Unraveling trends in schistosomiasis: deep learning insights into national control programs in China by Qing Su, Cici Xi Chen Bauer, Robert Bergquist, Zhiguo Cao, Fenghua Gao, Zhijie Zhang, Yi Hu

“…METHODS We obtained village-level parasitological data from cross-sectional surveys combined with environmental data in Anhui Province, China from 1997 to 2015. A convolutional neural network (CNN) based on a hierarchical integro-difference equation (IDE) framework (i.e., CNN-IDE) was used to model spatio-temporal variations in schistosomiasis. …”
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Development of a CNN-based decision support system for lung disease diagnosis using chest radiographs by B. T. Magar, M. A. Rahman, P. K. Saha, M. Ahmad, M. A. Rashid, H. Higa

“…This study presents CXRNet, a novel, efficient convolutional neural network (CNN)-based framework designed for multi-class classification of common chest diseases, including cardiomegaly, COVID-19, pneumonia, tuberculosis, and normal. …”
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AI Techniques and Applications for Online Social Networks and Media: Insights From BERTopic Modeling by Prema Nedungadi, G. Veena, Kai-Yu Tang, Remya R. K. Menon, Raghu Raman

“…Although AI techniques and multimodal frameworks have significantly improved content personalization, challenges like algorithmic bias and echo chambers remain. …”
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Learning Power Systems Waveform Incipient Patterns Through Few-Shot Meta-Learning by Lixian Shi, Qiushi Cui, Yang Weng, Yigong Zhang, Shilong Chen, Jian Li, Wenyuan Li

“…To resolve these problems, a few-shot meta-learning framework for incipient fault detection (FSMLF-IFD) is proposed in this paper. …”
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Flood Classification and Improved Loss Function by Combining Deep Learning Models to Improve Water Level Prediction in a Small Mountain Watershed by Rukai Wang, Ximin Yuan, Fuchang Tian, Minghui Liu, Xiujie Wang, Xiaobin Li, Minrui Wu

“…The integration of Graph Convolutional and Time Aware models enables the model to recognize the spatiotemporal flood characteristics, overcoming limitations of prevailing methods and ensuring long‐term forecast accuracy. …”
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GhostConv+CA-YOLOv8n: a lightweight network for rice pest detection based on the aggregation of low-level features in real-world complex backgrounds by Fei Li, Yang Lu, Qiang Ma, Shuxin Yin, Rui Zhao

“…To overcome these limitations, this paper introduces GhostConv+CA-YOLOv8n, a lightweight object detection framework was proposed, which incorporates several innovative features: GhostConv replaces standard convolutional operations with computationally efficient ghost modules in the YOLOv8n’s backbone structure, reducing parameters by 40,458 while maintaining feature richness; a Context Aggregation (CA) module is applied after the large and medium-sized feature maps were output by the YOLOv8n’s neck structure. …”
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The JPEG Pleno Learning-Based Point Cloud Coding Standard: Serving Man and Machine by Andre F. R. Guarda, Nuno M. M. Rodrigues, Fernando Pereira

“…Color compression performance is less competitive but this is overcome by the power of a full learning-based coding framework for both geometry and color and the associated effective compressed domain processing.…”
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Development of an optimized deep learning model for predicting slope stability in nano silica stabilized soils by Ishwor Thapa, Sufyan Ghani, Prabhu Paramasivam, Mitiku Adare Tufa

“…This study suggests a hybrid classification model of deep learning, integration of convolutional neural networks (CNN), long short-term memory (LSTM), and recurrent neural networks (RNN), optimized by Optuna to predict the stability of NS stabilized infinite slope. …”
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Topic Words-Based Multilingual Hateful Linguistic Resources Construction for Developing Multilingual Hateful Content Detection Model Using Deep Learning Technique by Naol Bakala Defersha, Kula Kekeba Tune, Solomon Teferra Abate

“…Finally, their performance was compared by integrating them into deep learning-based low-resource Ethiopian languages’ hateful content detection framework. Among applied deep learning algorithms with Ethiopian language linguistic resources, word2vec-based multilingual lexicons with convolutional neural network (CNN) outperform than others. …”
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Tricho-Vision: The use of computer vision in trichotaxonomy for enhancing wildlife conservation of priority species by Alloy Das, Priyanka Banerjee, Sanket Biswas, Manokaran Kamalakannan, Joydev Chattopadhyay, Dhriti Banerjee, Tanoy Mukherjee

