Log in

Relevant bibliographies by topics / VGG16 pretrained model / Journal articles

To see the other types of publications on this topic, follow the link: VGG16 pretrained model.

Journal articles on the topic 'VGG16 pretrained model'

Author: Grafiati

Published: 5 June 2025

Last updated: 16 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'VGG16 pretrained model.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Desai, Chitra. "Image Classification Using Transfer Learning and Deep Learning." International Journal of Engineering and Computer Science 10, no. 9 (2021): 25394–98. http://dx.doi.org/10.18535/ijecs/v10i9.4622.

Full text

Abstract:

Deep learning models have demonstrated improved efficacy in image classification since the ImageNet Large Scale Visual Recognition Challenge started since 2010. Classification of images has further augmented in the field of computer vision with the dawn of transfer learning. To train a model on huge dataset demands huge computational resources and add a lot of cost to learning. Transfer learning allows to reduce on cost of learning and also help avoid reinventing the wheel. There are several pretrained models like VGG16, VGG19, ResNet50, Inceptionv3, EfficientNet etc which are widely used. Thi

APA, Harvard, Vancouver, ISO, and other styles

2

Al-Sarem, Mohammed, Mohammed Al-Asali, Ahmed Yaseen Alqutaibi, and Faisal Saeed. "Enhanced Tooth Region Detection Using Pretrained Deep Learning Models." International Journal of Environmental Research and Public Health 19, no. 22 (2022): 15414. http://dx.doi.org/10.3390/ijerph192215414.

Full text

Abstract:

The rapid development of artificial intelligence (AI) has led to the emergence of many new technologies in the healthcare industry. In dentistry, the patient’s panoramic radiographic or cone beam computed tomography (CBCT) images are used for implant placement planning to find the correct implant position and eliminate surgical risks. This study aims to develop a deep learning-based model that detects missing teeth’s position on a dataset segmented from CBCT images. Five hundred CBCT images were included in this study. After preprocessing, the datasets were randomized and divided into 70% trai

APA, Harvard, Vancouver, ISO, and other styles

3

Jain, Mriga, and Brajesh Kumar Singh. "Leveraging Lightweight Pretrained Model for Brain Tumour Detection." BIO Web of Conferences 65 (2023): 05051. http://dx.doi.org/10.1051/bioconf/20236505051.

Full text

Abstract:

This study presents an analysis of two deep learning models deployed for brain tumour detection: the lightweight pretrained MobileNetV2 and a novel hybrid model by combining light-weight MobileNetV2 with VGG16. The aim is to investigate the performance and efficiency of these models in terms of accuracy and training time. The new hybrid model integrates the strengths of both architectures, leveraging the depth-wise separable convolutions of MobileNetV2 and the deeper feature extraction capabilities of VGG16. Through experimentation and evaluation using a publicly available benchmark brain tumo

APA, Harvard, Vancouver, ISO, and other styles

4

Saravagi, Deepika, Shweta Agrawal, Manisha Saravagi, Jyotir Moy Chatterjee, and Mohit Agarwal. "Diagnosis of Lumbar Spondylolisthesis Using Optimized Pretrained CNN Models." Computational Intelligence and Neuroscience 2022 (April 13, 2022): 1–12. http://dx.doi.org/10.1155/2022/7459260.

Full text

Abstract:

Spondylolisthesis refers to the slippage of one vertebral body over the adjacent one. It is a chronic condition that requires early detection to prevent unpleasant surgery. The paper presents an optimized deep learning model for detecting spondylolisthesis in X-ray radiographs. The dataset contains a total of 299 X-ray radiographs from which 156 images are showing the spine with spondylolisthesis and 143 images are of the normal spine. Image augmentation technique is used to increase the data samples. In this study, VGG16 and InceptionV3 models were used for the image classification task. The

APA, Harvard, Vancouver, ISO, and other styles

5

Yogapriya, J., Venkatesan Chandran, M. G. Sumithra, P. Anitha, P. Jenopaul, and C. Suresh Gnana Dhas. "Gastrointestinal Tract Disease Classification from Wireless Endoscopy Images Using Pretrained Deep Learning Model." Computational and Mathematical Methods in Medicine 2021 (September 11, 2021): 1–12. http://dx.doi.org/10.1155/2021/5940433.

Full text

Abstract:

Wireless capsule endoscopy is a noninvasive wireless imaging technology that becomes increasingly popular in recent years. One of the major drawbacks of this technology is that it generates a large number of photos that must be analyzed by medical personnel, which takes time. Various research groups have proposed different image processing and machine learning techniques to classify gastrointestinal tract diseases in recent years. Traditional image processing algorithms and a data augmentation technique are combined with an adjusted pretrained deep convolutional neural network to classify dise

APA, Harvard, Vancouver, ISO, and other styles

6

Kukadiya, Hirenkumar, Nidhi Arora, Divyakant Meva, and Shilpa Srivastava. "An ensemble deep learning model for automatic classification of cotton leaves diseases." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 3 (2024): 1942–49. https://doi.org/10.11591/ijeecs.v33.i3.pp1942-1949.

