Photovoltaic module dataset for automated fault detection and analysis in large photovoltaic systems using photovoltaic module fault detectionMendeley Data by Rotimi-Williams Bello, Pius A. Owolawi, Etienne A. van Wyk, Chunling Du

“…Because the data comes from a single-day collection and one week laboratory experiment, it makes the data more suitable for testing algorithms designed for fault detection. The dataset is publicly and freely available to the scientific community at 10.17632/5ssmfpgrpc.1…”
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Automatic Differentiation‐Based Full Waveform Inversion With Flexible Workflows by Feng Liu, Haipeng Li, Guangyuan Zou, Junlun Li

“…The AD‐based framework not only includes forword modeling and associated gradient computations for wave equations in various types of media from isotropic acoustic to vertically or horizontally transverse isotropic elastic, but also incorporates a suite of objective functions, regularization techniques, and optimization algorithms. By leveraging state‐of‐the‐art AD, objective functions such as soft dynamic time warping and Wasserstein distance, which are difficult to apply in traditional FWI are also easily integrated into ADFWI. …”
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Clinical characteristics of adrenal crisis in 371 adult patients with glucocorticoid-induced adrenal insufficiency by Ying Qiu, Ying Qiu, Ying Luo, Xinqian Geng, Yujian Li, Yujian Li, Yunhua Feng, Ying Yang

“…Among the prediction models constructed by machine learning algorithms, logistic regression model had the best prediction effect.ConclusionThis study investigated the clinical characteristics of AC in GIAI patients. …”
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Intra-Technology Enhancements for Multi-Service Multi-Priority Short-Range V2X Communication by Ihtisham Khalid, Vasilis Maglogiannis, Dries Naudts, Adnan Shahid, Ingrid Moerman

“…To bridge this gap, we propose intelligent Multi-Attribute Decision-Making algorithms for adaptive AC selection in ITS-G5 and RRI adjustment in C-V2X PC5, tailored to the varying priorities of active V2X services. …”
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Understanding and detection of process instabilities in wire arc directed energy deposition additive manufacturing using meltpool imaging and machine learning by André Ramalho, Anis Assad, Benjamin Bevans, Fernando Deschamps, Telmo G. Santos, J.P. Oliveira, Prahalada Rao

“…The objectives were two-fold: (1) observe and understand, through in-operando high-speed meltpool imaging, the causal dynamics of two common WA-DED process instabilities, namely, humping and humping-induced porosity; and (2) leverage the high-speed meltpool imaging data within machine learning algorithms for real-time detection of process instabilities. …”
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To be or not to be, when synthetic data meet clinical pharmacology: A focused study on pharmacogenetics by Jean‐Baptiste Woillard, Clément Benoist, Alexandre Destere, Marc Labriffe, Giulia Marchello, Julie Josse, Pierre Marquet

“…We also investigated the effect of applying the algorithms multiple times to improve results stability in terms of HR estimation. …”
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Detection of Psychomotor Retardation in Youth Depression: A Machine Learning Approach to Kinematic Analysis of Handwriting by Vladimir Džepina, Nikola Ivančević, Sunčica Rosić, Blažo Nikolić, Dejan Stevanović, Jasna Jančić, Milica M. Janković

“…After recursive feature elimination, classification was achieved through machine learning algorithms: logistic regression, support vector machine, and random forest. …”
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Reference Gene Selection for Quantitative Real-Time RT-PCR Normalization in Iris. lactea var. chinensis Roots under Cadmium, Lead, and Salt Stress Conditions by Chun-Sun Gu, Liang-qin Liu, Chen Xu, Yan-hai Zhao, Xu-dong Zhu, Su-Zhen Huang

“…GeNorm and NormFinder algorithms were used to assess the suitable reference genes. …”
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DGFEG: Dynamic Gate Fusion and Edge Graph Perception Network for Remote Sensing Change Detection by Shengning Zhou, Genji Yuan, Zhen Hua, Jinjiang Li

“…Benefiting from continuous innovations in deep learning algorithms, the accuracy of building change detection (BCD) in remote sensing (RS) has significantly improved. …”
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Early detection and analysis of accurate breast cancer for improved diagnosis using deep supervised learning for enhanced patient outcomes by Mandika Chetry, Ruiling Feng, Samra Babar, Hao Sun, Imran Zafar, Mohamed Mohany, Hassan Imran Afridi, Najeeb Ullah Khan, Ijaz Ali, Muhammad Shafiq, Sabir Khan

“…This study compares the performance of various machine learning (ML) algorithms, including convolutional neural networks (CNNs), logistic regression (LR), support vector machines (SVMs), and Gaussian naive Bayes (GNB), on two key datasets, Wisconsin Diagnostic Breast Cancer (WDBC) and Breast Cancer Histopathological Image Classification (BreaKHis). …”
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Deep Learning Model for CS-Based Signal Recovery for IRS-Assisted Near-Field THz MIMO System by Vaishali Sharma, Prakhar Keshari, Sanjeev Sharma, Kuntal Deka, Ondrej Krejcar, Vimal Bhatia

