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DAS: A DRL-Based Scheme for Workload Allocation and Worker Selection in Distributed Coded machine learning

IEEE INTERNET OF THINGS JOURNAL 2024年第15期11卷 26076-26090页

作者： Zhou, Yitong Ye, Qiang Huang, Hui Dalhousie Univ Fac Comp Sci Halifax NS B3H 4R2 Canada

machine learning (ML) has been widely applied to successfully address a variety of different problems across diverse domains, such as robotics, healthcare, and finance. However, high-complexity ML algorithms often require overlong computation time, which significantly impacts their feasibility. Distributed ML (DML) has been used to tackle the slow computation problem with high-complexity ML algorithms. Nevertheless, with DML, the computation results from all participating computing devices need to be collected in order to complete an ML task. When part of the participating devices, known as the stragglers, cannot return their results in time, the overall computation time will be extended. Distributed coded ML (DCML) is a promising solution to mitigate the negative impact of the stragglers. With DCML, redundancy is injected into an ML task so that only a subset of the results from participating devices are required to finish the ML task. In DCML, how to select proper participating devices, referred to as workers, and how to allocate appropriate workloads to the selected workers are two challenging problems. In this article, we consider a DCML scenario where numerous computing devices are available for an ML task. These devices are willing to offer their computation capacity in exchange for compensation. To encourage the computing devices to participate in the distributed computation, a reverse auction-based incentive mechanism is employed. With the objective of minimizing both the completion time of the ML task and the compensation for participating devices, we propose a deep-reinforcement-learning-based workload allocation and worker selection scheme for DCML, DAS. To our knowledge, this is the first attempt to simultaneously tackle both the workload allocation and worker selection issues in DCML. Our experimental results indicate that DAS outperforms the state-of-the-art schemes in terms of completion time and compensation.

关键词： Task analysis Encoding Resource management Termination of employment machine learning algorithms Vectors Optimization Deep reinforcement learning (DRL) distributed coded machine learning (DCML) worker selection workload allocation

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Disseminating the Risk Factors With Enhancement in Precision Medicine Using Comparative machine learning Models for Healthcare Data

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IEEE ACCESS 2024年 12卷 72794-72812页

作者： Sheik Abdullah, A. Naga Pranava Shashank, V. Altrin Lloyd Hudson, D. Vellore Inst Technol Sch Comp Sci & Engn Chennai 600127 Tamil Nadu India

The field of healthcare analytics, as an expanding discipline that integrates data analysis, machine learning, and clinical expertise, is seen to hold great promise for improving patient outcomes and the overall delivery of healthcare services. With the increasing availability of Electronic Health Records (EHRs), a wealth of healthcare data has emerged, presenting opportunities to enhance disease prediction and personalize treatments. The objective of the research is to develop and evaluate machine-learning models for predicting cancer, diabetic, diabetic retina, and heart-related outcomes using demographic and clinical data from Electronic Health Records (EHRs). Through thorough testing on diverse datasets, the study aims to assess the performance of these models in terms of accuracy, precision, and recall metrics, with the ultimate goal of advancing disease prediction and enhancing patient outcomes within the field of healthcare analytics. The proposed model demonstrates high accuracy, particularly in predicting cancer (97.080%) and diabetic (97.33%) outcomes using Support Vector machines (SVM) and Decision Trees. Additionally, logistic regression achieves a notable accuracy of 76.521% for diabetic retina dataset, while Decision Trees exhibit 86.419% accuracy for heart-related predictions. SVM accuracy for Pima diabetic dataset stands at 79.746%. To assess the model's performance, thorough testing was conducted on a diverse and extensive dataset, employing a combination of accuracy, precision, and recall metrics. This research represents a substantial contribution to the field of healthcare analytics, emphasizing the potential of machine learning to advance disease prediction and, ultimately, enhance patient outcomes.

关键词： Medical services machine learning Predictive models Prediction algorithms machine learning algorithms Breast cancer Precision medicine Decision trees Random forests Support vector machines Diabetes Hyperparameter optimization decision trees logistic regression random forest classifier support vector machine breast cancer heart disease PIMA Indian diabetes diabetic retinopathy diabetes comparative analysis hyperparameter tuning

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Predicting Patent Quality Based on machine learning Approach

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IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT 2024年 71卷 3144-3157页

作者： Erdogan, Zulfiye Altuntas, Serkan Dereli, Turkay Iskenderun Tech Univ Dept Ind Engn TR-31200 Antakya Turkiye Yildiz Tech Univ Dept Ind Engn TR-34349 Istanbul Turkiye Hasan Kalyoncu Univ Off President TR-27000 Gaziantep Turkiye

