Vulnerability scanning is a process that involves using software tools to identify security weaknesses in computer systems, networks, or applications. There are various architectures that can be used for vulnerability...
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Support Vector machine (SVM) is a most widely used classification technique in machinelearning as it gives a better accuracy than most of the existing techniques. These days, users frequently run across glitches and ...
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The Internet of Vehicles (IoV) has replaced vehicular networks as the preferred paradigm as a result of the enormous expansion in computer and network capabilities. Because of the dynamic IoV's diverse nature nece...
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The detection of anomalies in streaming data is crucial in enterprise operations, employing statistical and machinelearning methods to identify irregularities. This enhances service stability and reduces operational ...
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In this paper we present our ongoing research on the design and development of a service model for predictive vehicle maintenance, specifically suitable for autonomous vehicles and connected vehicles. We include a hig...
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ISBN:
(纸本)9798350340754
In this paper we present our ongoing research on the design and development of a service model for predictive vehicle maintenance, specifically suitable for autonomous vehicles and connected vehicles. We include a high-level service model architecture for autonomous vehicles/connected vehicles. The aim of this work is a service model that will enable the development of integrated, multiparty, end-to-end maintenance systems.
Recommender systems are unsupervised machinelearning algorithms that recommend goods, content, or services to people online based on their past actions and anticipated future preferences. With the help of advancing A...
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The Internet of Things (IoT) is a network of physical objects, automobiles, household appliances, and other items that are integrated with sensors, software, and connections to gather and share data via the Internet. ...
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In softwareengineering, maintenance effort estimation is a challenging research topic. Several empirical studies have focused on Maintenance Effort Estimation for Open Source software (O-MEE) using machinelearning (...
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The analysis of legislative data using machinelearning has the potential to greatly enhance parliamentary policymaking. With a focus on estimating when laws and amendments will be published in Parliament, this litera...
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ISBN:
(数字)9783031537318
ISBN:
(纸本)9783031537301;9783031537318
The analysis of legislative data using machinelearning has the potential to greatly enhance parliamentary policymaking. With a focus on estimating when laws and amendments will be published in Parliament, this literature review seeks to learn more about how machinelearning can be used in legislative decision-making. However, more than a basic platform with integrated tools and software applications is required for the legislative workspace, particularly during the policy development stage, where many users, such as parliamentary actors and/or stakeholders, are frequently active. To find studies about machinelearning application to parliamentary research, a thorough search of electronic databases was performed. To better understand how machinelearning is being used in the legislative branch, we conducted a Systematic Literature Review (SLR) of 35 primary papers. The objective of this study is to examine the use of machinelearning in legislative decision-making. In addition, we pointed out research needs and gaps and predicted developments in this area.
Sentiment analysis for softwareengineering(SA4SE) is a research domain with huge potential, with applications ranging from monitoring the emotional state of developers throughout a project to deciphering user feedbac...
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ISBN:
(纸本)9789897586477
Sentiment analysis for softwareengineering(SA4SE) is a research domain with huge potential, with applications ranging from monitoring the emotional state of developers throughout a project to deciphering user feedback. There exist two main approaches to sentiment analysis for this purpose: a lexicon-based approach and a machinelearning-based approach. Extensive research has been conducted on the former;hence this work explores the efficacy of the ML-based approach through an LSTM model for classifying the sentiment of the text. Three different data sets, StackOverflow, JIRA, and AppReviews, have been used to ensure consistent performance across multiple applications of sentiment analysis. This work aims to analyze how LSTM models perform sentiment prediction across various kinds of textual content produced in the softwareengineering industry to improve the predictive ability of the existing state-of-the-art models.
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