Log in

Relevant bibliographies by topics / Efficientnetv2s / Journal articles

To see the other types of publications on this topic, follow the link: Efficientnetv2s.

Journal articles on the topic 'Efficientnetv2s'

Author: Grafiati

Published: 7 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 'Efficientnetv2s.'

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

Abioye, Oluwasegun A., Abraham E. Evwiekpaefe, and Awujoola J. Awujoola. "PERFORMANCE EVALUATION OF EFFICIENTNETV2 MODELS ON THE CLASSIFICATION OF HISTOPATHOLOGICAL BENIGN BREAST CANCER IMAGES." Science Journal of University of Zakho 12, no. 2 (2024): 208–14. http://dx.doi.org/10.25271/sjuoz.2024.12.2.1261.

Full text

Abstract:

In the field of breast cancer diagnosis, the precise classification of benign images plays a pivotal role in ensuring effective patient care. This research undertakes a detailed examination of EfficientNetV2 models, specifically focusing on their ability to discern benign histopathology breast cancer images. The dataset were carefully curated to include diverse benign cases such as adenosis, fibroadenoma, phyllodes_tumor, and tubular_adenoma of image level for 40X magnification factor underwent thorough preprocessing before being divided into training and testing sets. Various variants of the

APA, Harvard, Vancouver, ISO, and other styles

2

Manolescu, Denis, Neil Buckley, and Emanuele Lindo Secco. "Machine Learning Models for Probability Classification in Spectrographic EEG Seizures Dataset." WSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE 21 (September 23, 2024): 260–71. http://dx.doi.org/10.37394/23208.2024.21.27.

Full text

Abstract:

The examination of brain signals, namely the Electroencephalogram (EEG) signals, is an approach to possibly detect seizures of the brain. Due to the nature of these signals, deep learning techniques have offered the opportunity to perform automatic or semi-automatic analysis which could support decision and therapeutical approaches. This paper focuses on the possibility of classifying EEG seizure using convolutional layers (namely EfficientNetV2 architectures, i.e., EfficientNetV2S and EfficientNetV2B2), Long Short-Term Memory (LSTM) units, and fine-tuned mechanisms of attention. We use these

APA, Harvard, Vancouver, ISO, and other styles

3

Yan, Xingchen. "Galaxy recognition based on improved efficientNetV2S." Applied and Computational Engineering 21, no. 1 (2023): 134–42. http://dx.doi.org/10.54254/2755-2721/21/20231132.

Full text

Abstract:

In order to identify effective metrics that can accurately duplicate the probability distributions resulting from human classifications, this paper analyzes an improved approach for galaxy morphologies classification. At the present stage, this field still faces the problem of insufficient quality and quantity of image data, low accuracy of computer recognition and weak generalization ability of the model. From the previous research, Convolution Neural Network (CNN) can be a valid technique to complete this task but usually spends a large time and space complexity. For the purpose of increasin

APA, Harvard, Vancouver, ISO, and other styles

4

Purnomo, Juanda Gilang, Sigit Birowo, and Muhammad Akbar Maulana. "Identifikasi Malaria Pada Citra Darah Dengan Convolutional Neural Network." bit-Tech 7, no. 2 (2024): 406–12. https://doi.org/10.32877/bt.v7i2.1828.

Full text

Abstract:

Penelitian ini membahas penerapan model pretrained EfficientNetV2S dan ConvNeXtBase untuk mendeteksi keberadaan parasit malaria dalam citra darah mikroskopis. Dataset yang digunakan terdiri dari 27.588 citra darah manusia, yang terbagi menjadi dua kategori, yaitu Parasitized dan Uninfected. Model dilatih menggunakan dua pengaturan learning rate: minimum (0.0001) dan maksimum (0.001), untuk membandingkan performa dan stabilitasnya dalam proses klasifikasi. Hasil penelitian menunjukkan bahwa model EfficientNetV2S dengan learning rate minimum mencapai akurasi validasi tertinggi sebesar 96,58%, me

APA, Harvard, Vancouver, ISO, and other styles

5

Nurhopipah, Ade, Jali Suhaman, and Anan Widianto. "Exploring Pre-Trained Model and Language Model for Translating Image to Bahasa." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 17, no. 4 (2023): 347. http://dx.doi.org/10.22146/ijccs.76389.

Full text

Abstract:

In the last decade, there have been significant developments in Image Caption Generation research to translate images into English descriptions. This task has also been conducted to produce texts in non-English, including Bahasa. However, the references in this study are still limited, so exploration opportunities are open widely. This paper presents comparative research by examining several state-of-the-art Deep Learning algorithms to extract images and generate their descriptions in Bahasa. We extracted images using three pre-trained models, namely InceptionV3, Xception, and EfficientNetV2S.

