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Journal articles on the topic 'LeNet-1'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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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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Suherman, Abdul Hafiz, Nur Ibrahim, Heri Syahrian, Vitria Puspitasari Rahadi, and Muhammad Khais Prayoga. "KLASIFIKASI DAUN TEH GAMBUNG VARIETAS ASSAMICA MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK DENGAN ARSITEKTUR LENET-5." JOURNAL OF ELECTRICAL AND SYSTEM CONTROL ENGINEERING 4, no. 2 (2021): 63–71. http://dx.doi.org/10.31289/jesce.v4i2.4136.

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Indonesia merupakan salah satu pengolahan produk teh gambung terbesar. Produk teh gambung dihasilkan dengan jenis teh yang berbeda. Namun, kualitas system pengolahan produk mengalami penurunan dikarenakan pekebun sulit membedakan jenis daun teh produksi dengan daun teh unggul dan masih menggunakan prosedur pengolahan daun secara manual. Diketahui, daun teh gambung memiliki 11 klon jenis. Daun teh GMB (1-11) merupakan klon unggul jenis teh dari jenis assamica maupun jenis sinensis dari hasil riset Pusat Penelitian Teh dan Kina (PPTK). Oleh karena itu, diperlukan teknologi pengenalan jenis daun

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Wei, Shiyu. "Impact of multi-GPUs and hyperparameters on parallel algorithms based on pytorch distributed." Journal of Physics: Conference Series 2580, no. 1 (2023): 012045. http://dx.doi.org/10.1088/1742-6596/2580/1/012045.

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Abstract Neural networks, as powerful models for solving many genuine problems, are suffering from the issue of being too computationally intensive due to the excessive size of the models themselves and the large datasets used for training. This paper’s work focuses on a parallel algorithm, experimented on the classical convolutional neural network LeNet-5, aiming to reduce the time it takes for the neural network to train. This applies a massively parallel GPU structure to take full advantage of its high computing power. Among the approaches to improve the performance of parallel algorithms,

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Mu, Junsheng, Youheng Tan, Dongliang Xie, Fangpei Zhang, and Xiaojun Jing. "CNN and DCGAN for Spectrum Sensors over Rayleigh Fading Channel." Wireless Communications and Mobile Computing 2021 (August 6, 2021): 1–12. http://dx.doi.org/10.1155/2021/9970600.

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Spectrum sensing (SS) has attracted much attention in the field of Internet of things (IoT) due to its capacity of discovering the available spectrum holes and improving the spectrum efficiency. However, the limited sensing time leads to insufficient sampling data due to the tradeoff between sensing time and communication time. In this paper, deep learning (DL) is applied to SS to achieve a better balance between sensing performance and sensing complexity. More specifically, the two-dimensional dataset of the received signal is established under the various signal-to-noise ratio (SNR) conditio

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Kita, Akinobu, Hidehiko Okazawa, Katsuya Sugimoto, Nobuyuki Kosaka, Eiji Kidoya, and Tetsuya Tsujikawa. "Specific Binding Ratio Estimation of [123I]-FP-CIT SPECT Using Frontal Projection Image and Machine Learning." Diagnostics 13, no. 8 (2023): 1371. http://dx.doi.org/10.3390/diagnostics13081371.

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This study aimed to develop a new convolutional neural network (CNN) method for estimating the specific binding ratio (SBR) from only frontal projection images in single-photon emission-computed tomography using [123I]ioflupane. We created five datasets to train two CNNs, LeNet and AlexNet: (1) 128FOV used a 0° projection image without preprocessing, (2) 40FOV used 0° projection images cropped to 40 × 40 pixels centered on the striatum, (3) 40FOV training data doubled by data augmentation (40FOV_DA, left-right reversal only), (4) 40FOVhalf, and (5) 40FOV_DAhalf, split into left and right (20 ×

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Chen, RiXin, ZhiXian Lin, and Jianpu Lin. "P‐3.12: Lightweight CNN Design and Implementation for Handwritten Digit Recognition on Resource Constrained Devices." SID Symposium Digest of Technical Papers 56, S1 (2025): 909–13. https://doi.org/10.1002/sdtp.18962.

