Results 21 to 30 of about 48,436 (195)
DDoS attacks and machine‐learning‐based detection methods: A survey and taxonomy
Engineering Reports, Volume 5, Issue 12, December 2023., 2023 This review paper discusses the Distributed Denial of Service (DDoS) attacks, the machine learning‐based detection methods of these attacks and the existing challenges. Some of the most commonly used public datasets are also compared, and their strengths and shortcomings are discussed.
Mohammad Najafimehr +2 more
wiley +1 more source
Scalable multi‐site photovoltaic power forecasting based on stream computing
IET Renewable Power Generation, Volume 17, Issue 9, Page 2379-2390, 6 July 2023., 2023 This work proposes a multi‐site photovoltaic forecasting system that contains message queue and stream engine, where a forecasting model is continuously updated using real‐time data. A benchmark with 60 sites served was performed to verify the scalability of the system.
Yuxi Sun +4 more
wiley +1 more source
A NOVEL TRUE REAL-TIME SPATIOTEMPORAL DATA STREAM PROCESSING FRAMEWORK
Jordanian Journal of Computers and Information Technology, 2022 The ability to interpret spatiotemporal data streams in real-time is critical for a range of systems. However, processing vast amounts of spatiotemporal data out of several sources, such as online traffic, social platforms, sensor networks, and other ...
ATURE ANGBERA, HUAH YONG CHAN
doaj +1 more source
An investigation of distributed computing for combinatorial testing
Software Testing, Verification and Reliability, Volume 33, Issue 4, June 2023., 2023 Combinatorial test generation is the process of generating sets of input parameters for a system under test, by considering interactions between t values of multiple parameters; the paper investigates the use of distributed algorithms to generate such test suites.
Edmond La Chance, Sylvain Hallé
wiley +1 more source
SPOT: Testing Stream Processing Programs with Symbolic Execution and Stream Synthesizing
Applied Sciences, 2021 Adoption of distributed stream processing (DSP) systems such as Apache Flink in real-time big data processing is increasing. However, DSP programs are prone to be buggy, especially when one programmer neglects some DSP features (e.g., source data ...
Qian Ye, Minyan Lu
doaj +1 more source
Automated issue assignment using topic modelling on Jira issue tracking data
IET Software, Volume 17, Issue 3, Page 333-344, June 2023., 2023 In this work, we provide a methodology for automated issue assignment, designed using Jira issue tracking data extracted from the Apache Software Foundation that describe both features and bugs. Our methodology employs topic modelling to extract the semantics of text features, while optimising the LDA algorithm (number of topics) using the assignment ...
Themistoklis Diamantopoulos +2 more
wiley +1 more source
Wireless Communications and Mobile Computing, Volume 2023, Issue 1, 2023., 2023 In the current digital era, data is budding tremendously from various sources like banks, businesses, education, entertainment, etc. Due to its significant consequence, it became a prominent proceeding for numerous research areas like the semantic web, machine learning, computational intelligence, and data mining.
Sandeep Dasari +2 more
wiley +1 more source
Data mining in predictive maintenance systems: A taxonomy and systematic review
WIREs Data Mining and Knowledge Discovery, Volume 12, Issue 5, September/October 2022., 2022 Predictive Maintenance from a Data Mining perspective: this review analyzes the most significant predictive maintenance (PdM) contributions in recent years from Data Mining (DM) perspective. An exhaustive study is carried out to determine the most used DM techniques for solving each specific PdM problem.
Aurora Esteban +2 more
wiley +1 more source
Machine learning‐based prognostic and metastasis models of kidney cancer
Cancer Innovation, Volume 1, Issue 2, Page 124-134, August 2022., 2022 We used the data of 12,394 kidney cancer patients in the SEER (surveillance, epidemiology, and final results) database to construct a research cohort, combine with statistical relevance and clinical experience to screen for factors related to kidney cancer survival and prognosis.
Yuxiang Zhang +13 more
wiley +1 more source
Business Process Event Prediction Through Scalable Online Learning
IEEE Access, 2021 Predictive process monitoring techniques aim to forecast outcomes of running business process instances. These techniques are based on using predictive models built from past observed behavior, i.e., in an offline setting.
Pedro Rico +4 more
doaj +1 more source