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Relevant bibliographies by topics / LeNet-1

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Academic literature on the topic 'LeNet-1'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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Journal articles on the topic "LeNet-1"

1

Hadi, Risman, Ramaditia Dwiyansaputra, and Pahrul Irfan. "IMPLEMENTASI LENET-5 DAN MOBILENET-V2 UNTUK KLASIFIKASI KEMATANGAN BUAH CABAI BERBASIS COMPUTER VISION." JATI (Jurnal Mahasiswa Teknik Informatika) 9, no. 1 (2025): 1485–93. https://doi.org/10.36040/jati.v9i1.12725.

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Tingginya konsumsi cabai rawit menjadikannya komoditas dengan permintaan yang selalu tinggi, sehingga memiliki nilai ekonomi yang signifikan bagi para petani . Terlebih lagi, makanan khas Pulau Lombok seperti pelecing kangkung, ayam taliwang, dan bebalung sangat ditentukan cita rasanya oleh cabai sebagai salah satu bahan utama untuk bumbu. Permintaan cabai yang cenderung tinggi sepanjang tahun tanpa mengenal musim menciptakan tantangan dalam pengelolaan kualitas cabai di pasaran. Identifikasi kualitas buah cabai yang masih dilakukan secara visual oleh petani sering kali menghasilkan kesalahan

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Munsarif, Muhammad, Muhammad Sam'an, and Andrian Fahrezi. "Convolution neural network hyperparameter optimization using modified particle swarm optimization." Bulletin of Electrical Engineering and Informatics 13, no. 2 (2024): 1268–75. http://dx.doi.org/10.11591/eei.v13i2.6112.

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Based on the literature review, a convolutional neural network (CNN) is one of the deep learning techniques most often used for classification problems, especially image classification. Various approaches have been proposed to improve accuracy performance. In CNN architecture, parameter determination is very influential on accuracy performance. Particle swarm optimization (PSO) is a type of metaheuristic algorithm widely used for hyperparameter optimization. PSO convergence is faster than genetic algorithm (GA) and attracts many researchers for further studies such as genetic algorithms and an

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Nayak, Jithendra P. R., and Parameshachari B. D. "Defect Detection in Printed Circuit Boards Using Leaky-LeNet 5." International Journal of Software Innovation 10, no. 1 (2022): 1–13. http://dx.doi.org/10.4018/ijsi.309726.

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Each electronic device includes printed circuit boards (PCBs), where defect detection is an important process to enhance the quality of PCB production. To accomplish error-free PCBs, the researchers and experts converted traditional manual inspections into automated systems. The manual inspection results are ineffective, where the non-defective PCBs are classified as defective PCBs. A subsequent study added a technique called LeNetwork-5 (LeNet-5) and speeded up robust feature extraction (SURF) techniques to identify defects. The existing method was unreliable, so further research was conducte

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Das, Sumit, Dipansu Mondal, and Diprajyoti Majumdar. "Intelligent Application of Laser for Medical Prognosis: An Instance for Laser Mark Diabetic Retinopathy." Biosciences Biotechnology Research Asia 20, no. 2 (2023): 547——559. http://dx.doi.org/10.13005/bbra/3109.

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ABSTRACT: Refractive laser surgery is all about the accuracy, whether screening or surgery, given the age and profile of the patient enduring these trials, there is no margin for error. Most of them are for aesthetic reasons, contact lens intolerance, or professional reasons, including athletes. In this article, the role of artificial intelligence and deep learning in laser eye surgeries has been introduced. The presence of lingering laser spots on the retina after refractive laser surgery in diabetic retinopathy poses a potential risk to visual integrity and ocular well-being. The hypothesis

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Bao, Rongxin, Xu Yuan, Zhikui Chen, and Ruixin Ma. "Cross-Entropy Pruning for Compressing Convolutional Neural Networks." Neural Computation 30, no. 11 (2018): 3128–49. http://dx.doi.org/10.1162/neco_a_01131.

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The success of CNNs is accompanied by deep models and heavy storage costs. For compressing CNNs, we propose an efficient and robust pruning approach, cross-entropy pruning (CEP). Given a trained CNN model, connections were divided into groups in a group-wise way according to their corresponding output neurons. All connections with their cross-entropy errors below a grouping threshold were then removed. A sparse model was obtained and the number of parameters in the baseline model significantly reduced. This letter also presents a highest cross-entropy pruning (HCEP) method that keeps a small p

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Vijayanandan, Thanoshan, Kuhaneswaran Banujan, Ashan Induranga, Banage T. G. S. Kumara, and Kaveenga Koswattage. "LeONet: A Hybrid Deep Learning Approach for High-Precision Code Clone Detection Using Abstract Syntax Tree Features." Big Data and Cognitive Computing 9, no. 7 (2025): 187. https://doi.org/10.3390/bdcc9070187.

