DPD-v2: Generalised deep particle diffusometry for varied particle shapes and experimental conditions by Pranshul Sardana, Steven T. Wereley

“…In microfluidic set-ups, the observed particles are often defocused and their motion is affected by factors such as fluid flow, which leads to high errors for conventional and deep learning-based PD (DPD) algorithms. This work improves the performance of DPD models by updating their architecture, avoiding temporal averaging in the input, and exploring the impact of various choices during training. …”
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A Comprehensive Approach to Rustc Optimization Vulnerability Detection in Industrial Control Systems by Kaifeng Xie, Jinjing Wan, Lifeng Chen, Yi Wang

“…Compiler optimization is a critical component for improving program performance. However, the Rustc optimization process may introduce vulnerabilities due to algorithmic flaws or issues arising from component interactions. …”
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Machine learning in dentistry and oral surgery: charting the course with bibliometric insights by Shuangwei Liu, Yuquan Hao, Shijie Zhu, Liyao Wan, Zhe Yi, Zhichang Zhang

“…Moreover, challenges, such as data availability and security, algorithmic biases, and “black-box models”, must be addressed. …”
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Task-Driven Real-World Super-Resolution of Document Scans by Maciej Zyrek, Tomasz Tarasiewicz, Jakub Sadel, Aleksandra Krzywon, Michal Kawulok

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Optimized customer churn prediction using tabular generative adversarial network (GAN)-based hybrid sampling method and cost-sensitive learning by I Nyoman Mahayasa Adiputra, Paweena Wanchai, Pei-Chun Lin

“…Additionally, this study provided a robustness measurement for algorithms, demonstrating that CostLearnGAN outperforms other sampling methods in improving the performance of classical machine learning models with a 5.68 robustness value on average.…”
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Applications of Machine Learning for Wine Recognition Based on ¹H-NMR Spectroscopy by Ariana Raluca Hategan, Adrian Pirnau, Dana Alina Magdas

“…The present study aims to explore the possibility of applying machine learning (ML) algorithms for the development of reliable wine authentication instruments able to encompass the exhaustive performances offered by learning-based methods, and, at the same time, to propose reference practices for improving the recognition ability of the developed models. …”
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ARCHITECTURE AND FUNCTIONALITY OF INTEGRATED INFORMATION SYSTEM FOR ANALYSIS OF POTENTIAL OF RENEWABLE ENERGY SOURCES by B. A. Tonkonogov, S. P. Kundas, A. E. Moroz

“…The required functionality of system has led to the solution of a number of problems in the development of appropriate software modules that implement methods, models and algorithms for assessing the energy potential and economic efficiency of the use of renewable energy sources (RES). …”
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A Critical Review of the Prospect of Integrating Artificial Intelligence in Infectious Disease Diagnosis and Prognosis by Shuaibu Abdullahi Hudu, Ahmed Subeh Alshrari, Esra’a Jebreel Ibrahim Abu-Shoura, Amira Osman, Abdulgafar Olayiwola Jimoh

“…This paper reviews how AI-driven models improve diagnostic accuracy, predict patient outcomes, and contribute to effective disease management. …”
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An open paradigm dataset for intelligent monitoring of underground drilling operations in coal mines by Pengzhen Zhao, Xichao Wang, Shuainan Yu, Xiangqing Dong, Baojiang Li, Haiyan Wang, Guochu Chen

“…The results indicate the proposed dataset highlights areas for improvement in algorithmic models and fills the data gap in the coal mining, providing valuable resources for developing coal mine safety monitoring.…”
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Enhancing e-learning through AI: advanced techniques for optimizing student performance by Rund Mahafdah, Seifeddine Bouallegue, Ridha Bouallegue

“…AI algorithms, known for their cognitive ability and capacity to learn, adapt, and make decisions, are employed to analyze and forecast student performance, thereby improving educational quality and outcomes. …”
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Integrating artificial intelligence into thermodynamics: A new paradigm for sustainable future by Marwan Al-Raeei

“…By integrating machine learning algorithms and statistical techniques into predictive modeling, we demonstrate that it is possible to develop highly accurate models that forecast performance based on historical data. …”
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Enhancing Visitor Forecasting with Target-Concatenated Autoencoder and Ensemble Learning by Ray-I Chang, Chih-Yung Tsai, Yu-Wei Chang

“…Regression models, on the other hand, face challenges when dealing with high-dimensional data. …”
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DNA familial binding profiles made easy: comparison of various motif alignment and clustering strategies. by Shaun Mahony, Philip E Auron, Panayiotis V Benos

“…In addition, multiple-alignment strategies for binding profiles and tree-building methods are tested for their efficiency in constructing generalized binding models. A new method for automatic determination of the optimal number of clusters is developed and applied in the construction of a new set of familial binding profiles which improves upon TF classification accuracy. …”
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The potential of machine learning in diagnosing neurological and psychiatric diseases: a review by Claudia Ricetti, Luca Carrara, Davide La Torre

“…Findings The examined technologies have shown considerable success in early diagnosis, as well as in the identification of risk factors and symptom management for diseases such as Alzheimer’s, Parkinson’s and psychiatric disorders. These models could help improve diagnostic accuracy and enable a more personalized therapeutic approach by utilizing large datasets with information such as biomarkers and medical images. …”
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Exploring the Applications of Explainability in Wearable Data Analytics: Systematic Literature Review by Yasmin Abdelaal, Michaël Aupetit, Abdelkader Baggag, Dena Al-Thani

“…Post hoc methods such as Shapley Additive Explanations have gained traction for their adaptability in explaining complex algorithms visually. However, user evaluation remains an area in which improvement is needed, and involving users in the development process can contribute to more transparent and reliable artificial intelligence models in health care applications. …”
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AI-powered Somatic Cancer Cell Analysis for Early Detection of Metastasis: The 62 principal Cancer Types by Sandile Buthelezi, Solly Matshonisa Seeletse, Taurai Hungwe, Vimbai Mbirimi-Hungwe

“…Background: Early detection of metastasis is critical in improving survival outcomes in cancer patients, with artificial intelligence offering advanced tools for predictive analytics. …”
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Integrated Platform for Bio-Image Processing and Identification of Cervical Cancer by Larisa-Dalina LAZĂR, Diogen BABUC, Todor IVAŞCU

“…Following this, the cervical cells are segmented and classified using a combination of machine learning algorithms, such as shallow machine learning models and Convolutional Neural Networks (CNN). …”
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From macro to micro: Enhancing real GDP predictions through business tendency and bank loans surveys by Oguzhan Cepni, Furkan Emirmahmutoglu

“…Moreover, refining the prediction models by strategically reducing the number of factors using variable selection algorithms and choosing the most significant ones further enhanced their forecast accuracy. …”
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The Role of Time in Facial Dynamics and Challenges in Automatic Emotion Recognition (2019–2024) by Williams Contreras-Higuera, Lucrezia Crescenzi-Lanna

“…It also addresses the limitations of current models, including cultural biases and the need for large, diverse datasets. …”
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Investigation of the impact of service parameters on the degree of passenger satisfaction based on the application of the apparatus of neural networks by S. S. Pastukhov, K. V. Stel’Mashenko

“…Based on the analysis of the advantages and disadvan-luation models has been identified and justified. These models could tages of various methodological approaches to the search for ways be used to guide management to implement several areas of service to increase the quality of service existing in the marketing practice of improvement at the same time, and also to make efficient decisions in the passenger complex, the need for further development of such the context of possible synergies between the multiple aspects of the robust nonparametric mechanisms for constructing predictive va-service being investigated in their impact on customer satisfaction. …”
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