NavBLIP: a visual-language model for enhancing unmanned aerial vehicles navigation and object detection by Ye Li, Li Yang, Meifang Yang, Fei Yan, Tonghua Liu, Chensi Guo, Rufeng Chen

“…Traditional methods for UAV navigation and object detection have often relied on either handcrafted features or unimodal deep learning approaches. While these methods have seen some success, they frequently encounter limitations in dynamic environments, where robustness and computational efficiency become critical for real-time performance. …”
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Research on new energy power plant network traffic anomaly detection method based on EMD by Danni Liu, Shengda Wang, YutongLi, Ji Du, Jia Li

“…The incorporation of these state-of-the-art convolutional methods into the CNN-GRU model enhances detection capabilities and opens up new avenues for exploration in the realm of anomaly detection based on deep learning. Results The grid deployment of large-scale PV power facilities relies heavily on dependable communication networks. …”
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pACP-HybDeep: predicting anticancer peptides using binary tree growth based transformer and structural feature encoding with deep-hybrid learning by Shahid, Maqsood Hayat, Wajdi Alghamdi, Shahid Akbar, Ali Raza, Rabiah Abdul Kadir, Mahidur R. Sarker

“…The selected feature vector is subsequently trained using a CNN + RNN-based deep learning model. Our proposed pACP-HybDeep model demonstrated a high training accuracy of 95.33%, and an AUC of 0.97. …”
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Seasonal Land Use and Land Cover Mapping in South American Agricultural Watersheds Using Multisource Remote Sensing: The Case of Cuenca Laguna Merín, Uruguay by Giancarlo Alciaturi, Shimon Wdowinski, María del Pilar García-Rodríguez, Virginia Fernández

“…Future research should explore approaches such as Deep Learning and pixel-based and object-based classification integration to address the identified challenges. …”
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Artificial Intelligence in Pediatric Epilepsy Detection: Balancing Effectiveness With Ethical Considerations for Welfare by Marina Ramzy Mourid, Hamza Irfan, Malik Olatunde Oduoye

“…Specific methods, like EEG‐based AI for detecting interictal discharges, showed high specificity (93.33%–96.67%) and sensitivity (76.67%–93.33%), while neuroimaging approaches using rs‐fMRI and DTI reached up to 97.5% accuracy in identifying microstructural abnormalities. Deep learning models, such as CNN‐LSTM, have also enhanced seizure detection from video by capturing subtle movement and expression cues. …”
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Novel Speech-Based Emotion Climate Recognition in Peers’ Conversations Incorporating Affect Dynamics and Temporal Convolutional Neural Networks by Ghada Alhussein, Mohanad Alkhodari, Ahsan H. Khandoker, Leontios J. Hadjileontiadis

“…Moreover, there is a distinct improvement when the AD are combined with the TCNN, compared to the baseline deep learning approaches. These results demonstrate the effectiveness of AffECt in speech-based EC recognition, paving the way for many applications, e.g., in patients’ group therapy, negotiations, and emotion-aware mobile applications.…”
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TransDeep: Transformer-Integrated DeepLabV3+ for Image Semantic Segmentation by Tengfei Chai, Zhiguo Xiao, Xiangfeng Shen, Qian Liu, NianFeng Li, Tong Guan, Jia Tian

“…In recent years, image semantic segmentation algorithms have made significant progress driven by deep learning technology, and are widely used in fields such as medical image analysis, assistive technology for the visually impaired people, and autonomous driving. …”
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Data-knowledge hybrid driven intelligent prediction method of tunnel excavation profiles geometric deformation by Ya Hu, Jingyi Lu, Jun Zhu, Huixin Zhang, Ying Ren, Jianlin Wu, Jianbo Lai, Heng Zhang, Hongyue Zhao, Xiang Zeng

“…A dynamic data-driven tunnel excavation knowledge extraction method is designed, along with the construction of a multi-process coupled tunnel excavation knowledge base. Additionally, a deep learning-based excavation profile deformation prediction model is developed, integrating the data-knowledge hybrid driven method to improve prediction performance. …”
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Peningkatan Performa Pengenalan Wajah pada Gambar Low-Resolution Menggunakan Metode Super-Resolution by Muhammad Imaduddin Abdur Rohim, Auliati Nisa, Muhammad Nurkhoiri Hindratno, Radhiyatul Fajri, Gembong Satrio Wibowanto, Nova Hadi Lestriandoko, Pesigrihastamadya Normakristagaluh

“…Kami menginvestigasi penggunaan metode super-resolution (SR) berbasis deep learning, termasuk DFDNet, LapSRN, GFPGAN, Real-ESRGAN, Real-ESRGAN+GFPGAN, dan FaceSPARNet. …”
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Enhancing Precipitation Nowcasting Through Dual-Attention RNN: Integrating Satellite Infrared and Radar VIL Data by Hao Wang, Rong Yang, Jianxin He, Qiangyu Zeng, Taisong Xiong, Zhihao Liu, Hongfei Jin

