Optimization of Temperature Schedule Parameters on Heat Supply in Power-and-Heat Supply Systems by V. A. Sednin, A. V. Sednin, M. L. Bogdanovich

“…The paper considers problems concerning optimization of a temperature schedule in the district heating systems with steam-turbine thermal power stations having average initial steam parameters. …”
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Evaluation of the Human Immunodeficiency Virus viral load surveillance system, national perspective in Tanzania: A descriptive cross-sectional study by Peter Richard Torokaa, Loveness John Urio, Ambwene Mwakalobo, Alex Sifaeli Magesa, James Njoroge Allan, David John Osima, Focus Medard Shao, Joseph Mziray, Yulitha Barnabas, Agricola Joachim, Mtebe Majigo

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Formation of biocenoses in roadside landscapes of Cherkasy Region by Ірина Кравцова, Сергій Сонько, Ольга Василенко, Ігор Гурський, Станіслав Огілько

“…Formulation of the problem. The problem solved in the article is relevant primarily due to the constant growth of the density of the transport network, and, therefore, the ever-increasing "theft" of natural ecosystems by humans from aboriginal plants and animals. …”
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A three year review of Tuberculosis surveillance data in Montserrado County, Liberia, 2016-2018 by Alberta Berlynda Corvah, Himiede Wilson Sesay, Delia Akosua Bando, Patrick Konwloh, Benedict Calys-Tagoe, Ernest Kenu

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Methodology of Determining the Location of the Distribution Center of Material Flows in a Combined Scheme of Goods Delivery by Svetlana A. Zhestkova

“…Despite the sufficient number of existing ways to solve this problem, they have some disadvantages: they are not entirely realistic and do not completely meet the requirements of logistical optimization. …”
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Federated Learning-Based Credit Card Fraud Detection: A Comparative Analysis of Advanced Machine Learning Models by Zheng Han

“…This paper introduced federated learning and discussed a few federated learning algorithms applied to the problem—these methods include Federated Graph Attention Network with Dilated Convolution Neural Network (FedGAT-DCNN), FedAvg with Convolutional Neural Network (CNN), and Federated Averaging with Distance-based Weighted Aggregation (FedAvg-DWA) with Random Forest (RF). …”
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Using Genetic Algorithms for Hardware Core Placement and Mapping in NoC-Based Reconfigurable Systems by Jonas Gomes Filho, Marius Strum, Wang Jiang Chau

“…In the last years, partial reconfigurable systems (PRSs) have included Networks-on-Chips (NoCs) as their communication structure, adding complexity to the problem of mapping. …”
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Enhanced Lightweight YOLO Model for Efficient Vehicle Detection in Satellite Imagery by Mohamad Haniff Junos, Anis Salwa Mohd Khairuddin, Elmi Abu Bakar, Ahmad Faizul Hawary

“…Additionally, an improved small efficient layer aggregation network is adopted in the modified path aggregation network to enhance feature extraction across various scales. …”
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A Novel Deep Hybrid Model for Automatic Femoral Stem Classification in Hip Arthroplasty From Radiographs: MSFT-Net With CBAM and Transformer Modules by Emre Gogus, Atinc Yilmaz, Meric Enercan

“…The proposed multi-scale feature transformer network was trained and validated on a dataset comprising 1266 anteroposterior (A.P.) hip radiographs of 10 different femoral stem implant types. …”
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Federated learning and GWO-enabled consumer-centric healthcare internet of things for pancreatic tumour by Nuha Alruwais, Ghada Moh. Samir Elhessewi, Muhammad Kashif Saeed, Menwa Alshammeri, Othman Alrusaini, Abdulwhab Alkharashi, Samah Al Zanin, Yahia Said

“…The proposed model was more network efficient, performed better in data imbalance scenarios, and led to lower communication costs than the currently available federated average model. …”
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Towards automatic US-MR fetal brain image registration with learning-based methods by Qi Zeng, Weide Liu, Bo Li, Ryne Didier, P. Ellen Grant, Davood Karimi

“…In this work, we present a novel atlas-assisted multi-task learning technique to address this problem. Instead of training the registration model solely with intra-subject US-MR image pairs, our approach enables the network to also learn from domain-specific image-to-atlas registration tasks. …”
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Sound-Based Unsupervised Fault Diagnosis of Industrial Equipment Considering Environmental Noise by Jeong-Geun Lee, Kwang Sik Kim, Jang Hyun Lee