APA, Harvard, Vancouver, ISO, and other styles

6

Efendi, Ilham Julian, Jabir Muktabir, Khaerunni Salsa Billah, Muhammad Vannes Al Qadri, Wa Ode Asriyani, and Amar Adi Ismoyo. "DIAGNOCAR: PENDETEKSIAN OTOMATIS KERUSAKAN MOBIL MENGGUNAKAN DEEP LEARNING BERBASIS CITRA LEWAT PERANGKAT MOBILE." Simtek : jurnal sistem informasi dan teknik komputer 10, no. 1 (2025): 199–205. https://doi.org/10.51876/simtek.v10i1.1547.

Full text

Abstract:

Penelitian ini bertujuan mengembangkan sistem klasifikasi otomatis berbasis arsitektur Convolutional Neural Network untuk mendeteksi jenis kerusakan pada mobil melalui data citra. Sistem ini diharapkan dapat mempercepat dan meningkatkan akurasi proses penilaian kerusakan pada klaim asuransi dan perbaikan kendaraan. Lima arsitektur telah diimplementasikan, yaitu MobileNetV2, EfficientNetV2S, NASNetMobile, ResNet50, dan model konvolusional yang dirancang sendiri, dengan menggunakan dataset berjumlah 1.594 citra yang terbagi dalam enam kelas kerusakan. Proses pelatihan mencakup praproses data, au

APA, Harvard, Vancouver, ISO, and other styles

7

Grd, Petra, Igor Tomičić, and Ena Barčić. "Transfer Learning with EfficientNetV2S for Automatic Face Shape Classification." JUCS - Journal of Universal Computer Science 30, no. 2 (2024): 153–78. http://dx.doi.org/10.3897/jucs.104490.

Full text

Abstract:

The classification of human face shapes, a pivotal aspect of one&rsquo;s appearance, plays a crucial role in diverse fields like beauty, cosmetics, healthcare, and security. In this paper, we present a multi-step methodology for face shape classification, harnessing the potential of transfer learning and a pretrained EfficientNetV2S neural network. Our approach comprises key phases, including preprocessing, augmentation, training, and testing, ensuring a comprehensive and reliable solution. The preprocessing step involves precise face detection, cropping, and image scaling, laying a solid

APA, Harvard, Vancouver, ISO, and other styles

8

Grd, Petra, Igor Tomičić, and Ena Barčić. "Transfer Learning with EfficientNetV2S for Automatic Face Shape Classification." JUCS - Journal of Universal Computer Science 30, no. (2) (2024): 153–78. https://doi.org/10.3897/jucs.104490.

Full text

Abstract:

The classification of human face shapes, a pivotal aspect of one's appearance, plays a crucial role in diverse fields like beauty, cosmetics, healthcare, and security. In this paper, we present a multi-step methodology for face shape classification, harnessing the potential of transfer learning and a pretrained EfficientNetV2S neural network. Our approach comprises key phases, including preprocessing, augmentation, training, and testing, ensuring a comprehensive and reliable solution. The preprocessing step involves precise face detection, cropping, and image scaling, laying a solid foundation

APA, Harvard, Vancouver, ISO, and other styles

9

Shehzad, Khurram, Tan Zhenhua, Shifa Shoukat, et al. "A Deep-Ensemble-Learning-Based Approach for Skin Cancer Diagnosis." Electronics 12, no. 6 (2023): 1342. http://dx.doi.org/10.3390/electronics12061342.

Full text

Abstract:

Skin cancer is one of the widespread diseases among existing cancer types. More importantly, the detection of lesions in early diagnosis has tremendously attracted researchers’ attention. Thus, artificial intelligence (AI)-based techniques have supported the early diagnosis of skin cancer by investigating deep-learning-based convolutional neural networks (CNN). However, the current methods remain challenging in detecting melanoma in dermoscopic images. Therefore, in this paper, we propose an ensemble model that uses the vision of both EfficientNetV2S and Swin-Transformer models to detect the e

APA, Harvard, Vancouver, ISO, and other styles

10

Liana, Verianti, Rizal Arifiandika, Bagas Rohmatulloh, et al. "Enzyme dosage detection to degrade feathers in edible bird’s nests: A comparative convolutional neural networks study." Advances in Food Science, Sustainable Agriculture and Agroindustrial Engineering 6, no. 4 (2023): 382–98. http://dx.doi.org/10.21776/ub.afssaae.2023.006.04.6.

Full text

Abstract:

Edible Bird’s Nest (EBN), a costly food product made from swiftlet’s saliva, has encountered a longstanding problem of plucking the swiftlet’s feather from the nests. The destructive and inefficient manual process of plucking the feathers can be substituted with a serine protease enzyme alternative. Accurate detection of enzyme dosage is crucial for ensuring efficient feather degradation with cost-effective enzyme usage. This research employed the transfer learning method using pretrained Convolutional Neural Networks (Pt-CNN) to detect enzyme dosage based on EBN’s images. This study aimed to

APA, Harvard, Vancouver, ISO, and other styles

11

Saeed, Adnan, Shifa Shoukat, Khurram Shehzad, et al. "A Deep Learning-Based Approach for the Diagnosis of Acute Lymphoblastic Leukemia." Electronics 11, no. 19 (2022): 3168. http://dx.doi.org/10.3390/electronics11193168.

