Relevant bibliographies by topics / AlexNet CNN

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'AlexNet CNN'

Author: Grafiati
Published: 5 June 2025
Last updated: 15 July 2025

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

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'AlexNet CNN.'

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.

Journal articles on the topic "AlexNet CNN"

1

Aditya, Kakde Nitin Arora Durgansh Sharma. "A COMPARATIVE STUDY OF DIFFERENT TYPES OF CNN AND HIGHWAY CNN TECHNIQUES." Global Journal of Engineering Science and Research Management 6, no. 4 (2019): 18–31. https://doi.org/10.5281/zenodo.2639265.

Full text
Abstract:
In recent years, convolutional networks have shown breakthrough performance in image classification and detection. The main reason behind the performance of convnets is that they are inspired from the mammal’s visual cortex. In this paper, we have investigated the performance of four models that are Alexnet, Highway Convolutional Neural Network, Convolutional Neural Network and an evolutionary approach on highway convolutional neural network on the basis of train loss, test loss, train accuracy and test accuracy. These models are tested on two datasets that are WANG dataset and Simpsons dataset. In WANG dataset, Alexnet model achieved the highest test accuracy of 0.2625 and the highest train accuracy of 0.2193. Evolutionary Highway CNN has the least train loss of 0.1599 and CNN has the least test loss of 0.1604. In Simpsons dataset, Evolutionary Highway CNN has the Highest test accuracy of 0.5780. Highway CNN has the highest train accuracy of 0.5662 and for loss domain; Evolutionary Highway CNN has the least train and test loss
APA, Harvard, Vancouver, ISO, and other styles
2

Sachin, B. Jadhav, R. Udupi Vishwanath, and B. Patil Sanjay. "Convolutional neural networks for leaf image-based plant disease classification." International Journal of Artificial Intelligence (IJ-AI) 8, no. 4 (2019): 328–41. https://doi.org/10.11591/ijai.v8.i4.pp328-341.

Full text
Abstract:
Plant pathologists desire soft computing technology for accurate and reliable diagnosis of plant diseases. In this study, we propose an efficient soybean disease identification method based on a transfer learning approach by using a pre-trained convolutional neural network (CNN’s) such as AlexNet, GoogleNet, VGG16, ResNet101, and DensNet201. The proposed convolutional neural networks were trained using 1200 plant village image dataset of diseased and healthy soybean leaves, to identify three soybean diseases out of healthy leaves. Pre-trained CNN used to enable a fast and easy system implementation in practice. We used the five-fold cross-validation strategy to analyze the performance of networks. In this study, we used a pre-trained convolutional neural network as feature extractors and classifiers. The experimental results based on the proposed approach using pre-trained AlexNet, GoogleNet, VGG16, ResNet101, and DensNet201 networks achieve an accuracy of 95%, 96.4%, 96.4%, 92.1%, 93.6% respectively. The experimental results for the identification of soybean diseases indicated that the proposed networks model achieves the highest accuracy.
APA, Harvard, Vancouver, ISO, and other styles
3

Nur, Azida Muhammad, Ab Nasir Amelina, Ibrahim Zaidah, and Sabri Nurbaity. "Evaluation of CNN, Alexnet and GoogleNet for Fruit Recognition." Indonesian Journal of Electrical Engineering and Computer Science 12, no. 2 (2018): 468–75. https://doi.org/10.11591/ijeecs.v12.i2.pp468-475.

Full text
Abstract:
Fruit recognition is useful for automatic fruit harvesting. Fruit recognition application can reduce or minimize human intervention during fruit harvesting operation. However, in computer vision, fruit recognition is very challenging because of similar shapes, colors and textures among various fruits. Illuminations changes due to weather condition also lead to a challenging task for fruit recognition. Thus, this paper tends to investigate the performance of basic Convolutional Neural Network (CNN), Alexnet and Googlenet in recognizing nine different types of fruits from a publicly available dataset. The experimental results indicate that all these techniques produce excellent recognition accuracy, but basic CNN achieves the fastest recognition result compared with Alexnet and Googlenet.
APA, Harvard, Vancouver, ISO, and other styles
4

Akbar, Fauzan, Achmad Hidayatno, and Aris Triwiyatno. "PERANCANGAN PROGRAM PENGENALAN ISYARAT TANGAN DENGAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN)." Transient: Jurnal Ilmiah Teknik Elektro 9, no. 1 (2020): 26–36. http://dx.doi.org/10.14710/transient.v9i1.26-36.

