Comparative Analysis of Traditional and Modern NLP Techniques on the CoLA Dataset: From POS Tagging to Large Language Models by Abdessamad Benlahbib, Achraf Boumhidi, Anass Fahfouh, Hamza Alami

“…In this article, we compare a range of techniques, from traditional methods such as Part-of-Speech (POS) tagging and feature extraction methods like CountVectorizer with Term Frequency-Inverse Document Frequency (TF-IDF) and N-grams, to modern embeddings such as FastText and Embeddings from Language Models (ELMo), as well as deep learning architectures like transformers and Large Language Models (LLMs). …”
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Epilepsy Diagnosis from EEG Signals Using Continuous Wavelet Transform-Based Depthwise Convolutional Neural Network Model by Fırat Dişli, Mehmet Gedikpınar, Hüseyin Fırat, Abdulkadir Şengür, Hanifi Güldemir, Deepika Koundal

“…Epilepsy diagnosis traditionally relies on analyzing EEG signals, with recent deep learning methods gaining prominence due to their ability to bypass manual feature extraction. …”
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Enhancing Binary Change Detection in Hyperspectral Images Using an Efficient Dimensionality Reduction Technique Within Adversarial Learning by Amel Oubara, Falin Wu, Guoxin Qu, Reza Maleki, Gongliu Yang

“…Detecting binary changes in co-registered bitemporal hyperspectral images (HSIs) using deep learning methods is challenging due to the high dimensionality of spectral data and significant variations between images. …”
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Ensemble Learning for Three-dimensional Medical Image Segmentation of Organ at Risk in Brachytherapy Using Double U-Net, Bi-directional ConvLSTM U-Net, and Transformer Network by Soniya Pal, Raj Pal Singh, Anuj Kumar

“…Aim: This article presents a novel approach to automate the segmentation of organ at risk (OAR) for high-dose-rate brachytherapy patients using three deep learning models combined with ensemble learning techniques. …”
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Blockchain-Powered Secure and Scalable Threat Intelligence System With Graph Convolutional Autoencoder and Reinforcement Learning Feedback Loop by Mohamad Khayat, Ezedin Barka, Mohamed Adel Serhani, Farag Sallabi, Khaled Shuaib, Heba M. Khater

“…This paper proposes an approach that integrates secure blockchain technology with data preprocessing, deep learning, and reinforcement learning to enhance threat detection and response capabilities. …”
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DeepTGIN: a novel hybrid multimodal approach using transformers and graph isomorphism networks for protein-ligand binding affinity prediction by Guishen Wang, Hangchen Zhang, Mengting Shao, Yuncong Feng, Chen Cao, Xiaowen Hu

“…Scientific contribution DeepTGIN is a novel hybrid multimodal deep learning model for predict protein-ligand binding affinity. …”
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Aircraft Detection for Remote Sensing Images Based on Deep Convolutional Neural Networks by Liming Zhou, Haoxin Yan, Yingzi Shan, Chang Zheng, Yang Liu, Xianyu Zuo, Baojun Qiao

“…Aircraft detection for remote sensing images, as one of the fields of computer vision, is one of the significant tasks of image processing based on deep learning. Recently, many high-performance algorithms for aircraft detection have been developed and applied in different scenarios. …”
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Minimal sourced and lightweight federated transfer learning models for skin cancer detection by Vikas Khullar, Prabhjot Kaur, Shubham Gargrish, Anand Muni Mishra, Prabhishek Singh, Manoj Diwakar, Anchit Bijalwan, Indrajeet Gupta

“…Here minimal resource based pre-trained deep learning models including EfficientNetV2S, EfficientNetB3, ResNet50, and NasNetMobile have been used to apply transfer learning on data of shape $$\:\:224\times\:224\times\:3$$ . …”
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Research Status and Prospects of Key Technologies for Rice Smart Unmanned Farms by YU Fenghua, XU Tongyu, GUO Zhonghui, BAI Juchi, XIANG Shuang, GUO Sien, JIN Zhongyu, LI Shilong, WANG Shikuan, LIU Meihan, HUI Yinxuan

“…The field weed management and disease diagnosis technology mainly recognizes rice weeds as well as diseases through deep learning and other methods, and combines them with precision application technology for prevention and intervention. …”
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Enhancing pediatric congenital heart disease detection using customized 1D CNN algorithm and phonocardiogram signals by Ihtisham Ul Haq, Ghassan Husnain, Yazeed Yasin Ghadi, Nisreen Innab, Masoud Alajmi, Hanan Aljuaid

