Enhancing Kalman Filter Performance: Design and Estimation with Fading-Memory Kalman Filter for Reduced Modeling Error and Improved Robustness by Jalil Manafian, Mehdi Fazli, Onur Alp Ilhan, Somaye Malmir, Sukaina Tuama Ghafel

“…The Kalman filter is a widely employed algorithm for state estimation and sensor fusion in various fields. …”
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Testing the Perturbation Sensitivity of Abortion-Crime Regressions by Michał Brzeziński, Maria Halber

“…The hypothesis that the legalisation of abortion contributed significantly to the reduction of crime in the United States in 1990s is one of the most prominent ideas from the recent “economics-made-fun” movement sparked by the book Freakonomics. …”
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Balanced operation strategies of district heating systems based on dynamic hydraulic-thermal modeling by Xiaojie Lin, Ning Zhang, Zheng Luo, Encheng Feng, Wei Zhong

“…The heating supply imbalance of the primary side return temperatures among different heating substations is defined and optimized based on the dynamic hydraulic-thermal model and the particle swarm optimization algorithm. In the case analysis, the results of heating supply imbalance before and after optimization are 0.81 K and 0.47 K, respectively, with a 42.0 % reduction in heating imbalances after optimization. …”
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Scalable data assimilation with message passing by Oscar Key, So Takao, Daniel Giles, Marc Peter Deisenroth

“…In combination with a GPU-accelerated implementation, we can scale the algorithm to very large grid sizes while retaining good accuracy and compute and memory requirements.…”
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The role of nanomedicine and artificial intelligence in cancer health care: individual applications and emerging integrations—a narrative review by Prasanthi Samathoti, Rajasekhar Komarla Kumarachari, Sarad Pawar Naik Bukke, Eashwar Sai Komarla Rajasekhar, Ashish Ashokkumar Jaiswal, Zohre Eftekhari

“…Nanomedicine leverages nanoparticles for targeted drug delivery, enhancing therapeutic efficacy while minimizing adverse effects. Concurrently, AI algorithms facilitate early cancer detection, personalized treatment planning, and predictive analytics, thereby optimizing clinical outcomes. …”
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Quantum Computation of the Cobb–Douglas Utility Function via the 2D Clairaut Differential Equation by Isabel Cristina Betancur-Hinestroza, Éver Alberto Velásquez-Sierra, Francisco J. Caro-Lopera, Álvaro Hernán Bedoya-Calle

“…Our findings, validated through IBM-Q simulations, align with the predictions, demonstrating the robustness of our approach. …”
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A New Method for Camera Auto White Balance for Portrait by Sicong Zhou, Kaida Xiao, Changjun Li, Peihua Lai, Hong Luo, Wenjun Sun

“…Experimental results on difficult skin color reproduction task demonstrate that SCR-AWB significantly outperforms traditional AWB algorithms. It achieves lower reproduction angle errors and more accurate CCT predictions, with deviations below 300 K in most cases. …”
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Advances in Breast Cancer Care: The Role of Artificial Intelligence and Digital Pathology in Precision Medicine by Ayşe Hümeyra Dur Karasayar, İbrahim Kulaç, Nilgün Kapucuoğlu

“…The application of machine learning algorithms has enhanced the ability of AI systems to classify breast cancer subtypes, grade tumors, and quantify key biomarkers, thereby improving diagnostic accuracy and prognostic precision. …”
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Interpretability in deep learning for finance: A case study for the Heston model by Damiano Brigo, Xiaoshan Huang, Andrea Pallavicini, Haitz Sáez de Ocáriz Borde

“…In this study, we focused on the calibration of a stochastic volatility model, a subject recently tackled by deep-learning algorithms. We analyzed the Heston model in particular, as this model’s properties are well known, resulting in an ideal benchmark case. …”
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Combined L-Band Polarimetric SAR and GPR Data to Develop Models for Leak Detection in the Water Pipeline Networks by Yuyao Zhang, Hongliang Guan, Fuzhou Duan

“…In this paper, we combine the SAOCOM-1A L-band synthetic-aperture radar (SAR) and the ground-penetrating radar (GPR) data to develop regression models that predict the SSRDC values. The model features are selected with the Boruta wrapper algorithm based on the SAOCOM-1A images after pre-processing, and the SSRDC values at sampling locations within the research area are calculated with the reflected wave method based on the GPR data. …”
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High concentration formulation developability approaches and considerations by Jonathan Zarzar, Tarik Khan, Maniraj Bhagawati, Benjamin Weiche, Jasmin Sydow-Andersen, Alavattam Sreedhara

