Relevant bibliographies by topics / Inception-V3 model

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  2. Dissertations / Theses
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  4. Conference papers

Academic literature on the topic 'Inception-V3 model'

Author: Grafiati
Published: 7 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "Inception-V3 model"

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B P, Aishwarya, Chandan M S, Preethu K S, and Swathi K A. "Diabetic Retinopathy using Inception V3 Model." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 4612–16. http://dx.doi.org/10.22214/ijraset.2023.51304.

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Abstract: The retina of the eye is affected by a diabetic condition known as diabetic retinopathy (DR). It causes damage to the light source based on the blood vessels in the human retina. If diabetes is not properly treated, it is more likely to occur, making it the most common cause of visual impairment in adults during their working years. There are procedures for detecting DR, but they need an ophthalmologist to manually examine the retinal image. Deep Convolution neural networks based on the Inception V3 model are used in the design. The model was about 97 percent accurate after undergoing GPU training on 35,126 images that eyePACS made available for public viewing on the Kaggle website. 1
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Ghifari, Dloifur Rohman Al, Ema Utami, and Dhani Ariatmanto. "Deteksi Penyakit Gigi dan Mulut Menggunakan Algoritma Inception-V3 Detection of Dental and Oral Diseases Using Inception-V3." Jurnal Pendidikan Indonesia 6, no. 4 (2025): 1775–88. https://doi.org/10.59141/japendi.v6i4.7649.

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Kesehatan gigi dan mulut sangat penting bagi kesejahteraan umum, namun banyak orang mengabaikan pengobatan karena kurangnya kesadaran atau tantangan diagnostik. Metode diagnostik tradisional sering kali kurang akurat dan efisien. Penelitian ini bertujuan mengembangkan sistem otomatis untuk mengklasifikasikan penyakit gigi dan mulut menggunakan algoritma deep learning Inception-V3 guna meningkatkan akurasi diagnostik. Penelitian menggunakan dataset 8.776 citra oral yang diseimbangkan dengan SMOTE dan diproses dengan teknik augmentasi. Inception-V3 dilatih dan dibandingkan dengan CNN, VGG-16, ResNet50, serta model machine learning tradisional. Model Inception-V3 mencapai akurasi 94%, mengungguli model lain (CNN: 81%, VGG-16: 88.7%, ResNet50: 76.25%) dan menunjukkan stabilitas tanpa overfitting. Studi ini menegaskan potensi Inception-V3 dalam analisis gambar medis, menawarkan alat diagnostik yang andal untuk deteksi dini penyakit gigi dan mulut, sehingga dapat meningkatkan hasil layanan kesehatan.
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Sharma, Shagun, Kalpna Guleria, Sushil Kumar, and Sunita Tiwari. "Deep Learning based Model for Detection of Vitiligo Skin Disease using Pre-trained Inception V3." International Journal of Mathematical, Engineering and Management Sciences 8, no. 5 (2023): 1024–39. http://dx.doi.org/10.33889/ijmems.2023.8.5.059.

