Výsledky vyhledávání - "convolutional neural network (cnn)"

Akademický článek

Enhancing advanced cervical cell categorization with cluster-based intelligent systems by a novel integrated CNN approach with skip mechanisms and GAN-based augmentation

Autor: Gunjan Shandilya, Sheifali Gupta, Ahmad Almogren, Salil Bharany, Ayman Altameem, Ateeq Ur Rehman, Seada Hussen

Publikováno v: Scientific Reports, Vol 14, Iss 1, Pp 1-18 (2024)

Abstract Cervical cancer is one of the biggest challenges in global health, thus it forms a critical need for early detection technologies that could improve patient prognosis and inform treatment decisions. This development in the form of an early d

Externí odkaz: https://doaj.org/article/c18e4551514c43418a36d1d8151e6c27

Zobrazit plný text záznamu

Akademický článek

An Explainable Deep Learning Approach for Classifying Monkeypox Disease by Leveraging Skin Lesion Image Data

Autor: Andino Maseleno, Miftachul Huda, Chotirat Ann Ratanamahatana

Publikováno v: Emerging Science Journal, Vol 8, Iss 5, Pp 1875-1897 (2024)

According to the World Health Organization's (WHO) external situation report on the multi-country outbreak of Monkeypox in 2023, from 11 countries in Southeast Asia Regions, Thailand recorded the highest reported cases, totaling 461. The ongoing Monk

Externí odkaz: https://doaj.org/article/9f7c11e10fa043a1ab82406c7e02f4b4

Zobrazit plný text záznamu

Akademický článek

A Prediction Method of New Power System Frequency Characteristics Based on Convolutional Neural Network

Autor: LU Wen’an, ZHU Qingxiao, LI Zhaowei, LIU Hui, YU Yiping

Publikováno v: Shanghai Jiaotong Daxue xuebao, Vol 58, Iss 10, Pp 1500-1512 (2024)

In order to solve the problems existing in the traditional frequency analysis method for the frequency analysis of grids with a high proportion of new energy, such as the large amount of calculation, the difficulty of modeling, and the prominent cont

Externí odkaz: https://doaj.org/article/fdcc2ecfb7904363a8f9f6da87a665cc

Zobrazit plný text záznamu

Akademický článek

A convolutional neural network machine learning based navigation of underwater vehicles under limited communication

Autor: Sarada Prasanna Sahoo, Bibhuti Bhusan Pati, Bikramaditya Das

Publikováno v: Archives of Control Sciences, Vol vol. 34, Iss No 3, Pp 537-568 (2024)

This paper proposes navigation of multiple autonomous underwater vehicles (AUVs) by employing machine learning approach for wide area surveys in underwater environment. Wide area survey in underwater environment is affected by low data rate.We consid

Externí odkaz: https://doaj.org/article/36486a955ae34ed38e1a9048fef8d058

Zobrazit plný text záznamu

Akademický článek

Improved Urdu-English Neural Machine Translation with a fully Convolutional Neural Network Encoder

Autor: Huma Israr, Muhammad Khuram Shahzad, Shahid Anwar

Publikováno v: International Journal of Mathematical, Engineering and Management Sciences, Vol 9, Iss 5, Pp 1067-1088 (2024)

Neural machine translation (NMT) approaches driven by artificial intelligence (AI) has gained more and more attention in recent years, mainly due to their simplicity yet state-of-the-art performance. Despite NMT models with attention mechanism relyin

Externí odkaz: https://doaj.org/article/8b4cbf9c6c60438588518cb75c813067

Zobrazit plný text záznamu

Akademický článek

Leveraging immuno-fluorescence data to reduce pathologist annotation requirements in lung tumor segmentation using deep learning

Autor: Hatef Mehrabian, Jens Brodbeck, Peipei Lyu, Edith Vaquero, Abhishek Aggarwal, Lauri Diehl

Publikováno v: Scientific Reports, Vol 14, Iss 1, Pp 1-16 (2024)

Abstract The main bottleneck in training a robust tumor segmentation algorithm for non-small cell lung cancer (NSCLC) on H&E is generating sufficient ground truth annotations. Various approaches for generating tumor labels to train a tumor segmentati

Externí odkaz: https://doaj.org/article/d3c7dacee6e841c6b4657d2155e93c6c

Zobrazit plný text záznamu

Plný text ve formátu HTML

Akademický článek

Enhancing sewage flow prediction using an integrated improved SSA-CNN-Transformer-BiLSTM model

Autor: Jiawen Ye, Lei Dai, Haiying Wang

Publikováno v: AIMS Mathematics, Vol 9, Iss 10, Pp 26916-26950 (2024)

Accurate prediction of sewage flow is crucial for optimizing sewage treatment processes, cutting down energy consumption, and reducing pollution incidents. Current prediction models, including traditional statistical models and machine learning model

Externí odkaz: https://doaj.org/article/74bf350d35f04f6a9f54463179929e38

Zobrazit plný text záznamu

Akademický článek

Development of Artificial Intelligence/Machine Learning (AI/ML) Models for Methane Emissions Forecasting in Seaweed

Autor: Clifford Jaylen Louime, Tariq Asleem Raza

Publikováno v: Methane, Vol 3, Iss 3, Pp 485-499 (2024)

This research project aimed to address the growing concern about methane emissions from seaweed by developing a Convolutional Neural Network (CNN) model capable of accurately predicting these emissions. The study used PANDAS to read and analyze the d

Externí odkaz: https://doaj.org/article/702e0ef15be544b0974f447a08abb607

Zobrazit plný text záznamu

Akademický článek

ENHANCED DEEP LEARNING BASED PRECISION AGRICULTURE: A DECISION SUPPORT SYSTEM FOR ENHANCING CROP RECOMMENDATION ACCURACY USING CONVOLUTIONAL NEURAL NETWORKS (CNN)

Autor: Muhammad Nabeel Amin, Shreeraz Memon, Arshad Ali, Hamayun Khan, Roshan Joshi, Muhammad Tausif Afzal Ran, Yazed ALsaawy

Publikováno v: Journal of Mechanics of Continua and Mathematical Sciences, Vol 19, Iss 9, Pp 166-187 (2024)

Machine learning-based crop recommendation models are invaluable tools for enhancing modern AI-based farming, assisting in decisions about the selection of crops to optimize yield performance and growth. This research introduces an intelligent strate

Externí odkaz: https://doaj.org/article/982a33e1a64e4428a1e20fd5cdd890a4

Zobrazit plný text záznamu

Vyhledávací nástroje:

Upřesnit hledání