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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Leveraging machine learning techniques to analyze nutritional content in processed foods by K. A. Muthukumar, Soumya Gupta, Doli Saikia

“…The findings reveal that the SVR model is particularly effective in predicting nutrient retention, outperforming the RF model. …”
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Local Diversity-Guided Weakly Supervised Fine-Grained Image Classification Method by Yuebo Meng, Xianglong Luo, Hua Zhan, Bo Wang, Shilong Su, Guanghui Liu

“…Extensive experiments conducted on four fine-grained datasets and explainable visualization demonstrate that the LDGNet can effectively enhance discriminative region localization and detailed feature acquisition for fine-grained objects, achieving competitive performance over other state-of-the-art algorithms.…”
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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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An Enhanced Seamless Localization Framework Using Spatial-temporal Uncertainty Predictor Under Obscured Indoor and Outdoor Scenes by Y. Yu, S. Bai, J. Wang, K. Tang, S. Bao, L. Chen, R. Chen

“…An iPDR-based trajectory estimation structure is proposed, using the integration of INS/PDR mechanizations, magnetic observations, and deep-learning based speed estimation to enhance the performance of traditional PDR algorithm. A period of human motion features extracted from hybrid location sources are modelled instead of only one or adjacent location points to realize time-varying measured uncertainty errors prediction, and the predicted uncertainty errors of different indoor and outdoor location sources are integrated with iPDR to realize robust seamless positioning performance. …”
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Klasifikasi Multilabel Pada Gaya Belajar Siswa Sekolah Dasar Menggunakan Algoritma Machine Learning by I Kadek Nicko Ananda, Ni Putu Novita Puspa Dewi, Ni Wayan Marti, Luh Joni Erawati Dewi

“…The machine learning algorithms used to build the model are Decision Tree, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). …”
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Climatic variables determining <i>Rhododendron</i> sister taxa distributions and distributional overlaps in the Himalayas by Madan Krishna Suwal, Ole Reidar Vetaas

“…We used Generalized Linear Modelling to select variables, and modelled the distribution of each species using Random Forest algorithms, predicting their potential distribution in current and future climates. …”
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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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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

“…Sea ice segmentation from synthetic aperture radar (SAR) imagery is a key task in polar sea ice monitoring, which is crucial for global climate prediction and polar route planning. 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. …”
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Scalable Semantic Adaptive Communication for Task Requirements in WSNs by Hong Yang, Xiaoqing Zhu, Jia Yang, Ji Li, Linbo Qing, Xiaohai He, Pingyu Wang

“…The experimental results show that the proposed SSAC is more robust than traditional and other semantic communication algorithms in image classification tasks, and achieves scalable compression rates without sacrificing classification performance, while improving the bandwidth utilization of the communication system.…”
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Machine Learning and Digital-Twins-Based Internet of Robotic Things for Remote Patient Monitoring by Sehat Ullah, Sangeen Khan, David Vanecek, Inam Ur Rehman

“…The system was also tested in the clinical setting to collect patient data and the best-performing algorithm (KNN) was used for status prediction, obtaining 98% accuracy.…”
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An Optimization Framework for Waste Treatment Center Site Selection Considering Nighttime Light Remote Sensing Data and Waste Production Fluctuations by Junbao Xia, Yanping Liu, Haozhong Yang, Guodong Zhu

“…By processing remote sensing data to mitigate noise and integrating it with conventional urban datasets, an innovative index system and predictive model were developed. Using Beijing as a case study, the gradient boosting regression algorithm yielded a prediction accuracy of 92%. …”
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A Review of Recent Advances in Roll Stability Control in On-Road and Off-Road Vehicles by Jie Chen, Ruochen Wang, Wei Liu, Dong Sun, Yu Jiang, Renkai Ding

“…Furthermore, the integration of machine learning and advanced predictive algorithms promises to enhance the intelligence and versatility of roll stability control systems.…”
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The Impact of Integrating Variable Renewable Energy Sources into Grid-Connected Power Systems: Challenges, Mitigation Strategies, and Prospects by Emmanuel Ejuh Che, Kang Roland Abeng, Chu Donatus Iweh, George J. Tsekouras, Armand Fopah-Lele

“…Many research efforts in using prediction models have developed real-time monitoring of variability and machine learning predictive algorithms in contrast to the conventional methods of studying variability. …”
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Establishment and validation of an immune-related nomogram for the prognosis of pancreatic adenocarcinoma by Kan Wang, Yunkun Lu, Yanfei Cao, Ping Feng, Qiu Wu, Peng Xiao, Yimin Ding

“…This study aims to improve prognosis prediction to guide therapeutic decision-making, and to identify novel targets for immunotherapy of PDAC. …”
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Deepmol: an automated machine and deep learning framework for computational chemistry by João Correia, João Capela, Miguel Rocha

“…Despite its potential to revolutionize the field, researchers are often encumbered by obstacles, such as the complexity of selecting optimal algorithms, the automation of data pre-processing steps, the necessity for adaptive feature engineering, and the assurance of model performance consistency across different datasets. …”
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Scalable Clustering of Complex ECG Health Data: Big Data Clustering Analysis with UMAP and HDBSCAN by Vladislav Kaverinskiy, Illya Chaikovsky, Anton Mnevets, Tatiana Ryzhenko, Mykhailo Bocharov, Kyrylo Malakhov

“…The study aims to apply unsupervised clustering algorithms to ECG data to detect latent risk profiles related to heart failure, based on distinctive ECG features. …”
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Leveraging machine learning to identify determinants of zero utilization of maternal continuum of care in Ethiopia: Insights from SHAP analysis and the 2019 mini DHS. by Shimels Derso Kebede, Agmasie Damtew Walle, Daniel Niguse Mamo, Ermias Bekele Enyew, Jibril Bashir Adem, Meron Asmamaw Alemayehu

“…Conversely, secondary or higher education, being married, higher wealth status, and having multiple children were associated with lower likelihoods of zero care utilization. …”
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Research and application of intelligent learning path optimization based on LSTM-Transformer model by Jinling Wang, Wandong Chai

“…In the global wave of digital learning, how to optimize personalized learning paths and improve learning efficiency has become a key issue to be solved urgently in the field of education. Based on this, this study proposes a hypothesis: the intelligent learning path optimization strategy based on the LSTM-Transformer model can achieve accurate prediction and personalized optimization of learners' learning paths with the help of deep learning technology. …”
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Integrating particle swarm optimization with backtracking search optimization feature extraction with two-dimensional convolutional neural network and attention-based stacked bidir... by Jyotirmayee Rautaray, Sangram Panigrahi, Ajit Kumar Nayak

“…In the realm of automatic text summarization, advanced methods such as evolutionary algorithms, deep learning, and clustering have demonstrated promising outcomes. …”
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