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Journal articles on the topic 'Wine Review Dataset'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Tian, Qiuyun, Brittany Whiting, and Bernard Chen. "Wineinformatics: Comparing and Combining SVM Models Built by Wine Reviews from Robert Parker and Wine Spectator for 95 + Point Wine Prediction." Fermentation 8, no. 4 (2022): 164. http://dx.doi.org/10.3390/fermentation8040164.

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Wineinformatics is among the new fields in data science that use wine as domain knowledge. To process large amounts of wine review data in human language format, the computational wine wheel is applied. In previous research, the computational wine wheel was created and applied to different datasets of wine reviews developed by Wine Spectator. The goal of this research is to explore the development and application of the computational wine wheel to reviews from a different reviewer, Robert Parker. For comparison, this research collects 513 elite Bordeaux wines that were reviewed by both Robert

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Oliveira, Luís, Rodrigo Rocha Silva, and Jorge Bernardino. "Wine Ontology Influence in a Recommendation System." Big Data and Cognitive Computing 5, no. 2 (2021): 16. http://dx.doi.org/10.3390/bdcc5020016.

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Wine is the second most popular alcoholic drink in the world behind beer. With the rise of e-commerce, recommendation systems have become a very important factor in the success of business. Recommendation systems analyze metadata to predict if, for example, a user will recommend a product. The metadata consist mostly of former reviews or web traffic from the same user. For this reason, we investigate what would happen if the information analyzed by a recommendation system was insufficient. In this paper, we explore the effects of a new wine ontology in a recommendation system. We created our o

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Brandon, Gowray, Yadav Omprakash, Keerimolel Albert, Galsulkar Sharifa, and Revulagadda Akshita. "WordCloud Generation using NLP." Advancement of Computer Technology and its Applications 4, no. 2 (2021): 1–3. https://doi.org/10.5281/zenodo.4991876.

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Natural Language Processing is one of the most powerful tool that we have today for machines to understand how and what we are thinking and saying, what we mean with the power to understand sentence in various languages and process it to give us desired outputs in various instances like frequency of a particular word, summarization etc. WordCloud is one such application where we can using concepts of NLP to create beautiful and aesthetically pleasing, decorative wordclouds which can be used for various purposes such as on websites, greeting cards etc.

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Kopsacheilis, Orestis, Pantelis P. Analytis, Karthikeya Kaushik, Stefan M. Herzog, Bahador Bahrami, and Ophelia Deroy. "Crowdsourcing the assessment of wine quality: Vivino ratings, professional critics, and the weather." Journal of Wine Economics 19, no. 3 (2024): 285–304. https://doi.org/10.1017/jwe.2024.20.

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AbstractCrowdsourcing platforms—such as Vivino—that aggregate the opinions of large numbers of amateur wine reviewers represent a new source of information on the wine market. We assess the validity of aggregated Vivino ratings based on two criteria: correlation with professional critics’ ratings and sensitivity to weather conditions affecting the quality of grapes. We construct a large, novel dataset consisting of Vivino ratings for a portfolio of red wines from Bordeaux, review scores from professional critics, and weather data from a local weather station. Vivino ratings correlate substanti

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Puga, German, James Fogarty, Atakelty Hailu, and Alejandro Gennari. "Modeling Grape Price Dynamics in Mendoza: Lessons for Policymakers." Journal of Wine Economics 14, no. 4 (2019): 343–55. http://dx.doi.org/10.1017/jwe.2019.29.

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AbstractMendoza is the main wine-producing province of Argentina, and the government is currently implementing a range of policies that seek to improve grape grower profitability, including a vineyard replanting program. This study uses a dataset of all grape sales recorded in Mendoza from 2007 to 2018, totaling 90,910 observations, to investigate the determinants of grape prices. Key findings include: smaller volume transactions receive lower-average prices per kilogram sold; the discount for cash payments is higher in less-profitable regions; and the effect of wine stock levels on prices is

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Dong, Zeqing, Xiaowan Guo, Syamala Rajana, and Bernard Chen. "Understanding 21st Century Bordeaux Wines from Wine Reviews Using Naïve Bayes Classifier." Beverages 6, no. 1 (2020): 5. http://dx.doi.org/10.3390/beverages6010005.

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Wine has been popular with the public for centuries; in the market, there are a variety of wines to choose from. Among all, Bordeaux, France, is considered as the most famous wine region in the world. In this paper, we try to understand Bordeaux wines made in the 21st century through Wineinformatics study. We developed and studied two datasets: the first dataset is all the Bordeaux wine from 2000 to 2016; and the second one is all wines listed in a famous collection of Bordeaux wines, 1855 Bordeaux Wine Official Classification, from 2000 to 2016. A total of 14,349 wine reviews are collected in

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Gatou, Paraskevi, Xanthi Tsiara, Alexandros Spitalas, Spyros Sioutas, and Gerasimos Vonitsanos. "Artificial Intelligence Techniques in Grapevine Research: A Comparative Study with an Extensive Review of Datasets, Diseases, and Techniques Evaluation." Sensors 24, no. 19 (2024): 6211. http://dx.doi.org/10.3390/s24196211.

