Generalization of scientific and institutional prerequisites for risk management of technological integration by A. E. Miller, L. M. Davidenko

“…The authors highlight the trends of technological transformation of the global energy complex, which determine the promising directions of technological integration of the oil and gas sector for the development of carbon-neutral energy. As a positive vector, the development and implementation of innovations that improve environmental safety, as well as new technologies for the production of pure hydrogen from natural gas and recycling processes are shown. …”
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Hybrid Approach of Cotton Disease Detection for Enhanced Crop Health and Yield by Rahul Kumar, Ashok Kumar, Karamjit Bhatia, Kottakkaran Sooppy Nisar, Siddharth Singh Chouhan, Priti Maratha, Anoop Kumar Tiwari

“…” These models include Random Forest, Support Vector Machine (SVM), Multi-Class SVM, and an Ensemble model. …”
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Flexible Baseband-Unit Aggregation Enabled by Reconfigurable Multi-IF Over WDM Fronthaul by Haiyun Xin, Hao He, Kuo Zhang, Syed Baqar Hussain, Weisheng Hu

“…For 4G+ deployment as specified in 3GPP protocol, 100-MHz orthogonal frequency-division multiplexing signal with 64 quadratic-amplitude modulation format after transmission shows that 3.5% error vector magnitude can be achieved.…”
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Obstacle Detection and Warning System for Visually Impaired Using IoT Sensors by Sunnia Ikram, Imran Sarwar Bajwa, Amna Ikram, Isabel de la Torre Diez, Carlos Eduardo Uc Rios, Angel Kuc Castilla

“…The system is equipped with ultrasonic sensor, PIR sensor and a buzzer, with data processing managed by an Arduino Uno microcontroller. …”
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Interpretation of Heritage in Mountain Areas (Leitariegos and Cueto Arbás (Asturias, Spain)) Through Experience Based on Virtual Reality by Daniel Herrera, Carmen Rodríguez, Juan Sevilla

“…Using a VR tool, we sought to enhance the identification and interpretation of the keys that lead to the initiation and consolidation of the patrimonialization process, uncovering the processes and agents through their practices, the vectors around which the process pivots, and the conflicts in the competition for land use.…”
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Machine Learning Models for Carbonation Depth Prediction in Reinforced Concrete Structures: A Comparative Study by Rafael Aredes Couto, Igor Augusto Guimarães Campos, Elvys Dias Reis, Daniel Hasan Dalip, Flávia Spitale Jacques Poggiali, Péter Ludvig

“…This study applied machine learning (ML) techniques—Random Forest (RF), Support Vector Regression (SVR), and Artificial Neural Networks (ANNs)—to predict carbonation depth using a synthetic dataset of 20,000 instances generated from the validated Possan equation. …”
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Polarization-Independent Broadband Infrared Selective Absorber Based on Multilayer Thin Film by Shenglan Wu, Hao Huang, Xin Wang, Chunhui Tian, Zhenyong Huang, Zhiyong Zhong, Shuang Liu

“…Moreover, the proposed planarized structure design is compatible with standard fabrication processes and has good scalability, which can be applied to other electromagnetic wave bands. …”
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Real-Time Intrusion Detection in Power Grids Using Deep Learning: Ensuring DPU Data Security by Maoran Xiao, Qi Zhou, Zhen Zhang, Junjie Yin

“…This paper explores the use of deep learning for real-time intrusion detection in power grids with a primary focus on safeguarding the integrity and security of Data Processing Units (DPUs). An evaluation of various machine learning models, including Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Decision Trees, and Random Forests, is conducted to detect various types of intrusions, including Fault, Injection, Masquerade, Normal, and Replay. …”
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Methodology For Extracting Poplar Planted Fields From Very High-Resolution Imagery Using Object-Based Image Analysis and Feature Selection Strategy by E. O. Yilmaz, T. Kavzoglu, I. Colkesen, H. Tonbul, A. Teke

“…The delineation and monitoring of poplar cultivated areas are invaluable for decision-making processes. With the remote sensing technology, accurate detection of poplar planted areas could be determined much faster, more economically, and with minimum labor requirements. …”
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MSJosSAR Configuration Optimization and Scattering Mechanism Classification Based on Multi-Dimensional Features of Attribute Scattering Centers by Shuo Liu, Fubo Zhang, Longyong Chen, Minan Shi, Tao Jiang, Yuhui Lei

