Impact of bridging the gap between Artificial Intelligence and nanomedicine in healthcare by Divyam Mishra, Bhavishya Chaturvedi, Vishal Soni, Dhairya Valecha, Megha Goel, Jamilur R. Ansari

“…We will also assess the long-term implications of lipid nanoparticles in drug delivery applications. Machine Learning algorithms are employed to create data-driven adaptive nanomaterials and paradigms, further advancing the field. …”
Get full text

Embedded Processor-in-the-Loop Implementation of ANFIS-Based Nonlinear MPPT Strategies for Photovoltaic Systems by Khalil Chnini, Mahamadou Abdou Tankari, Houda Jouini, Hatem Allagui, Mostafa Ahmed Ibrahim, Ezzeddine Touti

“…The methodology adheres to a Model-Based Design (MBD) framework, ensuring systematic development, implementation, and verification of the MPPT algorithms in an embedded environment. Experimental results demonstrate that the proposed controllers achieve high efficiency, rapid convergence, and robust maximum power point tracking under varying operating conditions. …”
Get full text

Hybrid Machine-Learning Model for Accurate Prediction of Filtration Volume in Water-Based Drilling Fluids by Shadfar Davoodi, Mohammed Al-Rubaii, David A. Wood, Mohammed Al-Shargabi, Mohammad Mehrad, Valeriy S. Rukavishnikov

“…Therefore, employing radial basis function neural network (RBFNN) and multilayer extreme learning machine (MELM) algorithms integrated with the growth optimizer (GO), predictive hybrid ML (HML) models are developed to reliably predict the FV using only two easy-to-measure input variables: drilling fluid density (FD) and Marsh funnel viscosity (MFV). …”
Get full text

Computer Viewing Model for Classification of Erythrocytes Infected with <i>Plasmodium</i> spp. Applied to Malaria Diagnosis Using Optical Microscope by Eduardo Rojas, Irene Cartas-Espinel, Priscila Álvarez, Matías Moris, Manuel Salazar, Rodrigo Boguen, Pablo Letelier, Lucia San Martín, Valeria San Martín, Camilo Morales, Neftalí Guzmán

“…Six models (five machine learning algorithms and one pre-trained for a convolutional neural network) were assessed, and the performance of each was measured using metrics like accuracy (A), precision (P), recall, F1 score, and area under the curve (AUC). …”
Get full text

On the Synergy of IoMT Devices and Ceiling-Mounted Systems for Advanced Medical Data Analytics by Andreas Andreou, Constandinos X. Mavromoustakis, Evangelos K. Markakis, Athina Bourdena, George Mastorakis

“…Specifically, Twin-Delayed Deep Deterministic Policy Gradients (TD3) and Soft Actor-Critic (SAC) algorithms are adopted to optimize task scheduling and system decisions in dynamic, resource-constrained settings. …”
Get full text

Climate-resilient water management: Leveraging IoT and AI for sustainable agriculture by Syed Muhammad Zaigham Abbas Naqvi, Saddam Hussain, Muhammad Awais, Muhammad Naveed Tahir, Shoaib Rashid Saleem, Fuad A.M. Al-Yarimi, Mirjalol Ashurov, Oumaima Saidani, M.Ijaz Khan, Junfeng Wu, Zhang Wei, Jiandong Hu

“…They collect the data from field and analyze using artificial intelligence based algorithmic models. The irrigation management strategies using the artificial intelligence (AI) to mitigate the climate change impacts by reducing the wastage of essential resources in the environment has not been adopted by many developed countries. …”
Get full text

Spatiotemporal estimation of ambient forest phytoncides: Unveiling patterns through geospatial-based machine learning approach by Aji Kusumaning Asri, Hao-Ting Chang, Chia-Pin Yu, Wan-Yu Liu, Yinq-Rong Chern, Rui-Hao Xie, Shih-Chun Candice Lung, Kai Hsien Chi, Yu-Cheng Chen, Sen-Sung Cheng, Gary Adamkiewicz, John D. Spengler, Chih-Da Wu

“…The results showed that RF and XGB were the most effective algorithms, explaining approximately 83.3% and 98.4% of the spatiotemporal variability in camphene and α-pinene, respectively. …”
Get full text

Impact of tertiary lymphoid structure-associated biomarkers on pancreatic cancer via a dual-disease analysis of psoriasis and pancreatic cancer by Hao Yu, Quan Yuan, Liu-Xing Feng, Jia-Huan Xu, Shen-Yuan Zhan, Zhi-min Li, Shuang-Jia Wang

“…Gene expression profiles from public databases were analyzed, and TLS-associated genes were subjected to Cox regression and Kaplan-Meier analyses. Machine learning algorithms identified GBP2 as a risk factor and ZNF814 as a protective factor. …”
Get full text

Evaluating the Effect of Thermal Treatment on Phenolic Compounds in Functional Flours Using Vis–NIR–SWIR Spectroscopy: A Machine Learning Approach by Achilleas Panagiotis Zalidis, Nikolaos Tsakiridis, George Zalidis, Ioannis Mourtzinos, Konstantinos Gkatzionis

“…This study explores the thermal stability of phenolic compounds in various functional flours using visible, near and shortwave-infrared (Vis–NIR–SWIR) spectroscopy (350–2500 nm), integrated with machine learning (ML) algorithms. Random Forest models were employed to classify samples based on flour type, baking temperature, and phenolic concentration. …”
Get full text

