Development of ANPR Framework for Pakistani Vehicle Number Plates Using Object Detection and OCR by Salma, Maham Saeed, Rauf ur Rahim, Muhammad Gufran Khan, Adil Zulfiqar, Muhammad Tahir Bhatti

“…For comparison and validation, we implemented a LeNet Convolutional Neural Network (CNN) architecture which uses the segmented image as an input. …”
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Increasing Mosquito Abundance Under Global Warming by Gokul Nair, Hong‐Yi Li, Jon Schwenk, Kaitlyn Martinez, Carrie Manore, Chonggang Xu

“…The LSTM is able to explain, on average, approximately 40% of the variance in the observed mosquito abundance data. …”
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Deep Reinforcement Learning for Optimal Replenishment in Stochastic Assembly Systems by Lativa Sid Ahmed Abdellahi, Zeinebou Zoubeir, Yahya Mohamed, Ahmedou Haouba, Sidi Hmetty

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Using artificial intelligence techniques and econometrics model for crypto-price prediction by Abhidha Verma, Jeewesh Jha

“…To achieve accurate predictions for Ethereum's price one day ahead, we develop a hybrid algorithm combining Genetic Algorithms (GA) and Artificial Neural Networks (ANN). Furthermore, regression analysis serves as an additional prediction tool. …”
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Compatibility Between OLCI Marine Remote-Sensing Reflectance from Sentinel-3A and -3B in European Waters by Frédéric Mélin, Ilaria Cazzaniga, Pietro Sciuto

“…The products derived from these missions should be consistent and allow the analysis of long-term multi-mission data records, particularly for climate science. …”
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High-accuracy prediction of mutations in nine genes in lung adenocarcinoma via two-stage multi-instance learning on large-scale whole-slide images by Lingyu Zhao, Na Zhao, Ruiqi Zhong, Yiru Niu, Ziyi Chang, Peng Su, Zhihui Wang, Lifang Cui, Bei Wang, Huang Chen, Xiaowen Wang, Xiangbing Kong, Baolin Du, Fei Ren, Dingrong Zhong

“…The AUC value on the external test data set is 0.516–0.843. Furthermore, when comparing EGFR gene mutation prediction between pathologists and the GAMIL model, GAMIL exhibited a significantly higher AUC value of 0.810, exceeding the average AUC value of 0.508 achieved by pathologists. …”
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IoT‐Based Elderly Health Monitoring System Using Firebase Cloud Computing by Adhan Efendi, Muhammad Imam Ammarullah, Indra Griha Tofik Isa, Meli Puspita Sari, Jasmine Nurul Izza, Yohanes Sinung Nugroho, Hamid Nasrullah, Denny Alfian

“…Methods The system architecture adopts the IoT layer including physical layer, network layer, and application layer. Device validation is conducted by involving six participants to measure the real‐time data of heart‐rate, oxygen saturation, and body temperature, then analysed by mean average percentage error (MAPE) to define error rate. …”
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Fire Detection with Deep Learning: A Comprehensive Review by Rodrigo N. Vasconcelos, Washington J. S. Franca Rocha, Diego P. Costa, Soltan G. Duverger, Mariana M. M. de Santana, Elaine C. B. Cambui, Jefferson Ferreira-Ferreira, Mariana Oliveira, Leonardo da Silva Barbosa, Carlos Leandro Cordeiro

“…The review identifies “Remote Sensing” as the primary platform for FDDL research dissemination and highlights the field’s collaborative nature, with an average of 5.02 authors per paper. The co-occurrence network analysis reveals diverse research themes, spanning technical approaches and practical applications, with significant contributions from China, the United States, South Korea, Brazil, and Australia. …”
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Seed multispectral imaging combined with machine learning algorithms for distinguishing different varieties of lettuce (Lactuca sativa L.) by Jinpeng Wei, Zhangyan Dai, Qi Zhang, Le Yang, Zhaoqi Zeng, Yuliang Zhou, Jun Liu, Bingxian Chen

“…The LDA model based on morphological and spectral fusion feature data performed best, and the average classification accuracy was 92.7 %. …”
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Adaptive User Pairing With Non-Orthogonal Medium Access Choices for Balanced Coexistence of Mission-Critical and eMBB Services in Cellular IoT by Farnaz Khodakhah, Aamir Mahmood, Patrik Osterberg, Mikael Gidlund

“…By deriving a signal-to-noise ratio (SNR) gap threshold between a pair of eMBB and MC users, we identify optimal access scheme&#x2014;puncturing, non-orthogonal multiple access (NOMA), or rate-splitting multiple access (RSMA)&#x2014;with respect to activation probability <inline-formula> <tex-math notation="LaTeX">$(p_{m})$ </tex-math></inline-formula> and cellular network radius. By using this derived threshold, we design an adaptive pairing algorithm that achieves near-optimal QoS for MC users and maximizes eMBB data rates. …”
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Exploring the effect of training set size and number of categories on ice crystal classification through a contrastive semi-supervised learning algorithm by Y. Chu, Y. Chu, H. Zhang, X. Li, J. Henneberger