Full text

Abstract:

Cotton plant (Gossypium herbaceum), is one of the significant fiber crop grown worldwide. However, the crop is quite prone to leaves diseases, for which deep learning (DL) techniques can be utilized for early disease prediction and prevent stakeholders from losing the harvest. The objective of this paper is to develop a novel ensemble based deep convolutional neural network (DCNN) model developed on two base pretrained models named: VGG16 and InceptionV3 for early detection of cotton leaves diseases. The proposed ensemble model trained on cotton leaves dataset reports higher training and testi

APA, Harvard, Vancouver, ISO, and other styles

7

Kukadiya, Hirenkumar, Nidhi Arora, Divyakant Meva, and Shilpa Srivastava. "An ensemble deep learning model for automatic classification of cotton leaves diseases." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 3 (2024): 1942. http://dx.doi.org/10.11591/ijeecs.v33.i3.pp1942-1949.

Full text

Abstract:

<p>Cotton plant (Gossypium herbaceum), is one of the significant fiber crop grown worldwide. However, the crop is quite prone to leaves diseases, for which deep learning (DL) techniques can be utilized for early disease prediction and prevent stakeholders from losing the harvest. The objective of this paper is to develop a novel ensemble based deep convolutional neural network (DCNN) model developed on two base pretrained models named: VGG16 and InceptionV3 for early detection of cotton leaves diseases. The proposed ensemble model trained on cotton leaves dataset reports higher training

APA, Harvard, Vancouver, ISO, and other styles

8

Kaushik, Bhavana, Deepika Koundal, Neelam Goel, Atef Zaguia, Assaye Belay, and Hamza Turabieh. "Computational Intelligence-Based Method for Automated Identification of COVID-19 and Pneumonia by Utilizing CXR Scans." Computational Intelligence and Neuroscience 2022 (July 4, 2022): 1–12. http://dx.doi.org/10.1155/2022/7124199.

Full text

Abstract:

Chest X-ray (CXR) scans are emerging as an important diagnostic tool for the early spotting of COVID and other significant lung diseases. The recognition of visual symptoms is difficult and can take longer time by radiologists as CXR provides various signs of viral infection. Therefore, artificial intelligence-based method for automated identification of COVID by utilizing X-ray images has been found to be very promising. In the era of deep learning, effective utilization of existing pretrained generalized models is playing a decisive role in terms of time and accuracy. In this paper, the bene

APA, Harvard, Vancouver, ISO, and other styles

9

Huan, Er-Yang, and Gui-Hua Wen. "Multilevel and Multiscale Feature Aggregation in Deep Networks for Facial Constitution Classification." Computational and Mathematical Methods in Medicine 2019 (December 20, 2019): 1–11. http://dx.doi.org/10.1155/2019/1258782.

Full text

Abstract:

Constitution classification is the basis and core content of TCM constitution research. In order to improve the accuracy of constitution classification, this paper proposes a multilevel and multiscale features aggregation method within the convolutional neural network, which consists of four steps. First, it uses the pretrained VGG16 as the basic network and then refines the network structure through supervised feature learning so as to capture local image features. Second, it extracts the image features of different layers from the fine-tuned VGG16 model, which are then dimensionally reduced

APA, Harvard, Vancouver, ISO, and other styles

10

Singh, Tajinder Pal, Sheifali Gupta, Meenu Garg, et al. "Transfer and Deep Learning-Based Gurmukhi Handwritten Word Classification Model." Mathematical Problems in Engineering 2023 (May 3, 2023): 1–20. http://dx.doi.org/10.1155/2023/4768630.

Full text

Abstract:

The world is having a vast collection of text with abandon of knowledge. However, it is a difficult and time-taking process to manually read and recognize the text written in numerous regional scripts. The task becomes more critical with Gurmukhi script due to complex structure of characters motivated from the challenges in designing an error-free and accurate classification model of Gurmukhi characters. In this paper, the author has customized the convolutional neural network model to classify handwritten Gurmukhi words. Furthermore, dataset has been prepared with 24000 handwritten Gurmukhi w

APA, Harvard, Vancouver, ISO, and other styles

11

Muliawan, Nicholas Hans, Edbert Valencio Angky, and Simeon Yuda Prasetyo. "Age estimation through facial images using Deep CNN Pretrained Model and Particle Swarm Optimization." E3S Web of Conferences 426 (2023): 01041. http://dx.doi.org/10.1051/e3sconf/202342601041.

Full text

Abstract:

There has been a lot of recent study on age estimates utilizing different optimization techniques, architecture models, and diverse strategies with some variations. However, accuracy improvement in age estimation studies remains a challenge due to the inability of traditional approaches to effectively capture complex facial features and variations. Therefore, this study investigates the usage of Particle Swarm Optimization in Deep CNN models to improve accuracy. The focus of the study is on exploring different feature extractors for the age estimation task, utilizing pre-trained CNN models suc

APA, Harvard, Vancouver, ISO, and other styles

12

Sumon, Shakil Ahmed, Raihan Goni, Niyaz Bin Hashem, Tanzil Shahria, and Rashedur M. Rahman. "Violence Detection by Pretrained Modules with Different Deep Learning Approaches." Vietnam Journal of Computer Science 07, no. 01 (2019): 19–40. http://dx.doi.org/10.1142/s2196888820500013.

Full text

Abstract:

In this paper, we have explored different strategies to find out the saliency of the features from different pretrained models in detecting violence in videos. A dataset has been created which consists of violent and non-violent videos of different settings. Three ImageNet models; VGG16, VGG19, ResNet50 are being used to extract features from the frames of the videos. In one of the experiments, the extracted features have been feed into a fully connected network which detects violence in frame level. Moreover, in another experiment, we have fed the extracted features of 30 frames to a long sho

APA, Harvard, Vancouver, ISO, and other styles

13

Sharma, Jatin, Sahil Sharma, Vijay Kumar, Hany S. Hussein, and Hammam Alshazly. "Deepfakes Classification of Faces Using Convolutional Neural Networks." Traitement du Signal 39, no. 3 (2022): 1027–37. http://dx.doi.org/10.18280/ts.390330.

