Research on water and fertilizer irrigation system of tea plantation by Xuetao Jia, Ying Huang, Yanhua Wang, Daozong Sun

“…The principal component factor is extracted and input into the back propagation neural network to judge the quality of the tea. The number of principal component factors of image information and spectral information is set to six and three; the overall recognition rate reached 97.8%. …”
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Enhanced Disc Herniation Classification Using Grey Wolf Optimization Based on Hybrid Feature Extraction and Deep Learning Methods by Yasemin Sarı, Nesrin Aydın Atasoy

“…The proposed approach begins with feature extraction using ResNet50, a deep convolutional neural network known for its robust feature representation capabilities. …”
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Analysis of Feature Extraction and Anti-Interference of Face Image under Deep Reconstruction Network Algorithm by Jin Yang, Yuxuan Zhao, Shihao Yang, Xinxin Kang, Xinyan Cao, Xixin Cao

“…To explore the anti-interference performance of convolutional neural network (CNN) reconstructed by deep learning (DL) framework in face image feature extraction (FE) and recognition, in the paper, first, the inception structure in the GoogleNet network and the residual error in the ResNet network structure are combined to construct a new deep reconstruction network algorithm, with the random gradient descent (SGD) and triplet loss functions as the model optimizer and classifier, respectively, and it is applied to the face recognition in Labeled Faces in the Wild (LFW) face database. …”
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Violence Detection From Industrial Surveillance Videos Using Deep Learning by Hamza Khan, Xiaohong Yuan, Letu Qingge, Kaushik Roy

“…The lightweight convolutional neural network (CNN) model initially identifies individuals in the video stream to minimize the processing of irrelevant frames. …”
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Evaluating GRU Algorithm and Double Moving Average for Predicting USDT Prices: A Case Study 2017-2024 by RAHMAT, Munirul ula, Zara Yunizar

“…GRU, a deep learning-based recurrent neural network, processes sequential data using a gating mechanism, making it effective for capturing short-term price dynamics. …”
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From non-human to human primates: a translational approach to enhancing resection, safety, and indications in glioma surgery while preserving sensorimotor abilities by Matteo Gambaretti, Matteo Gambaretti, Luca Viganò, Luca Viganò, Matteo Gallo, Giovanni Pratelli, Tommaso Sciortino, Lorenzo Gay, Marco Conti Nibali, Alberto Luigi Gallotti, Alberto Luigi Gallotti, Leonardo Tariciotti, Luca Mattioli, Lorenzo Bello, Lorenzo Bello, Gabriella Cerri, Gabriella Cerri, Marco Rossi, Marco Rossi, Marco Rossi

“…The main goal, and, at the same time, the main challenge, of oncological neurological surgery is to avoid permanent neurological deficit while reaching maximal resection, particularly when the tumor infiltrates the neural network subserving motor functions. Brain mapping techniques were developed using neurophysiological probes to identify the areas and tracts subserving sensorimotor function, ensuring their preservation during the resection. …”
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Correction of CAMS PM₁₀ Reanalysis Improves AI-Based Dust Event Forecast by Ron Sarafian, Sagi Nathan, Dori Nissenbaum, Salman Khan, Yinon Rudich

“…To evaluate the contribution, we train a deep neural network to predict city-scale dust events (0–72 h) over the Balkans using PM₁₀ fields. …”
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Predictive model of acute kidney injury in critically ill patients with acute pancreatitis: a machine learning approach using the MIMIC-IV database by Shengwei Lin, Wenbin Lu, Ting Wang, Ying Wang, Xueqian Leng, Lidan Chi, Peipei Jin, Jinjun Bian

“…Model construction involved an ensemble of ML, including random forest (RF), support vector machine (SVM), k-nearest neighbors (KNN), naive Bayes (NB), neural network (NNET), generalized linear model (GLM), and gradient boosting machine (GBM). …”
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Using a robust model to detect the association between anthropometric factors and T2DM: machine learning approaches by Nafiseh Hosseini, Hamid Tanzadehpanah, Amin Mansoori, Mostafa Sabzekar, Gordon A. Ferns, Habibollah Esmaily, Majid Ghayour-Mobarhan

“…The performance of the KNN model was compared with Artificial neural network (ANN) and support vector machine (SVM) models. …”
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Forecasting Weather using Deep Learning from the Meteorological Stations Data : A Study of Different Meteorological Stations in Kaski District, Nepal by Supath Dhital, Kapil Lamsal, Sulav Shrestha, Umesh Bhurtyal

“…This project aims to forecast the next 2-hour Precipitation and Air Temperature for Pokhara Domestic Airport meteorological station and the next day's Precipitation, Maximum and Minimum Air Temperature forecast for Lumle, Begnas, and Lamachaur meteorological station, total of four meteorological stations of the Kaski District, Nepal using Long Short-Term Memory (LSTM): a Recurrent Neural Network (RNN) and deploy the outputs through the web portal. …”
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Design a Robust DDoS Attack Detection and Mitigation Scheme in SDN-Edge-IoT by Leveraging Machine Learning by Habtamu Molla Belachew, Mulatu Yirga Beyene, Abinet Bizuayehu Desta, Behaylu Tadele Alemu, Salahadin Seid Musa, Alemu Jorgi Muhammed

