Deep Learning Model Size Performance Evaluation for Lightning Whistler Detection on Arase Satellite Dataset by I Made Agus Dwi Suarjaya, Desy Purnami Singgih Putri, Yuji Tanaka, Fajar Purnama, I Putu Agung Bayupati, Linawati, Yoshiya Kasahara, Shoya Matsuda, Yoshizumi Miyoshi, Iku Shinohara

“…To achieve this, we transformed the entirety of raw data from the Arase (ERG) Satellite for August 2017 into 2736 images, which were then used to train the models. …”
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Intelligent predictive networks for MHD nanofluid with carbon nanotubes and thermal conductivity along a porous medium by Hafiz Muhammad Shahbaz, Iftikhar Ahmad

“…The LMA-RNNs is performed with 80% of data to train the model and another 10% is used to test the model and the left over 10% is used for validation. …”
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Hybrid EXGBStackQoE Classifier and Stackelberg Game-Theoretic Approaches for Enhanced QoE in Video Services Over 5G Wireless Networks by K. B. Ajeyprasaath, Vetrivelan Pandu

“…This model employs economic incentives and traffic load balancing to strategically select users for offloading, prioritizing those with lower QoS based on the Received Signal Strength Indicator (RSSI). By optimizing data distribution between MBS and Heterogeneous Networks (HetNets), the SbSG model enhances both network throughput and service quality. …”
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YOLOv8 framework for COVID-19 and pneumonia detection using synthetic image augmentation by Uddin A Hasib, Raihan Md Abu, Jing Yang, Uzair Aslam Bhatti, Chin Soon Ku, Lip Yee Por

“…Synthetic images are generated using Feature Interpolation through Linear Mapping and principal component analysis to enrich dataset diversity and balance class distribution. YOLOv8 and InceptionV3 models, fine-tuned via transfer learning, are trained on the augmented dataset. …”
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HIV Pre-Exposure Prophylaxis (PrEP) in Clinical Settings in Western Nigeria: Are Healthcare Providers Prepared? by Afusat Adesina, Saheed Opeyemi Usman*, Ahmad Abdurrahman, Tijani Adesina, Olaiya Paul Abiodun, Temitope Christopher Omisakin, Ibiwumi Nafisat Usman, Ndumiso Tshuma, Olusola John Fatunmbi

“…The target population were physicians and nurses largely involved in the antiretroviral clinics in the hospitals. Data was collected by trained volunteers and supervised by appointed supervisors by a face-to-face interview. …”
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Factors associated with COVID-19 vaccine uptake and hesitancy among healthcare workers in the Democratic Republic of the Congo. by Michel K Nzaji, Jean de Dieu Kamenga, Christophe Luhata Lungayo, Aime Cikomola Mwana Bene, Shanice Fezeu Meyou, Anselme Manyong Kapit, Alanna S Fogarty, Dana Sessoms, Pia D M MacDonald, Claire J Standley, Kristen B Stolka

“…We implemented a cross-sectional Knowledge, Attitudes, and Practices (KAP) questionnaire targeting HCWs, administered by trained data collectors in Haut-Katanga, Kasaï Orientale, Kinshasa, Kongo Centrale, Lualaba, North Kivu, and South Kivu provinces. …”
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A study on forest fire risk assessment in jiangxi province based on machine learning and geostatistics by Jinping Lu, Mangen Li, Yaozu Qin, Niannan Chen, Lili Wang, Wanzhen Yang, Yuke Song, Yisu Zheng

“…In addition, the models trained with WoE-selected negative samples exhibited enhanced accuracy, with area under the curve values increasing from 0.946 to 0.995 for RF, 0.8344 to 0.925 for SVM, and 0.832 to 0.850 for BPNN, compared to those trained with randomly sampled negative data. …”
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Modeling the Influence of Disturbances in High-Speed Railway Systems by Ping Huang, Chao Wen, Qiyuan Peng, Chaozhe Jiang, Yuxiang Yang, Zhuan Fu

“…We first extracted data about the disturbances and their affected train groups from historical train operation records of Wuhan-Guangzhou (W-G) HSR in China. …”
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Study of Pantograph Catenary Dynamic Interaction Performance for Beijing Metro Line 14 by ZHANG Huiqing, WANG Miao, TANG Changchun, FANG Yan

“…Pantograph catenary working principle of the measurement system was introduced, and combined with the test data, the distribution of contact force, contact force’s the most value, pantograph head vibration acceleration, pantograph and catenary arc phenomenon, the height of the catenary anchor point and the pulling value test results were analyzed respectively. …”
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A Novel Approach of Optimal Signal Streaming Analysis Implicated Supervised Feedforward Neural Networks by Farhan Ali, He Yigang