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In the last few years, the agricultural field has undergone a digital transformation, incorporating artificial intelligence systems to make good employment of the growing volume of data from various sources and derive value from it. Within artificial intelligence, Machine Learning is a powerful tool for confronting the numerous challenges of developing knowledge-based farming systems. This study aims to comprehensively review the current scientific literature from 2017 to 2023, emphasizing Machine Learning in agriculture, especially viticulture, to detect and predict grape infections. Most of

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Le, Long, Pedro Navarrete Hurtado, Ian Lawrence, Qiuyun Tian, and Bernard Chen. "Applying Neural Networks in Wineinformatics with the New Computational Wine Wheel." Fermentation 9, no. 7 (2023): 629. http://dx.doi.org/10.3390/fermentation9070629.

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Wineinformatics involves the application of data science techniques to wine-related datasets generated during the grape growing, wine production, and wine evaluation processes. Its aim is to extract valuable insights that can benefit wine producers, distributors, and consumers. This study highlights the potential of neural networks as the most effective black-box classification algorithm in wineinformatics for analyzing wine reviews processed by the Computational Wine Wheel (CWW). Additionally, the paper provides a detailed overview of the enhancements made to the CWW and presents a thorough c

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Chen, Bernard, Valentin Velchev, James Palmer, and Travis Atkison. "Wineinformatics: A Quantitative Analysis of Wine Reviewers." Fermentation 4, no. 4 (2018): 82. http://dx.doi.org/10.3390/fermentation4040082.

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Data Science is a successful study that incorporates varying techniques and theories from distinct fields including Mathematics, Computer Science, Economics, Business and domain knowledge. Among all components in data science, domain knowledge is the key to create high quality data products by data scientists. Wineinformatics is a new data science application that uses wine as the domain knowledge and incorporates data science and wine related datasets, including physicochemical laboratory data and wine reviews. This paper produces a brand-new dataset that contains more than 100,000 wine revie

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Dong, Zeqing, Travis Atkison, and Bernard Chen. "Wineinformatics: Using the Full Power of the Computational Wine Wheel to Understand 21st Century Bordeaux Wines from the Reviews." Beverages 7, no. 1 (2021): 3. http://dx.doi.org/10.3390/beverages7010003.

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Although wine has been produced for several thousands of years, the ancient beverage has remained popular and even more affordable in modern times. Among all wine making regions, Bordeaux, France is probably one of the most prestigious wine areas in history. Since hundreds of wines are produced from Bordeaux each year, humans are not likely to be able to examine all wines across multiple vintages to define the characteristics of outstanding 21st century Bordeaux wines. Wineinformatics is a newly proposed data science research with an application domain in wine to process a large amount of wine

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Nagayoshi, Yuka, and Bernard Chen. "Wineinformatics: Wine Score Prediction with Wine Price and Reviews." Fermentation 10, no. 12 (2024): 598. http://dx.doi.org/10.3390/fermentation10120598.

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Wineinformatics is a new field that applies data science to wine-related data. The goal of this paper is to determine whether incorporating wine price can improve the accuracy of score prediction. To explore the relationship between wine price and wine score, naive Bayes classifier and support vector machine (SVM) classifier are employed to predict the scores as either equal to or above 90 or below 90. The price values are normalized using four different methods: mean, median, boxplot mean, and boxplot median. To conduct a proper comparison, the original dataset from previous research, which i

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A, Stella, Deepthi K, Karthik D S, Mohammed Maaz Ahmed, and Mohak Pal. "REVIEW SPAM DETECTION USING MACHINE LEARNING." International Research Journal of Computer Science 9, no. 8 (2022): 256–61. http://dx.doi.org/10.26562/irjcs.2022.v0908.20.

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Reviews, ratings, and experience stories left by customers on e-commerce websites and other online services are helpful to both buyers and sellers. The reviewer can foster more brand loyalty and aid in the understanding of other consumers' product experiences. Similar to how reviews help customers earn more profiles, reviews help businesses sell more things by enhancing customer satisfaction. However, suppliers may, unfortunately, abuse these review processes. For instance, one might fabricate positive evaluations to boost the reputation of a brand or attempt to denigrate rival brands' goods b

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Atif, Muhammad, Muhammad Shafiq, Muhammad Farooq, Gohar Ayub, Friedrich Leisch, and Muhammad Ilyas. "Monitoring Changes in Clustering Solutions: A Review of Models and Applications." Journal of Probability and Statistics 2023 (November 3, 2023): 1–15. http://dx.doi.org/10.1155/2023/7493623.

