Deep‐NCA: A deep learning methodology for performing noncompartmental analysis of pharmacokinetic data by Gengbo Liu, Logan Brooks, John Canty, Dan Lu, Jin Y. Jin, James Lu

“…Although the existing NCA algorithms are very well‐established and widely utilized, they suffer from low accuracies in the setting of sparse PK samples. …”
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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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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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Development of New Radar and Pyroelectric Sensors for Road Safety Increase in Cloud-Based Multi-Agent Control Application by Leslie Robert Adrian, Ansis Avotins, Donato Repole, Olegs Tetervenoks

“…The proposed sensor solutions can detect the road user (vehicle or pedestrian) and determine its movement direction and approximate speed that can be used for dynamic lighting control algorithms, traffic intensity prediction, and increased safety for both driver and pedestrian traffic. …”
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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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Detection and Tracking of Environmental Sensing System for Construction Machinery Autonomous Operation Application by Junyi Chen, Qipeng Cai, Xinhai Hu, Qihuai Chen, Tianliang Lin, Haoling Ren

“…By taking the environmental characteristics and operating conditions of construction machinery into consideration, a set of environmental sensing algorithms based on LiDAR for construction machinery scenarios is studied. …”
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Driving range estimation for electric bus based on atomic orbital search and back propagation neural network by Hanchen Ke, Jun Bi, Yongxing Wang, Yu Zhang

“…Simulation and experimental analysis show that the algorithm introduced in this paper has higher prediction accuracy and efficiency compared to the traditional machine learning algorithms, that compared with BPNN, AOSBP reduced MAE, RMSE and MAPE by 85.6%, 50.9% and 64.6%, respectively, which effectively relieves range anxiety, and ensures the normal operation of the electric bus fleet.…”
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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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Challenges in COVID-19 Pandemic Triaging: An Indian and US Perspective by Muralidhar Varma, Robin Sudandiradas, Mauli Mahendra Patel, Trini Ann Mathew, Marcus Zervos, Shashikiran Umakanth, Asha Kamath, Mahadev Rao, Vandana Kalwaje Eshwara, Chiranjay Mukhopadhyay, Vijaya Arun Kumar

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Discovering action insights from large-scale assessment log data using machine learning by Minyoung Yun, Minjeong Jeon, Heyoung Yang

“…This approach demonstrates potential applications in personalized education, healthcare diagnostics, and consumer behavior prediction, advancing the understanding of human behavior through digital footprints.…”
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Discovering the Influences of Complex Network Effects on Recovering Large Scale Multiagent Systems by Yang Xu, Pengfei Liu, Xiang Li, Wei Ren

“…Those interesting discoveries are helpful to predict how complex network attributes influence on system performance and in turn are useful for new algorithm designs that make a good use of those attributes.…”
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LoRa Technology Enhanced with a Custom-Designed High-Gain Yagi-Uda Antenna for Data Transmission from Misti Volcano Monitoring to Arequipa City by Flor de Milagro Yesit Arana Medina, Jorge Rendulich

“…In response, this project integrates Internet of Things (IoT) technology, the LoRa (Long Range) network, and an optimized seven-element Yagi-Uda antenna, developed using advanced optimization algorithms to enhance transmission efficiency. The primary objective is to facilitate the reliable collection and transmission of critical sensor data for subsequent analysis by volcanological experts, thereby supporting improved prediction and mitigation of potential volcanic hazards. …”
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House Value Estimation using Different Regression Machine Learning Techniques by Tarek Ghamrawi, Müesser Nat

“…This study investigates the effectiveness of various regression algorithms in estimating house values using a dataset sourced from Zillow.com, encompassing 15,000 residential properties from Denver, Colorado. …”
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Learning Optimal Dynamic Treatment Regime from Observational Clinical Data through Reinforcement Learning by Seyum Abebe, Irene Poli, Roger D. Jones, Debora Slanzi

“…Our study aims to evaluate the performance and feasibility of such algorithms: tree-based reinforcement learning (T-RL), DTR-Causal Tree (DTR-CT), DTR-Causal Forest (DTR-CF), stochastic tree-based reinforcement learning (SL-RL), and Q-learning with Random Forest. …”
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