An analytical examination of the performance assessment of CNN-LSTM architectures for state-of-health evaluation of lithium-ion batteries by Arun Jose, Sonam Shrivastava

“…In this study, an assessment was conducted on various Convolutional Neural Network-Long Short-Term Memory architectures to explore their complete potential, with the generic architecture yielding the most favorable outcomes. …”
Get full text

Using Deep Learning to Predict Complex Systems: A Case Study in Wind Farm Generation by J. M. Torres, R. M. Aguilar

“…We also conduct a sensitivity analysis to determine which estimator type is most robust to perturbations. An analysis of our findings shows that the most accurate and robust estimators are those based on feedforward neural networks with a SELU activation function and convolutional neural networks.…”
Get full text

A deep fusion‐based vision transformer for breast cancer classification by Ahsan Fiaz, Basit Raza, Muhammad Faheem, Aadil Raza

“…Abstract Breast cancer is one of the most common causes of death in women in the modern world. …”
Get full text

Advanced phenotyping in tomato fruit classification through artificial intelligence by Sandra Eulália Santos Faria, Alcinei Místico Azevedo, Nayany Gomes Rabelo, Varlen Zeferino Anastácio, Valentina de Melo Maciel, Deltimara Viana Matos, Elias Barbosa Rodrigues, Phelipe Souza Amorim, Janete Ramos da Silva, Fernanda de Souza Santos

“…This study aimed to classify tomato fruits based on shape, group, color, and defects using Convolutional Neural Networks (CNNs). The performance of five architectures - VGG16, InceptionV3, ResNet50, EfficientNetB3, and InceptionResNetV2 was evaluated to identify and determine the most efficient one for this classification. …”
Get full text

A Rolling-Bearing-Fault Diagnosis Method Based on a Dual Multi-Scale Mechanism Applicable to Noisy-Variable Operating Conditions by Jing Kang, Taiyong Wang, Ye Wei, Usman Haladu Garba, Ying Tian

“…This approach targets the intrinsic mode functions (IMFs), which capture information across multiple scales, to obtain the most precise denoised signal possible. Subsequently, we introduce the Dynamic Weighted Multi-Scale Feature Convolutional Neural Network (DWMFCNN) model, which integrates two structures: multi-scale feature extraction and dynamic weighting of these features. …”
Get full text

Deep learning classification integrating embryo images with associated clinical information from ART cycles by Mohamed Salih, Christopher Austin, Krishna Mantravadi, Eva Seow, Sutthipat Jitanantawittaya, Sandeep Reddy, Beverley Vollenhoven, Hamid Rezatofighi, Fabrizzio Horta

“…From the visualisation process we found that female and male age to be the most clinical factors, whilst Trophectoderm to be the most important blastocyst feature. …”
Get full text

Precision forecasting for hybrid energy systems using five deep learning algorithms for meteorological parameter prediction by Ceren Ceylan, Zehra Yumurtacı

“…In addition, most of the research uses the same algorithmic solution to all the meteorological parameters without identifying parameter-specific optimization potential, and recent research is verified on actual future time steps instead of historical train-test split. …”
Get full text

Multiclass Supervised Learning Approach for SAR-COV2 Severity and Scope Prediction: SC2SSP Framework by Shaik Khasim Saheb, B. Narayanan, T.V. Narayana Rao

“…Although a CT scan is the most common method for diagnosis, CXR is the most frequently utilized since it is more accessible, quicker, and less expensive. …”
Get full text

Artificial intelligence in ophthalmology: a bibliometric analysis of the 5-year trends in literature by Bosen Peng, Jiancheng Mu, Feng Xu, Wanyue Guo, Chuhuan Sun, Wei Fan

“…These papers were authored by 87,695 individuals, with Wang Y, Liu Y, and Zhang Y the most prolific authors. Ting DSW was the most co-cited author. …”
Get full text

Comparative Analysis of AlexNet, ResNet-50, and VGG-19 Performance for Automated Feature Recognition in Pedestrian Crash Diagrams by Baraah Qawasmeh, Jun-Seok Oh, Valerian Kwigizile

