AI-based virtual immunocytochemistry for rapid and robust fine needle aspiration biopsy diagnosis by Irfan Ahmed, Wei Zhang, Pikting Cheung, Vardhan Basnet, Zulfiqar Ali, May PY Tse, Fraser Hill, Tom Tak Lam Chan, Haibo Hu, Xinyue Li, Condon Lau

“…It offers a rapid, accurate, and precise evaluation of FNA samples and has the potential to help advance diagnostic cellular and molecular pathology capabilities.…”
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Machine learning reveals the dynamic importance of accessory sequences for Salmonella outbreak clustering by Chao Chun Liu, William W. L. Hsiao

“…To assess the analytical value of bacterial accessory genomes in clustering epidemiologically related cases, we trained classifiers on a set of genomes collected from 24 Salmonella enterica outbreaks of food, animal, or environmental origin. …”
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Multiparameter diagnostic model using S100A9, CCL5 and blood biomarkers for nasopharyngeal carcinoma by Lu Long, Ya Tao, Wenze Yu, Qizhuo Hou, Yunlai Liang, Kangkang Huang, Huidan Luo, Bin Yi

“…NPC prediction models were developed using four machine-learning algorithms, and their performance was evaluated with ROC curves. …”
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Real-World Parkinson’s Hand Tremor Detection Using Ensemble Learning Techniques by Sungwook Hur, Jieming Zhang, Moon-Hyun Kim, Tai-Myoung Chung

“…Finally, unlike traditional approaches that use uniform feature weighting, our proposed variance adaptive AdaBoost (VA-AdaBoost) algorithm uniquely incorporates accelerometer signal variance to adaptively weight samples for feature training. …”
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Therapy and rehabilitation of women with diabetic adhesive capsulitis by Iryna Zharova, Yevhen Orlenko

“…Results. We developed and evaluated the effectiveness of a program of therapy and rehabilitation of women with diabetic adhesive capsulitis, implemented in three stages: preparatory (at a clinical institution and/or outpatient), main and final (outpatient and home training programs). …”
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Artificial Intelligence (AI) in Neurofeedback Therapy Using Electroencephalography (EEG), Heart Rate Variability (HRV), and Galvanic Skin Response (GSR): A Review by Teddy Marcus Zakaria, Armein Zainal R. Langi, Muhamad Sophian Nazaruddin, Isa Anshori

“…The objective of this review is to offer a fair evaluation of the current state of artificial intelligence in NFT, identify gaps in the literature, and propose future research directions. …”
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Detecting unsafe behavior in neural network imitation policies for caregiving robotics by Andrii Tytarenko

“…While leveraging advancements in deep learning and control algorithms, the study focuses on training neural network policies using offline demonstrations. …”
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Predicting soil organic carbon with ensemble learning techniques by using satellite images for precision farming by Shyamal Mundada, Pooja Jain

“…The goal of the research is to create a system for evaluating soil organic carbon based on topographic features and soil properties incorporating machine learning algorithms. …”
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Establishing a preoperative predictive model for gallbladder adenoma and cholesterol polyps based on machine learning: a multicentre retrospective study by Yubing Wang, Chao Qu, Jiange Zeng, Yumin Jiang, Ruitao Sun, Changlei Li, Jian Li, Chengzhi Xing, Bin Tan, Kui Liu, Qing Liu, Dianpeng Zhao, Jingyu Cao, Weiyu Hu

“…Using 12 machine learning algorithms, 110 combination predictive models were constructed. …”
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Enhancing Pollen Prediction in Beijing, a Chinese Megacity: Leveraging Ensemble Learning Models for Greater Accuracy by Wenxi Ruan, Ziming Li, Zhaobin Sun, Xingqin An, Yuxin Zhao, Shuwen Zhang, Yinglin Liang, Yaqin Bu, Jingyi Xin, Xiaoyi Hang

“…Specifically, it forecasts pollen concentrations in Beijing, utilizing R2 and RMSE as evaluation metrics. The findings reveal that the CatBoost, Extra Trees, and XGBoost algorithms perform well for three-day consecutive pollen predictions. …”
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Application of Mask R-CNN for automatic recognition of teeth and caries in cone-beam computerized tomography by Yujie Ma, Maged Ali Al-Aroomi, Yutian Zheng, Wenjie Ren, Peixuan Liu, Qing Wu, Ye Liang, Canhua Jiang

