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Journal articles on the topic 'Streaming Data Processing for Machine Learning'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Bajwa, Waheed U., Volkan Cevher, Dimitris Papailiopoulos, and Anna Scaglione. "Machine Learning From Distributed, Streaming Data [From the Guest Editors]." IEEE Signal Processing Magazine 37, no. 3 (2020): 11–13. http://dx.doi.org/10.1109/msp.2020.2972654.

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Khan, Muhammad Taimoor, Mehr Durrani, Shehzad Khalid, and Furqan Aziz. "Online Knowledge-Based Model for Big Data Topic Extraction." Computational Intelligence and Neuroscience 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/6081804.

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Lifelong machine learning (LML) models learn with experience maintaining a knowledge-base, without user intervention. Unlike traditional single-domain models they can easily scale up to explore big data. The existing LML models have high data dependency, consume more resources, and do not support streaming data. This paper proposes online LML model (OAMC) to support streaming data with reduced data dependency. With engineering the knowledge-base and introducing new knowledge features the learning pattern of the model is improved for data arriving in pieces. OAMC improves accuracy as topic cohe

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Bandi, Raswitha, J. Amudhavel, and R. Karthik. "Machine Learning with PySpark - Review." Indonesian Journal of Electrical Engineering and Computer Science 12, no. 1 (2018): 102. http://dx.doi.org/10.11591/ijeecs.v12.i1.pp102-106.

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<p>A reasonable distributed memory-based Computing system for machine learning is Apache Spark. Spark is being superior in computing when compared with Hadoop. Apache Spark is a quick, simple to use for handling big data that has worked in modules of Machine Learning, streaming SQL, and graph processing. We can apply machine learning algorithms to big data easily, which makes it simple by using Spark and its machine learning library MLlib, even this can be made simpler by using the Python API PySpark. This paper presents the study on how to develop machine learning algorithms in PySpark.

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Boachie, Emmanuel, and Chunlin Li. "Big Data Processing with Apache Spark in University Institutions: Spark Streaming and Machine Learning Algorithm." International Journal of Continuing Engineering Education and Life-Long Learning 28, no. 4 (2018): 1. http://dx.doi.org/10.1504/ijceell.2018.10017171.

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Boachie, Emmanuel, and Chunlin Li. "Big data processing with Apache Spark in university institutions: spark streaming and machine learning algorithm." International Journal of Continuing Engineering Education and Life-Long Learning 29, no. 1/2 (2019): 5. http://dx.doi.org/10.1504/ijceell.2019.099217.

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Hassan, Fawzya, and Masoud E. Shaheen. "Predicting Diabetes from Health-based Streaming Data using Social Media, Machine Learning and Stream Processing Technologies." International Journal of Engineering Research and Technology 13, no. 8 (2020): 1957. http://dx.doi.org/10.37624/ijert/13.8.2020.1957-1967.

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Borodo, Salisu Musa, Siti Mariyam Shamsuddin, and Shafaatunnur Hasan. "Big Data Platforms and Techniques." Indonesian Journal of Electrical Engineering and Computer Science 1, no. 1 (2016): 191. http://dx.doi.org/10.11591/ijeecs.v1.i1.pp191-200.

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Data is growing at unprecedented rate and has led to huge volume generated; the data sources include mobile, internet and sensors. This voluminous data is generated and updated at high velocity by batch and streaming platforms. This data is also varied along structured and unstructured types. This volume, velocity and variety of data led to the term big data. Big data has been premised to contain untapped knowledge, its exploration and exploitation is termed big data analytics. This literature reviewed platforms such as batch processing, real time processing and interactive analytics used in b

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Mahmood, Tariq, and Tatheer Fatima. "CONCEPT DRIFT IN STREAMING DATA: A SYSTEMATIC LITERATURE REVIEW." KIET Journal of Computing and Information Sciences 4, no. 1 (2021): 17. http://dx.doi.org/10.51153/kjcis.v4i1.43.

