Gotowe bibliografie tematyczne / InceptionResNetV2

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Gotowa bibliografia na temat „InceptionResNetV2”

Autor: Grafiati
Data publikacji: 11 września 2023
Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "InceptionResNetV2"

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Ullah, Naeem, Javed Ali Khan, Mohammad Sohail Khan, et al. "An Effective Approach to Detect and Identify Brain Tumors Using Transfer Learning." Applied Sciences 12, no. 11 (2022): 5645. http://dx.doi.org/10.3390/app12115645.

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Brain tumors are considered one of the most serious, prominent and life-threatening diseases globally. Brain tumors cause thousands of deaths every year around the globe because of the rapid growth of tumor cells. Therefore, timely analysis and automatic detection of brain tumors are required to save the lives of thousands of people around the globe. Recently, deep transfer learning (TL) approaches are most widely used to detect and classify the three most prominent types of brain tumors, i.e., glioma, meningioma and pituitary. For this purpose, we employ state-of-the-art pre-trained TL techni
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Wijaya, Jodi, and Kusrini Kusrini. "Classification of Coffee Fruit Ripe Levels from the Tree Using InceptionResnetv2, and MobileNetv2." Bitnet: Jurnal Pendidikan Teknologi Informasi 10, no. 1 (2025): 70–79. https://doi.org/10.33084/bitnet.v10i1.9278.

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Tanaman kopi adalah komoditas ekpor terbesar dalam peningkatan ekonomi negara berkembang. Kopi memiliki peran penting dalam budaya Indonesia, dengan sejarah panjang dan berbagai citra rasa yang kaya. Ada dua jenis utama kopi di Indonesia: Arabika dan Robusta. Kematangan dan pengolahan biji kopi menjadi salah satu pengaruh citra rasa kopi. Untuk menghasilkan biji kopi berkualitas tinggi, proses pemanenan biji kopi yang tepat pada tingkat kematangan yang ideal sangat penting. MobileNet adalah arsitektur jaringan saraf tiruan CNN yang menggunakan konvolusi yang dipisahkan secara kedalaman (depthw
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Kurniawan, Vallent Austin Theasar, Elan Cahya Niswary, Christian S.k.aditya, and Didih Rizki Chandranegara. "BRAIN TUMOR CLASSIFICATION USING INCEPTIONRESNET-V2 AND TRANSFER LEARNING APPROACH." JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) 10, no. 1 (2024): 91–99. http://dx.doi.org/10.33480/jitk.v10i1.5223.

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Brain, a highly intricate organ within the central nervous system, plays a fundamental role in information processing, cognition, motor control, and consciousness. Brain tumors pose severe threats to brain function and overall human well-being. Timely detection of these tumors is imperative for life-saving interventions. A dataset comprising four categories: no tumors, meningioma tumors, glioma tumors, and pituitary tumors was regarded in this research. The employed of the InceptionResNet-V2 architecture combined with Transfer Learning and data augmentation proposed to obtain optimal results o
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Yazid Aufar, Muhammad Helmy Abdillah, and Jiki Romadoni. "Web-based CNN Application for Arabica Coffee Leaf Disease Prediction in Smart Agriculture." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 7, no. 1 (2023): 71–79. http://dx.doi.org/10.29207/resti.v7i1.4622.

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In the agriculture industry, plant diseases provide difficulty, particularly for Arabica coffee production. A first step in eliminating and treating infections to avoid crop damage is recognizing ailments on Arabica coffee leaves. Convolutional neural networks (CNN) are rapidly advancing, making it possible to diagnose Arabica coffee leaf damage without a specialist's help. CNN is aimed to find features adaptively through backpropagation by adding layers including convolutional layers and pooling layers. This study aims to optimize and increase the accuracy of Arabica coffee leaf disease class
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PROCHUKHAN, Dmitro. "FEATURES OF THE MODIFICATION OF THE INCEPTIONRESNETV2 ARCHITECTURE AND THE CREATION OF A DIAGNOSTIC SYSTEM FOR DETERMINING THE DEGREE OF DAMAGE TO RETINAL VESSELS." Computer systems and information technologies, no. 1 (March 28, 2024): 27–32. http://dx.doi.org/10.31891/csit-2024-1-3.

