Compositional modeling of solution gas–oil ratio (Rs): a comparative study of tree-based models, neural networks, and equations of state by Aydin Larestani, Sara Sahebalzamani, Abdolhossein Hemmati-Sarapardeh, Ali Naseri

“…A relevancy factor analysis quantified the influence of each input parameter on model outputs, whereas the Leverage technique identified outliers and defined the parameter ranges for optimal algorithm performance. While the ML models achieved high predictive reliability, their computational demands and complexity may limit deployment in resource-constrained environments and decision-critical applications. …”
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An online multi-camera multi-vehicle tracking using lightweight YOLO11 and improved association strategies by Feijiang Huang, Jialong Yao, Chengyue Su, Sheng Xu

“…For cross-camera trajectory association, a hierarchical clustering algorithm based on cosine distance and Dunn’s index automatically optimizes clustering parameters to enhance matching precision. …”
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Combination of Feature Selection and Learning Methods for IoT Data Fusion by V. Sattari-Naeini, Zahra Parizi-Nejad

“…In this paper, we propose five data fusion schemes for the Internet of Things (IoT) scenario,which are Relief and Perceptron (Re-P), Relief and Genetic Algorithm Particle Swarm Optimization (Re-GAPSO), Genetic Algorithm and Artificial Neural Network (GA-ANN), Rough and Perceptron (Ro-P)and Rough and GAPSO (Ro-GAPSO). …”
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Linear Continuous-Time Regression and Dequantizer for Lithium-Ion Battery Cells with Compromised Measurement Quality by Zoltan Mark Pinter, Mattia Marinelli, M. Scott Trimboli, Gregory L. Plett

“…Then, the Linear Continuous-Time Regression (LCTR) algorithm extracts exponential parameters from first-order or overdamped second-order systems, deducing ECM parameters and guaranteeing optimality with respect to RMSE. …”
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Blasting Vibration Control Using an Improved Artificial Neural Network in the Ashele Copper Mine by Shida Xu, Tianxiao Chen, Jiaqi Liu, Chenrui Zhang, Zhiyang Chen

“…In this paper, a genetic algorithm (GA) and an artificial neural network (ANN) algorithm were jointly used to construct a neural network model with a 4-5-1 topology to predict the PPV. …”
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Trade-off between gradient measurement efficiency and expressivity in deep quantum neural networks by Koki Chinzei, Shinichiro Yamano, Quoc Hoan Tran, Yasuhiro Endo, Hirotaka Oshima

“…Abstract Quantum neural networks (QNNs) require an efficient training algorithm to achieve practical quantum advantages. …”
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A Simulation Based Metaheuristics for Capacitated Vehicle Routing Problem by Tamara Latifah Jasmine, Heru Purboyo Hidayat Putro, Niken Prilandita, Gatot Yudoko

“…The methodology utilizes a simheuristics approach, integrating a Genetic Algorithm (GA) to determine optimal routing solutions, while employing Discrete Event Simulation (DES) to incorporate key economic, environmental, and social variables. …”
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Lightweight YOLOv8s-Based Strawberry Plug Seedling Grading Detection and Localization via Channel Pruning by CHEN Junlin, ZHAO Peng, CAO Xianlin, NING Jifeng, YANG Shuqin

“…Consequently, it was necessary to experiment extensively to identify the optimal pruning rate that balanced model accuracy and speed. …”
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Mortality prediction of heart transplantation using machine learning models: a systematic review and meta-analysis by Ida Mohammadi, Setayesh Farahani, Asal Karimi, Saina Jahanian, Shahryar Rajai Firouzabadi, Mohammadreza Alinejadfard, Alireza Fatemi, Bardia Hajikarimloo, Mohammadhosein Akhlaghpasand

“…Among the algorithms, CatBoost demonstrated the highest accuracy (AUC 0.80, 95% CI: 0.74, 0.86), while K-nearest neighbor had the lowest accuracy (AUC 0.53, 95% CI: 0.50, 0.55). …”
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Genotyping Identification of Maize Based on Three-Dimensional Structural Phenotyping and Gaussian Fuzzy Clustering by Bo Xu, Chunjiang Zhao, Guijun Yang, Yuan Zhang, Changbin Liu, Haikuan Feng, Xiaodong Yang, Hao Yang