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World is generating immeasurable amount of data every minute, that needs to be analyzed for better decision making. In order to fulfil this demand of faster analytics, businesses are adopting efficient stream processing and machine learning techniques. However, data streams are particularly challenging to handle. One of the prominent problems faced while dealing with streaming data is concept drift. Concept drift is described as, an unexpected change in the underlying distribution of the streaming data that can be observed as time passes. In this work, we have conducted a systematic literature

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Cumbane, Silvino Pedro, and Gyozo Gidófalvi. "Review of Big Data and Processing Frameworks for Disaster Response Applications." ISPRS International Journal of Geo-Information 8, no. 9 (2019): 387. http://dx.doi.org/10.3390/ijgi8090387.

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Natural hazards result in devastating losses in human life, environmental assets and personal, and regional and national economies. The availability of different big data such as satellite imageries, Global Positioning System (GPS) traces, mobile Call Detail Records (CDRs), social media posts, etc., in conjunction with advances in data analytic techniques (e.g., data mining and big data processing, machine learning and deep learning) can facilitate the extraction of geospatial information that is critical for rapid and effective disaster response. However, disaster response systems development

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Kim, Yoon-Ki, and Yongsung Kim. "DiPLIP: Distributed Parallel Processing Platform for Stream Image Processing Based on Deep Learning Model Inference." Electronics 9, no. 10 (2020): 1664. http://dx.doi.org/10.3390/electronics9101664.

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Recently, as the amount of real-time video streaming data has increased, distributed parallel processing systems have rapidly evolved to process large-scale data. In addition, with an increase in the scale of computing resources constituting the distributed parallel processing system, the orchestration of technology has become crucial for proper management of computing resources, in terms of allocating computing resources, setting up a programming environment, and deploying user applications. In this paper, we present a new distributed parallel processing platform for real-time large-scale ima

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Dabas, Chetna, Aditya Agarwal, Naman Gupta, Vaibhav Jain, and Siddhant Pathak. "Machine Learning Evaluation for Music Genre Classification of Audio Signals." International Journal of Grid and High Performance Computing 12, no. 3 (2020): 57–67. http://dx.doi.org/10.4018/ijghpc.2020070104.

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Music genre classification has its own popularity index in the present times. Machine learning can play an important role in the music streaming task. This research article proposes a machine learning based model for the classification of music genre. The evaluation of the proposed model is carried out while considering different music genres as in blues, metal, pop, country, classical, disco, jazz and hip-hop. Different audio features utilized in this study include MFCC (Mel Frequency Spectral Coefficients), Delta, Delta-Delta and temporal aspects for processing the data. The implementation o

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Lobo, Jesus L., Igor Ballesteros, Izaskun Oregi, Javier Del Ser, and Sancho Salcedo-Sanz. "Stream Learning in Energy IoT Systems: A Case Study in Combined Cycle Power Plants." Energies 13, no. 3 (2020): 740. http://dx.doi.org/10.3390/en13030740.

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The prediction of electrical power produced in combined cycle power plants is a key challenge in the electrical power and energy systems field. This power production can vary depending on environmental variables, such as temperature, pressure, and humidity. Thus, the business problem is how to predict the power production as a function of these environmental conditions, in order to maximize the profit. The research community has solved this problem by applying Machine Learning techniques, and has managed to reduce the computational and time costs in comparison with the traditional thermodynami

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Kanavos, Andreas, Maria Trigka, Elias Dritsas, Gerasimos Vonitsanos, and Phivos Mylonas. "A Regularization-Based Big Data Framework for Winter Precipitation Forecasting on Streaming Data." Electronics 10, no. 16 (2021): 1872. http://dx.doi.org/10.3390/electronics10161872.

