Simultaneous detection of human neutrophil elastase and cathepsin G on a single substrate using a fluorometric quantum dots probe and chemometric models by Fátima A.R. Mota, Rafael C. Castro, David S.M. Ribeiro, João L.M. Santos, Ricardo N.M.J. Páscoa, Marieta L.C. Passos, M. Lúcia M.F.S. Saraiva

“…These second-order data were processed using various chemometric models, including unfolded partial least-squares with residual bilinearization (U-PLS/RBL), radial basis function artificial neural network (RBF-ANN), and partial least squares-discriminant analysis (PLS-DA), to guarantee a detailed and precise analysis. …”
Get full text

Prediksi Detak Jantung Berbasis LSTM pada Raspberry Pi untuk Pemantauan Kesehatan Portabel by Ahmad Foresta Azhar Zen, Eko Sakti Pramukantoro, Kasyful Amron, Viera Wardhani, Putri Annisa Kamila

“…LSTM models are a type of artificial neural network architecture known for their ability to handle sequential data effectively, making them highly suitable for sequential heart rate monitoring and prediction. …”
Get full text

A Local Adversarial Attack with a Maximum Aggregated Region Sparseness Strategy for 3D Objects by Ling Zhao, Xun Lv, Lili Zhu, Binyan Luo, Hang Cao, Jiahao Cui, Haifeng Li, Jian Peng

“…The increasing reliance on deep neural network-based object detection models in various applications has raised significant security concerns due to their vulnerability to adversarial attacks. …”
Get full text

600 meters to VO2max: Predicting Cardiorespiratory Fitness with an Uphill Run by Kübra Stoican, Regina Oeschger

“…Discussion/Conclusion These results suggest that our short, high-intensity field test, when combined with a neural network model, can provide accurate predictions of VO2max. …”
Get full text

Template switching during DNA replication is a prevalent source of adaptive gene amplification by Julie N Chuong, Nadav Ben Nun, Ina Suresh, Julia Cano Matthews, Titir De, Grace Avecilla, Farah Abdul-Rahman, Nathan Brandt, Yoav Ram, David Gresham

“…Using a CNV reporter system and neural network simulation-based inference (nnSBI) we quantified the formation rate and fitness effect of CNVs for each strain. …”
Get full text

A multidimensional assessment of adverse events associated with paliperidone palmitate: a real-world pharmacovigilance study using the FAERS and JADER databases by Siyu Lou, Zhiwei Cui, Yingyong Ou, Junyou Chen, Linmei Zhou, Ruizhen Zhao, Chengyu Zhu, Li Wang, Zhu Wu, Fan Zou

“…Utilizing disproportionality analyses such as the reporting odds ratios (ROR), proportional reporting ratios (PRR), Bayesian confidence propagation neural network (BCPNN), and multi-item Poisson shrinkage (MGPS), significant associations between ADEs and paliperidone palmitate were evaluated. …”
Get full text

Fine particulate matter concentrations forecasting using long short-term memory network and meteorological inputs by T. Istiana, B. Kurniawan, S. Soekirno, A. Wihono, D.E. Nuryanto, B.A. Pertala, A. Sopaheluwakan

“…This study introduces the long short-term memory deep learning model and contrasts it with the one-dimensional convolution neural network as well as their hybrid counterpart. The dataset is split into 80 percent training and 20 percent testing data. …”
Get full text

Safety profiles of IDH inhibitors: a pharmacovigilance analysis of the FDA Adverse Event Reporting System (FAERS) database by Ximu Sun, Han Zhou, Yanming Li, Yanhui Luo, Qixiang Guo, Yixin Sun, Chenguang Jia, Bin Wang, Maoquan Qin, Peng Guo

“…Disproportionality analyses including the reporting odds ratio and the Bayesian confidence propagation neural network were performed in data mining to assess IDH inhibitor-relatedAEs. …”
Get full text

Deep learning-based CT radiomics predicts prognosis of unresectable hepatocellular carcinoma treated with TACE-HAIC combined with PD-1 inhibitors and tyrosine kinase inhibitors by Linan Yin, Ruibao Liu, Wei Li, Shijie Li, Xunbo Hou

“…Residual convolutional neural network (ResNet) technology was used to extract image features. …”
Get full text

Spatiotemporal variation in biomass abundance of different algal species in Lake Hulun using machine learning and Sentinel-3 images by Zhaojiang Yan, Chong Fang, Kaishan Song, Xiangyu Wang, Zhidan Wen, Yingxin Shang, Hui Tao, Yunfeng Lyu

