Výsledky vyhľadávania - Special Issue on Timely Advances of Deep Learning with applications AND Data Driven Modeling*

Deep learning approaches and data augmentation for melanoma detection Autor Alzamel, Mai, Iliopoulos, Costas, Lim, Zara

“… Machine learning (ML) and deep learning (DL) techniques have shown a promising results in prediction and classification tasks…”
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Exploring distribution-based approaches for out-of-distribution detection in deep learning models Autor 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 classification…”
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CM-MLP: hybrid convmixer-deep MLP architecture for enhanced identification of corn and apple leaf diseases Autor Li, Li-Hua, Tanone, Radius

“… We propose a novel hybrid ConvMixer and Deep MLP architecture called CM-MLP that combines the strengths of ConvMixer with various Deep Multi-layer Perceptron (MLP…”
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Researching the detection of continuous gravitational waves based on signal processing and ensemble learning Autor Pintelas, Emmanuel, Livieris, Ioannis E., Pintelas, Panagiotis

“…). In contrast to the complex burst nature of B-GWs, C-GWs have an elegant and significantly simpler form while they are able to provide higher quality data for the exploration of the universe…”
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Assessment and deployment of a LSTM-based virtual sensor in an industrial process control loop Autor González-Herbón, Raúl, González-Mateos, Guzmán, Rodríguez-Ossorio, José R., Prada, Miguel A., Morán, Antonio, Alonso, Serafín, Fuertes, Juan J., Domínguez, Manuel

“… Data-driven techniques are well-suited for this aim, given their capacity to model potentially complex industrial processes…”
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DACL+: domain-adapted contrastive learning for enhanced low-resource language representations in document clustering tasks Autor Zaikis, Dimitrios, Vlahavas, Ioannis

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

“… Advances in data-driven models provide techniques like Non-Intrusive Load Monitoring (NILM), which estimates the energy demand of appliances from total consumption…”
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Zero-day Android botnet detection using neural networks Autor 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…”
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A fuzzy zeroing neural network and its application on dynamic Hill cipher Autor 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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Feature analysis and ensemble-based fault detection techniques for nonlinear systems Autor 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 detection, control, and various other areas…”
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A novel human action recognition using Grad-CAM visualization with gated recurrent units Autor Jayamohan, M., Yuvaraj, S.

“…Human action recognition is a vital aspect of computer vision, with applications ranging from security systems to interactive technology…”
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Memetic algorithm-based optimization of hybrid forecasting systems for multivariate time series Autor Padilha, Guilherme Afonso Galindo, Jung, Jason J., de Mattos Neto, Paulo S. G.

“…), a hybrid system that combines linear statistical and deep learning (DL) models employing a novel memetic algorithm (MA…”
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A deep residual sequence autoencoder for future state estimation and aiding prognostics and diagnostics in machines: a case study of mechanical rolling elements Autor Ramadhan, Bwambale Rashid, Cahit, Perkgoz

“… However, the existing impediments known in RNNs that cause nonconvergence during training have created setbacks in their application in fields with data characterized by long dependent series…”
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A parameter-free nearest neighbor algorithm with reduced prediction time and improved performance through injected randomness Autor 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…”
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An explainable approach for prediction of remaining useful life in turbofan condition monitoring Autor Mansourvar, Zahra, Jahangoshai Rezaee, Mustafa, Eshkevari, Milad

“… In the second phase, among various machine learning and deep learning approaches, bidirectional long short-term memory (BiLSTM…”
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Efficient deterministic renewable energy forecasting guided by multiple-location weather data Autor Symeonidis, Charalampos, Nikolaidis, Nikos

“…Electricity generated from renewable energy sources has been established as an efficient remedy for both energy shortages and the environmental pollution…”
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Residual connections improve click-through rate and conversion rate prediction performance Autor Biçici, Ergun

“… As the learning models become more complex with increasing depth, it has become increasingly challenging to predict CTR and CVR accurately…”
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Dual Bi-LSTM-GRU based stance detection in tweets ordered classes Autor 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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WaveFLSTM: Wavelet-based fuzzy LSTM model for forecasting complex time series data Autor Sarkar, Anita, Yeasin, Md, Paul, Ranjit Kumar, Paul, A. K., Singh, Ankit Kumar

“…The forecasting of time series continues to be a prominent area of interest among researchers exploring advanced learning techniques…”
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Novel wavelet-LSTM approach for time series prediction Autor 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…”
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