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Diabetic retinopathy is a retinal disease caused by diabetes. The progression of this disease can lead to blindness. Every year, the number of patients with this disease increases. Diabetic retinal damage can be slowed if it is diagnosed early. The article describes the features of the creation of a neural network model and the development of a system with high accuracy rates for the recognition of diabetic retinopathy. The advantages of the InceptionResNetv2 convolutional neural network architecture are considered. This network uses residual connections that help facilitate the learning proce
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Ninama, Hitesh, Jagdish Raikwal, and Pushpa Raikwal. "Ensemble Transfer Learning for Automated Gauge Reading Detection and Prediction." Scalable Computing: Practice and Experience 26, no. 5 (2025): 2130–44. https://doi.org/10.12694/scpe.v26i5.4786.

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Pressure gauges and automatic reading methods for pointer gauges were the root causes of the problems that have motivated the development of an ensemble transfer learning strategy. The study suggests that to effectively generate and predict the present measurements of the gauge, it is necessary to use an ensemble learning method that incorporates transfer learning framework designs such as InceptionResnetV2 or DenseNet 201. The suggested methodology involves integrating the given data with ensemble model architectures and qualifying InceptionResnetV2 and Dense Net 201 models to forecast the pr
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Jiang, Kaiyuan, Jiawei Zhang, Haibin Wu, Aili Wang, and Yuji Iwahori. "A Novel Digital Modulation Recognition Algorithm Based on Deep Convolutional Neural Network." Applied Sciences 10, no. 3 (2020): 1166. http://dx.doi.org/10.3390/app10031166.

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The modulation recognition of digital signals under non-cooperative conditions is one of the important research contents here. With the rapid development of artificial intelligence technology, deep learning theory is also increasingly being applied to the field of modulation recognition. In this paper, a novel digital signal modulation recognition algorithm is proposed, which has combined the InceptionResNetV2 network with transfer adaptation, called InceptionResnetV2-TA. Firstly, the received signal is preprocessed and generated the constellation diagram. Then, the constellation diagram is us
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Kalkan, Muruvvet, Mehmet S. Guzel, Fatih Ekinci, Ebru Akcapinar Sezer, and Tunc Asuroglu. "Comparative Analysis of Deep Learning Methods on CT Images for Lung Cancer Specification." Cancers 16, no. 19 (2024): 3321. http://dx.doi.org/10.3390/cancers16193321.

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Background: Lung cancer is the leading cause of cancer-related deaths worldwide, ranking first in men and second in women. Due to its aggressive nature, early detection and accurate localization of tumors are crucial for improving patient outcomes. This study aims to apply advanced deep learning techniques to identify lung cancer in its early stages using CT scan images. Methods: Pre-trained convolutional neural networks (CNNs), including MobileNetV2, ResNet152V2, InceptionResNetV2, Xception, VGG-19, and InceptionV3, were used for lung cancer detection. Once the disease was identified, the tum
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Huang, Youran. "Research on garbage sorting robotic arm based on image vision." Journal of Physics: Conference Series 2741, no. 1 (2024): 012020. http://dx.doi.org/10.1088/1742-6596/2741/1/012020.

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Abstract This paper aims to investigate the application of artificial intelligence in robotic arm automation control for the accurate identification of different types of garbage using deep learning algorithms. The goal is to enable the robotic arm to autonomously classify and handle garbage. The appropriate garbage classification dataset was selected and subjected to data preprocessing in this study. After comparing various well-established convolutional neural network models, including VGG16, InceptionResNetV2, Xception, and InceptionResNetV3, in terms of performance and suitability on the t
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Cheritha, K., S. Akhil, V. Bhanu Prakash, and A. Akhil Reddy. "Deepfake Detection using Deep Learning with InceptionV3." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44058.