Full text
Abstract:
Convolutional Neural Network is one of the Deep Learning Algorithms. CNN itself is developed from Multilayer Peceptron (MLP) method. CNN and MLP are algorithms that focused on processing data in two dimensions form, such as pictures or sounds. CNN is made with the principle of translation invariance. That means CNN is able to recognize objects at various possible positions. There are 150 sign language images that are classified using Alexnet in this system. Alexnet is Krizhevsky's work at developing CNN method as a clessifier. CNN architecture developed by Alex has eight feature extraction layers. The layer consists of five convolution layers and three pooling layers. In its classification layer, Alexnet has two fully connected layers, each of them has 4096 neurons. At the end of the layer, there are classifications into 5 categories using softmax activation. The average accuracy of the classification results even reaches 100%.
APA, Harvard, Vancouver, ISO, and other styles
5

Denis, Eka Cahyani, Dwi Hariadi Anjar, Farris Setyawan Faisal, Gumilar Langlang, and Setumin Samsul. "COVID-19 classification using CNN-BiLSTM based on chest X-ray images." Bulletin of Electrical Engineering and Informatics 12, no. 3 (2023): 1773~1782. https://doi.org/10.11591/eei.v12i3.4848.

Full text
Abstract:
Cases of the COVID-19 virus continue to spread still needs to be considered even though we have entered the post-pandemic era. Rapid identification of COVID-19 cases is necessary to prevent the virus from spreading further. This study developed a chest X-ray-based (CXR) COVID-19 classification for COVID-19 detection using the convolutional neural networkbidirectional long short-term memory (CNN-BiLSTM) combination model and compared the CNN-BiLSTM combination model with CNN models. The CNN models used in this study are the transfer learning models, namely Resnet50, VGG19, InceptionV3, Xception, and AlexNet. This research classifies CXR into three groups: COVID-19, normal, and viral pneumonia. In comparison to other models, the Resnet50-BiLSTM model is the most accurate and hence the best. The accuracy of the Resnet50-BiLSTM model was 98.48%. The model that obtains the next highest accuracy i.e Resnet50, VGG19-BiLSTM, VGG19, InceptionV3-BiLSTM, InceptionV3, XceptionBiLSTM, Xception, AlexNet-BiLSTM, and AlexNet. In this study, precision, recall, and F1-measure are also employed to demonstrate that Resnet50-BiLSTM achieves the highest value compared to other approaches. When compared to previous studies, this study enhances classification performance results.
APA, Harvard, Vancouver, ISO, and other styles
6

Dyah Irawati, Indrarini, Gelar Budiman, Sofia Saidah, Suci Rahmadiani, and Rohaya Latip. "Block-based compressive sensing in deep learning using AlexNet for vegetable classification." PeerJ Computer Science 9 (November 16, 2023): e1551. http://dx.doi.org/10.7717/peerj-cs.1551.

Full text
Abstract:
Vegetables can be distinguished according to differences in color, shape, and texture. The deep learning convolutional neural network (CNN) method is a technique that can be used to classify types of vegetables for various applications in agriculture. This study proposes a vegetable classification technique that uses the CNN AlexNet model and applies compressive sensing (CS) to reduce computing time and save storage space. In CS, discrete cosine transform (DCT) is applied for the sparsing process, Gaussian distribution for sampling, and orthogonal matching pursuit (OMP) for reconstruction. Simulation results on 600 images for four types of vegetables showed a maximum test accuracy of 98% for the AlexNet method, while the combined block-based CS using the AlexNet method produced a maximum accuracy of 96.66% with a compression ratio of 2×. Our results indicated that AlexNet CNN architecture and block-based CS in AlexNet can classify vegetable images better than previous methods.
APA, Harvard, Vancouver, ISO, and other styles
7

C, Sridevi, and Kannan M. "Convolutional Neural Network Architecture-Inception (GoogLeNet) For Deep Architected Learning-Assisted Lung Cancer Classification in Computed Tomography Images." Indian Journal of Science and Technology 18, no. 7 (2025): 504–16. https://doi.org/10.17485/IJST/v18i7.3086.