Full text

Abstract:

Leukemia is a deadly disease caused by the overproduction of immature white blood cells (WBS) in the bone marrow. If leukemia is detected at the initial stages, the chances of recovery are better. Typically, morphological analysis for the identification of acute lymphoblastic leukemia (ALL) is performed manually on blood cells by skilled medical personnel, which has several disadvantages, including a lack of medical personnel, sluggish analysis, and prediction that is dependent on the medical personnel’s expertise. Therefore, we proposed the Multi-Attention EfficientNetV2S and EfficientNetB3 s

APA, Harvard, Vancouver, ISO, and other styles

12

Researcher. "EVALUATION OF DEEP LEARNING FOR THE DIAGNOSIS OF LEUKEMIA BLOOD CANCER." International Journal of Advanced Research in Engineering and Technology (IJARET) 11, no. 3 (2024): 661–72. https://doi.org/10.5281/zenodo.13836317.

Full text

Abstract:

One of the crucial challenges in disease diagnostics is the timely identification of leukemia, primarily the distinction between various malignant leukocytes in an initial stage of an illness. The DL model, which is used to diagnose leukemia, a blood malignancy, is the main focus of this work. This specific research aims to evaluate the feasibility of deep learning approach to that of Leukemia detection from blood smear images used in the C-NMC 2019 dataset. For the next stage, the C-NMC 2019 dataset that was collected from Kaggle were pre-processed. It also involved uses like transforms in th

APA, Harvard, Vancouver, ISO, and other styles

13

Alberry, Hesham A., M. E. Khalifa, and Ahmed Taha. "Abnormal Behavior Detection in Surveillance Systems Using a Hybrid EfficientNet-Transformer Model." Statistics, Optimization & Information Computing 13, no. 4 (2025): 1610–22. https://doi.org/10.19139/soic-2310-5070-2259.

Full text

Abstract:

Anomaly detection in video surveillance is vital for public safety, but challenges arise from the unpredictability of abnormal behaviors and large-scale systems. We propose a hybrid architecture combining EfficientNetV2S for efficient feature extraction with a transformer encoder to capture long-range dependencies through self-attention. This model robustly detects abnormal events by modeling local and global patterns in video frames. Evaluated on UCSD Ped1, UCSD Ped2, and Avenue datasets, our approach achieved accuracies of 99.51, 99.80, and 94.82, outperforming existing methods and proving t

APA, Harvard, Vancouver, ISO, and other styles

14

Suleiman, Dima, Ruba Obiedat, Rizik Al-Sayyed, Shadi Saleh, Wolfram Hardt, and Yazan Al-Zain. "Employing CNN mobileNetV2 and ensemble models in classifying drones forest fire detection images." International Journal of Data and Network Science 9, no. 2 (2025): 297–316. https://doi.org/10.5267/j.ijdns.2024.10.004.

Full text

Abstract:

In recent years, the adoption of advanced machine learning techniques has revolutionized approaches to solving complex problems, such as identifying occurrences of forest fires. Among these techniques, the use of Convolutional Neural Networks (CNNs) combined with ensemble methods is particularly promising. To investigate the feasibility of detecting fires using video streams from Unmanned Aerial Vehicles (UAVs), the lightweight CNN architecture MobileNetV2 was utilized for real-time detection. Several experiments were conducted on the DeepFire dataset, which comprises an equal number of images

APA, Harvard, Vancouver, ISO, and other styles

15

Yurni Oktarina, Zainuddin Nawawi, Bhakti Yudho Suprapto, and Tresna Dewi. "TOWARDS ECOLOGICAL SUSTAINABILITY: HARVEST PREDICTION IN AGRIVOLTAIC CHILI FARMING WITH CNN TRANSFER LEARNING." IRAQI JOURNAL OF AGRICULTURAL SCIENCES 55, no. 6 (2024): 1910–26. https://doi.org/10.36103/5gdhkh84.

Full text

Abstract:

Agrivoltaic systems, which integrate agricultural production with solar energy generation, present a promising approach to ecological sustainability. This study focuses on predicting chili harvests within an agrivoltaic setup using Convolutional Neural Networks (CNN) with transfer learning. Accurate yield prediction is vital for optimizing both agricultural output and energy generation. The study evaluates three pre-trained CNN models—EfficientNetV2L, EfficientNetV2M, and ResNet 50—fine-tuned with specific agrivoltaic data. The experimental setup includes a solar-powered greenhouse with IoT-co

APA, Harvard, Vancouver, ISO, and other styles

16

Oh, Je-Seok, Su-Hyeon Jeong, and Doo-Hyun Choi. "Gender Classification in Korean Handwriting Using EfficientNetV2S: Performance Analysis Across Word Length and Age Groups." Asia-pacific Journal of Convergent Research Interchange 10, no. 11 (2024): 501–12. http://dx.doi.org/10.47116/apjcri.2024.11.37.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

Hui, Wuyu, Zheng Robert Jia, Hansheng Li, and Zijian Wang. "Galaxy Morphology Classification with DenseNet." Journal of Physics: Conference Series 2402, no. 1 (2022): 012009. http://dx.doi.org/10.1088/1742-6596/2402/1/012009.