A Multi‐Task Self‐Supervised Strategy for Predicting Molecular Properties and FGFR1 Inhibitors by Xin Yang, Yang Wang, Ye Lin, Mingxuan Zhang, Ou Liu, Jianwei Shuai, Qi Zhao

“…Overall, MTSSMol provides an effective algorithmic framework for enhancing molecular representation learning and identifying potential drug candidates, offering a valuable tool to accelerate drug discovery processes. …”
Get full text

A novel approach for the effective prediction of cardiovascular disease using applied artificial intelligence techniques by Azka Mir, Attique Ur Rehman, Tahir Muhammad Ali, Sabeen Javaid, Maram Fahaad Almufareh, Mamoona Humayun, Momina Shaheen

“…Methods In this paper, we have utilized machine learning algorithms to predict cardiovascular disease on the basis of symptoms such as chest pain, age and blood pressure. …”
Get full text

Prediction of Voice Therapy Outcomes Using Machine Learning Approaches and SHAP Analysis: A K-VRQOL-Based Analysis by Ji Hye Park, Ah Ra Jung, Ji-Na Lee, Ji-Yeoun Lee

“…Multiple regression analysis and four machine learning algorithms—random forest (RF), gradient boosting (GB), light gradient boosting machine (LightGBM), and extreme gradient boosting (XGBoost)—are applied to predict changes in K-VRQOL scores across the total, physical, and emotional domains. …”
Get full text

MFGC-Net: Bridging and Fusing Multiscale Features and Global Contexts for Multitask Sea Ice Fine Segmentation by Tianen Ma, Xinwei Chen, Linlin Xu, Pengfei Ma, Peilin Yu

“…However, the existing sea ice segmentation algorithms for SAR images often fail to consider long-range contextual dependencies when capturing multiscale features, resulting in an inability to fully exploit multiscale global contextual information. …”
Get full text

Wearable Solutions Using Physiological Signals for Stress Monitoring on Individuals with Autism Spectrum Disorder (ASD): A Systematic Literature Review by Sandra Cano, Claudio Cubillos, Rodrigo Alfaro, Andrés Romo, Matías García, Fernando Moreira

“…However, limitations include small sample sizes, variability in study conditions, and the need for customization in stress detection algorithms. In addition, there is a need to customize the stress threshold due to the device’s high individual variability and sensitivity. …”
Get full text

A novel ensemble model for multi-temporal forest vegetation classification: integrating spectral-temporal features and topographic constraints by Rongfei Duan, Chunlin Huang, Peng Dou, Jinliang Hou, Ying Zhang

“…While remote sensing combined with modeling algorithms enables efficient forest monitoring, existing approaches frequently treat multi-temporal imagery as one-dimensional sequences, capturing only temporal trends within spectral bands and neglecting the joint spectral-temporal dynamics essential for fine-scale forest classification. …”
Get full text

An Approach for Multi-Source Land Use and Land Cover Data Fusion Considering Spatial Correlations by Jing Yang, Yiheng Jiang, Qirui Song, Zheng Wang, Yang Hu, Kaiqiang Li, Yizhong Sun

“…Although existing research has explored land use type recognition from remote sensing imagery, interpretation algorithms, and other perspectives, significant spatial discrepancies exist between these data products. …”
Get full text

Monitoring and modeling hydrologic conditions in Ukraine for hydropower generation by Matthew J. McCarthy, Jesus D. Gomez-Velez, David Hughes, Shannon Meade

“…New hydrological insights for the region: We ran new algorithms on 144 WorldView-2 and WorldView-3 satellite images to map rivers and extract width, one of which was validated against river gauge data located along the same river but in a neighboring country. …”
Get full text

Differences in parameter estimates derived from various methods for the ORYZA (v3) Model by Jun-wei TAN, Qing-yun DUAN, Wei GONG, Zhen-hua DI

“…The parameter estimates given by the frequentist methods were obviously sensitive to initial values, and the extent of the sensitivity varied with algorithms and objective functions. Among the frequentist methods, the SCE-UA was recommended due to the balance between stable convergence and high efficiency. …”
Get full text

Differentiable Deep Learning Surrogate Models Applied to the Optimization of the IFMIF-DONES Facility by Galo Gallardo Romero, Guillermo Rodríguez-Llorente, Lucas Magariños Rodríguez, Rodrigo Morant Navascués, Nikita Khvatkin Petrovsky, Rubén Lorenzo Ortega, Roberto Gómez-Espinosa Martín

“…The substantial speed-up factors enable the application of online reinforcement learning algorithms, and the differentiable nature of the models allows for seamless integration with differentiable programming techniques, facilitating the solving of inverse problems to find the optimal parameters for a given objective. …”
Get full text

Enhanced supervised classification of seasonal pastures on the Qinghai-Tibet Plateau (1990–2020) using Landsat optimal time window by Panfei Fang, Jia Wang, Haifeng Xu, Ruonan Li, Long Yu, Shaodong Huang, Yuying Liang, Pengfei Zheng

“…The framework leverages Landsat imagery within the optimal time window (Days of Year, DOYs 190–280), incorporates algorithms for automated sample generation and refinement, and employs the Random Forest (RF) classifier to enable fully automated seasonal pasture mapping. …”
Get full text