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This article comprehensively reviews the applications and algorithms used for monitoring the evolution of clustering solutions in data streams. The clustering technique is an unsupervised learning problem that involves the identification of natural subgroups in a large dataset. In contrast to supervised learning models, clustering is a data mining technique that retrieves the hidden pattern in the input dataset. The clustering solution reflects the mechanism that leads to a high level of similarity between the items. A few applications include pattern recognition, knowledge discovery, and mark

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Mankin, Kyle R., Sushant Mehan, Timothy R. Green, and David M. Barnard. "Review of gridded climate products and their use in hydrological analyses reveals overlaps, gaps, and the need for a more objective approach to selecting model forcing datasets." Hydrology and Earth System Sciences 29, no. 1 (2025): 85–108. https://doi.org/10.5194/hess-29-85-2025.

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Abstract. Climate forcing data accuracy drives performance of hydrologic models and analyses, yet each investigator needs to select from among the numerous gridded climate dataset options and justify their selection for use in a particular hydrologic model or analysis. This study aims to provide a comprehensive compilation and overview of gridded datasets (precipitation, air temperature, humidity, wind speed, solar radiation) and considerations for historical climate product selection criteria for hydrologic modeling and analyses based on a review and synthesis of previous studies conducting d

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Vimala, Dhulepalla. "Detection of Fake Online Reviews Using Semi Supervised and Supervised Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem31613.

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Consumers reviews on ecommerce websites, online services, ratings and experience stories are useful for the user as well as the vendor. The reviewer can increase their brand’s loyalty and help other customers understand their experience with the product. Similarly reviews help the vendors gain more profiles by increasing their sale of products, if consumers leave positive feedback on their product review. But unfortunately, these review mechanisms can be misused by vendors. For example, one may create fake positive reviews to promote brand’s reputation or try to demote competitor’s products by

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Palmer, James, and Bernard Chen. "Wineinformatics: Regression on the Grade and Price of Wines through Their Sensory Attributes." Fermentation 4, no. 4 (2018): 84. http://dx.doi.org/10.3390/fermentation4040084.

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Wineinformatics is a field that uses machine-learning and data-mining techniques to glean useful information from wine. In this work, attributes extracted from a large dataset of over 100,000 wine reviews are used to make predictions on two variables: quality based on a “100-point scale”, and price per 750 mL bottle. These predictions were built using support vector regression. Several evaluation metrics were used for model evaluation. In addition, these regression models were compared to classification accuracies achieved in a prior work. When regression was used for classification, the resul

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Callaghan, Sarah, Fiona Murphy, Jonathan Tedds, et al. "Processes and Procedures for Data Publication: A Case Study in the Geosciences." International Journal of Digital Curation 8, no. 1 (2013): 193–203. http://dx.doi.org/10.2218/ijdc.v8i1.253.

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The Peer REview for Publication and Accreditation of Research Data in the Earth sciences (PREPARDE) project is a JISC and NERC funded project which aims to investigate the policies and procedures required for the formal publication of research data, ranging from ingestion into a data repository, through to formal publication in a data journal. It also addresses key issues arising in the data publication paradigm, including, but not limited to, issues related to how one peer reviews a dataset, what criteria are needed for a repository to be considered objectively trustworthy, and how datasets a

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Ilieva, Iliana, Margarita Terziyska, and Teofana Dimitrova. "From Words to Ratings: Machine Learning and NLP for Wine Reviews." Beverages 11, no. 3 (2025): 80. https://doi.org/10.3390/beverages11030080.

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Wine production is an important sector of the food industry in Bulgaria, contributing to both economic development and cultural heritage. The present study aims to show how natural language processing (NLP) and machine learning methods can be applied to analyze expert-written Bulgarian wine descriptions and to extract patterns related to wine quality and style. Based on a bilingual dataset of reviews (in Bulgarian and English), semantic analysis, classification, regression and clustering models were used, which combine textual and structured data. The descriptions were transformed into numeric

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Pinto, Teresa, Fernanda Cosme, Elisete Correia, and Alice Vilela. "Spectroscopic Techniques Application for Wine and Wine Byproduct Authentication." Applied Sciences 15, no. 8 (2025): 4457. https://doi.org/10.3390/app15084457.