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With the continuous advancement of embedded system technology, embedded deployment of deep learning models has become a hot research issue. Resource‐constrained devices such as low‐performance microcontrollers have stringent computational and memory requirements, so how to design efficient and lightweight convolutional neural networks (CNNs) to adapt to the limitations of these devices has become a key research direction. In this paper, we propose a lightweight CNN design scheme based on a simplified LeNet‐5 [1] architecture, aiming at efficient handwritten digit recognition through integer we

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Saito, Michiyuki, Mizuho Mitamura, Yuki Ito, Hiroaki Endo, Satoshi Katsuta, and Susumu Ishida. "Inter-Relationships Between the Deep Learning-Based Pachychoroid Index and Clinical Features Associated with Neovascular Age-Related Macular Degeneration." Journal of Clinical Medicine 14, no. 9 (2025): 3245. https://doi.org/10.3390/jcm14093245.

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Background/Objectives: To investigate the impact of pachychoroid on the clinical features of neovascular age-related macular degeneration (nAMD) in Japan using the deep learning-based Hokkaido University pachychoroid index (HUPI), which has a high discriminative ability for pachychoroid. Methods: This retrospective observational study examined 124 eyes of 111 treatment-naïve nAMD patients, including 44 eyes with type 1 macular neovascularization (MNV), 26 eyes with type 2 MNV, and 54 eyes with polypoidal choroidal vasculopathy (PCV). HUPI was calculated for each eye from EDI-OCT choroidal imag

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Latif, Ghazanfar, Kévin Bouchard, Julien Maitre, Arnaud Back, and Léo Paul Bédard. "Deep-Learning-Based Automatic Mineral Grain Segmentation and Recognition." Minerals 12, no. 4 (2022): 455. http://dx.doi.org/10.3390/min12040455.

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A multitude of applications in engineering, ore processing, mineral exploration, and environmental science require grain recognition and the counting of minerals. Typically, this task is performed manually with the drawback of monopolizing both time and resources. Moreover, it requires highly trained personnel with a wealth of knowledge and equipment, such as scanning electron microscopes and optical microscopes. Advances in machine learning and deep learning make it possible to envision the automation of many complex tasks in various fields of science at an accuracy equal to human performance

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Zhang, Lifu, and Tarek S. Abdelrahman. "Pipelined Training with Stale Weights in Deep Convolutional Neural Networks." Applied Computational Intelligence and Soft Computing 2021 (September 21, 2021): 1–16. http://dx.doi.org/10.1155/2021/3839543.

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The growth in size and complexity of convolutional neural networks (CNNs) is forcing the partitioning of a network across multiple accelerators during training and pipelining of backpropagation computations over these accelerators. Pipelining results in the use of stale weights. Existing approaches to pipelined training avoid or limit the use of stale weights with techniques that either underutilize accelerators or increase training memory footprint. This paper contributes a pipelined backpropagation scheme that uses stale weights to maximize accelerator utilization and keep memory overhead mo

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Tao, Sichen, Ruihan Zhao, Yifei Yang, Hiroyoshi Todo, Zheng Tang, and Yuki Todo. "Artificial Visual Network with Fully Modeled Retinal Direction-Selective Neural Pathway for Motion Direction Detection in Grayscale Scenes." Symmetry 16, no. 12 (2024): 1592. http://dx.doi.org/10.3390/sym16121592.

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The complexity and functional evolution of mammalian visual systems have always been a focal point in neuroscience and biological science research. The primary neurons that output motion direction signals have been a focal point of research in visual neuroscience for nearly 130 years. These neurons are widely present in the cortex and retina of mammals. Although the relevant pathways have been discovered and studied for almost 60 years due to experimental accessibility, research still remains at the cellular level. The specific functions and overall operational mechanisms of the component neur

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Gao, Mingyu, Fei Wang, Junyan Liu, et al. "Estimation of the convolutional neural network with attention mechanism and transfer learning on wood knot defect classification." Journal of Applied Physics 131, no. 23 (2022): 233101. http://dx.doi.org/10.1063/5.0087060.

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In the intelligent production process of wood products, the classification system of wood knot defects is a very practical solution. However, traditional image processing methods cannot handle it well due to the uncertainty of manually extracted features. Therefore, a lightweight and reliable artificial neural network model is proposed to classify and identify our objective. To solve this problem, a wood knot defect recognition model named SE-ResNet18 combining convolutional neural network, attention mechanism, and transfer learning is proposed in this paper. First, the Sequence-and-Exception

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Ma, Li, Lei Zhao, Liying Cao, Dongming Li, Guifen Chen, and Ye Han. "Inversion of Soil Organic Matter Content Based on Improved Convolutional Neural Network." Sensors 22, no. 20 (2022): 7777. http://dx.doi.org/10.3390/s22207777.