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Skin diseases are commonly identified problems all over the world. There are various kinds of skin diseases, such as skin cancer, vulgaris, ichthyosis, and eczema. Vitiligo is one of the skin diseases that can occur in any area of the body, including the inner part of the mouth. This type of skin can have immense negative impacts on the human body, involving memory issues, hypertension, and mental health problems. Conventionally, dermatologists use biopsy, blood tests, and patch testing to identify the presence of skin diseases and provide medications to patients. However, these treatments don't always provide results due to the transformation of a macule into a patch. Various machine learning (ML) and deep learning (DL) models have been developed for the early identification of macules to avoid delays in treatments. This work has implemented a DL-based model for predicting and classifying vitiligo skin disease in healthy skin. The features from the images have been extracted using a pre-trained Inception V3 model and substituted for each classifier, namely, naive Bayes, convolutional neural network (CNN), random forest, and decision tree. The results have been determined as accuracy, recall, precision, area under the curve (AUC), and F1-score for Inception V3 with naive Bayes as 99.5%, 0.995, 0.995, 0.997, and 0.995, respectively. The Inception V3 with CNN has achieved 99.8% accuracy, 0.998 recall, 0.998 precision, 1.00 AUC, and 0.998 F1-score. Further, Inception V3 with random forest shows 99.9% accuracy, 0.999 recall, 0.999 precision, 1.00 AUC, and 0.999 F1-score values whereas, Inception V3 with decision tree classifier shows an accuracy value of 97.8%, 0.978 recall, 0.977 precision, 0.969 AUC, and 0.977 F1-score. Results exhibit that Inception V3 with a random forest classifier outperforms in terms of accuracy, recall, precision, and F1-score, whereas for the AUC metric, Inception V3 with a random forest and Inception V3 with CNN have shown the same outcomes of 1.00.
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Sirait, Dheo Ronaldo, Sutikno Sutikno, and Priyo Sidik Sasongko. "Improved inception-V3 model for apple leaf disease classification." International Journal of Informatics and Communication Technology (IJ-ICT) 13, no. 2 (2024): 161. http://dx.doi.org/10.11591/ijict.v13i2.pp161-167.

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Apple, a nutrient-rich fruit belonging to the genus Malus, is recognized for its fiber, vitamins, and antioxidants, giving health benefits such as improved digestion and reduced cardiovascular disease risk. In Indonesia, the soil and climate create favorable conditions for apple cultivation. However, it is essential to prioritize the health of the plant. Biotic factors, such as fungal infections like apple scabs and pests, alongside abiotic factors like temperature and soil moisture, impact the health of apple plants. Computer vision, specifically convolution neural network (CNN) inception-V3, proves effective in aiding farmers in identifying these diseases. The output layer in inception-V3 is essential, generating predictions based on input data. For this reason, in this paper, we add an output layer in inception-V3 architecture to increase the accuracy of apple leaf disease classification. The added output layers are dense, dropout, and batch normalization. Adding a dense layer after flattening typically consolidates the extracted features into a more compact representation. Dropout can help prevent overfitting by randomly deactivating some units during training. Batch normalization helps normalize activations across batches, speeding up training and providing stability to the model. Test results show that the proposed method produced an accuracy of 99.27% and can increase accuracy by 1.85% compared to inception-V3. These enhancements showcase the potential of leveraging computer vision for precise disease diagnosis in apple crops.
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Dheo, Ronaldo Sirait, Sutikno, and Sidik Sasongko Priyo. "Improved inception-V3 model for apple leaf disease classification." International Journal of Informatics and Communication Technology 13, no. 2 (2024): 161–67. https://doi.org/10.11591/ijict.v13i2.pp161-167.

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Apple, a nutrient-rich fruit belonging to the genus Malus, is recognized for its fiber, vitamins, and antioxidants, giving health benefits such as improved digestion and reduced cardiovascular disease risk. In Indonesia, the soil and climate create favorable conditions for apple cultivation. However, it is essential to prioritize the health of the plant. Biotic factors, such as fungal infections like apple scabs and pests, alongside abiotic factors like temperature and soil moisture, impact the health of apple plants. Computer vision, specifically convolution neural network (CNN) inception-V3, proves effective in aiding farmers in identifying these diseases. The output layer in inception-V3 is essential, generating predictions based on input data. For this reason, in this paper, we add an output layer in inception-V3 architecture to increase the accuracy of apple leaf disease classification. The added output layers are dense, dropout, and batch normalization. Adding a dense layer after flattening typically consolidates the extracted features into a more compact representation. Dropout can help prevent overfitting by randomly deactivating some units during training. Batch normalization helps normalize activations across batches, speeding up training and providing stability to the model. Test results show that the proposed method produced an accuracy of 99.27% and can increase accuracy by 1.85% compared to inception-V3. These enhancements showcase the potential of leveraging computer vision for precise disease diagnosis in apple crops.
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Kohsasih, Kelvin Leonardi, Muhammad Dipo Agung Rizky, Rika Rosnelly, and Willy Wira Widjaja. "A deep learning model to detect the brain tumor based on magnetic resonance images." JURNAL INFOTEL 14, no. 3 (2022): 203–8. http://dx.doi.org/10.20895/infotel.v14i3.793.

