M<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula>Convformer: Multiscale Masked Hybrid Convolution-Transformer Network for Hyperspectral Image Super-Res... by Shuo Wang, Boneng Shi, Ninglian Wang, Yuzhu Zhang, Yan Zhu

“…This work focuses on the single hyperspectral image super-resolution problem and develops a multiscale masked hybrid convolution-transformer framework. The starting point of this work is an attempt to add a random mask to the input signal to reduce the redundancy of the original features, which the model combines with multiscale representation inference to improve its learning and generalization capabilities. …”
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YOLOv9-AAG: Distinguishing Birds and Drones in Infrared and Visible Light Scenarios by Peichun Suo, Jincan Zhu, Qingyang Zhou, Weili Kou, Xiuyan Wang, Wen Suo

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State of Health Prediction for Lithium-Ion Batteries Based on Gated Temporal Network Assisted by Improved Grasshopper Optimization by Xiankun Wei, Silun Peng, Mingli Mo

“…The experimental results demonstrate that the proposed IGOA-GGNN-TCN framework offers a novel and effective approach for state-of-health (SOH) estimation in lithium-ion batteries. …”
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Comparative analysis of deep learning and traditional methods for IoT botnet detection using a multi-model framework across diverse datasets by Saeed Ullah, Junsheng Wu, Zhijun Lin, Mian Muhammad Kamal, Hala Mostafa, Muhammad Sheraz, Teong Chee Chuah

“…This study focuses on traditional machine learning and deep learning approaches, proposes a novel ensemble framework to address these issues, integrating Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), Random Forest (RF), and Logistic Regression (LR) via a weighted soft-voting mechanism. …”
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Enhancing microgrid forecasting accuracy with a TCNN-TLS framework: A novel approach to mitigating uncertainty in renewable energy and load predictions by Md. Omer Faruque, Md. Majharul Islam, Md Jakaria Talukder, Arif Mia, Shahriar Tasnim, Md. Alamgir Hossain, S.M. Muyeen

“…This paper introduces a novel framework designed to improve the accuracy of short-term forecasting of the aforementioned energies and load profiles in an isolated microgrid. …”
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TrioConvTomatoNet-BiLSTM: An Efficient Framework for the Classification of Tomato Leaf Diseases in Real Time Complex Background Images by S. Ledbin Vini, P. Rathika

“…As a result, the proposed framework achieves remarkable accuracy of 99.65%, 98.83%, and 99.20% in classifying tomato leaf disease images with non-uniform, synthetic, and real-time complex backgrounds against the TrioConvTomatoNet and TrioConvTomatoNet-LSTM frameworks. …”
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Innovative Expert-Based Tools for Spatiotemporal Shallow Landslides Mapping: Field Validation of the GOGIRA System and Ex-MAD Framework in Western Greece by Michele Licata, Francesco Seitone, Efthimios Karymbalis, Konstantinos Tsanakas, Giandomenico Fubelli

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Enhanced skin cancer diagnosis: a deep feature extraction-based framework for the multi-classification of skin cancer utilizing dermoscopy images by Hadeel Alharbi, Gabriel Avelino Sampedro, Roben A. Juanatas, Se-jung Lim

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Hybrid deep learning-enabled framework for enhancing security, data integrity, and operational performance in Healthcare Internet of Things (H-IoT) environments by Nithesh Naik, Neha Surendranath, Sai Annamaiah Basava Raju, Chennaiah Madduri, Nagaraju Dasari, Vinod Kumar Shukla, Vathsala Patil

“…This paper proposes a novel trust-aware hybrid framework integrating Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) models, and Variational Autoencoders (VAE) to analyze spatial, temporal, and latent characteristics of physiological signals. …”
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MultiRepPI: a cross-modal feature fusion-based multiple characterization framework for plant peptide-protein interaction prediction by Yu Zhiguo, Li Zixuan, Li Peng