“…Traditional deep learning-based prediction methods predominantly rely on weather radar data to quantify precipitation, often neglecting the integration of the thermal processes involved in the formation and dissipation of precipitation, which leads to reduced prediction accuracy. …”
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hERGAT: predicting hERG blockers using graph attention mechanism through atom- and molecule-level interaction analyses by Dohyeon Lee, Sunyong Yoo

“…Scientific contribution: hERGAT is a deep learning model for predicting hERG blockers by combining GAT and GRU, enabling it to capture complex interactions at atomic and molecular levels. …”
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Micro-Expression Recognition Using Convolutional Variational Attention Transformer (ConVAT) With Multihead Attention Mechanism by Hafiz Khizer Bin Talib, Kaiwei Xu, Yanlong Cao, Yuan Ping Xu, Zhijie Xu, Muhammad Zaman, Adnan Akhunzada

“…Despite significant advancements in deep learning models, challenges persist in accurately handling the nuanced and fleeting nature of micro-expressions, particularly when applied across diverse datasets with varied expressions. …”
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Counterfactual Based Approaches for Feature Attributions of Stress Factors Affecting Rice Yield by Nisha P. Shetty, Balachandra Muniyal, Ketavarapu Sriyans, Kunyalik Garg, Shiv Pratap, Aman Priyanshu, Dhruthi Kumar

“…The increased integration of Deep Learning (DL) and Machine Learning (ML) into agriculture has enabled substantial advancements in predicting crop yields and analyzing factors affecting them. …”
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EPRNG: Effective Pseudo-Random Number Generator on the Internet of Vehicles Using Deep Convolution Generative Adversarial Network by Chenyang Fei, Xiaomei Zhang, Dayu Wang, Haomin Hu, Rong Huang, Zejie Wang

“…Before generating the encryption keys, a random number generator (RNG) plays an important component in cybersecurity. Several deep learning-based RNGs have been deployed to train the initial value and generate pseudo-random numbers. …”
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Predicting water level fluctuations in glacier-fed lakes by ensembling individual models into a quad-meta model by Shoukat Ali Shah, Songtao Ai, Hanxiao Yuan

“…This study highlights the potential of ensemble learning for enhanced prediction accuracy in glacier-fed lake systems, recommending future research directions that incorporate the QM model with deep learning and long-term forecasting to expand predictive capabilities on a global scale.…”
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Characteristics of successful expansion of tumor-infiltrating lymphocytes from colorectal cancer liver metastasis by Jina Baek, Gyuheon Choi, GunHee Lee, Hyun Lee, Gyungyub Gong, Hye Seon Park, Chae-Lyul Lim, Joo Young Kim, Hee Jin Lee

“…Histologic examination including the level of stromal TILs and Klintrup-Mäkinen score, were assessed by pathologists and deep learning-derived spatial analysis. We performed correlation analysis between expanded TILs and histopathologic factors. …”
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Establishment and Evaluation of Atmospheric Water Vapor Inversion Model Without Meteorological Parameters Based on Machine Learning by Ning Liu, Yu Shen, Shuangcheng Zhang, Xuejian Zhu

“…For this reason, based on the data of 17 ground-based GNSS stations and water vapor reanalysis products over 2 years in the Hong Kong region, a new model for water vapor inversion without the Tm parameter is established by deep learning in this paper, the research results showed that, compared with the PWV information calculated by the traditional model using Tm parameter, the accuracy of the PWV retrieved by the new model proposed in this paper is higher, and its accuracy index parameters BIAS, MAE, and RMSE are improved by 38% on average. …”
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R-CNN Based Vehicle Object Detection via Segmentation Capabilities in Road Scenes by Bisma Riaz Chughtai, Haifa F. Alhasson, Mohammed Alnusayri, Mohammed Alatiyyah, Hanan Aljuaid, Ahmad Jalal, Jeongmin Park

“…We propose a novel approach that leverages state-of-the-art machine learning and deep learning algorithms to enhance accuracy and efficiency. …”
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Precision Imaging for Early Detection of Esophageal Cancer by Po-Chun Yang, Chien-Wei Huang, Riya Karmakar, Arvind Mukundan, Tsung-Hsien Chen, Chu-Kuang Chou, Kai-Yao Yang, Hsiang-Chen Wang

“…This study underscores the potential of deep learning identification models to aid in medical detection research. …”
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Semantic Tokenization-Based Mamba for Hyperspectral Image Classification by Ri Ming, Na Chen, Jiangtao Peng, Weiwei Sun, Zhijing Ye

“…Experimental results on three HSI datasets demonstrate that the proposed STMamba outperforms existing state-of-the-art deep learning and transformer-based methods.…”
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