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Journal articles on the topic 'Nadam optimizer'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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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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Mondal, M. Rubaiyat Hossain, Subrato Bharati, and Prajoy Podder. "CO-IRv2: Optimized InceptionResNetV2 for COVID-19 detection from chest CT images." PLOS ONE 16, no. 10 (2021): e0259179. http://dx.doi.org/10.1371/journal.pone.0259179.

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This paper focuses on the application of deep learning (DL) in the diagnosis of coronavirus disease (COVID-19). The novelty of this work is in the introduction of optimized InceptionResNetV2 for COVID-19 (CO-IRv2) method. A part of the CO-IRv2 scheme is derived from the concepts of InceptionNet and ResNet with hyperparameter tuning, while the remaining part is a new architecture consisting of a global average pooling layer, batch normalization, dense layers, and dropout layers. The proposed CO-IRv2 is applied to a new dataset of 2481 computed tomography (CT) images formed by collecting two ind

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Podder, Prajoy, Sanchita Rani Das, M. Rubaiyat Hossain Mondal, et al. "LDDNet: A Deep Learning Framework for the Diagnosis of Infectious Lung Diseases." Sensors 23, no. 1 (2023): 480. http://dx.doi.org/10.3390/s23010480.

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This paper proposes a new deep learning (DL) framework for the analysis of lung diseases, including COVID-19 and pneumonia, from chest CT scans and X-ray (CXR) images. This framework is termed optimized DenseNet201 for lung diseases (LDDNet). The proposed LDDNet was developed using additional layers of 2D global average pooling, dense and dropout layers, and batch normalization to the base DenseNet201 model. There are 1024 Relu-activated dense layers and 256 dense layers using the sigmoid activation method. The hyper-parameters of the model, including the learning rate, batch size, epochs, and

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Adiatma, Biva Candra Lutfi, Ema Utami, and Anggit Dwi Hartanto. "PENGENALAN EKSPRESI WAJAH MENGGUNAKAN DEEP CONVOLUTIONAL NEURAL NETWORK." EXPLORE 11, no. 2 (2021): 75. http://dx.doi.org/10.35200/explore.v11i2.478.

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Pengenalan ekspresi wajah menjadi salah satu bidang penelitian aktif dalam beberapa tahun terakhir. Pendekatan yang ada saat ini sebagian besar menggunakan metode tradisional seperti SIFT, HOG, LBP, yang diikuti oleh klasifikasi yang dilatih dari data gambar atau video. Sebagian besar mendapatkan hasil yang cukup baik ketika menggunakan data citra yang terkontrol , tetapi tidak bekerja dengan baik pada kumpulan data yang lebih sulit dimana terdapat banyak bagian wajah dengan banyak variasi gambar. Banyak penelitian yang telah mengusulkan kerangka kerja untuk pengenalan ekspresi wajah menggunak

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Liu, Songang. "An improvement on common optimization methods based on SuperstarGAN." Applied and Computational Engineering 50, no. 1 (2024): 46–51. http://dx.doi.org/10.54254/2755-2721/50/20241169.

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Image processing has long been a focal point of research, offering avenues to enhance image clarity and transfer image features. Over the past decade, Generative Adversarial Networks (GANs) have played a pivotal role in the field of image conversion. This study delves into the world of GANs, focusing on the SuperstarGAN model and its optimization techniques. SuperstarGAN, an evolution of the well-known StarGAN, excels in multi-domain image-to-image conversion, overcoming limitations and offering versatility. To better understand its optimization, this study explored the effects of different op

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Khozaimi, Ach, and Wayan Firdaus Mahmudy. "New insight in cervical cancer diagnosis using convolution neural network architecture." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 3092. http://dx.doi.org/10.11591/ijai.v13.i3.pp3092-3100.

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<p>The Pap smear is a screening method for early cervical cancer diagnosis. The selection of the right optimizer in the convolutional neural network (CNN) model is key to the success of the CNN in image classification, including the classification of cervical cancer Pap smear images. In this study, stochastic gradient descent (SGD), root mean square propagation (RMSprop), Adam, AdaGrad, AdaDelta, Adamax, and Nadam optimizers were used to classify cervical cancer Pap smear images from the SipakMed dataset. Resnet-18, Resnet-34, and VGG-16 are the CNN architectures used in this study, and

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Masruroh, Fitriana, Bayu Surarso, and Budi Warsito. "Perbandingan Kinerja Inception- Resnetv2, Xception, Inception-v3, dan Resnet50 pada Gambar Bentuk Wajah." Jurnal Teknologi Informasi dan Ilmu Komputer 10, no. 1 (2023): 11–20. https://doi.org/10.25126/jtiik.1014941.

