Scalable and lightweight deep learning for efficient high accuracy single-molecule localization microscopy by Yue Fei, Shuang Fu, Wei Shi, Ke Fang, Ruixiong Wang, Tianlun Zhang, Yiming Li

“…To address these challenges, we present LiteLoc, a scalable analysis framework for high-throughput SMLM data analysis. …”
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THE DEVELOPMENT OF TOURISTIC SERVICES THROUGH INDIVIDUAL AND ORGANIZATIONAL LEARNING. STUDY CASE: ROMANIA AND SPAIN by Dan Popescu, Iulia Chivu, Alina Ciocârlan-Chitucea, Daniela-Oana Popescu

“…Organizational learning is focused on the learning process in the organization which has direct influence on the goods and services produced. …”
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Application of machine learning in diabetes prediction based on electronic health record data analysis by Yang Zihan

“…With the application of electronic health records (EHRs) in the medical field, the use of machine learning to predict disease has become one of the important research hotspots in the healthcare industry. …”
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Escape Classroom or How to Transform the Foreign Language Classroom into a Mysterious Learning Space by Irina Mishkova-Yotova

“…The methodology of educational puzzle rooms is used to increase competences by creating a motivating and engaging learning experience. The Educational Puzzle Room game is characterized by a specific focus on learning objectives that are woven into the overall design, concept, content and experience. …”
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A Problem-Based Learning Electrochemistry Course for Undergraduate Students to Develop Complex Thinking by Aurora Ramos-Mejía, Kira Padilla

“…This paper presents a Problem-Based Learning (PBL) electrochemistry course contextualized within a real-world problem of wastewater treatment, designed to enhance students’ subject matter knowledge. …”
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Data‐sharing strategies in medical consortium based on master‐slave multichain and federated learning by Bohan Kang, Ning Zhang, Jianming Zhu

“…Abstract In order to encourage participants to actively join the data sharing and to meet the distributed structure and privacy requirement in the medical consortium, the data‐sharing strategy based on the master‐slave multichain is presented in this paper. According to the different computing resources and the responsibility of participants, the adaptive Proof of Liveness and Quality consensus and hierarchical federated learning algorithm for master‐slave multichain are proposed. …”
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Development of data-driven machine learning models and their potential role in predicting dengue outbreak by Bushra Mazhar, Nazish Mazhar Ali, Farkhanda Manzoor, Muhammad Kamran Khan, Muhammad Nasir, Muhammad Ramzan

“…The current article endeavors to present an overview of predicting dengue outbreaks through data-based machine-learning models. …”
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Communication Strategies Used by Students in English Learning-teaching: A Discourse-ethnographic Study by John Okafor Diobi, Nashruddin Nashruddin

“…The data were then analyzed using performance analysis technique, namely an analysis based on the speech that occurs naturally and the linguistic competence. This present study found that students used two communication strategies, namely verbal communication strategy and nonverbal communication strategy, in English teaching-learning activity. …”
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A Machine Learning-Based Approach to Analyze and Visualize Time-Series Sentencing Data by Eugene Pinsky, Kandaswamy Piranavakumar

“…Analyzing time-series sentencing data presents many challenges. The data have many dimensions and change with time. …”
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Wheat yield prediction of Rajasthan using climatic and satellite data and machine learning techniques by KAVITA JHAJHARIA

“…The solar induced chlorophyll fluorescence is more sensitive to photosynthesis than any other vegetation indices, so it is crucial to uncover its potential for accurately predicting wheat yields. In the present study, we implemented three machine learning algorithms, support vector regression, Random Forest and XGBoost, one linear regression method, Least Absolute Shrinkage and Selection Operator regression, and one deep learning method, long short-term memory, to predict the wheat yield prediction from 2008 to 2019 using satellite data (SIF) and vegetation indices. …”
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Lifelong Learning as a Research Topic in European Journals: Prevalence, Subjects, and Types of Research by Kristinka Č. Ovesni, Nataša B. Matović

“…The paper discusses lifelong learning as a research topic in European journals through three research questions concerning its prevalence, the subjects of lifelong learning and lifelong education–related papers, and the type of research. …”
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Modularized Learning for Hate Speech Detection in Korean: Integrating Emotions and Multi-Faceted Attributes by Hyeun Jeong Min

“…Detecting hate speech is critical both online and offline, particularly on social media, in personal reviews, and during interpersonal conversations. In this study, we present a modularized learning algorithm for hate speech detection in Korean that integrates diverse features, including emotional indicators and attributes capturing expressions of hatred. …”
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Cue-reliance and vowel perceptual patterns of Cypriot Greek children who learn English by Georgios P. Georgiou

“… The aim of the present study is to investigate the vowel perceptual patterns of Cypriot Greek children who learn English as a foreign language in Cyprus and their cue-reliance during L2 vowel categorization. …”
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Deep Learning Model for COVID-19 Diagnosis: Improving Accuracy and Sensitivity in Early Detection by Ahmed Majid Taha, Munera A. Jabaar, Nadia Mahmood Ali

“…The continuous COVID-19 pandemic, caused by the SARS-CoV-2 virus, required fast and efficient diagnostic tools. This work presents a deep learning-based system, using convolutional neural networks, for the detection and diagnosis of COVID-19 through computed tomography tests, aiming to assist specialized medical professionals. …”
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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

“…On-grid predictive energy management using machine learning is presented in this paper. A photovoltaic array considered in this study is one of the kinds of a renewable sources of energy, where the battery bank acts as a technology for energy storage, in order to optimise energy exchange with the utility grid using logistic regression. …”
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Project-Based Learning in Analytic Geometry Courses to Support Student’s Mathematic Communication Skill by Yunita Oktavia Wulandari, Eko Yuniarto, Cynthia Tri Octavianti

“…Definitions, methods for analytically characterizing parabolas, ellipses, and hyperbolas, as well as example problems and exercises, are all included in the learning videos. The video showed that students could audio-visually express mathematical ideas about conic slices, presenting in algebraic form or using mathematical symbols. …”
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A digital photography dataset for Vaccinia Virus plaque quantification using Deep Learning by Trina De, Subasini Thangamani, Adrian Urbański, Artur Yakimovich

“…We demonstrate how analysis of these plates can be performed using deep learning by training models based on the leading architecture for biomedical instance segmentation - StarDist. …”
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Learning the diffusion of nanoparticles in liquid phase TEM via physics-informed generative AI by Zain Shabeeb, Naisargi Goyal, Pagnaa Attah Nantogmah, Vida Jamali

“…However, linking motion to interactions to decode the underlying mechanisms of motion and interpret interactive forces at play is challenging, particularly when closed-form Langevin-based equations are not available to model the motion. Herein, we present LEONARDO, a deep generative model that leverages a physics-informed loss function and an attention-based transformer architecture to learn the stochastic motion of nanoparticles in LPTEM. …”
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CALM: Cultivating Awareness, Learning, and Mastery to Reduce Anger and Violence Through Combat Sports by Andrew M. Lane

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Stock market trading via actor-critic reinforcement learning and adaptable data structure by Cesar Guevara

“…Currently, the stock market is attractive, and it is challenging to develop an efficient investment model with high accuracy due to changes in the values of the shares for political, economic, and social reasons. This article presents an innovative proposal for a short-term, automatic investment model to reduce capital loss during trading, applying a reinforcement learning (RL) model. …”
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