Improving Localization in Wireless Sensor Networks for the Internet of Things Using Data Replication-Based Deep Neural Networks by Jehan Esheh, Sofiene Affes

“…Localization is one of the most challenging problems in wireless sensor networks (WSNs), primarily driven by the need to develop an accurate and cost-effective localization system for Internet of Things (IoT) applications. While machine learning (ML) algorithms have been widely applied in various WSN-based tasks, their effectiveness is often compromised by limited training data, leading to issues such as overfitting and reduced accuracy, especially when the number of sensor nodes is low. …”
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The State of Criminality in Tajikistan by Yuldoshev Rifat Rahmadjonovich, Safarzoda Anvar Islom, Mahmadzoda Neru

“…The research analyzed the “pros” and “cons” of the social control of criminality as well as specified the collection, processing and thorough analysis of statistical data. The research takes timely decisions and a transition to the digitalization format of the entire criminal policy process. …”
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Privacy, ethics, transparency, and accountability in AI systems for wearable devices by Petar Radanliev, Petar Radanliev

“…This study undertakes a systematic examination of these challenges, highlighting the risks posed by unregulated data aggregation, biased model training, and inadequate transparency in AI-powered health applications. …”
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TEEMLEAP—A New Testbed for Exploring Machine Learning in Atmospheric Prediction for Research and Education by J. Wilhelm, J. Quinting, M. Burba, S. Hollborn, U. Ehret, I. Pena Sánchez, S. Lerch, J. Meyer, B. Verfürth, P. Knippertz

“…Abstract In the past 5 years, data‐driven prediction models and Machine Learning (ML) techniques have revolutionized weather forecasting. …”
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Innamark: A Whitespace Replacement Information-Hiding Method by Malte Hellmeier, Hendrik Norkowski, Ernst-Christoph Schrewe, Haydar Qarawlus, Falk Howar

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IACGAN-ST: A Fault Diagnosis Method for Scraper Conveyor Bearings Under Imbalanced Data Conditions by Hongwei Wang, Shengyu Guan, Xuesong Wang, Lijie Wang, Qun Zhang, Kaiman Liu, Zhiwen Fang, Hongli Wang

“…Experimental results show that the Swin Transformer achieves superior diagnostic accuracy under various imbalance conditions, reaching 99.1% when the data is balanced at a 1:1 ratio. Additionally, during a field application at the E2312 mining face of Gaohe Energy Coal Mine, the IACGAN-ST model attained a diagnostic accuracy of 96.06%, significantly outperforming benchmark models such as SVM, CNN, and ResNet. …”
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Differential Diagnosis of Jejunum and Ileum Tumours Based on Video Capsule Endoscopy Data Using Mathematical Analysis by E. D. Fedorov, E. V. Ivanova, S. E. Rauzina, D. E. Seleznev, A. V. Budykina

“…In the study, we analysed data on the examination and treatment of 65 patients (35 men and 30 women aged 18–80 years (mean age 46 ± 28 years)), who underwent VCE in the City Clinical Hospital No. 31 and in the Clinica K+31 during the period October 2008 — April 2017. …”
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Virtual metering of gas-liquid two-phase flow in oil nozzles based on physical-data fusion by Qing LI, Hongfei LIU, Manqing JIN, Ying ZHANG, Fachun LIANG

“…Additionally, low costs for data acquisition and modeling enhance the method's potential for widespread adoption and application.…”
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Research on the Detection Method of Cyanobacteria in Lake Taihu Based on Hyperspectral Data from ZY-1E by Qinshun Luo, Dongzhi Zhao, Zhongfeng Qiu, Sheng Jiang, Yuanzhi Zhang

“…The model demonstrates good detecting performance. Regarding its application on the ZY-1E satellite, we validated the detection results accuracy through matching synchronized in situ algal density data. …”
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Improving chlorophyll-a estimation using Sentinel-2 data: a comparative analysis of augmented datasets by Jinmyeong Lee, Do Hyuck Kwon, Heewon Jeong, Gibeom Nam, Euiho Hwang, Jin Hwi Kim, Kyung Hwa Cho, Hyo Gyeom Kim

