METHODS OF ASSESSING PUBLIC(ECOLOGICAL AND ECONOMIC) EFFECTIVENESS OF TRANSPORT PROJECTS IN RUSSIA by A. I. Artemenkov, O. E. Medvedeva, P. V. Medvedev, Yu. V. Trofimenko

“…The application of the considered method is also illustrated in the article through an analysis of a project to deploy a network of high-speed highways in Russia expected to be made operational by the year 2030. …”
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Untrained perceptual loss for image denoising of line-like structures in MR images. by Elisabeth Pfaehler, Daniel Pflugfelder, Hanno Scharr

“…In particular, we investigate if the special characteristics of these datasets (connectivity, sparsity) benefit from the use of special loss functions for network training. We hereby translate the Perceptual Loss to 3D data by comparing feature maps of untrained networks in the loss function. …”
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A Closer Look at the Origin of LINER Emission in Later-type Galaxies and Its Connection to Evolved Stars with a Machine Learning Classification Scheme by Ahmad Nemer, Ivan Yu. Katkov, Joseph D. Gelfand, Changhyun Cho

“…We show in this work that the neural-network-based encoder was able identify LINER sources from the stellar continuum alone. …”
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A high-gain THz microstrip patch antenna designed for IoT and 6G communications with predicted efficiency using machine learning approaches by Md Sharif Ahammed, Redwan A. Ananta, Jun-Jiat Tiang, Mouaaz Nahas, Narinderjit Singh Sawaran Singh, Md. Ashraful Haque

“…This study highlights the potential of integrating THz technology with machine learning to enhance antenna design, presenting a novel framework for the evolution of future wireless networks with improved performance and energy efficiency.…”
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Quantitative Structure Activity Relationship Studies of Topoisomerase I Inhibitors as Potent Antibreast Cancer Agents by Supriya Singh, Sucheta Das, Anubhuti Pandey, Sarvesh Paliwal, Rajeev Singh

“…So, with an aim to elucidate the important features responsible for their activity, QSAR studies on breast cancer cell line using stepwise multiple linear regressions, partial least square, and neural network were performed. …”
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Classification of Liver Fibrosis From Heterogeneous Ultrasound Image by Yunsang Joo, Hyun-Cheol Park, O-Joun Lee, Changhan Yoon, Moon Hyung Choi, Chang Choi

“…With the advances in deep learning, including Convolutional Neural Networks (CNN), automated diagnosis technology using medical images has received considerable attention in medical science. …”
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Enhancing Intrusion Detection Systems with Dimensionality Reduction and Multi-Stacking Ensemble Techniques by Ali Mohammed Alsaffar, Mostafa Nouri-Baygi, Hamed Zolbanin

“…We employ the LogitBoost algorithm with XGBRegressor for feature selection, complemented by a Residual Network (ResNet) deep learning model for feature extraction. …”
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LSTM time series prediction of soil moisture content in kiwifruit root zone based on meteorological data fusion by Jingyuan He, Weifeng Li, Shijia Pan, Nikolaos Sygrimis, Zijie Niu, Dongyan Zhang, Dong Han, Petro A. Roussos

“…To fully evaluate the model performance, we compared ATT-LSTM with traditional artificial neural network models, including non-temporal feedforward neural network (FFNN) and temporal Long Short-Term Memory (LSTM). …”
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Delayed diagnosis of POEMS syndrome, how AI tools can enhance diagnostic accuracy by Max Jin, Sowbharnika Arivazhagan, Prerna Mewawalla, Santhosh Sadashiv, Arjun Lakshman

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Intrusion Detection in IoT and IIoT: Comparing Lightweight Machine Learning Techniques Using TON_IoT, WUSTL-IIOT-2021, and EdgeIIoTset Datasets by Shereen Ismail, Salah Dandan, Ala'a Qushou

“…Furthermore, a cross-dataset transfer learning approach was applied to evaluate how models trained on the TON_IoT dataset generalize when tested on the WUSTL-IIoT-2021 dataset, demonstrating the ability of the models to generalize across datasets with common features and attack labels. …”
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A Computational Offloading Method for Edge Server Computing and Resource Allocation Management by Muna Al-Razgan, Taha Alfakih, Mohammad Mehedi Hassan

“…In this study, we proposed a model to evaluate the efficiency of the close-end network computation offloading in MEC. …”
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Bearing remaining useful life prediction based on optimized VMD and BiLSTM-CBAM. by Wei Liu, Sen Liu