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The growing concern regarding the authenticity of wine and wine byproducts, particularly in terms of their origin and grape varieties, is of great importance to both consumers and the wine industry. Verifying the accuracy of information presented on labels is crucial for this sector, as regulatory frameworks strictly govern the veracity of claims made to consumers. This concern has driven the development and application of innovative analytical methods, such as spectroscopic techniques, which utilize different wavelengths of the electromagnetic spectrum, including the visible (Vis), ultraviole

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Mustafa, Dheya, Safaa M. Khabour, Mousa Al-kfairy, and Ahmed Shatnawi. "Leveraging sentiment analysis of food delivery services reviews using deep learning and word embedding." PeerJ Computer Science 11 (February 19, 2025): e2669. https://doi.org/10.7717/peerj-cs.2669.

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Companies that deliver food (food delivery services, or FDS) try to use customer feedback to identify aspects where the customer experience could be improved. Consumer feedback on purchasing and receiving goods via online platforms is a crucial tool for learning about a company’s performance. Many English-language studies have been conducted on sentiment analysis (SA). Arabic is becoming one of the most extensively written languages on the World Wide Web, but because of its morphological and grammatical difficulty as well as the lack of openly accessible resources for Arabic SA, like as dictio

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Wysocki, Olaf, Benedikt Schwab, Christof Beil, Christoph Holst, and Thomas H. Kolbe. "Reviewing Open Data Semantic 3D City Models to Develop Novel 3D Reconstruction Methods." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4-2024 (October 21, 2024): 493–500. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-2024-493-2024.

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Abstract. Structured semantic 3D city models are pivotal in creating urban 3D digital twins. The wide adoption of such models has been primarily enabled by robust, model-based, and automatic 3D reconstruction methods. However, these methods impose requirements on the reconstruction, mainly restricting the solution space to several model types and relying on accurate 2D footprints. Recent research shows that deep-learning-based methods promise highly generic solution space and are footprint-free. Yet, the current training and test datasets are limited, hindering the methods’ development. In thi

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Rakhi A. Kalantri. "Advancing Cyber Threat Detection with Ai: Cutting-Edge Techniques and Future Trends." Journal of Information Systems Engineering and Management 10, no. 14s (2025): 338–52. https://doi.org/10.52783/jisem.v10i14s.2301.

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The digital age has made cyberspace indispensable for economic, social, and governmental functions, thus intensifying the critical need for robust cybersecurity. Our increasing dependence on digital platforms has exposed systems to a wide array of sophisticated cyber threats, including malware, phishing, distributed denial-of-service (DDoS) attacks, ransomware, and insider threats, often motivated by financial gain, political agendas, or espionage. These challenges underscore the urgent requirement for flexible and resilient cybersecurity strategies. Traditional signature-based and rule-based

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Wu, Burton, Clare A. McGrory, and Anthony N. Pettitt. "The Variational Bayesian Approach to Fitting Mixture Models to Circular Wave Direction Data." Journal of Applied Meteorology and Climatology 51, no. 10 (2012): 1750–62. http://dx.doi.org/10.1175/jamc-d-11-0124.1.

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AbstractThe emerging variational Bayesian (VB) technique for approximate Bayesian statistical inference is a non-simulation-based and time-efficient approach. It provides a useful, practical alternative to other Bayesian statistical approaches such as Markov chain Monte Carlo–based techniques, particularly for applications involving large datasets. This article reviews the increasingly popular VB statistical approach and illustrates how it can be used to fit Gaussian mixture models to circular wave direction data. This is done by taking the straightforward approach of padding the data; this me

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Sielemann, Katharina, Alenka Hafner, and Boas Pucker. "The reuse of public datasets in the life sciences: potential risks and rewards." PeerJ 8 (September 22, 2020): e9954. http://dx.doi.org/10.7717/peerj.9954.

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The ‘big data’ revolution has enabled novel types of analyses in the life sciences, facilitated by public sharing and reuse of datasets. Here, we review the prodigious potential of reusing publicly available datasets and the associated challenges, limitations and risks. Possible solutions to issues and research integrity considerations are also discussed. Due to the prominence, abundance and wide distribution of sequencing data, we focus on the reuse of publicly available sequence datasets. We define ‘successful reuse’ as the use of previously published data to enable novel scientific findings

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Pokhun, Leeveshkumar, and M. Yasser Chuttur. "Emotions in texts." Bulletin of Social Informatics Theory and Application 4, no. 2 (2020): 59–69. http://dx.doi.org/10.31763/businta.v4i2.256.

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Several studies have used different techniques to detect and identify emotions expressed in various sets of texts corpora. In this paper, we review different emotion models, emotion datasets and the corresponding techniques used for emotion analysis in past studies. We observe that researchers have been using a wide variety of techniques to detect emotions in texts and that there is currently no gold standard on which dataset or which emotion model to use. Consequently, although the field of emotion analysis has gained much momentum in previous years, there seems to be little progress into rel

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Wilson, David, Simon Hulka, and Leon Bennun. "A review of raptor carcass persistence trials and the practical implications for fatality estimation at wind farms." PeerJ 10 (November 15, 2022): e14163. http://dx.doi.org/10.7717/peerj.14163.

