Automation in Construction (2000–2023): Science Mapping and Visualization of Journal Publications by Mohamed Marzouk, Abdulrahman A. Bin Mahmoud, Khalid S. Al-Gahtani, Kareem Adel

“…Finally, the publications are analyzed using keyword co-occurrence and keyword thematic evolution analyses, revealing five major research clusters: (i) foundational optimization, (ii) deep learning and computer vision, (iii) building information modeling, (iv) 3D printing and robotics, and (v) machine learning. …”
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HARDWARE AND SOFTWARE COMPLEX FOR FRICTION AND WEAR INVESTIGATIONS BY THE «DISC ON PLATE» METHOD by F. F. Komarov, V. V. Pilko, V. N. Kuleshov

“…Another important requirement is modeling in the experiment of acyclic friction process, as close as possible to the real operating conditions of components and parts. …”
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Motion Planning Methods for Automated Parking: A Comprehensive Review by CHEN Xiaoming, LI Bai, FAN Lili, ZHANG Youmin

“…This paper offers a thorough review of the motion planning methods applied within the automated parking domain, categorizing them into geometric methods, sampling methods, search methods, numerical optimization methods, and machine learning methods. This paper also retrospectively examines their development and characteristics. …”
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Smoothing the Subjective Financial Risk Tolerance: Volatility and Market Implications by Wookjae Heo, Eunchan Kim

“…Seven smoothing methods were applied to derive smoothed mean and standard deviation values, including exponential smoothing, ARIMA, and Kalman filter. Machine learning models, including support vector machines and neural networks, were used to assess predictive performance. …”
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Estimation of state of health for lithium-ion batteries using advanced data-driven techniques by Smitanjali Rout, Sudhansu Kumar Samal, Demissie Jobir Gelmecha, Satyasis Mishra

“…This study proposes an advanced framework that leverages machine learning models to model the nonlinear degradation patterns of lithium-ion batteries by focusing on key features such as voltage, current, internal resistance, and temperature. …”
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Non-Destructive Detection of Soybean Storage Quality Using Hyperspectral Imaging Technology by Yurong Zhang, Wenliang Wu, Xianqing Zhou, Jun-Hu Cheng

“…Partial least squares regression (PLSR), support vector machine (SVM), and extreme learning machine (ELM) models were developed to predict crude fatty acid values of soybeans. …”
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Free-form型机床的切齿优化（一）——优化模型的建立 by 张艳红, 郭九生, 王小椿

“…In this paper, Free-form style machine tool setting parameters is analyzed, and a mathematical model of optimal synthesis is established. …”
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A multi objective collaborative reinforcement learning algorithm for flexible job shop scheduling by Jian Li, Shifa Li, Pengbo He, Huankun Li

“…First, a mathematical model for flexible job shop scheduling optimization is established, with the makespan and total energy consumption of the shop as optimization objectives, and a disjunctive-graph is introduced to represent state features. …”
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Real-Time Power Management of Plug-In Electric Vehicles and Renewable Energy Sources in Virtual Prosumer Networks with Integrated Physical and Network Security Using Blockchain by Nikolaos Sifakis, Konstantinos Armyras, Fotis Kanellos

“…Future research directions include investigating more advanced consensus mechanisms (e.g., Proof of Task), machine learning-driven predictive optimization, real-world large-scale testing, and federated learning models for decentralized decision-making. …”
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Prediction of pathological complete response to neoadjuvant chemoimmunotherapy in non–small cell lung cancer using 18F-FDG PET radiomics features of primary tumour and lymph nodes... by Xingbiao Liu, Zhilin Ji, Libo Zhang, Linlin Li, Wengui Xu, Qian Su

“…Our study aimed to establish a machine learning model that accurately predicts the pathological complete response (pCR) using 18F-FDG PET radiomics features. …”
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IoT‐Based Elderly Health Monitoring System Using Firebase Cloud Computing by Adhan Efendi, Muhammad Imam Ammarullah, Indra Griha Tofik Isa, Meli Puspita Sari, Jasmine Nurul Izza, Yohanes Sinung Nugroho, Hamid Nasrullah, Denny Alfian

