Self-Healing of Active Distribution Networks by Accurate Fault Detection, Classification, and Location by Sally El-Tawab, Hassan S. Mohamed, Amr Refky, A. M. Abdel-Aziz

“…The fault location is achieved by integrating DWT and support vector machine (SVM). The data for training were extracted using DWT and collected, and then SVM was trained to locate the faulted section. …”
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Acoustic Source Localization Using Kernel-based Extreme Learning Machine in Distributed Microphone Array by Rong WANG, Zhe CHEN, Fuliang YIN

“…After the kernel-based extreme learning machine (K-ELM) is well trained, the measured generalized cross correlation data are fed into the K-ELM classifier, and the output is the estimated acoustic source position. …”
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Transient Stability Assessment Model With Sample Selection Method Based on Spatial Distribution by Yongbin Li, Yiting Wang, Jian Li, Huanbei Zhao, Huaiyuan Wang, Litao Hu

“…With the phasor measurement units (PMUs) being widely utilized in power systems, a large amount of data can be stored. If transient stability assessment (TSA) method based on the deep learning model is trained by this dataset, it requires high computation cost. …”
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Gear Fault Diagnosis based on Distribution Adaptation Layer and Soft Label Learning by Zhenguo Jie, Xiyang Wang, Tingkai Gong

“…The intelligent gear recognition method based on convolutional neural network can effectively identify the gear fault， but the convolutional neural network needs a lot of labeled training data， which limits the application of convolutional neural network in gear fault diagnosis. …”
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An unsupervised approach for the detection of zero‐day distributed denial of service attacks in Internet of Things networks by Monika Roopak, Simon Parkinson, Gui Yun Tian, Yachao Ran, Saad Khan, Balasubramaniyan Chandrasekaran

“…This system can identify anomalies without needing prior knowledge or training on attack information. Zero‐day attacks exploit previously unknown vulnerabilities, making them hard to detect with traditional deep learning and machine learning systems that require pre‐labelled data. …”
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Predicting the temporal distribution of origin-destination traffic demand using machine learning by Keyvan Pourhassan, Mojgan Pourhassan, Sekhar Somenahalli

“…Results show that the trained models accurately predict the temporal distribution of origin-destination traffic demand. …”
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Prediction of Temperature Distribution with Deep Learning Approaches for SM1 Flame Configuration by Gökhan Deveci, Özgün Yücel, Ali Bahadır Olcay

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Statistical resolution of ambiguous HLA typing data. by Jennifer Listgarten, Zabrina Brumme, Carl Kadie, Gao Xiaojiang, Bruce Walker, Mary Carrington, Philip Goulder, David Heckerman

“…Our method, which requires an independent, high-resolution training data set drawn from the same population as the data to be refined, uses linkage disequilibrium in HLA haplotypes as well as four-digit allele frequency data to probabilistically refine HLA typings. …”
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Elastic Optimization for Stragglers in Edge Federated Learning by Khadija Sultana, Khandakar Ahmed, Bruce Gu, Hua Wang

“…The distributed collaborative training in EFL deals with delay and privacy issues compared to traditional centralized model training. …”
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Human Papillomavirus Prevalence and Genotype Distribution in Cervical Swab Samples in Istanbul, Turkey by Sema Alacam, Ayfer Bakir

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Strategic vegetable crop distribution in Himachal Pradesh for sustainable economic and environmental outcomes by Nikhil Chandel, S. A. Wani, Mushtaq A. Lone, Ohud A. Alqasem, Aafaq A. Rather, S. A. Mir, Danish Qayoom, M. Nagy, Getachew Tekle Mekiso, Oluwafemi Samson Balogun

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A dataset to train intrusion detection systems based on machine learning models for electrical substationsZenodo by Esteban Damián Gutiérrez Mlot, Jose Saldana, Ricardo J. Rodríguez, Igor Kotsiuba, Carlos Gañán