Full text

Abstract:

Abstract Galaxy classification is crucial in astronomy, as galaxy types reveal information on how the galaxy was formed and evolved. While manually conducting the classification task requires extensive background knowledge and is time-consuming, deep learning algorithms provide a time-efficient and expedient way of accomplishing this task. Hence, this paper utilizes transfer learning from pre-trained CNN models and compares their performances on the Galaxy10 DECals Dataset. This paper applies opening operation, data augmentation, class weights, and learning rate decay to further improve the mo

APA, Harvard, Vancouver, ISO, and other styles

18

Ren, Guanyu. "Monkeypox Disease Detection with Pretrained Deep Learning Models." Information Technology and Control 52, no. 2 (2023): 288–96. http://dx.doi.org/10.5755/j01.itc.52.2.32803.

Full text

Abstract:

Monkeypox has been recognized as the next global pandemic after COVID-19 and its potential damage cannot be neglected. Computer vision-based diagnosis and detection method with deep learning models have been proven effective during the COVID-19 period. However, with limited samples, the deep learning models are difficult to be full trained. In this paper, twelve CNN-based models, including VGG16, VGG19, ResNet152, DenseNet121, DenseNet201, EfficientNetB7, EfficientNetV2B3, EfficientNetV2M and InceptionV3, are used for monkeypox detection with limited skin pictures. Numerical results suggest th

APA, Harvard, Vancouver, ISO, and other styles

19

Akhyar, Fityanul, Ledya Novamizanti, Inung Wijayanto, et al. "Fish grades identification system with ensemble-based key feature learning." ITM Web of Conferences 67 (2024): 01034. http://dx.doi.org/10.1051/itmconf/20246701034.

Full text

Abstract:

Indonesia has already contacted the maritime nations due to its 5.8 million km2 of coastline. Consequently, fish products are among the most important commodities. Moreover, fish grading is a crucial step in the process of exporting fisheries products. Currently, in Indonesia, the process itself is manually inspected by an expert. In addition, this paper proposes to assist the industry by suggesting a method for grading fish. This method involves combining two essential fish parts with different resolutions: the high-level feature (the body) and the low-level feature (the eye) serve as definin

APA, Harvard, Vancouver, ISO, and other styles

20

Sun, Jiarui, Xiaokang Liu, Yunfei Huang, et al. "Automatic identification and morphological comparison of bivalve and brachiopod fossils based on deep learning." PeerJ 11 (October 11, 2023): e16200. http://dx.doi.org/10.7717/peerj.16200.

Full text

Abstract:

Fossil identification is an essential and fundamental task for conducting palaeontological research. Because the manual identification of fossils requires extensive experience and is time-consuming, automatic identification methods are proposed. However, these studies are limited to a few or dozens of species, which is hardly adequate for the needs of research. This study enabled the automatic identification of hundreds of species based on a newly established fossil dataset. An available “bivalve and brachiopod fossil image dataset” (BBFID, containing >16,000 “image-label” data pairs, taxon

APA, Harvard, Vancouver, ISO, and other styles

21

Al-Kumate, Mohammed, Essam El-Deen Fakharany, and Yasser Moustafa Kamal Omar. "Enhancing Galaxy Morphology Classification using Ensemble Learning." Journal of Advanced Research in Applied Sciences and Engineering Technology 62, no. 4 (2024): 175–84. https://doi.org/10.37934/araset.62.4.175184.

Full text

Abstract:

In recent decades, due to progress made in the field of astronomy and the development of specialized equipment, large-scale sky surveys have generated immense amounts of data. To address this issue, astronomers have utilized the power of crowdsourcing provided by the Galaxy Zoo project for the morphological classification of galaxies. However, with the upcoming generation of surveys, relying on crowdsourcing is not optimal and the adoption of automated strategies for the morphological classification of galaxies is essential. The aim in this work is to design a model focused on the classificati

APA, Harvard, Vancouver, ISO, and other styles

22

Zulcaffle, Tengku, Fatih Kurugollu, Kuryati Kipli, Annie Joseph, and David Bong. "Front and Back Views Gait Recognitions Using EfficientNets and EfficientNetV2 Models Based on Gait Energy Image." International Journal of Computing and Digital Systems 14, no. 1 (2023): 749–58. http://dx.doi.org/10.12785/ijcds/140157.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

Khan, Zeshan Aslam, Muhammad Waqar, Hashir Ullah Khan, et al. "Fine-tuned deep transfer learning: an effective strategy for the accurate chronic kidney disease classification." PeerJ Computer Science 11 (April 8, 2025): e2800. https://doi.org/10.7717/peerj-cs.2800.

