Drug Efficacy Recommendation System of Glioblastoma (GBM) Using Deep Learning by Sajid Naveed, Mujtaba Husnain, Ali Samad, Amna Ikram, Hina Afreen, Ghulam Gilanie, Najah Alsubaie

“…A panel of 47 genes associated with GBM was processed using two deep learning models: Artificial Neural Network (ANN) and Convolutional Neural Network (CNN). …”
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Drying kinetic for moisture content prediction of peels Tahiti lemon (Citrus latifolia): Approach by machine learning and optimization - genetic algorithms and nonlinear programmin... by Maressa O. Camilo, Romero F. Carvalho, Ariany B.S. Costa, Esly F.C. Junior, Andréa O.S. Costa, Robson C. Sousa

“…The application of a versatile approach for modeling and prediction the moisture content of dried peels was evaluated using both empirical and semi-empirical equations (Lewis, Page, Henderson and Pabis, Modified Page, Logarithmic, and Modified Logistic) as well as machine learning models (K-nearest neighbor | KNN, Decision Tree | DT, Artificial Neural Network | ANN and Support Vector Regression | SVR). …”
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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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Identification and preliminary validation of biomarkers associated with mitochondrial and programmed cell death in pre-eclampsia by Rong Lin, Rong Lin, XiaoYing Weng, XiaoYing Weng, Liang Lin, Liang Lin, XuYang Hu, XuYang Hu, ZhiYan Liu, ZhiYan Liu, Jing Zheng, Jing Zheng, FenFang Shen, FenFang Shen, Rui Li, Rui Li

“…Their performance was assessed through nomogram and artificial neural network models. Biomarkers were subjected to localization, functional annotation, regulatory network analysis, and drug prediction. …”
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Modelling of a new form of nitrogen doped activated carbon for adsorption of various dyes and hexavalent chromium ions by Mohamed A. El-Nemr, Uyiosa Osagie Aigbe, Kingsley Eghonghon Ukhurebor, Kingsley Obodo, Adetunji Ajibola Awe, Mohamed A. Hassaan, Safaa Ragab, Ahmed El Nemr

“…AB14 and AO7 dyes and Cr6+ ions adsorption to synthesised AC5-600 was predicted employing the response surface methodology (RSM) and artificial neural network (ANN) models. The ANN model was more effective in predicting AB14 and AO7 dyes and Cr6+ ions adsorption than the RSM, and it was highly applicable in the sorption process.…”
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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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Advancing Horticultural Crop Loss Reduction Through Robotic and AI Technologies: Innovations, Applications, and Practical Implications by H. W. Gammanpila, M. A. Nethmini Sashika, S. V. G. N. Priyadarshani

“…For instance, Ji et al. in 2007 developed an artificial neural network (ANN)-based system for rice yield prediction in Fujian, China, improving accuracy over traditional models. …”
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Developing a decision support tool to predict delayed discharge from hospitals using machine learning by Mahsa Pahlevani, Enayat Rajabi, Majid Taghavi, Peter VanBerkel

“…Three ML classifiers, Random Forest (RF), Artificial Neural Network (ANN), and eXtreme Gradient Boosting (XGB), were tested to classify patients as ALC or not. …”
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Multiview Multimodal Feature Fusion for Breast Cancer Classification Using Deep Learning by Sadam Hussain, Mansoor Ali Teevno, Usman Naseem, Daly Betzabeth Avendano Avalos, Servando Cardona-Huerta, Jose Gerardo Tamez-Pena

“…Imaging features were extracted using a Squeeze-and-Excitation (SE) network-based ResNet50 model, while textual features were extracted using an artificial neural network (ANN). Afterwards, extracted features from both modalities were fused using a late feature fusion strategy. …”
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Geospatial Data and Deep Learning Expose ESG Risks to Critical Raw Materials Supply: The Case of Lithium by Christopher J. M. Lawley, Marcus Haynes, Bijal Chudasama, Kathryn Goodenough, Toni Eerola, Artem Golev, Steven E. Zhang, Junhyeok Park, Eleonore Lèbre