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Deep learning techniques have been widely used in everything from analyzing medical information to tools for making medical diagnoses. One of the most feared diseases in modern medicine is a brain tumor. MRI is a radiological method that can be used to identify brain tumors. However, manual segmentation and analysis of MRI images is time-consuming and can only be performed by a professional neuroradiologist. Therefore automatic recognition is required. This study propose a deep learning method based on a hybrid multi-layer perceptron model with Inception-v3 to predict brain tumors using MRI images. The research was conducted by building the Inception-v3 and multilayer perceptron model, and comparing it with the proposed model. The results showed that the hybrid multilayer perceptron model with inception-v3 achieved accuracy, recall, precision, and fi-score of 92%. While the inception-v3 and multilayer perceptron models only obtained 66% and 56% accuracy, respectively. This research shows that the proposed model successfully predicts brain tumors and improves performance
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Metlek, Sedat, and Halit Çetiner. "INCEPTION SH: A NEW CNN MODEL BASED ON INCEPTION MODULE FOR CLASSIFYING SCENE IMAGES." Mühendislik Bilimleri ve Tasarım Dergisi 12, no. 2 (2024): 328–44. http://dx.doi.org/10.21923/jesd.1372788.

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In this study, a light-weight model with an optimum block structure that can be used in autonomous unmanned aerial vehicles (UAVs) was designed. The Inception SH model, which was developed based on the Inception V3 model, was compared on "Intel Image Dataset", a publicly available dataset in the literature. As a result of the comparison, values of 0.882, 0.883, 0.882 and 0.882 were obtained for the accuracy, precision, recall, and F1 score metrics for the Inception V3 model, respectively. In the Inception SH model, values of 0.958, 0.957, 0.974 and 0.967 were obtained for accuracy, precision, recall and F1 score metrics, respectively. As can be seen from these values, the proposed Inception SH model offers higher performance values than the underlying Inception V3 model. The Inception SH model was compared with different models in the literature using the same data set and was superior in accuracy, precision, recall and F1 score metrics compared to the compared models. According to the results obtained, it is predicted that the Inception SH model can be used as a lightweight model in various IoT devices, considering the popularity of autonomous UAVs.
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Rashid, Iqbal, and Basit Abdul. "Enhancing Vehicle Classification Accuracy: A Convolutional Neural Network (CNN) Based Model." LC International Journal of STEM 5, no. 1 (2024): 1–11. https://doi.org/10.5281/zenodo.11074270.

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Using the convolutional neural network (CNN) fine-tuned method, this article introduces a vehicle categorization system. The system's goal is to properly categorize popular vehicle types in the domestic market, which will help with traffic control, monitoring, and traffic accident prevention. The efficacy of VGG-16 and Inception V3 architectures is demonstrated by their evaluation of a real-world dataset consisting of 2000 photos of vehicles. While VGG-16 attains an accuracy of 99.11%, Inception V3 reaches an accuracy of 96.43%. In terms of overall accuracy, VGG-16 outperforms Inception V3, highlighting the importance of architectural decisions in achieving accurate vehicle classification. The suggested technique significantly improves computer vision applications in the domain of vehicle classification, making valuable contributions to traffic management and accident prevention efforts.
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Degaonkar, Swarali, and Aarti Agarkar. "Real Time Face Mask Detection Using Inception-V3." International Journal for Research in Applied Science and Engineering Technology 11, no. 11 (2023): 973–79. http://dx.doi.org/10.22214/ijraset.2023.56652.

