Enhancing Regional Topsoil Total Nitrogen Mapping Through Differentiated Fusion of Ground Hyperspectral Data and Satellite Images Under Low Vegetation Cover by Rongpeng He, Jihua Meng, Yanfei Du, Zhenxin Lin, Xinyan You, Xinyu Gao

“…Therefore, a differentiated fusion of enhanced multispectral image bands (DFE_MSIBs) method combined with Random Forest (RF) algorithms was developed for spectral inversion of STN content. …”
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AI-driven pharmacovigilance: Enhancing adverse drug reaction detection with deep learning and NLP by Dr. Bharti Khemani, Dr. Sachin Malave, Samyukta Shinde, Mandvi Shukla, Razzaq Shikalgar, Harshita Talwar

“…This research underscores the potential of predictive modeling to enhance pharmacovigilance efforts and ensure safer clinical trial outcomes. • The research methodology includes a comparison of supervised learning algorithms, such as Logistic Regression, Random Forest, Gradient Boost, CNN, and genetic algorithms, to identify patterns and anomalies in clinical trial data. …”
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Enhancing Wind Turbine Power Output Estimation Using Causal Inference and Adaptive Neuro-Fuzzy Inference System ANFIS by Ahmed A. Mostfa, Nawfal A. Zakar, Rasha Raad Al-Mola, Abdel-Nasser Sharkawy

“…To meet the demand for renewable energy at the lowest cost, wind energy became the target of machine learning algorithms and was employed to predict the output power of wind turbines. …”
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A Study on the Management and Evolution of Land Use and Land Cover in Romania During the Period 1990–2022 in the Context of Political and Environmental Changes by Jenica Călina, Aurel Călina, Gheorghe Marian Vangu, Alin Constantin Croitoru, Marius Miluț, Nicolae Ion Băbucă, Ion Stan

“…Land use and land cover are the main anthropogenic factors that lead to the rapid and aggressive degradation of land and interfere with the functioning of ecosystems, especially through the expansion of urbanization and the reduction in forested areas. The purpose of this article is to identify sources of official data and to build an updated dataset upon which analysis algorithms can be applied. …”
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Optimization and benefit evaluation model of a cloud computing-based platform for power enterprises by Xueli Yin, Xuguang Zhang, Luyao Pei, Rong Hu, Kaihui Ye, Kangkang Cai

“…In addition, through containerized deployment and intelligent orchestration, it achieves a 43% reduction in monthly operating costs. A multi-level benefit evaluation system—spanning power generation, grid operations, and end-user services—is established, integrating historical data, expert weighting, and dynamic optimization algorithms to enable quantitative performance assessment and decision support. …”
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Novel Spatio-Temporal Joint Learning-Based Intelligent Hollowing Detection in Dams for Low-Data Infrared Images by Lili Zhang, Zihan Jin, Yibo Wang, Ziyi Wang, Zeyu Duan, Taoran Qi, Rui Shi

“…Furthermore, it attained a sub-10% cross-sectional calculation error for hollowing dimensions, outperforming maximum entropy (70.5% error reduction) and OTSU (7.4% error reduction) methods, which shows our method being one novel method for automated intelligent hollowing detection.…”
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The role of artificial intelligence in breast cancer screening as a supportive tool for radiologists by Agata Król, Katarzyna Kwaterska, Karol Kutyłowski, Paweł Łuckiewicz

“…Early diagnosis is crucial for cancer-related burden and mortality reduction. For this reason several countries have implemented breast cancer screening programme. …”
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On the Potential of Bayesian Neural Networks for Estimating Chlorophyll-a Concentration from Satellite Data by Mohamad Abed El Rahman Hammoud, Nikolaos Papagiannopoulos, George Krokos, Robert J. W. Brewin, Dionysios E. Raitsos, Omar Knio, Ibrahim Hoteit

“…Our results suggest that BNNs perform at least as well as established methods, and they could achieve 20–40% lower mean squared errors when additional input variables are included, such as the sea surface temperature and its climatological mean alongside the coordinates of the prediction. The BNNs offer means for uncertainty quantification by estimating the probability distribution of [CHL-a], building confidence in the [CHL-a] predictions through the variance of the predictions. …”
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Harnessing AI forward and backward chaining with telemetry data for enhanced diagnostics and prognostics of smart devices by Muhammad Shoaib Farooq, Rizwan Pervez Mir, Atif Alvi, Kilian Tutusaus, Eduardo Garcia Villena, Fadwa Alrowais, Hanen Karamti, Imran Ashraf

“…The capacity to precisely diagnose and preemptively predict potential failures holds the potential to considerably amplify maintenance efficiency, diminish downtime, and optimize resource allocation. …”
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Wearable Artificial Intelligence for Sleep Disorders: Scoping Review by Sarah Aziz, Amal A M Ali, Hania Aslam, Alaa A Abd-alrazaq, Rawan AlSaad, Mohannad Alajlani, Reham Ahmad, Laila Khalil, Arfan Ahmed, Javaid Sheikh

