Kaartdijin Bidi (Learning Journey): Place-based Cultural Regeneration at University by Sandra Wooltorton, Lauren Stephenson, Kathie Ardzejewska, Len Collard

“…Within this worldview, the authors propose collaborative ways of governing, teaching, learning, and leading that is necessary for place-based cultural regeneration. …”
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High-Performance stacking ensemble learning for thermoelectric figure-of-merit prediction by Yuelin Wang, Chengquan Zhong, Jingzi Zhang, Honghao Yao, Junjie Chen, Xi Lin

“…However, accurately predicting their efficiency, quantified by the figure of merit (zT), remains challenging, especially for doped materials. Here we present a machine learning (ML) approach, the stacking model, that significantly improves zT prediction accuracy for doped thermoelectric. …”
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Lessons learned from shallow subglacial bedrock drilling campaigns in Antarctica by Scott Braddock, Ryan A. Venturelli, Keir Nichols, Elliot Moravec, Grant V. Boeckmann, Seth Campbell, Greg Balco, Robert Ackert, David Small, Joanne S. Johnson, Nelia Dunbar, John Woodward, Sujoy Mukhopadhyay, Brent Goehring

“…Here we summarize the lessons learned from five field seasons tasked with obtaining bedrock cores from shallow depths (<120 m beneath ice surface) across West Antarctica since 2016. …”
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A Machine Learning Implementation to Predictive Maintenance and Monitoring of Industrial Compressors by Ahmad Aminzadeh, Sasan Sattarpanah Karganroudi, Soheil Majidi, Colin Dabompre, Khalil Azaiez, Christopher Mitride, Eric Sénéchal

“…Integrating machine learning algorithms leveraged by advanced data acquisition systems is emerging as a pivotal approach in predictive maintenance. …”
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Deep Learning Based Cross Domain Sentiment Classification for Urdu Language by Amna Altaf, Muhammad Waqas Anwar, Muhammad Hasan Jamal, Sana Hassan, Usama Ijaz Bajwa, Gyu Sang Choi, Imran Ashraf

“…Sentiment classification is performed using machine learning and deep learning classifiers. The proposed method achieves an accuracy, precision, recall, and F1 scores of 0.77, 0.83, 0.68, and 0.75, respectively.…”
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EEG-CLIP: learning EEG representations from natural language descriptions by Tidiane Camaret Ndir, Tidiane Camaret Ndir, Robin T. Schirrmeister , Robin T. Schirrmeister , Tonio Ball , Tonio Ball 

“…Overall, we show that EEG-CLIP manages to non-trivially align text and EEG representations. Our work presents a promising approach to learn general EEG representations, which could enable easier analyses of diverse decoding questions through zero-shot decoding or training task-specific models from fewer training examples. …”
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Machine Learning‐Enhanced Nanoparticle Design for Precision Cancer Drug Delivery by Qingquan Wang, Yujian Liu, Chenchen Li, Bin Xu, Shidang Xu, Bin Liu

“…Recent advancements in Machine Learning (ML) and computational methods have shown great promise for precision cancer drug delivery. …”
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Predicting Insemination Outcome in Holstein Dairy Cattle using Deep Learning by Mohammad Alishahi, Mahdi Ravakhah

“…Introduction: Development of a predictive model using machine learning can help livestock farmers to increase their understanding of the performance potential of their livestock. …”
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Mapping taluses using deep learning and high-resolution satellite images by Decai Jiang, Min Feng, Dezhao Yan, Yingzheng Wang, Jinhao Xu, Ning Wang, Jianbang Wang, Xin Li

“…Despite their critical environmental and geohazard roles, taluses have only been mapped in limited regions. This study presents an effective approach to identifying taluses by capturing their morphological features using deep learning (DeepLab V3+ with an attention mechanism) and high-resolution satellite images. …”
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Active-Darknet: An Iterative Learning Approach for Darknet Traffic Detection and Categorization by Sidra Abbas, Imen Bouazzi, Gabriel Avelino Sampedro, Shtwai Alsubai, Ahmad S. Almadhor, Abdullah Al Hejaili, Natalia Kryvinska