“…Simulation results for symbol error rate (SER) and normalized mean square error (NMSE) performance indicate that the proposed DL-based receiver outperforms conventional recovery algorithms based on orthogonal matching pursuit (OMP) CS-recovery and dictionary-shrinkage estimation (DSE).…”
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Fractional Optimizers for LSTM Networks in Financial Time Series Forecasting by Mustapha Ez-zaiym, Yassine Senhaji, Meriem Rachid, Karim El Moutaouakil, Vasile Palade

“…We extend several widely used optimization algorithms—including Adam, RMSprop, SGD, Adadelta, FTRL, Adamax, and Adagrad—by incorporating fractional derivatives into their update rules. …”
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Remote monitoring of Tai Chi balance training interventions in older adults using wearable sensors and machine learning by Giulia Corniani, Stefano Sapienza, Gloria Vergara-Diaz, Andrea Valerio, Ashkan Vaziri, Paolo Bonato, Peter M. Wayne

“…Our framework comprises a model for identifying the specific Tai Chi movement being performed and a model to assess performance proficiency, both employing Random Forest algorithms and features from IMU signals. The movement identification model achieved a micro F1 score of 90.05%. …”
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Pitik: A Cebuano-Binisaya Intent-Based Chatbot for Cardiovascular Disease Patient Profiling and Risk Factor Recommendations by Joseph Cedeño, Andrew Manteza, Nicole Nacar, Merhamdin Umbukan, Cherrie Muaña, Ma. Juliet Vasay-Cruz, Ceasar Ian Benablo, Kristine Mae Adlaon

“…The chatbot integrated the Diag-Ex framework alongside Pre-Intent and Post-Intent Matching algorithms. Gricean Maxims guided its conversational design to enhance communication accuracy and user interaction quality. …”
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Identification and analysis of pyroptosis-related key genes in heart failure by Jing Zhang, Zhijie Yue, Na Zhu, Na Zhao

“…Further, the key genes were screened based on machine learning algorithms. Furthermore, immune infiltration analysis and mRNA–Transcription factor (TF)/drug regulatory networks construction were implemented. …”
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NeuAFG: Neural Network-Based Analog Function Generator for Inference in CIM by Pengcheng Feng, Yihao Chen, Jinke Yu, Zhelong Jiang, Junjia Su, Qian Zhou, Hao Yue, Zhigang Li, Haifang Jian, Huaxiang Lu, Wan'Ang Xiao, Gang Chen

“…NeuAFG utilizes a one-hidden-layer ReLU neural network (ReLUNet) to approximate a variety of activation functions, incorporating two novel optimization algorithms: Low-Discrepancy Search (LDSearch) for efficient parameter initialization and KDE-FocusSampler for focused sampling of regions that are difficult to approximate. …”
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A Comprehensive Review of Thermal Management Methods and Ideal System Design for Improved Electric Vehicle Battery Pack Performance and Safety by M. Murugan, P. V. Elumalai, KCK Vijayakumar, M. Babu, K. Suresh Kumar, M. Ganesh, Liu Kuang, S. Prabhakar

“…This is shown through the industry's constant pursuit to develop in this critical area through the discovery of novel technologies, including predictive control algorithms and superior thermal materials. It discusses in depth how heat management is integrated into the general vehicle design and how this impacts battery lifespan, charging speed, and range. …”
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Architected Design and Fabrication of Soft Mechanical Metamaterials by Thileepan Stalin, Aby Raj Plamootil Mathai, Naresh Kumar Thanigaivel, Elgar Kanhere, Saikrishna Dontu, Gumawang Hiramandala, Aaron Chooi, Arturo Castillo Ugalde, Pablo Valdivia Y Alvarado

“…Planar printing tool paths, utilized in layer‐by‐layer printing such as fused deposition modeling (FDM) and prevalently used in most slicing algorithms, severely limit the realization of complex topologies required in metamaterials. …”
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Development and validation of an early predictive model for hemiplegic shoulder pain: a comparative study of logistic regression, support vector machine, and random forest by Qiang Wu, Qiang Wu, Fang Zhang, Yuchang Fei, Zhenfen Sima, Shanshan Gong, Qifeng Tong, Qingchuan Jiao, Hao Wu, Jianqiu Gong, Jianqiu Gong

“…ObjectiveIn this study, we aim to identify the predictive variables for hemiplegic shoulder pain (HSP) through machine learning algorithms, select the optimal model and predict the occurrence of HSP.MethodsData of 332 stroke patients admitted to a tertiary hospital in Zhejiang Province from January 2022 to January 2023 were collected. …”
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Physics-informed deep learning model for line-integral diagnostics across fusion devices by Cong Wang, Weizhe Yang, Haiping Wang, Renjie Yang, Jing Li, Zhijun Wang, Yixiong Wei, Xianli Huang, Chenshu Hu, Zhaoyang Liu, Xinyao Yu, Changqing Zou, Zhifeng Zhao

“…The incorporation of the PILF has been shown to correct the model’s predictions, bringing the back-projections closer to the actual inputs and reducing the errors associated with inversion algorithms. Besides, we have developed a synthetic data model to generate customized line-integral diagnostic datasets and have also collected soft x-ray diagnostic datasets from EAST and HL-2A. …”
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