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Bird and bat turbine collision fatalities are a principal biodiversity impact at wind energy facilities. Raptors are a group at particular risk and often the focus of post-construction fatality monitoring programs. To estimate fatalities from detected carcasses requires correction for biases, including for carcasses that are removed or decompose before the following search. This is addressed through persistence trials, where carcasses are monitored until no longer detectable or the trial ends. Sourcing sufficient raptor carcasses for trials is challenging and surrogates that are typically used

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Sunaryo, Budi, Muhammad Ilhamdi Rusydi, Ariadi Hazmi, and Minoru Sasaki. "A Systematic Literature Review of Automation Quality of Service in Computer Networks: Research Trends, Datasets, and Methods." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 7, no. 2 (2023): 353–66. http://dx.doi.org/10.29207/resti.v7i2.4810.

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The article is a systematic literature review of the use of automation for quality of service (QoS) in computer networks. It summarizes the research trends, datasets, and methods used in the field and provides an overview of the current state of the art. The focus of the review is on the use of automation for QoS management and improvement in computer networks, including the use of machine learning, artificial intelligence, and other computational techniques. The review highlights the need for further research and development in this area and provides insights into future directions for the fi

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Morales, Jesús, Ricardo Vázquez-Martín, Anthony Mandow, David Morilla-Cabello, and Alfonso García-Cerezo. "The UMA-SAR Dataset: Multimodal data collection from a ground vehicle during outdoor disaster response training exercises." International Journal of Robotics Research 40, no. 6-7 (2021): 835–47. http://dx.doi.org/10.1177/02783649211004959.

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This article presents a collection of multimodal raw data captured from a manned all-terrain vehicle in the course of two realistic outdoor search and rescue (SAR) exercises for actual emergency responders conducted in Málaga (Spain) in 2018 and 2019: the UMA-SAR dataset. The sensor suite, applicable to unmanned ground vehicles (UGVs), consisted of overlapping visible light (RGB) and thermal infrared (TIR) forward-looking monocular cameras, a Velodyne HDL-32 three-dimensional (3D) lidar, as well as an inertial measurement unit (IMU) and two global positioning system (GPS) receivers as ground t

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Sh. Othman, Renjbar, and Hajar Maseeh Yasin. "Applications of deep learning in automated ‎image classification: a review." International Journal of Scientific World 11, no. 1 (2025): 104–13. https://doi.org/10.14419/qra5nc04.

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Deep learning's developments have greatly changed automatic picture categorization, proving ‎great accuracy and efficiency in many fields. Medical imaging, agriculture, and environmental ‎monitoring are just a few innovative uses for techniques including hybrid models, transfer ‎learning, and convolutional neural networks (CNNs). Improved precision using ensemble and ‎attention-based models has helped medical diagnosis including diabetic retinopathy and breast ‎cancer detection benefit. Likewise, CNNs are used in environmental monitoring systems for ‎species identification and wind turbine ins

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Thomas, Neha, and Susan Elias. "Classification of Fake Product Ratings Using a Timeline Based Approach." International Journal of Business Administration and Management Research 3, no. 2 (2017): 12. http://dx.doi.org/10.24178/ijbamr.2017.3.2.12.

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Abstract— Detection of fake review and reviewers is currently a challenging problem in cyber space. It is challenging primarily due to the dynamic nature of the methodology used to fake the review. There are several aspects to be considered when analyzing reviews to classify them effective into genuine and fake. Sentiment analysis, opinion mining and intend mining are fields of research that try to accomplish the goal through Natural Language Processing of the text content of the review. In this paper, an approach that uses the review ratings evaluated along a timeline is presented. An Amazon

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Kumar, Raman, Tushar Anand, Vishal Vyas, Sushant Kumar, and Swati Kashyap. "Review Paper: Social Media Sentiment Analysis Using Twitter Dataset." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 11 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem27294.

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The expansion of social media platforms, particularly Twitter, has transformed them into vast databases of user-generated content expressing a wide range of thoughts and emotions. This review delves into the field of conceptual analysis applied to Twitter datasets, focusing on machine learning. This article provides an overview of methods, techniques, and operational models for sentiment analysis using machine learning in the specific context of Twitter. Introducing the evolution of machine learning applied to the Twitter dataset, starting by exploring the main concepts of sentiment analysis.