“…The primary selection criterion was the inclusion of studies that utilized AI algorithms to detect or predict various sleep disorders using data from wearable devices. …”
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Micro hole drilling and multi criteria optimization of soda lime glass via ultrasonic assisted rotary electrochemical discharge drilling by Sahil Grover, Viveksheel Rajput, Sanjay Kumar Mangal, Sarbjit Singh, Sandeep Singh, Shubham Sharma, Ehab El Sayed Massoud, Dražan Kozak, Jasmina Lozanovic

“…Machine learning-based algorithms are also used to predict the responses using Random Forest and Gradient Boost approaches. …”
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Enhancing PV feed-in power forecasting through federated learning with differential privacy using LSTM and GRU by Pascal Riedel, Kaouther Belkilani, Manfred Reichert, Gerd Heilscher, Reinhold von Schwerin

“…By leveraging advanced FL algorithms such as FedYogi and FedAdam, we propose a method that not only predicts sequential energy data with high accuracy, achieving an R2 of 97.68%, but also adheres to stringent privacy standards, offering a scalable solution for the challenges of smart grids analytics, thus clearly showing that the proposed approach is promising and worth being pursued further.…”
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Automated Detection of Reduced Ejection Fraction Using an ECG-Enabled Digital Stethoscope by Ling Guo, PhD, Gregg S. Pressman, MD, Spencer N. Kieu, BS, Scott B. Marrus, MD, PhD, George Mathew, PhD, John Prince, PhD, Emileigh Lastowski, MS, Rosalie V. McDonough, MD, MSc, Caroline Currie, BA, John N. Maidens, PhD, Hussein Al-Sudani, MD, Evan Friend, BA, Deepak Padmanabhan, MD, Preetham Kumar, MD, Edward Kersh, MD, Subramaniam Venkatraman, PhD, Salima Qamruddin, MD

“…Results: The CNN model demonstrated an area under the receiver operating characteristic curve of 0.85, with a sensitivity of 77.5%, specificity of 78.3%, positive predictive value of 20.3%, and negative predictive value of 98.0%. …”
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The Emergence of AI-Driven Virtual Hospitals: Redefining Patient Care Beyond Physical Boundaries by Ifrah Hameed, Hafiz Muhammad Haseeb Khaliq

“…It also reduces the rate of readmissions as well. Predictive analysis has been proven to lower hospital readmissions by 32% which is beneficial for cost savings for healthcare systems 4. …”
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MHC2-SCALE enhances identification of immunogenic neoantigens by Joshua G. Gober, Aude-Hélène Capietto, Reyhane Hoshyar, Martine Darwish, Richard Vandlen, Jonathan L. Linehan, Lélia Delamarre, Adel M. ElSohly

“…We validated MHC-II peptide candidates predicted by the immune epitope database (IEDB) algorithm, as well as uncovered many true and immunogenic MHC-II binders that were not predicted by IEDB. …”
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Differentiating Pulmonary Nodule Malignancy Using Exhaled Volatile Organic Compounds: A Prospective Observational Study by Guangyu Lu, Zhixia Su, Xiaoping Yu, Yuhang He, Taining Sha, Kai Yan, Hong Guo, Yujian Tao, Liting Liao, Yanyan Zhang, Guotao Lu, Weijuan Gong

“…We applied five machine learning (ML) algorithms to develop predictive models which were evaluated using area under the curve (AUC), sensitivity, specificity, and other relevant metrics. …”
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Renal parenchymal volume analysis: Clinical and research applications by Carlos Munoz‐Lopez, Kieran Lewis, Nityam Rathi, Eran Maina, Akira Kazama, Anne Wong, Angelica Bartholomew, Worapat Attawettayanon, Yunlin Ye, Zhiling Zhang, Wen Dong, Rebecca A. Campbell, Nicholas Heller, Erick Remer, Christopher Weight, Steven C. Campbell

“…This simple principle forms the basis for parenchymal volume analysis (PVA) with semiautomated software, which can be leveraged to predict SRF and new‐baseline glomerular filtration rate (NBGFR) following nephrectomy. …”
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Neutrosophic OWA-TOPSIS Model for Decision-Making in AI Systems with Large Volumes of Data by Juan Roberto Pereira Salcedo, Karla Melissa Ruiz Quezada, Edison Luis Cruz Navarrete, Pedro Manuel García Arias

“…The theoretical contribution to the academic literature expands notions of AI multi-criteria decision making process; the practical application lends itself to scalable possibilities within big data reliant cases, especially predictive sentiment analysis or resource allocation/optimization. …”
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Interpretable machine learning modeling of temperature rise in a medium voltage switchgear using multiphysics CFD analysis by Mahmood Matin, Amir Dehghanian, Amir Hossein Zeinaddini, Hossein Darijani

“…SHAP analysis identified the most significant variables affecting temperature prediction as current, air velocity, duct area, and switchgear conditions, in that order.…”
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Combining first principles and machine learning for rapid assessment response of WO3 based gas sensors by Ran Zhang, Guo Chen, Shasha Gao, Lu Chen, Yongchao Cheng, Xiuquan Gu, Yue Wang

“…The collected data was subsequently utilized to develop a correlation model linking the multi-physical parameters to gas sensitive performance using intelligent algorithms. The model’s performance was assessed through receiver operating characteristic (ROC) curves, confusion matrices, and other evaluation metrics, ultimately achieving a prediction accuracy of 90% for identifying key features influencing gas adsorption performance. …”
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