Analysis of Exergy Efficiency and Ways of Energy Saving in Air Conditioning System for a Cleanroom by Dmytro Harasym, Volodymyr Labay

“…So, reducing the cost of energy consumed by air conditioning systems preconditions the need for its optimization, which can be fully achieved by virtue of exergy analysis that takes into account not only the quantity but also the quality of energy spent. …”
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DeepDTAGen: a multitask deep learning framework for drug-target affinity prediction and target-aware drugs generation by Pir Masoom Shah, Huimin Zhu, Zhangli Lu, Kaili Wang, Jing Tang, Min Li

“…Abstract Identifying novel drugs that can interact with target proteins is a highly challenging, time-consuming, and costly task in drug discovery and development. Numerous machine learning-based models have recently been utilized to accelerate the drug discovery process. …”
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Prediction of Li-ion conductivity in Ca and Si co-doped LiZr2(PO4)3 using a denoising autoencoder for experimental data by Yumika Yokoyama, Shuto Noguchi, Kazuki Ishikawa, Naoto Tanibata, Hayami Takeda, Masanobu Nakayama, Ryo Kobayashi, Masayuki Karasuyama

“…However, a recent use of a materials informatics approach utilizing machine learning shows promise for more efficient property optimization. …”
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A GNSS-IR Soil Moisture Inversion Method Considering Multi-Factor Influences Under Different Vegetation Covers by Yadong Yao, Jixuan Yan, Guang Li, Weiwei Ma, Xiangdong Yao, Miao Song, Qiang Li, Jie Li

“…A multi-factor SMC inversion dataset was constructed, and three machine learning models were selected to develop the SMC prediction model: Support Vector Regression (SVR), suitable for small and medium-sized regression tasks; Convolutional Neural Networks (CNN), with robust feature extraction capabilities; and NRBO-XGBoost, which supports automatic optimization. …”
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Deep Metric Learning-Assisted 3D Audio-Visual Speaker Tracking via Two-Layer Particle Filter by Yidi Li, Hong Liu, Bing Yang, Runwei Ding, Yang Chen

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Intelligent Data Processing Methods for the Atypical Values Correction of Stock Quotes by T. V. Zolotova, D. A. Volkova

“…As part of further work, it is possible to consider the optimization of the parameters used in the methods of detecting and correcting outliers to study their effect on the results of the models.…”
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Evaluating techniques from low-shot learning on traditional imbalanced classification tasks by Preston Billion-Polak, Taghi M. Khoshgoftaar

“…In this paper, we aim to fill this gap by selecting two LSL papers from prior literature (representing two major approaches to LSL, optimization-based and contrastive), and reevaluate their models on two highly-imbalanced tabular fraud detection datasets, including a “big-data” Medicare dataset. …”
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Ensemble Transformer–Based Detection of Fake and AI–Generated News by Md. Ishraquzzaman, Mohammed Ashraful Islam Chowdhury, Shahreen Rahman, Riasat Khan

“…Among the machine learning models, random forest achieved the highest performance, with an accuracy of 92.49% and an F1 score of 92.60%. …”
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Numerical analysis method of stress wave transmission attenuation of coal and rock structural plane by Wenlong SHEN, Renren ZHU, Ziqiang CHEN, Guocang SHI

“…Given the one-dimensional dynamic joint angle and axial static load difference of the coal rock structural plane under the bearing damage of the stress wave transmittance problem, the mechanism of interface inclination and axial static load on the transmitted stress wave of the coal-rock structural surface was revealed by using indoor experiments, theoretical analysis and computer simulation. The simulation and machine learning of stress wave transmission in the experimental process of Split Hopkinson Pressure Bar (SHPB) were carried out by combining the Barton-Bandis nodal ontology model, UDEC discrete element simulation and Gray Wolf Algorithm optimized BP neural network technology. …”
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基于MED-SVM的齿轮箱故障诊断方法 by 刘志川, 唐力伟, 曹立军, 张远刚

