Machine learning-driven multi-targeted drug discovery in colon cancer using biomarker signatures by Tingting Liu, Lifan Zhong, Xizhe Sun, Zhijiang He, Witiao Lv, Liyun Deng, Yanfei Chen

“…The CatBoost algorithm efficiently classifies patients based on molecular profiles and predicts drug responses. …”
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In Silico Evaluation of Algorithm-Based Clinical Decision Support Systems: Protocol for a Scoping Review by Michael Dorosan, Ya-Lin Chen, Qingyuan Zhuang, Shao Wei Sean Lam

“… BackgroundIntegrating algorithm-based clinical decision support (CDS) systems poses significant challenges in evaluating their actual clinical value. …”
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Aerodynamic Parameter Identification of Projectile Based on Improved Extreme Learning Machine and Ensemble Learning Theory by Tianyi Wang, Wenjun Yi, Youran Xia

“…The improved particle swarm optimization algorithm (IPSO) with an adaptive update strategy is used to optimize the weight and threshold of ELM. …”
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Constant Cutting Force Control for CNC Machining Using Dynamic Characteristic-Based Fuzzy Controller by Hengli Liu, Taiyong Wang, Dong Wang

“…This paper presents a dynamic characteristic-based fuzzy adaptive control algorithm (DCbFACA) to avoid the influence of cutting force changing rapidly on the machining stability and precision. …”
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Study of model construction of fuel production from waste plastic pyrolysis based on machine learning by CHEN Sihan, YUAN Zhilong, WANG Ye, SUN Yifei*

“…The Gradient Boosting Regression (GBR) algorithm has the best fitting performance for predicting oil yield (R^2=0.91, RMSE=7.78), while the adaptive boosting algorithm (AdaBoost) has the best fitting performance for predicting gas yield (R^2=0.83, RMSE=6.42), enabling accurate prediction of reaction conditions. …”
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Variability-Aware Machine Learning Model Selection: Feature Modeling, Instantiation, and Experimental Case Study by Cristina Tavares, Nathalia Nascimento, Paulo Alencar, Donald Cowan

“…In general, although the current literature provides a wide variety of ML techniques and algorithms, there is a lack of design approaches to support algorithm selection. …”
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Student dropout prediction through machine learning optimization: insights from moodle log data by Markson Rebelo Marcolino, Thiago Reis Porto, Tiago Thompsen Primo, Rafael Targino, Vinicius Ramos, Emanuel Marques Queiroga, Roberto Munoz, Cristian Cechinel

“…This study seeks to advance the field of dropout and failure prediction through the application of artificial intelligence with machine learning methodologies. In particular, we employed the CatBoost algorithm, trained on student activity logs from the Moodle platform. …”
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Real-Time Acoustic Measurement System for Cutting-Tool Analysis During Stainless Steel Machining by Tom Salm, Kourosh Tatar, José Chilo

“…Using the TreeBagger machine-learning algorithm, the system accurately predicts tool wear, detecting both gradual and abrupt wear patterns. …”
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Fault diagnosis of ZDJ7 railway point machine based on improved DCNN and SVDD classification by Zengshu Shi, Yiman Du, Xinwen Yao

“…Aiming at the unbalanced features of the railway point machine sample, an improved quantity learning algorithm for hypersphere coordinate mapping based on SVDD is proposed. …”
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A Comparative Evaluation of Machine Learning-Based Intrusion Detection Systems for Securing Cloud Environments by Mohammad Shadi Alhakeem, Khawla Bin Ajlan

“…Using a recently developed, reputable dataset and concentrating on attack types that pose significant threats to cloud environments, our experimental results offer a comprehensive evaluation of these techniques, including a variety of machine learning algorithm performance metrics.…”
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AAGP integrates physicochemical and compositional features for machine learning-based prediction of anti-aging peptides by Saptashwa Datta, Jen-Chieh Yu, Yi-Hsiang Lin, Yun-Chen Cheng, Ching-Tai Chen

“…Peptides were encoded using 4,305 features, followed by adaptive feature selection with a heuristic algorithm on both datasets. …”
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Modeling the impacts of governmental and human responses on COVID-19 spread using statistical machine learning by Binbin Lin, Yimin Dai, Lei Zou, Ning Ning