The investment budget allocated by companies in R&D activities has increased due to increased competition in the market. Applications for industrial property rights by countries, investors, companies, and universities to protect inventions obtained as an outcome of investments have also increased. The selection of the patent to be invested becomes more difficult with the increasing number of applications. Therefore, predicting patent quality is quite significant for companies to be successful in the future. The level to which a patent meets the expectations of decision makers is referred to as patent quality. Patent indices represent decision makers' expectations. In this study, an approach is proposed to predict patent quality in practice. The proposed approach uses supervised learning algorithms and analytic hierarchy process (AHP) method. The proposed approach is applied to patents related to personal digital assistant technologies. The performances of individual and ensemble machine learning methods have been also analyzed to establish the prediction model. In addition, 75% split ratio and the five-fold cross-validation methods have been used to verify the prediction model. The multilayer perceptron algorithm has 76% accuracy value. The proposed prediction model is essential in directing R&D studies to the right technology areas and transferring the incentives to patent applications with a high quality rate.

关键词： Patents Codes Clustering algorithms Prediction algorithms machine learning algorithms Predictive models Technological innovation Analytic hierarchy process (AHP) machine learning multilayer perceptron patent indices supervised learning algorithms

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Employing machine learning algorithm for properties of wood ceramics prediction: A case study of ammonia nitrogen adsorption capacity, apparent porosity, surface hardness and burn-off for wood ceramics

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CERAMICS INTERNATIONAL 2024年第8期50卷 13820-13831页

作者： Jiang, Wenjun Guo, Xiurong Guan, Qi Zhang, Yanlin Du, Danfeng Northeast Forestry Univ Mech & Elect Engn Inst Harbin 150040 Peoples R China

The estimation of material performance plays a crucial role in practical life, enabling the rational allocation of time and resources while enhancing the practical application of materials. Therefore, this study investigates the analytical and predictive capabilities of five machine learning (ML) algorithms (the Random Forest algorithm - RF, the Adaboost algorithm - AB, the Gradient Boosting algorithm - GB, the Extra Trees algorithm - ET and linear models - LM) for comprehensive performance parameters of wood ceramics (ammonia nitrogen adsorption quantity - Q, open porosity - P, burn-off, and hardness). In the analysis of model prediction capabilities, five key statistical parameters (Root Mean Square Error - RMSE, Mean Square Error - MSE, Coefficient of Correlation - R, Determination Coefficient - R-2, and Mean Absolute Relative Error - MARE) were calculated. The results indicate the following: (1) Among various ML models, the GB model exhibits the most superior performance with an R-2 >= 0.9594. (2) However, in predicting the four performance parameters of wood ceramics, the AB model with an R-2 ranging from 0.0078 to 0.45, indicating notably poor predictive capabilities. Despite integrating the Support Vector Regression (SVR) module, no enhancement in predictive accuracy was observed. (3) While forecasting the four performance parameters of wood ceramics, the LM model demonstrates predictive accuracy relatively akin to that of the RF and ET models among the ML models. (4) Though the feature importance scores vary across distinct input variable models, there is a consistent trend of change. (5) Variables B and D exhibit some level of correlation with other variables. In summary, the results of this study suggest that, for the analysis and prediction of wood ceramic performance, the GB model among the five regression models demonstrates outstanding simulation performance.

关键词： Performance estimation machine learning algorithms Wood ceramic performance Analytical and predictive capabilities

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A machine learning-based electronic nose for detecting neonatal sepsis: Analysis of volatile organic compound biomarkers in fecal samples

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CLINICA CHIMICA ACTA 2025年 565卷 119974页

作者： Kombo, Kombo Othman Hidayat, Shidiq Nur Puspita, Mayumi Kusumaatmaja, Ahmad Roto, Roto Nirwati, Hera Susilowati, Rina Haksari, Ekawaty Lutfia Wibowo, Tunjung Wandita, Setya Wahyono Julia, Madarina Triyana, Kuwat Univ Gadjah Mada Fac Math & Nat Sci Dept Phys Sekip Utara POB BLS 21 Yogyakarta 55281 Indonesia Univ Gadjah Mada Fac Math & Nat Sci Dept Chem Yogyakarta 55281 Indonesia Univ Gadjah Mada Fac Med Publ Hlth & Nursing Dept Microbiol Yogyakarta 55281 Indonesia Univ Gadjah Mada Fac Med Publ Hlth & Nursing Dept Histol & Cell Biol Yogyakarta 55281 Indonesia Univ Gadjah Mada Dr Sardjito Hosp Fac Med Publ Hlth & Nursing Dept Child Hlth Yogyakarta 55281 Indonesia Univ Gadjah Mada Dept Comp Sci & Elect Sekip Utara BLS 21 Yogyakarta 55281 Indonesia Mbeya Univ Sci & Technol Coll Sci & Tech Educ Dept Nat Sci POB 131 Mbeya Tanzania