Full text
Abstract:
<strong>Objectives:</strong>&nbsp;The primary objective of this study is to investigate the performance of AlexNet and GoogLeNet architectures in classifying lung cancer images from the LIDC-IDRI dataset. Additionally, the AlexNet architecture is modified to generate three classes (benign, malignant, and non-nodules) using a new dataset.&nbsp;<strong>Methods:</strong>&nbsp;This study utilizes the LIDC-IDRI dataset, consisting of 1018 thoracic CT scans from 1010 patient cases, with annotations from four radiologists. The AlexNet and GoogLeNet architectures are employed for image classification, with the following Hyperparameters: AlexNet uses a learning rate of 0.01 and a dropout of 0.5, while GoogLeNet uses a learning rate of 0.03 and a dropout of 0.4. Both architectures are trained and tested using the LIDC-IDRI dataset. The training accuracy achieved for VGG 16 and GoogLeNet is 0.952 and 0.963 respectively with low log loss.&nbsp;<strong>Findings:</strong>&nbsp;The findings of this study indicate that GoogLeNet outperforms VGG 16 in terms of accuracy, with a classification accuracy of 0.963 compared to 0.952 for VGG 16. Additionally, GoogLeNet demonstrates a higher F1-score (0.946) compared to AlexNet (0.714) and VGG 16 (0.825). The receiver operating characteristic (ROC) curve analysis reveals that GoogLeNet has a higher area under the curve (AUC) value (0.973) compared to AlexNet (0.923) and VGG 16 (0.968). This study demonstrates the effectiveness of deep learning architectures in classifying lung cancer images, with GoogLeNet outperforming AlexNet in terms of accuracy and F1-score. The proposed methodology can be extended to develop a hybrid convolutional neural network for identifying cancer cells in image datasets with improved performance and reduced computational time.&nbsp;<strong>Novelty/application:</strong>&nbsp;Future studies can focus on developing a hybrid convolutional neural network that combines the strengths of AlexNet and GoogLeNet for improved performance and reduced computational time. Additionally, investigating the application of transfer learning and fine-tuning techniques can improve the performance of deep learning architectures in lung cancer image classification. <strong>Keywords:</strong>&nbsp;Lung Cancer, CNN Architecture, GoogLeNet, AlexNet, Performance Analysis &nbsp;
APA, Harvard, Vancouver, ISO, and other styles
8

Pratama, Aan Rachmatullah, and Adi Fajaryanto Cobantoro. "KLASIFIKASI CITRA PNEUMONIA MENGGUNAKAN ARSITEKTUR CONVOLUTIONAL NEURAL NETWORK (CNN)." Networking Engineering Research Operation 8, no. 2 (2023): 133–44. https://doi.org/10.21107/nero.v8i2.18992.

Full text
Abstract:
Pneumonia adalah infeksi atau peradangan akut pada bagian jaringan paru yang disebabkan oleh berbagai mikroorganisme seperti bakteri, virus, parasit, jamur, kerusakan fisik paru ataupun bahan kimia. Pneumonia dapat menyerang orang dewasa maupun anak-anak, banyak kasus yang terjadi, terutama pada Negara berkembang dimana kebanyakan mengandalkan energi yang berpontensi menyebabkan polusi udara yang akan berdampak pada pernafasan manusia. Klasifikasi citra Pneumonia dari hasil rontgen dengan algoritma Convolutional Neural Network yang memiliki metode alur pemecahan masalah yang menyerupai pola pikir manusia. Pada program ini melakukan penelitian tentang membandingkan performa dari kedua model arsitektur Convolutional Neural Network arsitektur AlexNet dengan GoogleNet. Pada hasil confusion matrix mendapatkan hasil tingkat akurasi 0,79 untuk arsitektur Alexnet dan untuk arsitektur GoogLeNet mendapatkan hasil akurasi 0,78. Umumnya akurasi dari GoogLeNet lebih tinggi namun pada penelitian ini AlexNet mendapatkan akurasi yang lebih tinggi, namun GoogLeNet memiliki loss yang lebih rendah, loss dan Accuracy diperngaruhi callback yang didalamanya terdapat epoch. Pada hasil implementasi kedua model dari web app menggunakan flask dan Google colab, dari jumlah masukan 16 citra 15 prediksi dilakukan benar dan 1 salah mendapatkan hasil akurasi 0,94.Kata kunci : AlexNet, CNN, GoogLeNet, Pneumonia
APA, Harvard, Vancouver, ISO, and other styles
9