“…The proposed Tricho-Vision framework offers significant applications in biodiversity monitoring and wildlife crime investigation, facilitating accurate species identification from forensic hair samples. …”
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Underground helmet detection algorithm based on improved YOLOv8s by Jiaru YANG, Yinan QIN, Tianxu LI, Han ZHUANG

“…Then, the SPD-Conv convolution module is added, and the non-step convolutional layer is used to reduce the situations that the redundant information of small targets is filtered and fine-grained information is lost. …”
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Accelerating spin Hall conductivity predictions via machine learning by Jinbin Zhao, Junwen Lai, Jiantao Wang, Yi‐Chi Zhang, Junlin Li, Xing‐Qiu Chen, Peitao Liu

“…Here, we have developed a residual crystal graph convolutional neural network (Res‐CGCNN) deep learning model to classify and predict SHCs solely based on the structural and compositional information. …”
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P-68 LIVGUARD, A DEEP NEURAL NETWORK FOR CIRRHOSIS DETECTION IN LIVER ULTRASOUND (USD) IMAGES by DIEGO ARUFE, Pablo Gomez del Campo, Ezequiel Demirdjian, Carlos Galmarini

“…Further work is required to validate this algorithmic framework in prospective cohorts of patients in additional clinical trials and/or real-world datasets.…”
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Multi-User Activity Recognition Using Plot Images Based on Ambiental Sensors by Anca Roxana Alexan, Alexandru Iulian Alexan, Stefan Oniga

“…In the second stage, the generated data are provided to a sequential convolutional neural network, which predicts the 16 activities developed by two users. …”
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Research on Interval Probability Prediction and Optimization of Vegetation Productivity in Hetao Irrigation District Based on Improved TCLA Model by Jie Ren, Delong Tian, Hexiang Zheng, Guoshuai Wang, Zekun Li

“…We propose a multimodal regression prediction model utilizing the TCLA framework—comprising the Transient Trigonometric Harris Hawks Optimizer (TTHHO), Convolutional Neural Networks (CNN), Least Squares Support Vector Machine (LSSVM), and Adaptive Bandwidth Kernel Density Estimation (ABKDE)—with the Hetao Irrigation District, a vast irrigation basin in China, serving as the study area. …”
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A hybrid CNN-LSTM model with adaptive instance normalization for one shot singing voice conversion by Assila Yousuf, David Solomon George

“…In the proposed singing voice conversion technique, an encoder decoder framework was implemented using a hybrid model of convolutional neural network (CNN) accompanied by long short term memory (LSTM). …”
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RT-DETR-Smoke: A Real-Time Transformer for Forest Smoke Detection by Zhong Wang, Lanfang Lei, Tong Li, Xian Zu, Peibei Shi

“…To tackle these issues, we propose RT-DETR-Smoke, a specialized real-time transformer-based smoke-detection framework. First, we designed a high-efficiency hybrid encoder that combines convolutional and Transformer features, thus reducing computational cost while preserving crucial smoke details. …”
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A New Hybrid Wavelet Transform-Deep Learning for Smart Resilient Inverters in Microgrids Against Cyberattacks by Chou-Mo Yang, Pei-Min Huang, Chun-Lien Su, Mahmoud Elsisi

“…This paper proposes a new hybrid deep learning framework that integrates Long Short-Term Memory (LSTM) networks with the Gaussian Continuous Wavelet Transform (GCWT), a signal processing technique excelling at time-frequency feature extraction from power signals to detect severe FDIA in smart inverters, which are designed to significantly alter operational data and destabilize the grid. …”
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LeafDNet: Transforming Leaf Disease Diagnosis Through Deep Transfer Learning by Tofayet Sultan, Mohammad Sayem Chowdhury, Nusrat Jahan, M. F. Mridha, Sultan Alfarhood, Mejdl Safran, Dunren Che

“…These results emphasize the potential of this advanced deep learning framework as a scalable, efficient, and highly accurate solution for early plant disease detection, providing substantial benefits for plant health management and supporting sustainable agricultural practices.…”
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IMViT: Adjacency Matrix-Based Lightweight Plain Vision Transformer by Qihao Chen, Yunfeng Yan, Xianbo Wang, Jishen Peng

“…While extensive experiments prove its outstanding ability for large models, transformers with small sizes are not comparable with convolutional neural networks in various downstream tasks due to its lack of inductive bias which can benefit image understanding. …”
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