“…This study compared and evaluated 6 commonly used machine learning models, including extreme gradient boosting (XGBoost), support vector regression (SVR), backpropagation neural network (BP), gradient boosting decision tree (GBDT), random forest (RF), and categorical boosting (CatBoost). …”
Get full text

Deep Transfer Learning for Classification of Late Gadolinium Enhancement Cardiac MRI Images into Myocardial Infarction, Myocarditis, and Healthy Classes: Comparison with Subjective... by Amani Ben Khalifa, Manel Mili, Mezri Maatouk, Asma Ben Abdallah, Mabrouk Abdellali, Sofiene Gaied, Azza Ben Ali, Yassir Lahouel, Mohamed Hedi Bedoui, Ahmed Zrig

“…A spatial attention mechanism was implemented as a part of the neural network architecture. The MLP architecture was used for the classification. …”
Get full text

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. …”
Get full text

Peningkatan Performa Pengelompokan Siswa Berdasarkan Aktivitas Belajar pada Media Pembelajaran Digital Menggunakan Metode Adaptive Moving Self-Organizing Maps by Onky Prasetyo, Ahmad Afif Supianto, Syaiful Anam, Hilman Ferdinandus Pardede, Vicky Zilvan, R. Budiarianto Suryo Kusumo

“…One of the most frequently used clustering methods is Self-Organizing Maps (SOM), SOM is a neural network method to maintain data topology when multidimensional input data is converted into output data with lower dimensions. …”
Get full text

An optimized approach for predicting water quality features and a performance evaluation for mapping surface water potential zones based on Discriminant Analysis (DA), Geographical... by Abhijeet Das

“…Again, this research used a strong methodology by incorporating Machine learning (ML) algorithms, such as: Artificial Neural Network (ANN), Gaussian Process Regression (GPR), Support Vector Machine (SVM), and Linear Regression Model (LRM), were applied to forecast and confirm the quality of the water. …”
Get full text

Presenting a Prediction Model for CEO Compensation Sensitivity using Meta-heuristic Algorithms (Genetics and Particle Swarm) by Saeed Khaljastani, Habib Piri, Reza Sotoudeh

“…Results The results demonstrate the superiority of the deep neural network model in terms of the coefficient of determination and MSE index. …”
Get full text

Synthesizing field plot and airborne remote sensing data to enhance national forest inventory mapping in the boreal forest of Interior Alaska by Pratima Khatri-Chhetri, Hans-Erik Andersen, Bruce Cook, Sean M. Hendryx, Liz van Wagtendonk, Van R. Kane

“…To achieve this goal, we compared the performance of two advanced modeling approaches, the convolutional neural network (CNN) and the XGBoost model. Our datasets included field and high-resolution topographic metrics including elevation, slope, aspect, and solar radiation and canopy height derived from lidar (1 m) and 44 vegetation indices derived from high-resolution (1 m) visible to near infrared (VNIR) hyperspectral data collected by NASA Goddard's Lidar, Hyperspectral and Thermal Imager (G-LiHT) sensor. …”
Get full text

Advances in machine learning applications to resource technology for organic solid waste by Hongzhi MA, Yichan LIU, Jihua ZHAO, Fan FEI, Ming GAO, Qunhui WANG

“…This study explores a range of commonly used ML models, including artificial neural network (ANN), support vector machine (SVM), decision tree, random forest, and extreme gradient boosting (XGBoost). …”
Get full text

Evaluation of linear, nonlinear and ensemble machine learning models for landslide susceptibility assessment in southwest China by Bingwei Wang, Qigen Lin, Tong Jiang, Huaxiang Yin, Jian Zhou, Jinhao Sun, Dongfang Wang, Ran Dai

“…Linear models represented by logistic regression (LR), nonlinear models represented by support vector machine (SVM), artificial neural network (ANN) and classification 5.0 decision tree (C5.0 DT), and ensemble models represented by random forest (RF) and categorical boosting (Catboost) were selected. …”
Get full text

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. …”
Get full text

In-vivo high-resolution χ-separation at 7T by Jiye Kim, Minjun Kim, Sooyeon Ji, Kyeongseon Min, Hwihun Jeong, Hyeong-Geol Shin, Chungseok Oh, Robert J. Fox, Ken E. Sakaie, Mark J. Lowe, Se-Hong Oh, Sina Straub, Seong-Gi Kim, Jongho Lee

“…To address these challenges, we developed a novel deep neural network, R2PRIMEnet7T, designed to convert a 7T R2* map into a 3T R2′ map. …”
Get full text