Bioinsecticide Production from Cigarette Wastes by Badhane Gudeta, Solomon K, M. Venkata Ratnam

“…In addition, artificial neural network (ANN) studies with MATLAB were used to accurately forecast extraction yield. …”
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Development of face image recognition algorithm using CNN in airport security checkpoints for terrorist early detection by Eca Indah Anggraini, Fachdy Nurdin, Mohammad Obie Restianto, Sudarti Dahsan, Andini Aprilia Ardhana, Asep Adang Supriyadi, Yahya Darmawan, Syachrul Arief, Agus Haryanto Ikhsanudin

“…This paper presents a novel approach to enhancing security measures at airport checkpoints by applying Convolutional Neural Network (CNN) and Artificial Neural Network (ANN) algorithms in face image recognition. …”
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Metabolomics Biomarkers in Prediction of Sudden Infant Death Syndrome: The Role of Short Chain Fatty Acids by Maria Aslam, Omer Riaz, Jawaria Aslam, Dost Muhammad Khan, Mustafa Hameed, Muhammad Suleman, Rizwan Shahid, Turke Althobaiti, Naeem Ramzan

“…The application of ML, particularly the Artificial Neural Network (ANN) and Stacking model, demonstrated exceptional accuracy of 94% and 96.15% with a recall of 100% and 92.31%, respectively. …”
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An Investigation of Ionospheric TEC Prediction Maps Over China Using Bidirectional Long Short‐Term Memory Method by Shuangshuang Shi, Kefei Zhang, Suqin Wu, Jiaqi Shi, Andong Hu, Huajing Wu, Yu Li

“…The root mean square errors of the bi‐LSTM‐based model’s 1 and 2 hr ahead predictions on the test data set (from June 2021 to December 2021) are 1.12 and 1.68 TECU, respectively, which are 75/50/32% and 72/48/22% smaller than those of the IRI‐2016, artificial neural network and LSTM‐based models, correspondingly. …”
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Leveraging AI to improve disease screening among American Indians: insights from the Strong Heart Study by Paul Rogers, Thomas McCall, Ying Zhang, Jessica Reese, Dong Wang, Weida Tong

“…Logistic Regression, Artificial Neural Network, and Random Forest were utilized as in silico screening tests within the SHS group. …”
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Investigation of groundwater quality indices and health risk assessment of water resources of Jiroft city, Iran, by machine learning algorithms by Sobhan Maleky, Maryam Faraji, Majid Hashemi, Akbar Esfandyari

“…The random forest model with the highest accuracy (R 2 = 0.986) was the best prediction model, while logistic regression (R 2 = 0.98), decision tree (R 2 = 0.979), K-nearest neighbor (R 2 = 0.968), artificial neural network (R 2 = 0.955), and support vector machine (R 2 = 0.928) predicted GWQI with lower accuracy. …”
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A multi-task model for failure identification and GPS assessment in metro trains by Pratik Vinayak Jadhav, Sairam V. A, Siddharth Sonkavade, Shivali Amit Wagle, Preksha Pareek, Ketan Kotecha, Tanupriya Choudhury

“…A multi-task artificial neural network was developed for the simultaneous identification of failures and GPS quality assessment. …”
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Forecasting SYM‐H Index: A Comparison Between Long Short‐Term Memory and Convolutional Neural Networks by F. Siciliano, G. Consolini, R. Tozzi, M. Gentili, F. Giannattasio, P. De Michelis

“…To forecast SYM‐H, we built two artificial neural network (ANN) models and trained both of them on two different sets of input parameters including interplanetary magnetic field components and magnitude and differing for the presence or not of previous SYM‐H values. …”
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Machine learning-based estimation of crude oil-nitrogen interfacial tension by Safia Obaidur Rab, Subhash Chandra, Abhinav Kumar, Pinank Patel, Mohammed Al-Farouni, Soumya V. Menon, Bandar R. Alsehli, Mamata Chahar, Manmeet Singh, Mahmood Kiani

“…In this work, we aim to utilize eight machine learning methods of Decision Tree (DT), AdaBoost (AB), Random Forest (RF), K-nearest Neighbors (KNN), Ensemble Learning (EL), Support Vector Machine (SVM), Convolutional Neural Network (CNN) and Multilayer Perceptron Artificial Neural Network (MLP-ANN) to construct data-driven intelligent models to predict crude oil – nitrogen IFT based upon experimental data of real crude oils samples encountered in underground oil reservoirs. …”
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Machine learning-based modelling and analysis of carbonation depth of recycled aggregate concrete by Xuyong Chen, Xuan Liu, Shukai Cheng, Xiaoya Bian, Xixuan Bai, Xin Zheng, Xiong Xu, Zhifeng Xu

“…On this basis, six machine learning models were employed to predict RAC carbonation depth: Artificial Neural Network, Decision Tree, Support Vector Regression, Random Forest, Extreme Gradient Boosting, and Light Gradient Boosting. …”
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Influences of Solar Wind Parameters on Energetic Electron Fluxes at Geosynchronous Orbit Revealed by the Deep SHAP Method by Jianhang Wang, Zheng Xiang, Binbin Ni, Deyu Guo, Yangxizi Liu, Junhu Dong, Jingle Hu, Haozhi Guo

