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Monia Hamdi, Inès Hilali-Jaghdam, Manal M. Khayyat, Bushra M. E. Elnaim, Sayed Abdel-Khalek and Romany F. Mansour
Data mining (DM) involves the process of identifying patterns, correlation, and anomalies existing in massive datasets. The applicability of DM includes several areas such as education, healthcare, business, and finance. Educational Data Mining (EDM) is ...
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Warawut Narkbunnum and Kittipol Wisaeng
Depression is becoming one of the most prevalent mental disorders. This study looked at five different classification techniques to predict the risk of students? depression based on their socio-demographics, internet addiction, alcohol use disorder, and ...
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Vagan Terziyan and Anton Nikulin
Operating with ignorance is an important concern of geographical information science when the objective is to discover knowledge from the imperfect spatial data. Data mining (driven by knowledge discovery tools) is about processing available (observed, k...
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Fatima Abdullah, Limei Peng and Byungchul Tak
The volume of streaming sensor data from various environmental sensors continues to increase rapidly due to wider deployments of IoT devices at much greater scales than ever before. This, in turn, causes massive increase in the fog, cloud network traffic...
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Despoina Mouratidis and Katia Lida Kermanidis
Machine translation is used in many applications in everyday life. Due to the increase of translated documents that need to be organized as useful or not (for building a translation model), the automated categorization of texts (classification), is a pop...
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Despoina Mouratidis and Katia Lida Kermanidis
Machine translation is used in many applications in everyday life. Due to the increase of translated documents that need to be organized as useful or not (for building a translation model), the automated categorization of texts (classification), is a pop...
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Qing Zhu, Junxiao Zhang, Yulin Ding, Mingwei Liu, Yun Li, Bin Feng, Shuangxi Miao, Weijun Yang, Huagui He and Jun Zhu
Although abundant spatiotemporal data are collected before and after landslides, the volume, variety, intercorrelation, and heterogeneity of multimodal data complicates disaster assessments, so it is challenging to select information from multimodal spat...
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Yu-Pin Lin, Wei-Chih Lin, Wan-Yu Lien, Johnathen Anthony and Joy R. Petway
The purpose of this study is to increase the number of species occurrence data by integrating opportunistic data with Global Biodiversity Information Facility (GBIF) benchmark data via a novel optimization technique. The optimization method utilizes Natu...
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Álvaro Herce and Manuel Salvador
This paper proposes the use of Bayesian inference techniques to search for and obtain valid instruments in dynamic panel data models where endogenous variables may exist. The use of Principal Component Analysis (PCA) allows for obtaining a reduced number...
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Philipp Schlieper, Mischa Dombrowski, An Nguyen, Dario Zanca and Bjoern Eskofier
Time series forecasting has witnessed a rapid proliferation of novel neural network approaches in recent times. However, performances in terms of benchmarking results are generally not consistent, and it is complicated to determine in which cases one app...
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Jie Zhao, Junwei Chen, Yangze Liang and Zhao Xu
The stability of scaffolding structures is crucial for quality management in construction. Currently, scaffolding assembly quality monitoring relies on visual inspections performed by designated on-site personnel, which are highly subjective, inaccurate,...
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Daniel Maposa, Amon Masache, Precious Mdlongwa, Caston Sigauke
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Thomas Parr, Karl Friston and Peter Zeidman
Bayesian inference typically focuses upon two issues. The first is estimating the parameters of some model from data, and the second is quantifying the evidence for alternative hypotheses?formulated as alternative models. This paper focuses upon a third ...
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Zvonimir Dabcevic, Branimir ?kugor, Ivan Cvok and Jo?ko Deur
The paper presents a novel approach for predicting battery energy consumption in electric city buses (e-buses) by means of a trip-based data-driven regression model. The model was parameterized based on the data collected by running a physical experiment...
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Yibrah Gebreyesus, Damian Dalton, Sebastian Nixon, Davide De Chiara and Marta Chinnici
The need for artificial intelligence (AI) and machine learning (ML) models to optimize data center (DC) operations increases as the volume of operations management data upsurges tremendously. These strategies can assist operators in better understanding ...
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Sa?ad Ibrahim
Land use and land cover (LULC) mapping can be of great help in changing land use decisions, but accurate mapping of LULC categories is challenging, especially in semi-arid areas with extensive farming systems and seasonal vegetation phenology. Machine le...
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Hailun Xie and Lars Johanning
In this research, a hierarchical met-ocean data selection model is proposed to reduce the computational cost in stochastic simulation of operation and maintenance (O&M) and enable rapid evaluation of offshore renewable energy systems. The proposed mo...
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Mohammad H. Nadimi-Shahraki, Zahra Asghari Varzaneh, Hoda Zamani and Seyedali Mirjalili
Feature selection is an NP-hard problem to remove irrelevant and redundant features with no predictive information to increase the performance of machine learning algorithms. Many wrapper-based methods using metaheuristic algorithms have been proposed to...
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Sayanti Guha Majumdar, Anil Rai and Dwijesh Chandra Mishra
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Jiaqi Zhao, Baiyi Zong and Ling Wu
Based on a study of the spatial distribution of coffee shops in the main urban area of Beijing, the main influencing factors were selected based on the multi-source space data. Subsequently, three regression models were compared, and the best site select...
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