“…Abstract Objectives Deep convolutional neural networks (CNNs) are advancing rapidly in medical research, demonstrating promising results in diagnosis and prediction within radiology and pathology. This study evaluates the efficacy of deep learning algorithms for detecting and diagnosing dental caries using cone-beam computed tomography (CBCT) with the Mask R-CNN architecture while comparing various hyperparameters to enhance detection. …”
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WGAN-DL-IDS: An Efficient Framework for Intrusion Detection System Using WGAN, Random Forest, and Deep Learning Approaches by Shehla Gul, Sobia Arshad, Sanay Muhammad Umar Saeed, Adeel Akram, Muhammad Awais Azam

“…In our efficient and novel framework, we integrate an oversampling strategy that uses Generative Adversarial Networks (GANs) to overcome the difficulties introduced by imbalanced datasets, and we use the Random Forest (RF) importance algorithm to select a subset of features that best represent the dataset to reduce the dimensionality of a training dataset. …”
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Predicting the risk of heart failure after acute myocardial infarction using an interpretable machine learning model by Qingqing Lin, Qingqing Lin, Wenxiang Zhao, Wenxiang Zhao, Hailin Zhang, Hailin Zhang, Wenhao Chen, Sheng Lian, Qinyun Ruan, Qinyun Ruan, Zhaoyang Qu, Zhaoyang Qu, Yimin Lin, Yimin Lin, Dajun Chai, Dajun Chai, Dajun Chai, Dajun Chai, Xiaoyan Lin, Xiaoyan Lin, Xiaoyan Lin, Xiaoyan Lin

“…The performance evaluation of the prediction model was carried out on the training set and the testing set, utilizing metrics including AUC (Area under the receiver operating characteristic curve), calibration plot, and decision curve analysis (DCA). …”
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Usage of the dwarf mongoose optimization-based ANFIS on the static strength of seasonally frozen soils by Bowen Liu, Junbin Chen, Xiaoguang Zhang, Zhenwei Wang

“…To find hyper-parameters of models as ideally as possible, the dwarf mongoose optimization algorithm (DMOA) was employed (ANF DW, and SVR DW). …”
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Memory Efficient Local Features Descriptor for Identity Document Detection on Mobile and Embedded Devices by Daniil P. Matalov, Elena E. Limonova, Natalya S. Skoryukina, Vladimir V. Arlazarov

“…In this paper, we propose a data-driven approach to training a memory-efficient local feature descriptor for identity documents location and classification on mobile and embedded devices. …”
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Comparison in prostate cancer diagnosis with PSA 4–10 ng/mL: radiomics-based model VS. PI-RADS v2.1 by Chunxing Li, Zhicheng Jin, Chaogang Wei, Guangcheng Dai, Jian Tu, Junkang Shen

“…Abstract Background To evaluate accuracy of MRI-based radiomics in diagnosing prostate cancer (PCa) in patients with PSA levels between 4 and 10 ng/mL and compare it with the latest Prostate Imaging Reporting and Data System (PI-RADS v2.1) score. …”
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A privacy-enhanced framework with deep learning for botnet detection by Guangli Wu, Xingyue Wang

“…In order to further ensure the privacy protection of the feature extractor during the training process, we train the feature extractor in the federated learning training mode. …”
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Satellite-Derived Bathymetry Combined With Sentinel-2 and ICESat-2 Datasets Using Deep Learning by Weidong Zhu, Yanying Huang, Tiantian Cao, Xiaoshan Zhang, Qidi Xie, Kuifeng Luan, Wei Shen, Ziya Zou

“…However, existing deep learning models are limited by simple architectures and low efficiency in hyperparameter optimization, resulting in suboptimal training performance. This article proposes a convolutional neural network and bidirectional long short-term memory hybrid model based on the Bayesian optimization algorithm (BOA-CNN-BILSTM) to enhance bathymetric inversion accuracy and efficiency. …”
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Understanding the flowering process of litchi through machine learning predictive models by SU Zuanxian, NING Zhenchen, WANG Qing, CHEN Houbin

“…The algorithms (RF and STR) with the smallest Mean Absolute Error (MAE) and the highest residual error (RMSE) and the highest correlation coefficient (RP2) were selected for further parameter optimization and evaluation. …”
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Averaged Soft Actor-Critic for Deep Reinforcement Learning by Feng Ding, Guanfeng Ma, Zhikui Chen, Jing Gao, Peng Li

“…The experimental results show that the Averaged-SAC algorithm effectively improves the performance of the SAC algorithm and the stability of the training process.…”
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