The role of continuous monitoring in acute-care settings for predicting all-cause 30-day hospital readmission: A pilot study by Michael Joseph Pettinati, Kyriakos Vattis, Henry Mitchell, Nicole Alexis Rosario, David Michael Levine, Nandakumar Selvaraj

“…The influence of different data sources, including remotely monitored continuous vital signs and activity, on machine learning (ML) models’ performances is examined for predicting all-cause unplanned 30-day readmission. …”
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Supraglacial Lake Depth Retrieval from ICESat-2 and Multispectral Imagery Datasets by Quan Zhou, Qi Liang, Wanxin Xiao, Teng Li, Lei Zheng, Xiao Cheng

“…In this study, we present a machine learning-based method for estimating the depth of supraglacial lakes through the combination of ICESat-2 ATL03 data with multispectral imagery. …”
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Survey on Byzantine attacks and defenses in federated learning by ZHAO Xiaojie, SHI Jinqiao, HUANG Mei, KE Zhenhan, SHEN Liyan

“…Federated learning as an emerging distributed machine learning, can solve the problem of data islands. …”
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A review of displacement cascade simulations using molecular dynamics emphasizing interatomic potentials for TPBAR components by Ankit Roy, Giridhar Nandipati, Andrew M. Casella, David J. Senor, Ram Devanathan, Ayoub Soulami

“…Abstract This review explores molecular dynamics simulations for studying radiation damage in Tritium Producing Burnable Absorber Rod (TPBAR) materials, emphasizing the role of interatomic potentials in displacement cascades. Recent machine learning potentials (MLPs), trained on quantum data, enhance prediction accuracy over traditional models like EAM. …”
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treeducken: An R package for simulating cophylogenetic systems by Wade Dismukes, Tracy A. Heath

“…This allows easier performance testing of methods and has potential applications in machine learning (ML) and approximate Bayesian computation (ABC) approaches.…”
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Maximal Information Coefficient-Based Undersampling Method for Highly-Imbalanced Learning by Haiou Qin

“…Learning from highly-imbalanced datasets is still a big challenge in the field of machine learning because models created by general learning algorithms are weak in recognizing the samples from the minority class correctly. …”
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E-commerce Service Chatbot Application Design using KNN and Random Forest Methods by Fardan Zamakhsyari

“…In response to this need, the researcher has developed a chatbot application aimed at improving customer service, employing machine learning techniques with the KNN and Random Forest algorithms. …”
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Artificial Intelligence - Blessing or Curse in Dentistry? - A Systematic Review by Y Greeshma Vani, Suma B. Chalapathy, Pallavi Pandey, Shailendra K. Sahu, A Ramesh, Jayashree Sajjanar

“…The search was conducted using the terms “Artificial Intelligence,” “Dentistry,” “Machine learning,” “Deep learning,” and “Diagnostic System.” …”
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Multilayer neural network model for unbalanced data by Xue ZHANG, Zhiguo SHI, Xuan LIU

“…Classification of unbalanced data often has low performance of the classifier because of the unbalance of data between classes.Using AUC (the area under the ROC curve) as evaluation index,combined with one class F-score feature selection and genetic algorithm,a multilayer neural network model was established,and a more favorable feature set for unbalanced data classification was selected,so as to establish a deeper model suitable for classification of unbalanced data.Based on Tensor Flow,a multilayer neural network model was established.Using four different UCI datasets for testing,and comparing with the traditional machine learning algorithms such as Naive Bayesian,KNN,neural networks,etc,the performance of the proposed model built on the unbalanced data classification is more excellent.…”
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Low complexity radar signal classification based on spectrum shape by Liang YIN, Rui LIN, Xiaolei WANG, Yuliang YAO, Lin ZHOU, Yuan HE

“…In order to solve the problems of high computational complexity, low recognition accuracy of low signal to noise ratio (SNR) environment and low fidelity of simulation data in radar signal modulation recognition, a low complexity radar signal classification algorithm based on spectrum shape was proposed.Signal spectrum was normalized, feature parameters were extracted by spectrum sampling method, and then machine learning classification model was trained.The test results of the data generated by the radar signal source show that the classification accuracy of Barker code, Frank code, LFM code, BPSK, QPSK modulation and conventional radar signals is more than 90% (SNR≥3 dB).The algorithm has low computational complexity, can adapt to the change of signal parameters, and has good generalization.…”
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Correlation Analysis between Exchange Rate Fluctuations and Oil Price Changes Based on Copula Function by Xiaodong Huang

“…Moreover, this paper improves some defects in the algorithm and combines some new learning frameworks in machine learning to generalize the copula function to a variety of learning models. …”
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Method of anti-confusion texture feature descriptor for malware images by Yashu LIU, Zhihai WANG, Hanbing YAN, Yueran HOU, Yukun LAI