“…The contributions are (1) uncovering the spatiotemporal variations in governmental and human responses during COVID-19; (2) developing a statistical machine learning algorithm that incorporates spatiotemporal dependencies and temporal lag effects to model the relationships between governmental and human responses and the pandemic spread; (3) dissecting the impacts of human responses on the pandemic across space and time. …”
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Prediction on Slip Modulus of Screwed Connection for Timber–Concrete Composite Structures Based on Machine Learning by Wen-Wu Lu, Yu-Wei Chen, Ji-Gang Xu, Hui-Feng Yang, Hao-Tian Tao, Wei Zheng, Ben-Kai Shi

“…Four ML methods, including decision tree (DT), random forest (RF), adaptive boosting machine (AdaBoost), and gradient boosting regression tree (GBRT), are adopted to develop the ML algorithm. …”
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Adaptive MCS selection and resource planning for energy-efficient communication in LTE-M based IoT sensing platform. by Nhu-Ngoc Dao, Minho Park, Joongheon Kim, Sungrae Cho

“…Focusing on this circumstance, we propose a novel adaptive modulation and coding selection (AMCS) algorithm to address the energy consumption problem in the LTE-M based IoT-sensing platform. …”
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Functional Diagnostic System for Multichannel Mine Lifting Machine Working in Factor Cluster Analysis Mode by Zimovets V. I., Shamatrin S. V., Olada D. E., Kalashnykova N. I.

“…In this case, the synthesized FDS must be adaptive to arbitrary initial conditions of the technological process and practically invariant to the multidimensionality of the space of diagnostic features, an alphabet of recognition classes, which characterize the possible technical states of the units and devices of the machine. …”
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A state-of-the-art review of soft computing-based monitoring and control in the machining of hard alloys by Lokesh Kumar, Ashish Goyal, Vimal Kumar Pathak, Abhijit Bhowmik

“…These innovations are supported by suitable modeling and metaheuristic techniques, including adaptive neuro-fuzzy inference systems (ANFIS), simulated annealing, teaching-learning-based optimization (TLBO), the finite element method (FEM), and the non-dominated sorting genetic algorithm II (NSGA-II). …”
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Optimization of Flavor Quality of Lactic Acid Bacteria Fermented Pomegranate Juice Based on Machine Learning by Wenhui ZOU, Fei PAN, Junjie YI, Linyan ZHOU

“…Binary classification models of HWPS and LWPS were established by random forest (RF) and adaptive boosting (AdaBoost) algorithms, and RF algorithm had higher prediction precision and accuracy. …”
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Photovoltaic Farm Power Generation Forecast Using Photovoltaic Battery Model with Machine Learning Capabilities by Agboola Benjamin Alao, Olatunji Matthew Adeyanju, Manohar Chamana, Stephen Bayne, Argenis Bilbao

“…Existing models often lack predictive accuracy, computational efficiency, and adaptability to changing environmental conditions. To address these limitations, the proposed model integrates an Adaptive Neuro-Fuzzy Inference System (ANFIS) with a multi-input multi-output (MIMO) prediction algorithm, utilizing historical temperature and irradiance data for accurate and efficient forecasting. …”
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Machine learning techniques for predicting the peak response of reinforced concrete beam subjected to impact loading by Ali Husnain, Munir Iqbal, Hafiz Ahmed Waqas, Mohammed El-Meligy, Muhammad Faisal Javed, Rizwan Ullah

“…To address these challenges, this study investigates various ensemble and non-ensemble machine learning techniques—including support vector machine, gaussian process regression (GPR), k-nearest neighbor (KNN), gene expression programming, random forest, decision tree, boosted tree, adaptive boosting tree, gradient boosting algorithm, stochastic gradient descent, and artificial neural network—for predicting the peak response of RC beams under impact loads. …”
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DEVELOPMENT OF DIAGNOSTIC METHOD’S REALIZING ALGORITHM OF THE EQUIPPED WITH THE COURSE STABILITY SYSTEM VEHICLES AT OPERATION STAGE by Il. V. Denisov, I. A. Terentyev

“…As a result, realizing algorithm of the transport machines’ diagnostic method, equipped with the course stability system and based on the three-stage system of technical diagnostics was developed and implemented.Discussion and conclusions. …”
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