Role,application and challenges of IoT in smart EV charging management:a review by Tripti Kunj, Kirti Pal

“…Additionally, the paper emphasizes the importance of adaptive algorithms and machine learning models for predictive maintenance and efficient resource allocation. …”
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Inverse link prediction with graph convolutional networks for knowledge-preserving sparsification in cheminformatics by Elnaz Bangian Tabrizi, Mehrdad Jalali, Mahboobeh Houshmand

“…Validated on MOF similarity graphs, the sparsified graphs maintain structural integrity and support robust performance across both graph-based (GCN, GraphRAGE) and non-graph-based (Gradient Boosting Trees, Logistic Regression, Naïve Bayes, Deep Neural Networks) machine learning models for tasks such as pore limiting diameter prediction. …”
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Q-Gen: A Parameterized Quantum Circuit Generator by Yikai Mao, Shaswot Shresthamali, Masaaki Kondo

“…This dataset enables researchers to statistically analyze the structure, complexity, and performance of large-scale quantum circuits or quickly train novel machine learning models without worrying about the exponentially growing simulation time. …”
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Innovative Strategies and Methodologies in Antimicrobial Peptide Design by Devesh Pratap Verma, Amit Kumar Tripathi, Ashwani Kumar Thakur

“…The incorporation of artificial intelligence (AI) and machine learning (ML) tools into AMP research has further accelerated the identification, optimization, and application of these peptides. …”
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A networked station system for high-resolution wind nowcasting in air traffic operations: A data-augmented deep learning approach. by Décio Alves, Fábio Mendonça, Sheikh Shanawaz Mostafa, Diogo Freitas, João Pestana, Dinarte Vieira, Marko Radeta, Fernando Morgado-Dias

“…This study introduces a high-resolution wind nowcasting model designed for aviation applications at Madeira International Airport, a location known for its complex wind patterns. …”
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A general approach for predicting the behavior of the Supreme Court of the United States. by Daniel Martin Katz, Michael J Bommarito, Josh Blackman

“…Building on developments in machine learning and prior work in the science of judicial prediction, we construct a model designed to predict the behavior of the Supreme Court of the United States in a generalized, out-of-sample context. …”
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Synergistic Integration of Edge Computing and 6G Networks for Real-Time IoT Applications by Ahmed M. Alwakeel

“…Our comprehensive methodology involves collecting and preprocessing data from IoT devices, extracting relevant features, predicting resource demand, optimizing task offloading, and continuously monitoring and adjusting resource allocation using advanced machine learning techniques. …”
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Free-form型机床切齿优化（二）——混合进化遗传算法 by 张艳红, 郭九生, 王小椿

“…In view of the situation that the model of optimal synthesis for Free-form style machine tool setting parameters is a multidimensional, nonlinear and high order one, a hybrid evolutionary genetic algorithms is proposed in this paper. …”
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Enhancing uncertainty quantification in drug discovery with censored regression labels by Emma Svensson, Hannah Rosa Friesacher, Susanne Winiwarter, Lewis Mervin, Adam Arany, Ola Engkvist

“…Therefore, it is becoming essential to accurately quantify the uncertainty in machine learning predictions, such that resources can be used optimally and trust in the models improves. …”
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PV Generation Prediction Using Multilayer Perceptron and Data Clustering for Energy Management Support by Fachrizal Aksan, Vishnu Suresh, Przemysław Janik

“…The elbow method identified four optimal clusters, and individual MLP models were trained on each, reducing data complexity and improving model focus. …”
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Real-time bearing fault classification of induction motor using enhanced inception ResNet-V2 by Karan Kumar K, Srihari Mandava

“…Furthermore, the model underwent training using k-fold method to assess its resilience with the use of optimal values obtained from hyperparameter tuning. …”
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An Innovative Study for Tool Wear Prediction Based on Stacked Sparse Autoencoder and Ensemble Learning Strategy by Zhaopeng He, Tielin Shi, Xu Chen

