Vegetarianism Discourse in Russian Social Media: A Case Study by Nikita Gorduna, Natalia Vanetik

“…Through extensive experimentation, we demonstrate that contrastive learning significantly outperforms traditional machine learning and fine-tuned transformer models, achieving the best classification performance for distinguishing pro- and anti-vegetarian opinions. …”
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Advances in lignocellulosic feedstocks for bioenergy and bioproducts by Daniel B. Sulis, Nathalie Lavoine, Heike Sederoff, Xiao Jiang, Barbara M. Marques, Kai Lan, Carlos Cofre-Vega, Rodolphe Barrangou, Jack P. Wang

“…It also explores emerging biotechnologies such as CRISPR-based genome editing informed by machine learning, aimed at improving feedstock traits and reducing the environmental impact of fossil fuel dependence.…”
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POSITIVE, NEGATIVE RULE AND APPLICATION by Nguyễn Đức Thuần, Phạm Quang Tùng, Hồ Thị Thu Sa

“…Experimental results obtained on data sets from the UCI repository of machine learning databases and the result of an experiment performed on a real-world dataset, the teaching and learning database at Nhatrang University were discussed.…”
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Correlation between the Quality of Talent Training and Regional Economic Development Based on Multivariate Statistical Analysis Model by Yana Yuan

“…This paper incorporates machine learning algorithms to construct a multivariate statistics analysis model based on the correlation analysis model between talent training quality and regional economic development. …”
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Object Detection Techniques in Autonomous Driving by Yu Jianlei

“…It discusses various methodologies, including the application of machine learning (ML) techniques, and the integration of sensors like LiDAR and radar, which enhance the system’s ability to accurately identify and track nearby objects, such as pedestrians, vehicles, and obstacles, in real-time. …”
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Research on person re-identification algorithm based on multi-task learning by MI Rongxin, YAO Wenwen, WU Binghao

“…A novel approach was proposed which combined representation learning and metric learning methods based on the multi-task learning machine learning paradigm. By capitalizing on the advantages of both feature representation and distance metric, and concurrently training the model using classification loss and triplet loss, comprehensive training for both feature extraction and similarity measurement was ensured. …”
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Warpage in wafer-level packaging: a review of causes, modelling, and mitigation strategies by Pallavi Praful, Chris Bailey

“…Warpage characterization techniques and modeling approaches, including theoretical, numerical, and emerging artificial intelligence and machine learning (AI/ML) methods, have been analyzed. The structural parameters and properties of the constituent materials of the reconstituted wafer and the FOWLP process have been considered to evaluate the effectiveness of these methods in predicting and analyzing warpage. …”
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Developing a Travel Time Estimation Method of Freeway Based on Floating Car Using Random Forests by Juan Cheng, Gen Li, Xianhua Chen

“…Different from other machine learning algorithm as black boxes, Random Forests can provide interpretable results through variable importance. …”
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Rolling Bearing Fault Diagnosis based on Wavelet and Deep Wavelet Auto-encoder by Xiaolei Du, Zhigang Chen, Nan Zhang, Xingguo Guo

“…Compared with traditional machine learning methods，the proposed method has better generalization ability，feature extraction ability and recognition ability in the case of insufficient target vibration data.…”
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Knowledge and innovation management model in the mezcal industry in Mexico by Antonia Terán-Bustamante, Sandra Nelly Leyva-Hernández, Antonieta Martínez-Velasco

“…This research aims to study and analyze knowledge management in the mezcal sector in Mexico and its impact on the development of rural communities through Bayesian-networks with machine learning techniques. A model is made in which the critical factors that impact is identified and quantified to optimally manage the knowledge that generates value and translates into innovation and competitive advantages. …”
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Out-of-Sample Predictability of the Equity Risk Premium by Daniel de Almeida, Ana-Maria Fuertes, Luiz Koodi Hotta

“…A comprehensive out-of-sample forecast evaluation exercise based on statistical and economic loss functions demonstrates the superiority of the proposed approach versus combined forecasts from linear models or Markov switching models and forecasts from machine learning methods such as random forests and gradient boosting. …”
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A Scheme Based on Deep Learning for Fruit Classification by Ali Orangzeb Panhwar, Anwar Ali Sathio, Nadeem Manzoor Shah, Sumaira Memon

“…Grading and classifying fruits are critical due to automated machine learning systems. In computer vision, different fruits have large complexity and similarity to identify the fruit types. …”
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Constrained Bayesian Optimization: A Review by Sasan Amini, Inneke Vannieuwenhuyse, Alejandro Morales-Hernandez

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Risks in Work-Integrated Learning: A Data-Driven Analysis by Xiao Xu

“…This study employs advanced data-driven and machine learning techniques to critically assess the integration of Work-Integrated Learning (WIL) into academic programs, with a focus on psychological well-being, financial, and equity and inclusion risks. …”
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UAV swarms: research, challenges, and future directions by Yunes Alqudsi, Murat Makaraci

“…Key areas such as coordinated path planning, task assignment, formation control, and security considerations are examined, highlighting how Artificial Intelligence (AI) and Machine Learning (ML) are integrated to improve decision-making and adaptability. …”
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Modeling and Analysis of Multifocus Picture Division Algorithm Based on Deep Learning by Hongxia You

“…As a complex machine learning algorithm, deep learning can extract object shape information and more complex and advanced information in images by using a deep learning model. …”
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Lower bounds for quantum-inspired classical algorithms via communication complexity by Nikhil S. Mande, Changpeng Shao

“…Quantum-inspired classical algorithms provide us with a new way to understand the computational power of quantum computers for practically-relevant problems, especially in machine learning. In the past several years, numerous efficient algorithms for various tasks have been found, while an analysis of lower bounds is still missing. …”
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BIBLIOMETRIC ANALYSIS OF THE CURRENT STATE OF RESEARCH IN THE FIELD OF COGNITIVE TECHNOLOGIES IN LOGISTICS by Anna HORZELA-MIŚ, Mariusz KMIECIK

“…Key themes include demographics, controlled studies, cognition, logistics, machine learning, and technology. The research highlights the importance of bibliometrics in understanding knowledge dynamics and suggests further exploration of cognitive technologies in Logistics.…”
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The Relationship Between Traffic Flow Forecasting and Traffic Accident Forecasting and the Possible Combination Points by Wu Haoyu

“…Secondly, the differences between neural networks and traditional machine learning are compared in terms of model structure, feature extraction, applicability, computational resources and time, and model complexity'. …”
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The Construction of Sports Health Management Model Based on Deep Learning by Junniao Meng, Song Wang

“…Deep learning is a new direction in the field of machine learning. It learns the inherent laws and representation levels of sample data. …”
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