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In the current paper, we propose a machine learning forecasting model for the accurate prediction of qualitative weather information on winter precipitation types, utilized in Apache Spark Streaming distributed framework. The proposed model receives storage and processes data in real-time, in order to extract useful knowledge from different sensors related to weather data. In following, the numerical weather prediction model aims at forecasting the weather type given three precipitation classes namely rain, freezing rain, and snow as recorded in the Automated Surface Observing System (ASOS) ne

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Li, Guang Di, Guo Yin Wang, Xue Rui Zhang, Wei Hui Deng, and Fan Zhang. "Forest Cover Types Classification Based on Online Machine Learning on Distributed Cloud Computing Platforms of Storm and SAMOA." Advanced Materials Research 955-959 (June 2014): 3803–12. http://dx.doi.org/10.4028/www.scientific.net/amr.955-959.3803.

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Storm is the most popular realtime stream processing platform, which can be used to deal with online machine learning. Similar to how Hadoop provides a set of general primitives for doing batch processing, Storm provides a set of general primitives for doing realtime computation. SAMOA includes distributed algorithms for the most common machine learning tasks like Mahout for Hadoop. SAMOA is both a platform and a library. In this paper, Forest cover types, a large benchmaking dataset available at the UCI KDD Archive is used as the data stream source. Vertical Hoeffding Tree, a parallelizing st

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AlQabbany, Abdulaziz O., and Aqil M. Azmi. "Measuring the Effectiveness of Adaptive Random Forest for Handling Concept Drift in Big Data Streams." Entropy 23, no. 7 (2021): 859. http://dx.doi.org/10.3390/e23070859.

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We are living in the age of big data, a majority of which is stream data. The real-time processing of this data requires careful consideration from different perspectives. Concept drift is a change in the data’s underlying distribution, a significant issue, especially when learning from data streams. It requires learners to be adaptive to dynamic changes. Random forest is an ensemble approach that is widely used in classical non-streaming settings of machine learning applications. At the same time, the Adaptive Random Forest (ARF) is a stream learning algorithm that showed promising results in

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Seo, Wonik, Sanghoon Cha, Yeonjae Kim, Jaehyuk Huh, and Jongse Park. "SLO-Aware Inference Scheduler for Heterogeneous Processors in Edge Platforms." ACM Transactions on Architecture and Code Optimization 18, no. 4 (2021): 1–26. http://dx.doi.org/10.1145/3460352.

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With the proliferation of applications with machine learning (ML), the importance of edge platforms has been growing to process streaming sensor, data locally without resorting to remote servers. Such edge platforms are commonly equipped with heterogeneous computing processors such as GPU, DSP, and other accelerators, but their computational and energy budget are severely constrained compared to the data center servers. However, as an edge platform must perform the processing of multiple machine learning models concurrently for multimodal sensor data, its scheduling problem poses a new challen

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Liu, Luhang, Qiang Zhang, Dazhong Wei, et al. "Chaotic Ensemble of Online Recurrent Extreme Learning Machine for Temperature Prediction of Control Moment Gyroscopes." Sensors 20, no. 17 (2020): 4786. http://dx.doi.org/10.3390/s20174786.

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Control moment gyroscopes (CMG) are crucial components in spacecrafts. Since the anomaly of bearing temperature of the CMG shows apparent correlation with nearly all critical fault modes, temperature prediction is of great importance for health management of CMGs. However, due to the complicity of thermal environment on orbit, the temperature signal of the CMG has strong intrinsic nonlinearity and chaotic characteristics. Therefore, it is crucial to study temperature prediction under the framework of chaos time series theory. There are also several other challenges including poor data quality,

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Aminudin, Aminudin, and Eko Budi Cahyono. "Pengukuran Performa Apache Spark dengan Library H2O Menggunakan Benchmark Hibench Berbasis Cloud Computing." Jurnal Teknologi Informasi dan Ilmu Komputer 6, no. 5 (2019): 519. http://dx.doi.org/10.25126/jtiik.2019651520.

