DrugBERT: a BERT-based approach integrating LDA topic embedding and efficacy-aware mechanism for predicting anti-tumor drug efficacy by Weiwei Zhu, Xiaodong Jiang, Lei Zhang, Peng Zhou, Xinping Xie, Hongqiang Wang

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Design of performance evaluation method for higher education reform based on adaptive fuzzy algorithm by Dakun Yang, Muhammad Sheraz Arshad Malik

“…The results demonstrate that the proposed model outperforms conventional methods, such as backpropagation (BP) neural networks and support vector machines (SVMs), in terms of accuracy, precision, and overall performance. …”
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Federated learning: a privacy-preserving approach to data-centric regulatory cooperation by Alexander Horst, Paul Loustalot, Sanjeev Yoganathan, Ting Li, Joshua Xu, Weida Tong, David Schneider, Nicolas Löffler-Perez, Erminio Di Renzo, Michael Renaudin

“…In this paper, we propose federated learning as an innovative method to enhance data-centric collaboration among regulatory agencies by enabling collaborative training of machine learning models without the need for direct data sharing, thereby preserving privacy and overcoming legal hurdles. …”
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Comparative Analysis of Chilling Injury in Banana Fruit During Storage: Physicochemical and Microstructural Changes, and Early Optical-Based Nondestructive Identification by Hui Ma, Lingmeng Hu, Jingyuan Zhao, Jie He, Anqi Wen, Daizhu Lv, Zhi Xu, Weijie Lan, Leiqing Pan

“…Hyperspectral microscope imaging (HMI) captured chilling-induced spectral variations (400–1000 nm), enabling the t-SNE-based clustering of CI-affected tissues. Machine learning models using first derivative (1-st)-processed spectra achieved a high accuracy. …”
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Innovative Technologies and Technical Means for Industrial Nursery Farming by A. A. Zavrazhnov, A. Yu. Izmaylov, A. I. Zavrazhnov, Ya. P. Lobachevskiy, V. Yu. Lantsev

“…Establishing an optimal environment for plant development using a machine with an active working unit for hilling growing shoots increases the output of standard layering up to 86.2-90.6 percent. …”
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Graph-based reinforcement learning for software-defined networking traffic engineering by Jingwen Lu, Chaowei Tang, Wenyu Ma, Wenjuan Xing

“…Recent machine learning approaches show promise but still face fundamental limitations in handling complex network constraints and maintaining performance across different network scales. …”
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Prediction of Three-Dimensional Milling Forces Based on Finite Element by Lida Zhu, Chunxia Zhu, Jiaying Pei, Xuebing Li, Wei Wang

“…It can be shown that milling forces match well between simulation and experiment results, which can provide many good basic data and analysis methods to optimize the machining parameters, reduce tool wear, and improve the workpiece surface roughness and adapt to the programming strategy of high speed machining.…”
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Investigation of a novel heat extraction configuration for boosting photovoltaic panel efficiency by Muhammad Usman, Hafiz Waqar Ul Haq, Fahid Riaz, Muhammad Sher Ali, Muhammad Imran Masood, Yasser Fouad, Shahmeer Khalid Chatha, Muhammad Nasir Bashir

“…Future work could enhance PV panel performance by using extracted heat for domestic water heating or industrial processes and optimizing cooling techniques through a parametric study and real-time machine learning models.…”
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Association between admission Braden Skin Score and delirium in surgical intensive care patients: an analysis of the MIMIC-IV database by Meiling Shang, Meiling Shang, Ailing Shang, Yu Xu, Yu Xu

“…Feature importance of BSS was initially assessed using a machine learning algorithm, while restricted cubic spline (RCS) models and multivariable logistic analysis evaluated the relationship between BSS and delirium. …”
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A computational framework for processing time-series of earth observation data based on discrete convolution: global-scale historical Landsat cloud-free aggregates at 30 m spatial... by Davide Consoli, Leandro Parente, Rolf Simoes, Murat Şahin, Xuemeng Tian, Martijn Witjes, Lindsey Sloat, Tomislav Hengl

“…The resulting reconstructed images can be used as input for machine learning models or to map biophysical indices. …”
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Revolutionizing Drug Delivery: The Impact of Advanced Materials Science and Technology on Precision Medicine by Mohamed El-Tanani, Shakta Mani Satyam, Syed Arman Rabbani, Yahia El-Tanani, Alaa A. A. Aljabali, Ibrahim Al Faouri, Abdul Rehman

