Secret Key Generation Driven by Attention-Based Convolutional Autoencoder and Quantile Quantization for IoT Security in 5G and Beyond by Anas Alashqar, Ehsan Olyaei Torshizi, Raed Mesleh, Werner Henkel

“…Specifically, a two-dimensional convolutional neural network–based autoencoder (2D CNN–AE) with a spatial self-attention (SSA) mechanism is developed to efficiently extract and learn channel reciprocity features in time-division duplex (TDD)-based fifth-generation (5G) networks. …”
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BDSER-InceptionNet: A Novel Method for Near-Infrared Spectroscopy Model Transfer Based on Deep Learning and Balanced Distribution Adaptation by Jianghai Chen, Jie Ling, Nana Lei, Lingqiao Li

“…The key contributions include: (1) RX-Inception multi-scale structure: Combines Xception’s depthwise separable convolution with ResNet’s residual connections to strengthen global–local feature coupling. (2) Squeeze-and-Excitation (SE) attention: Dynamically recalibrates spectral band weights to enhance discriminative feature representation. (3) Systematic evaluation of six transfer strategies: Comparative analysis of their impacts on model adaptation performance. …”
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Wearable sensor-based fall detection for elderly care using ensemble machine learning techniques by Ch Gangadhar, P Pavithra Roy, R. Dinesh Kumar, Janjhyam Venkata Naga Ramesh, S. Ravikanth, N. Akhila

“…Enhancing the quality of services for older people requires the development of a computerized surveillance network that can anticipate accidents before occur, offer protection throughout the incident, and send out remote warnings following an accident. …”
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SVR-Optimized ANN Model for Predicting Earthquake Risk in Electrical Substations Based on Disaster Datasets in the Aceh Region, Indonesia by Elvy Sahnur Nasution, Yuwaldi Away, Syahrial, Ira Devi Sara, Andri Novandri

“…This paper proposes a predictive model based on an Artificial Neural Network (ANN) optimized with Support Vector Regression (SVR) to predict seismic parameters as part of earthquake risk mitigation for substations. …”
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Novel dual gland GAN architecture improves human protein localization classification using salivary and pituitary gland inspired loss functions by Hanaa Salem Marie, Moatasem M. Draz, Waleed Abd Elkhalik, Mostafa Elbaz

“…This paper introduces a novel approach that employs two complementary loss functions within a Generative Adversarial Network (GAN) framework for processing images from the Human Protein Atlas dataset. …”
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Improvement of YOLO v8 Segmentation Algorithm and Its Study in the Identification of Hazards in Plateau Pika by Yaosheng Han, Yunpeng Jin, Chunmei Li, Xiangjie Huang

“…The specific improvements are as follows: firstly, the Contextual Transformer module is introduced in YOLO v8 to improve the global modeling capability; secondly, the DRConv dynamic region-aware convolution is introduced in YOLO v8 to improve the convolutional representation capability; thirdly, the attention mechanism is incorporated in the backbone of YOLO v8 to enhance the feature extraction capability of the network capability. …”
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Robust Automated Mouse Micro-CT Segmentation Using Swin UNEt TRansformers by Lu Jiang, Di Xu, Qifan Xu, Arion Chatziioannou, Keisuke S. Iwamoto, Susanta Hui, Ke Sheng

“…Swin UNETR reformulates mouse organ segmentation as a sequence-to-sequence prediction task using a hierarchical Swin Transformer encoder to extract features at five resolution levels, and it connects to a Fully Convolutional Neural Network (FCNN)-based decoder via skip connections. …”
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A Remote Sensing Semantic Self-Supervised Segmentation Model Integrating Local Sensitivity and Global Invariance by Buxun Zhang, Xiaoyan Guo, Sen Yang

“…This enables the model to learn feature representations that balance local sensitivity and global invariance in the semantically complex remote sensing images. …”
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Advanced object detection for smart accessibility: a Yolov10 with marine predator algorithm to aid visually challenged people by Mahir Mohammed Sharif Adam, Hussah Nasser AlEisa, Samah Al Zanin, Radwa Marzouk

