A non-asymptotic analysis of adaptive TD(λ) learning in wireless sensor networks by Bing Li, Tao Li, Muhua Liu, Junlong Zhu, Mingchuan Zhang, Qingtao Wu

“…(identically and independently distributed) noise in reinforcement learning. Moreover, we also present non-asymptotic analysis of ADTD( λ ) with both constant and diminishing step-sizes. …”
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Developing national frameworks for climate services: Experiences, challenges and learnings from across Europe by Nicola Golding, Keith Lambkin, Louise Wilson, Rozemien De Troch, Andreas Marc Fischer, Hans Olav Hygen, Angela Michiko Hama, Anita Verpe Dyrrdal, Ella Jamsin, Piet Termonia, Christopher Hewitt

“…Based on this experience, we propose an additional step as part of the WMO’s ‘Step-by-step Guidelines for Establishing a National Framework for Climate Services’ (WMO, 2018) to support countries to maintain and evolve their operational frameworks. …”
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Best practices for developing microbiome-based disease diagnostic classifiers through machine learning by Peikun Li, Min Li, Wei-Hua Chen

“…Finally, at the ML algorithm selection step, we found that Ridge and Random Forest ranked the best. …”
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Skin Cancer Classification: A Transfer Learning Approach Using Inception-v3 by Yaarob Younus Al Badrani, Abbas Mgharbel

“…The suggested framework is divided into several steps. The initial phase is the data augmentation of images. …”
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It takes a village to educate a child: LLV’s evolving story of building innovation in a context of constraint by Fiona Young, Meredith Ash, Stephanie McConnell

“…Since opening in 2019, the school has continually piloted and evolved its pedagogical practices and learning spaces. This paper discusses the process of creating the school’s vision and educational model, and key steps in implementing a process of pedagogical transformation to enable the school as it is today. …”
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Machine learning accelerated descriptor design for catalyst discovery in CO2 to methanol conversion by Prajwal Pisal, Ondřej Krejčí, Patrick Rinke

“…The descriptor is versatile and can be adjusted to a specific reaction through careful choice of the key-step reactants and reaction intermediates. By applying unsupervised machine learning and statistical analysis to a dataset comprising nearly 160 metallic alloys, we offer a powerful tool for catalyst discovery. …”
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Using genomic data and machine learning to predict antibiotic resistance: A tutorial paper. by Faye Orcales, Lucy Moctezuma Tan, Meris Johnson-Hagler, John Matthew Suntay, Jameel Ali, Kristiene Recto, Phelan Glenn, Pleuni Pennings

“…Given that ML is likely to become an ever more important tool in medicine, we believe that it is important for pre-health students and others in the life sciences to learn to use ML tools. This paper provides a step-by-step tutorial to train 4 different ML models (logistic regression, random forests, extreme gradient-boosted trees, and neural networks) to predict drug resistance for Escherichia coli isolates and to evaluate their performance using different metrics and cross-validation techniques. …”
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Modelling continual learning in humans with Hebbian context gating and exponentially decaying task signals. by Timo Flesch, David G Nagy, Andrew Saxe, Christopher Summerfield

“…Here, we propose novel computational constraints for artificial neural networks, inspired by earlier work on gating in the primate prefrontal cortex, that capture the cost of interleaved training and allow the network to learn two tasks in sequence without forgetting. We augment standard stochastic gradient descent with two algorithmic motifs, so-called "sluggish" task units and a Hebbian training step that strengthens connections between task units and hidden units that encode task-relevant information. …”
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AIME-solve: Accessible interactive mathematical expressions solver for optimized learning and visual empowerment by Amjad Ali, Shah Khusro, Fakhre Alam, Inayat Khan, Shabbab Ali Algamdi

“…However, students with visual disabilities still face significant challenges in mathematics learning, such as real-time feedback, error correction, step-by-step guidance, and interactive learning using digital devices such as smartphones and computers. …”
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From genetic data to kinship clarity: employing machine learning for detecting incestuous relations by Dejan Šorgić, Aleksandra Stefanović, Mladen Popović, Dušan Keckarević