“…The research results showed that the Gaussian Fuzzy Clustering algorithm was the optimal choice for clustering maize genotypes. …”
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Multi-Feature Long Short-Term Memory Facial Recognition for Real-Time Automated Drowsiness Observation of Automobile Drivers with Raspberry Pi 4 by Michael Julius R. Moredo, James Dion S. Celino, Joseph Bryan G. Ibarra

“…The model was trained on the NTHU-DDD dataset and optimized using long short-term memory (LSTM) deep learning algorithms implemented using TensorFlow version 2.14.0. …”
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DRACO: Decentralized Asynchronous Federated Learning Over Row-Stochastic Wireless Networks by Eunjeong Jeong, Marios Kountouris

“…Through a thorough convergence analysis, we show that DRACO achieves high performance in decentralized optimization while maintaining low variance across users even without predefined scheduling policies. …”
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Distance Estimation with a Stereo Camera and Accuracy Determination by Arnold Zaremba, Szymon Nitkiewicz

“…These results provide valuable information on the capabilities and limitations of the measurement system used, while pointing out directions for its further optimization.…”
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Neural Network-Based Imitation Learning for Approximating Stochastic Battery Management Systems by Andrea Pozzi, Alessandro Incremona, Daniele Toti

“…Traditional predictive control methods are limited by their reliance on precise models, which are often hindered by uncertainties in battery parameters due to aging, production variability, and operational conditions. While stochastic predictive control policies can address these uncertainties by incorporating them directly into the optimization process, they typically introduce considerable computational complexity. …”
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Artificial Neural Network Prediction on negative and positive activation energy of magnetohydrodynamic nanofluid flow with multiple slips by Shovan Sarkar, Hiranmoy Mondal, Prabir Kumar Kundu

“…This study can be helpful to identify the optimal conditions for heat and mass transfer enhancement in magnetohydrodynamic nanofluid flow with multiple slips. …”
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PV<i>k</i>NN: A Publicly Verifiable and Privacy-Preserving Exact <i>k</i>NN Query Scheme for Cloud-Based Location Services by Jingyi Li, Yuqi Song, Chengliang Tian, Weizhong Tian

“…Leveraging Paillier’s homomorphic cryptosystem and public-key signature techniques, our design achieves heightened security by safeguarding databases, query access patterns, and result integrity while enabling public verification. Additionally, our scheme enhances computational efficiency through optimized data-packing techniques and simplified Voronoi diagram-based ciphertext index construction, leading to substantial savings in computational and communication overheads. …”
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Low-Power Portable System for Power Grid Foreign Object Detection Based on the Lightweight Model of Improved YOLOv7 by Yonghuan He, Rong Wu, Chao Dang

“…Foreign objects caught and entangled on power grid transmission lines, power towers and other equipment can pose potential threats to the power system. The detection algorithm for foreign objects in the power grid cannot achieve the optimal balance in terms of accuracy and efficiency. …”
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AI and Machine Learning in V2G technology: A review of bi-directional converters, charging systems, and control strategies for smart grid integration by Nagarajan Munusamy, Indragandhi Vairavasundaram

“…AI-powered predictive analytics can forecast energy demand and supply, enabling proactive charging and discharging strategies. ML algorithms can optimize charging rates, battery health, and grid stability while also detecting anomalies and preventing potential faults. …”
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Multi-Objective-Based Multi-Heterogeneous- Agent Deep Reinforcement Learning for Minimization of Voltage Deviation and Operation Cost in Active Distribution System by Anurak Deanseekeaw, Watcharakorn Pinthurat, Boonruang Marungsri

“…The proposed framework employs a multi-objective optimization approach, integrating three advanced algorithms: multi-agent proximal policy optimization (MAPPO), multi-agent asynchronous actor-critic (MAA2C), and multi-agent twin delayed deep deterministic policy gradient (MATD3). …”
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Artificial intelligence-empowered functional design of semi-transparent optoelectronic and photonic devices via deep Q-learning by Caglar Cetinkaya, Erman Cokduygulular, Muhammed Yusuf Aykut, Okan Erkal, Fatma Aydogmus, Baris Kinaci

“…Abstract Photonic-based design of semi-transparent organic solar cells (ST-OSCs) demands a careful balance between optical transparency and photovoltaic efficiency, often requiring trade-offs that complicate optimization. This study, for the first time, employs deep Q-learning, a reinforcement learning algorithm, to address this challenge, integrating transfer matrix method for precise optical calculations. …”
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