“…A comparative experiment is conducted to define the optimal supervised machine learning model to be deployed into the system by analysing evaluation metrics. …”
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A novel flexible near-infrared endoscopic device that enables real-time artificial intelligence fluorescence tissue characterization. by Gareth Gallagher, Ra'ed Malallah, Jonathan P Epperlein, Jeffrey Dalli, Niall Hardy, Abhinav Jindal, Pol G MacAonghusa, Ronan A Cahill

“…Here, to close this technical gap, we present our development of a colonoscope-compatible flexible imaging probe for NIR-ICG visualization combined with a full field of view machine learning (ML) algorithm for fluorescence quantification and perfusion pattern cross-correlation (including first in human testing). …”
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AI-driven diagnosis and health management of autonomous electric vehicle powertrains: An empirical data-driven approach by Hicham El hadraoui, Adila El maghraoui, Oussama Laayati, Erroumayssae Sabani, Mourad Zegrari, Ahmed Chebak

“…The approach leverages vibration signals acquired from accelerometers and employs a hybrid machine learning (ML) framework. The study focuses on identifying the most informative features from time, frequency, and wavelet domains, followed by dimensionality reduction using Principal Component Analysis (PCA) and Correlation Analysis (CA) to enhance classification performance, reduce complexity, and improve model interpretability. …”
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Faster Image2Video Generation: A Closer Look at CLIP Image Embedding’s Impact on Spatio-Temporal Cross-Attentions by Ashkan Taghipour, Morteza Ghahremani, Mohammed Bennamoun, Aref Miri Rekavandi, Zinuo Li, Hamid Laga, Farid Boussaid

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Research Status and Prospects of Distribution Network Planning Technology Based on Artificial Intelligence by LI Jingru, LI Hongjun, MA Liang, JIANG Shigong, MU Chaoxu, SI Chenyi

“…However， it still faces critical issues， such as insufficient model accuracy and poor quality of generated solutions. …”
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Data-driven fault detection and positioning of eccentric rolls in roll-to-roll systems using wrap angle and sensor proximity by Yoonjae Lee, Minjae Kim, Jaehyun Noh, Gyoujin Cho, Changwoo Lee

“…Classification algorithms were used to compare the performance of models employing these optimized FCM-based features against traditional models utilizing a full suite of 40 statistical features. …”
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Clc-db: an open-source online database of chiral ligands and catalysts by Gufeng Yu, Kaiwen Yu, Xi Wang, Chenxi Zhang, Yicong Luo, Xiaohong Huo, Yang Yang

“…Recent advancements have demonstrated that computational methods, particularly machine learning, offer transformative potential by significantly accelerating these processes through enhanced prediction and modeling capabilities. …”
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Applications of artificial intelligence in hospital quality management: a review of digital strategies in healthcare settings by Rosa Elgiba García-García

“…The research identifies the main applications of AI in the automation of clinical and administrative processes, clinical decision support, triage optimization, and detection of adverse events. The most widely used technologies include machine learning, deep neural networks, expert systems, and natural language processing. …”
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A computational framework for inferring species dynamics and interactions with applications in microbiota ecology by Yuanwei Xu, Georgios V. Gkoutos

“…In this work, we contextualize the framework in microbial ecosystem modeling by coupling a modified generalized Lotka-Volterra formulation with machine learning optimization. …”
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6G native intelligence network architecture enabled by intent abstraction and knowledge by Jingya YANG, Xiaogang TANG, Yiqing ZHOU, Ling LIU, Wang Jiangzhou

“…6G will evolve to an intelligent network, featured by native intelligence and openness.The study of standardization of intelligent network emphasizes the importance of intent-driven network for network intelligence.However, current intent-based networking takes the intent as “What to do” rather than “What you want”.Moreover, current knowledge defined network (KDN) can partially fulfill “How to configure the network” according to “What to do”.Therefore, a 6G native intelligent network architecture based on the intent abstraction and knowledge was proposed, aiming to achieve“How to configure the network” according to “What you want”.Firstly, an intent abstraction module was designed to obtain “What to do” from “What you want”, composed of intent acquisition, intent translation, intent mapping, and intent modeling.Secondly, the cognitive module was proposed to achieve “How to configure the network” according to “What to do”, which got network knowledge through joint optimization of machine learning and logical reasoning.Finally, enabling key technologies such as intent mapping, network information measurement, network policy generation, and network policy verification were introduced to support the implementation of 6G native intelligence.…”
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