“…The deep belief network (DBN) technique is used for the classification process. …”
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Enhancing aviation safety: Multifunctional graphene nanostructured foams for lightweight fire suppression materials by M. Bakir, R. Asmatulu

“…Graphene foams have a unique open-cell morphology with three dimensional (3D) continuous and interconnected network structures and hollow features, which can be suitable for aircraft and defense applications. …”
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Multi-Class Urinary Sediment Particles Detection Based on YOLOv7 With Attention Modules by Tatsuki Komori, Hiroki Nishikawa, Keita Sasaki, Ittetsu Taniguchi, Takao Onoye

“…Urine sediment analysis plays a vital role in the evaluation of kidney health. Traditional machine learning techniques approach the task of urine sediment particle detection as an image classification problem, wherein the particles are segmented based on features like edges or thresholds. …”
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Crop yield prediction using machine learning: An extensive and systematic literature review by Sarowar Morshed Shawon, Falguny Barua Ema, Asura Khanom Mahi, Fahima Lokman Niha, H.T. Zubair

“…According to this analysis, the most used features are temperature, soil type, and vegetation. …”
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Emerging advances in spinal cord injury: An introductory overview by Michael G. Fehlings, Shintaro Honda, Peter Vajkoczy, Narihito Nagoshi

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Parameter Prediction for Metaheuristic Algorithms Solving Routing Problem Instances Using Machine Learning by Tomás Barros-Everett, Elizabeth Montero, Nicolás Rojas-Morales

“…We propose a methodology to use k-nearest neighbours and artificial neural network algorithms to predict suitable parameter values based on instance features. …”
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Methodology for detecting anomalies in cyber attack assessment data using Random Forest and Gradient Boosting in machine learning by A. S. Kechedzhiev, O. L. Tsvetkova, A. I. Dubrovina

“…It includes several key steps and methods that allow us to evaluate the effectiveness of the model, identify important features, and analyze performance for various attacks.…”
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Dual-coding Contrastive Learning Based on the ConvNeXt and ViT Models for Morphological Classification of Galaxies in COSMOS-Web by Shiwei Zhu, Guanwen Fang, Chichun Zhou, Jie Song, Zesen Lin, Yao Dai, Xu Kong

“…In this study, we propose a self-supervised method called contrastive learning to upgrade the unsupervised machine learning (UML) part of the USmorph framework, aiming to improve the efficiency of feature extraction in this step. The upgraded UML method primarily consists of the following three aspects. (1) We employ a convolutional autoencoder to denoise galaxy images and adaptive polar coordinate transformation to enhance the model’s rotational invariance. (2) A pretrained dual-encoder convolutional neural network based on ConvNeXt and a vision transformer is used to encode the image data, while contrastive learning is then applied to reduce the dimension of the features. (3) We adopt a bagging-based clustering model to cluster galaxies with similar features into distinct groups. …”
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Discovering linked data collections through a new national metadata platform by Kate M Miller, Felicity Flack, Merran Smith, Carina Ecremen Marshall, Vicki Bennett

“…Conclusion The Population Health Research Network developed a metadata platform to enable researchers to evaluate the suitability of Australian data collections for linked data projects more effectively. …”
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Key Characteristics of Digital Ecosystems in Politics by A. A. Frolov, A. V. Sokolov, D. V. Egorov

“…The paper discusses the main approaches to understanding the ecosystem phenomenon. The evolution of its interpretation is demonstrated. The authors characterize various approaches to understanding digital ecosystems, their main elements, characteristic features, features, as well as the main mechanisms for creating ecosystem values. …”
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Pathway-like Activation of 3D Neuronal Constructs with an Optical Interface by Saeed Omidi, Yevgeny Berdichevsky

“…Responses of the network to the stimulation possessed features of neuronal population code, including separability by input pattern and mixed selectivity of individual neurons. …”
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Enhanced Domain Tuned Yolo-Driven Intelligent Fault Identification Method: Application in Selection and Construction of Gas Storage by BAI Xuefeng, ZHANG Fengyuan, ZOU Huanyu, HUANG Famu, LI Junlun, ZHAO Shijie, ZHANG Li, TANG Jizhou

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