“…In this study, we use the Deep SHAP method to quantify contributions of different solar wind parameters with an artificial neural network (ANN) model. Backpropagating the prediction results of this ANN model from 2011 to 2020, SHAP values for four solar wind parameters (interplanetary magnetic field (IMF) BZ, solar wind speed, solar wind dynamic pressure, and proton density) are calculated and comprehensively analyzed. …”
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Unveiling shadows: A data-driven insight on depression among Bangladeshi university students by Sanjib Kumar Sen, Md. Shifatul Ahsan Apurba, Anika Priodorshinee Mrittika, Md. Tawhid Anwar, A.B.M. Alim Al Islam, Jannatun Noor

“…Seven machine learning models, including Support Virtual Machine (SVM), K-Nearest Neighbor (K-NN), Gaussian Naive Bayes (GNB), Decision Tree (DT), Random Forest Classifier (RFC), Artificial Neural Network (ANN), and Gradient Boosting (GB), were trained and tested using the collected data (n = 750) to identify the most effective method for predicting depression. …”
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Spatial Downscaling of Daily Temperature Minima Using Machine Learning Methods and Application to Frost Forecasting in Two Alpine Valleys by Sudheer Bhakare, Michael Matiu, Alice Crespi, Dino Zardi

“…This study examines the performance of three machine learning models—namely, Artificial Neural Network (ANN), Random Forest (RF), and Convolutional Neural Network (CNN)—for spatial downscaling of seasonal forecasts of daily minimum temperature from 12 km to 250 m horizontal resolution. …”
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Predicting home delivery and identifying its determinants among women aged 15–49 years in sub-Saharan African countries using a Demographic and Health Surveys 2016–2023: a machine... by Adem Tsegaw Zegeye, Binyam Chaklu Tilahun, Makida Fekadie, Eliyas Addisu, Birhan Wassie, Berihun Alelign, Mequannet Sharew, Nebebe Demis Baykemagn, Abdulaziz Kebede, Tirualem Zeleke Yehuala

“…Machine learning models such as Random Forest, Decision Tree, K-Nearest Neighbor, Logistic Regression, Extreme Gradient Boosting, AdaBoost, Artificial Neural Network, and Naive Bayes were used. The predictive model was evaluated by area under the curve, accuracy, precision, recall, and F-measure. …”
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A performance-based generative design framework based on a design grammar for high-rise office towers during early design stage by Liwei Chen, Ye Zhang, Yue Zheng

“…Case study results demonstrate that, with the support of Artificial Neural Network, utilizing this system can not only globally explore the diversity of tower morphologies but also efficiently uncover greater energy-saving potential in complex architectural forms compared to simpler cubic forms, with an improvement of up to 7.76% during the early stages of design. …”
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Cache Aging with Learning (CAL): A Freshness-Based Data Caching Method for Information-Centric Networking on the Internet of Things (IoT) by Nemat Hazrati, Sajjad Pirahesh, Bahman Arasteh, Seyed Salar Sefati, Octavian Fratu, Simona Halunga

“…The proposed method uses an artificial neural network to make predictions. These predictions closely match the actual values, with a low error margin of 0.0121. …”
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Decentralized control system for unlimited street lighting poles with an intelligent, energy-saving off-grid maximum power point tracking battery charger by Hussain Attia, Ali Al-Ataby, Maen Takruri, Amjad Omar

“…Additionally, we investigate how solar energy as a clean renewable source might be included in the system, offering an off-grid street lighting dimming solution. A deep artificial neural network (ANN) algorithm is designed to have an effective response of maximum power point tracking (MPPT) in terms of accuracy and speed to obtain maximum electrical power from the incident light on a pair of photovoltaic panels fixed above an off-grid street light pole. …”
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Gaussian process latent variable models-ANN based method for automatic features selection and dimensionality reduction for control of EMG-driven systems by Maham Nayab, Asim Waris, Muhammad Jawad Khan, Dokhyl AlQahtani, Ahmed Imran, Syed Omer Gilani, Umer Hameed Shah

“…To overcome such issues, this paper proposes a novel approach that integrates feature reduction techniques with an artificial neural network (ANN) classifier to enhance the accuracy of high-dimensional EMG classification. …”
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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. …”
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An Explainable Artificial Intelligence Model for the Classification of Breast Cancer by Tarek Khater, Abir Hussain, Riyad Bendardaf, Iman M. Talaat, Hissam Tawfik, Sam Ansari, Soliman Mahmoud

“…The best-performing machine-learning model has achieved an accuracy of 97.7% using k-nearest neighbors and a precision of 98.2% based on the Wisconsin breast cancer dataset and an accuracy of 98.6% using the artificial neural network with 94.4% precision based on the Wisconsin diagnostic breast cancer dataset. …”
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