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Soil organic matter (SOM) is an important source of nutrients required during crop growth and is an important component of cultivated soil. In this paper, we studied the possibility of using deep learning methods to establish a multi-feature model to predict SOM content. Moreover, using Nong’an County of Changchun City as the study area, Sentinel-2A remote sensing images were taken as the data source to construct the dataset by using field sampling and image processing. The LeNet-5 convolutional neural network model was chosen as the deep learning model, which was improved based on the basic m

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Hazarika, Ruhul Amin, Arnab Kumar Maji, Debdatta Kandar, et al. "An Approach for Classification of Alzheimer’s Disease Using Deep Neural Network and Brain Magnetic Resonance Imaging (MRI)." Electronics 12, no. 3 (2023): 676. http://dx.doi.org/10.3390/electronics12030676.

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Alzheimer’s disease (AD) is a deadly cognitive condition in which people develop severe dementia symptoms. Neurologists commonly use a series of physical and mental tests to diagnose AD that may not always be effective. Damage to brain cells is the most significant physical change in AD. Proper analysis of brain images may assist in the identification of crucial bio-markers for the disease. Because the development of brain cells is so intricate, traditional image processing algorithms sometimes fail to perceive important bio-markers. The deep neural network (DNN) is a machine learning techniqu

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Bati, Jen, and Pankaj Raj Dawadi. "Ranjana Script Handwritten Character Recognition using CNN." JOIV : International Journal on Informatics Visualization 7, no. 3 (2023): 984. http://dx.doi.org/10.30630/joiv.7.3.1725.

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This paper proposes a public image database for Ranjana script Handwritten Character Datasets (RHCD), publicly available for Ranjana script researchers or anyone interested in the subject. To the best of our knowledge, the Ranjana script Handwritten Character Dataset (RHCD) is the first publicly available database for Ranjana script researchers. Ranjana script descended from the Brahmi script, consists of 36 consonant letters, 16 vowel letters, and 10 numerical letters. The focus of this research is three-fold: the first is to create a new database for Ranjana script Handwritten Character Reco

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Dou, Shiqing, Xinze Ren, Xiangqian Qi, et al. "Prediction of Citrus Leaf Water Content Based on Multi-Preprocessing Fusion and Improved 1-Dimensional Convolutional Neural Network." Horticulturae 11, no. 4 (2025): 413. https://doi.org/10.3390/horticulturae11040413.

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The leaf water content (LWC) of citrus is a pivotal indicator for assessing citrus water status. Addressing the limitations of traditional hyperspectral modelling methods, which rely on single preprocessing techniques and struggle to fully exploit the complex information within spectra, this study proposes a novel strategy for estimating citrus LWC by integrating spectral preprocessing combinations with an enhanced deep learning architecture. Utilizing a citrus plantation in Guangxi as the experimental site, 240 leaf samples were collected. Seven preprocessing combinations were constructed bas

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Sheu, Ruey-Kai, Lun-Chi Chen, Mayuresh Sunil Pardeshi, Kai-Chih Pai, and Chia-Yu Chen. "AI Landing for Sheet Metal-Based Drawer Box Defect Detection Using Deep Learning (ALDB-DL)." Processes 9, no. 5 (2021): 768. http://dx.doi.org/10.3390/pr9050768.

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Sheet metal-based products serve as a major portion of the furniture market and maintain higher quality standards by being competitive. During industrial processes, while converting a sheet metal to an end product, new defects are observed and thus need to be identified carefully. Recent studies have shown scratches, bumps, and pollution/dust are identified, but orange peel defects present overall a new challenge. So our model identifies scratches, bumps, and dust by using computer vision algorithms, whereas orange peel defect detection with deep learning have a better performance. The goal of

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Jung, Wonho, Hyunseok Oh, Dong Ho Yun, Young Gon Kim, Jong Pil Youn, and Jae Hong Park. "Modified Recurrence Plot for Robust Condition Monitoring of Electrode Tips in a Resistance Spot Welding System." Applied Sciences 10, no. 17 (2020): 5860. http://dx.doi.org/10.3390/app10175860.