“…The research highlights the promise of combining modern signal processing with deep learning for efficient CHD screening. The suggested model exhibits outstanding performance yet, issues like dataset variability and noise persist. …”
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Discovery of novel PRMT1 inhibitors: a combined approach using AI classification model and traditional virtual screening by Jungan Zhang, Yixin Ren, Yixin Ren, Yun Teng, Han Wu, Jingsu Xue, Lulu Chen, Xiaoyue Song, Yan Li, Ying Zhou, Zongran Pang, Zongran Pang, Hao Wang, Hao Wang, Hao Wang

“…Although extensive research has been conducted on PRMT1, the reported inhibitors have not successfully passed clinical trials. In this study, deep learning was employed to analyze the characteristics of existing PRMTs inhibitors and to construct a classification model for PRMT1 inhibitors. …”
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Explainability of Subfield Level Crop Yield Prediction Using Remote Sensing by Hiba Najjar, Miro Miranda, Marlon Nuske, Ribana Roscher, Andreas Dengel

“…Crop yield forecasting plays a significant role in addressing growing concerns about food security and guiding decision-making for policymakers and farmers. When deep learning is employed, understanding the learning and decision-making processes of the models, as well as their interaction with the input data, is crucial for establishing trust in the models and gaining insight into their reliability. …”
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Cross-modal feature interaction network for heterogeneous change detection by Zhiwei Yang, Xiaoqin Wang, Haihan Lin, Mengmeng Li, Mengjing Lin

“…Recently, research has focused on feature space translation methods based on deep learning technology for heterogeneous images. However, these types of methods often lead to the loss of original image information, and the translated features cannot be efficiently compared, further limiting the accuracy of change detection. …”
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SDRG-Net: Integrating multi-level color transformation encryption and ICNN-IRDO feature analysis for robust diabetic retinopathy diagnosis by Venkata Kotam Raju Poranki, B. Srinivasarao

“…The encrypted images are then fed into an Iterative Convolutional Neural Network (ICNN) architecture for feature extraction, leveraging deep learning capabilities to learn discriminative features from the retinal images automatically. …”
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Benchmark dataset on feeding intensity of the pearl gentian grouper(Epinephelus fuscoguttatus♀×E. lanceolatus♂) by Haijing Qin, Yunchen Tian, Jianing Quan, Xueqi Cong, Qingfei Li, Jinzhu Sui

“…In the factory-based recirculating water high-density aquaculture environment, images have disadvantages such as uneven contrast and blurring, and there are difficulties in manually extracting image features. Although the deep learning-based fish feeding intensity assessment model has higher recognition accuracy and better robustness, the conventional differentiation of feeding intensity usually relies on manual experience to divide the feeding intensity dataset, which is subjective and uncertain, and the annotation is observed by the aquaculture experienced personnel to increase the labor and time cost. …”
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Meso Hybridized Silk Fibroin Watchband for Wearable Biopotential Sensing and AI Gesture Signaling by Xiao Wang, Changsheng Lu, Zerong Jiang, Guangwei Shao, Jingzhe Cao, Xiang Yang Liu

“…Through smart raining via deep learning, we achieved an unparalleled recognition rate (96% across 20 volunteers of different genders) among other EMG sensing devices. …”
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Automatic Comparative Chest Radiography Using Deep Neural Networks by Redwan Sony, Carolyn V. Isaac, Alexis Vanbaarle, Clara J. Devota, Todd Fenton, Joseph T. Hefner, Arun Ross

“…We then explored three families of deep neural networks, each selected for its unique strengths in addressing specific challenges in deep learning for processing these ROIs as well as the entire radiograph. …”
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HFIFNet: Hierarchical Feature Interaction Network With Multiscale Fusion for Change Detection by Mingzhi Han, Tao Xu, Qingjie Liu, Xiaohui Yang, Jing Wang, Jiaqi Kong

“…Change detection (CD) from remote sensing images has been widely used in land management and urban planning. Benefiting from deep learning, numerous methods have achieved significant results in the CD of clearly changed targets. …”
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Dual‐fluorescence imaging and automated trophallaxis detection for studying multi‐nutrient regulation in superorganisms by Lior Baltiansky, Einav Sarafian‐Tamam, Efrat Greenwald, Ofer Feinerman

“…Additionally, our image‐based deep learning algorithm for automatic detection of ant trophallaxis events efficiently yields a detailed record of all food‐transfer interactions. …”
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A graph neural network approach for hierarchical mapping of breast cancer protein communities by Xiao Zhang, Qian Liu

“…Existing approaches are subjective and fail to take information from protein sequences into consideration. Deep learning can automatically learn features from protein sequences and protein–protein interactions for hierarchical clustering. …”
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