“…When tested on the unseen CLOUDLAB dataset, CSSL shows superior generalization, improving accuracy by an average of 2.19 %. Our analysis also reveals that both CSSL and purely supervised algorithms exhibit inherent instability when trained on small dataset sizes, and the performance difference between them converges as the training set size exceeds 2048 samples. …”
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Enhancing Drought Forecast Accuracy Through Informer Model Optimization by Jieru Wei, Wensheng Tang, Pakorn Ditthakit, Jiandong Shang, Hengliang Guo, Bei Zhao, Gang Wu, Yang Guo

“…This study employed the Informer model to forecast drought and conducted a comparative analysis with Autoregressive Integrated Moving Average (ARIMA), long short-term memory (LSTM), and Convolutional Neural Network (CNN) models. …”
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High partner specificity in an algal-salamander mutualism at continental scale by Shannon E. Wallace, John A. Burns, Rebecca E. Hale, Ryan R. Kerney, Cy L. Mott, Cory D. Bishop

“…Pairwise alignments of ASVs from the V4 region revealed that, on average, more divergent ASVs were rarer. Haplotype network analysis revealed ASV A as the ancestral variant and amplicon sequence analysis from newly isolated cultures of O. amblystomatis demonstrate that ASVs A and B are intragenomic variants that can segregate. …”
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Applying machine learning techniques to predict the risk of distant metastasis from gastric cancer: a real world retrospective study by Xinxin Qin, Binxu Qiu, Litao Ge, Song Wu, Yuye Ma, Wei Li

“…We used data from 1595 gastric cancer patients in the First Hospital of Jilin University for external validation. …”
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Improved estimation of two-phase capillary pressure with nuclear magnetic resonance measurements via machine learning by Oriyomi Raheem, Misael M. Morales, Wen Pan, Carlos Torres-Verdín

“…Recently, artificial intelligence methods have also been applied to capillary pressure prediction (Qi et al., 2024).Currently, no readily applicable predictive model exists for estimating an entire capillary pressure curve directly from standard petrophysical logs and core data. Although porescale imaging and network modeling techniques can compute capillary pressure from micro-CT rock images (Øren and Bakke, 2003; Valvatne and Blunt, 2004), these approaches are time-consuming, limited to small sample volumes, and not yet practical for routine reservoir evaluation. …”
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The More, the Better? Evaluating the Role of EEG Preprocessing for Deep Learning Applications by Federico Del Pup, Andrea Zanola, Louis Fabrice Tshimanga, Alessandra Bertoldo, Manfredo Atzori

“…The last decade has witnessed a notable surge in deep learning applications for electroencephalography (EEG) data analysis, showing promising improvements over conventional statistical techniques. …”
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Practical Challenges in the Diagnosis of SARS-CoV-2 Infection in Children by Alina Petronela Bouari-Coblișan, Claudia Felicia Pop, Valentina Sas, Adina Georgiana Borcău, Teodora Irina Bonci, Paraschiva Cherecheș-Panța

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The Association between Ambient Fine Particulate Matter and Oral Neoplasm among Smokers and Betel Quids Chewers by Mei-Sheng Ku, Pallop Siewchaisakul, Amy Ming-Fang Yen, Chen-Yu Liu

“…The purpose of this study is to elucidate the association between PM2.5 and oral neoplasm, including oral potentially malignant disorder (OPMD) and oral cancer (OC), taking into account the geographical heterogeneity. Data for analysis were derived from nationwide OC screening program, targeting Taiwanese cigarette smokers and/or betel quid chewers, and the Taiwan Air Quality Monitoring Network between 2006 and 2016. …”
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IOT based wearable sensor system architecture for classifying human activity by V. Mahalakshmi, Pramod Kumar, Manisha Bhende, Ismail Keshta, Swatiben Yashvantbhai Rathod, Janjhyam Venkata Naga Ramesh

“…The proposed system follows a structured workflow: (i) channel state information (CSI) is extracted from ESP32 modules, (ii) data preprocessing is performed using Hampel and Gaussian filters for noise and outlier removal, (iii) dimensionality reduction is achieved through Principal Component Analysis (PCA) and Discrete Wavelet Transform (DWT), and (iv) activity classification is conducted using Dynamic Time Warping (DTW) and the K-Nearest Neighbors (KNN) algorithm. …”
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Expression patterns of protein kinases correlate with gene architecture and evolutionary rates. by Aleksey Y Ogurtsov, Leonardo Mariño-Ramírez, Gibbes R Johnson, David Landsman, Svetlana A Shabalina, Nikolay A Spiridonov

“…<h4>Principal findings</h4>Our comparative analysis demonstrates that genomic organization of regulatory PK genes differs from organization of other protein coding genes. …”
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