Machine learning framework for oxytetracycline removal using nanostructured cupric oxide supported on magnetic chitosan alginate biocomposite by Hassan Rasoulzadeh, Hossein Azarpira, Mojtaba Pourakbar, Amir Sheikhmohammadi, Alieh Rezagholizade-shirvan

“…Prior to applying machine learning models, preprocessing steps were performed, including normalization using Min–Max Scaling to confine all features within the [0,1] range, outlier detection and removal of anomalous values, correlation analysis to avoid redundancy and multicollinearity, and data splitting into training and testing sets at an 80:20 ratio, along with K-fold cross-validation (k = 5) for robust model evaluation. …”
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Author’s agency in a research article: From the grammar of language to the grammar of communication by S. A. Sheypak

“…The second cycle is focused on step-by-step manuscript revisions. Finally, a change of journal and/or author whose manuscript is discussed involves the third cycle of expansive learning.Scientific novelty. …”
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Automatic Generation Control of Multi-Area Power System Using a Fuzzy Wavelet Neural Network Load Frequency Controller Combined With Shuffled Frog Leaping Algorithm by Leila Esteki, Abas Ali Zamani, Seyed Mohamad Kargar, SayedAli Mousavi

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Automatic Liver segmentation Using Vector Field Convolution and Artificial Neural Network in MRI Images by Hassan Masoumi, Ahad Salimi, Hamidreza Sadeghi Madavani

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Cyclical hybrid imputation technique for missing values in data sets by Kurban Kotan, Serdar Kırışoğlu

“…Abstract The problem of missing data in data sets is the most important first step to be addressed in the preprocessing phase. …”
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Brand visibility in packaging: a deep learning approach for logo detection, saliency-map prediction, and logo placement analysis by Alireza Hosseini, Kiana Hooshanfar, Pouria Omrani, Reza Toosi, Ramin Toosi, Zahra Ebrahimian, Mohammad Ali Akhaee

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Investigating the Effectiveness of Cognitive Vocabulary Strategy Instruction in Raising Learners’ Metacognitive Awareness for Long-Term Mass Lexis Learning by Naouel Dib

“… This paper reports on a study that investigates the role of learning vocabulary and the importance of intentionally instructing learners the techniques of vocabulary learning strategies. …”
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Semi-supervised segmentation of cardiac chambers from LGE-CMR using feature consistency awareness by Hairui Wang, Helin Huang, Jing Wu, Nan Li, Kaihao Gu, Xiaomei Wu

“…Deep learning methods have demonstrated extremely promising performance. …”
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Supercontinuum Generation at 1310nm in a Highly Nonlinear Photonic Crystal Fiber with a Minimum Anomalous Group Velocity Dispersion by Ashkan Ghanbari, Ali Sadr, Hadi Tat Hesari

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Recognition of anxiety and depression using gait data recorded by the kinect sensor: a machine learning approach with data augmentation by Milad Shoryabi, Ahmad Hajipour, Afshin Shoeibi, Ali Foroutannia

“…Key kinematic features such as step length, step width, cadence, and eight additional gait parameters were extracted from the recorded data. …”
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Gender Differences in Pain-Physical Activity Linkages among Older Adults: Lessons Learned from Daily Life Approaches by Amy Ho, Maureen C. Ashe, Anita DeLongis, Peter Graf, Karim M. Khan, Christiane A. Hoppmann

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LSTM-H: A Hybrid Deep Learning Model for Accurate Livestock Movement Prediction in UAV-Based Monitoring Systems by Ayub Bokani, Elaheh Yadegaridehkordi, Salil S. Kanhere

“…The results demonstrate that LSTM-H achieves a mean error of just 11.51 m for the first step and 40.68 m over a 30-step prediction horizon, outperforming state-of-the-art models by 4.3–14.8 times. …”
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Landslide Displacement Prediction Using Kernel Extreme Learning Machine with Harris Hawk Optimization Based on Variational Mode Decomposition by Chenhui Wang, Gaocong Lin, Cuiqiong Zhou, Wei Guo, Qingjia Meng

“…Therefore, under the premise of VMD effectively decomposing displacement data, combined with the global optimization ability of the HHO heuristic algorithm and the fast-learning ability of KELM, HHO-KELM can be used for displacement prediction of step-like landslides in the TGRA.…”
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Mapping sugarcane plantations in Northeast Thailand using multi-temporal data from multi-sensors and machine-learning algorithms by Savittri Ratanopad Suwanlee, Surasak Keawsomsee, Emma Izquierdo-Verdiguier, Jaturong Som-Ard, Alvaro Moreno-Martinez, Vorraveerukorn Veerachit, Jantima Polpinij, Kanokporn Rattanasuteerakul

“…In the second step, the discretization of sugarcane from non-sugarcane classes in the agricultural category was conducted using four efficient machine learning algorithms (decision tree (DT), random forest (RF), support vector machine (SVM), and one-class SVM). …”
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A novel deep learning model for air quality index prediction integrating time series decomposition and intelligent optimization by Guangyao Ma, Kai Xu, Yue Zhang, Lanhe Zhang, Zicheng Chen

“…Empirical results demonstrate that STL-TimesNet-iTransformer-CPO consistently outperforms existing methods in both single-step and multi-step forecasting tasks, offering a robust and accurate tool for regional air quality prediction and decision support.…”
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Co‐producing a board game to learn and engage about dementia inequalities: First impacts on knowledge in the general population by Clarissa Giebel, Kerry Hanna, Hilary Tetlow, Mark Gabbay, Jacqui Cannon

“…Results Forty stakeholders attended four workshops. Workshops provided step‐by‐step thoughts on how the game could be designed or modified. …”
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Lessons learned from implementation of the Workload Indicator of Staffing Need (WISN) methodology: An international Delphi study of expert users. by Grace , Nyendwoha Namaganda, Audrey, Whitright, Everd, Bikaitwoha Maniple

“…Key lessons learned were that: the benefits gained from applying the WISN outweigh the challenges faced in understanding the technical steps; benchmarking during WISN implementation saves time; data quality is critical for successful implementation; and starting with small-scale projects sets the ground better for more effective scale-up than attempting massive national application of the methodology the first time round. …”
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Predicting depression and unravelling its heterogeneous influences in middle-aged and older people populations: a machine learning approach by Ling Zhang, Ruigang Wei, Jingwen Zhou, Lin Tan, Xiaolong Che, Minqinag Zhang, Xiaoyue Ning, Zhiliang Zhong

“…Methods Using a two-step hybrid model combining long short-term memory (LSTM) and machine learning (ML), we compared 20 depression risk/protective factors in a balanced panel dataset of middle-aged and elderly Chinese adults (N = 3706; aged 45–94; 64.65% female; 41.20% middle-aged) from the China Health and Retirement Longitudinal Study (CHARLS). …”
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Facial Emotion Recognition of 16 Distinct Emotions From Smartphone Videos: Comparative Study of Machine Learning and Human Performance by Marie Keinert, Simon Pistrosch, Adria Mallol-Ragolta, Björn W Schuller, Matthias Berking

“…For our deep learning models, we used the appearance features, the deep-learned features, and their concatenation as inputs. …”
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A robust tuned EfficientNet-B2 using dynamic learning for predicting different grades of brain cancer by A. A. Abd El-Aziz, Mohammed Elmogy, Sameh Abd El-Ghany

“…Our model incorporates the adaptive learning rate (ALR) technique, where the learning rate (LR) adjusts automatically at the beginning of each step based on the training accuracy and loss value from the previous step. …”
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