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35 articles for “Big Data Mining”
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Enhanced Association Rule Mining Algorithm (EARMA) for Reducing Computational Time on Large Data Set
Abstract: Association rule learning is a trendy process for discovering exciting relationships between variables in big database. It is frequently used in market basket analysis field e.g. if a buyer buys onions and potatoes then he also purchases beef. But, in fact, it can be implemented in different application area where we want to determine the association among variables. The APRIORI method is definitely the trendiest. But, even with its good …
Published in Research & Reviews: Discrete Mathematical Structures · Vol. 3, Issue 2, 2016 · pp. 20–25 Read article
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Generation of Business Intelligence by Sentimental Analysis through Big Data and Hadoop
Abstract: AbstractOnline networking gives clients a platform to discuss successfully with companions, family, and partners, and gives them a stage to discuss their top pick (and minimum most loved brands). This "unstructured" discussion can provide organizations crucial understanding into how buyers see their image, and enable them to effectively settle on business choices to keep up their horizons. Fast in the volume of conclusion, rich online networking on the web has …
Published in Journal of Advanced Database Management & Systems · Vol. 4, Issue 3, 2017 · pp. 20–25 Read article
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Supply Chain Optimization Using Big Data
Abstract: In the references to add productivity and short the appendences, and increase customer value, supply chain optimization is a very crucial part of contemporary business operations. Supply chain management has been transformed by the emergence of big data, which can provide analysis and insight from many different sources of information. This content explores the importance of using big data analytics to improve the delivery process. Thanks to the evolution of …
Published in Journal of Production Research & Management · Vol. 14, Issue 2, 2024 · pp. 19–25 Read article
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Efficient Clustering Techniques for Data Stream Mining
Abstract: Data mining mainly works on a massive database for storing heavy amount of data. It is generally essential for extracting the meaning insights from the massive, continuously growing database. The traditional method often struggles with sheer volume and the dynamic nature of the modern data. Data stream mining allows for the real-time analysis, means insights are generated as the data arrives, and not after the long batch process. This continuous …
Published in Recent Trends in Parallel Computing · Vol. 12, Issue 2, 2025 · pp. 26–32 Read article
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Voting Classification Method for Network Traffic Prediction
Abstract: Prediction analysis (PA) is a data mining-based technique. The futuristic outcomes based on present data can be predicted using this technique. As the dataset is large and complex, network traffic classification is a big concern in prediction analysis. Three phases are included in network traffic strategies. The data collection is obtained in the first step of pre-processing, and it is analysed to delete incomplete and obsolete values. The relationship between …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 8, Issue 1, 2021 · pp. 23–30 Read article
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Strategies to Control Quality in Crowdmining
Abstract: Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 Crowd sourcing is emerging as an effective method for data gathering and analysis, which introduces a new paradigm of data mining process. Crowd workers perform small chunks of larger tasks in return for a reward, which can be monetary, material, psychological and so on. One of the biggest challenges of crowd mining is controlling the quality of the answers collected from …
Published in Current Trends in Information Technology · Vol. 6, Issue 1, 2016 · pp. 1–5 Read article
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Scientific Paper Reviews Identification by Sentiment Analysis in Data Mining
Abstract: AbstractSentiment analysis (SA) is one of the rapidly growing fields of research in computer science and makes it difficult to track all activities in the area. Analyzes of thoughts and/or perceptions are used for automating subjective information identification such as opinions, attitudes, emotions, and feelings. Hundreds of thousands are concerned about science and take a long time to select appropriate papers for their research. These opinions are numerous and SA …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 7, Issue 3, 2020 · pp. 1–9 Read article
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DESIGN PERSONALIZATION CLASSIFICATION MODEL USING SEMI-SUPERVISED SUPPORT VECTOR MACHINES
Abstract: Abstract Internet user is growing everyday as an outcome of this huge volume of data is being produced constantly. Thus Mining and evaluating such data can support an organization in services extending from website personalization and usage classification. The pre-existing machine learning algorithms are unable to solve this in a better way. The current application for data classification is really expensive in nature. Improve the recommendation technique using map reduce …