“…Although remote sensing using machine learning techniques can effectively monitor harmful algal blooms, their application is often limited by data availability. The synergetic impacts of rapid urbanization and climate change contribute to the unprecedented occurrence of severe algal blooms, which require sufficient high-concentration data for successful model training. …”
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Teachers’ team innovativeness in TALIS 2018: An empirical and simulation study using glmmLasso for multilevel data by Miryeong Koo, Jin Eun Yoo

“…Future research should explore the applicability of glmmLasso across diverse educational contexts and consider the development of advanced penalized regression techniques to address challenges such as the optimal selection count thresholds and the appropriate size of ICC within multilevel data structures.…”
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Estimating within-stride metabolic cost from stride-average data using autoencoders and expander networks by Manal Mustafa, Alex C. Dzewaltowski, Alex C. Dzewaltowski, Philippe Malcolm, Keegan J. Moore, Keegan J. Moore

“…This research could also be applied to expand sparse measurements to locations or times that were not measured explicitly. This application would reduce the number of measurement points required to capture the response of a system.…”
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Perbandingan Metode Jaringan Saraf Tiruan, Fuzzy, Dan Anfis Pada Peramalan Data Inflasi Indonesia by Dwi Ayu Lusia, Karen Semathea, Eni Sumarminingsih, Achmad Efendi

“… Peramalan adalah teknik penting untuk mengestimasi nilai masa depan berdasarkan data historis. Namun, metode peramalan sering menghadapi tantangan dalam memilih model dengan tingkat akurasi terbaik. …”
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Enhancing Surgical Safety and Efficiency: Systematic Review and Single-Arm Meta-Analysis of Surgical Data Recorders by Niels Siegel, André Rotärmel, Georgios Polychronidis, Gabriel Salg, Rosa Klotz, Pascal Probst, Thomas Pausch

“…These devices allow for unique insights in the operating room by providing new data capture capabilities. No systematic review has been carried out to evaluate the areas of application of surgical data recorders to date. …”
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Extraction of Building Construction Time Using the LandTrendr Model With Monthly Landsat Time Series Data by Tengyun Hu, Meng Zhang, Xuecao Li, Tinghai Wu, Qiwei Ma, Jianneng Xiao, Xieqin Huang, Jinchen Guo, Yangchun Li, Donglie Liu

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TIRDH: A Novel Three-Shadow-Image Reversible Data Hiding Algorithm Using Weight and Modulo by Li-Chian Chin, Yu-Chen Li, Hung-Mo Hsieh, Chung-Ming Wang

“…Reversible data hiding (RDH) in image media is crucial for securely embedding confidential information while ensuring complete recovery of both the cover image and the hidden data. …”
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Classification of single tree decay stages from combined airborne LiDAR data and CIR imagery by Tsz-Chung Wong, Abubakar Sani-Mohammed, Jinhong Wang, Puzuo Wang, Wei Yao, Marco Heurich

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Evaluating machine leaning algorithms for accuracy, stability, and among-predictors discriminability in modeling species-richness across ten datasets by Yong Cao, Tyler E. Schartel, David C. Houghton, Jared Ross, Dana M. Infante

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A Low Power Memory-Integrated Hardware BNN-MLP Model on an FPGA for Current Signals in a Biosensor by Geon-Hoe Kim, Dong-Gyun Kim, Sung-Jae Lee, Jong-Han Kim, Da-Yeong An, Hyejin Kim, Young-Gun Pu, Heejeong Jasmine Lee, Jun-Eun Park, Kang-Yoon Lee

“…Finally, the inference time for a single data point was 0.018 ms, which is much faster than in previous studies, confirming its potential for low-power and compact applications in biosensors.…”
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A Novel Method for Traffic Parameter Extraction and Analysis Based on Vehicle Trajectory Data for Signal Control Optimization by Yizhe Wang, Yangdong Liu, Xiaoguang Yang

“…The extracted parameters can directly support signal optimization applications such as traffic signal coordination, timing optimization, and congestion management, providing crucial support for implementing data-driven intelligent traffic control. …”
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