Search Results - "S.I.: Timely Advances of Deep Learning with applications and Data Driven Modeling"

Zero-day Android botnet detection using neural networks by Seraj, Saeed, Pimenidis, Elias, Trovati, Marcello, Polatidis, Nikolaos

“…Android devices have evolved to offer a diverse array of services, spanning applications related to banking, business, health, and entertainment. The…”
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Deep learning approaches and data augmentation for melanoma detection by Alzamel, Mai, Iliopoulos, Costas, Lim, Zara

“…Skin cancer is one of the most common and dangerous forms of cancer. Diagnosis in the preliminary stages plays a significant role in determining the…”
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CM-MLP: hybrid convmixer-deep MLP architecture for enhanced identification of corn and apple leaf diseases by Li, Li-Hua, Tanone, Radius

“…This study aims to identify diseases impacting the agricultural sector, specifically focusing on corn and apple leaves. We propose a novel hybrid ConvMixer and…”
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Assessment and deployment of a LSTM-based virtual sensor in an industrial process control loop by González-Herbón, Raúl, González-Mateos, Guzmán, Rodríguez-Ossorio, José R., Prada, Miguel A., Morán, Antonio, Alonso, Serafín, Fuertes, Juan J., Domínguez, Manuel

“…Measurement of certain variables within the industrial sector remains a challenge due to the prohibitive costs of sensors, the intricate installation…”
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Researching the detection of continuous gravitational waves based on signal processing and ensemble learning by Pintelas, Emmanuel, Livieris, Ioannis E., Pintelas, Panagiotis

“…The detection of Gravitational Waves has introduced a new era for physics, astronomy, and astrophysics, unveiling new universe mysteries. Unfortunately,…”
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Residual connections improve click-through rate and conversion rate prediction performance by Biçici, Ergun

“…The prediction of click-through rate (CTR) and conversion rate (CVR) are crucial tasks in online advertising and recommendation systems. As the learning models…”
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A parameter-free nearest neighbor algorithm with reduced prediction time and improved performance through injected randomness by Singh, Manpreet, Chhabra, Jitender Kumar

“…K-nearest neighbor is considered in top machine learning algorithms because of its effectiveness in pattern classification and simple implementation. However,…”
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A fuzzy zeroing neural network and its application on dynamic Hill cipher by Jin, Jie, Lei, Xiaoyang, Chen, Chaoyang, Lu, Ming, Wu, Lianghong, Li, Zhijing

“…Cryptography is the core of information security, and the Hill cipher is one of the most important methods for cryptography. For the purpose of the improvement…”
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Exploring distribution-based approaches for out-of-distribution detection in deep learning models by Carvalho, Thiago, Vellasco, Marley, Amaral, José Franco

“…Detecting unknown samples is a crucial task for deep learning applications, especially when considering open-set problems such as autonomous driving or disease…”
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Feature analysis and ensemble-based fault detection techniques for nonlinear systems by Bolboacă, Roland, Haller, Piroska, Genge, Bela

“…Machine learning approaches play a crucial role in nonlinear system modeling across diverse domains, finding applications in system monitoring, anomaly/fault…”
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Memetic algorithm-based optimization of hybrid forecasting systems for multivariate time series by Padilha, Guilherme Afonso Galindo, Jung, Jason J., de Mattos Neto, Paulo S. G.

“…In recent decades, wind speed’s growing use in electricity generation has posed challenges due to its intermittent and fluctuating nature, hindering its…”
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DACL+: domain-adapted contrastive learning for enhanced low-resource language representations in document clustering tasks by Zaikis, Dimitrios, Vlahavas, Ioannis

“…Low-resource languages in natural language processing present unique challenges, marked by limited linguistic resources and sparse data. These challenges…”
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Conditioned fully convolutional denoising autoencoder for multi-target NILM by García, Diego, Pérez, Daniel, Papapetrou, Panagiotis, Díaz, Ignacio, Cuadrado, Abel A., Enguita, José M., Domínguez, Manuel

“…Energy management requires reliable tools to support decisions aimed at optimising consumption. Advances in data-driven models provide techniques like…”
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Novel wavelet-LSTM approach for time series prediction by Tamilselvi, C., Paul, Ranjit Kumar, Yeasin, Md, Paul, A. K.

“…Time series prediction often faces challenges due to hidden patterns and noise within the data. This paper presented a novel algorithm that combines wavelet…”
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A novel human action recognition using Grad-CAM visualization with gated recurrent units by Jayamohan, M., Yuvaraj, S.

“…Human action recognition is a vital aspect of computer vision, with applications ranging from security systems to interactive technology. Our study presents a…”
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An explainable approach for prediction of remaining useful life in turbofan condition monitoring by Mansourvar, Zahra, Jahangoshai Rezaee, Mustafa, Eshkevari, Milad

“…The goal of this research is to present an approach that predicts the remaining useful life (RUL) estimation of a turbofan engine system within specific time…”
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Forecasting stock market volatility using social media sentiment analysis by Saravanos, Christina, Kanavos, Andreas

“…In the era where social media significantly influences public sentiment, platforms such as Twitter have become vital in predicting stock market trends. This…”
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Dual Bi-LSTM-GRU based stance detection in tweets ordered classes by Poonam, Km, Ramakrishnudu, Tene

“…There has been a tremendous increase in social media text-based opinions and reviews as a result of the quick development of social media. It emphasizes those…”
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Artificial neural network to characterize spatially varying quantity through random field approach by Kumar, Pratyush

“…Random field theory is commonly employed to characterize spatially varying quantities by decomposing them into deterministic and random components. The unknown…”
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Stacked semi-supervised autoencoder-regularized RVFLNs for reliable prediction of molten iron quality in blast furnace by Zhou, Ping, Zhao, Peng, Ou, Zihui, Chai, Tianyou

“…This paper proposes a novel stacked semi-supervised autoencoder-regularized random vector functional-link networks (RVFLNs) for reliable prediction of molten…”
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