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Degraded electrodes in a resistance spot welding system should be replaced to ensure that weld quality is maintained. Welding electrodes are subjected to different environmental and operational loading conditions during use. When they are replaced with a fixed interval, replacement may occur too early (raising maintenance costs) or too late (leading to quality issues). This motivates condition monitoring strategies for resistance spot welding electrode tips. Thus, this paper proposes a modified recurrence plot (RP) for robust condition monitoring of welding electrode tips in resistance spot we

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Meng, Fanjie, Xinqing Wang, Faming Shao, Dong Wang, and Xia Hua. "Energy-Efficient Gabor Kernels in Neural Networks with Genetic Algorithm Training Method." Electronics 8, no. 1 (2019): 105. http://dx.doi.org/10.3390/electronics8010105.

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Deep-learning convolutional neural networks (CNNs) have proven to be successful in various cognitive applications with a multilayer structure. The high computational energy and time requirements hinder the practical application of CNNs; hence, the realization of a highly energy-efficient and fast-learning neural network has aroused interest. In this work, we address the computing-resource-saving problem by developing a deep model, termed the Gabor convolutional neural network (Gabor CNN), which incorporates highly expression-efficient Gabor kernels into CNNs. In order to effectively imitate th

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Xie, Xiaoyun, Ming Xie, Ata Jahangir Moshayedi, and Mohammad Hadi Noori Skandari. "A Hybrid Improved Neural Networks Algorithm Based on L2 and Dropout Regularization." Mathematical Problems in Engineering 2022 (November 3, 2022): 1–19. http://dx.doi.org/10.1155/2022/8220453.

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Small samples are prone to overfitting in the neural network training process. This paper proposes an optimization approach based on L2 and dropout regularization called a hybrid improved neural network algorithm to overcome this issue. The proposed model was evaluated based on the Modified National Institute of Standards and Technology (MNIST, grayscale-28 × 28 × 1) and Canadian Institute for Advanced Research 10 (CIFAR10, RGB - 32 × 32 × 3) as the training data sets and data applied to the LeNet-5 and Autoencoder neural network architectures. The evaluation is conducted based on cross-valida

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Wang, Shiqi, Kevin Eykholt, Taesung Lee, Jiyong Jang, and Ian Molloy. "Adaptive Verifiable Training Using Pairwise Class Similarity." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 11 (2021): 10201–9. http://dx.doi.org/10.1609/aaai.v35i11.17223.

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Verifiable training has shown success in creating neural networks that are provably robust to a given amount of noise. However, despite only enforcing a single robustness criterion, its performance scales poorly with dataset complexity. On CIFAR10, a non-robust LeNet model has a 21.63% error rate, while a model created using verifiable training and a L-infinity robustness criterion of 8/255, has an error rate of 57.10%. Upon examination, we find that when labeling visually similar classes, the model's error rate is as high as 61.65%. Thus, we attribute the loss in performance to inter-class si

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Yunita Astuti, Lalu Puji Indra Kharisma, and Ahmad Ashril Rizal. "Aplikasi Ujian Berbasis Komputer Pada MTs NW Lenek 1." TEKNIMEDIA: Teknologi Informasi dan Multimedia 1, no. 2 (2021): 26–32. http://dx.doi.org/10.46764/teknimedia.v1i2.21.

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Dalam era globalisasi sekarang ini, tekonologi informasi berkembang dengan sangat pesat, baik dari segi infrastruktur, jaringan, perangkat dan juga perangkat lunak. Hal tersebut mendorong digitalisasi dalam segala aspek kehidupan, tak terkecuali dalam pelaksanaan ujian pada berbagai lembaga pendidikan. Ujian sebagai salah satu cara untuk mengevaluasi proses belajar untuk mengukur taraf pencapaian kemampuan, kepribadian dan intelegensi peserta didik. Sehingga menjadikan ujian merupakan tahapan penting dalam proses belajar mengajar. Beberapa tahun yang lalu, pelaksanaan ujian diberbagai sekolah

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Antony, A., A. Biju, A. Rambojun, and W. Tillett. "AB1569 THE USE OF MACHINE LEARNING IN DIAGNOSING AND DETECTING DAMAGE IN THE HANDS AND FEET OF PATIENTS WITH RHEUMATOID ARTHRITIS AND PSORIATIC ARTHRITIS: A SCOPING REVIEW." Annals of the Rheumatic Diseases 82, Suppl 1 (2023): 2017.2–2018. http://dx.doi.org/10.1136/annrheumdis-2023-eular.3426.