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<p class="Judul2">Apache Spark merupakan platform yang dapat digunakan untuk memproses data dengan ukuran data yang relatif besar (<em>big data</em>) dengan kemampuan untuk membagi data tersebut ke masing-masing cluster yang telah ditentukan konsep ini disebut dengan parallel komputing. Apache Spark mempunyai kelebihan dibandingkan dengan framework lain yang serupa misalnya Apache Hadoop dll, di mana Apache Spark mampu memproses data secara streaming artinya data yang masuk ke dalam lingkungan Apache Spark dapat langsung diproses tanpa menunggu data lain terkumpul. Agar di da

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Demertzis, Konstantinos, Nikos Tziritas, Panayiotis Kikiras, Salvador Llopis Sanchez, and Lazaros Iliadis. "The Next Generation Cognitive Security Operations Center: Adaptive Analytic Lambda Architecture for Efficient Defense against Adversarial Attacks." Big Data and Cognitive Computing 3, no. 1 (2019): 6. http://dx.doi.org/10.3390/bdcc3010006.

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A Security Operations Center (SOC) is a central technical level unit responsible for monitoring, analyzing, assessing, and defending an organization’s security posture on an ongoing basis. The SOC staff works closely with incident response teams, security analysts, network engineers and organization managers using sophisticated data processing technologies such as security analytics, threat intelligence, and asset criticality to ensure security issues are detected, analyzed and finally addressed quickly. Those techniques are part of a reactive security strategy because they rely on the human f

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Hafsa, Mounir, and Farah Jemili. "Comparative Study between Big Data Analysis Techniques in Intrusion Detection." Big Data and Cognitive Computing 3, no. 1 (2018): 1. http://dx.doi.org/10.3390/bdcc3010001.

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Cybersecurity ventures expect that cyber-attack damage costs will rise to $11.5 billion in 2019 and that a business will fall victim to a cyber-attack every 14 seconds. Notice here that the time frame for such an event is seconds. With petabytes of data generated each day, this is a challenging task for traditional intrusion detection systems (IDSs). Protecting sensitive information is a major concern for both businesses and governments. Therefore, the need for a real-time, large-scale and effective IDS is a must. In this work, we present a cloud-based, fault tolerant, scalable and distributed

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Awan, Mazhar Javed, Rafia Asad Khan, Haitham Nobanee, et al. "A Recommendation Engine for Predicting Movie Ratings Using a Big Data Approach." Electronics 10, no. 10 (2021): 1215. http://dx.doi.org/10.3390/electronics10101215.

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In this era of big data, the amount of video content has dramatically increased with an exponential broadening of video streaming services. Hence, it has become very strenuous for end-users to search for their desired videos. Therefore, to attain an accurate and robust clustering of information, a hybrid algorithm was used to introduce a recommender engine with collaborative filtering using Apache Spark and machine learning (ML) libraries. In this study, we implemented a movie recommendation system based on a collaborative filtering approach using the alternating least squared (ALS) model to p

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Kelevitz, Krisztina, Kristy F. Tiampo, and Brianna D. Corsa. "Improved Real-Time Natural Hazard Monitoring Using Automated DInSAR Time Series." Remote Sensing 13, no. 5 (2021): 867. http://dx.doi.org/10.3390/rs13050867.

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As part of the collaborative GeoSciFramework project, we are establising a monitoring system for the Yellowstone volcanic area that integrates multiple geodetic and seismic data sets into an advanced cyber-infrastructure framework that will enable real-time streaming data analytics and machine learning and allow us to better characterize associated long- and short-term hazards. The goal is to continuously ingest both remote sensing (GNSS, DInSAR) and ground-based (seismic, thermal and gas observations, strainmeter, tiltmeter and gravity measurements) data and query and analyse them in near-rea

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Stringham, Oliver C., Stephanie Moncayo, Katherine G. W. Hill, et al. "Text classification to streamline online wildlife trade analyses." PLOS ONE 16, no. 7 (2021): e0254007. http://dx.doi.org/10.1371/journal.pone.0254007.