Full text

Abstract:

In the recent years, petabytes of data is being generated and uploaded online every second. To successfully detect fake contents, a deepfake detection technique is used to determine whether the uploaded content is real or fake. In this paper, a convolutional neural network-based model is proposed to detect the fake face images. The generative adversarial networks and data augmentation are used to generate the face dataset for real and fake face classification. Transfer learning techniques from pretrained deep models such as VGG16 and ResNet50 are employed in the proposed model. The proposed mo

APA, Harvard, Vancouver, ISO, and other styles

14

Li, Xiaojing, Jiandong Fang, and Yvdong Zhao. "A Multi-Target Identification and Positioning System Method for Tomato Plants Based on VGG16-UNet Model." Applied Sciences 14, no. 7 (2024): 2804. http://dx.doi.org/10.3390/app14072804.

Full text

Abstract:

The axillary buds that grow between the main and lateral branches of tomato plants waste nutrients and lead to a decrease in yield, necessitating regular removal. Currently, these buds are removed manually, which requires substantial manpower and incurs high production costs, particularly on a large scale. Replacing manual labor with robots can lead to cost reduction. However, a critical challenge is the accurate multi-target identification of tomato plants and precise positioning for axillary bud removal. Therefore, this paper proposes a multi-target identification and localization method for

APA, Harvard, Vancouver, ISO, and other styles

15

Jahan, Israt, K. M. Aslam Uddin, Saydul Akbar Murad, et al. "4D: A Real-Time Driver Drowsiness Detector Using Deep Learning." Electronics 12, no. 1 (2023): 235. http://dx.doi.org/10.3390/electronics12010235.

Full text

Abstract:

There are a variety of potential uses for the classification of eye conditions, including tiredness detection, psychological condition evaluation, etc. Because of its significance, many studies utilizing typical neural network algorithms have already been published in the literature, with good results. Convolutional neural networks (CNNs) are employed in real-time applications to achieve two goals: high accuracy and speed. However, identifying drowsiness at an early stage significantly improves the chances of being saved from accidents. Drowsiness detection can be automated by using the potent

APA, Harvard, Vancouver, ISO, and other styles

16

Gul, Zeki, and Sebnem Bora. "Exploiting Pre-Trained Convolutional Neural Networks for the Detection of Nutrient Deficiencies in Hydroponic Basil." Sensors 23, no. 12 (2023): 5407. http://dx.doi.org/10.3390/s23125407.

Full text

Abstract:

Due to the integration of artificial intelligence with sensors and devices utilized by Internet of Things technology, the interest in automation systems has increased. One of the common features of both agriculture and artificial intelligence is recommendation systems that increase yield by identifying nutrient deficiencies in plants, consuming resources correctly, reducing damage to the environment and preventing economic losses. The biggest shortcomings in these studies are the scarcity of data and the lack of diversity. This experiment aimed to identify nutrient deficiencies in basil plants

APA, Harvard, Vancouver, ISO, and other styles

17

Twum, Frimpong, Charlyne Carol Eyram Ahiable, Stephen Opoku Oppong, Linda Banning, and Kwabena Owusu-Agyemang. "Employing transfer learning for breast cancer detection using deep learning models." PLOS Digital Health 4, no. 6 (2025): e0000907. https://doi.org/10.1371/journal.pdig.0000907.

Full text

Abstract:

Breast cancer remains a critical global health concern, affecting countless lives worldwide. Early and accurate detection plays a vital role in improving patient outcomes. The challenge lies with the limitations of traditional diagnostic methods in terms of accuracy. This study proposes a novel model based on the four pretrained deep learning models, Mobilenetv2, Inceptionv3, ResNet50, and VGG16, which were also used as feature extractors and fed on multiple supervised learning models using the BUSI dataset. Mobiletnetv2, inceptionv3, ResNet50 and VGG16 achieved an accuracy of 85.6%, 90.8%, 89

APA, Harvard, Vancouver, ISO, and other styles

18

Ko, Hoon, Heewon Chung, Wu Seong Kang, et al. "COVID-19 Pneumonia Diagnosis Using a Simple 2D Deep Learning Framework With a Single Chest CT Image: Model Development and Validation." Journal of Medical Internet Research 22, no. 6 (2020): e19569. http://dx.doi.org/10.2196/19569.

Full text

Abstract:

Background Coronavirus disease (COVID-19) has spread explosively worldwide since the beginning of 2020. According to a multinational consensus statement from the Fleischner Society, computed tomography (CT) is a relevant screening tool due to its higher sensitivity for detecting early pneumonic changes. However, physicians are extremely occupied fighting COVID-19 in this era of worldwide crisis. Thus, it is crucial to accelerate the development of an artificial intelligence (AI) diagnostic tool to support physicians. Objective We aimed to rapidly develop an AI technique to diagnose COVID-19 pn

APA, Harvard, Vancouver, ISO, and other styles

19

Cai, Yijie, Zhe Xu, Quan Wen, et al. "Fault States Diagnosis of Marine Diesel Engine Valve Based on a Modified VGG16 Transfer Learning Method." Mathematical Problems in Engineering 2023 (May 8, 2023): 1–14. http://dx.doi.org/10.1155/2023/1225536.

