Autoencoder untuk Sistem Prediksi Berat Lahir Bayi by Fitra Septia Nugraha, Hilman Ferdinandus Pardede

“…So that the autoencoder can model the correlation between features. By varying the parameters of the autoencoder, the best performance is MSE 0.002, MAE 0.029, R2 0.991 with an autoencoder with 4 hidden layer encoder and decoder layers. …”
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Explainable Artificial Intelligence to Predict the Water Status of Cotton (<i>Gossypium hirsutum</i> L., 1763) from Sentinel-2 Images in the Mediterranean Area by Simone Pietro Garofalo, Anna Francesca Modugno, Gabriele De Carolis, Nicola Sanitate, Mesele Negash Tesemma, Giuseppe Scarascia-Mugnozza, Yitagesu Tekle Tegegne, Pasquale Campi

“…The random forest model using Sentinel-2 bands’ reflectance as predictors showed the highest performance, with an R² of 0.75 (±0.07) and an RMSE of 0.11 (±0.02). …”
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A retrospective study on a nomogram combining clinical and ultrasound parameters for differentiating solitary parathyroid adenoma from carcinoma or atypical tumors by Chunrui Liu, Mingxia Li, Wenxian Li, Haiyan Xue, Yidan Zhang, Shuping Wei, Jian He, Jing Yao, Zhengyang Zhou

“…Decision and calibration curve analyses were performed to assess the clinical value and calibration of each model, respectively.ResultsIn the training set, there were 181 cases of PA and 26 cases of APC/PC. …”
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A lightweight object detection approach based on edge computing for mining industry by Muhammad Wahab Hanif, Zhanli Li, Zhenhua Yu, Rehmat Bashir

“…A model is proposed to improve real‐time performance for edge mining detection tasks. …”
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Efficient urinary stone type prediction: a novel approach based on self-distillation by Kun Liu, Xuanqi Zhang, Haiyun Yu, Jie Song, Tianxiao Xu, Min Li, Chang Liu, Shuang Liu, Yucheng Wang, Zhenyu Cui, Kun Yang

“…This method circumvents the extra computational expenses and performance reduction linked with model compression and removes the reliance on external teacher models, markedly enhancing the efficacy of lightweight models. achieved a classification accuracy of 74.96% on a proprietary dataset, outperforming current techniques. …”
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Advanced Diagnosis of Cardiac and Respiratory Diseases from Chest X-Ray Imagery Using Deep Learning Ensembles by Hemal Nakrani, Essa Q. Shahra, Shadi Basurra, Rasheed Mohammad, Edlira Vakaj, Waheb A. Jabbar

“…Using the NIH Chest X-ray dataset, the methodology involved comprehensive preprocessing, data augmentation, and model optimization techniques to address challenges such as label imbalance and feature variability. …”
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Domain generalization for image classification based on simplified self ensemble learning. by Zhenkai Qin, Xinlu Guo, Jun Li, Yue Chen

“…In this paper, we present an approach that transcends merely eliminating domain discrepancies by enhancing the model's adaptability to improve its performance in unseen domains. …”
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Optimizing Wet Fingerprint Denoising Net for Enhanced Biometric Security by Mao-Hsiu Hsu, Ying-Hong Shi

“…By integrating scale-invariant feature transform, WFDN effectively restores critical minutiae and significantly enhances feature preservation compared with existing models. …”
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DIAGNOSTICS AND PLANNING OF ORTHODONTIC TREATMENT OF PATIENTS WITH DENTAL ARCH NARROWING USING 3D TECHNOLOGIES by Yu.M. Voliak, Z.R. Ozhohan

“…We used both calipers and 3SHAPЕ program on three-dimensional models to measure the models. It is not necessarily to keep numerous stone models to create an archive data of orthodontic patients before and after treatment. …”
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Design optimization of university ideological and political education system based on deep learning by Shangle Ai, Huanhuan Ding

“…The hybrid model combines the advantages of CNN in feature extraction and the ability of LSTM in processing sequence data to realize accurate analysis of IPE data. …”
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End-to-End Architecture for English Reading and Writing Content Assessment Based on Prompt Learning by Su-Qin Wu, Ming-Yong Pang, Xue-Mei Sun, Xin-Jian Wang, Yun-Peng Ji