Background: Neonatal sepsis is a global health threat, contributing to high morbidity and mortality rates among newborns. Recognizing the profound impact of neonatal sepsis on long-term health outcomes emphasizes the critical need for timely detection to mitigate its consequences and ensure optimal health for the affected newborns. Currently, various diagnostic approaches have been implemented, but they are limited by their invasiveness, high costs, centralized testing, frequent delays, inaccuracies in results, and the need for sophisticated laboratory equipment. Methods: We introduced a novel, non-invasive, cost-efficient, and easy-to-use technology that can provide rapid results at a point-of-care. The technology utilized a lab-built metal oxide semiconductor-based electronic nose (cNose) combined with volatile organic compound (VOC) biomarkers identified through gas chromatographymass spectrometry (GC-MS) analysis. The system was evaluated using fecal profiling tests involving a total of 32 samples, including 17 positive and 15 negative sepsis, confirmed by blood culture. To assess the performance in discriminating patients from healthy controls, four machine learning algorithms were implemented. Results: Based on the cross-validation results, the MLPNN model provided the best results in distinguishing between neonates with positive and negative sepsis, achieving high-performance results of 90.63 % accuracy, 88.24 % sensitivity, and 93.33 % specificity at a 95 % confidence interval. Specific VOCs associated with neonatal sepsis, such as alcohols, acids, and esters, were successfully identified through GC-MS analysis, further validating the diagnostic capability of the cNose device. Conclusion: The overall observations show the feasibility of using cNose system as a promising tool for real-time and bedside sepsis detection, potentially improving patient outcomes.

关键词： Neonatal sepsis Electronic nose Volatile organic compounds Biomarkers machine learning algorithms

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Trihalomethane prediction model for water supply system based on machine learning and Log-linear regression

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ENVIRONMENTAL GEOCHEMISTRY AND HEALTH 2024年第2期46卷 31-31页

作者： Li, Hui Chu, Yangyang Zhu, Yanping Han, Xiaomeng Shu, Shihu Donghua Univ Coll Environm Sci & Engn 2999 North Renmin Rd Shanghai 201620 Peoples R China

Laboratory determination of trihalomethanes (THMs) is a very time-consuming task. Therefore, establishing a THMs model using easily obtainable water quality parameters would be very helpful. This study explored the modeling methods of the random forest regression (RFR) model, support vector regression (SVR) model, and Log-linear regression model to predict the concentration of total-trihalomethanes (T-THMs), bromodichloromethane (BDCM), and dibromochloromethane (DBCM), using nine water quality parameters as input variables. The models were developed and tested using a dataset of 175 samples collected from a water treatment plant. The results showed that the RFR model, with the optimal parameter combination, outperformed the Log-linear regression model in predicting the concentration of T-THMs (N25 = 82-88%, rp = 0.70-0.80), while the SVR model performed slightly better than the RFR model in predicting the concentration of BDCM (N25 = 85-98%, rp = 0.70-0.97). The RFR model exhibited superior performance compared to the other two models in predicting the concentration of T-THMs and DBCM. The study concludes that the RFR model is superior overall to the SVR model and Log-linear regression models and could be used to monitor THMs concentration in water supply systems.

关键词： Disinfection by-products Predictive models machine learning algorithms Random forest Support vector regression

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Achieving Equity via Transfer learning With Fairness Optimization

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IEEE ACCESS 2024年 12卷 195229-195241页

作者： Wang, Xiaoyang Chang, Chia-Hsuan Yang, Christopher C. Drexel Univ Coll Comp & Informat Philadelphia PA 19104 USA

machine learning algorithms are increasingly used in real-world decision-making systems, raising concerns about potential biases and unfairness. Existing in-processing bias mitigation approaches often focus on achieving numerical parity across demographic groups while neglecting the performance impact on individual groups and the overall performance-fairness trade-off. This can lead to increased discriminatory outcomes and hinder efforts to develop truly fair AI systems. This paper proposes Transfer learning with Fairness Optimization (TLFO), a novel framework that serially optimizes predictive performance and fairness in machine learning models. TLFO leverages transfer learning by dividing the training process into two distinct phases: (1) initial learning for performance optimization and (2) subsequent fine-tuning for fairness enhancement. This sequential approach enables fine-grained control over fairness constraints, minimizing the performance-fairness trade-off. Extensive experiments on two real-world datasets demonstrate TLFO's effectiveness. TLFO consistently achieves superior fairness with minimal performance degradation compared to state-of-the-art in-processing bias mitigation approaches, highlighting its potential for generating fair and accurate classifiers with versatile applications.