Full text

Abstract:

The marine diesel engine is an important power machine for ships. Traditional machine learning methods for diesel engine fault diagnosis usually require a large amount of labeled training data, and the diagnosis performance may decline when encounters vibrational and environmental interference. A transfer learning convolutional neural network model based on VGG16 is introduced for diesel engine valve leakage fault diagnosis. The acquired diesel engine cylinder head vibration signal is first converted to time domain, frequency domain, and wavelet decomposition images. Secondly, the VGG16 deep c

APA, Harvard, Vancouver, ISO, and other styles

20

Senthilkumar, Chamirti, Sindhu C, G. Vadivu, and Suresh Neethirajan. "Early Detection of Lumpy Skin Disease in Cattle Using Deep Learning—A Comparative Analysis of Pretrained Models." Veterinary Sciences 11, no. 10 (2024): 510. http://dx.doi.org/10.3390/vetsci11100510.

Full text

Abstract:

Lumpy Skin Disease (LSD) poses a significant threat to agricultural economies, particularly in livestock-dependent countries like India, due to its high transmission rate leading to severe morbidity and mortality among cattle. This underscores the urgent need for early and accurate detection to effectively manage and mitigate outbreaks. Leveraging advancements in computer vision and artificial intelligence, our research develops an automated system for LSD detection in cattle using deep learning techniques. We utilized two publicly available datasets comprising images of healthy cattle and tho

APA, Harvard, Vancouver, ISO, and other styles

21

Sadewa, Bagas Ahmad, and Yuni Yamasari. "Implementasi Deep Transfer Learning untuk Klasifikasi Nominal Uang Kertas Rupiah." Journal of Informatics and Computer Science (JINACS) 5, no. 04 (2024): 543–51. http://dx.doi.org/10.26740/jinacs.v5n04.p543-551.

Full text

Abstract:

Penerapan teknologi otomatisasi dilakukan dalam proses transaksi jual beli menggunakan mesin sebagai perantara. Mesin tersebut bertindak sebagai penjual yang memiliki kemampuan serupa dengan otak manusia, termasuk kemampuan membaca dan mengenali nominal uang dengan cepat dan tepat. Penggunaan teknologi otomatisasi ini menjadi solusi kemudahan dalam kegiatan jual beli. Untuk itu, penelitian ini memfokuskan pada pengenalan nominal mata uang kertas rupiah dengan menerapkan teknologi Deep Transfer Learning - algoritma Convolutional Neural Network (CNN). Lebih jauh, penelitian ini melakukan pemilih

APA, Harvard, Vancouver, ISO, and other styles

22

Irem, Ecem. "Brain tumor classification and detection using a hybrid deep learning model." Global Journal of Computer Sciences: Theory and Research 14, no. 2 (2024): 24–29. https://doi.org/10.18844/gjcs.v14i2.9604.

Full text

Abstract:

Dignosis of brain tumor is an important topic in medical area. It involves a combination of medical imaging techniques, clinical assessments and sometimes molecular analysis. However, classification of brain tumor can be accomplished by deep learning methods easily and accurately. Therefore, automatized medical systems integrated with deep learning are highly demanded nowadays. Deep learning is a subset of machine learning, which itself is a branch of artificial intelligence (AI). It's based on the idea of artificial neural networks which is inspired by the structure and function of the human

APA, Harvard, Vancouver, ISO, and other styles

23

Yang, Cheng-Jung, Wei-Kai Huang, and Keng-Pei Lin. "Three-Dimensional Printing Quality Inspection Based on Transfer Learning with Convolutional Neural Networks." Sensors 23, no. 1 (2023): 491. http://dx.doi.org/10.3390/s23010491.

Full text

Abstract:

Fused deposition modeling (FDM) is a form of additive manufacturing where three-dimensional (3D) models are created by depositing melted thermoplastic polymer filaments in layers. Although FDM is a mature process, defects can occur during printing. Therefore, an image-based quality inspection method for 3D-printed objects of varying geometries was developed in this study. Transfer learning with pretrained models, which were used as feature extractors, was combined with ensemble learning, and the resulting model combinations were used to inspect the quality of FDM-printed objects. Model combina

APA, Harvard, Vancouver, ISO, and other styles

24

Rismiyati, Rismiyati, and Ardytha Luthfiarta. "VGG16 Transfer Learning Architecture for Salak Fruit Quality Classification." Telematika 18, no. 1 (2021): 37. http://dx.doi.org/10.31315/telematika.v18i1.4025.

Full text

Abstract:

Purpose: This study aims to differentiate the quality of salak fruit with machine learning. Salak is classified into two classes, good and bad class.Design/methodology/approach: The algorithm used in this research is transfer learning with the VGG16 architecture. Data set used in this research consist of 370 images of salak, 190 from good class and 180 from bad class. The image is preprocessed by resizing and normalizing pixel value in the image. Preprocessed images is split into 80% training data and 20% testing data. Training data is trained by using pretrained VGG16 model. The parameters th

APA, Harvard, Vancouver, ISO, and other styles

25

Zhang, Baocan, Wennan Wang, Yutian Xiao, et al. "Cross-Subject Seizure Detection in EEGs Using Deep Transfer Learning." Computational and Mathematical Methods in Medicine 2020 (May 8, 2020): 1–8. http://dx.doi.org/10.1155/2020/7902072.