“…Finally, the degradation feature set is input into a BiLSTM network integrated with the CBAM* for RUL prediction of bearings. …”
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SE-ResNet based disturbance identification algorithm for microthrust measurement system by Mingming Han

“…The Squeeze-and-Excitation (SE) module is incorporated to optimize the network, as it can adaptively enhance important features. …”
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A novel deep learning approach to field-road semantic segmentation by Bei Wang, Wenze Wang

“…Additionally, the model identifies unique features by evaluating GNSS points’ similarities to adjacent points and their global class counterparts. …”
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Clinical, dermoscopic, and histopathological analysis of Becker’s nevus (BN): A case series in Indian skin by Siddharth Mani, Manish Khandare, Aradhana Raut, Benjith Paul

“…Four dermatologists independently evaluated the dermoscopic findings, which were then correlated with histopathological features. …”
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Naxos Disease and Related Cardio-Cutaneous Syndromes by Alexandros Protonotarios, MD, Angeliki Asimaki, Ph.D, Cristina Basso, MD, Zafeirenia Xylouri, MD, Emanuele Monda, MD, Ioannis Protonotarios, MD, Giulia Crisci, MD, Dominic JR. Abrahms, MMBS, MD, MBA, Aris Anastasakis, MD, Loizos Antoniades, MD, Athanasios Bakalakos, MD, Andreina Carbone, MD, Aman S. Coonar, MD, Juan Ramon Gimeno, MD, George Lazaros, MD, Stamatis Lerakis, MD, Luisa Mestroni, MD, George Papadopoulos, MD, Leandro Pecchia, MD, Francesca Romana Prandi, MD, Petros Syrris, MD, Julia Cadrin-Turigny, MD, Anargyros Vasilakis, MSc, Jeffrey E. Saffitz, MD, PhD, Stamatios Gaetano Thiene, MD, Perry M. Elliott, MD, Juan Pablo Kaski, MD, William J. McKenna, MD, Eduardo Bossone, MD, Giuseppe Limongelli, MD, Adalena Tsatsopoulou, MD

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Dunes Identification Based on Attention Mechanism With Dual-Branch Codec by Zhaobin Wang, Yan Li, Yue Shi, Yaonan Zhang, Xuejun Guo

“…The codec hybrid module makes the deep global information interact with the shallow detail information in the dual-branch network to obtain richer feature information. The multiscale mixed attention module is used to extract deep features, and lightweight upsampling operator is used to achieve feature recombination and reduce the number of network parameters. …”
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Dual-stream interactive mechanism with multi-modal hierarchical aggregation transformer for gait recognition by Jinghang Liu, Xiangyuan Xu, Yan Qiu, Chunzhi Wang

“…This mechanism incorporates batch normalization and residual connections to ensure stable and effective feature extraction. Furthermore, DSM integration with Horizontal Pyramid Pooling (HPP) strengthens inter-part anatomical associations in gait features through attentional interactions between adjacent feature strips.The proposed MHAT establishes dynamic feature interactions among silhouette representations, heatmap characteristics, and fused features through modality-specific query features and shared key-value features. …”
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Analysis of the most influential factors affecting outcomes of lung transplant recipients: a multivariate prediction model based on UNOS Data by Reza Safdari, Marsa Gholamzadeh, Hamidreza Abtahi, Mehrnaz Asadi Gharabaghi

“…Our primary objective was to identify the key factors that influence the allocation of priorities in LTx using machine learning (ML) techniques to enhance the process of prioritising patients.Design Developing a prediction model.Setting and participants Our data were retrieved from the United Network for Organ Sharing (UNOS) open-source database of transplant patients between 2005 and 2023.Interventions After the preprocessing process, a feature engineering technique was employed to select the most relevant features. …”
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Enhanced cardiovascular risk prediction in the Western Pacific: A machine learning approach tailored to the Malaysian population. by Sazzli Kasim, Putri Nur Fatin Amir Rudin, Sorayya Malek, Nurulain Ibrahim, Xue Ning Kiew, Nafiza Mat Nasir, Khairul Shafiq Ibrahim, Raja Ezman Raja Shariff

“…<h4>Methods</h4>Utilizing data from the REDISCOVER Registry (5,688 participants from 2007 to 2017), 30 clinically relevant features were selected, and several ML algorithms were trained: Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Neural Network (NN) and Naive Bayes (NB). …”
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