Ong, Jia Heng, Pauline Ong, and Kiow Lee Woon. "IMAGE-BASED OIL PALM LEAVES DISEASE DETECTION USING CONVOLUTIONAL NEURAL NETWORK." Journal of Information and Communication Technology 21, No.3 (2022): 383–410. http://dx.doi.org/10.32890/jict2022.21.3.4.

Full text
Abstract:
Over the years, numerous studies have been conducted on the integration of computer vision and machine learning in plant disease detection. However, these conventional machine learning methods often require the contour segmentation of the infected region from the entire leaf region and the manual extraction of different discriminative features before the classification models can be developed. In this study, deep learning models, specifically, the AlexNet convolutional neural network (CNN) and the combination of AlexNet and support vector machine (AlexNet-SVM), which overcome the limitation of handcrafting of feature representation were implemented for oil palm leaf disease identification. The images of healthy and infected leaf samples were collected, resized, and renamed before the model training. These images were directly used to fit the classification models, without the need for segmentation and feature extraction as in models, without the need for segmentation and feature extraction as in the conventional machine learning methods. The optimal architecture of AlexNet CNN and AlexNet-SVM models were then determined and subsequently applied for the oil palm leaf disease identification.Comparative studies showed that the overall performance of the AlexNet CNN model outperformed AlexNet-SVM-based classifier.
APA, Harvard, Vancouver, ISO, and other styles
10

Damatraseta, Febri, Rani Novariany, and Muhammad Adlan Ridhani. "Real-time BISINDO Hand Gesture Detection and Recognition with Deep Learning CNN." Jurnal Informatika Kesatuan 1, no. 1 (2021): 71–80. http://dx.doi.org/10.37641/jikes.v1i1.774.

Full text
Abstract:
BISINDO is one of Indonesian sign language, which do not have many facilities to implement. Because it can cause deaf people have difficulty to live their daily life. Therefore, this research tries to offer an recognition or translation system of the BISINDO alphabet into a text. The system is expected to help deaf people to communicate in two directions. In this study the problems encountered is small datasets. Therefore this research will do the testing of hand gesture recognition, by comparing two model CNN algorithms, that is LeNet-5 and Alexnet. This test will look for which classification technique is better if the dataset conditions in an amount that does not reach 1000 images in each class. After testing, the results found that the CNN technique on the Alexnet architectural model is better to used, this is because when doing the testing process by using still-image and Alexnet model data which has been released in training process, Alexnet model data gives greater prediction results that is equal to 76%. While the LeNet model is only able to predict with the percentage of 19%. When that Alexnet data model used on the system offered, only able to predict correcly by 60%.&#x0D; &#x0D; Keywords: Sign language, BISINDO, Computer Vision, Hand Gesture Recognition, Skin Segmentation, CIELab, Deep Learning, CNN.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "AlexNet CNN"

1

Balestri, Roberto. "Intelligenza artificiale e industrie culturali storia, tecnologie e potenzialità dell’ia nella produzione cinematografica." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amslaurea.unibo.it/25176/.