Full text

Abstract:

Electroencephalography (EEG) plays an import role in monitoring the brain activities of patients with epilepsy and has been extensively used to diagnose epilepsy. Clinically reading tens or even hundreds of hours of EEG recordings is very time consuming. Therefore, automatic detection of seizure is of great importance. But the huge diversity of EEG signals belonging to different patients makes the task of seizure detection much challenging, for both human experts and automation methods. We propose three deep transfer convolutional neural networks (CNN) for automatic cross-subject seizure detec

APA, Harvard, Vancouver, ISO, and other styles

26

Sieradzki, Alexander, Jakub Bednarek, Albina Jegorowa, and Jarosław Kurek. "Explainable AI (XAI) Techniques for Convolutional Neural Network-Based Classification of Drilled Holes in Melamine Faced Chipboard." Applied Sciences 14, no. 17 (2024): 7462. http://dx.doi.org/10.3390/app14177462.

Full text

Abstract:

The furniture manufacturing sector faces significant challenges in machining composite materials, where quality issues such as delamination can lead to substandard products. This study aims to improve the classification of drilled holes in melamine-faced chipboard using Explainable AI (XAI) techniques to better understand and interpret Convolutional Neural Network (CNN) models’ decisions. We evaluated three CNN architectures (VGG16, VGG19, and ResNet101) pretrained on the ImageNet dataset and fine-tuned on our dataset of drilled holes. The data consisted of 8526 images, divided into three cate

APA, Harvard, Vancouver, ISO, and other styles

27

Kirtida Naik, Bindu Garg. "Comparative Study and Detection of Lymphoma through Medical Imaging and modified ResNet and VGG Models." Journal of Information Systems Engineering and Management 10, no. 13s (2025): 544–53. https://doi.org/10.52783/jisem.v10i13s.2110.

Full text

Abstract:

Diagnosis and treatment of lymphoma, an aggressive neoplasm of the lymphatic system, becomes extremely difficult when the patients present with various sub-types of lymphoma because of their peculiar heterogeneity in clinical presentation. The most probable chances of successful management or curing patients depend on early and accurate diagnosis. Most of the traditional tools they used like; biopsy and histopathological examination are invasive, long duration test procedures. This will reduce the time of diagnosis of lymphoma by using the combination of convolutional neural networks (CNN)-par

APA, Harvard, Vancouver, ISO, and other styles

28

Staines J1, Noble, and Justin Moses Selvamony2. "CHARACTERISATION OF CRACKS IN THE BUILDING USING DEEP LEARNING TECHNIQUE." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–8. https://doi.org/10.55041/ijsrem.icites009.

Full text

Abstract:

Detecting and characterizing cracks in buildings using deep learning techniques is a crucial research area. Cracks in buildings could lead to catastrophic structural failures, which can be hazardous to human life and property. Deep learning techniques can aid in addressing this problem by enabling the detection and classification of different types of cracks with high accuracy. This study investigates the effect of image pre-processing on the performance of DL crack detection using a data set of 5000 images. The results showed that using a pretrained model with RGB weights does not affect the

APA, Harvard, Vancouver, ISO, and other styles

29

Sobti, Priyal, Anand Nayyar, Niharika, and Preeti Nagrath. "EnsemV3X: a novel ensembled deep learning architecture for multi-label scene classification." PeerJ Computer Science 7 (May 25, 2021): e557. http://dx.doi.org/10.7717/peerj-cs.557.

Full text

Abstract:

Convolutional neural network is widely used to perform the task of image classification, including pretraining, followed by fine-tuning whereby features are adapted to perform the target task, on ImageNet. ImageNet is a large database consisting of 15 million images belonging to 22,000 categories. Images collected from the Web are labeled using Amazon Mechanical Turk crowd-sourcing tool by human labelers. ImageNet is useful for transfer learning because of the sheer volume of its dataset and the number of object classes available. Transfer learning using pretrained models is useful because it

APA, Harvard, Vancouver, ISO, and other styles

30

Wiriyasirivaj, Budsaba, Sawit Limkiatsataporn, Apisit Pukinghin, et al. "DEEP LEARNING DEVELOPMENTAL MODEL FOR EMBRYO GRADING IN THAI POPULATION." Suranaree Journal of Science and Technology 31, no. 4 (2024): 010322(1–12). http://dx.doi.org/10.55766/sujst-2024-04-e05589.

Full text

Abstract:

In light of the growing challenges associated with infertility, an increasing number of researchers are resorting to assisted reproductive technologies such as In Vitro Fertilization (IVF). Embryo grading is a crucial step in the IVF process that requires embryologists’ expertise. However, their limited availability has led to the exploration of technological alternatives. This study aims to use deep learning for human embryo grading, with models specifically designed for the dataset in Thailand at Vajira Hospital. The process of IVF at Vajira Hospital presents its own set of challenges since

APA, Harvard, Vancouver, ISO, and other styles

31

Abdul Majeed, Anwar P. P., Muhammad Amirul Abdullah, Ahmad Fakhri Ab. Nasir, Mohd Azraai Mohd Razman, Wei Chen, and Eng Hwa Yap. "Surface Defect Detection: A feature-based transfer learning approach." Journal of Physics: Conference Series 2762, no. 1 (2024): 012088. http://dx.doi.org/10.1088/1742-6596/2762/1/012088.

Full text

Abstract:

Abstract Surface defect detection is critical for maintaining product quality in manufacturing. In this work, we apply a feature-based transfer learning approach for surface defect classification on the NEU surface defect database. The database contains defects across 6 categories captured under various conditions. We utilised two pretrained convolutional neural network (CNN) architectures - VGG16 and InceptionV3 - by removing the final classification layer and using the CNN as a fixed feature extractor. The output feature vectors were classified using a logistic regression (LR) model. The dat

APA, Harvard, Vancouver, ISO, and other styles

32

Wang, Bowei. "Convolutional Neural Network Based on Brain Tumor Identification and Classification." Highlights in Science, Engineering and Technology 16 (November 10, 2022): 453–60. http://dx.doi.org/10.54097/hset.v16i.2611.

