Discovering opioid slang on social media: A Word2Vec approach with reddit data by E. Holbrook, B. Wiskur, Z. Nagykaldi

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SMART 2.0: Social Media Analytics and Reporting Tool Applied to Misinformation Tracking by Mahmoud Mousa Hamad, Gopichandh Danala, Wolfgang Jentner, David Ebert

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Analysis of plant metabolomics data using identification‐free approaches by Xinyu Yuan, Nathaniel S. S. Smith, Gaurav D. Moghe

“…We further focus on alternative strategies that bypass the need for metabolite identification, allowing researchers to interpret global metabolic patterns and pinpoint key metabolite signals. These methods include molecular networking, distance‐based approaches, information theory–based metrics, and discriminant analysis. …”
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Cold-start visualization recommendation driven by large language models for ocean data analysis by Xin Li, Jixiu Liao, Wen Liu, Yu Miao, Leyu Wang, Shuqing Sun

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PLM-ATG: Identification of Autophagy Proteins by Integrating Protein Language Model Embeddings with PSSM-Based Features by Yangying Wang, Chunhua Wang

“…Although current computational approaches have addressed experimental recognition’s costly and time-consuming challenges, they still have room for improvement since handcrafted features inadequately capture the intricate patterns and relationships hidden in sequences. In this study, we propose PLM-ATG, a novel computational model that integrates support vector machines with the fusion of protein language model (PLM) embeddings and position-specific scoring matrix (PSSM)-based features for the ATG identification. …”
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Enhanced automated art curation using supervised modified CNN for art style classification by Weiwei Li

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Recent advances in the inverse design of silicon photonic devices and related platforms using deep generative models by Sun Jae Baek, Minhyeok Lee

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Multi-omics derivation of a core gene signature for predicting therapeutic response and characterizing immune dysregulation in inflammatory bowel disease by Mingming Wang, Liping Liang, Zibo Tang, Jimin Han, Lele Wu, Le Liu, Le Liu, Ye Chen, Ye Chen

“…Single-cell RNA sequencing revealed cell-type-specific expression patterns: PDK2 demonstrated widespread expression across epithelial cycling cells and stem cells, UGT2A3 showed preferential epithelial localization, and CDC14A exhibited selective enrichment in innate lymphoid cells. …”
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Optimisation-Based Feature Selection for Regression Neural Networks Towards Explainability by Georgios I. Liapis, Sophia Tsoka, Lazaros G. Papageorgiou

“…Regression is a fundamental task in machine learning, and neural networks have been successfully employed in many applications to identify underlying regression patterns. …”
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Toward Generative AI-Based Intrusion Detection Systems for the Internet of Vehicles (IoV) by Isra Mahmoudi, Djallel Eddine Boubiche, Samir Athmani, Homero Toral-Cruz, Freddy I. Chan-Puc

“…Traditional IDS approaches, such as rule-based and signature-based methods, are often inadequate in detecting novel and sophisticated attacks due to their limited adaptability and dependency on predefined patterns. To overcome these limitations, machine learning (ML) and deep learning (DL)-based IDS have been introduced, offering better generalization and the ability to learn from data. …”
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Artificial Intelligence Models and Tools for the Assessment of Drug–Herb Interactions by Marios Spanakis, Eleftheria Tzamali, Georgios Tzedakis, Chryssalenia Koumpouzi, Matthew Pediaditis, Aristides Tsatsakis, Vangelis Sakkalis

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Low-Parameter Critic-Based Multivariate WGAN Model for Clogging Detection in Drives by Surówka Artur Dawid, Kosiba Marcin, Mikkelä Teemu, Kavala Asko, Firla Marcin

“…Smart condition monitoring tools, especially those using machine/deep learning, have proven effective for failure detection. …”
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Hybrid Deep Learning Models for Predicting Student Academic Performance by Kuburat Oyeranti Adefemi, Murimo Bethel Mutanga, Vikash Jugoo

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Gene expression-based modeling of overall survival in Black or African American patients with lung adenocarcinoma by Bin Zhu, Stephanie S. McHale, Michelle Van Scoyk, Gregory Riddick, Pei-Ying Wu, Chu-Fang Chou, Ching-Yi Chen, Robert A. Winn

“…Black/African American (B/AA) populations, in particular, exhibit the highest incidence and mortality rates of lung adenocarcinoma (LUAD) in the United States.MethodsThis study aims to explore gene expression patterns linked to LUAD in B/AA and case-matched white patients, with the goal of developing predictive models for prognosis. …”
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Decoding and generating synergy-based hand movements using electroencephalography during motor execution and motor imagery by Dingyi Pei, Ramana Vinjamuri

“…Brain-machine interfaces (BMIs) have proven valuable in motor control and rehabilitation. …”
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An online learning method for assessing smart grid stability under dynamic perturbations by Alaa Alaerjan, Randa Jabeur

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Exploration of AI-Powered Tools for Risk Assessment in General Dentistry by Abdalwhab Zwiri, Ravi Jotish, Mohammad Khursheed Alam, Norah Khalid S. Almuhanna, Alanoud Mamluh Alazmi, Nor Farid Bin Mohd Noor, Mohammad Saiful Islam

“…The tool analyzed factors, such as oral hygiene habits, dietary patterns, and medical history, to generate individualized risk scores. …”
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Mitigating class imbalance in churn prediction with ensemble methods and SMOTE by R. Suguna, J. Suriya Prakash, H. Aditya Pai, T. R. Mahesh, Venkatesan Vinoth Kumar, Temesgen Engida Yimer

“…Abstract This study examines how imbalanced datasets affect the accuracy of machine learning models, especially in predictive analytics applications such as churn prediction. …”
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Identification of diagnostic genes for myocardial ischemia reperfusion injury associated with metabolic syndrome through the integration of bioinformatics analysis, molecular docki... by Shufang Liu, Shufang Liu, Yan Zhang, Yan Zhang, Yanan Zhao, Ping Wu, Ping Wu, Shouyuan Tian, Han-Qing Pang, Han-Qing Pang, Zhifang Wu, Zhifang Wu, Sijin Li, Sijin Li

“…Drug candidates were predicted via PPI networks, CTD, and molecular docking, followed by experimental evaluation of dexamethasone.ResultsFive hub genes—DAK, GTF3C5, KCNMB1, TRAF1, and ZNF692—were identified, with distinct expression patterns (DAK/GTF3C5 downregulated; KCNMB1/TRAF1/ZNF692 upregulated). …”
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Applying Multi-Sensor Satellite Data to Identify Key Natural Factors in Annual Livestock Change and Winter Livestock Disaster (<i>Dzud</i>) in Mongolian Nomadic Pasturelands by Sinkyu Kang, Nanghyun Cho, Amartuvshin Narantsetseg, Bolor-Erdene Lkhamsuren, Otgon Khongorzul, Tumendemberel Tegshdelger, Bumsuk Seo, Keunchang Jang

“…The primary factors of livestock change and <i>Dzud</i> showed regional patterns: dryness in the south, temperature in the north, and foraging resource in the central and western regions. …”
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