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learning From Heterogeneous Data Based on Social Interactions Over Graphs

IEEE TRANSACTIONS ON INFORMATION THEORY 2023年第5期69卷 3347-3371页

作者： Bordignon, Virginia Vlaski, Stefan Matta, Vincenzo Sayed, Ali H. Ecole Polytech Fed Lausanne EPFL Sch Engn CH-1015 Lausanne Switzerland Ecole Polytech Fed Lausanne EPFL Adapt Syst Lab CH-1015 Lausanne Switzerland Imperial Coll London Dept Elect & Elect Engn London SW7 2BT England Univ Salerno Dept Informat & Elect Engn & Appl Math DIEM I-84084 Fisciano Italy Natl Interuniv Consortium Telecommun CNIT Rome Italy

This work proposes a decentralized architecture, where individual agents aim at solving a classification problem while observing streaming features of different dimensions and arising from possibly different distributions. In the context of social learning, several useful strategies have been developed, which solve decision making problems through local cooperation across distributed agents and allow them to learn from streaming data. However, traditional social learning strategies rely on the fundamental assumption that each agent has significant prior knowledge of the underlying distribution of the observations. In this work we overcome this issue by introducing a machine learning framework that exploits social interactions over a graph, leading to a fully data-driven solution to the distributed classification problem. In the proposed social machine learning (SML) strategy, two phases are present: in the training phase, classifiers are independently trained to generate a belief over a set of hypotheses using a finite number of training samples;in the prediction phase, classifiers evaluate streaming unlabeled observations and share their instantaneous beliefs with neighboring classifiers. We show that the SML strategy enables the agents to learn consistently under this highly-heterogeneous setting and allows the network to continue learning even during the prediction phase when it is deciding on unlabeled samples. The prediction decisions are used to continually improve performance thereafter in a manner that is markedly different from most existing static classification schemes where, following training, the decisions on unlabeled data are not re-used to improve future performance.

关键词： Training Data models Random variables Videos Predictive models machine learning algorithms Distributed databases Distributed classification social learning neural networks diffusion strategies

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Prediction of Single Event Effect in Inverter Circuit Based on Deep learning

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IEEE JOURNAL OF THE ELECTRON DEVICES SOCIETY 2025年 13卷 431-438页

作者： Huang, Jin Zhao, Rong Wang, Shulong Yan, Xingyuan Zhou, Hao Li, Liutao Chen, Shupeng Liu, Hongxia Xidian Univ Sch Microelect Key Lab Wide Band Gap Semicond Mat & Devices Educ Xian 710071 Peoples R China

Fully Depleted Silicon on Insulator (FDSOI) technology can solve the short channel effect very effectively, with low power consumption, and low voltage, and can improve the subthreshold characteristics of the device. In addition, FDSOI devices have good radiation resistance, which has become an important research object in the field of device research. Single event effect (SEE) is an important index of radiation resistance of FDSOI devices. At present, the research on SEE of FDSOI devices typically employs heavy-ion irradiation experiments and TCAD software simulations. Taking FDSOI technology as an example, this paper presents a research method of device modeling and performance prediction based on deep learning. The accuracy of the peak of transient current (I-peak) predicted by this method is 96.45%, the accuracy of total collected charge (Q(total))$ is 97.86%, and the determination coefficient of drain transient current pulse (It) is 0.97717. This method can obviously improve the simulation speed and reduce the calculation cost, and provide a new feasible method for the research of FDSOI devices.

关键词： Inverters Silicon-on-insulator Transient analysis Integrated circuit modeling Deep learning Software Performance evaluation Ions Voltage machine learning algorithms Deep learning (DL) FDSOI devices single event effect (SEE) inverter circuit

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MedAi: A Smartwatch-Based Application Framework for the Prediction of Common Diseases Using machine learning

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IEEE ACCESS 2023年 11卷 12342-12359页

作者： Himi, Shinthi Tasnim Monalisa, Natasha Tanzila Whaiduzzaman, M. D. Barros, Alistair Uddin, Mohammad Shorif Jahangirnagar Univ Dept Comp Sci & Engn Dhaka Bangladesh Queensland Univ Technol Sch Informat Syst Brisbane Qld 4000 Australia