“…We evaluated four popular classifiers (K-Nearest Neighbor (K-NN), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and FeedForward Neural Network (FFNN)) on benchmark datasets CICIDS2017 and Edge-IIoTset, conducting both binary and multi-class classifications. …”
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Monitoring changes of forest height in California by Samuel Favrichon, Jake Lee, Yan Yang, Yan Yang, Ricardo Dalagnol, Ricardo Dalagnol, Fabien Wagner, Le Bienfaiteur Sagang, Le Bienfaiteur Sagang, Sassan Saatchi, Sassan Saatchi, Sassan Saatchi

“…Exploring the reliability of machine learning methods for temporal monitoring of forest is still a developing field. We train a deep neural network to predict forest height metrics at 10-m resolution from radar and optical satellite imagery. …”
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Multi-task aquatic toxicity prediction model based on multi-level features fusion by Xin Yang, Jianqiang Sun, Bingyu Jin, Yuer Lu, Jinyan Cheng, Jiaju Jiang, Qi Zhao, Jianwei Shuai

“…Objectives: This article presents ATFPGT-multi, an advanced multi-task deep neural network prediction model for organic toxicity. …”
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Rapid diagnosis of bacterial vaginosis using machine-learning-assisted surface-enhanced Raman spectroscopy of human vaginal fluids by Xin-Ru Wen, Jia-Wei Tang, Jie Chen, Hui-Min Chen, Muhammad Usman, Quan Yuan, Yu-Rong Tang, Yu-Dong Zhang, Hui-Jin Chen, Liang Wang

“…Multiple ML models were constructed and optimized, with the convolutional neural network (CNN) model achieving the highest prediction accuracy at 99%. …”
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Comparisons of adverse events associated with immune checkpoint inhibitors in the treatment of non-small cell lung cancer: a real-world disproportionality analysis based on the FDA... by Ruichen Gao, Wenjun Liang, Jintao Chen, Mingxia Yang, Xiaowei Yu, Xiaohua Wang

“…Methods Disproportionality analysis and Bayesian confidence propagation neural network (BCPNN) were utilized to identify pharmacovigilance signals from the FDA Adverse Event Reporting System (FAERS). …”
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Aging Alters Olfactory Bulb Network Oscillations and Connectivity: Relevance for Aging-Related Neurodegeneration Studies by A. Ahnaou, D. Rodriguez-Manrique, S. Embrechts, R. Biermans, N. V. Manyakov, S. A. Youssef, W. H. I. M. Drinkenburg

“…However, age-dependent alterations in neural network appeared spontaneously in the OB circuit, suggesting the neurophysiological basis of synaptic deficits underlying olfactory processing. …”
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Correlation-guided decoding strategy for low-resource Uyghur scene text recognition by Miaomiao Xu, Jiang Zhang, Lianghui Xu, Wushour Silamu, Yanbing Li

“…Specifically, (1) CGDS employs a hybrid encoding strategy that combines Convolutional Neural Network (CNN) and Transformer. This hybrid encoding effectively leverages the advantages of both methods: On one hand, the convolutional properties and shared weight mechanism of CNN allow for efficient extraction of local features, reducing dependency on large datasets and minimizing errors caused by similar characters. …”
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Corrosion inhibition effects of eco-friendly clarithromycin molecules on aluminium in hydrochloric acid solution via experimental, theoretical and optimization approach by O.D. Onukwuli, I.A. Nnanwube, F.O. Ochili, M. Omotioma, J.I. Obibuenyi

“…Optimization by RSM gave an optimum IE of 85.43 %, from which artificial neural network (ANN) predicted improved inhibition efficiency. …”
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Evaluation of Rainfall-Induced Accumulation Landslide Susceptibility Based on Remote Sensing Interpretation by Zhen Wu, Runqing Ye, Jue Huang, Xiaolin Fu, Yao Chen

“…Various machine learning models, such as Random Forest (RF), Support Vector Machine (SVM), and BP Neural Network models, were employed to assess the susceptibility of rainfall-induced accumulation landslides in the study area. …”
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The Use of Machine Learning to Create a Risk Score to Predict Survival in Patients with Hepatocellular Carcinoma: A TCGA Cohort Analysis by Samer Tohme, Hamza O Yazdani, Amaan Rahman, Sanah Handu, Sidrah Khan, Tanner Wilson, David A Geller, Richard L Simmons, Michele Molinari, Christof Kaltenmeier

“…The current study uses Artificial Neural Network (ANN) and Classification Tree Analysis (CTA) to create a gene signature score that can help predict survival in patients with HCC. …”
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