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Academic literature on the topic 'Nadam optimizer'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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Journal articles on the topic "Nadam optimizer"

1

Zhang, Qikun, Yuzhi Zhang, Yanling Shao, et al. "Boosting Adversarial Attacks with Nadam Optimizer." Electronics 12, no. 6 (2023): 1464. http://dx.doi.org/10.3390/electronics12061464.

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Deep neural networks are extremely vulnerable to attacks and threats from adversarial examples. These adversarial examples deliberately crafted by attackers can easily fool classification models by adding imperceptibly tiny perturbations on clean images. This brings a great challenge to image security for deep learning. Therefore, studying and designing attack algorithms for generating adversarial examples is essential for building robust models. Moreover, adversarial examples are transferable in that they can mislead multiple different classifiers across models. This makes black-box attacks f

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Praharsha, Chittathuru Himala, Alwin Poulose, and Chetan Badgujar. "Comprehensive Investigation of Machine Learning and Deep Learning Networks for Identifying Multispecies Tomato Insect Images." Sensors 24, no. 23 (2024): 7858. https://doi.org/10.3390/s24237858.

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Deep learning applications in agriculture are advancing rapidly, leveraging data-driven learning models to enhance crop yield and nutrition. Tomato (Solanum lycopersicum), a vegetable crop, frequently suffers from pest damage and drought, leading to reduced yields and financial losses to farmers. Accurate detection and classification of tomato pests are the primary steps of integrated pest management practices, which are crucial for sustainable agriculture. This paper explores using Convolutional Neural Networks (CNNs) to classify tomato pest images automatically. Specifically, we investigate

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Riyadi, Willy, and Jasmir Jasmir. "PREDICTION PERFORMANCE OF AIRPORT TRAFFIC USING BILSTM AND CNN-BI-LSTM MODELS." JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) 9, no. 1 (2023): 1–7. http://dx.doi.org/10.33480/jitk.v9i1.4191.

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The COVID-19 pandemic has had a significant and enduring impact on the aviation industry, necessitating the accurate prediction of airport traffic. This study compares the predictive accuracy of biLSTM (Bidirectional Long Short-Term Memory) and CNN-biLSTM (Convolutional Neural Network-Bidirectional Long Short-Term Memory) models using various optimization techniques such as RMSProp, Stochastic Gradient Descent (SGD), Adam, Nadam, and Adamax. The evaluation is based on Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) indices. In the United States, the biLSTM model utilizing t

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Ismanto, Edi, and Noverta Effendi. "An LSTM-based prediction model for gradient-descending optimization in virtual learning environments." Computer Science and Information Technologies 4, no. 3 (2023): 199–207. https://doi.org/10.11591/csit.v4i3.pp199-207.

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A virtual learning environment (VLE) is an online learning platform that allows many students, even millions, to study according to their interests without being limited by space and time. Online learning environments have many benefits, but they also have some drawbacks, such as high dropout rates, low engagement, and students' self-regulated behavior. Evaluating and analyzing the students' data generated from online learning platforms can help instructors to understand and monitor students learning progress. In this study, we suggest a predictive model for assessing student success in online

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Susetyo, Yosia Adi, Hanna Arini Parhusip, Suryasatriya Trihandaru, and Bambang Susanto. "LSTM-IOT (LSTM-based IoT) untuk Mengatasi Kehilangan Data Akibat Kegagalan Koneksi." Jurnal Teknologi Informasi dan Ilmu Komputer 12, no. 1 (2025): 175–86. https://doi.org/10.25126/jtiik.20251219157.

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Masalah dalam industri terkait kehilangan data suhu dan kelembaban sering terjadi akibat gangguan perangkat atau hilangnya koneksi. Data ini penting untuk menentukan kelayakan produk yang akan didistribusikan. Untuk mengatasi permasalahan tersebut, dikembangkan inovasi LSTM-IOT, yaitu perangkat IoT yang terintegrasi dengan model Long Short-Term Memory (LSTM) dalam arsitektur Environment Intelligence. Arsitektur ini telah dioptimalkan melalui eksperimen menggunakan berbagai jenis optimizer, seperti Adam, RMSprop, AdaGrad, SGD, Nadam, dan Adadelta. Dari hasil optimasi, kombinasi Nadam Optimizer

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Susetyo, Yosia Adi, Hanna Arini Parhusip, Suryasatriya Trihandaru, and Bambang Susanto. "LSTM-IOT (LSTM-based IoT) untuk Mengatasi Kehilangan Data Akibat Kegagalan Koneksi." Jurnal Teknologi Informasi dan Ilmu Komputer 12, no. 1 (2025): 175–86. https://doi.org/10.25126/jtiik.2025129157.

