Event Forecasting in Organizational Networks: A Discrete Dynamical System Approach by Piotr Śliwa

“…The network perspective is believed to successfully model most of the socioeconomic phenomena, which, in combination with the prospects of continuously advancing tools for automated data mining and machine learning, gives a tempting promise to effectively forecast socioeconomic events occurring in our societies and businesses. …”
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Restricted Isometry Property of Principal Component Pursuit with Reduced Linear Measurements by Qingshan You, Qun Wan, Haiwen Xu

“…The principal component prsuit with reduced linear measurements (PCP_RLM) has gained great attention in applications, such as machine learning, video, and aligning multiple images. …”
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Method for Creating Domain-Specific Dataset Ontologies from Text in Uncontrolled English by Minab Shokoufeh Salem, Nazaruka Erika

“…The prominent technologies for implementation of the proposed method are machine learning, including classification algorithms and natural language processing using a large language model. …”
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Effects of Organizational Culture on Employer Attractiveness of Hotel Firms: Topic Modeling Approach by Kyoung-Joo Lee, Sun-Yong Choi

“…This study combines an unsupervised machine learning tool for topic modeling (latent Dirichlet allocation) with the coding process of researchers to measure the different cultural attributes of hotel firms. …”
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Learning the simplicity of scattering amplitudes by Clifford Cheung, Aurélien Dersy, Matthew D. Schwartz

“…This work explores the application of machine learning to a particular facet of this challenge: the task of simplifying scattering amplitudes expressed in terms of spinor-helicity variables. …”
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Application of multimodal large language models for safety indicator calculation and contraindication prediction in laser vision correction by Joon Yul Choi, Doo Eun Kim, Sung Jin Kim, Hannuy Choi, Tae Keun Yoo

“…ChatGPT-4 effectively analyzed ocular data, calculated key indicators, generated calculator codes, and outperformed traditional machine learning models and indicators in handling unstructured data and corneal topography. …”
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Short-term power consumption forecasting using neural networks with first- and second-order differencing by Meftah Elsaraiti

“…Recent advancements in machine learning, particularly long short-term memory (LSTM) networks, have addressed some of these limitations by leveraging neural network architectures capable of learning complex temporal dependencies. …”
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Southeast Australia encapsulates the recent decade of extreme global weather and climate events by Milton Speer, Lance Leslie

“…Accelerated GW amplifies the impacts of SEAUS climate drivers, depending on their phases. We used machine learning attribution to identify climate drivers responsible for a range of extreme events. …”
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Transverse cracking in glass fibre-reinforced composites monitored with synchrotron X-ray multi-projection imaging by Elise Van Vlierberghe, Jeroen Soete, Eleni Myrto Asimakopoulou, Zisheng Yao, Julia Rogalinski, Zhe Hu, Kannara Mom, Bratislav Lukić, Christian Breite, Pablo Villanueava Perez, Yentl Swolfs

“…An extensive data set was gathered to make a 3D reconstruction of the crack evolution over time using XMPI and machine learning-based reconstruction algorithms.  …”
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Prediction of Compressive Strength of Concrete and Rock Using an Elementary Instance-Based Learning Algorithm by Shun-Chieh Hsieh

“…The use of machine learning techniques to predict material strength is becoming popular. …”
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Features of using Cox regression in various instrumental environments by I. V. Kramarenko, L. A. Konstantinova

“…The presence of large amounts of data in information and analytical systems makes it necessary to study them using machine learning and artificial intelligence methods. These models require the definition of tuning parameters related to the specifics of the subject area. …”
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Prediction of G Protein-Coupled Receptors with SVM-Prot Features and Random Forest by Zhijun Liao, Ying Ju, Quan Zou

“…All of the above indicate that our machine-learning method can successfully distinguish GPCRs from non-GPCRs.…”
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Urban Vitality Measurement Through Big Data and Internet of Things Technologies by Young-Long Kim

“…This approach offers a more dynamic and comprehensive picture of urban vitality, facilitated by advanced analytical tools such as machine learning and predictive analytics, which can interpret complex datasets to offer real-time insights and better decision-making for urban planning. …”
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RespiroDynamics: A Multifaceted Dataset for Enhanced Lung Health Assessment Using Deep Learning by Ahmed Sharshar, Muhammad Sharshar, Hosam Elhady, Ahmed Aboeitta, Youssef Nafea, Yasser Ashraf, Mohammad Yaqub, Mohsen Guizani

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Sentiment Analysis Twitter Bahasa Indonesia Berbasis WORD2VEC Menggunakan Deep Convolutional Neural Network by Hans Juwiantho, Esther Irawati Setiawan, Joan Santoso, Mauridhi Hery Purnomo

“…Penggunaan metode classical machine learning yang sudah banyak diterapkan pada sentiment analysis, tetapi metode tersebut tidak memperhatikan pentingnya urutan kata pada suatu kalimat. …”
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Enhancing Semi-Supervised Learning With Concept Drift Detection and Self-Training: A Study on Classifier Diversity and Performance by Jose L. M. Perez, Roberto S. M. Barros, Silas G. T. C. Santos

“…Machine learning algorithms that assist in decision-making are becoming crucial in several areas, such as healthcare, finance, marketing, etc. …”
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Dengue dynamics, predictions, and future increase under changing monsoon climate in India by Yacob Sophia, Mathew Koll Roxy, Raghu Murtugudde, Anand Karipot, Amir Sapkota, Panini Dasgupta, Kalpana Baliwant, Sujata Saunik, Abhiyant Tiwari, Rajib Chattopadhyay, Revati K. Phalkey

“…Based on these weather-dengue associations, we developed a machine-learning model utilizing the random forest regression algorithm. …”
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Application of ATR-FTIR spectroscopy and multivariate statistical analysis in cancer diagnosis by Ao Song, Wanli Yang, Jun Wang, Yisa Cai, Lizheng Cai, Nan Pang, Ruihua Yu, Zhikun Liu, Chao Yang, Feng Jiang

“…The findings suggest that the integration of ATR-FTIR spectroscopy with multiple machine learning models represents a promising auxiliary diagnostic method for clinical lung cancer diagnosis, enabling detection at the molecular level.…”
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Designing host-associated microbiomes using the consumer/resource model by Germán Plata, Karthik Srinivasan, Madan Krishnamurthy, Lukas Herron, Purushottam Dixit

“…For example, there has been a boom in machine-learning models aiding in the design of proteins with user-specified structures or functions. …”
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Self-reports map the landscape of task states derived from brain imaging by Brontë Mckeown, Ian Goodall-Halliwell, Raven Wallace, Louis Chitiz, Bridget Mulholland, Theodoros Karapanagiotidis, Samyogita Hardikar, Will Strawson, Adam Turnbull, Tamara Vanderwal, Nerissa Ho, Hao-Ting Wang, Ting Xu, Michael Milham, Xiuyi Wang, Meichao Zhang, Tirso RJ Gonzalez Alam, Reinder Vos de Wael, Boris Bernhardt, Daniel Margulies, Jeffrey Wammes, Elizabeth Jefferies, Robert Leech, Jonathan Smallwood

“…., those inferred from measures of brain activity). Here, we used machine learning to show that self-reported descriptions of experiences across tasks can reliably map the objective landscape of task states derived from brain activity. …”
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