Machine learning-driven development of a stratified CES-D screening system: optimizing depression assessment through adaptive item selection by Ruo-Fei Xu, Zhen-Jing Liu, Shunan Ouyang, Qin Dong, Wen-Jing Yan, Dong-Wu Xu

“…Results The resulting stratified screening system consists of an initial four-item rapid screening layer (encompassing emotional, cognitive, and interpersonal dimensions) for detecting probable depression (AUC = 0.982, sensitivity = 0.945, specificity = 0.926), followed by an enhanced assessment layer with five additional items. …”
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Giant Chemo-Resistive Response of POSS Nano-Spacers in PS- and PMMA-Based Quantum Resistive Vapour Sensors (vQRS) Used for Cancer Biomarker Analysis by Abhishek Sachan, Mickaël Castro, Veena Choudhary, Jean-François Feller

“…All transducers were made by spray layer by layer (sLbL) to obtain a hierarchically structured conducting architecture. …”
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Modeling the unconfined compressive strength of lateritic soil treated with FGD gypsum as a partial cement replacement by Chidananda M Linganagoudar, Shiva Kumar G, M S Ujwal, G Rohith, A Vinay, Poornachandra Pandit

“…To support predictive insights, machine learning models including Decision Tree, Random Forest, and Multi-Layer Perceptron (MLP) were trained on 168 data samples. …”
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Predicting Readmission Among High-Risk Discharged Patients Using a Machine Learning Model With Nursing Data: Retrospective Study by Eui Geum Oh, Sunyoung Oh, Seunghyeon Cho, Mir Moon

“…The 6 algorithms of logistic regression, random forest, decision tree, XGBoost, CatBoost, and multiperceptron layer were employed to develop predictive models. …”
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Enhancing IoT security and healthcare data protection in the metaverse: A Dynamic Adaptive Security Mechanism by Saima Siraj Qureshi, Jingsha He, Nafei Zhu, Ahsan Nazir, Juan Fang, Xiangjun Ma, Ahsan Wajahat, Faheem Ullah, Sirajuddin Qureshi, Sahroui Dhelim, Muhammad Salman Pathan

“…Although the LSTM model demonstrates strong accuracy, the ensemble approach of Random Forest balances computational efficiency and performance. …”
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Advancing patient care: Machine learning models for predicting grade 3+ toxicities in gynecologic cancer patients treated with HDR brachytherapy. by Andres Portocarrero-Bonifaz, Salman Syed, Maxwell Kassel, Grant W McKenzie, Vishwa M Shah, Bryce M Forry, Jeremy T Gaskins, Keith T Sowards, Thulasi Babitha Avula, Adrianna Masters, Jose G Schneider, Scott R Silva

“…Seven supervised classification machine learning models (Logistic Regression, Random Forest, K-Nearest Neighbors, Support Vector Machines, Gaussian Naive Bayes, Multi-Layer Perceptron Neural Networks, and XGBoost) were constructed and evaluated. …”
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Research on Traffic Accident Severity Level Prediction Model Based on Improved Machine Learning by Jiming Tang, Yao Huang, Dingli Liu, Liuyuan Xiong, Rongwei Bu

“…Decision tree, XGBoost, and random forest algorithms, respectively, were applied for the secondary prediction. …”
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Parametric Modelling of Fibre-Concrete Interface and Prediction of Compressive Strength of Concrete With Waste Tyre Steel Fibres Using Artificial Neural Network Model by Daudi Salezi Augustino

“…In addition, the application of fibres in concrete is based on random mixing into the matrix without focusing on the interfacial properties of the fibre and concrete. …”
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Hyperspectral Images Fusion Classification Based on the DS Evidence Theory by LI Hao, YU Hong, RAO Tong, ZHOU Shuai, SHEN Feng

“…In this fusion method, a multi-layer perceptron network and random forest network are used for fusion classification experiments. …”
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Prediction of R1234yf flow boiling behavior in horizontal, vertical, and inclined tubes using machine learning techniques by Farzaneh Abolhasani, Behrang Sajadi, Mohammad Ali Akhavan-Behabadi