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Srinath Jettaboina and Dr. Amit Sharma. "AI and Machine Learning Approaches for Chronic Kidney Disease Progression: A Systematic Review." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 3128–34. https://doi.org/10.32628/cseit25112791.

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Chronic Kidney Disease also known as CKD is a health issue of the global significance with millions of affected clients across the globe. They result in early CKD progression prediction and monitoring, which is vital for better patient prognosis and inequalities’ decrease with concern to healthcare expenses. Over time however, the terms’ AI and ML have become revolutionary technologies in healthcare by providing better methodologies to diagnose CKD progression with high efficiency. This systematic review therefore aims to use a wide NM touched algorithm, dataset, prediction technologies, and o

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Luo, Zhengtao, Liyi Yu, Zhaochun Xu, Kening Liu, and Lichuan Gu. "Comprehensive Review and Assessment of Computational Methods for Prediction of N6-Methyladenosine Sites." Biology 13, no. 10 (2024): 777. http://dx.doi.org/10.3390/biology13100777.

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N6-methyladenosine (m6A) plays a crucial regulatory role in the control of cellular functions and gene expression. Recent advances in sequencing techniques for transcriptome-wide m6A mapping have accelerated the accumulation of m6A site information at a single-nucleotide level, providing more high-confidence training data to develop computational approaches for m6A site prediction. However, it is still a major challenge to precisely predict m6A sites using in silico approaches. To advance the computational support for m6A site identification, here, we curated 13 up-to-date benchmark datasets f

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Badache, Ismail, Adrian-Gabriel Chifu, and Sébastien Fournier. "Unsupervised and Supervised Methods to Estimate Temporal-Aware Contradictions in Online Course Reviews." Mathematics 10, no. 5 (2022): 809. http://dx.doi.org/10.3390/math10050809.

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The analysis of user-generated content on the Internet has become increasingly popular for a wide variety of applications. One particular type of content is represented by the user reviews for programs, multimedia, products, and so on. Investigating the opinion contained by reviews may help in following the evolution of the reviewed items and thus in improving their quality. Detecting contradictory opinions in reviews is crucial when evaluating the quality of the respective resource. This article aims to estimate the contradiction intensity (strength) in the context of online courses (MOOC). T

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Wang, Bingkun, Bing Chen, Li Ma, and Gaiyun Zhou. "User-Personalized Review Rating Prediction Method Based on Review Text Content and User-Item Rating Matrix." Information 10, no. 1 (2018): 1. http://dx.doi.org/10.3390/info10010001.

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With the explosive growth of product reviews, review rating prediction has become an important research topic which has a wide range of applications. The existing review rating prediction methods use a unified model to perform rating prediction on reviews published by different users, ignoring the differences of users within these reviews. Constructing a separate personalized model for each user to capture the user’s personalized sentiment expression is an effective attempt to improve the performance of the review rating prediction. The user-personalized sentiment information can be obtained n

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Lanciotti, Sabrina, Elena Ridolfi, Fabio Russo, and Francesco Napolitano. "Intensity–Duration–Frequency Curves in a Data-Rich Era: A Review." Water 14, no. 22 (2022): 3705. http://dx.doi.org/10.3390/w14223705.

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Intensity–duration–frequency (IDF) curves are widely used in the hydrological design of hydraulic structures. This paper presents a wide review of methodologies for constructing IDF curves with a specific focus on the choice of the dataset type, highlighting the main characteristics, possible uncertainties, and benefits that can be derived from their application. A number of studies based on updating IDFs in relation to climate change are analyzed. The research was based on a comprehensive analysis of more than 100 scientific papers and reports, of which 80 were found to be suitable for the ai

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Birari, Dipika, Gajanan Walunjkar, Aarti Dandavate, Sonali Mallinath Antad, and Sheetal Phatangare. "Integrated Approach for Emotion Detection via Speech and Text Analysis." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 7 (2023): 282–93. http://dx.doi.org/10.17762/ijritcc.v11i7.7938.

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This paper aims to provide a comprehensive solution for effective reviews using deep learning models. Customers often have difficulty to find accurate reviews of the things they are interested in. The proposed framework implements a review mechanism to address this problem, which will give customers relevant reviews based on video reviews supplied in the product description. The goal of this system is to turn video reviews into a particular rating so that viewers may get a summary of the review without having to watch the full thing by simply glancing at the rating. Deep learning neural networ

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Sourabh. "A SYSTEMATIC REVIEW ON FAKE NEWS DETECTION USING MACHINE LEARNING APPROACHES." International Journal of Engineering Research and Modern Education (IJERME) 8, no. 1 (2023): 40–44. https://doi.org/10.5281/zenodo.8073703.

