Mitigating Cyber Risks in Smart Cyber-Physical Power Systems Through Deep Learning and Hybrid Security Models by M. A. S. P. Dayarathne, M. S. M. Jayathilaka, R. M. V. A. Bandara, V. Logeeshan, S. Kumarawadu, Chathura Wanigasekara

“…By incorporating a novel pre-processing method that leverages feature derivatives, the proposed models achieve over 98% accuracy in detecting cyber threats, providing a robust framework for protecting smart power grids from evolving cyber risks.…”
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WindDefNet: A Multi-Scale Attention-Enhanced ViT-Inception-ResNet Model for Real-Time Wind Turbine Blade Defect Detection by Majad Mansoor, Xiyue Tan, Adeel Feroz Mirza, Tao Gong, Zhendong Song, Muhammad Irfan

“…This work introduces an enhanced deep learning-based framework for real-time detection of wind turbine blade defects. …”
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The integration of artificial intelligence in assisted reproduction: a comprehensive review by Pragati Kakkar, Shruti Gupta, Kasmiria Ioanna Paschopoulou, Ilias Paschopoulos, Ioannis Paschopoulos, Vassiliki Siafaka, Orestis Tsonis

“…AI's capacity for precise image-based analysis, leveraging convolutional neural networks, stands out as a promising avenue. …”
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AI-Driven UAV Surveillance for Agricultural Fire Safety by Akmalbek Abdusalomov, Sabina Umirzakova, Komil Tashev, Nodir Egamberdiev, Guzalxon Belalova, Azizjon Meliboev, Ibragim Atadjanov, Zavqiddin Temirov, Young Im Cho

“…In this study, we propose an advanced deep learning-based fire-detection framework that integrates the Single-Shot MultiBox Detector (SSD) with the computationally efficient MobileNetV2 architecture. …”
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CABAD: A video dataset for benchmarking child aggression recognition by Shehzad Ali, Md Tanvir Islam, Ik Hyun Lee, Mohammad Hijji, Khan Muhammad

“…Leveraging CABAD, we propose CABA_Net, a multi-stage deep-learning framework integrating MobileViT for spatial feature extraction, Temporal Convolutional Networks (TCN) for sequential modeling, and an Attention LSTM for refined temporal attention on behavioral patterns. …”
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Multimodal Emotion Recognition Based on Facial Expressions, Speech, and EEG by Jiahui Pan, Weijie Fang, Zhihang Zhang, Bingzhi Chen, Zheng Zhang, Shuihua Wang

“…Specifically, the proposed Deep-Emotion framework consists of three branches, i.e., the facial branch, speech branch, and EEG branch. …”
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DCANet: A Dual-Branch Cross-Scale Feature Aggregation Network for Remote Sensing Image Semantic Segmentation by Yanhong Yang, Fei Wang, Haozheng Zhang, Yushan Xue, Guodao Zhang, Shengyong Chen

“…In this article, we introduce DCANet, a dual-branch cross-scale feature aggregation network based on an encoder–decoder framework, incorporating visual-state-space (VSS) blocks in the encoder branch to overcome the limitations of conventional convolutional neural networks in capturing global contextual information. …”
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HAMF: A Novel Hierarchical Attention-Based Multi-Modal Fusion Model for Parkinson’s Disease Classification and Severity Prediction by Anitha Rani Palakayala, P. Kuppusamy, D. Kothandaraman, Gunakala Archana, Jaideep Gera

“…A comprehensive approach is a multi-modal framework that overcomes these limitations by integration of brain Magnetic Resonance Imaging (MRI) data, gait analysis, and speech signals for enhanced classification and severity estimation of PD. …”
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Enhancing Lung Cancer Detection through Dual Imaging Modality Integration by Costin Teodor STREBA, Mircea-Sebatian ŞERBĂNESCU, Liliana STREBA, Alin Dragoş DEMETRIAN, Andreea-Georgiana GHEORGHE, Mădălin MĂMULEANU, Daniel-Nicolae PIRICI

“…By integrating histological and pCLE imaging, the dual TL framework significantly improves classification accuracy for lung cancer detection, making it a promising technique for further developments. …”
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Deep Learning-Based Detection and Digital Twin Implementation of Beak Deformities in Caged Layer Chickens by Hengtai Li, Hongfei Chen, Jinlin Liu, Qiuhong Zhang, Tao Liu, Xinyu Zhang, Yuhua Li, Yan Qian, Xiuguo Zou

“…Additionally, the standard convolutional blocks in the neck of the original model were replaced with Grouped Shuffle Convolution (GSConv) modules, effectively reducing information loss during feature extraction. …”
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Robust Image Steganography Approach Based on Edge Detection Combined With CNN Algorithm by Rana Al-Rawashdeh, Md Mahfuzur Rahman, Mahmood Niazi

“…In this research, a new framework is proposed that integrates the edge detection strategy (using edge detectors) with the deep learning methods, such as a convolutional neural network (CNN), for making secret data embedding and extraction processes efficient. …”
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Construction and application of foundational models for intelligent processing of microseismic events in mines by Anye CAO, Maotao LI, Xu YANG, Yao YANG, Sen LI, Yaoqi LIU, Changbin WANG