Full text
Abstract:
Negli ultimi anni stiamo assistendo, in svariati campi, a un sempre più vasto utilizzo di tecnologie che utilizzano quella che viene comunemente chiamata “intelligenza artificiale”. Anche il settore audiovisivo, da sempre recettore di novità e incline a evolversi continuamente, sta già vivendo quei processi che lo porteranno a essere rivoluzionato da questo tipo di tecnologie. In un periodo di frenetico progresso scientifico è difficile riuscire a fissare nel tempo e su carta lo stato attuale dello sviluppo tecnologico, dato che ciò che oggi viene considerato come novità domani potrebbe già essere stato superato. È necessario, quindi, uno strumento che riesca a catalogare, se non tutte, almeno le più importanti rivoluzionarie tecnologie d’intelligenza artificiale che hanno investito il mondo della produzione artistica e delle industrie culturali. Uno studio approfondito è dedicato, in particolare, all’industria cinematografica. Dopo una breve introduzione di carattere storico, vengono descritti i principali tipi di rete neurale artificiale e la loro evoluzione. Sono poi delineate e descritte le principali tecnologie d’IA applicate all’elaborazione, comprensione e generazione automatica o assistita d’immagine e testo. Ancora più nel dettaglio sono osservate alcune soluzioni tecnologiche che interessano le varie fasi del processo di produzione cinematografica, come la fase di scrittura e analisi della sceneggiatura, quella di editing e montaggio video, così come quelle riguardanti l’implementazione di effetti visivi e la composizione musicale. Il testo risulta essere, da un lato, una fotografia sul passato che ha interessato lo sviluppo delle tecnologie d’IA, dall’altro uno strumento che illustra il presente così da aiutarci, se non a predire, almeno a non trovarci completamente impreparati di fronte agli sviluppi futuri che interesseranno sia la produzione audiovisiva che, in senso più ampio, la nostra vita di tutti i giorni.
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "AlexNet CNN"

1

Sai Adhinesh Reddy, T., V. S. Yashwanth Varma Vadlamudi, Saket Acharya, Umashankar Rawat, and Roheet Bhatnagar. "Windows Malware Detection Using CNN and AlexNet Learning Models." In Proceedings of the 8th International Conference on Advanced Intelligent Systems and Informatics 2022. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-20601-6_25.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Barbhuiya, Abul Abbas, Ram Kumar Karsh, and Samiran Dutta. "AlexNet-CNN Based Feature Extraction and Classification of Multiclass ASL Hand Gestures." In Lecture Notes in Electrical Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0275-7_7.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Sultan, Shahrukh, and Yana Bekeneva. "A Comparative Analysis of a Designed CNN and AlexNet for Image Classification on Small Datasets." In Intelligent Distributed Computing XIV. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96627-0_40.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Shrivastava, Shubhi, Shanti Rathore, and Rahul Gedam. "Deep Feature Extraction and Classification of Diabetic Retinopathy Using AlexNet, InceptionV3, and VGG16 CNN Architectures." In Advances in Economics, Business and Management Research. Atlantis Press International BV, 2024. https://doi.org/10.2991/978-94-6463-612-3_7.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Hj Wan Yussof, Wan Nural Jawahir, Nurfarahim Shaharudin, Muhammad Suzuri Hitam, Ezmahamrul Afreen Awalludin, Mohd Uzair Rusli, and Daphne Z. Hoh. "Photo Identification of Sea Turtles Using AlexNet and Multi-Class SVM." In Knowledge Innovation Through Intelligent Software Methodologies, Tools and Techniques. IOS Press, 2020. http://dx.doi.org/10.3233/faia200549.

Full text
Abstract:
Up to now, identification of sea turtle species mainly for tracking the population usually relied on flipper tags or through other physical markers. However, this approach is not practical due to the missing tags over some period. Due to this matter, we propose a photo identification system of the individual sea turtle based on the convolutional neural network (CNN) using a pre-trained AlexNet CNN and error-correcting output codes (ECOC) SVM. Experiments were performed on 300 images obtained from Biodiversity Research Center, Academia Sinica, Taiwan. Using Alexnet and ECOC SVM, the overall accuracy achieved is 62.9%. The results indicate that features obtained from the CNN are capable of identifying photo of sea turtles.
APA, Harvard, Vancouver, ISO, and other styles
6

Das, Anuj Kumar, and Dr Syed Sazzad Ahmed. "CONVOLUTIONAL NEURAL NETWORKS." In Futuristic Trends in Artificial Intelligence Volume 2 Book 16. Iterative International Publishers, Selfypage Developers Pvt Ltd, 2023. http://dx.doi.org/10.58532/v2bs16ch18.