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Code duplication, commonly referred to as code cloning, is not inherent in software systems but arises due to various factors, such as time constraints in meeting project deadlines. These duplications, or “code clones”, complicate the program structure and increase maintenance costs. Code clones are categorized into four types: Type-1, Type-2, Type-3, and Type-4. This study aims to address the adverse effects of code clones by introducing LeONet, a hybrid Deep Learning approach that enhances the detection of code clones in software systems. The hybrid approach, LeONet, combines LeNet-5 with Or

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Sadik Croock, Muayad, and Sahar Salman Mahmood. "Management System of Smart Electric Vehicles Using Software Engineering Model." International journal of electrical and computer engineering systems 13, no. 5 (2022): 369–77. http://dx.doi.org/10.32985/ijeces.13.5.5.

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In this paper, a management system for smart electric vehicle is introduced using software engineering models and installed Sensor Network (SN). Two software engineering models are proposed to construct information exchange and available resource management algorithms, in which the required performance of vehicles is obtained. The resource management algorithm adopts the LeNet-5 deep-learning model in choosing the best driving mode. The datset is achieved from the simulated sensor Network (SN). The results show the satisfied performance of the electric cars in terms of information exchange and

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Mingalev, A. V., A. V. Belov, I. M. Gabdullin, R. R. Agafonova, and S. N. Shusharin. "Test-object recognition in thermal images." Computer Optics 43, no. 3 (2019): 402–11. http://dx.doi.org/10.18287/2412-6179-2019-43-3-402-411.

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The paper presents a comparative analysis of several methods for recognition of test-object position in a thermal image when setting and testing characteristics of thermal image channels in an automated mode. We consider methods of image recognition based on the correlation image comparison, Viola-Jones method, LeNet classificatory convolutional neural network, GoogleNet (Inception v.1) classificatory convolutional neural network, and a deep-learning-based convolutional neural network of Single-Shot Multibox Detector (SSD) VGG16 type. The best performance is reached via using the deep-learning

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Ковальчук, Р., та О. Польшакова. "CNN для вирішення задач Computer vision". Адаптивні системи автоматичного управління 1, № 44 (2024): 93–102. http://dx.doi.org/10.20535/1560-8956.44.2024.302422.

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Об'єктом дослідження є нейронні мережі в області комп’ютерного зору та аналізу даних. У статті розглядаються ключові принципи та аспекти, що лежать в основі функціонування нейронних мереж. Серед визначених обмежень – складність налаштування гіперпараметрів та обчислювальні витрати, пов’язані із збільшенням глибини мережі та розміру даних для навчання. Мета роботи полягає в аналізі сучасних рішень, пов’язаних із згортковими нейронними мережами (CNN), для вибору оптимальної топології, яка оптимізує вирішення типових завдань CV. Основні методи які розглянуто: згорткові, пулінгові, повнозв’язані,

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Rifqie, Dary Mochamad, Dewi Fatmarani Surianto, Sudarmanto Jayanegara, Muhammad Fajar B, and M. Miftach Fakhri. "Minimizing Multiplication of Kernel Computation in Convolutional Neural Networks Using Strassen Algorithm." Jurnal MediaTIK 6, no. 2 (2023): 52. http://dx.doi.org/10.26858/jmtik.v6i2.46016.

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Convolution neural networks (CNN) have been widely applied for the computer vision task. However, the success of CNN is limited by the computational complexity of the network, so it is difficult for the model to run the inference process in real time. In this paper, we apply Strassen matrix multiplication to reduce multiplications in convolution operations in CNN, in order to get faster execution for CNN. First, we transform the convolution operation into a matrix multiplication operation using the Toeplitz mapping method, then after that, we apply the Strassen method to these matrices. In the

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Books on the topic "LeNet-1"

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Tageskalender, Tagesplaner 2020 Frauen. Terminkalender 2020: F�r Lene Personalisierter Taschenkalender und Tagesplaner Ca DIN A5 - 376 Seiten - 1 Seite Pro Tag - Tagebuch - Wochenplaner. Independently Published, 2019.

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Berlin, Kreativwerkstatt. Hausaufgabenheft Lene: Personalisiertes Einhorn Hausaufgabenheft / Sch�lerplaner F�r 1 Schuljahr Mit Wochen�bersicht / Mit 2x Stundenplan / DIN a 5 / 112 Seiten. Independently Published, 2020.

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Book chapters on the topic "LeNet-1"

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Ren, Zenghui, Tao Liu, Zhaoyuan Liu, Min Tian, Ying Guo, and Jingshan Pan. "SW-LeNet: Implementation and Optimization of LeNet-1 Algorithm on Sunway Bluelight II Supercomputer." In Algorithms and Architectures for Parallel Processing. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0808-6_16.

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Verdhan, Vaibhav. "Image Classification Using LeNet." In Computer Vision Using Deep Learning. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6616-8_3.