关键词： Optimization Transfer learning Training Predictive models machine learning algorithms Prevention and mitigation Adaptation models Data models Accuracy Transforms AI fairness transfer learning in-processing bias mitigation performance-fairness trade-off

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A survey on machine learning algorithm applications in visible light communication systems

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OPTICAL AND QUANTUM ELECTRONICS 2024年第8期56卷 1351-1351页

作者： Sliti, Maha Mrabet, Manel Garai, Mouna Ammar, Lassaad Ben Univ Carthage Higher Sch Commun Tunis SUPCOM Commun Networks & Secur Res Lab LR11TIC04 Tunis Tunisia Univ Carthage Higher Sch Commun Tunis SUPCOM Green & Smart Commun Syst Res Lab LR11TIC02 Tunis Tunisia Prince Sattam Bin Abdulaziz Univ Al Kharj Saudi Arabia Manouba Univ Manouba Tunisia Higher Inst Technol Studies Med Medenine Tunisia

Visible Light Communication (VLC) technology offers a promising alternative for high-speed data transfer due to its ability to simultaneously illuminate and transport data. This research studies the application of machine learning (ML) algorithms for the purpose of addressing several issues associated with VLC technology. These challenges include the reduction of non-linearity, channel estimation, detection of modulation format, localization, enhancement of security, optimization of mobility, and management of resources. ML algorithms have the potential to enhance the efficiency and reliability of VLC systems, ensuring that data flow is uninterrupted. Additionally, the study analyzes the challenges that are involved with the application of ML algorithms in VLC technology as well as the potential future research topics that could be pursued.

关键词： Visible light communication (VLC) machine learning algorithms Applications Open research directions

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Novel Statistical Regularized Extreme learning Algorithm to Address the Multicollinearity in machine learning

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IEEE ACCESS 2024年 12卷 102355-102367页

作者： Yildirim, Hasan Karamanoglu Mehmetbey Univ Kamil Ozdag Fac Sci Dept Math TR-70100 Karaman Turkiye

The multicollinearity problem is a common phenomenon in data-driven studies, significantly affecting the performance of machine learning algorithms during the process of extracting information from data. Despite its widespread use across various fields, the extreme learning machine (ELM) also suffers from multicollinearity issues. To address this challenge, the ridge and Liu estimators, drawn from statistics literature, have been integrated into ELM theory, resulting in a notable advancement. This study aims to further enhance the capabilities of ridge and Liu estimators within the ELM framework by introducing two innovative two-parameter algorithms (TP1-ELM and TP2-ELM) that simultaneously incorporate both estimators. The proposed algorithms undergo comprehensive benchmarking against ELM, ELM-based algorithms, and other commonly used machine learning techniques across seven diverse datasets. Benchmark results demonstrate that the proposed algorithms consistently outperform both ELM-focused approaches and traditional machine learning algorithms on most datasets, yielding more generalizable and stable results. These findings suggest that the proposed algorithms offer a promising alternative to traditional machine learning techniques for regression and classification tasks, particularly in scenarios where multicollinearity is a concern.

关键词： machine learning algorithms Classification algorithms Tuning machine learning Prediction algorithms Extreme learning machines Computational modeling Extreme learning machine Liu estimator machine learning multicollinearity ridge estimator Tikhonov regularization

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A Scalable and Adaptable Supervised learning Approach for Solving the Traveling Salesman Problems

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第11期25卷 17092-17104页

作者： Lyu, Zefeng Islam, Md. Zahidul Yu, Andrew Junfang Univ Tennessee Knoxville Dept Ind & Syst Engn Knoxville TN 37996 USA Clemson Univ Dept Ind Engn Clemson SC 29634 USA

The Traveling Salesman Problem (TSP) is a well-known combinatorial optimization problem that has attracted extensive research efforts in developing exact methods and heuristics. However, the scalability and generalization of learning-based approaches remain significant challenges. This paper addresses this gap by proposing an approach that integrates supervised learning with traditional approaches. In particular, we introduce the concept of "anchors", which represents nodes that should be connected to their nearest neighbors in the optimal solution. Our approach differs from the previous supervised learning approaches in that, instead of using the whole distance matrix, our neural network leverages local information to make predictions, which enables it to handle arbitrarily large-scale TSP instances without a decline on prediction accuracy. Experimental results demonstrate that our model successfully identifies 87% of the anchors with a precision of over 95% for both generated instances and TSPLIB instances, which were unseen by the model during the training. The performance of the proposed framework is evaluated by integrating the predicted anchors into existing methods such as the Miller-Tucker-Zemlin (MTZ) model and the insertion algorithms. Our approach demonstrates remarkable improvements in solution quality and computational time. By comparing with the graph pointer network, a state-of-the-art learning-based approach, the proposed algorithm achieves a 28% reduction in the average gap and a 59% decrease in computational time for solving TSP instances with 1,000 nodes.

关键词： Supervised learning Prediction algorithms machine learning algorithms Scalability Heuristic algorithms Training Computational modeling Traveling salesman problem supervised learning constructive heuristics integer linear programming generalization adaptability

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