Full text
Abstract:
Convolutional Neural Network (CNN) is one of the most important algorithms used for computer vision task. CNN integrates feature extraction process along with classification. It can automatically extract features from images. Feature extraction is done through convolution operation in CNN. CNN also employs pooling operation for dimensionality reduction. Over the years researchers have proposed different architectures of CNN. The architectures differ based on the numbers of convolution layers, size of filters, number of pooling layers and activation functions used in the CNN model. LeNet 5, AlexNet, VGG16, VGG19 are some of the CNN architectures discussed here
APA, Harvard, Vancouver, ISO, and other styles
7

Bhanumathi M, Ravi Rithika, Roshni R, and Sona Selvaraj. "Underwater Fish Species Classification Using Alexnet." In Advances in Parallel Computing Algorithms, Tools and Paradigms. IOS Press, 2022. http://dx.doi.org/10.3233/apc220056.

Full text
Abstract:
There has been a constant need for the classification of fish species for a better understanding of the underwater ecological balance. Identifying the characteristics of different fish species plays a significant role in knowing the insights of marine ecology and is a great deal to many fisheries and industries. Manually classifying fish species is time-consuming and requires high sampling efforts. The behaviour of fishes can be well understood using an automated system that accurately classifies various fish species effectively. The classification of underwater images has difficulties like background noise interruption, image disruption, lower quality of images, occlusion. The proposed model lights up on the assortment of fish species using Alexnet. The knowledge of the previously trained model is given to the alexnet for improving the system. The performance of our improved model is demonstrated with real-world data from a research organization called Kaggle. CNN has used several layers trained for precise identification of the distinct features of a species and classify them accordingly. This paper ensures increased accuracy than the existing systems.
APA, Harvard, Vancouver, ISO, and other styles
8

Alaeddine, Hmidi, and Malek Jihene. "A Comparative Study of Popular CNN Topologies Used for Imagenet Classification." In Deep Neural Networks for Multimodal Imaging and Biomedical Applications. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-3591-2.ch007.

Full text
Abstract:
Deep Learning is a relatively modern area that is a very important key in various fields such as computer vision with a trend of rapid exponential growth so that data are increasing. Since the introduction of AlexNet, the evolution of image analysis, recognition, and classification have become increasingly rapid and capable of replacing conventional algorithms used in vision tasks. This study focuses on the evolution (depth, width, multiple paths) presented in deep CNN architectures that are trained on the ImageNET database. In addition, an analysis of different characteristics of existing topologies is detailed in order to extract the various strategies used to obtain better performance.
APA, Harvard, Vancouver, ISO, and other styles
9

Chantrapornchai, Chantana, and Samrid Duangkaew. "CNN Customizations With Transfer Learning for Face Recognition Task." In Handbook of Research on Deep Learning Innovations and Trends. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7862-8.ch003.

Full text
Abstract:
Several kinds of pretrained convolutional neural networks (CNN) exist nowadays. Utilizing these networks with the new classification task requires the retraining with new data sets. With the small embedded device, large network cannot be implemented. The authors study the use of pretrained models and customizing them towards accuracy and size against face recognition tasks. The results show 1) the performance of existing pretrained networks (e.g., AlexNet, GoogLeNet, CaffeNet, SqueezeNet), as well as size, and 2) demonstrate the layers customization towards the model size and accuracy. The studied results show that among the various networks with different data sets, SqueezeNet can achieve the same accuracy (0.99) as others with small size (up to 25 times smaller). Secondly, the two customizations with layer skipping are presented. The experiments show the example of SqueezeNet layer customizing, reducing the network size while keeping the accuracy (i.e., reducing the size by 7% with the slower convergence time). The experiments are measured based on Caffe 0.15.14.
APA, Harvard, Vancouver, ISO, and other styles
10

Mahalle, Vishwanath S., Narendra M. Kandoi, Santosh B. Patil, Abhijit Banubakode, and Vandana C. Bagal. "Enhancing Efficiency in Content-based Image Retrieval System Using Pre-trained Convolutional Neural Network Models." In Artificial Intelligence, Machine Learning and User Interface Design. BENTHAM SCIENCE PUBLISHERS, 2024. http://dx.doi.org/10.2174/9789815179606124010015.