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BackgroundMachine learning methods have the potential to optimise the diagnosis of inflammatory arthropathies and the quantification of damage over time.ObjectivesTo summarise the literature investigating the use of machine learning methods to diagnose and detect damage in the hands and feet of patients with Rheumatoid Arthritis (RA) and Psoriatic Arthritis (PsA).MethodsA literature search was undertaken in PubMed, EMBASE, Web of Science and the Cochrane Central Register for Controlled Trials using a pre-specified search strategy. Full-text articles and conference abstracts published in Englis

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Meri Yuliani, Muhamad Ridwan Habibi, and Sri Heni. "Penanaman Nilai-Nilai Karakter Siswa SD Melalui Pembelajaran IPS di Sekolah Dasar Negeri 1 Lenek Kecamatan Lenek Lombok Timur." KASTA : Jurnal Ilmu Sosial, Agama, Budaya dan Terapan 2, no. 3 (2022): 208–13. http://dx.doi.org/10.58218/kasta.v2i3.422.

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Globalisasi yang sedang kita hadapi adalah sebuah fakta yang tidak bisa dihindari. Karakter anak bangsa berubah menjadi rapuh. Tujuan penulisan jurnal ini adalah untuk menggali pentingnya penanaman nilai karakter di sekolah dasar. Pendidikan karakter ini penting diperhatikan, sebab globalisasi tidak hanya berdampak positif, tetapi juga negatif. Pendidikan karakter anak perlu ditanamkan sejak sekolah dasar karena usia sekolah dasar merupakan masa keemasan anak. Penulisan karya ini menggunakan metode kajian pustaka dengan pendekatan kualitatif deskriptif untuk memaparkan hasil pengamatan dan stu

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Khaer, Muhammad Hizbi, Sugeng Mulyono, Endang Suswati, and Sulaiman Sulaiman. "KOMPENSASI, IKLIM ORGANISASI, MOTIVASI KERJA DAN KOMITMEN ORGANISASIONAL KARYAWAN PONDOK PESANTREN DI KECAMATAN LENEK." SOSIO EDUKASI Jurnal Studi Masyarakat dan Pendidikan 6, no. 2 (2023): 136–43. http://dx.doi.org/10.29408/sosedu.v6i2.24139.

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This study was meant 1) to determine the partial influence of compensation, organizational climate and motivation on Employee Organizational Commitment in Islamic Boarding Schools in Lenek District, East Lombok Regency. 2) to find out whether the variables Compensation, Organizational Climate, and Work Motivation simultaneously influence the Employee Organizational Commitment in Islamic Boarding Schools in Lenek District, East Lombok Regency. 3) to find out among the variables Compensation, Organizational Climate, and Motivation, which variable had the most dominant influence on the Employee O

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AINI, AINI. "UPAYA MENINGKATKAN KEPERCAYAAN DIRI MELALUI LAYANAN KONSELING INDIVIDUAL SISWA KELS IX.A SMPN 1 LENEK MASA COVID-19 TAHUN PELAJARAN 2021/2022." TEACHING : Jurnal Inovasi Keguruan dan Ilmu Pendidikan 1, no. 4 (2021): 305–13. http://dx.doi.org/10.51878/teaching.v1i4.775.

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The purpose of this study is that the self-confidence of grade IX-A SMPN 1 Lenek students can increase through individual counseling services during the COVID-19 period of the 2021/2022 school year. This type of research is classroom action research (CAR). The stages in this research are: (1) plan; (2) actions; (3) observation; and (4) reflection. Research subjects were determined by using a proportional sampling technique. The research subject is class IX-A SMPN 1 Lenek. Data collection techniques used in this study are non-test and observation techniques. The instrument used to measure the l

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Hosseini, Morteza, and Tinoosh Mohsenin. "Binary Precision Neural Network Manycore Accelerator." ACM Journal on Emerging Technologies in Computing Systems 17, no. 2 (2021): 1–27. http://dx.doi.org/10.1145/3423136.