“…In order to solve the problem of gearbox fault diagnosis,a new method based on minimum entropy deconvolution（MED）and support vector machine（SVM）is proposed.MED is used for gearbox vibration acceleration signal under background noise,then feature parameters extracted on breadth domain,frequency domain and energy domain of decreased signal are carried out,and the feature vector is built.Taking the feature vector as input,the multi-classification support vector machine is established,and the model parameters optimized by cross validation method are used to identify gearbox fault types.The fault diagnosis result of practical gearbox vibration signals shows that the proposed method can effectively identify different fault types of gear and bearing,and the optimizing model parameters can evidently improve fault identification accuracy.…”
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Transient Synchronization Stability in Grid-Following Converters: Mechanistic Insights and Technological Prospects—A Review by Yang Liu, Lin Zhu, Xinya Xu, Dongrui Li, Zhiwei Liang, Nan Ye

“…Building on existing studies, the paper further explores innovative applications of artificial intelligence (AI) in transient stability assessment, including stability prediction based on deep learning, data-physics hybrid modeling, and human–machine collaborative optimization strategies. …”
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Network-based intrusion detection using deep learning technique by Muhammad Farhan, Hafiz Waheed ud din, Saadat Ullah, Muhammad Sajjad Hussain, Muhammad Amir Khan, Tehseen Mazhar, Umar Farooq Khattak, Ines Hilali Jaghdam

“…The interesting novelty of this study is the tactical use of ReLU-based DNN combined with feature optimization through the Extra Tree Classifier, which not only overcomes general problems like vanishing gradients and overfitting but also greatly increases the interpretability of the model and the efficiency of its computation. …”
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Dual Strategy Based Improved Swarm Intelligence and Stacked LSTM With Residual Connection for Land Use Land Cover Classification by Vinaykumar Vajjanakurike Nagaraju, Ananda Babu Jayachandra, Andrzej Stateczny, Swathi Holalu Yogesh, Raviprakash Madenur Lingaraju, Balaji Prabhu Baluvaneralu Veeranna

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Dynamic Rock-Breaking Process of TBM Disc Cutters and Response Mechanism of Rock Mass Based on Discrete Element by Qinglong Zhang, Yanwen Zhu, Canxun Du, Sanlin Du, Kun Shao, Zhihao Jin

“…Rock-breaking efficiency of full-face rock tunnel boring machine (TBM) is closely related to the performance of the disc cutter and the characteristics of the rock mass. …”
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The Modularized Development of a Wheel-Side Electric Drive System Using the Process of Hobbing and Form Grinding by Xiaoyu Ding, Wei Wang, Xinbo Chen

“…As a result, the gears are critical to output robustness and NVH performance. The modeling accuracy is decisive for simulations and tests, so it is necessary to build a precise geometric model instead of the data-fitting estimation. …”
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Simulation of Motion of Long Flexible Fibers with Different Linear Densities in Jet Flow by Peifeng Lin, Wenqian Xu, Yuzhen Jin, Zefei Zhu

“…Air-jet loom is a textile machine designed to drive the long fiber using a combination flow of high-pressure air from a main nozzle and a series of assistant nozzles. …”
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Deep Learning for Ore Haulage Monitoring: Vibrational Analysis Using a VGG16 Network by Artur Skoczylas, Pawel Stefaniak, Sergii Anufriiev, Wioletta Koperska

“…The proposed solution incorporates advanced deep learning models, specifically VGG16 and autoencoders, to process the sensor data and detect cycles effectively. …”
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Comparative Analysis of Control Strategies for Microgrid Energy Management with a Focus on Reinforcement Learning by Parisa Mohammadi, Razieh Darshi, Saeed Shamaghdari, Pierluigi Siano

“…However, these approaches often struggle with slow performance and high computational demands, making them less effective for real-time applications due to the need for frequent re-optimization. In contrast, reinforcement learning, a branch of machine learning, excels by continuously learning and optimizing through real-time interactions This approach offers greater flexibility and adaptability in complex and dynamic environments. …”
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Exploration of Cutting Processing Mode of Low-Rigidity Parts for Intelligent Manufacturing by Jianping Zhu, Xinna Liu, Hui Peng, Wei Liu, Zhiyong Li

“…The proposed architecture can provide a reference model for the research and application of intelligent cutting technology for low-rigidity parts.…”
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Enhancing Predictive Maintenance in Mining Mobile Machinery Through a Hierarchical Inference Network by Raul de la Fuente, Luciano Radrigan, Anibal S. Morales

“…This is critical to ensure machinery uptime in remote, rugged environments. The use of Tiny-Machine-Learning (TinyML) optimization approaches allow optimal accuracy and model compression for efficient deployment of deep learning models on IoT edge devices with limited hardware resources. …”
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