“…Pedestrians, as the most vulnerable road users in traffic crashes, prompt transportation researchers and urban planners to prioritize pedestrian safety due to the elevated risk and growing incidence of injuries and fatalities. …”
Get full text

Neurophysiological Approaches to Lie Detection: A Systematic Review by Bewar Neamat Taha, Muhammet Baykara, Talha Burak Alakuş

“…<b>Results:</b> CIT with ERP P300 was the most frequently employed protocol. The most used preprocessing method was Bandpass Filtering (BPF), and the Discrete Wavelet Transform (DWT) emerged as the preferred feature extraction technique due to its suitability for non-stationary EEG signals. …”
Get full text

CNN Based Automatic Speech Recognition: A Comparative Study by Hilal Ilgaz, Beyza Akkoyun, Özlem Alpay, M. Ali Akcayol

“…The most advanced results have been obtained with speech recognition systems created using convolutional neural network (CNN) and recurrent neural networks (RNN). …”
Get full text

A Research Approach to Port Information Security Link Prediction Based on HWA Algorithm by Zhixin Xia, Zhangqi Zheng, Lexin Bai, Xiaolei Yang, Yongshan Liu

“…At the same time, most of the algorithms only consider the general graph structure and do not fully consider the high-order information in the graph. …”
Get full text

Prediction of foreign currency exchange rates using an attention-based long short-term memory network by Shahram Ghahremani, Uyen Trang Nguyen

“…We conducted comprehensive experiments to evaluate and compare the performance of ALFA against several models used in previous work and against state-of-the-art deep learning models such as temporal convolutional networks (TCN) and Transformer. Experimental results show that ALFA outperforms the baseline models in most cases, across different currency pairs and feature sets, thanks to its attention mechanism that filters out irrelevant or redundant data to focus on important features. …”
Get full text

Fusion of Deep and Time–Frequency Local Features for Melanoma Skin Cancer Detection by Hamidreza Eghtesaddoust, Morteza Valizadeh, Mehdi Chehel Amirani

“…Skin cancer spreads quickly as the skin is the most vulnerable organ, and melanoma (MEL) is a fatal type of skin cancer. …”
Get full text

The Detection and Classification of Grape Leaf Diseases with an Improved Hybrid Model Based on Feature Engineering and AI by Fatih Atesoglu, Harun Bingol

“…Therefore, the early detection and classification of grape diseases with the latest artificial intelligence techniques and feature reduction techniques was carried out within the scope of this study. The most well-known convolutional neural network (CNN) architectures, texture-based Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG) methods, Neighborhood Component Analysis (NCA), feature reduction methods, and machine learning (ML) techniques are the methods used in this article. …”
Get full text

Design and Analysis of an Expert System for the Detection and Recognition of Criminal Faces by Rishi Gupta, Amit Kumar Gupta, Deepak Panwar, Ashish Jain, Partha Chakraborty

“…An analysis of these strategies is also conducted and then put into practice to those that seem to be the most effective for the designed criminal recognition system. …”
Get full text

Advancements in the use of AI in the diagnosis and management of inflammatory bowel disease by Dalia Braverman-Jaiven, Luigi Manfredi

“…There are several datasets publicly available for endoscopy images and videos, but most of them are solely specialized in polyps. The use of DL algorithms to detect IBD is still in its inception, most studies are based on assessing the severity of UC. …”
Get full text

Mast cell hyperactivity as the factor in the pathogenesis of male infertility by N. G. Kulchenko

“…Most infertile men have significant amounts of significant amounts of mast cells in their ejaculate. …”
Get full text

Evaluation of machine learning and deep learning algorithms for fire prediction in Southeast Asia by Aditya Eaturu, Krishna Prasad Vadrevu

“…In this study, we utilize Visible Infrared Imaging Radiometer Suite (VIIRS) satellite-derived fire data alongside six machine learning (ML) and deep learning (DL) models, Simple Persistence, Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), CNN-Long Short-Term Memory (CNN-LSTM), and Convolutional Long Short-Term Memory (ConvLSTM) to determine the most effective fire prediction model. …”
Get full text