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Abstract: This project was developed for detecting people’s faces and segregating them into two classes, masks and without masks is done with the help of image processing in real-time and deep learning. The proposed model is built by fine-tuning the pre-trained deep learning model, InceptionV3.The model was trained on a WIDER dataset of 8,262 images. The model performed greatly achieving an accuracy of 99% overall.
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Sarathambekai, S., T. Vairam, Krishna S. Harish, and A. Vishal. "Vehicle Classification for Traffic Signal Optimization via YOINS Transfer Learning Model." Journal of Research in Artificial Neural Network Systems 1, no. 1 (2025): 31–43. https://doi.org/10.5281/zenodo.15055065.

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<em>Intelligent transportation systems are increasingly being used in urban environments to enhance road safety, reduce congestion, and optimize vehicular movement. Traditional vehicle classification methods often fail to address dynamic traffic conditions, reducing real- time efficiency. The proposed hybrid deep learning-based approach, titled YOINS (YOLOv7</em> <em>+ Inception V3) Deep Learning Model, integrates YOLOv7 for realtime vehicle detection and Inception V3 for fine-grained classification into ten classes such as trucks, buses, SUVs, Family sedans, Fire engines, Heavy trucks, Jeeps, Minibuses, Racing cars and Taxis. Experimental results validate the effectiveness of this approach, with Inception V3 achieving an accuracy of 85% and YOLOv7 achieving 81%. When combined, the YOINS hybrid model improves classification accuracy to 87%, demonstrating a slight enhancement over the individual models. Additionally, the dynamic adjustment of green signal timing based on real-time vehicle density has significantly improved traffic flow and congestion management, contributing to smarter urban mobility. The choice of YOLOv7 and Inception V3 over alternatives like EfficientDet and MobileNet is based on their superior balance of speed, accuracy, and scalability in ITS applications. This approach offers a scalable and efficient solution for intelligent transportation management.</em>
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Dissertations / Theses on the topic "Inception-V3 model"

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Albert, Florea George, and Filip Weilid. "Deep Learning Models for Human Activity Recognition." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20201.

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AMI Meeting Corpus (AMI) -databasen används för att undersöka igenkännande av gruppaktivitet. AMI Meeting Corpus (AMI) -databasen ger forskare fjärrstyrda möten och naturliga möten i en kontorsmiljö; mötescenario i ett fyra personers stort kontorsrum. För attuppnågruppaktivitetsigenkänninganvändesbildsekvenserfrånvideosoch2-dimensionella audiospektrogram från AMI-databasen. Bildsekvenserna är RGB-färgade bilder och ljudspektrogram har en färgkanal. Bildsekvenserna producerades i batcher så att temporala funktioner kunde utvärderas tillsammans med ljudspektrogrammen. Det har visats att inkludering av temporala funktioner både under modellträning och sedan förutsäga beteende hos en aktivitet ökar valideringsnoggrannheten jämfört med modeller som endast använder rumsfunktioner[1]. Deep learning arkitekturer har implementerats för att känna igen olika mänskliga aktiviteter i AMI-kontorsmiljön med hjälp av extraherade data från the AMI-databas.Neurala nätverks modellerna byggdes med hjälp av KerasAPI tillsammans med TensorFlow biblioteket. Det finns olika typer av neurala nätverksarkitekturer. Arkitekturerna som undersöktes i detta projektet var Residual Neural Network, Visual GeometryGroup 16, Inception V3 och RCNN (LSTM). ImageNet-vikter har använts för att initialisera vikterna för Neurala nätverk basmodeller. ImageNet-vikterna tillhandahålls av Keras API och är optimerade för varje basmodell [2]. Basmodellerna använder ImageNet-vikter när de extraherar funktioner från inmatningsdata. Funktionsextraktionen med hjälp av ImageNet-vikter eller slumpmässiga vikter tillsammans med basmodellerna visade lovande resultat. Både Deep Learning användningen av täta skikt och LSTM spatio-temporala sekvens predikering implementerades framgångsrikt.<br>The Augmented Multi-party Interaction(AMI) Meeting Corpus database is used to investigate group activity recognition in an office environment. The AMI Meeting Corpus database provides researchers with remote controlled meetings and natural meetings in an office environment; meeting scenario in a four person sized office room. To achieve the group activity recognition video frames and 2-dimensional audio spectrograms were extracted from the AMI database. The video frames were RGB colored images and audio spectrograms had one color channel. The video frames were produced in batches so that temporal features could be evaluated together with the audio spectrogrames. It has been shown that including temporal features both during model training and then predicting the behavior of an activity increases the validation accuracy compared to models that only use spatial features [1]. Deep learning architectures have been implemented to recognize different human activities in the AMI office environment using the extracted data from the AMI database.The Neural Network models were built using the Keras API together with TensorFlow library. There are different types of Neural Network architectures. The architecture types that were investigated in this project were Residual Neural Network, Visual Geometry Group 16, Inception V3 and RCNN(Recurrent Neural Network). ImageNet weights have been used to initialize the weights for the Neural Network base models. ImageNet weights were provided by Keras API and was optimized for each base model[2]. The base models uses ImageNet weights when extracting features from the input data.The feature extraction using ImageNet weights or random weights together with the base models showed promising results. Both the Deep Learning using dense layers and the LSTM spatio-temporal sequence prediction were implemented successfully.
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Book chapters on the topic "Inception-V3 model"

