IoT security approach based random distribution of communication frequency by Manga Tonga V Joseph Salomon, Mpoh Lobe Henri Pascal-Will, Malong Yannick, Maka Maka Ebenezer, Ihonock Eyembe Luc, Essiben Dikoundou Jean-François, Paul-Marie Moulema Douala, Yong Suk Joe

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Velocity-Based Channel Charting With Spatial Distribution Map Matching by Maximilian Stahlke, George Yammine, Tobias Feigl, Bjoern M. Eskofier, Christopher Mutschler

“…However, even channel charting still requires data acquisition and reference signals, and its localization is slightly less accurate than FP. …”
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Multi-Objective Optimization Algorithm Based Bidirectional Long Short Term Memory Network Model for Optimum Sizing of Distributed Generators and Shunt Capacitors for Distribution S... by Amarendra Alluri, Srinivasa Rao Gampa, Balaji Gutta, Mahesh Babu Basam, Kiran Jasthi, Nibir Baran Roy, Debapriya Das

“…An adaptive moment estimation (adam) optimization approach is employed to train the BiLSTM ML model for identifying the ideal values of distributed generations and shunt capacitors at different load factors. …”
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Multi-Scenario Robust Distributed Permutation Flow Shop Scheduling Based on DDQN by Shilong Guo, Ming Chen

“…This approach reduces algorithm training complexity by abstracting away intricate workpiece allocation details. …”
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Graph Split Federated Learning for Distributed Large-Scale AIoT in Smart Cities by Hanyue Xu, Kah Phooi Seng, Li-Minn Ang, Wei Wang, Jeremy Smith

“…Distributed collaborative machine learning, particularly split federated learning, has emerged as a solution, enabling privacy-preserving, resource-efficient training on IoT devices. …”
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Unmanned Aerial Vehicle Photogrammetry Based Dataset of Halophyte Distribution in Jujin Estuary by Donguk Lee, Yeongjae Jang, Joo-Hyung Ryu, Hyeong-Tae Jou, Keunyong Kim

“…The classification results were validated using field control point data, confirming an approximate classification accuracy of 92%.…”
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Predicting species distributions in the open ocean with convolutional neural networks by Morand, Gaétan, Joly, Alexis, Rouyer, Tristan, Lorieul, Titouan, Barde, Julien

“…In this case study, we considered 38 taxa comprising pelagic fishes, elasmobranchs, marine mammals, marine turtles and birds. We trained a model to predict probabilities from the environmental conditions at any specific point in space and time, using species occurrence data from the Global Biodiversity Information Facility (GBIF) and environmental data from various sources. …”
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Ensemble Learning-Based Day-Ahead Power Forecasting of Distributed Photovoltaic Generation by Yijuan LIU, Yunlong CHEN, Jiyan LIU, Xuemei ZHANG, Xiaoyu WU, Weizheng KONG

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Operational Effects on Water Quality Evolution in Water Distribution Systems by Laura González, Yesid Coy, Dominic L. Boccelli, Juan Saldarriaga

“…For this work, two black-box models were compared to predict chlorine concentration at different nodes in a small network and a large network. The model was trained with synthetic data from simulations through the EPANET-Python Toolkit. …”
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A Data-Driven Approach for Urban Heat Island Predictions: Rethinking the Evaluation Metrics and Data Preprocessing by Berk Kıvılcım, Patrick Erik Bradley

“…Since the task is to explore patterns, i.e., urban heat islands, Gaussian blurring is implemented on these generated 2D raster data before the training process. This strengthens the visual capturing of spatial relationships, and as a result the correlation rate between air temperature and building volume data is also increased. …”
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State Estimation in Power Distribution Grids Using Deep Unfolding by Biswajeet Rout, Balasubramaniam Natarajan

“…To address these challenges, this paper proposes a novel model-based neural network approach, called deep unfolding, for fast and accurate estimation of system states in a low observable distribution network. Unlike model-agnostic neural networks (NNs), which are difficult to tune and train, the proposed model-based NN is created by “unfolding” the alternating direction method of multipliers (ADMM) solver. …”
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Prediction of Aerosol Particle Size Distribution Based on Neural Network by Yali Ren, Jiandong Mao, Hu Zhao, Chunyan Zhou, Xin Gong, Zhimin Rao, Qiang Wang, Yi Zhang

“…The results show that BP neural network has a better prediction effect than that of the RBF neural network and is an effective method to obtain the aerosol particle size distribution of the whole atmosphere column using the data of CE-318 and APS 3321.…”
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A Cross-Regional Load Forecasting Method Based on a Pseudo-Distributed Federated Learning Strategy by Jinsong Deng, Shaotang Cai, Weinong Wu, Rong Jiang, Hongyu Deng, Jinhua Ma, Yonghang Luo

“…To address this issue, this study proposed a collaborative training strategy based on pseudo-distributed federated learning. …”
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Distributed random vector functional link network with subspace-based local connections by YU Wanguo, YUAN Zhenhao, CHEN Jiaqi, HE Yulin

“…Firstly, in order to take advantage of the partition parallelism of resilient distributed dataset (RDD), the large-scale dataset stored in the Hadoop distributed file system HDFS is randomly divided into random sample partition (RSP) data blocks and each RSP data block corresponds to a partition of the RDD, where the RSP data block is a subset of data that maintains probability distribution consistency with the big data at a given significance level. …”
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Semi-Supervised Learned Autoencoder for Classification of Events in Distributed Fibre Acoustic Sensors by Artem Kozmin, Oleg Kalashev, Alexey Chernenko, Alexey Redyuk

“…The classifier, trained on labeled data, recognizes and classifies specific events based on these features. …”
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Federated learning and information sharing between competitors with different training effectiveness by Jiajun Meng, Jing Chen, Dongfang Zhao

“…Despite its substantial benefits, the adoption of FL in competitive markets faces significant challenges, particularly due to concerns about training effectiveness and price competition. In practice, data from different firms may not be independently and identically distributed (non-IID) and heterogenous, which can lead to differences in model training effectiveness when aggregated through FL. …”
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Leveraging generative adversarial networks for data augmentation to improve fault detection in wind turbines with imbalanced data by Subhajit Chatterjee, Yung-Cheol Byun

“…Incorporating conditional data generation contributes to training stability and sample quality while utilising wasserstein distance ensures a faster convergence rate. …”
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Distributed Deep Reinforcement Learning Via Split Computing For Connected Autonomous Vehicles by Rauch Robert, Gazda Juraj

“…Additionally, this methodology not only decreases the data transmission burden but also achieves comparable rewards. …”
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StrideHD: A Binary Hyperdimensional Computing System Utilizing Window Striding for Image Classification by Dehua Liang, Jun Shiomi, Noriyuki Miura, Hiromitsu Awano

“…StrideHD encodes data points to distributed binary hypervectors and eliminates the expensive Channel item Memory (CiM) and item Memory (iM) in the encoder, which significantly reduces the required hardware cost for inference. …”
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Training teachers to teach PISA-like reading: A case in Indonesia by Emi Emilia, Eva Tuckyta Sari Sujatna, Nia Kurniasih

“…The study uses a program evaluation with the data collected from four sources, including a phase of training, pre- and post-tests, collection of PISA-like reading materials, and questionnaires distributed before and after the training program. …”
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