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Masalah dalam industri terkait kehilangan data suhu dan kelembaban sering terjadi akibat gangguan perangkat atau hilangnya koneksi. Data ini penting untuk menentukan kelayakan produk yang akan didistribusikan. Untuk mengatasi permasalahan tersebut, dikembangkan inovasi LSTM-IOT, yaitu perangkat IoT yang terintegrasi dengan model Long Short-Term Memory (LSTM) dalam arsitektur Environment Intelligence. Arsitektur ini telah dioptimalkan melalui eksperimen menggunakan berbagai jenis optimizer, seperti Adam, RMSprop, AdaGrad, SGD, Nadam, dan Adadelta. Dari hasil optimasi, kombinasi Nadam Optimizer

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Krisna, Julius Immanuel Theo, Ardytha Luthfiarta, Leno Dwi Cahya, Sri Winarno, and Adhitya Nugraha. "Comparing Optimizer Strategies For Enhancing Emotion Classification In IndoBERT Models." Advance Sustainable Science, Engineering and Technology 6, no. 2 (2024): 0240203. http://dx.doi.org/10.26877/asset.v6i2.18228.

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Emotions are one of the reactions of human when they receive physical or verbal action. Every human action is based on emotion. Every opinion expressed in the comments column also contains the author's emotions. This research aims to classify five emotions, Marah, Takut, Senang, Cinta, and Sedih and evaluate the performance of three commonly used optimizer, Adam, RMSProp, and Nadam. The processed data used IndoBERT model for Indonesian text classification. The research purpose to search the best optimizer for text classification. The result shows classification used Adam Optimizer 90,21%, RMSP

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Wijaya, Angel Joanna, Windra Swastika, and Oesman Hendra Kelana. "PREDIKSI HARGA FOREIGN EXCHANGE MATA UANG EUR/USD DAN GBP/USD MENGGUNAKAN LONG SHORT-TERM MEMORY." Sainsbertek Jurnal Ilmiah Sains & Teknologi 2, no. 1 (2021): 16–31. http://dx.doi.org/10.33479/sb.v2i1.121.

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Foreign exchange (Forex) adalah perdagangan pasangan mata uang dari harga mata uang suatu negara terhadap mata uang negara lainnya. Pada penelitian ini menggunakan metode Long Short-Term Memory (LSTM) untuk memprediksi harga close mata uang EUR/USD (Euro terhadap Dolar Amerika) dan GBP/USD (Pound Sterling terhadap Dolar Amerika) pada candle D1 (1 hari) dengan input harga open dan close. Hasil yang diperoleh model EUR/USD dengan 1 input mendapatkan nilai Mean Squared Error (MSE) terendah yaitu 0,0535 dengan model 1 layer LSTM 10 node dan menggunakan optimizer Nadam. Pada model 3 input mendapatk

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Nurdiati, Sri, Mohamad Khoirun Najib, Fahren Bukhari, Refi Revina, and Fitra Nuvus Salsabila. "PERFORMANCE COMPARISON OF GRADIENT-BASED CONVOLUTIONAL NEURAL NETWORK OPTIMIZERS FOR FACIAL EXPRESSION RECOGNITION." BAREKENG: Jurnal Ilmu Matematika dan Terapan 16, no. 3 (2022): 927–38. http://dx.doi.org/10.30598/barekengvol16iss3pp927-938.

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A convolutional neural network (CNN) is one of the machine learning models that achieve excellent success in recognizing human facial expressions. Technological developments have given birth to many optimizers that can be used to train the CNN model. Therefore, this study focuses on implementing and comparing 14 gradient-based CNN optimizers to classify facial expressions in two datasets, namely the Advanced Computing Class 2022 (ACC22) and Extended Cohn-Kanade (CK+) datasets. The 14 optimizers are classical gradient descent, traditional momentum, Nesterov momentum, AdaGrad, AdaDelta, RMSProp,

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Shi, Wei, Jinzhu Zhang, Lina Li, et al. "Analysis of Efficient and Fast Prediction Method for the Kinematics Solution of the Steel Bar Grinding Robot." Applied Sciences 13, no. 2 (2023): 1212. http://dx.doi.org/10.3390/app13021212.