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Mahto, Manoj Kumar, Vinjamoori Manaswini, D. Akshara, and Indhirala Jayasree. "Identification of Medicinal Plants Using Inception V3 Model." In Springer Proceedings in Mathematics & Statistics. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-51338-1_47.

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Dash, Artatrana Biswaprsanna, Sachikanta Dash, Sasmita Padhy, Naween Kumar, Girish Kumar Pati, and Kanishka Uthansingh. "Leveraging inception-v3 CNN model for efficient image classification." In Intelligent Computing and Communication Techniques. CRC Press, 2025. https://doi.org/10.1201/9781003635680-52.

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Meena, Sitaram, Amod Kumar, Meenakshi Sood, and Rajesh Kumar Meena. "Lung Cancer Detection and Classification Model Using Inception V3 Algorithm." In Proceedings of Data Analytics and Management. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-6550-2_32.

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Ashwath Rao, B., Gopalakrishana N. Kini, and Joshua Nostas. "Content-Based Medical Image Retrieval Using Pretrained Inception V3 Model." In Algorithms for Intelligent Systems. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-5747-4_55.

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Sarah, Jessica, Amisha Michael Danny, and Juan Mark Deen. "Performance Enhancement of Action Recognition System Using Inception V3 Model." In Proceedings of the 13th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2021). Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96302-6_1.

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Jakhar, Shyo Prakash, Amita Nandal, and Rahul Dixit. "Classification and Measuring Accuracy of Lenses Using Inception Model V3." In Advances in Intelligent Systems and Computing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6067-5_42.

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AlAjlan, Shatha AbdulAziz, and Abdul Khader Jilani Saudagar. "Threat Detection in Social Media Images Using the Inception-v3 Model." In Proceedings of Fifth International Congress on Information and Communication Technology. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5859-7_57.

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Parveen, Zahida, Yumnah Hasan, Anzar Alam, Hafsa Abbas, and Muhammad Umair Arif. "Rice Classification Using Scale Conjugate Gradient (SCG) Backpropagation Model and Inception V3 Model." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01177-2_10.

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Zeng, Yuyu, and Xingsheng Zhu. "Skin Cancer Detection Based on Hybrid Model by Means of Inception V3 and ResNet 50." In Proceedings of the 2022 3rd International Conference on Artificial Intelligence and Education (IC-ICAIE 2022). Atlantis Press International BV, 2023. http://dx.doi.org/10.2991/978-94-6463-040-4_42.

