Deep Q-Networks for Minimizing Total Tardiness on a Single Machine by Kuan Wei Huang, Bertrand M. T. Lin

“…Due to its computational intractability, exact approaches such as dynamic programming algorithms and branch-and-bound algorithms struggle to produce optimal solutions for large-scale instances in a reasonable time. …”
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Sentiment Analysis Using Stacking Ensemble After the 2024 Indonesian Election Results by Andy Victor Pakpahan, Fahmi Reza Ferdiansyah, Robby Gustian, Muhammad Nur Faiz, Sukma Aji

“…Machine learning is commonly applied in this area, with algorithms such as Naïve Bayes, Support Vector Machine (SVM), and Random Forest being frequently used. …”
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Leveraging Multilingual Transformer for Multiclass Sentiment Analysis in Code-Mixed Data of Low-Resource Languages by Muhammad Kashif Nazir, Cm Nadeem Faisal, Muhammad Asif Habib, Haseeb Ahmad

“…The evaluation results showed a significant improvement in accuracy (+22.55%), precision (+21.06%), recall (+22.55%), and F-measure (+25.50%) compared to benchmark algorithms. Additionally, the proposed model outperformed other transformer-based models, as well as deep learning and machine learning algorithms in sentiment extraction from code-mixed data. …”
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HarmonizR: blocking and singular feature data adjustment improve runtime efficiency and data preservation by Simon Schlumbohm, Julia E. Neumann, Philipp Neumann

“…Due to the inherent presence of missing values by such methods and their additional introduction by means of data integration, renowned algorithms such as ComBat and limma are unable to perform batch effect adjustment. …”
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Geotri-VIO: geometrically consistent multi-prism projection for panoramic imaging in visual-inertial-odometry by Ruidong Mei, Yihan Tu, Lanxiang Zheng, MingXin Wei, Hui Cheng

“…However, the nonlinear distortions caused by the lack of geometric consistency in projection models for panoramic images pose significant challenges to feature extraction and tracking algorithms. In this paper, we present Geotri-VIO, a novel VIO system that addresses these challenges using a multi-prism projection model. …”
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A Comparative Study of Continual, Lifelong, and Online Supervised Learning Libraries by Nicholas Cummins, Brad Killen, Somayeh Bakhtiari Ramezani, Shahram Rahimi, Maria Seale, Sudip Mittal

“…Our comparison as developers takes maintenance, documentation, and offerings of state-of-the-art algorithms into account. As this is not necessarily free of bias, we also use benchmarks known to online learning to gather power usage, RAM usage, speed, and accuracy of these libraries to get an objective view. …”
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Analysing the effectiveness of unsignalized crossing infrastructure in improving pedestrian safety using multiple data-driven approaches by Shengqi Liu, Harry Evdorides

“…This study investigates the effectiveness of unsignalized crossings to enhance pedestrian safety through a robust data-driven approach utilizing multiple machine learning models, including the statistical classifier Logistic Regression, Decision Tree, Random Forest, and Neural Network Multi-Layer Perceptron (MLP). While numerous studies have applied predictive models to traffic crash data, few have systematically analysed pedestrian crash severity at unsignalized crossings using multiple machine learning algorithms. …”
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Prediction of Temperature Distribution on an Aircraft Hot-Air Anti-Icing Surface by ROM and Neural Networks by Ziying Chu, Ji Geng, Qian Yang, Xian Yi, Wei Dong

“…The POD-AlexNet model achieves a mean prediction accuracy of over 95%, while the MCG model reaches 96.97%. Furthermore, the proposed model demonstrates a prediction time of no more than 5.5 ms for individual temperature samples. …”
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Towards Economic Sustainability: A Comprehensive Review of Artificial Intelligence and Machine Learning Techniques in Improving the Accuracy of Stock Market Movements by Atoosa Rezaei, Iheb Abdellatif, Amjad Umar