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Aiming at the robotization of the grinding process in the steel bar finishing process, the steel bar grinding robot can achieve the goal of fast, efficient, and accurate online grinding operation, a multi-layer forward propagating deep neural network (DNN) method is proposed to efficiently predict the kinematic solution of grinding robot. The process and kinematics model of the grinding robot are introduced. Based on the proposed method, simulations of the end position and orientation, and joint angle of the grinding robot are given. Three different methods, including SGD + tanh, Nadam + tanh,

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Book chapters on the topic "Nadam optimizer"

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Alamsyah, Alamsyah, and Dinda Ayu Anggraeni. "Detection of Indonesian Sign Language System using Convolutional Neural Network (CNN) with Nadam Optimizer." In Advances in Computer Science Research. Atlantis Press International BV, 2024. http://dx.doi.org/10.2991/978-94-6463-589-8_32.

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Singh, Bhuvanesh, and Dilip Kumar Sharma. "Enhanced Steering Angle Prediction for Self-driving Cars Using a Refined CNN and NADAM Optimizer." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-5223-5_19.

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Pinto Bustamante, Boris Julián, Laura Bibiana Piñeros Hernández, Estefanía Zapata, et al. "Mi vida: cine, bioética y cuidados paliativos." In La muerte en el cine: ética narrativa en el final de la vida. Universidad del Rosario, 2020. http://dx.doi.org/10.12804/lm9789587844313.02.

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Los cuidados paliativos deben ser comprendidos desde una perspectiva más amplia que la noción de medicina paliativa, ofreciendo, desde un enfoque interdisciplinario, los cuidados necesarios para abordar las múltiples dimensiones del sufrimiento que experimentan pacientes y familiares enfrentados a una enfermedad en condición terminal. En este artículo se explorarán, con ayuda de la película My Life, los conceptos fundamentales de una atención centrada en el paciente, en el contexto de una enfermedad progresiva y sin alternativas terapéuticas. Allí, donde parece que no hay «nada más por hacer»,

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Conference papers on the topic "Nadam optimizer"

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Kissiedu, Alexander N. T., George Kwamina Aggrey, Maame Gyamfua Asante-Mensah, and Alexander Asante. "Development of Pneumonia Identification System: A Comparative Analysis of Some Selected CNN Architectures Using Adam, Nadam, and RAdam Optimizers." In 2024 IEEE SmartBlock4Africa. IEEE, 2024. https://doi.org/10.1109/smartblock4africa61928.2024.10779552.

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Xie, Zhiyi, Yanli Wang, and Shibao Sun. "TCNNn Text Classification Model based on nAdam Optimizer." In ICCSIE2022: 7th International Conference on Cyber Security and Information Engineering. ACM, 2022. http://dx.doi.org/10.1145/3558819.3565149.

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V, Harish, Vijaya Kumar T, Rajasekaran P, Poovizhi P, Jason Joshua P, and Sridhar R. "Classification of Early Skin Cancer Prediction using Nesterov- Accelerated Adaptive Moment Estimation (NADAM) Optimizer Algorithm." In 2024 International Conference on Cognitive Robotics and Intelligent Systems (ICC - ROBINS). IEEE, 2024. http://dx.doi.org/10.1109/icc-robins60238.2024.10533910.

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Gisbert, Fernando, David Cadrecha, Jaime Quintanal, et al. "Adapting Artificial Neural Networks Training Algorithms to Adjoint-Based Aerodynamic Shape Optimization." In ASME Turbo Expo 2023: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/gt2023-103906.

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Abstract Gradient-based algorithms are one of the pillars of automatic aerodynamic design in the turbomachinery field. Their use is largely extended due to their low computational demand when dealing with hundreds of design variables, especially if gradient computation is performed by means of an adjoint code. Difficulties arise when facing 3D aerodynamic inverse design, trying to obtain automatically a 3D blade geometry that produces a prescribed pressure distribution while fulfilling certain other aerodynamic constraints. Fine-tuning this target pressure distribution along the blade span — w

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P, Kuppusamy, Raga Siri P, Harshitha P, Dhanyasri M, and Celestine Iwendi. "Customized CNN with Adam and Nadam Optimizers for Emotion Recognition using Facial Expressions." In 2023 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET). IEEE, 2023. http://dx.doi.org/10.1109/wispnet57748.2023.10134002.

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Devi, M. Shyamala, D. Umanandhini, and Sakineti Aayush Kumar. "Seven Convolutional Nadam Optimized Deep Convolutional Neural Network based Cucumber Leaf Disease Prediction." In 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2023. http://dx.doi.org/10.1109/icccnt56998.2023.10307488.

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