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Kumar Dey, Samrat, Lubana Akter, Dola Saha, Mshura Akter, and Md Mahbubur Rahman. "DeshiFoodBD: Development of a Bangladeshi Traditional Food Image Dataset and Recognition Model Using Inception V3." In Machine Intelligence and Data Science Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2347-0_50.

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Conference papers on the topic "Inception-V3 model"

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Sharma, Jatin, Deepak Kumar, and Abhiraj Malhotra. "Weed Disease Classification using CNN and Inception V3 Model." In 2024 4th Asian Conference on Innovation in Technology (ASIANCON). IEEE, 2024. https://doi.org/10.1109/asiancon62057.2024.10837925.

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Sowjanya, Lingineni Lakshmi, Kodali Kowshya, Manduri Sai Roshini, Bogireddy Krishna, Venkatrama Phani Kumar S, and Venkata Krishna Kishore Kolli. "An Improved Inception V3 Deep learning model for Cardiovascular Disease Prediction." In 2025 International Conference on Emerging Systems and Intelligent Computing (ESIC). IEEE, 2025. https://doi.org/10.1109/esic64052.2025.10962696.

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Mugilan, A., R. Kanmani, V. Prabhu, Thilagavathi K, R. P. Arun Raj, and A. Kiren Karthi. "Enhancing Dog Breed Identification Accuracy Using Hybrid Inception V3 and Xception Model." In 2025 3rd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA). IEEE, 2025. https://doi.org/10.1109/icaeca63854.2025.11012309.

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Jabbar, Muhammad Faiq, Febryanti Sthevanie, and Kurniawan Nur Ramadhani. "Evaluation of Modified Inception-v3 Model in Tomato Fruit Ripeness Classification on Image." In 2024 International Conference on Intelligent Cybernetics Technology & Applications (ICICyTA). IEEE, 2024. https://doi.org/10.1109/icicyta64807.2024.10913221.

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Kumar, T. Siva, R. Lohanandd, K. Kaviyanandh, and J. Gunabharathi. "Expression of Concern for: Brain Tumor Classification with Inception V3 Network Model Using Transfer Learning." In 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE, 2023. http://dx.doi.org/10.1109/icaccs57279.2023.10703661.

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Jusman, Yessi, Dwi Ahirita Ramadani, and Muhammad Ahdan Fawwaz Nurkholid. "Classification of Prostate Precancerous Cells Using DenseNet-201 and Inception-v3 Models." In 2024 International Conference on Intelligent Cybernetics Technology & Applications (ICICyTA). IEEE, 2024. https://doi.org/10.1109/icicyta64807.2024.10913321.

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Jusman, Yessi, Masayu Alya Nur ‘Aini, Cahaya Aji Pamungkas, Fajar Aziz Wicaksono, Bintang Alvin Ardyansyah, and Muhammad Rijalul Arif. "Comparative Analysis of Inception V3 and Xception Models for Breast Ultrasound Image Classification." In 2024 7th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI). IEEE, 2024. https://doi.org/10.1109/isriti64779.2024.10963465.

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Degadwala, Sheshang, Dhairya Vyas, Haimanti Biswas, Utsho Chakraborty, and Sowrav Saha. "Image Captioning Using Inception V3 Transfer Learning Model." In 2021 6th International Conference on Communication and Electronics Systems (ICCES). IEEE, 2021. http://dx.doi.org/10.1109/icces51350.2021.9489111.

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Herath, Lakmini, Dulani Meedeniya, M. A. J. C. Marasingha, and Vajira Weerasinghe. "Autism spectrum disorder diagnosis support model using Inception V3." In 2021 International Research Conference on Smart Computing and Systems Engineering (SCSE). IEEE, 2021. http://dx.doi.org/10.1109/scse53661.2021.9568314.

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Thalanki, Vaibhav, R. Nagha Akshayaa, R. Krithika, and R. Jothi. "Voice-based Image Captioning using Inception-V3 Transfer Learning Model." In 2023 7th International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, 2023. http://dx.doi.org/10.1109/icoei56765.2023.10125754.

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