“…The approach transforms the configuration optimization problem into a vector separability problem commonly addressed in machine learning. …”
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CPS-LSTM: Privacy-Sensitive Entity Adaptive Recognition Model for Power Systems by Hao Zhang, Jing Wang, Xuanyuan Wang, Xuhui Lü, Zhenzhi Guan, Zhenghua Cai, Hua Zhang

“…We then introduce CPS-LSTM (Character-level Privacy-sensitive Entity Adaptive Recognition Model), which enhances the recognition capability of privacy-sensitive entities in mixed Chinese and English text through character-level embedding and word vector fusion. The model features a streamlined architecture, accelerating convergence and enabling parallel sentence processing. …”
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Advanced evaluation of performance of machine learning models for soapstock splitting optimisation under uncertainty by Bartosz Szeląg, Krzysztof Barbusiński, Michał Stachura, Przemysław Kowal, Adam Kiczko, Eldon R. Rene

“…Machine learning algorithms—Extreme Gradient Boosting (XGBoost) and Support Vector Machines (SVM)—were assessed in comparison with Response Surface Methodology (RSM). …”
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Mobile Accelerometer Applications in Core Muscle Rehabilitation and Pre-Operative Assessment by Aleš Procházka, Daniel Martynek, Marie Vitujová, Daniela Janáková, Hana Charvátová, Oldřich Vyšata

“…The study employs a range of machine learning methods, including support vector machines, Bayesian analysis, and neural networks, to evaluate the balance of various physical activities. …”
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A Fast Power Market Clearing Method Based on Active Constraints Identification by Deep Learning by Yunliang WU, Jianxin ZHANG, Bao LI, Peng LI, Zhiyong LI, Xin ZHOU, Yan YANG, Xiaowen LAI

“…Secondly, a deep learning strategy is proposed for identification of active constraint sets, which can provide technical support for deep neural networks to effectively identify the active constraints of SCED from two aspects: feature vector design and efficient processing of the results of deep neural network. …”
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Continuous prediction of human knee joint angle using a sparrow search algorithm optimized random forest model based on sEMG signals by Liuyi Ling, Zhu Lin, Bin Feng, Liyu Wei, Li Jin, Yiwen Wang

“…The performance of the proposed model was compared with those of traditional backpropagation neural network, support vector machine regression, and random forest models. …”
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Soil Organic Carbon Prediction and Mapping in Morocco Using PRISMA Hyperspectral Imagery and Meta-Learner Model by Yassine Bouslihim, Abdelkrim Bouasria, Budiman Minasny, Fabio Castaldi, Andree Mentho Nenkam, Ali El Battay, Abdelghani Chehbouni

“…The first layer consists of Random Forest (RF), Support Vector Regression (SVR), and Partial Least Squares Regression (PLSR). …”
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An Integrated Learning Approach for Municipal Solid Waste Classification by Hieu M. Sondao, Tuan M. Le, Hung V. Pham, Minh T. Vu, Son Vu Truong Dao

“…These selected features are then fed into machine learning classifiers—Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), and K-Nearest Neighbor (KNN)—for final predictions. …”
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Viral and Viroid Communities in Peach Cultivars Grown in Bulgaria by Mariyana Gozmanova, Vesselin Baev, Rumyana Valkova, Elena Apostolova-Kuzova, Stoyanka Jurac, Galina Yahubyan, Lilyana Nacheva, Snezhana Milusheva

“…Climate change may alter vector populations and lead to shifts in agricultural practices, influencing the spread of these viruses and viroids. …”
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IoT-MFaceNet: Internet-of-Things-Based Face Recognition Using MobileNetV2 and FaceNet Deep-Learning Implementations on a Raspberry Pi-400 by Ahmad Saeed Mohammad, Thoalfeqar G. Jarullah, Musab T. S. Al-Kaltakchi, Jabir Alshehabi Al-Ani, Somdip Dey

“…Additionally, an in-house database is compiled, capturing data from 50 individuals via a web camera and 10 subjects through a smartphone camera. Pre-processing of the in-house database involves face detection using OpenCV’s Haar Cascade, Dlib’s CNN Face Detector, and Mediapipe’s Face. …”
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Genomic selection in pig breeding: comparative analysis of machine learning algorithms by Ruilin Su, Jingbo Lv, Yahui Xue, Sheng Jiang, Lei Zhou, Li Jiang, Junyan Tan, Zhencai Shen, Ping Zhong, Jianfeng Liu

“…Machine learning (ML) methods are usually used to predict phenotypic values since their advantages in processing high dimensional data. While, the existing researches have not indicated which ML methods are suitable for most pig genomic prediction. …”
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