Full text

Abstract:

Kidney diseases are becoming an alarming concern around the globe. Premature diagnosis of kidney disease can save precious human lives by taking preventive measures. Deep learning demonstrates a substantial performance in various medical disciplines. Numerous deep learning approaches are suggested in the literature for accurate chronic kidney disease classification by compromising on architectural complexity, classification speed, and resource constraints. In this study, deep transfer learning is exploited by incorporating unexplored yet effective variants of ConvNeXt and EfficientNetV2 for ac

APA, Harvard, Vancouver, ISO, and other styles

24

Kim, Jeoung Kun, Donghwi Park, and Min Cheol Chang. "Assessment of Bone Age Based on Hand Radiographs Using Regression-Based Multi-Modal Deep Learning." Life 14, no. 6 (2024): 774. http://dx.doi.org/10.3390/life14060774.

Full text

Abstract:

(1) Objective: In this study, a regression-based multi-modal deep learning model was developed for use in bone age assessment (BAA) utilizing hand radiographic images and clinical data, including patient gender and chronological age, as input data. (2) Methods: A dataset of hand radiographic images from 2974 pediatric patients was used to develop a regression-based multi-modal BAA model. This model integrates hand radiographs using EfficientNetV2S convolutional neural networks (CNNs) and clinical data (gender and chronological age) processed by a simple deep neural network (DNN). This approach

APA, Harvard, Vancouver, ISO, and other styles

25

Hamed, Bahaa S., Mahmoud M. Hussein, and Afaf M. Mousa. "Plant Disease Detection Using Deep Learning." International Journal of Intelligent Systems and Applications 15, no. 6 (2023): 38–50. http://dx.doi.org/10.5815/ijisa.2023.06.04.

Full text

Abstract:

Agricultural development is a critical strategy for promoting prosperity and addressing the challenge of feeding nearly 10 billion people by 2050. Plant diseases can significantly impact food production, reducing both quantity and diversity. Therefore, early detection of plant diseases through automatic detection methods based on deep learning can improve food production quality and reduce economic losses. While previous models have been implemented for a single type of plant to ensure high accuracy, they require high-quality images for proper classification and are not effective with low-reso

APA, Harvard, Vancouver, ISO, and other styles

26

Krithika Alias Anbu Devi, M., and K. Suganthi. "A Convolutional Mixer-Based Deep Learning Network for Alzheimer’s Disease Classification from Structural Magnetic Resonance Imaging." Diagnostics 15, no. 11 (2025): 1318. https://doi.org/10.3390/diagnostics15111318.

Full text

Abstract:

Objective: Alzheimer’s disease (AD) is a neurodegenerative disorder that severely impairs cognitive function across various age groups, ranging from early to late sixties. It progresses from mild to severe stages, so an accurate diagnostic tool is necessary for effective intervention and treatment planning. Methods: This work proposes a novel AD classification architecture that integrates depthwise separable convolutional layers with traditional convolutional layers to efficiently extract features from structural magnetic resonance imaging (sMRI) scans. This model benefits from excellent featu

APA, Harvard, Vancouver, ISO, and other styles

27

Anhar, Anhar, and Dandi Septiandi. "Detection of COVID-19 Based on Synthetic Chest X-Ray (CXR) Images Using Deep Convolutional Generative Adversarial Networks (DCGAN) and Transfer Learning." Jurnal Ilmiah Teknik Elektro Komputer dan Informatika 9, no. 3 (2023): 832–53. https://doi.org/10.26555/jiteki.v9i3.26685.

Full text

Abstract:

The global COVID-19 pandemic has significantly impacted the health and lives of people worldwide, with high numbers of cases and fatalities. Rapid and accurate diagnosis is crucially important. Radiographic imaging, particularly chest radiography (CXR), has been considered for diagnosing suspected COVID-19 patients. CXR images offers quick imaging, affordability, and wide accessibility, making it pivotal for screening. However, the scarcity of CXR images remains due to the pandemic's recent emergence. To address this scarcity, this study harnesses the capabilities of Deep Convolutional Generat

APA, Harvard, Vancouver, ISO, and other styles

28

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

29

Wang, Zengkun, Yang Cao, Hongfei Yu, et al. "Scene Classification of Remote Sensing Images Using EfficientNetV2 with Coordinate Attention." Journal of Physics: Conference Series 2289, no. 1 (2022): 012026. http://dx.doi.org/10.1088/1742-6596/2289/1/012026.