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Deepfake technology has rapidly evolved, making it increasingly difficult to distinguish between real and manipulated videos. This poses serious risks, including misinformation, identity theft, and digital forgery. To address this challenge, we propose a deep learning-based deepfake detection model that leverages InceptionResNetV2, a hybrid architecture combining the strengths of Inception networks and Residual networks (ResNet). Our approach efficiently extracts key facial features from video frames and classifies them as real or fake. The detection pipeline includes video preprocessing, fram
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Rozprawy doktorskie na temat "InceptionResNetV2"

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RASOOL, AALE. "DETECTING DEEPFAKES WITH MULTI-MODEL NEURAL NETWORKS: A TRANSFER LEARNING APPROACH." Thesis, 2023. http://dspace.dtu.ac.in:8080/jspui/handle/repository/19993.

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The prevalence of deepfake technology has led to serious worries about the veracity and dependability of visual media. To reduce any harm brought on by the malicious use of this technology, it is essential to identify deepfakes. By using the Vision Transformer (ViT) model for classification and the InceptionResNetV2 architecture for feature extraction, we offer a novel approach to deepfake detection in this thesis. The highly discriminative features are extracted from the input photos using the InceptionResNetV2 network, which has been pre-trained on a substantial dataset. The Vi
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Części książek na temat "InceptionResNetV2"

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Ganesh, Mukkesh, Sanjana Dulam, and Pattabiraman Venkatasubbu. "Diabetic Retinopathy Diagnosis with InceptionResNetV2, Xception, and EfficientNetB3." In Artificial Intelligence and Technologies. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6448-9_41.

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Sharma, Osho, Akashdeep Sharma, and Arvind Kalia. "Windows Malware Hunting with InceptionResNetv2 Assisted Malware Visualization Approach." In Proceedings of International Conference on Computational Intelligence and Data Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0609-3_12.

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Vodnala, Deepika, Konkathi Shreya, Maduru Sandhya, and Cholleti Varsha. "Skin Cancer Detection Using Convolutional Neural Networks and InceptionResNetV2." In Algorithms for Intelligent Systems. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7041-2_50.

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Zubair, Fida, and Moutaz Saleh. "Enhancing Tomato Crop Health: Leveraging Modified InceptionResNetV2 for Disease Detection." In Communications in Computer and Information Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-82153-0_25.

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Simon, Philomina, and V. Uma. "Integrating InceptionResNetv2 Model and Machine Learning Classifiers for Food Texture Classification." In Advances in Cognitive Science and Communications. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8086-2_51.

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Akhand, Md Nafis Tahmid, Sunanda Das, and Mahmudul Hasan. "Traffic Density Estimation Using Transfer Learning with Pre-trained InceptionResNetV2 Network." In Machine Intelligence and Data Science Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2347-0_28.

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Mittal, Shubh, Tisha Chawla, and Hiteshwar Kumar Azad. "InceptionResNetV2 and KNN-Based Detection of Yellow Vein Mosaic Virus in Okra." In Innovative Computing and Communications. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3817-5_31.

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Singh, Saravjeet, Jatin Arora, and Monika Sethi. "Pothole Classification Using DenseNet Model: An Empirical Analysis with CNN and InceptionResNetV2." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-66594-3_4.

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Tamilkodi, R., A. Harika, Jetti Siva Kumar, P. M. V. D. Siva Kumar, Chadaram Krishnasai, and M. R. N. Venkata Akhil. "Enhancing Cybersecurity with AI: A Deepfake Detection Framework Using InceptionResNetV2 and LSTM." In Algorithms in Advanced Artificial Intelligence. CRC Press, 2025. https://doi.org/10.1201/9781003641537-16.