“…This technological breakthrough establishes a robust framework for intelligent monitoring and precise early warning of mine dynamic disasters, effectively overcoming the limitations of traditional methods in complex geological environments.…”
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State of Health Estimation for Lithium-Ion Batteries Based on TCN-RVM by Yu Zhao, Yonghong Xu, Yidi Wei, Liang Tong, Yiyang Li, Minghui Gong, Hongguang Zhang, Baoying Peng, Yinlian Yan

“…The dilated causal convolution of TCN is used to extract temporal local features of health factors, addressing the insufficient capture of long-range dependencies in traditional models; meanwhile, the Bayesian inference framework of RVM is integrated to enhance the nonlinear mapping capability and small-sample generalization, avoiding the overfitting tendency of complex models. …”
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Advanced Hybrid Transformer-CNN Deep Learning Model for Effective Intrusion Detection Systems with Class Imbalance Mitigation Using Resampling Techniques by Hesham Kamal, Maggie Mashaly

“…IDSs, classified as anomaly-based or signature-based, have increasingly incorporated deep learning models into their framework. Recently, significant advancements have been made in anomaly-based IDSs, particularly those using machine learning, where attack detection accuracy has been notably high. …”
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Med-DGTN: Dynamic Graph Transformer with Adaptive Wavelet Fusion for multi-label medical image classification by Guanyu Zhang, Yan Li, Tingting Wang, Guokun Shi, Li Jin, Zongyun Gu, Zongyun Gu

“…To address these challenges, we propose Med-DGTN, a dynamically integrated framework designed to advance multi-label classification performance in clinical imaging analytics.MethodsThe proposed Med-DGTN (Dynamic Graph Transformer Network with Adaptive Wavelet Fusion) introduces three key innovations: (1) A cross-modal alignment mechanism integrating convolutional visual patterns with graph-based semantic dependencies through conditionally reweighted adjacency matrices; (2) Wavelet-transform-enhanced dense blocks (WTDense) employing multi-frequency decomposition to amplify low-frequency pathological biomarkers; (3) An adaptive fusion architecture optimizing multi-scale feature hierarchies across spatial and spectral domains.ResultsValidated on two public medical imaging benchmarks, Med-DGTN demonstrates superior performance across modalities: (1) Achieving a mean average precision (mAP) of 70.65% on the retinal imaging dataset (MuReD2022), surpassing previous state-of-the-art methods by 2.68 percentage points. (2) On the chest X-ray dataset (ChestXray14), Med-DGTN achieves an average Area Under the Curve (AUC) of 0.841. …”
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Decoupled pixel-wise correction for abdominal multi-organ segmentation by Xiangchun Yu, Longjun Ding, Dingwen Zhang, Jianqing Wu, Miaomiao Liang, Jian Zheng, Wei Pang

“…The integration of our DPAM and DPSM into traditional network architectures facilitates the creation of an NMF-inspired ADNN framework, known as the DPC-Net, which comes in two variants: DPCA-Net for attention and DPCS-Net for self-attention. …”
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Seasonal quantile forecasting of solar photovoltaic power using Q-CNN-GRU by Louiza Ait Mouloud, Aissa Kheldoun, Samira Oussidhoum, Hisham Alharbi, Saud Alotaibi, Thabet Alzahrani, Takele Ferede Agajie

“…This paper presents a novel approach to probabilistic solar power forecasting by combining Convolutional Neural Networks (CNN) with Gated Recurrent Units (GRU) into a hybrid Quantile-CNN-GRU model. …”
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A deep-learning algorithm using real-time collected intraoperative vital sign signals for predicting acute kidney injury after major non-cardiac surgeries: A modelling study. by Sehoon Park, Soomin Chung, Yisak Kim, Sun-Ah Yang, Soie Kwon, Jeong Min Cho, Min Jae Lee, Eunbyeol Cho, Jiwon Ryu, Sejoong Kim, Jeonghwan Lee, Hyung Jin Yoon, Edward Choi, Kwangsoo Kim, Hajeong Lee

“…Using data from three hospitals, we constructed a convolutional neural network-based EfficientNet framework to analyze intraoperative data and created an ensemble model incorporating 103 baseline variables of demographics, medication use, comorbidities, and surgery-related characteristics. …”
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Random splicing assisted deep learning for breast cancer cell line classification via Raman spectroscopy by Yiheng Liu, Junfeng Liu, Jiayi Wan, Hongke Hao, Guangxing Liu, Xia Huang

“…Here, we developed Random Splicing-Convolutional Neural Network (RS-CNN), a deep learning framework that addresses data scarcity through spectral concatenation. …”
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Optimizing the automated recognition of individual animals to support population monitoring by Tijmen A. deLorm, Catharine Horswill, Daniella Rabaiotti, Robert M. Ewers, Rosemary J. Groom, Jessica Watermeyer, Rosie Woodroffe

“…In this study, we develop a framework that automatically selects images suitable for individual identification, and compare the performance of three commonly used identification software packages; Hotspotter, I3S‐Pattern, and WildID. …”
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