Full text

Abstract:

Abstract The high intra class diversity of remote sensing image scene often leads to the problem of difficult classification of remote sensing image scenes. Therefore, this paper proposes the CA-EfficientNetV2 model, embedding the coordinate attention into the head of the EfficientNetV2 network to enhance the classification effect. The coordinate attention is used to generate the position relationship between image spaces and channels so as to learn features efficiently. We trained three improved models CA-EfficientNetV2-S, CA-EfficientNetV2-M and CA-EfficientNetV2-L on UC Merced remote sensin

APA, Harvard, Vancouver, ISO, and other styles

30

Erin Eka Citra, Siti Mutmainah, and Bambang Hermanto. "Breast Cancer Detection Using EfficientNetV2 Variants and Data Augmentation: A Comparative Study." Jurnal Komputasi 13, no. 1 (2025): 13–24. https://doi.org/10.23960/komputasi.v13i1.281.

Full text

Abstract:

Kanker merupakan penyebab utama kematian kedua di dunia yang menyebabkan sekitar 9,6 juta kematian. Deteksi dini kanker dan penanganannya sedini mungkin dapat menurunkan angka kematian. Metode deep learning terbukti mampu mengenali pola dalam citra medis dan memberikan hasil klasifikasi yang akurat, EfficientNet merupakan salah satu metode deep learning. Penelitian ini bertujuan untuk mengeksplorasi penggunaan berbagai varian EfficientNetV2, yaitu EfficientNetV2-S, EfficientNetV2-M, dan EfficientNetV2-L untuk mendeteksi kanker payudara berdasarkan citra medis. Dataset yang digunakan yakni gamb

APA, Harvard, Vancouver, ISO, and other styles

31

Zarif, Sameh, Hatem Abdulkader, Ibrahim Elaraby, Abdullah Alharbi, Wail S. Elkilani, and Paweł Pławiak. "Using hybrid pre-trained models for breast cancer detection." PLOS ONE 19, no. 1 (2024): e0296912. http://dx.doi.org/10.1371/journal.pone.0296912.

Full text

Abstract:

Breast cancer is a prevalent and life-threatening disease that affects women globally. Early detection and access to top-notch treatment are crucial in preventing fatalities from this condition. However, manual breast histopathology image analysis is time-consuming and prone to errors. This study proposed a hybrid deep learning model (CNN+EfficientNetV2B3). The proposed approach utilizes convolutional neural networks (CNNs) for the identification of positive invasive ductal carcinoma (IDC) and negative (non-IDC) tissue using whole slide images (WSIs), which use pre-trained models to classify b

APA, Harvard, Vancouver, ISO, and other styles

32

Prathibha, Dr G., Y. Kavya, P. Vinay Jacob, and L. Poojita. "Speech Emotion Recognition Using Deep Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 07 (2024): 1–13. http://dx.doi.org/10.55041/ijsrem36262.

Full text

Abstract:

Speech is one of the primary forms of expression and is important for Emotion Recognition. Emotion Recognition is helpful to derive various useful insights about the thoughts of a person. Automatic speech emotion recognition is an active field of study in Artificial intelligence and Machine learning, which aims to generate machines that communicate with people via speech. In this work, deep learning algorithms such as Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) are explored to extract features and classify emotions such as calm, happy, fearful, disgust, angry, neutral

APA, Harvard, Vancouver, ISO, and other styles

33

McMahon, Niamh, Eoin M. Grua, and Ciarán Eising. "Leveraging EfficientNetV2 for litter detection." IET Conference Proceedings 2024, no. 10 (2024): 315–18. https://doi.org/10.1049/icp.2024.3322.

Full text

APA, Harvard, Vancouver, ISO, and other styles

34

Gencer, Kerem. "A Comparative Analysis of EfficientNetB0 and EfficientNetV2 Variants for Brain Tumor Classification Using MRI Images." International Journal of Innovative Engineering Applications 9, no. 1 (2025): 1–7. https://doi.org/10.46460/ijiea.1523782.

Full text

Abstract:

Accurate and early diagnosis of brain tumors is critical for effective treatment planning, yet traditional methods of analyzing Magnetic Resonance Imaging (MRI) scans are labor-intensive and prone to variability among experts. Deep learning, particularly Convolutional Neural Networks (CNNs), has emerged as a transformative tool in medical imaging by automating feature extraction and enhancing classification accuracy. This study provides a comparative analysis of EfficientNetB0 and three EfficientNetV2 variants (S, M, and L) for brain tumor classification using the Figshare Brain Tumor Dataset,

APA, Harvard, Vancouver, ISO, and other styles

35

Prabha, Chander, Retinderdeep Singh, Meena Malik, Manas Ranjan Pradhan, and Biswaranjan Acharya. "Advanced Gesture Recognition in Gaming: Implementing EfficientNetV2-B1 for "Rock, Paper, Scissors"." Engineering, Technology & Applied Science Research 15, no. 3 (2025): 23386–92. https://doi.org/10.48084/etasr.10373.