“…This study proposes a fault diagnosis method using a variational autoencoder (VAE) and domain adaptation neural network (DANN), both of which are based on unsupervised learning, to address this problem. …”
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A Secure COVID Affected CT Scan Image Encryption Scheme Using Hybrid MLSCM for IoMT Environment by Sasmita Padhy, Sachikanta Dash, Naween Kumar, Gyanendra Kumar

“…After proper diagnosis, digital images storing patient personal health information are saved and transferred over a public network. As a result, the security aspects of preserving sensitive patient health information are becoming crucial. …”
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Thermosolutal convection and entropy generation in hybrid nanofluids: FEM and ANN analysis of a magnetized wavy enclosure by Noor Zeb Khan, Muhammad Salim Khan, A.S. Shflot, M.Y. Malik

“…This study investigates the thermosolutal convection and entropy generation in a magnetohydrodynamic (MHD) hybrid nanofluid-filled enclosure featuring wavy vertical walls and a centrally placed star-shaped cylinder. The problem addresses the critical need to optimize heat and mass transfer in engineering systems, such as thermal management, energy storage, and electronic cooling. …”
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Synergistic Hierarchical Bi-Level Optimization of Demand Response Programs and Smart Inverter Volt/Var Control for Maximizing Hosting Capacity of Power Systems With Renewable-Based... by Zenhom M. Zenhom, Shady H. E. Abdel Aleem, Essam Aboul Zahab, Ziad M. Ali, Abdulaziz Almalaq, Tarek A. Boghdady

“…The pelican optimization algorithm (POA) is employed to solve the optimization problem and is compared with four other optimization techniques, demonstrating its good performance. …”
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Part of speech weighted multi-modal emotion analysis model with dynamic adjustment of semantic representation by HUA Qiang, CHEN Zhuo, ZHANG Feng, DONG Chunru

“…Comparative experimental results with the current advanced models such as tensor fusion network and low-rank multimodal fusion show that the average absolute errors of PW-DS model on public data sets CMU-MOSI and CMU-MOSEI are 0.607 and 0.510, respectively, and the binary classification accuracies are 89.02% and 86.93%, respectively, which is better than the models in the comparative experiments. …”
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Classification of Petrographic Thin Section Images With Depthwise Separable Convolution and Dilated Convolution by Shaowei Pan, Xingxing Cheng, Wenjing Fan

“…To enhance the precision and efficiency of petrographic thin section image classification and reduce the subjectivity resulting from manual classification methods, a new classification model (DC-PC-Dilated-IR-V2) in term of the deep convolutional network is constructed in this study. In the DC-PC-Dilated-IR-V2 model, the depthwise separable convolution (DSC) is applied to avoid the increase problem of parameters and calculations due to too many convolutional layers; the attention module is introduced to obtain a better representation of characteristics through information fusion and weighting, which allows the model to focus on the target area of thin section images; in addition, the dilated convolution is taken in, which enables the model to extract more global characteristics and higher-level semantic characteristics without increasing the convolution parameters, thereby improving the classification precision of the model. …”
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Spatial–Spectral Feature Fusion and Spectral Reconstruction of Multispectral LiDAR Point Clouds by Attention Mechanism by Guoqing Zhou, Haoxin Qi, Shuo Shi, Sifu Bi, Xingtao Tang, Wei Gong

“…Existing spectral reconstruction methods often require empirical parameter settings and involve high computational costs, limiting automation and complicating application. To address this problem, we introduce the dual attention spectral optimization reconstruction network (DossaNet), leveraging an attention mechanism and spatial–spectral information. …”
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Rapid screening and optimization of CO2 enhanced oil recovery operations in unconventional reservoirs: A case study by Shuqin Wen, Bing Wei, Junyu You, Yujiao He, Qihang Ye, Jun Lu

“…However, it remains challenging due to a limited understanding of fluid flow in multiscale porous media and the problem complexity invoked by numerous factors. This work developed a new interpretable machine learning (ML) framework to specifically address this issue. …”
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Predicting the impact of climate change on the re-emergence of malaria cases in China using LSTMSeq2Seq deep learning model: a modelling and prediction analysis study by Eric Kamana, Jijun Zhao, Di Bai

“…Objectives Malaria is a vector-borne disease that remains a serious public health problem due to its climatic sensitivity. Accurate prediction of malaria re-emergence is very important in taking corresponding effective measures. …”
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