“…The algorithm preprocesses the input English texts through the prompt learning framework and uses multi-scale attention mechanisms to improve the model&#x2019;s ability to capture features at different linguistic levels. …”
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Multi-physiological signal fusion for objective emotion recognition in educational human–computer interaction by Wanmeng Wu, Enling Zuo, Weiya Zhang, Xiangjie Meng

“…This study aimed to develop a robust emotion recognition system to enhance teacher–student interactions within educational HCI settings.MethodsA multi-physiological signal-based emotion recognition system was developed using wearable devices to capture electrocardiography (ECG), electromyography (EMG), electrodermal activity, and respiratory signals. Feature extraction was performed using time-domain and time-frequency domain analysis methods, followed by feature selection to eliminate redundant features. …”
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Transformer-generated atomic embeddings to enhance prediction accuracy of crystal properties with machine learning by Luozhijie Jin, Zijian Du, Le Shu, Yan Cen, Yuanfeng Xu, Yongfeng Mei, Hao Zhang

“…Here, we proposed an atomic embedding strategy called universal atomic embeddings (UAEs) for their broad applicability as atomic fingerprints, and generated the UAE tensors based on the proposed CrystalTransformer model. By performing experiments on widely-used materials database, our CrystalTransformer-based UAEs (ct-UAEs) are shown to accurately capture complex atomic features, leading to a 14% improvement in prediction accuracy on CGCNN and 18% on ALIGNN when using formation energies as the target, based on the Materials Project database. …”
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Super-resolution of 3D medical images by generative adversarial networks with long and short-term memory and attention by Qiong Zhang, Yiliu Hang, Fang Wu, Shentao Wang, Yue Hong

“…Deep convolutional networks perform well in extracting spatial features, but are less capable for processing sequence data compared to recurrent convolutional networks. …”
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Russian Historiography of Polish Nationalism in the End of the 19th – Beginning of the 20th Century: Network Analysis by Viktor Durov

“…The theoretical basis of network analysis is the mathematical theory of networks, which allows creating a model of the Russian scientific community thanks to the scientometric analysis of publications of historians. …”
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Deep Learning-Driven Multi-Temporal Detection: Leveraging DeeplabV3+/Efficientnet-B08 Semantic Segmentation for Deforestation and Forest Fire Detection by Joe Soundararajan, Andrew Kalukin, Jordan Malof, Dong Xu

“…In the Amazon forest segmentation dataset, the model achieves a validation Intersection over Union (IoU) of 0.9100 and a pixel accuracy of 0.9605, demonstrating strong performance in delineating forest boundaries. …”
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LENet: A Lightweight and Efficient High-Resolution Network for Human Pose Estimation by Ming Zhang, Xiandong Yu, Wenqiang Li, Xin Shu, Lei Pan, Zhongwei Shen

“…As a typical lightweight network, Lite-HRNet achieves high performance in human pose estimation with a relatively low model complexity, but its inference speed is not ideal in practical applications. …”
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Underground helmet detection algorithm based on improved YOLOv8s by Jiaru YANG, Yinan QIN, Tianxu LI, Han ZHUANG

“…In order to solve the above problems, this study proposes a detection algorithm for underground safety helmets based on improved YOLOv8s, which is called PBSS-YOLOv8. The PBSS-YOLOv8 model first adds the small target detection layer P2 to improve the detection performance of small targets, and on this basis, the BiFPN network framework is introduced to make the information in the network can be transmitted in both directions, which further improves the capture performance of the helmet, and the original P5 detection layer is deleted, which greatly reduces the number of parameters and calculations of the model. …”
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Application of the YOLOv11-seg algorithm for AI-based landslide detection and recognition by Luhao He, Yongzhang Zhou, Lei Liu, Yuqing Zhang, Jianhua Ma

“…The experimental results indicate that the YOLOv11-seg model excels in several key performance metrics, such as precision, recall, F1 score, and mAP. …”
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Explainable light-weight deep learning pipeline for improved drought stress identification by Aswini Kumar Patra, Aswini Kumar Patra, Lingaraj Sahoo

“…This architecture leverages the pre-trained network’s feature extraction capabilities while the custom layers enable targeted dimensionality reduction and enhanced regularization, ultimately leading to improved performance. …”
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