Full text

Abstract:

The human brain is one of the body's most major organs. If there are problems within the human brain, they may cause serious consequences, and even endangers the human life. One of the most fatal diseases for humans is a brain tumor. In the old days, tumor detection was done manually by doctors through reading magnetic resonance images, which might not be time efficient, and sometimes may even produce inaccurate results. Nowadays, with the development of science and technology, Artificial Intelligence (AI) is present in many fields in human life, including medical field. Tumor detection with A

APA, Harvard, Vancouver, ISO, and other styles

33

Wang, Jia-Jung, Alok Kumar Sharma, Shing-Hong Liu, Hangliang Zhang, Wenxi Chen, and Thung-Lip Lee. "Prediction of Vascular Access Stenosis by Lightweight Convolutional Neural Network Using Blood Flow Sound Signals." Sensors 24, no. 18 (2024): 5922. http://dx.doi.org/10.3390/s24185922.

Full text

Abstract:

This research examines the application of non-invasive acoustic analysis for detecting obstructions in vascular access (fistulas) used by kidney dialysis patients. Obstructions in these fistulas can interrupt essential dialysis treatment. In this study, we utilized a condenser microphone to capture the blood flow sounds before and after angioplasty surgery, analyzing 3819 sound samples from 119 dialysis patients. These sound signals were transformed into spectrogram images to classify obstructed and unobstructed vascular accesses, that is fistula conditions before and after the angioplasty pro

APA, Harvard, Vancouver, ISO, and other styles

34

R, Rohan, Vishnu Prakash R, Shibin K T, Akshay K, and Akhila E. "Underwater Image Restoration and Object Detection." Journal of Innovative Image Processing 6, no. 1 (2024): 74–83. http://dx.doi.org/10.36548/jiip.2024.1.007.

Full text

Abstract:

Underwater environments present unique challenges for imaging due to factors such as light attenuation, scattering, and colour distortion. This research combines advanced CNN models like CBAM(convolutional Block Attention Mod-ule) and VGG16 with state-of-the-art object detection methods of CNN like YOLO or RCNN to enhance the visual quality of underwater images and to detect the objects based on an accuracy rate. Leveraging the various capabilities of the VGG16 model, pretrained on extensive datasets, the system efficiently restores degraded underwater images by capturing and learning intricat

APA, Harvard, Vancouver, ISO, and other styles

35

Ahmeed, Marwah, and Othman Khalifa. "Improving Crowd Counting Performance: A Convolutional Neural Network Approach with Transfer Learning." Asian Journal of Electrical and Electronic Engineering 4, no. 2 (2024): 26–34. http://dx.doi.org/10.69955/ajoeee.24.v4i2.63.

Full text

Abstract:

Precise crowd counting is critical to public safety and smart city planning since it solves the problems associated with the time-consuming manual counting of people in photos and videos. Transfer learning has become a key building block for improving crowd counting techniques, especially when used to Convolutional Neural Networks (CNNs). Because pretrained models already know the pertinent weights and architecture, using them in transfer learning minimizes computational demands and shortens training time. This paper presents a crowd counting method with an emphasis on optimizing the VGG16 mod

APA, Harvard, Vancouver, ISO, and other styles

36

Pelluri Venkata Naga Balarama Murthy, Et al. "Inception-VR70: An Advanced Inception-Net Artificial Intelligence based Novel Hybrid Architecture." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 3295–300. http://dx.doi.org/10.17762/ijritcc.v11i9.9530.

Full text

Abstract:

In the present day, Convolutional Neural Network (CNN) architectures are undergoing a great deal of development, which has resulted in the creation of models like VGG16, ResNet50, InceptionV3 etc., that significantly increase accuracy. Yet, a network that can deal with overfitting, a significant challenge in deep learning besides having greater accuracy and extracting useful features is required. In this paper, we propose a Deep hybrid model which is an inception of pretrained models with a different input image size, significantly leading to improved accuracy which has been tested on various

APA, Harvard, Vancouver, ISO, and other styles

37

Bella Dwi Mardiana, Wahyu Budi Utomo, Ulfah Nur Oktaviana, Galih Wasis Wicaksono, and Agus Eko Minarno. "Herbal Leaves Classification Based on Leaf Image Using CNN Architecture Model VGG16." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 7, no. 1 (2023): 20–26. http://dx.doi.org/10.29207/resti.v7i1.4550.

Full text

Abstract:

Herbal leaves are a type that is often used by people in the health sector. The problem faced is the lack of knowledge about the types of herbal leaves and the difficulty of distinguishing the types of herbal leaves for ordinary people who do not understand plants. If any type of plant is used, it will have a negative impact on health. Automatic classification with the help of technology will reduce the risk of misidentification of herbal leaf types. To make identification, a precise and accurate herbal leaf detection process is needed. This research aims to facilitate the classification model

APA, Harvard, Vancouver, ISO, and other styles

38

Ahmed, Samit Hatem, Sabri Altememe Maha, and Abdulraheem Fadhel Mohammed. "Identifying corn leaves diseases by extensive use of transfer learning: a comparative study." Identifying corn leaves diseases by extensive use of transfer learning: a comparative study 29, no. 2 (2023): 1030–38. https://doi.org/10.11591/ijeecs.v29.i2.pp1030-1038.