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This article presents a low-power, programmable, domain-specific manycore accelerator, Binarized neural Network Manycore Accelerator (BiNMAC), which adopts and efficiently executes binary precision weight/activation neural network models. Such networks have compact models in which weights are constrained to only 1 bit and can be packed several in one memory entry that minimizes memory footprint to its finest. Packing weights also facilitates executing single instruction, multiple data with simple circuitry that allows maximizing performance and efficiency. The proposed BiNMAC has light-weight

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Kong, Deyu, Xuejun Zhang, Yini Wei, Xianfu Xu, Hongjie Zeng, and Bin Li. "Human Recognition Using Joint Coordinate Images (JCIs) with Convolutional Neural Network." Journal of Physics: Conference Series 2095, no. 1 (2021): 012056. http://dx.doi.org/10.1088/1742-6596/2095/1/012056.

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Abstract Human recognition with skeletal data has the advantage in detecting people without their face characteristic on image. However, the accuracy of recognition by this method is always low because it relies deeply on manual feature selection. We propose a novel human recognition method called Joint Coordinate Images (JCIs) with Convolutional Neural Network (CNN) based on the image generated from skeletal information tracked by KinectV1. In order to represent human physical skeletal characteristic, the coordinate values XYZ of human joint tracked by KinectV1 are firstly created in color im

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Busyairi, Ahmad, Aziz Anuri, and Sulaiman Sulaiman. "Faktor yang Mempengaruhi Terhambatnya Usaha Mikro di Kecamatan Lenek Kabupaten Lombok Timur." ARZUSIN 2, no. 4 (2022): 410–23. http://dx.doi.org/10.58578/arzusin.v2i4.508.

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The governments programs which are principally related to developing of micro enterprise instance societies enterprise credit financing, PEN financing, and marketing aspect. However many people don’t apply the loan since they are afraid and unconfident not to be able to return it. There for it implies to the progress of their bussines. This study aims to determine the factors that influence the inhibition of the development of micro-entreprises in Lenek. This research was quantitative research and the subjects are business actors in Lenek. The results of this study based on primary data testin

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Fitriana Surya, Rizqy, Siti Rohana Hariana Intiana, and Ida Ermiana. "HUBUNGAN MINAT MEMBACA DENGAN KEMAMPUAN BERBAHASA SISWA KELAS V SDN 1 LENEK LAUK TAHUN 2020." JURNAL ILMIAH PENDAS: PRIMARY EDUCATION JOURNAL 1, no. 2 (2020): 61–67. http://dx.doi.org/10.29303/pendas.v1i2.57.

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Penelitian ini bertujuan untuk mengetahui hubungan minat membaca dengan kemampuan berbahasa siswa kelas V SDN 1 Lenek Lauk. Penelitian ini menggunakan pendekatan kuantitatif dengan jenis penelitian survey. Populasi dalam penelitian ini adalah siswa kelas V SDN 1 Lenek Lauk yang berjumlah 28 orang. Teknik pengambilan sampel yang digunakan adalah sampling total, dengan demikian berarti seluruh populasi menjadi sampel. Instrumen penelitian yang digunakan adalah angket minat membaca dengan skala gautmen dan dokumentasi. Teknik analisis data menggunakan uji normalitas dan uji linieritas serta uji h

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Peng, Xinjie, Zhigang Wang, Binbin Li, Long Qian, and Bin Jiao. "Rolling Bearing Fault Diagnosis Based on PCA-ResNet." Journal of Physics: Conference Series 2218, no. 1 (2022): 012082. http://dx.doi.org/10.1088/1742-6596/2218/1/012082.

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Abstract Aiming at the situation that there are many interference factors in the collected signals of rolling bearings working in harsh and complex environment. Propose A method of noise reduction based on principal component analysis (PCA) and fault diagnosis and classification based on residual neural network (ResNet). Experimental results show that this method can effectively complete the task of fault identification and classification, and is superior to LeNet-5, SVM, DNN and other common deep learning methods.

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Nopiana, Tina, Candra Ayu, and Ibrahim Ibrahim. "Kontribusi Ekonomi Usaha Kerajinan Tikar Mendong (Fimbristylis Globulosa) Terhadap Pendapatan Rumah Tangga Petani di Kecamatan Lenek Kabupaten Lombok Timur." JURNAL AGRIMANSION 23, no. 1 (2022): 49–63. http://dx.doi.org/10.29303/agrimansion.v23i1.709.

