A Machine Learning Approach to Adapt Local Land Use Planning to Climate Change by Julia Forster, Stefan Bindreiter, Birthe Uhlhorn, Verena Radinger-Peer, Alexandra Jiricka-Pürrer

“…Integrating machine learning (ML) into systemic planning increases awareness of impacts by providing decision-makers with predictive analysis and risk mitigation tools. …”
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Diagnostic framework to validate clinical machine learning models locally on temporally stamped data by Maximilian Schuessler, Scott Fleming, Shannon Meyer, Tina Seto, Tina Hernandez-Boussard

“…This variability, if not addressed, can result in data shifts with potentially poor model performance. Presently, there are few easy-to-implement, model-agnostic diagnostic frameworks to vet machine learning models for future applicability and temporal consistency. …”
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Enhancing Recommendation Systems with Real-Time Adaptive Learning and Multi-Domain Knowledge Graphs by Zeinab Shahbazi, Rezvan Jalali, Zahra Shahbazi

“…These dynamic signals are mapped into evolving knowledge graphs, forming continuously updated learning charts that drive more context-aware and emotionally intelligent recommendations. …”
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Learning Manipulation from Expert Demonstrations Based on Multiple Data Associations and Physical Constraints by Yangqing Ye, Yaojie Mao, Shiming Qiu, Chuan’guo Tang, Zhirui Pan, Weiwei Wan, Shibo Cai, Guanjun Bao

“…Abstract Learning from demonstration is widely regarded as a promising paradigm for robots to acquire diverse skills. …”
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Modular deep learning approach for wind farm power forecasting and wake loss prediction by S. Ally, S. Ally, T. Verstraeten, T. Verstraeten, P.-J. Daems, A. Nowé, J. Helsen, J. Helsen

“…The deep learning model is not limited to steady-state situations but captures the influence of temporal wind dynamics and the farm power controller on the power production of the wind farm. …”
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Using e-scaffolding to develop students’ scientific reasoning through inquiry-based learning by S. Koes Handayanto, S. Fawaiz, A. Taufiq

“…Through inquiry-based learning (IBL), scaffolding is provided to help students develop their scientific reasoning (SR). …”
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Robust zero-watermarking for color images using hybrid deep learning models and encryption by Hager A. Gharib, Noha M. M. Abdelnapi, Khalid M. Hosny

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Text Mining and Unsupervised Deep Learning for Intrusion Detection in Smart-Grid Communication Networks by Joseph Azar, Mohammed Al Saleh, Raphaël Couturier, Hassan Noura

“…Additionally, a text-mining strategy based on a TF-IDF vectorizer and truncated SVD is presented for data preparation and feature extraction. …”
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Meta-Learning Approach for Adaptive Anomaly Detection from Multi-Scenario Video Surveillance by Deepak Kumar Singh, Dibakar Raj Pant, Ganesh Gautam, Bhanu Shrestha

“…Video surveillance is widely used in different areas like roads, malls, education, industries, retail, parks, bus stands, and restaurants, each presenting distinct anomaly patterns that demand specialized detection strategies. …”
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A Learning Emotion Recognition Model Based on Feature Fusion of Photoplethysmography and Video Signal by Xiaoliang Zhu, Zili He, Chuanyong Wang, Zhicheng Dai, Liang Zhao

“…The detection of learning emotions through the capture of convenient, non-intrusive signals such as photoplethysmography (PPG) and video offers good practicality; however, it presents new challenges. …”
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Decoding the footsteps of the African savanna: Classifying wildlife using seismic signals and machine learning by René Steinmann, Tarje Nissen‐Meyer, Fabrice Cotton, Frederik Tilmann, Beth Mortimer

“…To address the issue of the site effect, we trained machine learning models on data recorded on various sites. …”
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Study of line spectra emitted by hydrogen isotopes in tokamaks through Deep-Learning algorithms by N. Saura, M. Koubiti, S. Benkadda

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A Unified Deep Learning Framework for Short-Duration Speaker Verification in Adverse Environments by Youngmoon Jung, Yeunju Choi, Hyungjun Lim, Hoirin Kim

“…We combine SV, VAD, and SE models in a unified deep learning framework and jointly train the entire network in an end-to-end manner. …”
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Trust Region Policy Learning for Adaptive Drug Infusion with Communication Networks in Hypertensive Patients by Mai The Vu, Seong Han Kim, Ha Le Nhu Ngoc Thanh, Majid Roohi, Tuan Hai Nguyen

“…Secondly, a model-free deep reinforcement learning (MF-DRL) algorithm is integrated into the NBC to adjust dynamically the coefficients of the controller. …”
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Flash Card Learning Media in Physical Education Improves Students' Locomotor Movement Skills by Sauqi Sawa Bikalawan, Muchamad Arif Al Ardha, Nanik Indahwati, Andhega Wijaya, Nurhasan Nurhasan, Mochamad Ridwan, Chung Bing Yang

“… Students' learning of locomotor movement skills sometimes has obstacles that become a challenge for Physical Education (PE) teachers. …”
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Automated Breast Cancer Detection in Mammograms using Transfer Learningbased Deep Learning Models by Preeti Katiyar, Krishna Singh

“…For screening purposes, it is currently the preferred method. The present study employs deep learning models trained using Transfer Learning (TL) techniques. …”
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Health Workers’ Perspectives on Mobile Health Care Learning Stickiness: Mixed Methods Study by Sabila Nurwardani, Putu Wuri Handayani

“…ConclusionsThe results of this study will help mobile learning service providers increase user stickiness in mobile learning, for example, through processing speed, the quality of the information presented, security features, personalized content recommendations, and gamification.…”
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Implementation and Evaluation of Self-directed Learning Activity in Biochemistry for Phase I MBBS Students by Sushma B Jagannatha, Sumit Parashar

“…Aim and background: The teaching strategy, self-directed learning (SDL), is a very important and effective method used for teaching and learning that enables medical students for lifelong learning, enabling them to fulfill their learning requirements in self-directing way, identify ideal learning resources, identify objectives ideal for learning, and also assess the process involved in learning all through their career. …”
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A robust and statistical analyzed predictive model for drug toxicity using machine learning by Deepak Rawat, Rohit Bajaj, Rachit Manchanda, Ankush Mehta, Prabhu Paramasivam, Suraj Kumar Bhagat, Abinet Gosaye Ayanie

“…Further concept of W-saw score and L-saw is presented for all the scenarios. An optimized ensembled model using feature selection and resampling technique with tenfold cross-validation performs best among all machine learning models in all the scenarios.…”
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Machine learning classification and driver analysis of diel variability in dissolved oxygen in Taihu Lake by Tingting Luo, Yehui Zhang

“…The classification-based modeling approach improved performance significantly (R2 increased from 0.73 to > 0.8 in Type I and III). This study presents an integrated framework combining unsupervised clustering and interpretable machine learning to uncover the mechanisms of diel DO variation. …”
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