Full text

Abstract:

Deep learning is currently playing an important role in image analysis and classification. Diseases in maize diminish productivity, which is a major cause of economic damages in the agricultural business throughout the world. Researchers have previously utilized hand-crafted characteristics to classify images and identify leaf illnesses in Maize plants. With the advancement of deep learning, researchers can now significantly enhance the accuracy of object classification and identification. Using the "Corn or Maize Leaf Disease Dataset" from the Kaggle website, four forms of maize lea

APA, Harvard, Vancouver, ISO, and other styles

39

Abubakar, Aliyu. "Comparative Analysis of Classification Algorithms Using CNN Transferable Features: A Case Study Using Burn Datasets from Black Africans." Applied System Innovation 3, no. 4 (2020): 43. http://dx.doi.org/10.3390/asi3040043.

Full text

Abstract:

Burn is a devastating injury affecting over eleven million people worldwide and more than 265,000 affected individuals lost their lives every year. Low- and middle-income countries (LMICs) have surging cases of more than 90% of the total global incidences due to poor socioeconomic conditions, lack of preventive measures, reliance on subjective and inaccurate assessment techniques and lack of access to nearby hospitals. These factors necessitate the need for a better objective and cost-effective assessment technique that can be easily deployed in remote areas and hospitals where expertise and r

APA, Harvard, Vancouver, ISO, and other styles

40

Shchetinin, Eugeny Yu. "ON METHODS OF THE BRAIN TUMOR RECOGNITION WITH DEEP LEARNING METHODS." SOFT MEASUREMENTS AND COMPUTING 8, no. 57 (2022): 77–86. http://dx.doi.org/10.36871/2618-9976.2022.08.008.

Full text

Abstract:

In this paper computer studies of the effectiveness of using transfer learning methods to solve the problem of recognizing human brain tumor based on MRI images were performed. The deep convolutional networks VGG16, ResNet50, InceptionV3, Xception, DenseNet121 and MobileNet_v2 were used as the basic pretrained models. Various training and finetuning strategies for deep convolutional networks for recognizing brain tumor are proposed. An analysis of their performance showed that the strategy of finetuning of the Xception model on an extended MRIscans data set yielded higher accuracy, precision,

APA, Harvard, Vancouver, ISO, and other styles

41

Sokipriala, Jonah. "Prediction of Steering Angle for Autonomous Vehicles Using Pre-Trained Neural Network." European Journal of Engineering and Technology Research 6, no. 5 (2021): 171–76. http://dx.doi.org/10.24018/ejers.2021.6.5.2537.

Full text

Abstract:

Autonomous driving is one promising research area that would not only revolutionize the transportation industry but would as well save thousands of lives. accurate correct Steering angle prediction plays a crucial role in the development of the autonomous vehicle .This research attempts to design a model that would be able to clone a drivers behavior using transfer learning from pretrained VGG16, the results showed that the model was able to use less training parameters and achieved a low mean squared error(MSE) of less than 2% without overfitting to the training set hence was able to drive on

APA, Harvard, Vancouver, ISO, and other styles

42

Sokipriala, Jonah. "Prediction of Steering Angle for Autonomous Vehicles Using Pre-Trained Neural Network." European Journal of Engineering and Technology Research 6, no. 5 (2021): 171–76. http://dx.doi.org/10.24018/ejeng.2021.6.5.2537.

Full text

Abstract:

Autonomous driving is one promising research area that would not only revolutionize the transportation industry but would as well save thousands of lives. accurate correct Steering angle prediction plays a crucial role in the development of the autonomous vehicle .This research attempts to design a model that would be able to clone a drivers behavior using transfer learning from pretrained VGG16, the results showed that the model was able to use less training parameters and achieved a low mean squared error(MSE) of less than 2% without overfitting to the training set hence was able to drive on

APA, Harvard, Vancouver, ISO, and other styles

43

Turki, Amina, Omar Kahouli, Saleh Albadran, Mohamed Ksantini, Ali Aloui, and Mouldi Ben Amara. "A sophisticated Drowsiness Detection System via Deep Transfer Learning for real time scenarios." AIMS Mathematics 9, no. 2 (2024): 3211–34. http://dx.doi.org/10.3934/math.2024156.

Full text

Abstract:

<abstract> <p>Driver drowsiness is one of the leading causes of road accidents resulting in serious physical injuries, fatalities, and substantial economic losses. A sophisticated Driver Drowsiness Detection (DDD) system can alert the driver in case of abnormal behavior and avoid catastrophes. Several studies have already addressed driver drowsiness through behavioral measures and facial features. In this paper, we propose a hybrid real-time DDD system based on the Eyes Closure Ratio and Mouth Opening Ratio using simple camera and deep learning techniques. This system seeks to mode

APA, Harvard, Vancouver, ISO, and other styles

44

Suthaharan, Shan. "A Nature-Inspired Colony of Artificial Intelligence System with Fast, Detailed, and Organized Learner Agents for Enhancing Diversity and Quality." Proceedings of the AAAI Symposium Series 5, no. 1 (2025): 431–38. https://doi.org/10.1609/aaaiss.v5i1.35625.

