Accurate modeling and simulation of the effect of bacterial growth on the pH of culture media using artificial intelligence approaches by Suleiman Ibrahim Mohammad, Hamza Abu Owida, Asokan Vasudevan, Suhas Ballal, Shaker Al-Hasnaawei, Subhashree Ray, Naveen Chandra Talniya, Aashna Sinha, Vatsal Jain, Ahmad Abumalek

“…A range of sophisticated artificial intelligence methods, including One-Dimensional Convolutional Neural Network (1D-CNN), Artificial Neural Networks (ANN), Decision Tree (DT), Ensemble Learning (EL), Adaptive Boosting (AdaBoost), Random Forest (RF), and Least Squares Support Vector Machine (LSSVM), were utilized to model and predict pH variations with high accuracy. …”
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IHML: Incremental Heuristic Meta-Learner by Onur Karadeli, Kıymet Kaya, Şule Gündüz Öğüdücü

“…The landscape of machine learning constantly demands innovative approaches to enhance algorithms’ performance across diverse tasks. …”
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Advancing extrapolative predictions of material properties through learning to learn using extrapolative episodic training by Kohei Noda, Araki Wakiuchi, Yoshihiro Hayashi, Ryo Yoshida

“…Abstract Recent advancements in machine learning have demonstrated its potential to significantly accelerate the discovery of new materials. …”
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Research on Functional Modularity and Health Monitoring Design of Home Fitness Equipment by Xinyue Song, Cuiyu Li

“…Multimodal sensors collect real-time physiological data, processed via Kalman filtering and adaptive algorithms to generate personalized fitness recommendations. …”
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Prediction of the Calorific Value and Moisture Content of <i>Caragana korshinskii</i> Fuel Using Hyperspectral Imaging Technology and Various Stoichiometric Methods by Xuehong De, Haoming Li, Jianchao Zhang, Nanding Li, Huimeng Wan, Yanhua Ma

“…Meanwhile, to improve the prediction accuracy of the model to reduce the redundant wavelength data, we chose four feature extraction methods, competitive adaptive reweighted sampling (CARS), successive pojections algorithm (SPA), genetic algorithm (GA), iteratively retains informative variables (IRIV), and combined the three models to build a quantitative detection model for the characteristic wavelengths of moisture content and calorific value of <i>Caragana korshinskii</i> fuel. …”
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Dynamic Inversion Analysis of Structural Layer Modulus of Semirigid Base Pavement considering the Influence of Temperature and Humidity by Bei Zhang, Xu Zhang, Yanhui Zhong, Xiaolong Li, Meimei Hao, Jinbo Liu

“…The firefly algorithm was adapted to optimize the selection of support vector machine (SVM) parameters. …”
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BCI-DRONE CONTROL BASED ON THE CONCENTRATION LEVEL AND EYE BLINK SIGNALS USING A NEUROSKY HEADSET by Ali Abdulwahhab Mohammed, Ali H. Abdulwahhab, Alaa Hussein Abdulaal, Musaria Karim Mahmood, Indrit Myderrizi, Riyam Ali Yassin, Taha Talib Abdulridha, Morteza Valizadeh

“…Also, the experiment can support 16 commands, making the algorithm appropriate for various applications. …”
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Hybrid Renewable Energy Systems—A Review of Optimization Approaches and Future Challenges by Akvile Giedraityte, Sigitas Rimkevicius, Mantas Marciukaitis, Virginijus Radziukynas, Rimantas Bakas

“…Moreover, the integration of metaheuristic algorithms with machine learning has enabled dynamic adaptability and predictive optimization, paving the way for real-time energy management. …”
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A Review of Recent Advances in Roll Stability Control in On-Road and Off-Road Vehicles by Jie Chen, Ruochen Wang, Wei Liu, Dong Sun, Yu Jiang, Renkai Ding

“…Furthermore, the integration of machine learning and advanced predictive algorithms promises to enhance the intelligence and versatility of roll stability control systems.…”
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GA-PE-VMD and MSE Methods for Milling Chatter Feature Extraction of Thin-walled Parts by WANG Hanbin, LI Maoyue, LIU Xianli, WANG Zhixue, MENG Boyang