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Automated monitoring of websites that trade wildlife is increasingly necessary to inform conservation and biosecurity efforts. However, e-commerce and wildlife trading websites can contain a vast number of advertisements, an unknown proportion of which may be irrelevant to researchers and practitioners. Given that many wildlife-trade advertisements have an unstructured text format, automated identification of relevant listings has not traditionally been possible, nor attempted. Other scientific disciplines have solved similar problems using machine learning and natural language processing mode

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Kelling, Steve. "Technology Developments for Biodiversity Monitoring and Conservation." Biodiversity Information Science and Standards 2 (May 22, 2018): e25833. http://dx.doi.org/10.3897/biss.2.25833.

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Over the next 5 years major advances in the development and application of numerous technologies related to computing, mobile phones, artificial intelligence (AI), and augmented reality (AR) will have a dramatic impact in biodiversity monitoring and conservation. Over a 2-week period several of us had the opportunity to meet with multiple technology experts in the Silicon Valley, California, USA to discuss trends in technology innovation, and how they could be applied to conservation science and ecology research. Here we briefly highlight some of the key points of these meetings with respect t

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Siddiqui, Atif, Muhammad Yousuf Irfan Zia, and Pablo Otero. "A Universal Machine-Learning-Based Automated Testing System for Consumer Electronic Products." Electronics 10, no. 2 (2021): 136. http://dx.doi.org/10.3390/electronics10020136.

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Consumer electronic manufacturing (CEM) companies face a constant challenge to maintain quality standards during frequent product launches. A manufacturing test verifies product functionality and identifies manufacturing defects. Failure to complete testing can even result in product recalls. In this research, a universal automated testing system has been proposed for CEM companies to streamline their test process in reduced test cost and time. A universal hardware interface is designed for connecting commercial off-the-shelf (COTS) test equipment and unit under test (UUT). A software applicat

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Ferlin, Maria Anna, Michał Grochowski, Arkadiusz Kwasigroch, et al. "A Comprehensive Analysis of Deep Neural-Based Cerebral Microbleeds Detection System." Electronics 10, no. 18 (2021): 2208. http://dx.doi.org/10.3390/electronics10182208.

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Machine learning-based systems are gaining interest in the field of medicine, mostly in medical imaging and diagnosis. In this paper, we address the problem of automatic cerebral microbleeds (CMB) detection in magnetic resonance images. It is challenging due to difficulty in distinguishing a true CMB from its mimics, however, if successfully solved, it would streamline the radiologists work. To deal with this complex three-dimensional problem, we propose a machine learning approach based on a 2D Faster RCNN network. We aimed to achieve a reliable system, i.e., with balanced sensitivity and pre

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Andreetto, Paolo, Fulvia Costa, Alberto Crescente, et al. "Evolution of the CloudVeneto.it private cloud to support research and innovation." EPJ Web of Conferences 245 (2020): 07013. http://dx.doi.org/10.1051/epjconf/202024507013.

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CloudVeneto.it was initially funded and deployed by INFN in 2014 for serving the computational and storage demands of INFN research projects mainly related to HEP and Nuclear Physics. It is an OpenStack-based scientific cloud with resources spread across two different sites connected with a high speed optical link: INFN Padova Unit and the INFN Legnaro National Laboratories. The infrastructure has grown throughout the years with additional funds from ten University of Padova departments, and nowadays supports a broader range of scientific and engineering disciplines. Its hardware resources pro

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Egorochkina, I. O., E. S. Tsygankova, E. A. Shlyakhova, I. A. Serebryanaya, and D. S. Serebryanaya. "Information video analytics system for the prevention of physical inactivity in students." E3S Web of Conferences 273 (2021): 12116. http://dx.doi.org/10.1051/e3sconf/202127312116.

