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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Artificial intelligence in respiratory care by Manjush Karthika, Jithin K. Sreedharan, Madhuragauri Shevade, Chris Sara Mathew, Santosh Ray

“…Despite barriers, the current decade is witnessing an increased utility of AI into diverse specialities of the medical field to enhance precision medicine, predict diagnosis, therapeutic results, and prognosis; this includes respiratory medicine, critical care, and in their allied specialties. …”
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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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Trustworthiness of Deep Learning Under Adversarial Attacks in Power Systems by Dowens Nicolas, Kevin Orozco, Steve Mathew, Yi Wang, Wafa Elmannai, George C. Giakos

“…Nevertheless, these models are susceptible to adversarial attacks, which could lead to inaccurate predictions and system failure. In this paper, the impact of these attacks on DL models is analyzed by employing the use of defensive countermeasures such as Adversarial Training, Gaussian Augmentation, and Feature Squeezing, to investigate vulnerabilities in industrial control systems with potentially disastrous real-world impacts. …”
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Decentralized coordinated emergency frequency control strategy for renewables-rich power systems based on multi-agent reinforcement learning by Zhenglong Sun, Junpeng Han, Weihan Chen, Rui Zhang, Yong Liu, Yoash Levron, Juri Belikov

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Improving computational drug repositioning through multi-source disease similarity networks by Duc-Hau Le

“…We applied a tailored Random Walk with Restart (RWR) algorithm to predict novel drug-disease associations. …”
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Amortized Fairness for Drive-Thru Internet by Zhi Li, Limin Sun, Xinyun Zhou

“…The amortized fairness MAC requires predictions of future link quality. For this, we fully exploit the inner and inter-AP correlations revealed from our extensive field studies and design a link quality prediction algorithm. …”
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Deep Neural Network for Supervised Single-Channel Speech Enhancement by Nasir SALEEM, Muhammad IRFAN KHATTAK, Muhammad Yousaf ALI, Muhammad SHAFI

“…During the training stage the network learns and predicts the magnitude spectrums of the clean and noise signals from input noisy speech acoustic features. …”
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XFEM for Fracture Analysis of Centrally Cracked Laminated Plates Subjected to Biaxial Loads by Shailesh Palekar, Achchhe Lal

“…The effect of loadings on the crack growth and crack propagation direction and their effects on the MSIFs using global tracking crack growth algorithm is also presented. The results of the present investigation will be useful for accurate prediction of fracture response of cracked composite structures, crack growth and crack propagation behavior which ultimately effects on the structural safety and integrity of the composite structures.…”
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Fault Diagnosis of Industrial Process Based on FDKICA-PCA by ZHANG Jing, ZHU Fei-fei, LIU Jia-xing, WANG Jiang-tao

“…Because the dynamic characteristics of autocorrelation and lag correlation in production process are neglected in fault diagnosis,Kernel Independent Component AnalysisPrincipal Component Analysis (KICAPCA) is very poor in detecting small and gradual faults because of lacking available variable contribution analysis.In this paper, a dynamic kernel independent component analysis (KICAPCA) fault diagnosis method based on wavelet packet filtering is proposed.This method integrates wavelet packet filtering theory and AR model prediction data characteristics into KICAPCA to extract the feature information of process variable autocorrelation and lagrelated .In this paper, KICAPCA algorithm is used to extract the independent components and principal components of process variables to determine the control limits of three monitoring indicators T2, SPE,I2.Nonlinear contribution graph is used for fault diagnosis, and the advantage of FDKICAPCA method is verified by simulation results of Tennessee process.…”
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A precise estimation framework for individual tree AGB of Pinus kesiya var. Langbianensis utilizing point cloud registration Optimization by Zhibo Yu, Yong Wu, Ziyu Zhang, Chi Lu, Hong Wang, Zhi Liu, Xiaoli Zhang, Lei Bao, Jie Pan, Guanglong Ou, Hongbin Luo

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BSG-WSL: BackScatter-guided weakly supervised learning for water mapping in SAR images by Kai Wang, Zhongle Ren, Biao Hou, Weibin Li, Licheng Jiao

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Enhancing Sustainable Manufacturing in Industry 4.0: A Zero-Defect Approach Leveraging Effective Dynamic Quality Factors by Rouhollah Khakpour, Ahmad Ebrahimi, Seyed Mohammad Seyed Hosseini

“…Moreover, the gathered data from defect detection can be used in two ways: to prevent defect occurrence in the future (detect-prevent) and to design algorithms for predicting when a defect may occur in the future, hence, to prevent defects before they arise (predict-prevent). …”
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A Novel PAPR in OTFS Systems Through ISSA-PTS and ISPR Techniques by Gerges Mansour Salama, Amira A. Mohamed, Aziza I. Hussein, M. Mourad Mabrook

“…This study introduces two novel PAPR reduction techniques: a hybrid approach combining the Improved Salp Swarm Algorithm (ISSA) with Partial Transmit Sequence (PTS) and an innovative Iterative Sub-block Phase Rotation (ISPR) method. …”
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Thermal performance and passive energy saving optimization of prefabricated houses in Xinjiang region by Juan Zhao, Rui Liu, Botao Zhou, Yunchao Fu, Yongcai Li, Wenjie Zhang

“…Compared to original scheme1, it effectively achieves a reduction in the annual cost value to 6037.09CNY, representing a decrease of 489.853CNY, along with an energy consumption of 9428.36 kWh, reflectiong a reduction by 13.48 %.This study provides experimental data and passive energy-saving optimization solutions for improving thermal performance in retrofitting prefabricated houses.…”
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Handover Strategy for LEO Satellite Networks Using Bipartite Graph and Hysteresis Margin by Sahar Eydian, Maryam Hosseini, Gunes Karabulut Kurt

“…The numerical results demonstrate a significant reduction in handover rate and latency, while improving energy efficiency and achieving enhanced data rates. …”
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Combination treatment optimization using a pan-cancer pathway model. by Robin Schmucker, Gabriele Farina, James Faeder, Fabian Fröhlich, Ali Sinan Saglam, Tuomas Sandholm

“…In recent years, sophisticated mechanistic, ordinary differential equation-based pathways models that can predict treatment responses at a molecular level have been developed. …”
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Cost-Reference Particle Filter-Based Method for Constructing Effective Brain Networks: Application in Optically Pumped Magnetometer Magnetoencephalography by Yuyu Ma, Xiaoyu Liang, Huanqi Wu, Hao Lu, Yong Li, Changzeng Liu, Yang Gao, Min Xiang, Dexin Yu, Xiaolin Ning

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Methods to Quantitatively Evaluate the Effect of Shale Gas Fracturing Stimulation Based on Least Squares by DENG Cai, SUN Kexin, WEN Huan, HU Chaolang

“…During the step-down tests, a high level of agreement was observed between the predicted and measured friction pressure curves, confirming the model's robustness under complex field conditions. …”
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