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Bouti, Amal, Mohamed Adnane Mahraz, Jamal Riffi, and Hamid Tairi. "A Robust System for Road Sign Detection and Classification Using LeNet Architecture Based on Convolutional Neural Network." In Advancements in Computer Vision Applications in Intelligent Systems and Multimedia Technologies. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-4444-0.ch004.

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In this chapter, the authors report a system for detection and classification of road signs. This system consists of two parts. The first part detects the road signs in real time. The second part classifies the German traffic signs (GTSRB) dataset and makes the prediction using the road signs detected in the first part to test the effectiveness. The authors used HOG and SVM in the detection part to detect the road signs captured by the camera. Then they used a convolutional neural network based on the LeNet model in which some modifications were added in the classification part. The system obt

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Bhargavi, K., and B. Sathish Babu. "Application of Convoluted Neural Network and Its Architectures for Fungal Plant Disease Detection." In Artificial Intelligence and IoT-Based Technologies for Sustainable Farming and Smart Agriculture. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-1722-2.ch019.

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Eighty-five percent of the plants are affected by diseases caused by organisms like fungus, bacteria, and virus, which devastate the natural ecosystem. The most common clues provided by the plants affected by fungal diseases are defaming of the plant color. In literature, several traditional rule-based algorithms and normal image processing techniques are used to identify the fungal plant diseases. However, the traditional approach suffers from poor disease identification accuracy. Convoluted neural network (CNN) is one of the potential deep learning neural networks used for image recognition

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Janghel, Rekh Ram. "Deep-Learning-Based Classification and Diagnosis of Alzheimer's Disease." In Feature Dimension Reduction for Content-Based Image Identification. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5775-3.ch011.

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Alzheimer's is the most common form of dementia in India and it is one of the leading causes of death in the world. Currently it is diagnosed by calculating the MSME score and by manual study of MRI scan. In this chapter, the authors develop and compare different methods to diagnose and predict Alzheimer's disease by processing structural magnetic resonance image scans (MRI scans) with deep learning neural networks. The authors implement one model of deep-learning networks which are convolution neural network (CNN). They use four different architectures of CNN, namely Lenet-5, AlexNet, ZFNet,

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Janghel, Rekh Ram. "Deep-Learning-Based Classification and Diagnosis of Alzheimer's Disease." In Deep Learning and Neural Networks. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0414-7.ch076.

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Alzheimer's is the most common form of dementia in India and it is one of the leading causes of death in the world. Currently it is diagnosed by calculating the MSME score and by manual study of MRI scan. In this chapter, the authors develop and compare different methods to diagnose and predict Alzheimer's disease by processing structural magnetic resonance image scans (MRI scans) with deep learning neural networks. The authors implement one model of deep-learning networks which are convolution neural network (CNN). They use four different architectures of CNN, namely Lenet-5, AlexNet, ZFNet,

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"Handwriting 99 Multiplication on App Store." In Advances in Computer and Electrical Engineering. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1554-9.ch004.

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The Modified NIST (MNIST) database, consisting of 70,000 handwritten digit images, in partition to 60,000 training patterns and 10,000 testing patterns, serves as a typical benchmark of evaluating performance of handwritten digit classification. After the LeNet CNNs model proposed by LeCun, researchers regarded this example as “Hello, World” in the field of deep learning. This chapter compares traditional approaches with the CNN model. The dataset of training and testing CNN models here is expanded to the Extension-MNIST (EMNIST) database. It will be employed to pre-train a CNN model for recog

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Conference papers on the topic "LeNet-1"

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Abboud, A. W., D. P. Guillen, and R. Pokorny. "Convolutional Neural Network Model for the Prediction of Plenum Temperature in a Waste Glass Melter." In ASME 2020 Power Conference collocated with the 2020 International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/power2020-16993.

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Abstract Legacy radioactive tank waste is slated to undergo vitrification at the Waste Treatment and Immobilization Plate (WTP) using Joule-heated, ceramic-lined melters. A high-fidelity, computational fluid dynamics (CFD) model of the pilot-scale DM1200 melter has been developed to provide an understanding of the heat transfer and fluid dynamics within the WTP melters. Monitoring of the non-radioactive pilot-scale system has been primarily done through visual observations. However, visual observations will not be possible in the full-scale radioactive melter and process control will be based

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Hoang, Danny, Hanning Chen, Mohsen Imani, Ruimin Chen, and Farhad Imani. "Brief Paper: Multi-Task Brain-Inspired Learning for Interlinking Machining Dynamics With Parts Geometrical Deviations." In ASME 2024 19th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2024. http://dx.doi.org/10.1115/msec2024-125435.

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Abstract Increasing complexity, and requirements for the precise creation of parts, necessitate the use of computer numerical control (CNC) manufacturing. This process involves programmed instructions to remove material from a workpiece through operations such as milling, turning, and drilling. This manufacturing technique incorporates various process parameters (e.g., tools, spindle speed, feed rate, cut depth), leading to a highly complex operation. Additionally, interacting phenomena between the workpiece, tools, and environmental conditions further add to complexity which can lead to defec

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