Full text

Abstract:

The concepts of convolutional neural networks (CNNs) and multi-agent systems are two important areas of research in artificial intelligence (AI). In this paper, we present an approach that builds a CNN-based colony of AI agents to serve as a single system and perform multiple tasks (e.g., predictions or classifications) in an environment. The proposed system impersonates the natural environment of a biological system, like an ant colony or a human colony. The proposed colony of AI that is defined as a role-based system uniquely contributes to accomplish tasks in an environment by incorporating

APA, Harvard, Vancouver, ISO, and other styles

45

Liu, Xiongfei, Bengao Li, Xin Chen, Haiyan Zhang, and Shu Zhan. "Content-Based Attention Network for Person Image Generation." Journal of Circuits, Systems and Computers 29, no. 15 (2020): 2050250. http://dx.doi.org/10.1142/s0218126620502503.

Full text

Abstract:

This paper proposes a novel method for person image generation with arbitrary target pose. Given a person image and an arbitrary target pose, our proposed model can synthesize images with the same person but different poses. The Generative Adversarial Networks (GANs) are the major part of the proposed model. Different from the traditional GANs, we add attention mechanism to the generator in order to generate realistic-looking images, we also use content reconstruction with a pretrained VGG16 Net to keep the content consistency between generated images and target images. Furthermore, we test ou

APA, Harvard, Vancouver, ISO, and other styles

46

Hassan, Ahmed, Tehreem Masood, Hassan A. Ahmed, H. M. Shahzad, and Hafiz Muhammad Tayyab Khushi. "Benchmarking Pretrained Models for Speech Emotion Recognition: A Focus on Xception." Computers 13, no. 12 (2024): 315. http://dx.doi.org/10.3390/computers13120315.

Full text

Abstract:

Speech emotion recognition (SER) is an emerging technology that utilizes speech sounds to identify a speaker’s emotional state. Computational intelligence is receiving increasing attention from academics, health, and social media applications. This research was conducted to identify emotional states in verbal communication. We applied a publicly available dataset called RAVDEES. The data augmentation process involved adding noise, applying time stretching, shifting, and pitch, and extracting the features zero cross rate (ZCR), chroma shift, Mel-Frequency Cepstral Coefficients (MFCC), and a spe

APA, Harvard, Vancouver, ISO, and other styles

47

AlSayyed, Ayat, Abdullah Mahmoud Taqateq, Rizik Al-Sayyed, et al. "Employing CNN ensemble models in classifying dental caries using oral photographs." International Journal of Data and Network Science 7, no. 4 (2023): 1535–50. http://dx.doi.org/10.5267/j.ijdns.2023.8.009.

Full text

Abstract:

Dental caries is arguably the most persistent dental condition that affects most people over their lives. Carious lesions are commonly diagnosed by dentists using clinical and visual examination along with oral radiographs. In many circumstances, dental caries is challenging to detect with photography and might be mistaken as shadows for various reasons, including poor photo quality. However, with the introduction of Artificial Intelligence and robotic systems in dentistry, photographs can be a helpful tool in oral epidemiological research for the assessment of dental caries prevalence among t

APA, Harvard, Vancouver, ISO, and other styles

48

Devi, Bali, Sumit Srivastava, Vivek Kumar Verma, and Gaurav Aggarwal. "Transfer learning based novel intelligent classification for Alzheimer’s Dementia using duplex convolutional neural network." Journal of Discrete Mathematical Sciences and Cryptography 26, no. 5 (2023): 1367–79. http://dx.doi.org/10.47974/jdmsc-1758.

Full text

Abstract:

Goal: This work focuses on neuroimaging studies, including MRI analysis via machine learning and deep learning, which has led to an increase in computer vision research and the early detection of neural disorders. Methods: An adaptive implementation of transfer learning (TL) with a top-level VGG16 architecture is set up with pretrained weights for large MRI images dataset. Convolutional neural networks (CNN) are a bespoke version of a multi-layer view. Through experimentation on the ADNI dataset, the algorithm was trained and tested in binary and multiclass classification using the MRI scannin

APA, Harvard, Vancouver, ISO, and other styles

49

Oki, T., and Y. Ogawa. "A METHOD FOR REGIONAL ANALYSIS USING DEEP LEARNING BASED ON BIG DATA OF OMNIDIRECTIONAL IMAGES OF STREETS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2022 (June 2, 2022): 545–52. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2022-545-2022.

Full text

Abstract:

Abstract. In this paper, we propose a method for regional analysis using image recognition technology based on deep learning and big data of street images captured by omnidirectional cameras on vehicles. Specifically, we first construct a classification method of regions based on street images using a pretrained deep learning model (VGG16) for image recognition as a feature extractor. Next, we develop a method to evaluate the landscape and safety of streets based on the ratio of street components (such as buildings, roads, fences, vegetations, sky, street lights) at each shooting point, which

APA, Harvard, Vancouver, ISO, and other styles

50

Ishrak, Md Fatin, Md Maruf Rahman, Md Imran Kabir Joy, et al. "Vision Transformer Embedded Feature Fusion Model with Pre-Trained Transformers for Keratoconus Disease Classification." Emerging Science Journal 9, no. 2 (2025): 1037–75. https://doi.org/10.28991/esj-2025-09-02-027.

Full text

Abstract:

Keratoconus is a progressive eye disorder that, if undetected, can lead to severe visual impairment or blindness, necessitating early and accurate diagnosis. The primary objective of this research is to develop a feature fusion hybrid deep learning framework that integrates pretrained Convolutional Neural Networks (CNNs) with Vision Transformers (ViTs) for the automated classification of keratoconus into three distinct categories: Keratoconus, Normal, and Suspect. The dataset employed in this study is sourced from a widely recognized and publicly available online repository. Prior to model dev

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!