“…In summary, the dataset addresses the critical need for high-quality, targeted data for tuning IDS at electrical substations and contributes to the advancement of secure and reliable power distribution networks.…”
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National serosurvey and risk mapping reveal widespread distribution of Coxiella burnetii in Kenya by Lillian Wambua, Bernard Bett, Hussein M. Abkallo, Mathew Muturi, Daniel Nthiwa, Richard Nyamota, Enock Kiprono, Lynn Kirwa, Francis Gakuya, Andrew W. Bartlow, Earl A. Middlebrook, Jeanne Fair, Kariuki Njenga, John Gachohi, Athman Mwatondo, James M. Akoko

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Automatic Monitoring of Rock‐Slope Failures Using Distributed Acoustic Sensing and Semi‐Supervised Learning by Jiahui Kang, Fabian Walter, Patrick Paitz, Johannes Aichele, Pascal Edme, Lorenz Meier, Andreas Fichtner

“…Besides DAS, the dataset from 16 May to 30 June 2023 includes Doppler radar data for partially ground‐truth labeling. The proposed algorithm is capable of distinguishing between rock‐slope failures and background noise, including road and train traffic, with a detection precision of over 95%. …”
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A Novel Distributed Online Anomaly Detection Method in Resource-Constrained Wireless Sensor Networks by Zhiguo Ding, Haikuan Wang, Minrui Fei, Dajun Du

“…Firstly, the spatiotemporal correlation existing in the sensed data was exploited and a series of single anomaly detectors were built in each distributed deployment sensor node based on ensemble learning theory. …”
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Assessing machine learning models to generate permafrost distribution map in Solukhumbu, Nepal by Arnab Singh, Dibas Shrestha, Kaman Ghimire, Sangya Mishra, Darwin Rana, Sunil Acharya

“…Three machine learning models (Logistic Regression, Random Forest and Support Vector Machines) were trained to generate permafrost probability distribution maps based on their prediction of the probability of rock glaciers being intact as opposed to relict. …”
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Markarian Multiwavelength Data Center (MMDC): A Tool for Retrieving and Modeling Multitemporal, Multiwavelength, and Multimessenger Data from Blazar Observations by N. Sahakyan, V. Vardanyan, P. Giommi, D. Bégué, D. Israyelyan, G. Harutyunyan, M. Manvelyan, M. Khachatryan, H. Dereli-Bégué, S. Gasparyan

“…Another important distinguishing feature of MMDC is its ability to enable precise, self-consistent theoretical modeling of the observed data using machine learning algorithms trained on leptonic and lepto-hadronic models, which consider the injection of particles and all relevant cooling processes. …”
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FedACT: An adaptive chained training approach for federated learning in computing power networks by Min Wei, Qianying Zhao, Bo Lei, Yizhuo Cai, Yushun Zhang, Xing Zhang, Wenbo Wang

“…Federated Learning (FL) is a novel distributed machine learning methodology that addresses large-scale parallel computing challenges while safeguarding data security. …”
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Strategies for upgrading the training of personnel at the Shanghai Municipal and district Centers for Disease Control and Prevention by HE Yongchao, SUI Mengyun, LI Yugang

“…ObjectiveTo investigate the current status of personnel training, barriers and bottlenecks at Shanghai Municipal and district Centers for Disease Control and Prevention (hereinafter referred to as" CDC"), so as to provide a reference basis for the formulation of training policies that are consistent with the CDC’s staff development path, characteristics of the era, and features of Shanghai mega⁃city.MethodsQuestionnaire survey and qualitative interview were used to collect the data, covering the basic information of the research subjects, as well as training status, needs, barriers and problems. …”
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Development and Validation of Autotronic Training Module for Automobile Technology Students in Polytechnics in Southern Nigeria by Saue, Baritule Prince, Chukuigwe, Ogbondah Nndameka, Bassey, Imaobong Sunday

“…A research was carried out to create and verify an autotronic training module for students studying vehicle technology at polytechnics located in Southern Nigeria. …”
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