Health information technology is one of today's fastest-growing and most powerful technologies. This technology is used predominantly for predicting illness and obtaining medications quickly because visiting a doctor and performing pathological tests can be time-consuming and expensive. This has prompted many researchers to contribute by developing new disease prediction systems or improving existing ones. This paper presents a smartwatch-based prediction system named 'MedAi' for multiple diseases such as ischemic heart disease, hypertension, respiratory disease, hyperthyroidism, hypothyroidism, stroke, myocardial infarction, kidney failure, gallstones, diabetes, dyslipidemia using machine learning algorithms. It comprises three core modules: a prototype smartwatch 'Sense O'Clock' equipped with eleven sensors to collect bodily statistics, a machine learning model to analyze the data and make a prediction, and a mobile application to display the prediction result. A dataset consisting of patient bodily statistics was obtained from a local hospital according to ethical guidelines, such as obtaining the prior consent of both patients and doctors. We employ several machine learning algorithms, including Support Vector machine (SVM), Support Vector Regression (SVR), K-Nearest Neighbor (KNN), Extreme Gradient Boosting (XGBoost), Long Short Term Memory (LSTM), and Random Forest (RF) to investigate the best performing algorithm. Experimentation using our dataset shows that the RF algorithm outperforms other machine learning algorithms such as SVM, KNN, XGBoost, etc., in predicting aforementioned diseases with an accuracy of 99.4%. The system provides full-time assistance to the user by reporting his or her body condition and suggesting requisite remedies. It is a notable addition to early disease prediction systems and can predict multiple disease vulnerabilities before they reach an irrecoverable stage. Finally, we compare our method with the related existing methods.

关键词： Diseases Sensors machine learning Wearable Health Monitoring Systems Medical services Prototypes machine learning algorithms Smart devices Healthcare disease prediction machine learning smartwatch mobile application

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Green-IN machine learning at a Glance

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COMPUTER 2023年第6期56卷 35-43页

作者： Gutierrez, Maria Moraga, Ma Angeles Garcia, Felix Calero, Coral Univ Castilla La Mancha Toledo Spain Observ Social & Ethical Impact Artificial Intelli Toledo Spain

The use of machine learning (ML) algorithms has an environmental impact to be fully considered. This article presents a green-in-driven approach to the development of ML models. The aim thereof is to meet operational ... 详细信息

关键词： Energy consumption machine learning algorithms Computational modeling Green products machine learning

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Frequency Estimation in Data Streams: learning the Optimal Hashing Scheme

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第2期35卷 1541-1553页

作者： Bertsimas, Dimitris Digalakis, Vassilis MIT Sloan Sch Management Cambridge MA 02139 USA MIT Operat Res Ctr Cambridge MA 02139 USA

We present a novel approach for the problem of frequency estimation in data streams that is based on optimization and machine learning. Contrary to state-of-the-art streaming frequency estimation algorithms, which heavily rely on random hashing to maintain the frequency distribution of the data steam using limited storage, the proposed approach exploits an observed stream prefix to near-optimally hash elements and compress the target frequency distribution. We develop an exact mixed-integer linear optimization formulation, which enables us to compute optimal or near-optimal hashing schemes for elements seen in the observed stream prefix;then, we use machine learning to hash unseen elements. Further, we develop an efficient block coordinate descent algorithm, which, as we empirically show, produces high quality solutions, and, in a special case, we are able to solve the proposed formulation exactly in linear time using dynamic programming. We empirically evaluate the proposed approach both on synthetic datasets and on real-world search query data. We show that the proposed approach outperforms existing approaches by one to two orders of magnitude in terms of its average (per element) estimation error and by 45-90 percent in terms of its expected magnitude of estimation error.

关键词： Frequency estimation machine learning algorithms machine learning Signal processing algorithms Optimization Task analysis Monitoring Data streams streaming frequency estimation learning to hash optimal hashing scheme

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On the implementation of a new version of the Weibull distribution and machine learning approach to model the COVID-19 data

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2023年第1期20卷 337-364页

作者： Zhou, Yinghui Ahmad, Zubair Almaspoor, Zahra Khan, Faridoon tag-Eldin, Elsayed Iqbal, Zahoor El-Morshedy, Mahmoud Commun Univ China Sch Informat & Commun Engn Beijing Peoples R China Yazd Univ Dept Stat POB 89175741 Yazd Iran PIDE Islamabad PIDE Sch Econ Islamabad 44000 Pakistan Future Univ Egypt Fac Engn & Technol New Cairo 11835 Egypt Quaid i Azam Univ Dept Math Islamabad 44000 Pakistan Prince Sattam Bin Abdulaziz Univ Coll Sci & Humanities Dept Math Al Kharj 11942 Saudi Arabia Mansoura Univ Fac Sci Dept Math Mansoura 35516 Egypt