“…These technologies allow for predictive modeling and real-time adjustments to optimize drug delivery to the needs of individual patients. …”
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Trends of Soil and Solution Nutrient Sensing for Open Field and Hydroponic Cultivation in Facilitated Smart Agriculture by Md Nasim Reza, Kyu-Ho Lee, Md Rejaul Karim, Md Asrakul Haque, Emmanuel Bicamumakuba, Pabel Kanti Dey, Young Yoon Jang, Sun-Ok Chung

“…Key technologies include electrochemical and optical sensors, Internet of Things (IoT)-enabled monitoring, and the integration of machine learning (ML) and artificial intelligence (AI) for predictive modeling. …”
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Promoting the Integration of Public Perspective into Urban Design Decision-Making Based on Crowdsourced Visual Perception Method by Lu HUANG, Takuya (JPN) OKI

“…In the data analysis phase, statistical methods establish quantitative models linking built environment factors with visual perception evaluations, uncovering the interaction mechanisms between public preferences and built environment elements. …”
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Empirical Analysis of Data Sampling-Based Decision Forest Classifiers for Software Defect Prediction by Fatima Enehezei Usman-Hamza, Abdullateef Oluwagbemiga Balogun, Hussaini Mamman, Luiz Fernando Capretz, Shuib Basri, Rafiat Ajibade Oyekunle, Hammed Adeleye Mojeed, Abimbola Ganiyat Akintola

“…This involves integrating clean and preprocessed datasets, leveraging advanced machine learning (ML) methods, and optimizing key metrics. …”
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Commercial vision sensors and AI-based pose estimation frameworks for markerless motion analysis in sports and exercises: a mini review by Saeid Edriss, Cristian Romagnoli, Lucio Caprioli, Vincenzo Bonaiuto, Elvira Padua, Giuseppe Annino, Giuseppe Annino

“…Additionally, it discusses the optimal setup and perspectives for achieving accurate results in these studies. …”
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Advanced Classifiers and Feature Reduction for Accurate Insomnia Detection Using Multimodal Dataset by Ameya Chatur, Mostafa Haghi, Nagarajan Ganapathy, Nima TaheriNejad, Ralf Seepold, Natividad Martinez Madrid

“…Our findings emphasize the importance of tailoring feature sets and employing appropriate reduction techniques for optimal predictive modeling in sleep-related studies. …”
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Literature Review of Prognostic Factors in Secondary Generalized Peritonitis by Valerii Luțenco, Adrian Beznea, Raul Mihailov, George Țocu, Verginia Luțenco, Oana Mariana Mihailov, Mihaela Patriciu, Grigore Pascaru, Liliana Baroiu

“…Identifying prognostic factors remains essential for optimizing outcomes in secondary peritonitis, and future research should prioritize the clinical validation and integration of AI-based models into perioperative management protocols.…”
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Insights into KMT2A rearrangements in acute myeloid leukemia: from molecular characteristics to targeted therapies by Sara Zehtabcheh, Hamed Soleimani Samarkhazan, Marjan Asadi, Mitra Zabihi, Sahar Parkhideh, Mohammad Hossein Mohammadi

“…Diagnosing KMT2A-r AML requires precision, with traditional methods like FISH and RT-PCR being complemented by advanced technologies such as next-generation sequencing (NGS) and machine learning (ML). ML models, leveraging transcriptomic data, can predict KMT2A-r and identify biomarkers like LAMP5 and SKIDA1, improving risk stratification. …”
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Towards representation learning of radar altimeter waveforms for sea ice surface classification by L. Happ, L. Happ, S. Patil, S. Hendricks, R. Fellegara, L. Kaleschke, A. Gerndt, A. Gerndt

“…Traditional waveform representations are limited to a small set of parameters, leading to information loss. Moreover, machine learning models for sea ice classification often depend on supervised training, which is vulnerable to uncertainties in labeled data, especially in polar regions. …”
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Hybrid Recurrent Neural Network and Decision Tree Scheduling for Energy-Efficient Resource Allocation in Cloud Computing by Sefati Seyed Salar, Vulpe Alexandru, Popovici Eduard, Fratu Octavian

“…This paper proposes a hybrid scheduling framework that integrates Recurrent Neural Networks (RNNs) for execution time prediction and Decision Trees (DTs) for VM classification, enhancing resource allocation efficiency. The RNN model uses historical execution data to accurately predict task execution time, while the DT model classifies VMs based on performance characteristics, ensuring optimal task-to- VM assignments. …”
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