“…Chatter leads to poor surface quality, dimensional error and reducing the service life of tools and machines.Therefore, a reliable detection method is needed to identify chatter.Aiming at the problem of chatter detection in the milling process of thin-walled structures, a chatter feature extraction method of thin-walled parts based on optimal variational mode decomposition and multi-scale sample entropy is proposed.Firstly, in order to solve the problem of parameter selection in variational modal decomposition, a parameter adaptive method based on genetic algorithm optimization and minimum permutation entropy is proposed. …”
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Modelling and Solving Rescheduling Problems in Dynamic Permutation Flow Shop Environments by Pablo Valledor, Alberto Gomez, Paolo Priore, Javier Puente

“…Moreover, in manufacturing environments, disruptive changes usually emerge in scheduling problems, such as machine breakdowns or the arrival of new jobs, causing a need for fast schedule adaptation. …”
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Automated Design and Parametric Modeling of Excavator Buckets by Nikolay Stankov, Aleksandar Ivanov

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LuViRA Dataset Validation and Discussion: Comparing Vision, Radio, and Audio Sensors for Indoor Localization by Ilayda Yaman, Guoda Tian, Erik Tegler, Jens Gulin, Nikhil Challa, Fredrik Tufvesson, Ove Edfors, Kalle Astrom, Steffen Malkowsky, Liang Liu

“…Specifically, the evaluation covers the Oriented FAST and Rotated BRIEF simultaneous localization and mapping (SLAM) algorithm for vision-based localization with an RGB-D camera, a machine learning algorithm for radio-based localization with massive multiple-input multiple-output (MIMO) technology, and the StructureFromSound2 algorithm for audio-based localization with distributed microphones. …”
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Spectral Fingerprinting of Tencha Processing: Optimising the Detection of Total Free Amino Acid Content in Processing Lines by Hyperspectral Analysis by Qinghai He, Yihang Guo, Xiaoli Li, Yong He, Zhi Lin, Hui Zeng

“…This study employs VNIR-HSI combined with machine learning algorithms to develop a model for visualizing the total free amino acid content in Tencha samples that have undergone different processing steps on the production line. …”
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Fast sparse representative tree splitting via local density for large-scale clustering by Renmin Wang, Jie Li

“…Abstract Large-scale clustering remains an active yet challenging task in data mining and machine learning, where existing algorithms often struggle to balance efficiency, accuracy, and adaptability. …”
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Dynamic weighted ensemble model for predictive optimization in green sand casting: Advancing industry 4.0 manufacturing by Rajesh V․ Rajkolhe, Dr. Sanjay S․ Bhagwat, Dr. Priyanka V․ Deshmukh

“…The model dynamically allocates weights to top-performing algorithms based on their 10-fold cross-validated RMSE, ensuring robust and adaptive prediction performance.Five models—Linear Regression, Ridge Regression, Decision Tree, Random Forest, and Gradient Boosting—were evaluated over ten folds. …”
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Precise application of water and fertilizer to crops: challenges and opportunities by Yingying Xing, Xiukang Wang

“…It examines the integration of advanced sensors, remote sensing, and machine learning algorithms in precision agriculture, assessing their roles in optimizing irrigation and nutrient management. …”
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Research on Interval Probability Prediction and Optimization of Vegetation Productivity in Hetao Irrigation District Based on Improved TCLA Model by Jie Ren, Delong Tian, Hexiang Zheng, Guoshuai Wang, Zekun Li

“…Precise vegetation productivity monitoring and forecasting are crucial for the global carbon cycle. Traditional machine learning algorithms frequently experience overfitting when processing high-dimensional time-series data or substantial numbers of outliers, impeding the accurate prediction of various vegetation metrics. …”
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ENHANCING PROSTHETIC FUNCTIONALITY: A NOVEL APPROACH WITH MYOELECTRIC BIONIC ARM DESIGN by Nivethitha B, Sriram V, Mukundha Krishnan B, Manimaran V

“…The system captures muscle signals in real-time, processes them via a microcontroller, and translates them into precise motor-driven finger movements. Machine learning algorithms improve signal interpretation accuracy, ensuring responsive and natural hand motion. …”
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Collider-based movement detection and control of wearable soft robots for visually augmenting dance performance by Patrick Twomey, Vaibhavsingh Varma, Leslie L. Bush, Mitja Trkov

“…Additionally, machine learning algorithms are unsuitable for detecting the arbitrary motion of improvisational dancers due to the non-repetitive and unique nature of their movements, resulting in limited available training data. …”
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