“…Accurately predicting stock market movements remains a critical challenge in finance, driven by the increasing role of algorithmic trading and the centrality of financial markets in economic sustainability. …”
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Integrating Bayesian Knowledge Tracing and Human Plausible Reasoning in an Adaptive Augmented Reality System for Spatial Skill Development by Christos Papakostas, Christos Troussas, Akrivi Krouska, Cleo Sgouropoulou

“…The use of advanced adaptive algorithms in Augmented Reality (AR) systems works to advance spatial skills with valuable relevance in many professional spheres by providing personalized feedback in an immersive environment. …”
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DYMOS: A New Software for the Dynamic Identification of Structures by Fabrizio Gara, Simone Quarchioni, Vanni Nicoletti

“…Operational modal analysis (OMA) is widely used for its simplicity and reliance on ambient noise. While commercial OMA software exists, they often limit user control. …”
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The Use of AI in Elementary School Learning: A Systematic Literature Review by Natalina Purba, Desti Pujiati, Partohap Saut Raja Sihombing, Hendra Simanjuntak, Desi Sijabat

“…The study concludes that while AI holds great potential to transform primary education, its successful implementation depends on overcoming these barriers and fostering a culture of innovation within schools. …”
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Portfolio optimization with MOPSO-Shrinkage hybrid model by Minh Tran, Nhat M. Nguyen

“…The main contribution of this study lies in combining the adaptive search capabilities of evolutionary algorithms with robust covariance estimation techniques to enhance portfolio allocation in mature financial markets. …”
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Proposal of a Thermal Network Model for Fast Solution of Temperature Rise Characteristics of Aircraft Wire Harnesses by Tao Cao, Wei Li, Tianxu Zhao, Shumei Cui

“…This model successfully achieves a significant improvement in calculation speed while effectively ensuring useable temperature rise results, providing an effective method for EWIS design.…”
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Evaluation and Application of Machine Learning Techniques for Quality Improvement in Metal Product Manufacturing by Katarzyna Antosz, Lucia Knapčíková, Jozef Husár

“…A variety of classification algorithms, including neural networks (NNs), bagged trees (BT), and support vector machines (SVMs), were employed to efficiently analyse and predict defects. …”
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Advancing TinyML in IoT: A Holistic System-Level Perspective for Resource-Constrained AI by Leandro Antonio Pazmiño Ortiz, Ivonne Fernanda Maldonado Soliz, Vanessa Katherine Guevara Balarezo

“…We highlight both established and emergent techniques for optimizing resource usage while addressing security, privacy, and ethical concerns. …”
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Remote Sensing Perspective on Monitoring and Predicting Underground Energy Sources Storage Environmental Impacts: Literature Review by Aleksandra Kaczmarek, Jan Blachowski

“…Existing research focuses on monitoring subsurface elements of the storage, while on the surface it is limited to ground movement observations. …”
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Edge-aware multisensor brain image fusion via guided filtering in Laplacian domain by Shweta Sharma, Shalli Rani, Ayush Dogra, Mohammed Wasim Bhatt

“…This work presents a new edge-aware multi-sensor brain image fusion technique using Laplacian energy maps for edge preservation, guided filtering for image decomposition, and adaptive fusion algorithms for integration of base and detail layers. …”
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Students' performance prediction employing Decision Tree by Abtahi Ahmed, Farzana Akter Nipa, Wasi Uddin Bhuyian, Khaled Md Mushfique, Kamrul Islam Shahin, Huu-Hoa Nguyen, Dewan Md. Farid

“…Traditional machine learning algorithms perform quite well in general, with SVM giving a best accuracy of around 95% with academic data, while training and testing the model without academic data still gives a good performance of 93%. …”
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Optimizing coverage in wireless sensor networks using deep reinforcement learning with graph neural networks by G. Pushpa, R. Anand Babu, S. Subashree, S. Senthilkumar

“…These results validate the effectiveness of the proposed approach in enhancing WSN coverage while maintaining energy efficiency and minimal redundancy.…”
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