“…Compared to the single SSAE model and shallow machine learning models, the proposed method significantly improved both the prediction accuracy and reliability.…”
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Understanding mushroom farm environment using TinyML-based monitoring devices by Segun Adebayo, Halleluyah Oluwatobi Aworinde, Oluranti Olayinka Olufemi, Christian Okechukwu Osueke, Abidemi Emmanuel Adeniyi, Oluwasegun Julius Aroba

“…The optimization of environmental conditions in mushroom cultivation is pivotal for maximizing yield and quality. …”
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Artificial intelligence tools for engagement prediction in neuromotor disorder patients during rehabilitation by Simone Costantini, Anna Falivene, Mattia Chiappini, Giorgia Malerba, Carla Dei, Silvia Bellazzecca, Fabio A. Storm, Giuseppe Andreoni, Emilia Ambrosini, Emilia Biffi

“…Nested k-fold cross-validation was used to deal with model selection and optimization. Finally, the effects on classifiers performance of three dataset preparation techniques, such as unimodal or bimodal approach, feature reduction, and data augmentation, were also tested. …”
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Process Development for the Continuous Manufacturing of Carbamazepine-Nicotinamide Co-Crystals Utilizing Hot-Melt Extrusion Technology by Lianghao Huang, Wen Ni, Yaru Jia, Minqing Zhu, Tiantian Yang, Mingchao Yu, Jiaxiang Zhang

“…<b>Methods:</b> Using carbamazepine (CBZ) and nicotinamide (NIC) as model compounds, an HME platform compatible with PAT tools was established. …”
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A deep neural network framework for estimating coastal salinity from SMAP brightness temperature data by Yidi Wei, Qing Xu, Qing Xu, Xiaobin Yin, Xiaobin Yin, Yan Li, Yan Li, Kaiguo Fan

“…The framework leverages machine learning interpretability tools (Shapley Additive Explanations, SHAP) to optimize input feature selection and employs a grid search strategy for hyperparameter tuning.Results and discussionSystematic validation against independent in-situ measurements demonstrates that the baseline DNN model constructed for the entire region and time period outperforms conventional algorithms including K-Nearest Neighbors, Random Forest, and XGBoost and the standard SMAP SSS product, achieving a reduction of 36.0%, 33.4%, 40.1%, and 23.2%, respectively in root mean square error (RMSE). …”
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Identifying Patient-Reported Outcome Measure Documentation in Veterans Health Administration Chiropractic Clinic Notes: Natural Language Processing Analysis by Brian C Coleman, Kelsey L Corcoran, Cynthia A Brandt, Joseph L Goulet, Stephen L Luther, Anthony J Lisi

“…Cross-validation performance of the statistical and machine learning models for the note categorization task was very good overall, but lower than rule-based model performance. …”
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Risk Prediction Score for Thermal Mapping of Pharmaceutical Transport Routes in Brazil by Clayton Gerber Mangini, Nilsa Duarte da Silva Lima, Irenilza de Alencar Nääs

“…The machine learning model is the basis for an automated risk prediction for routes of pharmaceutical transportation; <i>Conclusions</i>: the developed MP model might automatically predict risk during the distribution of pharmaceutical products, which might lead to optimizing time and costs.…”
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Pneumothorax detection and segmentation from chest X-ray radiographs using a patch-based fully convolutional encoder-decoder network by Jakov Ivan S. Dumbrique, Jakov Ivan S. Dumbrique, Reynan B. Hernandez, Reynan B. Hernandez, Juan Miguel L. Cruz, Ryan M. Pagdanganan, Prospero C. Naval

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A stacked generalization approach for day ahead hourly photovoltaic power forecasting by Fatema Islam Tania, Pinki Rani, Tofael Ahmed, Shameem Ahmad

“…It proposes a stacked generalization approach with optimized ANN and XGBoost models as base learners. …”
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