“…The analysis and interpretation of enormous amounts of data generated by 5G networks present several challenges related to noise, precision, and feasibility validation. …”
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Engaging Community-Dwelling Older Adults in Research: Qualitative Substudy of Factors Impacting Participation by Bryah Boutilier, Grace Warner, Brianna Wolfe, Sorayya Askari, Elaine Moody, Parisa Ghanouni, Tanya Packer

“…For example, community-level data can proactively identify unmet health needs, inform preventative care strategies, and ensure the equitable distribution of resources that enable older adults to age in place. …”
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A machine-learned model for predicting weight loss success using weight change features early in treatment by Farzad Shahabi, Samuel L. Battalio, Angela Fidler Pfammatter, Donald Hedeker, Bonnie Spring, Nabil Alshurafa

“…From 2009 to 2021, 1058 individuals with obesity participated in three studies: SMART, Opt-IN, and ENGAGED. The model was trained on 80% of the SMART data (224 participants), and its in-distribution generalizability was tested on the remaining 20% (remaining 57 participants). …”
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PREDICTION OF PV POWER GENERATION USING ANFIS CONSIDERING UNCERTAINTIES by Ebenezer Narh Odonkor, Peter Musau Moses, Aloys Oriedi Akumu

“…Furthermore, ANFIS was used to train location weather data obtained and the prediction of PV power generation in MATLAB software. …”
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Study on the Effect of Temperature on the Alignment of a Long-Span Steel–Concrete Composite Beam Track Cable-Stayed Bridge by Xiaogang Li, Xiangsheng Huang, Peng Ding, Xiaohu Chen, Qiansong Wang, Sihan Cen

“…Based on the temperature and displacement monitoring system of the Chongqing Nanjimen track bridge, the temperature and displacement data in 2023 were obtained for comparative analysis. …”
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Deep Learning-Based Instance Segmentation of Galloping High-Speed Railway Overhead Contact System Conductors in Video Images by Xiaotong Yao, Huayu Yuan, Shanpeng Zhao, Wei Tian, Dongzhao Han, Xiaoping Li, Feng Wang, Sihua Wang

“…This work expands upon the YOLO11-seg model and introduces an instance segmentation approach for galloping video and image sensor data of OCS conductors. The algorithm, designed for the stripe-like distribution of OCS conductors in the data, employs four-direction Sobel filters to extract edge features in horizontal, vertical, and diagonal orientations. …”
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Comparison of nanoimaging and nanoflow based detection of extracellular vesicles at a single particle resolution by Shihan Xu, Zhengrong Zhang, Bridgette C. Melvin, Nibedita Basu Ray, Seiko Ikezu, Tsuneya Ikezu

“…Additionally, we applied machine learning models to correct for false positive particles identified in imaging‐based results by fitting size distribution data.…”
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Analyzing and predicting short-term substance use behaviors of persons who use drugs in the great plains of the U.S. by Nguyen Thach, Patrick Habecker, Bergen Johnston, Lillianna Cervantes, Anika Eisenbraun, Alex Mason, Kimberly Tyler, Bilal Khan, Hau Chan

“…The sample provides us longitudinal survey data regarding their individual attributes, including drug use behaviors, at two separate time periods spanning 4-12 months. …”
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Toward generalizable prediction of antibody thermostability using machine learning on sequence and structure features by Ameya Harmalkar, Roshan Rao, Yuxuan Richard Xie, Jonas Honer, Wibke Deisting, Jonas Anlahr, Anja Hoenig, Julia Czwikla, Eva Sienz-Widmann, Doris Rau, Austin J. Rice, Timothy P. Riley, Danqing Li, Hannah B. Catterall, Christine E. Tinberg, Jeffrey J. Gray, Kathy Y. Wei

“…Both of these models are trained over temperature-specific data (TS50 measurements) derived from multiple libraries of scFv sequences. …”
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Urban fabric decoded: High-precision building material identification via deep learning and remote sensing by Kun Sun, Qiaoxuan Li, Qiance Liu, Jinchao Song, Menglin Dai, Xingjian Qian, Srinivasa Raghavendra Bhuvan Gummidi, Bailang Yu, Felix Creutzig, Gang Liu

“…Here, we introduce a novel automated framework that harnesses recent advances in sensing technology and deep learning to identify roof and facade materials using remote sensing data and Google Street View imagery. The model was initially trained and validated on Odense's comprehensive dataset and then extended to characterize building materials across Danish urban landscapes, including Copenhagen, Aarhus, and Aalborg. …”
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Enhancing Autonomous Driving With Spatial Memory and Attention in Reinforcement Learning by Matvey Gerasyov, Andrey V. Savchenko, Ilya Makarov

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