Zobrazeno 1 - 10
of 2 363
pro vyhledávání: '"T58.5-58.64"'
Autor:
Hongfang Gong, Siyu Zhang
Publikováno v:
Journal of Big Data, Vol 11, Iss 1, Pp 1-23 (2024)
Abstract Aspect level sentiment analysis is a basic task to determine the sentiment bias based on the contextual information near the aspect words. Some sentences contain many confusing feature words due to incomplete structure or high complexity in
Externí odkaz:
https://doaj.org/article/affff25aad52430cb2787503c6bbad44
Publikováno v:
Journal of Big Data, Vol 11, Iss 1, Pp 1-38 (2024)
Abstract As the number and cleverness of cyber-attacks keep increasing rapidly, it's more important than ever to have good ways to detect and prevent them. Recognizing cyber threats quickly and accurately is crucial because they can cause severe dama
Externí odkaz:
https://doaj.org/article/c51d6368264e436a9feef175f66d34ea
Publikováno v:
Journal of Big Data, Vol 11, Iss 1, Pp 1-55 (2024)
Abstract The k-Nearest Neighbors (kNN) method, established in 1951, has since evolved into a pivotal tool in data mining, recommendation systems, and Internet of Things (IoT), among other areas. This paper presents a comprehensive review and performa
Externí odkaz:
https://doaj.org/article/47afd56c6b464b078637a5b751e3bc57
Publikováno v:
Journal of Big Data, Vol 11, Iss 1, Pp 1-27 (2024)
Abstract Prediction processes in areas ranging from climate and disease spread to disasters and air pollution rely heavily on spatial–temporal data. Understanding and forecasting the distribution patterns of disease cases and climate change phenome
Externí odkaz:
https://doaj.org/article/aeae42ded02146b190e2c4a63e56b19c
Publikováno v:
Journal of Big Data, Vol 11, Iss 1, Pp 1-39 (2024)
Abstract The convergence of artificial intelligence (AI), big data (DB), and Internet of Things (IoT) in Society 5.0, has given rise to Marketing 5.0, revolutionizing personalized customer experiences. In this study, a systematic literature review wa
Externí odkaz:
https://doaj.org/article/60e2ef7c518b4c12aad799a499c432ee
Autor:
Wing Keung Cheung, Ashkan Pakzad, Nesrin Mogulkoc, Sarah Helen Needleman, Bojidar Rangelov, Eyjolfur Gudmundsson, An Zhao, Mariam Abbas, Davina McLaverty, Dimitrios Asimakopoulos, Robert Chapman, Recep Savas, Sam M. Janes, Yipeng Hu, Daniel C. Alexander, John R. Hurst, Joseph Jacob
Publikováno v:
Journal of Big Data, Vol 11, Iss 1, Pp 1-30 (2024)
Abstract The morphology and distribution of airway tree abnormalities enable diagnosis and disease characterisation across a variety of chronic respiratory conditions. In this regard, airway segmentation plays a critical role in the production of the
Externí odkaz:
https://doaj.org/article/e9c934d8b58b43698c0b2721ecd382a0
Publikováno v:
Journal of Big Data, Vol 11, Iss 1, Pp 1-28 (2024)
Abstract With the growing complexity of data structures, which include high-dimensional and multilabel datasets, the significance of feature selection has become more emphasized. Multilabel feature selection endeavors to identify a subset of features
Externí odkaz:
https://doaj.org/article/823461e9a0614a81a3395777d5a84f13
Autor:
Prasannavenkatesan Theerthagiri, A. Usha Ruby, J. George Chellin Chandran, Tanvir Habib Sardar, Ahamed Shafeeq B. M.
Publikováno v:
Journal of Big Data, Vol 11, Iss 1, Pp 1-16 (2024)
Abstract The maize leaf diseases create severe yield reductions and critical problems. The maize leaf disease should be discovered early, perfectly identified, and precisely diagnosed to make greater yield. This work studies three main leaf diseases:
Externí odkaz:
https://doaj.org/article/31d74bf8dc584a5994cb1f3bcdaa38dc
Publikováno v:
Journal of Big Data, Vol 11, Iss 1, Pp 1-13 (2024)
Abstract Objective This paper addresses the Graeco-Latin square design (GLSD) under neutrosophic statistics. In this work, we propose a novel approach for analyzing Graeco-Latin square designs using uncertain observations. Method This approach involv
Externí odkaz:
https://doaj.org/article/57f2d270411240b2a68b3ae11ba8aa24
Autor:
Heinrich Peters, Yozen Liu, Francesco Barbieri, Raiyan Abdul Baten, Sandra C. Matz, Maarten W. Bos
Publikováno v:
Journal of Big Data, Vol 11, Iss 1, Pp 1-20 (2024)
Abstract The success of online social platforms hinges on their ability to predict and understand user behavior at scale. Here, we present data suggesting that context-aware modeling approaches may offer a holistic yet lightweight and potentially pri
Externí odkaz:
https://doaj.org/article/0a2fee6548584d60b2d982b2b6882de4