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 ABSTRACT  This study aims to: (1) analyze the types of productive economic activities of respondent farmer households in Lenek District East Lombok Regency; (2) analyzing the income from the mendong mat crafts business and its contribution to farmer households income in Lenek District of East Lombok Regency; (3) Knowing the problems and obstacles faced by the craftsmen in managing the mendong mat craft business in Lenek District East Lombok Regency. This research uses descriptive method. Analysis of the data used is the analysis of income and business contributions. The resu

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Sulistio, Iwan. "Manejemenen Pemasaran Sekolah dalam meningkatkan Imput Peserta Dididk di SMAN 1 Lenek." Jurnal Ilmiah Profesi Pendidikan 9, no. 4 (2024): 2540–46. http://dx.doi.org/10.29303/jipp.v9i4.2758.

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Salah satu problematika sebagian sekolah saat ini dengan adanya penurunan input peserta didik. Oleh karena itu, sekolah perlu melakukan promosi sekolah, promosi sekolah cukup refresentatif meningkatkan minat calon peserta didik baru. Penelitian ini difokuskan pada manajemen pemasaran dalam meningkatkan input pesertadidik, dengan melihat idetifikasi pemasaran dan implementasi pemasaran yang dilakukan oleh SMAN 1 Lenek sebagai lokasi penelitian. Penelitian ini menggunakan pendekatan kualitatif deskripif, data dikumpulkan melalui observasi, wawancara, dan dilengkapi dengan dokumentasi. Data diana

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Platschek, Johannes. "Zum Text von D. 1,16,6pr. (Ulp. 1 off. procons.) = Ulp. 2144 Lenel." Zeitschrift der Savigny-Stiftung für Rechtsgeschichte: Romanistische Abteilung 141, no. 1 (2024): 528–35. http://dx.doi.org/10.1515/zrgr-2024-0013.

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Summary The governor of a senatorial province (proconsul) can delegate iurisdictional power to his legati (iurisdictio mandata). He cannot transfer ius gladii and coercitio, nor the power to release a criminal suspect from custody. The governor’s practice however, as described by the transmitted text of D. 1,16,6pr., taken from Ulpian’s De officio proconsulis, is held to be interpolated. Reexamination of the passage shows there is no need to suppose any manipulation of the text with regards to content.

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Zago, Giovanni. "Decisa forficibus." DILEF. Rivista digitale del Dipartimento di Lettere e Filosofia, no. 1 (March 15, 2022): 1–6. http://dx.doi.org/10.35948/dilef/2022.3298.

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AbstractQuesto articolo offre note critico-testuali e nuove emendazioni a tre passi, Polyb. 7, 7, 7, Sen. Phae. 558, Ulpian. 1 ad edict. (Dig. 11, 4, 2 = Lenel, Paling. 185); mostra, inoltre, che il Ludus septem sapientum di Ausonio ha tra i suoi modelli letterari Phaedr. 5, 7 Z., e che Auian. fab. 9, 21 deriva da Catull. 115, 7.This article offers critical remarks and new emendations on three passages, Polyb. 7, 7, 7, Sen. Phae. 558, Ulpian. 1 ad edict. (Dig. 11, 4, 2 = Lenel, Paling. 185). It also shows that Ausonius’s Ludus septem sapientum has among its literary models Phaedr. 5, 7 Z., and

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Lestari, Haslina Putri, Moh Irawan Zain, and Baiq Niswatul Khair. "Pengembangan LKPD Bermuatan Kearifan Lokal Tema ‘Indahnya Kebersamaan’ dan Efektivitas Terhadap Karakter Nasionalisme Kelas IV SDN 3 Lenek Lauk." Jurnal Ilmiah Profesi Pendidikan 8, no. 1 (2023): 342–50. http://dx.doi.org/10.29303/jipp.v8i1.1169.

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Sekolah sebagai garda terdepan pendidikan harus bisa menjadi agen penjaga nasionalisme melalui proses pembelajaran. Oleh sebab itu, untuk menghindari lunturnya nilai nasionalosme pada diri peserta didik dapat dilakukan melalui pembelajaran. Salah satunya pembelajaran yang dikaitkan dengan kearifan lokal peserta didik. Tujuan Penelitian ini adalah menghasilkan produk LKPD bermuatan kearifan Lokal lenek yang efektif dan valid digunakan sekolah. Penelitian ini menggunakan pendekatan Penelitian dan Pengembangan Research and Development (R&D). Model pengembangan yang digunakan dalam penelitian

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Way, Laura. "Punkgirldiaries Blogzine 2, Lene Cortina and Vim Renault (2020)." Punk & Post-Punk 10, no. 1 (2021): 166–67. http://dx.doi.org/10.1386/punk_00079_5.