“…The same accuracy was obtained with a single-step mutation, while prediction based on child profiles exclusively yielded an accuracy of 90.37% in the U.S. population. …”
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Reinforcement Learning with Multi-Policy Movement Strategy for Weakly Supervised Temporal Sentence Grounding by Shan Jiang, Yuqiu Kong, Lihe Zhang, Baocai Yin

“…The RL-based framework initially sets a series of candidate windows and learns to adjust them step-by-step by maximizing the rewards, indicating the semantic alignment between the current window and the query. …”
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Assessment of the quality of digital services provided by an e-learning platform focused on sustainability issues by Beatriz Cruz Delgado, Antonio Carlos Farrapo Júnior, Ricardo Coser Mergulhão, Diogo Aparecido Lopes Silva

“…Implications for theory and practice Finally, the step-by-step methodological approach used by this paper should be adopted by other e-learning platforms to investigate quality and service management aspects. …”
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Deep Q-Learning Enhanced Variable Neighborhood Search for Influence Maximization in Social Networks by Afifeh Maleki Ghalghachi, Mehdy Roayaei

“…This paper introduces DQVNS (Deep Q-learning Variable Neighborhood Search), which integrates VNS with deep reinforcement learning (DRL) to enhance neighborhood structure determination in VNS. …”
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Teaching-learning in clinical education based on epistemological orientations: A multi-method study. by Hamed Khani, Soleiman Ahmady, Babak Sabet, Ali Namaki, Shirdel Zandi, Somayeh Niakan

“…In the second step, based on the framework of Ottenhoff- de Jonge et al., a three-dimensional matrix was developed concerning epistemological orientations about teaching and learning.…”
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INDONESIAN TRADITIONAL GAME «BANDHUL SADA» IN LEARNING ROTATION DYNAMIC CONCEPTS WITH CONTEXTUAL APPROACH by Siska Desy Fatmaryanti

“…We focused how to learn the rotational dynamic concept with traditional game «Bandhul Sada» and implementation learning steps with contextual approach.This identification includes analysis of rotation dynamic consepts in “Bandhul Sada”  and analysis of learning steps with a contextual approach to the application of the game. …”
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A Deep Curriculum Learning Semi-Supervised Framework for Remote Sensing Scene Classification by Qing Zhang, Jialu Chen, Baohua Yuan

“…Finally, the multimodal deep features are fed into multimodal curriculum learning methods for classification. Multimodal curriculum learning can integrate the easy curricula recommended by each modal according to the order of the samples of each modal and then learn step by step. …”
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Machine-learned interatomic potentials for accurate analysis of the mechanical properties of boron nitride sheets by Vijay Choyal, Mahesh Patil, Nitin Luhadiya, S I Kundalwal

“…We introduced a novel machine-learned interatomic potential (MLIP) by thoroughly discussing the step–by–step MLIP creation process using precise but limited data. …”
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Transfer learning based feature selection for feedforward neural network for speech emotion classifier by D. V. Krasnoproshin, M. I. Vashkevich

“…This work discusses speech emotion recognition via custom feature engineering and feature selection techniques using mel-frequency cepstral coefficients as initial audio features. Proposed transfer learning approach consist in employing the backward-step selection algorithm for feature selection using statistical learning classifiers, the obtained subset of features than subsequently used to train feedforward neural networks. …”
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Forming of pseudo-international words translation skills in the process of learning a foreign language by Amaliya L. Kyuregyan, Ekaterina A. Milyutina

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Energy Management Prediction in Hybrid PV-Battery Systems Using Deep Learning Architecture by Mohamad Reda A. Refaai, Shanmukha Naga Raju Vonteddu, Prasanthi Kumari Nunna, P. Suresh Kumar, C. Anbu, Mebratu Markos

“…The model of prediction can accurately estimate photovoltaic energy output and load one step ahead using a training technique. The optimization problem is constrained by the maximum amount of CO2 produced and the maximum amount of charge stored in a battery bank. …”
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