Full text

Abstract:

The study introduces a gesture recognition system for the classic "Rock, Paper, Scissors" game, based on a modified EfficientNetV2-B1 architecture. The dataset comprises 2,700 images, evenly divided among the three classes: "Rock", "Paper", and "Scissors". Leveraging the efficiency and accuracy of the EfficientNetV2-B1 model in image recognition tasks, the system was trained to classify these gestures effectively, and after fine-tuning, it achieved an accuracy of 98.89% and an Area Under the Curve (AUC) of ~1.0, indicating near-perfect classification across all classes. This performance highli

APA, Harvard, Vancouver, ISO, and other styles

36

Yang, Taewon, and Kunyeol Na. "Development of a Vehicle License Plate De-identification System using EfficientNetV2." Korean Journal of Computational Design and Engineering 28, no. 4 (2023): 503–13. http://dx.doi.org/10.7315/cde.2023.503.

Full text

APA, Harvard, Vancouver, ISO, and other styles

37

Kunduracıoğlu, İsmail, and İshak Paçal. "Deep Learning-Based Disease Detection in Sugarcane Leaves: Evaluating EfficientNet Models." Journal of Operations Intelligence 2, no. 1 (2024): 321–235. http://dx.doi.org/10.31181/jopi21202423.

Full text

Abstract:

Sugarcane is a crucial agricultural crop, providing 75% of the world's sugar production. Like all plant species, any disease that affects sugarcane can significantly impact yield and planning. Traditional manual methods for diagnosing diseases in sugarcane leaves are slow, inefficient, and often lack accuracy. In this study, we present a deep learning-based approach for the robust detection of diseases in sugarcane leaves. Specifically, we trained and evaluated all models from the EfficientNetv1 and EfficientNetv2 architectures, which are among the most notable convolutional neural network (CN

APA, Harvard, Vancouver, ISO, and other styles

38

Citra, Erin Eka, Dhomas Hatta Fudholi, and Chandra Kusuma Dewa. "Implementasi Arsitektur EfficientNetV2 Untuk Klasifikasi Gambar Makanan Tradisional Indonesia." JURNAL MEDIA INFORMATIKA BUDIDARMA 7, no. 2 (2023): 766. http://dx.doi.org/10.30865/mib.v7i2.5881.

Full text

Abstract:

Indonesia has many variations of traditional food and interesting tourist destinations. The large number of tourist destinations make people like traveling and try to enjoy their traditional food. However, when trying traditional foods, especially foods that are new to them, they must be more careful, because the various ingredients contained in them have an impact on health. This research will try to make an application that can recognize Indonesian traditional food. The hope is that it can provide complete information, so that it can be used to develop calorie counter applications in the fut

APA, Harvard, Vancouver, ISO, and other styles

39

C. K., Sunil, Jaidhar C. D., and Nagamma Patil. "Cardamom Plant Disease Detection Approach Using EfficientNetV2." IEEE Access 10 (2022): 789–804. http://dx.doi.org/10.1109/access.2021.3138920.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

Seng Tang, Michael Chi, Huong Yong Ting, Abdulwahab Funsho Atanda, and Kee Chuong Ting. "Transfer Learning with EfficientNetV2 forDiabetic Retinopathy Detection." International Journal of Engineering and Manufacturing 14, no. 6 (2024): 54–60. https://doi.org/10.5815/ijem.2024.06.05.

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

Sinaga, Novendra Adisaputra. "Analysis of EfficientNetV2 Model Usage in Predicting Gender on the Face of Mask Users." JATISI (Jurnal Teknik Informatika dan Sistem Informasi) 9, no. 3 (2022): 2487–94. http://dx.doi.org/10.35957/jatisi.v9i3.2975.

Full text

Abstract:

Biometrik merupakan metode untuk mengenali karakteristik fisik atau perilaku manusia yang digunakan sebagai input untuk pengenalan pola. Setiap bentuk biometrik tentunya menggunakan teknologi yang berbeda dalam mengidentifikasikannya. Sebuah gallery atau pertujukan seperti bioskop, pusat perbelanjaan, pameran membutuhkan informasi pengunjung dari acara tersebut untuk dilakukan sebuah kajian dalam menawarkan atau menjual produk sesuai dengan jenis kelamin dari pengunjung. Model EfficientNetV2 merupakan Family Baru dalam kelompok Covolution Neural Network (CNN) yang memiliki kecepatan pelatihan

APA, Harvard, Vancouver, ISO, and other styles

42

Rayhan, Ahmad, Rahmat Hidayat, and Rita Afyenni. "Perbandingan Akurasi EfficientNetV2 dan MobileNetV2 pada Klasifikasi Makanan Tradisional Indonesia." Journal Cerita 10, no. 2 (2024): 124–27. https://doi.org/10.33050/cerita.v10i2.3326.

Full text

Abstract:

Machine learning is a rapidly evolving technology that has led to numerous advancements, especially in the field of computer vision. Within this domain, many models have been introduced, including EfficientNetV2 and MobileNetV2. Given the wide range of model choices, it is essential to compare the accuracy of each model. This involves data collection, resizing and sorting data, visualizing data, and finally tuning and training processes. In this study, it was found that the accuracy of EfficientNetV2 is 31%, while MobileNetV2 achieves an accuracy of 99%.