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The project “SportTrainer” is presented -an information system of video analytics and personal recommendations for the prevention of hypo-dynamics and increasing the effectiveness of personal training of students who want to achieve a certain sports result and improve their figure. The developed system “SportTrainer” realizes consulting on training programs, nutrition, provides control over the implementation of the selected programs and the achievement of goals. A digital trainer can suggest how effective the selected program is for a particular individual, selects the necessary exercises, ma

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Yang, Yuan-Chi, Mohammed Ali Al-Garadi, Whitney Bremer, Jane M. Zhu, David Grande, and Abeed Sarker. "Developing an Automatic System for Classifying Chatter About Health Services on Twitter: Case Study for Medicaid." Journal of Medical Internet Research 23, no. 5 (2021): e26616. http://dx.doi.org/10.2196/26616.

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Background The wide adoption of social media in daily life renders it a rich and effective resource for conducting near real-time assessments of consumers’ perceptions of health services. However, its use in these assessments can be challenging because of the vast amount of data and the diversity of content in social media chatter. Objective This study aims to develop and evaluate an automatic system involving natural language processing and machine learning to automatically characterize user-posted Twitter data about health services using Medicaid, the single largest source of health coverage

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Gomes, Heitor Murilo, Jesse Read, Albert Bifet, Jean Paul Barddal, and João Gama. "Machine learning for streaming data." ACM SIGKDD Explorations Newsletter 21, no. 2 (2019): 6–22. http://dx.doi.org/10.1145/3373464.3373470.

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Deshmukh, Varad, Thomas E. Berger, Elizabeth Bradley, and James D. Meiss. "Leveraging the mathematics of shape for solar magnetic eruption prediction." Journal of Space Weather and Space Climate 10 (2020): 13. http://dx.doi.org/10.1051/swsc/2020014.

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Current operational forecasts of solar eruptions are made by human experts using a combination of qualitative shape-based classification systems and historical data about flaring frequencies. In the past decade, there has been a great deal of interest in crafting machine-learning (ML) flare-prediction methods to extract underlying patterns from a training set – e.g. a set of solar magnetogram images, each characterized by features derived from the magnetic field and labeled as to whether it was an eruption precursor. These patterns, captured by various methods (neural nets, support vector mach

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Shaout, Adnan, and Brennan Crispin. "Streaming Video Classification Using Machine Learning." International Arab Journal of Information Technology 17, no. 4A (2020): 677–82. http://dx.doi.org/10.34028/iajit/17/4a/13.

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This paper presents a method using neural networks and Markov Decision Process (MDP) to identify the source and class of video streaming services. The paper presents the design and implementation of an end-to-end pipeline for training and classifying a machine learning system that can take in packets collected over a network interface and classify the data stream as belonging to one of five streaming video services: You Tube, You Tube TV, Netflix, Amazon Prime, or HBO

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Aggarwal, Vaneet. "Machine Learning for Communications." Entropy 23, no. 7 (2021): 831. http://dx.doi.org/10.3390/e23070831.

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Due to the proliferation of applications and services that run over communication networks, ranging from video streaming and data analytics to robotics and augmented reality, tomorrow’s networks will be faced with increasing challenges resulting from the explosive growth of data traffic demand with significantly varying performance requirements [...]

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Nassif, Roula, Stefan Vlaski, Cedric Richard, Jie Chen, and Ali H. Sayed. "Multitask Learning Over Graphs: An Approach for Distributed, Streaming Machine Learning." IEEE Signal Processing Magazine 37, no. 3 (2020): 14–25. http://dx.doi.org/10.1109/msp.2020.2966273.

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Akilandeswari, P., R. Harshita, and Sumanth KO.M. "Sentiment Analysis using Machine Learning through Twitter Streaming API." International Journal of Engineering & Technology 7, no. 3.12 (2018): 1168. http://dx.doi.org/10.14419/ijet.v7i3.12.17781.

