Machine Learning Methods for Trust-based Selection of Web Services

doi:10.3837/tiis.2022.01.003

Cited by 3 publications

(2 citation statements)

References 45 publications

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“…Machine learning, referred to as ML, is a branch of artificial intelligence that enables machines to automatically learn and improve from experience without being explicitly programmed, and machine learning algorithms for classification problems have been applied to nearly every industry from healthcare to web services [15][16][17][18].…”

Section: Machine Learning For Motion Inferencementioning

confidence: 99%

Android IoT Lifelog System and Its Application to Motion Inference

Munkhtsetseg¹,

Seo²

2023

Computer Systems Science and Engineering

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In social science, health care, digital therapeutics, etc., smartphone data have played important roles to infer users' daily lives. However, smartphone data collection systems could not be used effectively and widely because they did not exploit any Internet of Things (IoT) standards (e.g., oneM2M) and class labeling methods for machine learning (ML) services. Therefore, in this paper, we propose a novel Android IoT lifelog system complying with oneM2M standards to collect various lifelog data in smartphones and provide two manual and automated class labeling methods for inference of users' daily lives. The proposed system consists of an Android IoT client application, an oneM2M-compliant IoT server, and an ML server whose high-level functional architecture was carefully designed to be open, accessible, and internationally recognized in accordance with the oneM2M standards. In particular, we explain implementation details of activity diagrams for the Android IoT client application, the primary component of the proposed system. Experimental results verified that this application could work with the oneM2M-compliant IoT server normally and provide corresponding class labels properly. As an application of the proposed system, we also propose motion inference based on three multi-class ML classifiers (i.e., k nearest neighbors, Naive Bayes, and support vector machine) which were created by using only motion and location data (i.e., acceleration force, gyroscope rate of rotation, and speed) and motion class labels (i.e., driving, cycling, running, walking, and stilling). When compared with confusion matrices of the ML classifiers, the k nearest neighbors classifier outperformed the other two overall. Furthermore, we evaluated its output quality by analyzing the receiver operating characteristic (ROC) curves with area under the curve (AUC) values. The AUC values of the ROC curves for all motion classes were more than 0.9, and the macro-average and micro-average ROC curves achieved very high AUC values of 0.96 and 0.99, respectively.

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Section: Machine Learning For Motion Inferencementioning

confidence: 99%

Android IoT Lifelog System and Its Application to Motion Inference

Munkhtsetseg¹,

Seo²

2023

Computer Systems Science and Engineering

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“…Various approaches have been proposed to address this challenge, encompassing mechanism-based models [5][15] [18,19][21-24], latent feature models [25][26][27][28][29], embedding methods [30][31][32][33], and community detection mechanisms [34]. Additionally, research has explored different network formations, including the integration of crowd-users rating information in social media platforms [35], trust-based selection network models of web services [36], and trajectory distance algorithms based on segment transformation distance [37]. Mechanism-based models can be further categorized as projection-based mechanisms [5,22,23], LCP mechanisms [18,19], PA mechanisms [15], and homophily mechanisms [21,24].…”

Section: Related Workmentioning

confidence: 99%

Link Prediction in Bipartite Network Using Composite Similarities

2023

KSII TIIS

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Analysis of a bipartite (two-mode) network is a significant research area to understand the formation of social communities, economic systems, drug side effect topology, etc. in complex information systems. Most of the previous works talk about a projection-based model or latent feature model, which predicts the link based on singular similarity. The projection-based models suffer from the loss of structural information in the projected network and the latent feature is hardly present. This work proposes a novel method for link prediction in the bipartite network based on an ensemble of composite similarities, overcoming the issues of modelbased and latent feature models. The proposed method analyzes the structure, neighborhood nodes as well as latent attributes between the nodes to predict the link in the network. To illustrate the proposed method, experiments are performed with five real-world data sets and compared with various state-of-art link prediction methods and it is inferred that this method outperforms with ∼3% to ∼9% higher using area under the precision-recall curve (AUC-PR) measure. This work holds great significance in the study of biological networks, e-commerce networks, complex web-based systems, networks of drug binding, enzyme protein, and other related networks in understanding the formation of such complex networks. Further, this study helps in link prediction and its usability for different purposes ranging from building intelligent systems to providing services in big data and web-based systems.

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A Semi-Supervised Learning Approach to Quality-Based Web Service Classification

Bonab,

Tanha,

Masdari

2024

IEEE Access

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The Internet provides a platform for sharing services, and web service brokers help users to choose the suitable service among similar services based on ranking. The quality of service is important in evaluating the services the user needs. However, finding a quality-based data label in many fields can be time-consuming and difficult. Thus, machine learning is required to classify and choose the best service in this field. The selection process is done through analysis and recommendations by the system. This article introduces the SSL-WSC algorithm, which classifies unlabeled data through semi-supervised self-training learning using a small amount of labeled data. This algorithm labels the data using a two-step method of calculating a score for each service and dynamic thresholding. The quality features of web services obtained from the QWS dataset were used to evaluate the performance of the proposed algorithm. The experimental results in different scenarios showed that using proposed semi-supervised learning algorithms to create classification models led to better results, so it improved the F1-score, accuracy, and precision, on average, by 11.26%, 9.43% and 9.53%, respectively, as compared to the supervised method.

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Machine Learning Methods for Trust-based Selection of Web Services

Cited by 3 publications

References 45 publications

Android IoT Lifelog System and Its Application to Motion Inference

Android IoT Lifelog System and Its Application to Motion Inference

Link Prediction in Bipartite Network Using Composite Similarities

A Semi-Supervised Learning Approach to Quality-Based Web Service Classification

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