Published in Journal of Web Engineering & Technology · Vol. 5, Issue 1, 2018 · pp. 17–24 Read article
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Big Data in Chemistry: Problems and Answers
Abstract: The rapid growth of experimental and computational chemistry data, researchers now have access to vast datasets, presenting both significant opportunities and challenges. This paper explores the primary challenges associated with managing, processing, and utilizing big data in chemistry, including data heterogeneity, integration across various scales and systems, lack of standardized formats, and the need for advanced tools for data analysis. Additionally, the paper discusses the ethical concerns of data ownership, …
Published in International Journal of Cheminformatics · Vol. 2, Issue 1, 2024 · pp. 9–14 Read article
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A Study on Privacy Preserving Techniques in Data Mining
Abstract: Data Mining (DM) is the technique of examining data from specific summarizing and perspectives the outcome as useful information. It has been describing as "the non-trivial procedure of new, potentially helpful and ultimately conceivable patterns and identifying valid in data". The word “Knowledge” in KDD submits to the discovery of example which is removing since the processed data. Preserving privacy is becoming a key apprehension as personal data is publicly …
Published in Journal of Operating Systems Development & Trends · Vol. 5, Issue 2, 2018 · pp. 18–23 Read article
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Enhanced Incremental DB Scan Algorithm using Map-Reduce
Abstract: Distributed data mining is more efficient, scalable and its performance is better than the central data mining techniques. Incremental DBSCAN algorithm is better than the other method DBSCAN. Incremental DBSCAN can give better performance in distributed environment in terms of run time complexity using Hadoop platform. The proposed system uses spatial dataset i.e. dataset are distributed among different sites. Initially, clusters are generated at each site using DBSCAN algorithm. Then …
Published in Research & Reviews: A Journal of Embedded System & Applications · Vol. 3, Issue 2, 2015 · pp. 1–6 Read article
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Data-Driven Life Prediction of Fiber-Reinforced Polymer Composites Using IoT Sensing and Machine Learning Algorithms
Abstract: The accurate prediction of fatigue life in fiber-reinforced polymer (FRP) composites remains a major challenge due to their nonlinear, multi-mechanism degradation behavior under variable loading conditions. This study presents a data-driven framework, H-LiProNet, which combines real-time IoT sensing with hybrid machine learning to estimate remaining useful life (RUL) in FRP composites. The proposed system integrates embedded Fiber Bragg Grating (FBG) and acoustic emission (AE) sensors to capture strain and damage …
Published in Journal of Polymer & Composites · Vol. 13, Issue 4, 2025 · pp. 116–130 Read article
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Smart City Solutions for Waste Management and Pollution Control
Abstract: Recent trends in the role of artificial intelligence, IoT, and other smart technologies have a critical role toward addressing urban environmental challenges related to air quality and waste management in the context of a smart city. This changes the scope of managing air quality as, with the integration of IoT sensors, big data, and AI, they are able to predict pollution levels through real time monitoring and analysis. These technologies …
Published in International Journal of Environmental Noise and Pollution Control · Vol. 3, Issue 1, 2025 · pp. 11–22 Read article
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Soil Analysis Mechanisms for Smart Agriculture-A Review
Abstract: Agriculture, Horticulture, as the single biggest client of freshwater on a worldwide premise and as a significant reason for corruption of surface and groundwater assets through disintegration and synthetic overflow, has cause to be worried about the worldwide ramifications of water quality. The related agro food preparing industry is likewise a huge wellspring of natural contamination in many nations. Hydroponics is currently perceived as a significant issue in freshwater, estuarine …
Published in Recent Trends in Sensor Research & Technology · Vol. 8, Issue 3, 2021 · pp. 6–10 Read article
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Modulation of Receptor Proteins: A Recent Paradigm in Drug Discovery and Target
Abstract: The main aim in drug discovery is to design different ligands those work on specific drug targets. However, various effective drugs work through the modulation of many proteins than single target. Advancement in system biology are establishing a phenotypic strength and a network structure, which suggests that, the excellent selective compounds may be performed less than desired clinical efficacy in comparison to multi-target drugs. In this new appreciation of the …
Published in Research & Reviews: A Journal of Drug Design & Discovery · Vol. 5, Issue 1, 2018 · pp. 14–24 Read article