Full text
Abstract:
Traditionally, image retrieval is done using a text-based approach. In the text-based approach, the user must query metadata or textual information, such as keywords, tags, or descriptions. The effectiveness and utility of this approach in the digital realm for solving image retrieval problems are limited. We introduce an innovative method that relies on visual content for image retrieval. Various visual aspects of the image, including color, texture, shape, and more, are employed to identify relevant images. The choice of the most suitable feature significantly influences the system's performance. Convolutional Neural Network (CNN) is an important machine learning model. Creating an efficient new CNN model requires considerable time and computational resources. There are many pre-trained CNN models that are already trained on large image datasets, such as ImageNet containing millions of images. We can use these pre-train CNN models by transferring the learned knowledge to solve our specific content-based image retrieval talk. In this chapter, we propose an efficient pre-trained CNN model for content-based image retrieval (CBIR) named as ResNet model. The experiment was conducted by applying a pre-trained ResNet model on the Paris 6K and Oxford 5K datasets. The performance of similar image retrieval has been measured and compared with the stateof-the-art AlexNet model. It is found that the AlexNet architecture takes a longer time to get more accurate results. The ResNet architecture does not need to fire all neurons at every epoch. This significantly reduces training time and improves accuracy. In the ResNet architecture, once the feature is extracted, it will not extract the feature again. It will try to learn a new feature. To measure its performance, we used the average mean precision. We obtained the result for Paris6K 92.12% and Oxford5K 84.81%. The Mean Precision at different ranks, for example, at the first rank in Paris6k, we get 100% result, and for Oxford5k, we get 97.06%.
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "AlexNet CNN"

1

Roshini, Polagani, Shaik Khajavali, M. L. Sneha Snigdha, Badugu Deva Harsha, Bandlamudi Srilakshmi, and Arepalli Gopi. "CNN Design with AlexNet Algorithm for Diagnosis of Diseases in Cassava Leaves." In 2024 International Conference on Expert Clouds and Applications (ICOECA). IEEE, 2024. http://dx.doi.org/10.1109/icoeca62351.2024.00129.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Sree, Pranitha, Surendran R, and Raveena Selvanarayanan. "Leveraging CNN and AlexNet Algorithms for Improved Coffee Leaf Disease Identification and Detection." In 2024 First International Conference on Innovations in Communications, Electrical and Computer Engineering (ICICEC). IEEE, 2024. https://doi.org/10.1109/icicec62498.2024.10808919.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Bandla, Rakesh, J. Swetha Priyanka, Yashash Chandra Dumpali, and Sunil Kumar M. Hattaraki. "Plant Identification and Analysis of Medicinal Properties Using Image Processing and CNN with MobileNetV2 and AlexNet." In 2024 International Conference on Innovation and Novelty in Engineering and Technology (INNOVA). IEEE, 2024. https://doi.org/10.1109/innova63080.2024.10847039.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Chandra Sekar, V., and V. Nagaraju. "Prediction of U2R attack in Wireless Networks using Novel CNN Alexnet in Comparison with Random Forest Algorithm." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10726240.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

S, Santhiya, T. Senthil Kumar, Jayadharshini P, Rohit P. V, Sriram V, and Sreepriyanth N. S. "Automated Detection of Tomato Leaf Disease Using Convolutional Neural Networks: Evaluation of GoogLeNet, AlexNet, and Custom CNN Architectures." In 2024 IEEE 4th International Conference on ICT in Business Industry & Government (ICTBIG). IEEE, 2024. https://doi.org/10.1109/ictbig64922.2024.10910930.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