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Patel, Vinitkumar Vasantbhai, and Arvind R. Yadav. "ISLO-Tuned InceptionResNetV2 and 3D U-Net: A Powerful Duo for Automated Renal Cancer Diagnosis." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-1188-1_1.

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Streszczenia konferencji na temat "InceptionResNetV2"

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Ravale, Ujwala, Riya Ramesh Tattu, Ashish Baban Bhoir, and Sneha Bhaskar Mahajan. "Deepfake Detection using InceptionResNetV2 Model." In 2024 IEEE 3rd World Conference on Applied Intelligence and Computing (AIC). IEEE, 2024. http://dx.doi.org/10.1109/aic61668.2024.10730917.

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Razlan, Nurul Natasha, Raihah Aminuddin, Nurbaity Sabri, Shafaf Ibrahim, and Anis Amilah Shari. "Visual Facial Paralysis Detection using InceptionResNetV2." In 2024 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS). IEEE, 2024. http://dx.doi.org/10.1109/i2cacis61270.2024.10649871.

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Kiran, V. Gopi, T. Hemanth Babu, Vidula N. A, and Rimjhim Padam Singh. "Enhanced Pomegranate Grading Using InceptionResNetV2 with Transformer Integration." In 2024 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE). IEEE, 2024. https://doi.org/10.1109/wiecon-ece64149.2024.10914841.

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Saini, Aradhna, Attiuttama, Sheenam Naaz, Anushka Shivhare, Gaurav Dhuriya, and Neha Yadav. "Deepfake Detection: Leveraging InceptionResNetV2 and LSTM for Enhanced Accuracy." In 2025 International Conference on Pervasive Computational Technologies (ICPCT). IEEE, 2025. https://doi.org/10.1109/icpct64145.2025.10941494.

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Kumar, Aditya, and Leema Nelson. "Advanced Melanoma Detection with InceptionResNetV2 Using Skin Lesion Images." In 2025 International Conference on Multi-Agent Systems for Collaborative Intelligence (ICMSCI). IEEE, 2025. https://doi.org/10.1109/icmsci62561.2025.10894382.

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R, Mahesh T., P. Shenbagavalli, T. Mohanraj, P. Amala, K. Anitha, and Arastu Thakur. "Enhancing Histopathological Image Classification through InceptionResNetV2: A Deep Learning Approach." In 2024 International Conference on Emerging Research in Computational Science (ICERCS). IEEE, 2024. https://doi.org/10.1109/icercs63125.2024.10895005.

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Nasra, Parul, Sheifali Gupta, Sripelli Jagadish, and Anmol Rattan Singh. "Deep Learning for Oral Disease Detection: Leveraging InceptionResNetV2 for Automated Diagnosis." In 2025 4th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0. IEEE, 2025. https://doi.org/10.1109/otcon65728.2025.11070336.

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Patnaik, Rahul, Noel Shaji Mathew, Muhammad Ammar Siddiqui, Mohana, Ramakanth Kumar P, and Geetha K. S. "Robust Brain Tumor Classification of MRI Images Using Deep Learning via InceptionResNetV2." In 2024 8th International Conference on Computational System and Information Technology for Sustainable Solutions (CSITSS). IEEE, 2024. https://doi.org/10.1109/csitss64042.2024.10817000.

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Kaushik, Pratham, and Pooja Sharma. "Efficient Eye Disease Detection: A Deep Learning Approach with InceptionResNetV2 for Retinal Image Classification." In 2024 International Conference on Advances in Computing, Communication and Materials (ICACCM). IEEE, 2024. https://doi.org/10.1109/icaccm61117.2024.11059202.

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Tamilselvi, M., R. Ashwini, S. Ravi, Rajalakshmi S, Chadge Rajkumar, and Hamed Fawareh. "Enhanced Model for Brain Tumor Detection Accuracy Using Inceptionresnetv2 compared to VGG19 and MobileNet Models." In 2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES). IEEE, 2024. https://doi.org/10.1109/icses63760.2024.10910388.

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