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Social media allows to share the experiences with many best suggestions and provides opportunities to share the ideas about any topics at any time. In the current trending, twitter is used to gather different kinds of information as user need and it is a social network service which enables the user for better communication and gaining of knowledge. Accurate representation of the user interactions can be done based on the facts sematic content. The pre-processed tweets which are stored in database are been identified and classified whether it relates to the user keywords related posts. The bes

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Viloria, Amelec, Omar Bonerge Pineda Lezama, and Nohora Mercado-Caruzo. "Unbalanced data processing using oversampling: Machine Learning." Procedia Computer Science 175 (2020): 108–13. http://dx.doi.org/10.1016/j.procs.2020.07.018.

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Zorich, L., K. Pichara, and P. Protopapas. "Streaming classification of variable stars." Monthly Notices of the Royal Astronomical Society 492, no. 2 (2019): 2897–909. http://dx.doi.org/10.1093/mnras/stz3426.

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ABSTRACT In the last years, automatic classification of variable stars has received substantial attention. Using machine learning techniques for this task has proven to be quite useful. Typically, machine learning classifiers used for this task require to have a fixed training set, and the training process is performed offline. Upcoming surveys such as the Large Synoptic Survey Telescope will generate new observations daily, where an automatic classification system able to create alerts online will be mandatory. A system with those characteristics must be able to update itself incrementally. U

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Li, Yanchao, Yongli Wang, Qi Liu, Cheng Bi, Xiaohui Jiang, and Shurong Sun. "Incremental semi-supervised learning on streaming data." Pattern Recognition 88 (April 2019): 383–96. http://dx.doi.org/10.1016/j.patcog.2018.11.006.

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Li, Guang, Zhushi He, Juzhi Deng, et al. "Robust CSEM data processing by unsupervised machine learning." Journal of Applied Geophysics 186 (March 2021): 104262. http://dx.doi.org/10.1016/j.jappgeo.2021.104262.

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Loh, Frank, Fabian Poignée, Florian Wamser, Ferdinand Leidinger, and Tobias Hoßfeld. "Uplink vs. Downlink: Machine Learning-Based Quality Prediction for HTTP Adaptive Video Streaming." Sensors 21, no. 12 (2021): 4172. http://dx.doi.org/10.3390/s21124172.

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Streaming video is responsible for the bulk of Internet traffic these days. For this reason, Internet providers and network operators try to make predictions and assessments about the streaming quality for an end user. Current monitoring solutions are based on a variety of different machine learning approaches. The challenge for providers and operators nowadays is that existing approaches require large amounts of data. In this work, the most relevant quality of experience metrics, i.e., the initial playback delay, the video streaming quality, video quality changes, and video rebuffering events

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Kenda, Klemen, Blaž Kažič, Erik Novak, and Dunja Mladenić. "Streaming Data Fusion for the Internet of Things." Sensors 19, no. 8 (2019): 1955. http://dx.doi.org/10.3390/s19081955.

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To achieve the full analytical potential of the streaming data from the internet of things, the interconnection of various data sources is needed. By definition, those sources are heterogeneous and their integration is not a trivial task. A common approach to exploit streaming sensor data potential is to use machine learning techniques for predictive analytics in a way that is agnostic to the domain knowledge. Such an approach can be easily integrated in various use cases. In this paper, we propose a novel framework for data fusion of a set of heterogeneous data streams. The proposed framework

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Behera, Ranjan, Sushree Das, Santanu Rath, Sanjay Misra, and Robertas Damasevicius. "Comparative Study of Real Time Machine Learning Models for Stock Prediction through Streaming Data." JUCS - Journal of Universal Computer Science 26, no. 9 (2020): 1128–47. http://dx.doi.org/10.3897/jucs.2020.059.