“…In order to accelerate and derisk development, new experimental and in-silico methods have emerged that can predict high concentration liabilities. Here, we review the challenges in developing high concentration formulations, the advances that have been made in establishing low mass and high-throughput predictive analytics, and advances in in-silico tools and algorithms aimed at identifying risks and understanding high concentration protein behavior.…”
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Method for Determining Igneous Rock Mineral Content Using Element Logging Data Based on Variational AutoEncoder by JIA Ruilong, PAN Baozhi, WANG Qinghui, LI Yan, GUAN Yao, WANG Xinru

“…It employs a VAE (Variational AutoEncoder) approach to predict mineral content and reconstruct the elemental weight content. …”
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High-performance sentiment classification of product reviews using GPU(parallel)-optimized ensembled methods by Annaluri Sreenivasa Rao, Yeruva Jaipal Reddy, Guggilam Navya, Neelima Gurrapu, Jala Jeevan, M. Sridhar, Desidi Narasimha Reddy, Siva Kumar Pathuri, Dama Anand

“…The GPU-CUDA-ENSEMBLED algorithm is a GPU-accelerated method for sentiment classification, enhancing predictive performance by minimizing variances and biases. …”
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The 3D tooth model segmentation method based on GAC+PointMLP network by Jianjun Chen, Liyuan Zheng, Huilai Zou, Jiafa Mao, Weiguo Sheng

“…Compared to other traditional segmentation algorithms, the proposed method shows improvements of 1.1, 2.04, 1.06, 2.2, and 1.8 percentage points in Overall Accuracy (OA), Sensitivity (SEN), Positive Predictive Value (PPV), and Intersection Over Union (IoU), respectively. …”
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Optimal Placement of Wind Power System Using Machine Learning by Abdul Karim, Muhammad Amir Raza, Darakhshan Ara, Muhammad Shahid, Shakir Ali Soomro

“…These models have taken raw data from 2000 to 2019 and tested from 2020 to 2023 and finally predict the future from 2023 to 2030. The results show that for renewable energy variables such as temperature and wind speed, statistical models such as SARIMAX perform better than traditional models such as LSTM, PT, SVR, LR, and KNN. …”
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Hilbert-Huang Transform and machine learning based electromechanical analysis of induction machine under power quality disturbances by V. Indragandhi, R. Senthil Kumar, R. Saranya

“…Monitoring and predicting Power Quality (PQ) is crucial for quickly minimizing risk, protecting induction machines (IMs), and increasing productivity. …”
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Artificial intelligence in respiratory care by Manjush Karthika, Jithin K. Sreedharan, Madhuragauri Shevade, Chris Sara Mathew, Santosh Ray

“…AI algorithms are widely studied in areas like mechanical ventilation, sleep medicine, lung ultrasound, and pulmonary function diagnostics and the results are found to be promising. …”
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BREAST-CAD: A Computer-Aided Diagnosis System for Breast Cancer Detection Using Machine Learning by Riyam M. Masoud, Ramadan Madi Ali Bakir, M. Sabry Saraya, Sarah M. Ayyad

“…In the final phase, the DT model was embedded within a user-friendly client application, empowering clinicians to input patient diagnostic data directly and receive immediate, AI-driven predictions of cancer probability, with results securely transmitted and managed by a dedicated server, facilitating remote access and centralized data storage and ensuring data integrity.…”
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High-resolution Annual Dynamic dataset of Curve Number from 2008 to 2021 over Conterminous United States by Qiong Wu, John J. Ramirez Avila, Jia Yang, Cunxiong Ji, Shanmin Fang

“…This developed CN dataset can be used as input for hydrological models or machine learning algorithms to simulate rainfall-runoff across multiple spatiotemporal scales.…”
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Trustworthiness of Deep Learning Under Adversarial Attacks in Power Systems by Dowens Nicolas, Kevin Orozco, Steve Mathew, Yi Wang, Wafa Elmannai, George C. Giakos

“…Emphasizing the inherent requirement of robust defense, this initiative lays the groundwork for follow-on initiatives to incorporate security and resilience into ML and DL algorithms and ensure mission-critical AI system dependability.…”
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