BBSNet: An Intelligent Grading Method for Pork Freshness Based on Few-Shot Learning by Chao Liu, Jiayu Zhang, Kunjie Chen, Jichao Huang

“…Deep learning approaches for pork freshness grading typically require large datasets, which limits their practical application due to the high costs associated with data collection. …”
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Exploring Requirements Engineering for Machine Learning via a Product Case Study by Lynn Vonderhaar, Timothy Elvira, Juan Couder, Omar Ochoa

“…However, these techniques are designed for describing deterministic software. Currently, Machine Learning (ML) is becoming ubiquitous, and many domains deploy it. …”
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Machine learning as a tool for diagnostic and prognostic research in coronary artery disease by B. I. Geltser, M. M. Tsivanyuk, K. I. Shakhgeldyan, V. Yu. Rublev

“…Machine learning (ML) are the central tool of artificial intelligence, the use of which makes it possible to automate the processing and analysis of large data, reveal hidden or non-obvious patterns and learn a new knowledge. …”
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Machine learning prediction of ammonia nitrogen adsorption on biochar with model evaluation and optimization by Chong Liu, Paramasivan Balasubramanian, Jingxian An, Fayong Li

“…Abstract In light of escalating nitrogen pollution in aquatic systems, this study presents a comprehensive machine learning (ML) approach to predict ammonia nitrogen adsorption capacity of biochar and identify optimal conditions. …”
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School bonding, attachment, and engagement through remote learning: Fostering school connectedness by Jennifer Charteris, Angela Page

“…Under challenging conditions, school leaders and teachers made rapid changes to pedagogy and curriculum to accommodate their students’ diverse range of learning needs. We present an interview drawn from a qualitative study undertaken in Australia to illustrate (from a teacher’s perspective) how a school response to COVID-19 integrates elements of school connectedness during the shift to distance learning. …”
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Music Generation Using Deep Learning and Generative AI: A Systematic Review by Rohan Mitra, Imran Zualkernan

“…This paper presents a systematic review of recent advances in music generation using deep learning techniques, categorizing the latest research in the field and identifying key contributions from various approaches. …”
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Trust-based Routing Optimization using Learning Automata in Wireless Sensor Network by Maryam Hajiee, Mehdi Fartash, Nafiseh Osati Eraghi

“…Due to the fact that this type of network is exposed to many attacks, identifying malicious nodes and removing them creates security in this type of network. This paper presents an energy-aware and trusted-based routing method using learning automata and an evaluation function. …”
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Noise Currents Measurement Method for Rail Vehicles Based on Broad Learning System by HAN Ran, LI Jie, LIAN Cheng, ZHANG Kun

“…In order to ensure the electromagnetic compatibility between train and trackside communication system, metro projects at home and abroad have strict limits for the noise current generated by train during operation. At present, current sensors and filters are commonly used in the test of noise current of metro vehicles. …”
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Comparative Analysis of Supervised Learning Techniques for Forecasting PV Current in South Africa by Ely Ondo Ekogha, Pius A. Owolawi

“…The optimal technique must effectively manage rapid weather fluctuations while maintaining high accuracy in forecasting the performance of a PV panel. This work presents a comparative examination of supervised learning algorithms optimized with particle swarm optimization for estimating photovoltaic output current. …”
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Ensemble learning driven Kolmogorov-Arnold Networks-based Lung Cancer classification. by Abdul Rahaman Wahab Sait, Eid AlBalawi, Ramprasad Nagaraj

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Hierarchy-boosted funnel learning for identifying semiconductors with ultralow lattice thermal conductivity by Mengfan Wu, Shenshen Yan, Jie Ren

“…Abstract Data-driven machine learning (ML) has demonstrated tremendous potential in material property predictions. …”
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Living, Caring, Learning – Setting boundaries for the wellbeing of everyone in bleeding disorders care by Harrison Cathy

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Comparative Analysis of Machine Learning Models for CO Emission Prediction in Engine Performance by Beytullah Eren, İdris Cesur

“…This study presents a comparative analysis of machine learning models for predicting carbon monoxide (CO) emissions in automotive engines. …”
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Developing Beginning Design Students’ Self-Directed Learning Through Leadership Activity by Dorna Eshrati, Kerry L. Priest

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Automated reinforcement learning for sequential ordering problem using hyperparameter optimization and metalearning by André Luiz Carvalho Ottoni

“…Abstract AutoML systems seek to assist Artificial Intelligence users in finding the best configurations for machine learning models. Following this line, recently the area of Automated Reinforcement Learning (AutoRL) has become increasingly relevant, given the growing increase in applications for reinforcement learning algorithms. …”
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A comprehensive survey of the machine learning pipeline for wildfire risk prediction and assessment by Naveed Ejaz, Salimur Choudhury

“…Wildfires, intensified by climate change and human activity, present a growing global threat to ecosystems, economies, and public safety. …”
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Ex vivo cortical circuits learn to predict and spontaneously replay temporal patterns by Benjamin Liu, Dean V. Buonomano

“…Furthermore, some neurons exhibited timed prediction errors as revealed by larger responses when the expected stimulus was omitted compared to when it was present. Mechanistically our results indicate that learning relied in part on asymmetric connectivity between distinct neuronal ensembles with temporally-ordered activation. …”
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Contrastive Learning with Global and Local Representation for Mixed-Type Wafer Defect Recognition by Shantong Yin, Yangkun Zhang, Rui Wang

“…In this paper, we present a self-supervised contrastive learning framework for the classification and segmentation of mixed-type WBM defect patterns. …”
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Quantitative Assessment of Regional Carbon Neutrality Policy Synergies Based on Deep Learning by Daiyang Zhang, Enmiao Feng

“… This study presents a comprehensive quantitative assessment framework for evaluating regional carbon neutrality policy synergies using deep learning techniques. …”
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Electric vehicle adoption in Indonesia: Lesson learned from developed and developing countries by Dzikri Firmansyah Hakam, Sari Jumayla

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