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A Review of Methods and Approaches" is a comprehensive review paper that explores the various methods and approaches employed in the detection of fake news. The paper provides an extensive overview of the existing literature, summarizing the key techniques and algorithms utilized in this field.The review highlights the importance of addressing the growing problem of fake news, particularly in the context of evolving communication channels and social media platforms. It emphasizes the need for effective detection mechanisms to combat the spread of misinformation and disinformation.The pape

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Singh, Anupriya. "Evaluate the Sentiment Analysis Performance of Several Classifiers Using Ensemble Feature Selection Method." International Journal for Research in Applied Science and Engineering Technology 12, no. 6 (2024): 1821–32. http://dx.doi.org/10.22214/ijraset.2024.63403.

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Abstract: The growing availability of digital text data has sparked a need for effective sentiment analysis methods, which enable the automatic extraction of sentiment from text for various purposes. This study investigates the application of sentiment analysis using the Amazon Reviews Polarity Dataset, a curated compilation of texts categorised into positive and negative attitudes based on review ratings. Despite the dataset containing a substantial amount of labelled data, its narrow focus and classification technique have difficulties in effectively capturing nuanced expressions of sentimen

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Banerjee, Amit, and Issam Abu-Mahfouz. "A Novel Adaptive FCM with Cooperative Multi-Population Differential Evolution Optimization." Algorithms 15, no. 10 (2022): 380. http://dx.doi.org/10.3390/a15100380.

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Fuzzy c-means (FCM), the fuzzy variant of the popular k-means, has been used for data clustering when cluster boundaries are not well defined. The of initial cluster prototypes (or the initialization of cluster memberships), and the fact that the number of clusters needs to be defined a priori are two major factors that can affect the performance of FCM. In this paper, we review algorithms and methods used to overcome these two specific drawbacks. We propose a new cooperative multi-population differential evolution method with elitism to identify near-optimal initial cluster prototypes and als

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Ricciardulli, Lucrezia, Gregory R. Foltz, Andrew Manaster, and Thomas Meissner. "Assessment of Saildrone Extreme Wind Measurements in Hurricane Sam Using MW Satellite Sensors." Remote Sensing 14, no. 12 (2022): 2726. http://dx.doi.org/10.3390/rs14122726.

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In 2021, a novel NOAA-Saildrone project deployed five uncrewed surface vehicle Saildrones (SDs) to monitor regions of the Atlantic Ocean and Caribbean Sea frequented by tropical cyclones. One of the SDs, SD-1045, crossed Hurricane Sam (Category 4) on September 30, providing the first-ever surface-ocean videos of conditions in the core of a major hurricane and reporting near-surface winds as high as 40 m/s. Here, we present a comprehensive analysis and interpretation of the Saildrone ocean surface wind measurements in Hurricane Sam, using the following datasets for direct and indirect compariso

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An, Yusung, Hayoung Oh, and Joosik Lee. "Marketing Insights from Reviews Using Topic Modeling with BERTopic and Deep Clustering Network." Applied Sciences 13, no. 16 (2023): 9443. http://dx.doi.org/10.3390/app13169443.

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The feedback shared by consumers on e-commerce platforms holds immense value in marketing, as it offers insights into their opinions and preferences, which are readily accessible. However, analyzing a large volume of reviews manually is impractical. Therefore, automating the extraction of essential insights from these data can provide more comprehensive and efficient information. This research focuses on leveraging clustering algorithms to automate the extraction of consumer intentions, related products, and the pros and cons of products from review data. To achieve this, a review dataset was

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Hamza, Ameer, Kashif Bilal Majeed, Muhammad Rashad, and Arfan Jaffar. "An Integrated Approach for Amazon Electronic Products Reviews by Using Sentiment Analysis." Bulletin of Business and Economics (BBE) 13, no. 2 (2024): 142–53. http://dx.doi.org/10.61506/01.00309.

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In our routine life, we interact a wide range of products, and frequently browse through digital media platforms to access their quality. Although the accessibility of online platforms, consumers often find it challenging to swiftly judge the quality of products on the basis of customer reviews. To cope this situation, the study addresses this problem by suggesting a machine learning-based solution to categorize product reviews. For this, we employ various machine learning techniques, including Random Forest, Naïve Bayes, Support Vector Machine (SVM), Stochastic Gradient Descent (SGD) Classifi

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Seker, Abdulkadir, Banu Diri, Halil Arslan, and Mehmet Fatih Amasyalı. "Open Source Software Development Challenges." International Journal of Open Source Software and Processes 11, no. 4 (2020): 1–26. http://dx.doi.org/10.4018/ijossp.2020100101.

