Review of State Estimation Methods for Autonomous Ground Vehicles: Perspectives on Estimation Objects, Vehicle Characteristics, and Key Algorithms by Xiaoyu Wang, Te Chen, Renzhong Wang, Jiankang Lu, Guowei Dou

“…Based on the discussion of the importance, development history, and application fields of this topic of research, it focuses on analyzing vehicle state estimation techniques from different perspectives, namely (1) from the perspective of the estimation objects, including vehicle attitude and driving state estimations, chassis component key dynamic parameter estimations, and vehicle driving environment state estimations; (2) from the perspective of vehicle characteristics, including vehicle dynamics coupling characteristics, vehicle multi-source information redundancy characteristics, and vehicle state transition characteristics; (3) from the perspective of key estimation algorithms, including model-based Kalman filtering algorithms, data-driven machine learning algorithms, and optimization estimation algorithms combining mechanism-based and data-driven approaches. …”
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The Collaborative Wall: A Technological Means to Improving the Teaching-Learning Process about Physics by Ricardo-Adán Salas-Rueda, Gustavo De-La-Cruz-Martínez, Clara Alvarado-Zamorano, Estefanía Prieto-Larios

“…This mixed research aims to analyse students’ perceptions about the use of the collaborative wall in the teaching-learning process for physics, considering data science. The collaborative wall is a web application that allows the participation of the students in the classroom through the dissemination of text and images. …”
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A deep classification model to assess environment following hazards using remote sensing images by Madhusmita Sahu, Rasmita Dash

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Chopped and minibars reinforced high-performance concrete: machine learning prediction of mechanical properties by Nikolai Ivanovich Vatin, Mohammad Hematibahar, Tesfaldet Hadgembes Gebre

“…Each model’s performance and application are examined thoroughly, leading to the identification of useful suggestions, existing knowledge gaps, and areas in need of further study.…”
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A Hardware-Aware Failure-Detection Method for GPU Control-Logic by Hiroaki Itsuji, Takumi Uezono, Tadanobu Toba, Kojiro Ito, Masanori Hashimoto

“…Graphics processing units (GPUs) are used for diverse applications and play a major role even in safety-critical applications. …”
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Advanced Financial Fraud Malware Detection Method in the Android Environment by Jaeho Shin, Daehyun Kim, Kyungho Lee

“…Undersampling was performed to adjust the proportion of benign applications in the training data because the data on benign and malicious apps were unbalanced. …”
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Global trends and hotspots in artificial intelligence for high myopia: a bibliometric analysis by Xuze Wang, Ailixiati Wumaier, Jun Wang, Dejuan Song, Yiting Cai, Jin Han, Wei Han, Zhi Fang

“…PurposeThis study aims to conduct a bibliometric analysis of global publications on the application of artificial intelligence (AI) in high myopia (HM).MethodsWe retrieved publications on AI in HM from the Web of Science Core Collection (WoSCC) database, MEDLINE and Chinese Science Citation Database (CSCD) with data up to 2024. …”
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Algorithmic fandom: how generative AI is reshaping sports marketing, fan engagement, and the integrity of sport by Hans Westerbeek

“…The integration of AI in gambling platforms and the commercialisation of fan data raise significant concerns around privacy, consent, and long-term wellbeing. …”
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Gate Control Mechanisms of Autoencoders for EEG Signal Reconstruction by Kangjing Li, Heba El-Fiqi, Min Wang

“…Such heavily contaminated and lost signal segments are usually removed manually, which can hinder practical system deployment and application performance, especially in scenarios where continuous signals are required. …”
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DualPFL: A Dual Sparse Pruning Method with Efficient Federated Learning for Edge-Based Object Detection by Shijin Song, Sen Du, Yuefeng Song, Yongxin Zhu

“…These findings indicate that DualPFL facilitates secure and efficient collaborative computing in smart city applications.…”
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An Integrated Approach to Stock Selection and Ranking: Combining Shannon Entropy Technique, DEA, and Inverse DEA by Temitope Olubanjo Kehinde, Sai-Ho Chung, Oludolapo A. Olanrewaju

“…This study proposes a novel, integrated framework that combines Shannon Entropy Technique (SET), Data Envelopment Analysis (DEA), and Inverse DEA (IDEA) for the objective evaluation and ranking of equities, with a case application to the tourism sector of the Taiwan Stock Exchange (TWSE). …”
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A robust method for calculating the vertical derivative of potential fields based on Hilbert transforms by Xin’gang Luo, Shengrong Liu, Xiaoping Tang

“…Abstract Calculation of high-order vertical derivatives represents a fundamental challenge in gravity and magnetic data processing, with critical applications in potential field separation, continuation analysis, and geological boundary identification. …”
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Implementation of flexible DEA structure in the management of business processes by Šegrt Slobodan, Tomić Radoljub, Anđelković Maja

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Imbalanced Text Classification on Tourism Reviews using Ada-boost Naïve Bayes by Ika Oktavia Suzanti, Fajrul Ihsan Kamil, Eka Mala Sari Rochman, Huzain Azis, Alfa Faridh Suni, Fika Hastarita Rachman, Firdaus Solihin

“…Initial analysis revealed an imbalance in sentiment data, with most reviews being positive. To address this, sampling techniques—both oversampling and undersampling—were applied to achieve a more balanced data distribution. …”
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Evaluating PurpleAir Sensors: Do They Accurately Reflect Ambient Air Temperature? by Justin Tse, Lu Liang

“…Finally, the practical application of PA temperature data for identifying heat wave events was investigated, including an assessment of associated uncertainties. …”
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Citation Analysis of Undergraduate Projects Report in the Department of Library and Information Science Kabale University Uganda: An In-Text Citation Study by Iroroeavwo Edwin, Achugbue, Benjamin, Ahimbisibwe, Olivia Nina, Rugambwa, Patrick, Odong

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Optimized deep learning models for stress-based stroke prediction from EEG signals by Sivasankaran Pichandi, Gomathy Balasubramanian, Venkatesh Chakrapani, J. Samuel Manoharan

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SGA-Driven feature selection and random forest classification for enhanced breast cancer diagnosis: A comparative study by Abrar Yaqoob, Navneet Kumar Verma, Mushtaq Ahmad Mir, Ghanshyam G. Tejani, Nashwa Hassan Babiker Eisa, Hind Mamoun Hussien Osman, Mohd Asif Shah

“…Abstract In this study, we propose a novel approach for breast cancer classification that integrates the Seagull Optimization Algorithm (SGA) for feature selection with the Random Forest (RF) classifier for effective data classification. The novelty of our approach lies in the first-time application of SGA for gene selection in breast cancer diagnosis, where SGA systematically explores the feature space to identify the most informative gene subsets, thereby improving classification accuracy and reducing computational complexity. …”
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Probabilistic Deep Learning for Highly Multivariate Spatio-Temporal Log-Gaussian Cox Processes by Achmad Choiruddin, Ekky Rino Fajar Sakti, Tintrim Dwi Ary Widhianingsih, Jorge Mateu, Kartika Fithriasari

“…We assess our model performance via a simulation study, and analyze the highly multivariate spatio-temporal point pattern data of Barro Colorado Island (BCI). Both the simulation study and application demonstrate our model effectiveness over previous approaches to handle highly multivariate point pattern data.…”
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An efficient hybrid filter-wrapper method based on improved Harris Hawks optimization for feature selection by Jamshid Pirgazi, Mohammad Mehdi Pourhashem Kallehbasti, Ali Ghanbari Sorkhi, Ali Kermani

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