IRFNet: Cognitive-Inspired Iterative Refinement Fusion Network for Camouflaged Object Detection by Guohan Li, Jingxin Wang, Jianming Wei, Zhengyi Xu

“…Camouflaged Object Detection (COD) aims to identify objects that are intentionally concealed within their surroundings through appearance, texture, or pattern adaptations. Despite recent advances, extreme object–background similarity causes existing methods struggle with accurately capturing discriminative features and effectively modeling multiscale patterns while preserving fine details. …”
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Anomaly Detection Based on Graph Convolutional Network–Variational Autoencoder Model Using Time-Series Vibration and Current Data by Seung-Hwan Choi, Dawn An, Inho Lee, Suwoong Lee

“…By combining the spatial feature extraction capability of Graph Convolutional Networks (GCNs) with the latent temporal feature modeling of Variational Autoencoders (VAEs), our method can effectively detect abnormal signs in the data, particularly in the lead-up to system failures. …”
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Evaluating the Effectiveness of Dimensionality Reduction on Machine Learning Algorithms in Time Series Forecasting by Rida Zaheer, Muhammad Kashif Hanif, Muhammad Umer Sarwar, Ramzan Talib

“…Certain methods excel at uncovering underlying patterns and improving predictive accuracy, while others offer computational advantages. …”
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An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection by Sarah Tipper, Hany F. Atlam, Harjinder Singh Lallie

“…CNNs enable the effective extraction of spatial features from video frames, such as facial textures and lighting, while LSTM analyses temporal patterns, detecting inconsistencies over time. …”
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Adaptive Taylor Kolmogorov–Arnold Network for Hyperspectral Image Classification by Yichen Liu, Xin Zhang, Zitong Zhang, Hanlin Feng

“…ATKAN applies KA operations to channels while sliding across the spatial domain to process high-dimensional spatial-spectral features. It also employs the Taylor series instead of B-spline functions for activation functions, ensuring smooth approximations and enhancing the ability to model nonlinear patterns. …”
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A Cross-Machine Intelligent Fault Diagnosis Method with Small and Imbalanced Data Based on the ResFCN Deep Transfer Learning Model by Juanru Zhao, Mei Yuan, Yiwen Cui, Jin Cui

“…In this paper, we propose a cross-machine IFD method based on a residual full convolutional neural network (ResFCN) transfer learning model, which leverages the time-series features of monitoring data. By incorporating sliding window (SW)-based data segmentation, network pretraining, and model fine-tuning, the proposed method effectively exploits fault-associated general features in the source domain and learns domain-specific patterns that better align with the target domain, ultimately achieving accurate fault diagnosis for the target equipment. …”
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Machine learning classification of chronic traumatic brain injury using diffusion tensor imaging and NODDI: A replication and extension study by J. Michael Maurer, Keith A. Harenski, Subhadip Paul, Victor M. Vergara, David D. Stephenson, Aparna R. Gullapalli, Nathaniel E. Anderson, Gerard J.B. Clarke, Prashanth K. Nyalakanti, Carla L. Harenski, Jean Decety, Andrew R. Mayer, David B. Arciniegas, Vince D. Calhoun, Todd B. Parrish, Kent A. Kiehl

“…Our research group previously used FA as a feature in a linear support vector machine (SVM) pattern classifier, observing high classification between individuals with and without acute TBI (i.e., an area under the curve [AUC] value of 75.50%). …”
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Comparative Analysis of Machine Learning Techniques for Fault Diagnosis of Rolling Element Bearing with Wear Defects by Devendra Sahu, Ritesh Kumar Dewangan, Surendra Pal Singh Matharu

“…The model was further refined by extracting 14 types of features from the SNR-enhanced vibration data, presenting a comprehensive depiction of fault patterns and finally, machine learning techniques were applied to categorize faults using the aforementioned datasets, facilitating a comparative analysis of results. …”
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Towards Robust Autonomous Driving: Out-of-Distribution Object Detection in Bird's Eye View Space by Muhammad Asad, Ihsan Ullah, Ganesh Sistu, Michael G. Madden