S, Harshavarthan, and Kavitha T. "Effective Forecasting for Enhancing the Precision in U2R Attacks in Wireless Networks Through the Use of New CNN Alexnet Compared to Support Vector Machine Algorithm." In 2024 International Conference on Advances in Computing Research on Science Engineering and Technology (ACROSET). IEEE, 2024. http://dx.doi.org/10.1109/acroset62108.2024.10743609.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Babu, Buvaneswari, Sindhuja Sekar, Srivenika Kumar, Sweatha Sivakumar, and Varshini Muralidsharan. "Detection of diabetic retinopathy using CNN and Alexnet." In SUSTAINABLE DEVELOPMENTS IN MATERIALS SCIENCE, TECHNOLOGY AND ENGINEERING: Sustainable Development in Material Science of Today Is the Innovation of Tomorrow. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0153887.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Chandu, Seethala Devi, P. Revathi, and N. A. S. Vinoth. "Discovering Knee Osteoarthritis Using CNN Enhanced with AlexNet." In 2024 5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV). IEEE, 2024. http://dx.doi.org/10.1109/icicv62344.2024.00028.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Rahmat, Rafhan Amnani, and Suhaili Beeran Kutty. "Malaysian Food Recognition using Alexnet CNN and Transfer Learning." In 2021 IEEE 11th IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE). IEEE, 2021. http://dx.doi.org/10.1109/iscaie51753.2021.9431833.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Agarwal, Aman, Kritik Patni, and Rajeswari D. "Lung Cancer Detection and Classification Based on Alexnet CNN." In 2021 6th International Conference on Communication and Electronics Systems (ICCES). IEEE, 2021. http://dx.doi.org/10.1109/icces51350.2021.9489033.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "AlexNet CNN"

1

Yu, Haichao, Haoxiang Li, Honghui Shi, Thomas S. Huang, and Gang Hua. Any-Precision Deep Neural Networks. Web of Open Science, 2020. http://dx.doi.org/10.37686/ejai.v1i1.82.

Full text
Abstract:
We present Any-Precision Deep Neural Networks (Any- Precision DNNs), which are trained with a new method that empowers learned DNNs to be flexible in any numerical precision during inference. The same model in runtime can be flexibly and directly set to different bit-width, by trun- cating the least significant bits, to support dynamic speed and accuracy trade-off. When all layers are set to low- bits, we show that the model achieved accuracy compara- ble to dedicated models trained at the same precision. This nice property facilitates flexible deployment of deep learn- ing models in real-world applications, where in practice trade-offs between model accuracy and runtime efficiency are often sought. Previous literature presents solutions to train models at each individual fixed efficiency/accuracy trade-off point. But how to produce a model flexible in runtime precision is largely unexplored. When the demand of efficiency/accuracy trade-off varies from time to time or even dynamically changes in runtime, it is infeasible to re-train models accordingly, and the storage budget may forbid keeping multiple models. Our proposed framework achieves this flexibility without performance degradation. More importantly, we demonstrate that this achievement is agnostic to model architectures. We experimentally validated our method with different deep network backbones (AlexNet-small, Resnet-20, Resnet-50) on different datasets (SVHN, Cifar-10, ImageNet) and observed consistent results.
APA, Harvard, Vancouver, ISO, and other styles
2

Forero Fuarez, Luis Carlos. Procesamiento de imágenes. Escuela Tecnológica Instituto Técnico Central - ETITC, 2023. http://dx.doi.org/10.55411/2023.4.

Full text
Abstract:
El semillero tiene como uno de sus objetivos, la enseñanza y la aplicación de técnicas y herramientas de inteligencia artificial en áreas de la ingeniería electromecánica y afines. Para ello se seguirá un proceso que requerirá en sus primeras etapas la recopilación de la información, su limpieza, transformación y análisis, persiguiendo mediante el aprendizaje continuo de los estudiantes y su desarrollo en posteriores etapas, la implementación de modelos y/o arquitecturas que permitan desarrollar un modelo de IA basado en técnicas de visión por computadora y aprendizaje automático para reconocer las placas de los vehículos que ingresan a la ETITC en tiempo real y/o aplicaciones en general, como procesos de regresión, clasificación, segmentación, etc. Considerando que a futuro se planteará el trabajar con imágenes, se sabe que este campo presenta gran auge en distintos campos, pues como lo menciona LeCun et al. 2015, el uso de redes convolucionales ha ampliado la capacidad para extraer características relevantes de las imágenes, lo que es fundamental para el reconocimiento de placas de vehículos. Adicionalmente, se han desarrollado métodos como el introducido por Redmon J et al. (2016), el cual es conocido actualmente como YOLO "You Only Look Once" que mediante redes convolucionales facilita el reconocimiento de objetos. Adicionalmente se tiene el ejemplo de Krizhevsky, A (2012), quien mediante el modelo AlexNet, presentó gran eficacia en tareas de reconocimiento de imágenes.
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'AlexNet CNN' for particular source types:
Journal articles
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!

To the bibliography