Search Results - Special Issue on Timely Advances of Deep Learning with applications AND Data-Driven Modeling

Deep learning approaches and data augmentation for melanoma detection by 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 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 classification…”
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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

“… Data-driven techniques are well-suited for this aim, given their capacity to model potentially complex industrial processes…”
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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

“… 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 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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DACL+: domain-adapted contrastive learning for enhanced low-resource language representations in document clustering tasks by Zaikis, Dimitrios, Vlahavas, Ioannis

“… To this end, we introduce a domain-adapted contrastive learning approach for low-resource Greek document clustering…”
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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…”
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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 detection, control, and various other areas…”
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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

“… 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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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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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.

“…), 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 by 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 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…”
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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…”
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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

“… In the second phase, among various machine learning and deep learning approaches, bidirectional long short-term memory (BiLSTM…”
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Residual connections improve click-through rate and conversion rate prediction performance by 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 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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Efficient deterministic renewable energy forecasting guided by multiple-location weather data by 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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DAMNet: lightweight dual attention mixed network for efficient image deraining by Thatikonda, Ragini, Cheruku, Ramalingaswamy, Kodali, Prakash

“…A long-standing problem in computer vision (CV) is image deraining. Current deraining networks frequently fail to achieve a good balance between low system…”
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Novel wavelet-LSTM approach for time series prediction by Tamilselvi, C., Paul, Ranjit Kumar, Yeasin, Md, Paul, A. K.

“…), followed by long short-term memory (LSTM) modeling on each denoised series. The hyperband search algorithm was employed to identify the optimal hyperparameter combination for each LSTM model, aiding in further fine-tuning the model…”
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