APA, Harvard, Vancouver, ISO, and other styles

43

Ms. Saranya K, Santhiya A, Sheebha Shanthini R, Sheshapriya N, and Varsha V. "Optimizing Wheat Rust Disease Detection with Efficient Net." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 04 (2025): 1846–50. https://doi.org/10.47392/irjaeh.2025.0267.

Full text

Abstract:

Wheat rust is one of the most destructive crop diseases, significantly impacting global wheat production. Traditional methods of disease detection are often time-consuming, labor-intensive, and lack the precision required for early intervention. This paper proposes a deep learning-based approach for optimizing wheat rust disease detection using the EfficientNetV2 model. EfficientNetV2 is a powerful convolutional neural network architecture known for its improved accuracy, faster training times, and computational efficiency. The model is trained on a large dataset of wheat leaf images to learn

APA, Harvard, Vancouver, ISO, and other styles

44

Ma, Yuzhao, Xu Tang, Yaxin Shi, and Pak-Wai Chan. "YOLOv8n–CBAM–EfficientNetV2 Model for Aircraft Wake Recognition." Applied Sciences 14, no. 17 (2024): 7754. http://dx.doi.org/10.3390/app14177754.

Full text

Abstract:

In the study of aircraft wake target detection, as the wake evolves and develops, the detection area of the LiDAR often shows the presence of two distinct vortices, one on each side. Sometimes, only a single wake vortex may be present. This can lead to a reduction in the accuracy of wake detection and an increased likelihood of missed detections, which may have a significant impact on the flight safety. Hence, we propose an algorithm based on the YOLOv8n–CBAM–EfficientNetV2 model for wake detection. The algorithm incorporates the lightweight network of EfficientNetV2 and the Convolutional Bloc

APA, Harvard, Vancouver, ISO, and other styles

45

Anavyanto, Arazka Firdaus, Maimunah Maimunah, Muhammad Resa Arif Yudianto, and Pristi Sukmasetya. "EfficientNetV2M for Image Classification of Tomato Leaf Deseases." PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic 11, no. 1 (2023): 55–76. http://dx.doi.org/10.33558/piksel.v11i1.5925.

Full text

Abstract:

Favorable climatic conditions make tomato plants (Solanum Lycopersicon) a widely cultivated horticultural crop in Indonesia. However, the increase in tomato production is often accompanied by a decrease in both the quantity and quality of the plants, which can be caused by a variety of factors such as bacteria, fungi, viruses, and insects like Late Blight and Two-Spotted Spider Mite diseases that attack the tomato leaves. To help farmers identify leaf diseases that have similar characteristics, this study employs image processing with the Convolutional Neural Network (CNN) algorithm and transf

APA, Harvard, Vancouver, ISO, and other styles

46

杨, 晖智. "Malware Classification Based on EfficientNetV2 and Feature Fusion." Computer Science and Application 14, no. 09 (2024): 151–60. http://dx.doi.org/10.12677/csa.2024.149196.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Song, Huaxiang. "FST-EfficientNetV2: Exceptional Image Classification for Remote Sensing." Computer Systems Science and Engineering 46, no. 3 (2023): 3959–78. http://dx.doi.org/10.32604/csse.2023.038429.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Sachin Sonawane. "Robust Classification of Black-Eyed Peas Based on Segment Anything Model and Transfer Learning." Journal of Information Systems Engineering and Management 10, no. 21s (2025): 66–80. https://doi.org/10.52783/jisem.v10i21s.3290.

Full text

Abstract:

Evaluating the physical quality of harvested black-eyed peas is essential to ensure their products meet high standards. Carefully designed and optimized machine learning models can provide better quality evaluation. A hybrid neural network integrating EfficientNetV2B1 and Vision Transformer (ViT) to classify black-eyed peas is introduced in this work. One of the main challenges in accurate classification was segmenting objects in a clustered view. Inconsistent lighting, variations in sample size, random placement of the objects, and neighboring objects touching each other make the task difficu

APA, Harvard, Vancouver, ISO, and other styles

49

Meng Lisha, 孟莉莎, 杨贤昭 Yang Xianzhao та 刘惠康 Liu Huikang. "基于CA-EfficientNetV2的蘑菇图像分类算法研究". Laser & Optoelectronics Progress 59, № 24 (2022): 2410005. http://dx.doi.org/10.3788/lop202259.2410005.

Full text

APA, Harvard, Vancouver, ISO, and other styles

50

Lee, Han-sung, and Hyun-chong Cho. "Wafer Map Defect Analysis Based on EfficientNetV2 using Grad-CAM." Transactions of The Korean Institute of Electrical Engineers 72, no. 4 (2023): 553–58. http://dx.doi.org/10.5370/kiee.2023.72.4.553.

Full text

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!