Object detection using YOLO: challenges, architectural successors, datasets and applications

Multimedia Tools and Applications, Journal Year: 2022, Volume and Issue: 82(6), P. 9243 - 9275

Published: Aug. 8, 2022

Language: Английский

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A Brain Tumor Identification and Classification Using Deep Learning based on CNN-LSTM Method

Computers & Electrical Engineering, Journal Year: 2022, Volume and Issue: 101, P. 107960 - 107960

Published: April 27, 2022

Language: Английский

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Application of Meta-Heuristic Algorithms for Training Neural Networks and Deep Learning Architectures: A Comprehensive Review

Neural Processing Letters, Journal Year: 2022, Volume and Issue: 55(4), P. 4519 - 4622

Published: Oct. 31, 2022

Language: Английский

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A comprehensive review on deep learning approaches in wind forecasting applications

CAAI Transactions on Intelligence Technology, Journal Year: 2022, Volume and Issue: 7(2), P. 129 - 143

Published: Jan. 18, 2022

The effective use of wind energy is an essential part the sustainable development human society, in particular, at recent unprecedented pressure shaping a low carbon environment. Accurate resource and power forecasting play key role improving penetration. However, it has not been well adopted real-world applications due to strong stochastic characteristics energy. In years, application boost deep learning methods provides new tools forecasting. This paper comprehensive overview models based on field Featured approaches include time-series-based recurrent neural networks, restricted Boltzmann machines, convolutional networks as auto-encoder-based approaches. addition, future directions deep-learning-based have also discussed.

Language: Английский

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An integrated mediapipe-optimized GRU model for Indian sign language recognition

Scientific Reports, Journal Year: 2022, Volume and Issue: 12(1)

Published: July 13, 2022

Abstract Sign language recognition is challenged by problems, such as accurate tracking of hand gestures, occlusion hands, and high computational cost. Recently, it has benefited from advancements in deep learning techniques. However, these larger complex approaches cannot manage long-term sequential data they are characterized poor information processing efficiency capturing useful information. To overcome challenges, we propose an integrated MediaPipe-optimized gated recurrent unit (MOPGRU) model for Indian sign recognition. Specifically, improved the update gate standard GRU cell multiplying reset to discard redundant past one screening. By obtaining feedback resultant gate, additional attention shown present input. Additionally, replace hyperbolic tangent activation GRUs with exponential linear SoftMax Softsign output layer cell. Thus, our proposed MOPGRU achieved better prediction accuracy, efficiency, capability, faster convergence than other models.

Language: Английский

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An Artificial Intelligence-Based Stacked Ensemble Approach for Prediction of Protein Subcellular Localization in Confocal Microscopy Images

Sustainability, Journal Year: 2023, Volume and Issue: 15(2), P. 1695 - 1695

Published: Jan. 16, 2023

Predicting subcellular protein localization has become a popular topic due to its utility in understanding disease mechanisms and developing innovative drugs. With the rapid advancement of automated microscopic imaging technology, approaches using bio-images for have gained lot interest. The Human Protein Atlas (HPA) project is macro-initiative that aims map human proteome utilizing antibody-based proteomics related c. Millions images been tagged with single or multiple labels HPA database. However, fewer techniques predicting location proteins devised, majority them relying on automatic single-label classification. As result, there need an sustainable system capable multi-label classification Deep learning presents potential option labeling protein’s localization, given vast image number generated by high-content microscopy fact manual both time-consuming error-prone. Hence, this research use ensemble technique improvement performance existing state-of-art convolutional neural networks pretrained models were applied; finally, stacked ensemble-based deep model was presented, which delivers more reliable robust classifier. F1-score, precision, recall used evaluation proposed model’s efficiency. In addition, comparison conducted respect method. results show strategy performed exponentially well images, recall, F1-score 0.70, 0.72, 0.71, respectively.

Language: Английский

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Probabilistic Deep Q Network for real-time path planning in censorious robotic procedures using force sensors

Journal of Real-Time Image Processing, Journal Year: 2021, Volume and Issue: 18(5), P. 1773 - 1785

Published: July 17, 2021

Language: Английский

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A New Hybrid Based on Long Short-Term Memory Network with Spotted Hyena Optimization Algorithm for Multi-Label Text Classification

Mathematics, Journal Year: 2022, Volume and Issue: 10(3), P. 488 - 488

Published: Feb. 2, 2022

An essential work in natural language processing is the Multi-Label Text Classification (MLTC). The purpose of MLTC to assign multiple labels each document. Traditional text classification methods, such as machine learning usually involve data scattering and failure discover relationships between data. With development deep algorithms, many authors have used MLTC. In this paper, a novel model called Spotted Hyena Optimizer (SHO)-Long Short-Term Memory (SHO-LSTM) for based on LSTM network SHO algorithm proposed. network, Skip-gram method embed words into vector space. new uses optimize initial weight network. Adjusting matrix major challenge. If neurons be accurate, then accuracy output will higher. population-based meta-heuristic that works mass hunting behavior spotted hyenas. algorithm, solution problem coded hyena. Then hyenas are approached optimal answer by following hyena leader. Four datasets (RCV1-v2, EUR-Lex, Reuters-21578, Bookmarks) evaluate proposed model. assessments demonstrate has higher rate than LSTM, Genetic Algorithm-LSTM (GA-LSTM), Particle Swarm Optimization-LSTM (PSO-LSTM), Artificial Bee Colony-LSTM (ABC-LSTM), Harmony Algorithm Search-LSTM (HAS-LSTM), Differential Evolution-LSTM (DE-LSTM). improvement SHO-LSTM four compared 7.52%, 7.12%, 1.92%, 4.90%, respectively.

Language: Английский

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Hand gesture recognition based on a Harris Hawks optimized Convolution Neural Network

Computers & Electrical Engineering, Journal Year: 2022, Volume and Issue: 100, P. 107836 - 107836

Published: March 4, 2022

Language: Английский

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Diagnosis of Brain Tumor Using Light Weight Deep Learning Model with Fine-Tuning Approach

Computational and Mathematical Methods in Medicine, Journal Year: 2022, Volume and Issue: 2022, P. 1 - 9

Published: July 1, 2022

Brain cancer is a rare and deadly disease with slim chance of survival. One the most important tasks for neurologists radiologists to detect brain tumors early. Recent claims have been made that computer-aided diagnosis-based systems can diagnose by employing magnetic resonance imaging (MRI) as supporting technology. We propose transfer learning approaches deep model malignant tumors, such glioblastoma, using MRI scans in this study. This paper presents learning-based approach tumor identification classification state-of-the-art object detection framework YOLO (You Only Look Once). The YOLOv5 novel technique requires limited computational architecture than its competing models. study used Brats 2021 dataset from RSNA-MICCAI radio genomic classification. has images annotated competition make sense an AI online tool labeling dataset. preprocessed data then divided into testing training model. provides precision 88 percent. Finally, our tested across whole dataset, it concluded able successfully.

Language: Английский

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