“…A total of 339 experimental data points sourced from the literature are employed to develop and train four methods of MLAs, including the multi-layer perceptron (MLP) neural network, support vector regression (SVR), random forest, and adaptive boosting (AdaBoost). …”
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Federated learning secrecy rate optimization for 6G intelligent reflecting surface assistance by WU Yingying, MAO Bomin

“…Simulation results demonstrated that deploying IRS in the system can greatly enhance the secrecy rate of the FL model update compared to artificial noise (AN), the block coordinate ascent method (BCAM), the scheme without IRS, and the scheme with random phase IRS, providing physical layer support for the subsequent deployment of FL algorithms in this communication system.…”
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Prediction of the packaging chemical migration into food and water by cutting-edge machine learning techniques by Behzad Vaferi, Mohsen Dehbashi, Reza Yousefzadeh, Ali Hosin Alibak

“…This research uses five renowned AI-based techniques (namely, long short-term memory, gradient boosting regressor, multi-layer perceptron, Random Forest, and convolutional neural networks) to anticipate chemical migration from packaging materials to the food/water structure, considering variables such as temperature, chemical characteristics, and packaging/food types. …”
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Lightweight Models for Real-Time Steganalysis: A Comparison of MobileNet, ShuffleNet, and EfficientNet by Achmad Bauravindah, Dhomas Hatta Fudholi

“…ShuffleNet and EfficientNet performed at random-guessing levels with 50% accuracy, reflecting the challenges of steganalysis on mobile platforms. …”
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Comparative Analysis of Machine Learning and Deep Learning Models for Lung Cancer Prediction Based on Symptomatic and Lifestyle Features by Bireswar Dutta

“…Machine learning classifiers, including Decision Trees, K-Nearest Neighbors, Random Forest, Naïve Bayes, AdaBoost, Logistic Regression, and Support Vector Machines, were implemented using Weka simultaneously with neural network models with 1, 2, and 3 hidden layers, which were developed in Python within a Jupyter Notebook environment. …”
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Graph-based two-level indicator system construction method for smart city information security risk assessment by Li Yang, Kai Zou, Yuxuan Zou

“…In this study, we proposed a graph-based two-level indicator system construction method. First, a random forest was used to extract the indicators' dependency graph from missing data. …”
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Angular Stabilization of a Multirotor Aircraft in Venus’ Atmosphere by Vladislav V. Ryzhkov

“…The relevance of this topic is driven by the need to obtain detailed data on the lower layers of Venus’ atmosphere, which is crucial for understanding climate processes in the Solar System as a whole. …”
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Designing a Neural Observer to Estimate the State Variables of the Dynamical System of a Specific Class of Leukaemia by Yousef Farshidi, Reza Ghasemi, Aminin Sharafian Ardekani

“…For this purpose, a two-layer feed forward neural network was applied. The weights of both layers are considered variables, depending on time. …”
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Crop Statistic to Annual Map: Tracking spatiotemporal dynamics of crop-specific areas through machine learning and statistics disaggregating by Xiyu Li, Le Yu, Zhenrong Du, Xiaoxuan Liu

“…Annual crop statistics were further disaggregated based on probabilistic layer and harmonized based on multiple constraints. …”
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Prediction and Parameter Optimization of Surface Settlement Induced by Shield Tunneling Using Improved Informer Algorithm by Shisen Zhao, Xianda Feng, Kefeng Peng

“…To address the limitations of the Informer algorithm in predicting surface settlement during shield tunneling, the standard convolution was replaced with dilated causal convolution, and three measures were employed: soil layer classification and characterization, a moving prediction window, and special factor handling. …”
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Design and Evaluation of a Framework for Cooperative and Adaptive QoS Control of DSRC Network for Road Safety Applications by Wenyang Guan, Jianhua He, Zuoyin Tang, Thomas M. Chen

“…A core design in the proposed QoS control framework is that network feedback and cross-layer design are employed to collaboratively achieve targeted QoS. …”
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