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Artiemjew, Piotr, Agnieszka Chojka, and Jacek Rapiński. "Deep Learning for RFI Artifact Recognition in Sentinel-1 Data." Remote Sensing 13, no. 1 (2020): 7. http://dx.doi.org/10.3390/rs13010007.

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Beyond the variety of unwanted disruptions that appear quite frequently in synthetic aperture radar (SAR) measurements, radio-frequency interference (RFI) is one of the most challenging issues due to its various forms and sources. Unfortunately, over the years, this problem has grown worse. RFI artifacts not only hinder processing of SAR data, but also play a significant role when it comes to the quality, reliability, and accuracy of the final outcomes. To address this issue, a robust, effective, and—importantly—easy-to-implement method for identifying RFI-affected images was developed. The ma

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Hamdi, Zainul, Sugianto Prajitno, Fahriana Azmi, and Made Agus Suanjaya. "Hubungan dan Prevalensi Tingkat Depresi, Tingkat Kecemasan, dan Tingkat Stres dengan Irritable Bowel Syndrome Pada Siswa/I SMAN 1 Lenek Kabupaten Lombok Timur." MAHESA : Malahayati Health Student Journal 4, no. 5 (2024): 1726–35. http://dx.doi.org/10.33024/mahesa.v4i5.14301.

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ABSTRACT Irritable bowel syndrome (IBS) is a chronic and recurrent functional intestinal disorder, in the form of abdominal pain or discomfort and defecation pattern disorders that occur 1 time per week for at least 3 months, without any underlying structural abnormalities. Irritable bowel syndrome is a multifactoral disease that has several pathogenesis. Psychological factors such as stress, anxiety and depression can strongly affect the work of the colon. The colon has many neurons associated with the cerebrum. Part of the colon is controlled by the CNS, which responds to stress. This study

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Li, Duo, Chaoqun Dong, and Qianchao Liu. "Robustness Testing Framework For Neural Network Image Classifier." Journal of Physics: Conference Series 2078, no. 1 (2021): 012050. http://dx.doi.org/10.1088/1742-6596/2078/1/012050.

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Abstract Neural network has made remarkable achievements in the field of image classification, but they are threatened by adversarial examples in the process of application, making the robustness of neural network classifiers face danger. Programs or software based on neural network image classifiers need to undergo rigorous robustness testing before use and promotion, in order to effectively reduce losses and security risks. To comprehensively test the robustness of neural network image classifiers and standardize the test process, starting from the two aspects of generated content and interf

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Gildin Zuckerman, Maja. "At beskytte demokratiet mod dets indre fjender." Nordisk judaistik/Scandinavian Jewish Studies 33, no. 1 (2022): 65–67. http://dx.doi.org/10.30752/nj.115520.

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Boganmeldelse af historiker Sofie Lene Baks seneste bog Racismeparagraffen. Bogen beskriver debatterne og de sociale og juridiske konsekvenser af indførelsen af racisme-paragraffen 1. juli 1939 i dansk straffelovgivning.

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Azwari, Riki, Muh Zubair, Sawaludin Sawaludin, and Yuliatin Yuliatin. "Potensi Tradisi Ngejot Sebagai Sumber Belajar PPKn." Eduka : Jurnal Pendidikan, Hukum, dan Bisnis 7, no. 1 (2022): 14. http://dx.doi.org/10.32493/eduka.v7i1.18480.

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This study aims to determine: (1) the implementation of the ngejot tradition in Lenek Pesiraman Village; (2) the character values contained in the implementation of the ngejot tradition in Lenek Pesiraman Village; (3) The Potential of the Ngejot Tradition as a Learning Source for Civics subjects. The approach used is a qualitative approach with ethnographic research. Data collection techniques used are observation, interviews and documentation. The data analysis technique used the Miles and Huberman analysis technique. The results showed that the implementation of the ngejot tradition had seve

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