Antero Kukko

“…In this study, we experiment with real-time semantic segmentation of high-density multispectral point clouds collected from 2D line scanners in urban environments using encoder - decoder convolutional neural network architectures. We introduce a rasterized multi-scan input format that can be constructed exclusively from the raw (non-georeferenced profiles) 2D laser scanner measurement stream without odometry information. …”
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Physics-Aware Deep-Learning-Based Proxy Reservoir Simulation Model Equipped With State and Well Output Prediction by Emilio Jose Rocha Coutinho, Emilio Jose Rocha Coutinho, Marcelo Dall’Aqua, Eduardo Gildin

“…We show our proxy’s accuracy and robustness by running two different neural network architectures (propositions 2 and 3), and we compare our results with the original E2C framework developed for reservoir simulation.…”
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Digital Twin Simulation Tools, Spatial Cognition Algorithms, and Multi-Sensor Fusion Technology in Sustainable Urban Governance Networks by Elvira Nica, Gheorghe H. Popescu, Milos Poliak, Tomas Kliestik, Oana-Matilda Sabie

“…Virtual simulation algorithms, deep learning neural network architectures, and cyber-physical cognitive systems articulate networked smart cities. …”
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Low-Cost, Open Source IoT-Based SCADA System Design Using Thinger.IO and ESP32 Thing by Lawrence Oriaghe Aghenta, Mohammad Tariq Iqbal

“…SCADA architectures have evolved over the years from monolithic (stand-alone) through distributed and networked architectures to the latest Internet of Things (IoT) architecture. …”
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Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning by Hammam Alshazly, Christoph Linse, Erhardt Barth, Thomas Martinetz

“…To this end, we adopted advanced deep network architectures and proposed a transfer learning strategy using custom-sized input tailored for each deep architecture to achieve the best performance. …”
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Land-Cover Classification Using Deep Learning with High-Resolution Remote-Sensing Imagery by Muhammad Fayaz, Junyoung Nam, L. Minh Dang, Hyoung-Kyu Song, Hyeonjoon Moon

“…By aligning with these advanced techniques, our research not only contributes to the evolution of remote-sensing methodologies but also underscores the paramount importance of incorporating cutting-edge methodologies, such as fine-tuning and the use of specific network architectures, in the continual enhancement of LC classification systems. …”
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Using malware for software-defined networking–based smart home security management through a taint checking approach by Ping Wang, Kuo-Ming Chao, Chi-Chun Lo, Wen-Hui Lin, Hsiao-Chung Lin, Wun-Jie Chao

“…Accordingly, this article proposes a taint propagation analysis model incorporating a weighted spanning tree analysis scheme for tracking data with taint marking using several taint checking tools with an open software-defined networking architecture for solving the dynamic taint propagation analysis problem. …”
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GeoDLS: A Deep Learning-Based Corn Disease Tracking and Location System Using RTK Geolocated UAS Imagery by Aanis Ahmad, Varun Aggarwal, Dharmendra Saraswat, Aly El Gamal, Gurmukh S. Johal

“…Five deep neural network architectures, VGG16, ResNet50, InceptionV3, DenseNet169, and Xception, were trained to identify diseased, healthy, and background regions in corn fields. …”
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Neural network interpretation techniques for analysis of histological images of breast abnormalities by Anna V. Fomina, Artem M. Borbat, Evgeny A. Karpulevich, Anton Yu. Naumov

“…In this paper, several methods of interpreting machine learning are studied using two different neural network architectures and a set of histological images of breast abnormalities. …”
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Signal Integration in Quorum Sensing Enables Cross-Species Induction of Virulence in <italic toggle="yes">Pectobacterium wasabiae</italic> by Rita S. Valente, Pol Nadal-Jimenez, André F. P. Carvalho, Filipe J. D. Vieira, Karina B. Xavier

“…Our work provides support for the hypothesis that interspecies interactions are among the major factors influencing the network architectures observed in bacterial quorum-sensing pathways.…”
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Novel angular and frequency manipulation of light in nano-scaled dielectric photonic systems by Shen, Yichen, Ph. D. Massachusetts Institute of Technology

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Tradespace exploration for space system architectures : a weighted graph framework by Davison, Peter Leslie

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Novel digital-twin modelling for smart building inspection by Mui, Jia Duan

“…After a comparison of the different neural network architectures, the CNN model was selected as most adept at predicting any of the 3 datasets given different strain and acceleration inputs from various floors. …”
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Training algorithm design and weight convergence analysis for discrete-time recurrent neural networks by Xu, Zhao

“…RNNs contain feedback loops in the structures which make them much more powerful in dynamical modeling of complex systems as compared with other neural network architectures. This thesis focuses on the design of robust training algorithms for RNNs based on the popular real time recurrent learning (RTRL) concept. …”
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3D Façade Labeling over Complex Scenarios: A Case Study Using Convolutional Neural Network and Structure-From-Motion by Rodolfo Georjute Lotte, Norbert Haala, Mateusz Karpina, Luiz Eduardo Oliveira e Cruz de Aragão, Yosio Edemir Shimabukuro

“…However, newer neural network architectures have brought back the interest in their application for autonomous classifiers, especially for image classification purposes. …”
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Deep-HPI-pred: An R-Shiny applet for network-based classification and prediction of Host-Pathogen protein-protein interactions by Muhammad Tahir ul Qamar, Fatima Noor, Yi-Xiong Guo, Xi-Tong Zhu, Ling-Ling Chen

“…Deep-HPI-pred use Multilayer Perceptron (MLP) models for HPI prediction, which is based on a comprehensive evaluation of topological features and neural network architectures. When subjected to independent validation datasets, the predicted models consistently surpassed a Matthews correlation coefficient (MCC) of 0.80 in host-pathogen interactions. …”
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Deep-Learning to Predict BRCA Mutation and Survival from Digital H&E Slides of Epithelial Ovarian Cancer by Camilla Nero, Luca Boldrini, Jacopo Lenkowicz, Maria Teresa Giudice, Alessia Piermattei, Frediano Inzani, Tina Pasciuto, Angelo Minucci, Anna Fagotti, Gianfranco Zannoni, Vincenzo Valentini, Giovanni Scambia

“…Based on these analyses, we have planned further steps of development such as proving a reference classification performance, exploring the hyperparameters space for training optimization, eventually tweaking the learning algorithms and the neural networks architecture for better suiting this specific task. …”
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A deep learning model integrating multisequence MRI to predict EGFR mutation subtype in brain metastases from non-small cell lung cancer by Ye Li, Xinna Lv, Cancan Chen, Ruize Yu, Bing Wang, Dawei Wang, Dailun Hou

“…An algorithm combining radiomics approach with graph convolutional networks architecture (Radio-GCN) was designed for the prediction of EGFR mutation status and subtype. …”
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Sentinel-1-Based Water and Flood Mapping: Benchmarking Convolutional Neural Networks Against an Operational Rule-Based Processing Chain by Max Bereczky, Marc Wieland, Christian Krullikowski, Sandro Martinis, Simon Plank

“…In this study, the effectiveness of several convolutional neural network architectures (AlbuNet-34&#x002F;FCN&#x002F;DeepLabV3&#x002B;&#x002F;U-Net&#x002F;U-Net&#x002B;&#x002B;) for water and flood mapping using Sentinel-1 amplitude data is compared to an operational rule-based processor (S-1FS). …”
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