Statistical methodologies have broader applications in almost every sector of life including education, hydrology, reliability, management, and healthcare sciences. Among these sectors, statistical modeling and predicting data in the healthcare sector is very crucial. In this paper, we introduce a new method, namely, a new extended exponential family to update the distributional flexibility of the existing models. Based on this approach, a new version of the Weibull model, namely, a new extended exponential Weibull model is introduced. The applicability of the new extended exponential Weibull model is shown by considering two data sets taken from the health sciences. The first data set represents the mortality rate of the patients infected by the coronavirus disease 2019 (COVID-19) in Mexico. Whereas, the second set represents the mortality rate of COVID-19 patients in Holland. Utilizing the same data sets, we carry out forecasting using three machine learning (ML) methods including support vector regression (SVR), random forest (RF), and neural network autoregression (NNAR). To assess their forecasting performances, two statistical accuracy measures, namely, root mean square error (RMSE) and mean absolute error (MAE) are considered. Based on our findings, it is observed that the RF algorithm is very effective in predicting the death rate of the COVID-19 data in Mexico. Whereas, for the second data, the SVR performs better as compared to the other methods.

关键词： family of distributions healthcare sector machine learning algorithms mathematical properties simulation statistical modeling

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Active learning for News Article's Authorship Identification

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IEEE ACCESS 2023年 11卷 98415-98426页

作者： Abbas, Sidra Alsubai, Shtwai Sampedro, Gabriel Avelino Abisado, Mideth Almadhor, Ahmad S. Kryvinska, Natalia Zaidi, Monji Mohamed COMSATS Univ Islamabad Dept Comp Sci Islamabad 45550 Pakistan Prince Sattam Bin Abdulaziz Univ Coll Comp Engn & Sci Al Kharj 16278 Saudi Arabia Open Univ Fac Informat & Commun Studies Univ Philippines Los Banos 4031 Philippines De La Salle Univ Ctr Computat Imaging & Visual Innovat Manila 1004 Philippines Natl Univ Coll Comp & Informat Technol Manila 1008 Philippines Jouf Univ Coll Comp & Informat Sci Dept Comp Engn & Networks Sakaka 72388 Saudi Arabia Comenius Univ Fac Management Informat Syst Dept Bratislava 81499 Slovakia King Khalid Univ Coll Engn Dept Elect Engn Abha 62529 Saudi Arabia

Over time, the amount of textual data has increased drastically, especially due to the publication of articles. As a consequence, there has been a rise in anonymous content. Research is being conducted to determine alternative methods for identifying unknown text authors. To this end, a system has to be developed to accurately determine the author of unknown texts, given a group of writing samples. Active learning is utilized in this study because it iteratively selects the most informative samples to include in the training set, which enables a more precise and accurate authorship identification approach with fewer examples. Makes it useful for analyzing the rising amount of anonymous content and identifying unknown text authors. This study proposes a novel approach that utilizes active learning (AL) based machine models, namely Logistic Regression (AL-LR), Random Forest (AL-RF), XGboost (AL-XGB), and Multilayer Perceptron (AL-MLP) for authorship identification. The proposed approach extracts valuable characteristics of the writer using the Term Frequency-Inverse Document Frequency (TF-IDF). This study's selected comprehensive dataset, "All the news," is divided into three subsets: Article 1, Article 2, and Article 3. We have restricted the dataset's scope and selected the top 50 authors for our experimentation. The experimental outcomes reveal that the proposed AL-XGB model achieves superior performance on Article 1 of the "All the news" dataset. Further, the AL-LR model performed well on Article 2, and the AL-MLP performed well on Article 3. The results suggest using the proposed approach for authorship identification.