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Stock prediction is one of the emerging applications in the field of data science which help the companies to make better decision strategy. Machine learning models play a vital role in the field of prediction. In this paper, we have proposed various machine learning models which predicts the stock price from the real-time streaming data. Streaming data has been a potential source for real-time prediction which deals with continuous ow of data having information from various sources like social networking websites, server logs, mobile phone applications, trading oors etc. We have adopted the d

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Zhang, Xiongwei, Hager Saleh, Eman M. G. Younis, Radhya Sahal, and Abdelmgeid A. Ali. "Predicting Coronavirus Pandemic in Real-Time Using Machine Learning and Big Data Streaming System." Complexity 2020 (December 19, 2020): 1–10. http://dx.doi.org/10.1155/2020/6688912.

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Twitter is a virtual social network where people share their posts and opinions about the current situation, such as the coronavirus pandemic. It is considered the most significant streaming data source for machine learning research in terms of analysis, prediction, knowledge extraction, and opinions. Sentiment analysis is a text analysis method that has gained further significance due to social networks’ emergence. Therefore, this paper introduces a real-time system for sentiment prediction on Twitter streaming data for tweets about the coronavirus pandemic. The proposed system aims to find t

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Madhusudhanan, Sathya, Suresh Jaganathan, and Jayashree L S. "Incremental Learning for Classification of Unstructured Data Using Extreme Learning Machine." Algorithms 11, no. 10 (2018): 158. http://dx.doi.org/10.3390/a11100158.

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Unstructured data are irregular information with no predefined data model. Streaming data which constantly arrives over time is unstructured, and classifying these data is a tedious task as they lack class labels and get accumulated over time. As the data keeps growing, it becomes difficult to train and create a model from scratch each time. Incremental learning, a self-adaptive algorithm uses the previously learned model information, then learns and accommodates new information from the newly arrived data providing a new model, which avoids the retraining. The incrementally learned knowledge

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King, Paul H. "Signal Processing and Machine Learning for Biomedical Big Data." IEEE Pulse 10, no. 3 (2019): 34–35. http://dx.doi.org/10.1109/mpuls.2019.2911803.

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Anderson, Brian E. "Signal Processing: Data analysis, machine learning, and imaging sources." Journal of the Acoustical Society of America 146, no. 4 (2019): 2870. http://dx.doi.org/10.1121/1.5136960.

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Katsamakas, Evangelos, and Hao Sun. "Machine Learning Crowdfunding." International Journal of Knowledge-Based Organizations 10, no. 2 (2020): 1–11. http://dx.doi.org/10.4018/ijkbo.2020040101.

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Crowdfunding is a novel and important economic mechanism for funding projects and promoting innovation in the digital economy. This article explores most recent structured and unstructured data from a crowdfunding platform. It provides an in-depth exploration of the data using text analytics techniques, such as sentiment analysis and topic modeling. It uses novel natural language processing to represent project descriptions, and evaluates machine learning models, including neural network models, to predict project fundraising success. It discusses the findings of the performance evaluation, an

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Omran, Nahla F., Sara F. Abd-el Ghany, Hager Saleh, and Ayman Nabil. "Breast Cancer Identification from Patients’ Tweet Streaming Using Machine Learning Solution on Spark." Complexity 2021 (January 27, 2021): 1–12. http://dx.doi.org/10.1155/2021/6653508.

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Twitter integrates with streaming data technologies and machine learning to add new value to healthcare. This paper presented a real-time system to predict breast cancer based on streaming patient’s health data from Twitter. The proposed system consists of two major components: developing an offline building model and an online prediction pipeline. For the first component, we made a correlation between the features to determine the correlation between features and reduce the number of features from the Breast Cancer Wisconsin Diagnostic dataset. Two feature selection algorithms are recursive f

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Rasool, Mir Junaid. "Spark based Health Status Prediction on Real Time Streaming Data using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 8, no. 11 (2020): 471–78. http://dx.doi.org/10.22214/ijraset.2020.32197.

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Suzuki, Yu. "Filtering Method for Twitter Streaming Data Using Human-in-the-Loop Machine Learning." Journal of Information Processing 27 (2019): 404–10. http://dx.doi.org/10.2197/ipsjjip.27.404.

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