“…To this end, we propose a multiple characterization framework based on cross-modal feature fusion-MultiRepPI-for efficient prediction of plant PepPI. …”
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Eeg-based detection of epileptic seizures in patients with disabilities using a novel attention-driven deep learning framework with SHAP interpretability by Tawfeeq Shawly, Ahmed A. Alsheikhy

“…These findings suggest that the proposed attention-enhanced CNN framework serves as a reliable and interpretable tool for the early detection of epilepsy and patient monitoring.…”
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CTSeg: CNN and ViT collaborated segmentation framework for efficient land-use/land-cover mapping with high-resolution remote sensing images by Jifa Chen, Gang Chen, Pin Zhou, Yufeng He, Lianzhe Yue, Mingjun Ding, Hui Lin

“…In this paper, we propose a novel CNN and ViT collaborated segmentation framework (CTSeg) to address these weaknesses. Following the encoder-decoder architecture, we first introduce an encoding backbone with multifarious attention mechanisms to respectively capture global and local contexts. …”
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DeepGenMon: A Novel Framework for Monkeypox Classification Integrating Lightweight Attention-Based Deep Learning and a Genetic Algorithm by Abdulqader M. Almars

“…This suggested framework leverages an attention-based convolutional neural network (CNN) and a genetic algorithm (GA) to enhance detection accuracy while optimizing the hyperparameters of the proposed model. …”
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Effective last-mile delivery using reinforcement learning and social media-based traffic prediction in underdeveloped megacities by Luis Rabelo, Cristian Rincón-Guio, Valeria Laynes, Edgar Gutierrez-Franco, Vasanth Bhat, Juan Zamora-Aguas, Marwen Elkamel

“…Leveraging a Graph Convolutional Networks and a Long Short-Term Memory model for traffic prediction, the framework incorporates multimodal data sources, such as social media sentiment analysis, to provide real-time insights into traffic dynamics. …”
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Rethinking model prototyping through the MedMNIST+ dataset collection by Sebastian Doerrich, Francesco Di Salvo, Julius Brockmann, Christian Ledig

“…Finally, by establishing a standardized evaluation framework, we aim to enhance transparency, reproducibility, and comparability within the MedMNIST+ dataset collection as well as future research. …”
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TMBO-AOD: Transparent Mask Background Optimization for Accurate Object Detection in Large-Scale Remote-Sensing Images by Tianyi Fu, Hongbin Dong, Benyi Yang, Baosong Deng

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A novel deep neural network-based technique for network embedding by Sabrina Benbatata, Bilal Saoud, Ibraheem Shayea, Naif Alsharabi, Abdulraqeb Alhammadi, Ali Alferaidi, Amr Jadi, Yousef Ibrahim Daradkeh

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EEG-Based Seizure Detection Using Dual-Branch CNN-ViT Network Integrating Phase and Power Spectrograms by Zhuohan Wang, Yaoqi Hu, Qingyue Xin, Guanghao Jin, Yazhou Zhao, Weidong Zhou, Guoyang Liu

“…<b>Methods:</b> In this study, we propose an effective epileptic seizure detection framework based on continuous wavelet transform (CWT) and a hybrid network consisting of convolutional neural network (CNN) and vision transformer (ViT). …”
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Dual-Path Adaptive Channel Attention Network Based on Feature Constraints for Face Anti-Spoofing by Nana Li, Zhipeng Weng, Fangmei Liu, Zuhe Li, Wei Wang

“…To address these limitations, we propose an innovative face anti-spoofing framework. Within this framework, we design a convolutional neural network (CNN) based on the Dual-path Adaptive Channel Attention (DACA) module, aiming to filter the features of the input facial images to extract key information. …”
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Research on Gear Surface Damage Recognition Based on Small Sample Deep Learning by Wang Xiaopeng, Hua Hongpeng, Lu Changqing, Peng Kun, Zhong Yuan, Wu Biqiong

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