“…In order to improve accuracy and precision in identifying illicit activities, this study presents a novel approach named Active-Darknet that uses an active learning-based machine learning model for detecting darknet traffic. …”
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Integration of UAV Remote Sensing and Machine Learning for Taro Blight Monitoring by Yushuai Wang, Yuxin Chen, Zhou Shu, Shaolong Zhu, Weijun Zhang, Tao Liu, Chengming Sun

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Boosting proactive motor control via statistical learning with brain stimulation by Giulia Ellena, Federica Contò, Michele Tosi, Lorella Battelli

“…The implicit nature of learning, as evidenced by subjects’ unawareness of probability distributions, underscores how proactive motor control quickly adapts to statistical regularities. …”
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Machine learning-assisted wearable sensing systems for speech recognition and interaction by Tao Liu, Mingyang Zhang, Zhihao Li, Hanjie Dou, Wangyang Zhang, Jiaqian Yang, Pengfan Wu, Dongxiao Li, Xiaojing Mu

“…Finally, the speech recognition system was able to recognize everyday sentences spoken by participants with an accuracy of 99.8% through a deep learning model. With advantages including a simple fabrication process, stable performance, easy integration, and low cost, SAAS presents a compelling solution for applications in voice control, HMI, and wearable electronics.…”
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Deep reinforcement learning approach for real-time airport gate assignment by Haonan Li, Xu Wu, Marta Ribeiro, Bruno Santos, Pan Zheng

“…We bridge this gap by looking at gate assignments as a dynamic decision-making process. This paper presents the Real-time Gate Assignment Problem Solution (REGAPS) algorithm, an innovative method adept at resolving pre-assignment issues and dynamically optimizing gate assignments in real-time at airports through the integration of Deep Reinforcement Learning (DRL). …”
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The discrepancy evaluation model in the implementation of online learning (on the basis of parents’ perceptions) by B. Bulkani, M. A. Setiawan, W. Wahidah

“…The evaluation of online learning is an attempt to see the extent of the education process in Indonesia. …”
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Non-Visual Interfaces for Visual Learners: Multisensory Learning of Graphic Primitives by Stacy A. Doore, Justin R. Brown, Saki Imai, Justin K. Dimmel, Nicholas A. Giudice

“…We used existing theories of multisensory information processing to study how sighted participants could learn and interpret spatial primitives and graphical concepts presented via three non-visual conditions: natural language (NL) descriptions, haptic renderings, and a NL-Haptic combination. …”
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Attentive Self-supervised Contrastive Learning (ASCL) for plant disease classification by Getinet Yilma, Mesfin Dagne, Mohammed Kemal Ahmed, Ravindra Babu Bellam

“…The ASCL framework enhances interpretability by incorporating attention mechanisms, such as squeeze-excitation and convolutional block attention module, which highlight key regions in plant images, aiding in transparent decision-making. In the present work, a pre-trained squeeze-excitation ResNet50 Siamese backbone network on the unlabeled PlantVillage dataset was used to validate the generalizability of the learned representations. …”
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Credit Scoring Prediction Using Deep Learning Models in the Financial Sector by Xi Shi, Dingfen Tang, Yike Yu

“…The increasing complexity and volume of financial and behavioral data in modern credit scoring and risk assessment present significant challenges to traditional modeling methods. …”
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Accelerating the Discovery of Steady‐States of Planetary Interior Dynamics With Machine Learning by Siddhant Agarwal, Nicola Tosi, Christian Hüttig, David S. Greenberg, Ali Can Bekar

“…Consequently, achieving steady‐state requires a large number of time steps due to the disparate time scales governing the stagnant and convecting regions. We present a concept for accelerating mantle convection simulations using machine learning. …”
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Interpretable Deep Learning for Efficient Code Smell Prioritization in Software Development by Maaeda M. Rashid, Mohd Hafeez Osman, Khaironi Yatim Sharif, Hazura Zulzalil

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