“…It is a new method that uses image processing and machine learning algorithms to classify malware samples in malware visualization field.The texture feature description method has great influence on the result.To solve this problem,a new method was presented that joints global feature of GIST with local features of LBP or dense SIFT in order to construct combinative descriptors of malware gray-scale images.Using those descriptors,the malware classification performance was greatly improved in contrast to traditional method,especially for those samples have higher similarity in the different families,or those have lower similarity in the same family.A lot of experiments show that new method is much more effective and general than traditional method.On the confusing dataset,the accuracy rate of classification has been greatly improved.…”
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Advances in Autism: a bibliometric analysis by Mehereen Chowdhury, Murdoc Gould, Latha Ganti

“…Institutions like Stanford University and McGill University demonstrate substantial research output, while authors such as Dennis Wall are prominent with contributions that make diagnosing Autism much more efficient with the use of AI. Keywords like "Machine learning", "Autism spectrum disorder", and “Children” dominate, reflecting ongoing efforts to leverage technology for ASD interventions. …”
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Survey on static software vulnerability detection for source code by Zhen LI, Deqing ZOU, Zeli WANG, Hai JIN

“…Static software vulnerability detection is mainly divided into two types according to different analysis objects:vulnerability detection for binary code and vulnerability detection for source code.Because the source codecontains more semantic information,it is more favored by code auditors.The existing vulnerability detection research works for source code are summarized from four aspects:code similarity-based vulnerability detection,symbolic execution-based vulnerability detection,rule-based vulnerability detection,and machine learning-based vulnerability detection.The vulnerability detection system based on source code similarity and the intelligent software vulnerability detection system for source code are taken as two examples to introduce the process of vulnerability detection in detail.…”
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APPLYING THE AUTOENCODER MODEL FOR URL PHISHING DETECTION by Dang Thi Mai

“…However, these techniques have significant limitations as they cannot identify new URLs. Many machine learning-based approaches have been researched and implemented to overcome these shortcomings. …”
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Research on coreference resolution technology of entity in information security by Han ZHANG, Yongjin HU, Yuanbo GUO, Jicheng CHEN

“…To solve the problem of coreference resolution in information security,a hybrid method was proposed.Based on the BiLSTM-attention-CRF model,the domain-dictionary matching mechanism was introduced and combined with the attention mechanism at the document level.As a new dictionary-based attention mechanism,the word features were calculated to solve the problem of weak recognition ability of rare entities and entities with long length when extracting candidates from text.And by summarizing the features of the domain texts,the candidates were coreferenced by rules and machine learning according to the part of speech to improve the accuracy.Through the experiments on security data set,the superiority of the method is proved from the aspects of coreference resolution and extraction of candidates from text .…”
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Survey of federated learning research by Chuanxin ZHOU, Yi SUN, Degang WANG, Huawei GE

“…Federated learning has rapidly become a research hotspot in the field of security machine learning in recent years because it can train the global optimal model collaboratively without the need for multiple data source aggregation.Firstly, the federated learning framework, algorithm principle and classification were summarized.Then, the main threats and challenges it faced, were analysed indepth the comparative analysis of typical research programs in the three directions of communication efficiency, privacy and security, trust and incentive mechanism was focused on, and their advantages and disadvantages were pointed out.Finally, Combined with application of edge computing, blockchain, 5G and other emerging technologies to federated learning, its future development prospects and research hotspots was prospected.…”
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A systemic approach and multiscale data management. A ‘refrigerator’ case study by Paolo Marco Tamborrini, Eleonora Fiore

“…Many digital technologies, such as the Internet of Things, Artificial Intelligence and Machine Learning, could radically change the way of conceiving a design process, especially when they are used to retrieve essential information to define a problem, identify the requirements and support design decisions, all of which are typical of the pre-design phase. …”
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Sandy Soil Liquefaction Prediction Based on Clustering-Binary Tree Neural Network Algorithm Model by Yu Wang, Jiachen Wang

“…The neural network algorithm is a small sample machine learning method built on the statistical learning theory and the lowest structural risk principle. …”
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Nowcasting of China’s industrial added value based on electric power big data by Fang PENG, Gaoqun PENG, Yaru QI, Tiantian LIU, Xiaolei ZHOU

“…Industrial added value is an important indicator to measure the operation of the real economy.In order to fully mine the value of power data in the current macroeconomic nowcasting, so as to serve the government policy making, the Bagging and Boosting algorithms in machine learning were applied to nowcast industrial added value based on electric power data as well as traditional statistical data.Firstly, traditional statistical data can significantly improve the forecasting effect of the ARIMA model.Secondly, the nowcasting ability of electric power data depends on the selection of electric power indicators, and the proper electric power index is helpful to predict industrial added value more timely and accurately.Thirdly, the prediction ability of electric power data to the industrial added value in the current period may be lower than that in the future, which means the power data is more likely to be used to predict ahead of time.…”
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