“…., disputes, protests, violence) that can negatively impact CRM supply chains: (1) a knowledge-driven fuzzy logic model that yields an area under the curve (AUC) for the receiver operating characteristics plot of 0.72 for the entire model; (2) a naïve Bayes model that yields an AUC of 0.81 for the test set; and (3) a deep learning model comprising stacked autoencoders and a feed-forward artificial neural network that yields an AUC of 0.91 for the test set. …”
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ANN-based two hidden layers computational procedure for analysis of heat transport dynamics in polymer-based trihybrid Carreau nanofluid flow over needle geometry by Adil Darvesh, Fethi Mohamed Maiz, Basma Souayeh, Luis Jaime Collantes Santisteban, Hakim AL. Garalleh, Afnan Al Agha, Lucerito Katherine Ortiz García, Nicole Anarella Sánchez-Miranda

“…Numerical computation of ODEs is made by a well-known bvp4c scheme and then an advanced artificial neural network (ANN) computational framework is integrated to train the resulting dataset, which is based on scaled conjugate gradient neural network (SCG-NN) to facilitate predictions regarding advanced solutions. …”
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Development and Validation of a Photoplethysmography System for Noninvasive Monitoring of Hemoglobin Concentration by Hongyun Liu, Fulai Peng, Minlu Hu, Jinlong Shi, Guojing Wang, Haiming Ai, Weidong Wang

“…To facilitate real-time total hemoglobin (tHb) monitoring, a portable prototype of a noninvasive Hb detection system was developed, and the accuracy of Hb predicted based on partial least squares (PLS) as well as backpropagation artificial neural network (BP-ANN) models was validated. Results. …”
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Estimating ocean heat content from the ocean thermal expansion parameters using satellite data by V. P. Kondeti, S. Palanisamy

“…To achieve this objective, artificial neural network (ANN) models were developed to derive thermosteric sea level (TSL) from a given dataset of sea surface temperature, sea surface salinity, geographical coordinates, and climatological TSL. …”
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Kombinasi Feature Selection Fisher Score dan Principal Component Analysis (PCA) untuk Klasifikasi Cervix Dysplasia by Krisan Aprian Widagdo, Kusworo Adi, Rahmat Gernowo

“…And then PCA transforms candidate features into a new uncorrelated dataset. Artificial Neural Network Backpropagation used to evaluate performance combination FScore PCA. …”
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Using Sequence Mining to Predict Complex Systems: A Case Study in Influenza Epidemics by Theyazn H. H. Aldhyani, Manish R. Joshi, Shahab A. AlMaaytah, Ahmed Abdullah Alqarni, Nizar Alsharif

“…This paper presents three adapting intelligence models: support vector machine regression (SVMR), artificial neural network using particle swarm optimisation (ANNPSO), and our intelligent time series (INTS) to predict influenza epidemics. …”
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Risk factors and machine learning prediction models for intrahepatic cholestasis of pregnancy by Yingchun Ren, Xiaoying Shan, Gengchao Ding, Ling Ai, Weiying Zhu, Ying Ding, Fuzhou Yu, Yun Chen, Beijiao Wu

“…Thirteen machine learning techniques, including Random Forest, Support Vector Machine, and Artificial Neural Network, were employed. Based on their various classification performances on the training set, the top five models were selected for internal validation. …”
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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. …”
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Identification of core genes related to exosomes and screening of potential targets in periodontitis using transcriptome profiling at the single-cell level by Wufanbieke Baheti, Diwen Dong, Congcong Li, Xiaotao Chen

“…Subsequently, a core gene-based artificial neural network (ANN) model was built to evaluate the predictive power of core genes for PD. …”
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Response surface methodology and adaptive neuro-fuzzy inference system for adsorption of reactive orange 16 by hydrochar by J. Oliver Paul Nayagam, K. Prasanna

“…This study validated adaptive neuro-fuzzy inference system, an artificial neural network with a fuzzy inference system, using response surface methodology projected experimental run with Box–Behnken method.FINDINGS: The adaptive neuro-fuzzy inference system model is created alongside the response surface methodology model to compare experimental outcomes. …”
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Effects of feature selection and normalization on network intrusion detection by Mubarak Albarka Umar, Zhanfang Chen, Khaled Shuaib, Yan Liu

“…Random forest (RF) models performed better on NSL-KDD and UNSW-NB15 datasets with accuracies of 99.86% and 96.01%, respectively, whereas artificial neural network (ANN) achieved the best accuracy of 95.43% on the CSE–CIC–IDS2018 dataset. …”
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