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Bochi Guo, Yu Liu, Hui Zhou, Wei Yan and Shuanggen Zhang
Automatic modulation recognition (AMR) provides excellent performance advantages over conventional algorithms and plays a key role in modern communication. However, a general challenge is that the channel errors greatly deteriorate the classification cap...
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Anuwat Boonprasope and Korrakot Yaibuathet Tippayawong
Following the COVID-19 pandemic, the healthcare sector has emerged as a resilient and profitable domain amidst market fluctuations. Consequently, investing in healthcare securities, particularly through mutual funds, has gained traction. Existing researc...
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C. Tamilselvi, Md Yeasin, Ranjit Kumar Paul and Amrit Kumar Paul
Denoising is an integral part of the data pre-processing pipeline that often works in conjunction with model development for enhancing the quality of data, improving model accuracy, preventing overfitting, and contributing to the overall robustness of pr...
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Arturs Kempelis, Inese Polaka, Andrejs Romanovs and Antons Patlins
Urban agriculture presents unique challenges, particularly in the context of microclimate monitoring, which is increasingly important in food production. This paper explores the application of convolutional neural networks (CNNs) to forecast key sensor m...
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Rejath Jose, Faiz Syed, Anvin Thomas and Milan Toma
The advancement of machine learning in healthcare offers significant potential for enhancing disease prediction and management. This study harnesses the PyCaret library?a Python-based machine learning toolkit?to construct and refine predictive models for...
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Benjamin Burrichter, Juliana Koltermann da Silva, Andre Niemann and Markus Quirmbach
This study employs a temporal fusion transformer (TFT) for predicting overflow from sewer manholes during heavy rainfall events. The TFT utilised is capable of forecasting overflow hydrographs at the manhole level and was tested on a sewer network with 9...
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Eyad K. Sayhood, Nisreen S. Mohammed, Salam J. Hilo and Salih S. Salih
This paper presents comprehensive empirical equations to predict the shear strength capacity of reinforced concrete deep beams, with a focus on improving the accuracy of existing codes. Analyzing 198 deep beams imported from 15 existing investigations, t...
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Maryam Badar and Marco Fisichella
Fairness-aware mining of data streams is a challenging concern in the contemporary domain of machine learning. Many stream learning algorithms are used to replace humans in critical decision-making processes, e.g., hiring staff, assessing credit risk, et...
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Eunju Hwang
Daily data on COVID-19 infections and deaths tend to possess weekly oscillations. The purpose of this work is to forecast COVID-19 data with partially cyclical fluctuations. A partially periodic oscillating ARIMA model is suggested to enhance the predict...
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Fátima Trindade Neves, Manuela Aparicio and Miguel de Castro Neto
In the rapidly evolving landscape of urban development, where smart cities increasingly rely on artificial intelligence (AI) solutions to address complex challenges, using AI to accurately predict real estate prices becomes a multifaceted and crucial tas...
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