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Surface Temperature Assisted State of Charge Estimation for Retired Power Batteries by Liangyu Xu, Wenxuan Han, Jiawei Dong, Ke Chen, Yuchen Li, Guangchao Geng

“…This improvement stems from the model’s ability to decode localized heating patterns and their hysteresis effects, which are particularly pronounced in aged batteries. …”
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DDoSNet: Detection and prediction of DDoS attacks from realistic multidimensional dataset in IoT network environment by Goda Srinivasa Rao, P. Santosh Kumar Patra, V.A. Narayana, Avala Raji Reddy, G.N.V. Vibhav Reddy, D. Eshwar

“…The ESN classifier utilizes the selected features to learn the underlying patterns and dynamics of network traffic, enabling accurate identification of DDoS attacks. …”
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A deep dive into artificial intelligence with enhanced optimization-based security breach detection in internet of health things enabled smart city environment by S Jayanthi, Sodagudi Suhasini, N. Sharmili, E. Laxmi Lydia, V. Shwetha, Bibhuti Bhusan Dash, Mrinal Bachute

“…Deep learning (DL) has recently been applied in attack detection because it can remove and learn deeper features of known attacks and identify unknown attacks by analyzing network traffic for anomalous patterns. …”
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Multimodal sleep staging network based on obstructive sleep apnea by Jingxin Fan, Jingxin Fan, Jingxin Fan, Mingfu Zhao, Li Huang, Li Huang, Bin Tang, Bin Tang, Lurui Wang, Zhong He, Zhong He, Xiaoling Peng

“…An improved Transformer encoder architecture ensures temporal consistency and effectively captures long-term dependencies in EEG and EOG signals. The Multi-Scale Feature Extraction Module (MFEM) employs convolutional layers with varying dilation rates to capture spatial patterns from fine to coarse granularity. …”
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Implementing large language model and retrieval augmented generation to extract geographic locations of illicit transnational kidney trade by Zifu Wang, Meng-Hao Li, Patrick Baxter, Olzhas Zhorayev, Jiaxin Wei, Valerie Kovacs, Qiuhan Zhao, Chaowei Yang, Naoru Koizumi

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Somatisms in the dialect world view of the Kuban subdialect speakers: A fulfilled cognitive potential by O.G. Borisova, L.Yu. Kostina

“…This paper refers to the ideas of cognitive dialectology and studies the somatic cultural pattern in the dialect world view of the Kuban subdialect speakers. …”
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Capturing Built Environment and Automated External Defibrillator Resource Interplay in Tianjin Downtown by Sara Grigoryan, Yike Hu, Nadeem Ullah

“…This study applied an Optuna-based extreme gradient boosting (OP_XGBoost) decision tree model with SHapely Additive exPlanations (SHAP) and partial dependence plots (PDPs) aiming to scrutinize the spatial effects, relative importance, and non-linear impact of BE features on AEDR intensity across grid and block urban patterns in Tianjin Downtown, China. …”
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Propagation of Radiofrequency Waves in Space by Bernard Démoulin

“…The principal antenna designs, radiation patterns, the effects of soil electrical conductivity and diffraction caused by obstacles such as buildings and mountains are discussed. …”
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Quelques aspects de la propagation des ondes radioélectriques by Bernard Démoulin

“…A short overview without mathematic requirements will be proposed about the basic features of electromagnetic wave propagation. Analysis of various antenna technologies, radiation patterns, effect of earth electrical conductivity and diffraction due to buildings and mountains bring some additional elements. …”
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Genome-wide characterization and expression profiling of the TGA gene family in sweet orange (Citrus sinensis) reveal CsTGA7 responses to multiple phytohormones and abiotic stresse... by Min Wang, Min Wang, Yue Ma, Yu-Xin Qiu, Si-Si Long, Wen-Shan Dai, Wen-Shan Dai

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SH-SDS: a new static-dynamic strategy for substation host security detection by Yang Diao, Hui Chen, Wei Liu, Abdur Rasool

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