关键词： Feature extraction Writing Analytical models Support vector machines machine learning algorithms Forensics Deep learning Text analysis machine learning Publishing Active learning authorship identification text analysis machine learning news articles

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Identification of Key Nodes in Complex Networks Based on Network Representation learning

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IEEE ACCESS 2023年 11卷 128175-128186页

作者： Zhang, Heping Zhang, Sicong Xie, Xiaoyao Zhang, Taihua Yu, Guojun Guizhou Normal Univ Key Lab Informat & Comp Sci Guizhou Prov Guiyang 550001 Peoples R China Guizhou Normal Univ Sch Mech & Elect Engn Guiyang 550025 Peoples R China

Recently, some research has utilized machine learning methods to identify critical nodes in complex networks. However, existing approaches often lack a comprehensive consideration of network structural features during node feature extraction. Benefiting from the powerful feature extraction capability of network representation learning methods, a simple and effective algorithm for identifying key nodes in complex networks, termed Network Representation learning and Key Node Identification (NRL_KNI), is proposed. The NRL_KNI algorithm utilizes network embedding techniques for learning node feature representations, followed by clustering and the utilization of quota-based limited sampling to obtain sampled nodes. Subsequently, these sampled nodes are employed to train a regression model for predicting the diffusion capability of unsampled nodes. To rank node influences, a Local Structure Influence Score (LSIS) based on the local structure is introduced to evaluate nodes' final impact. Experimental results on eight real-world datasets demonstrate that the NRL_KNI algorithm generally outperforms traditional centrality methods and network representation learning-based methods in terms of the Jaccard similarity coefficient and Kendall's Tau correlation coefficient evaluation metrics.

关键词： Representation learning Complex networks Training Predictive models machine learning algorithms Clustering algorithms Partitioning algorithms Regression analysis key node identification network representation learning regression model

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Explainable machine learning: The importance of a system-centric perspective [Lecture Notes]

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IEEE SIGNAL PROCESSING MAGAZINE 2023年第2期40卷 165-172页

作者： Narwaria, Manish Indian Inst Technol Jodhpur Dept Elect Engn Jodhpur Rajasthan India

The landscape in the context of several signal processing applications and even education [1] appears to be significantly affected by the emergence of machine learning (ML) and particularly deep learning (DL). The main reason for this is the ability of DL to model complex and unknown relationships between signals and the tasks of interest. In particular, supervised DL algorithms have been fairly successful at recognizing perceptually or semantically useful signal information in different applications (e.g., identifying objects or regions of interest from image/video signals or recognizing spoken words from speech signals, such as in speech recognition). In all of these tasks, the training process uses labeled data to learn a mapping function (typically implicitly) from signals to the desired information (class label or target label). The trained DL model is then expected to correctly recognize/classify relevant information in a given test signal. Therefore, a DL-based framework is, in general, very appealing since the features and characteristics of the required mapping are learned almost exclusively from the data without resorting to explicit model/system development.

关键词： Education Deep learning Image recognition machine learning algorithms Signal processing algorithms Speech recognition Signal processing

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Annealing Optimization for Progressive learning With Stochastic Approximation

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2023年第5期68卷 2862-2874页

作者： Mavridis, Christos N. Baras, John S. Univ Maryland Dept Elect & Comp Engn College Pk MD 20742 USA Univ Maryland Inst Syst Res College Pk MD 20742 USA

In this work, we introduce a learning model designed to meet the needs of applications in which computational resources are limited, and robustness and interpretability are prioritized. learning problems can be formulated as constrained stochastic optimization problems, with the constraints originating mainly from model assumptions that define a tradeoff between complexity and performance. This tradeoff is closely related to overfitting, generalization capacity, and robustness to noise and adversarial attacks, and depends on both the structure and complexity of the model, as well as the properties of the optimization methods used. We develop an online prototype-based learning algorithm based on annealing optimization that is formulated as an online gradient-free stochastic approximation algorithm. The learning model can be viewed as an interpretable and progressively growing competitive-learning neural network model to be used for supervised, unsupervised, and reinforcement learning. The annealing nature of the algorithm contributes to minimal hyperparameter tuning requirements, poor local minima prevention, and robustness with respect to the initial conditions. At the same time, it provides online control over the performance-complexity tradeoff by progressively increasing the complexity of the learning model as needed, through an intuitive bifurcation phenomenon. Finally, the use of stochastic approximation enables the study of the convergence of the learning algorithm through mathematical tools from dynamical systems and control, and allows for its integration with reinforcement learning algorithms, constructing an adaptive state-action aggregation scheme.

关键词： Approximation algorithms Optimization Heuristic algorithms Annealing machine learning algorithms Dynamical systems Complexity theory Optimization for machine learning annealing optimization online deterministic annealing (ODA) progressive learning reinforcement learning (RL) stochastic approximation

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