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GitHub is the most common code hosting and repository service for open-source software (OSS) projects. Thanks to the great variety of features, researchers benefit from GitHub to solve a wide range of OSS development challenges. In this context, the authors thought that was important to conduct a literature review on studies that used GitHub data. To reach these studies, they conducted this literature review based on a GitHub dataset source study instead of a keyword-based search in digital libraries. Since GHTorrent is the most widely known GitHub dataset according to the literature, they con

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Petersen, Ralph Alvin. "On the Impact and Benefits of AMDAR Observations in Operational Forecasting—Part I: A Review of the Impact of Automated Aircraft Wind and Temperature Reports." Bulletin of the American Meteorological Society 97, no. 4 (2016): 585–602. http://dx.doi.org/10.1175/bams-d-14-00055.1.

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Abstract This paper reviews the impact of World Meteorological Organization (WMO) Aircraft Meteorological Data Relay (AMDAR) observations on operational numerical weather prediction (NWP) forecasts at both regional and global scales that support national and local weather forecast offices across the globe. Over the past three decades, data collected from commercial aircraft have helped reduce flight-level wind and temperature forecast errors by nearly 50%. Improvements are largest in 3–48-h forecasts and in regions where the automated reports 1) are most numerous, 2) cover a broad area, and 3)

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Bai, Li, and Pierre Pinson. "Distributed Reconciliation in Day-Ahead Wind Power Forecasting." Energies 12, no. 6 (2019): 1112. http://dx.doi.org/10.3390/en12061112.

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With increasing renewable energy generation capacities connected to the power grid, a number of decision-making problems require some form of consistency in the forecasts that are being used as input. In everyday words, one expects that the sum of the power generation forecasts for a set of wind farms is equal to the forecast made directly for the power generation of that portfolio. This forecast reconciliation problem has attracted increased attention in the energy forecasting literature over the last few years. Here, we review the state of the art and its applicability to day-ahead forecasti

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Das, Madhusmita, Biju R. Mohan, and Ram Mohana Reddy Guddeti. "Fault Tree Analysis: A Review on Analysis, Simulation Tools and Reliability Dataset for Safety-critical Systems." Disaster Advances 18, no. 5 (2025): 186–90. https://doi.org/10.25303/185da1860190.

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Risk analysis is a crucial and prominent method to analyze the dependability attributes of safety-critical systems. Risk analysis comprises a wide variety of State-of-the-Art techniques. Out of these, this study only focuses on the Fault Tree Analysis (FTA) technique. Except for the evaluation techniques, we also paid attention to the survey of simulation tools along with the reliability datasets.

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Zhang, Wenqing. "Advances in Retrieving Soil Moisture Content in the Yellow River Basin Using Remote Sensing Satellite Data." International Journal of Natural Resources and Environmental Studies 6, no. 1 (2025): 14–24. https://doi.org/10.62051/ijnres.v6n1.02.

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Yellow River Basin, which suffered from water shortage and drought disaster frequently, has a need to use soil moisture information precisely to assist the agriculture, drought monitoring, and climate research. The high resolution dataset is very useful for the resource management. Conventional ground based monitoring is difficult and limited on precision due to their spatial resolution and costs, but remote sensing is able to overcome due to its scalability; however due to the limitation of satellite dataset resolution, remote sensing plays a limited role in soil moisture observation. Here, w

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Hayward, Leah, Malcolm Whitworth, Nick Pepin, and Steve Dorling. "Review article: A comprehensive review of datasets and methodologies employed to produce thunderstorm climatologies." Natural Hazards and Earth System Sciences 20, no. 9 (2020): 2463–82. http://dx.doi.org/10.5194/nhess-20-2463-2020.

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Abstract. Thunderstorm and lightning climatological research is conducted with a view to increasing knowledge about the distribution of thunderstorm-related hazards and to gain an understanding of environmental factors increasing or decreasing their frequency. There are three main methodologies used in the construction of thunderstorm climatologies: thunderstorm frequency, thunderstorm tracking or lightning flash density. These approaches utilise a wide variety of underpinning datasets and employ many different methods ranging from correlations with potential influencing factors and mapping th

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Andreas, Edgar L. "Relating the Drag Coefficient and the Roughness Length over the Sea to the Wavelength of the Peak Waves." Journal of Physical Oceanography 39, no. 11 (2009): 3011–20. http://dx.doi.org/10.1175/2009jpo4189.1.

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Abstract The standard 10-m reference height for computing the drag coefficient over the sea is admittedly arbitrary. The literature contains occasional suggestions that a scaling length based on the wavelength of the peak waves λp is a more natural reference height. Attempts to confirm this hypothesis must be done carefully, however, because of the potential for fictitious correlation between nondimensional dependent and independent variables. With the DMAJ dataset as an example, this study reviews the issue of fictitious correlation in analyses that use λp/2 as the reference height for evalua

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