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Saat ini, klasifikasi bentuk wajah banyak diterapkan dalam berbagai bidang. Dalam bidang industri fashion dapat digunakan untuk pemilihan gaya rambut, pemilihan bingkai kacamata, tata rias, dan mode lainnya. Selain itu, dalam bidang medis bentuk wajah digunakan untuk bedah plastik. Identifikasi bentuk wajah adalah tugas yang menantang karena kompleksitas wajah, ukuran, pencahayaan, usia dan ekspresi. Banyak metode yang dikembangkan untuk memberikan hasil akurasi terbaik dalam klasifikasi bentuk wajah. Deep learning menjadi tren dibidang komputer vision karena memberikan hasil yang paling baik

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Masruroh, Fitriana, Bayu Surarso, and Budi Warsito. "Perbandingan Kinerja Inception- Resnetv2, Xception, Inception-v3, dan Resnet50 pada Gambar Bentuk Wajah." Jurnal Teknologi Informasi dan Ilmu Komputer 10, no. 1 (2023): 11–20. https://doi.org/10.25126/jtiik.2023104941.

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Saat ini, klasifikasi bentuk wajah banyak diterapkan dalam berbagai bidang. Dalam bidang industri fashion dapat digunakan untuk pemilihan gaya rambut, pemilihan bingkai kacamata, tata rias, dan mode lainnya. Selain itu, dalam bidang medis bentuk wajah digunakan untuk bedah plastik. Identifikasi bentuk wajah adalah tugas yang menantang karena kompleksitas wajah, ukuran, pencahayaan, usia dan ekspresi. Banyak metode yang dikembangkan untuk memberikan hasil akurasi terbaik dalam klasifikasi bentuk wajah. Deep learning menjadi tren dibidang komputer vision karena memberikan hasil yang paling baik

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Najib, Mohamad Khoirun, Ade Irawan, Fitra Nuvus Salsabilla, and Sri Nurdiati. "Performance Comparison of Gradient-based Optimizer for Classification of Movie Genres." Indonesian Journal of Mathematics and Applications 3, no. 1 (2025): 1–18. https://doi.org/10.21776/ub.ijma.2025.003.01.1.

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In this digital era, artificial intelligence has become very popular due to its very wide scope of application. Various models and methods in artificial intelligence are developed with their respective purposes. However, each model and method certainly requires a reliable optimizer in the training process. Many optimizers have been developed and are increasingly reliable lately. In this article, we classify the synopsis texts of several movies into nine different genre classes, leveraging Natural Language Processing (NLP) with Long Short Term Memory (LSTM) and Embedding to build models. Models

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Sayın, Khuraman Aziz, Necla Kırcalı Gürsoy, Türkay Yolcu, and Arif Gürsoy. "On the Synergy of Optimizers and Activation Functions: A CNN Benchmarking Study." Mathematics 13, no. 13 (2025): 2088. https://doi.org/10.3390/math13132088.

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In this study, we present a comparative analysis of gradient descent-based optimizers frequently used in Convolutional Neural Networks (CNNs), including SGD, mSGD, RMSprop, Adadelta, Nadam, Adamax, Adam, and the recent EVE optimizer. To explore the interaction between optimization strategies and activation functions, we systematically evaluate all combinations of these optimizers with four activation functions—ReLU, LeakyReLU, Tanh, and GELU—across three benchmark image classification datasets: CIFAR-10, Fashion-MNIST (F-MNIST), and Labeled Faces in the Wild (LFW). Each configuration was asses

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Aldo, Dasril. "COMPARISON OF ACTIVATION AND OPTIMIZER PERFORMANCE IN LSTM MODEL FOR PURE BEEF PRICE PREDICTION." JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) 10, no. 3 (2025): 573–85. https://doi.org/10.33480/jitk.v10i3.6115.

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One of the primary factors impacting the economy is the ability to forecast the prices of commodities such as beef. This paper aims to evaluate the effectiveness of various activation functions and optimization strategies when integrated into the LSTM (Long Short-Term Memory) architecture model in predicting the price of lean beef in Aceh. The data sample utilized was obtained from the Indonesian National Food Agency panel, which shows daily prices for beef within the time frame of July 14th, 2022, to July 31st, 2024. As for the conducted research, the process of preparation data preprocessing

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Lopez-Betancur, Daniela, Efrén González-Ramírez, Carlos Guerrero-Mendez, et al. "Evaluation of Optimization Algorithms for Measurement of Suspended Solids." Water 16, no. 13 (2024): 1761. http://dx.doi.org/10.3390/w16131761.

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Advances in convolutional neural networks (CNNs) provide novel and alternative solutions for water quality management. This paper evaluates state-of-the-art optimization strategies available in PyTorch to date using AlexNet, a simple yet powerful CNN model. We assessed twelve optimization algorithms: Adadelta, Adagrad, Adam, AdamW, Adamax, ASGD, LBFGS, NAdam, RAdam, RMSprop, Rprop, and SGD under default conditions. The AlexNet model, pre-trained and coupled with a Multiple Linear Regression (MLR) model, was used to estimate the quantity black pixels (suspended solids) randomly distributed on a

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Fatima, Noor. "Enhancing Performance of a Deep Neural Network: A Comparative Analysis of Optimization Algorithms." ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 9, no. 2 (2020): 79–90. http://dx.doi.org/10.14201/adcaij2020927990.

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Adopting the most suitable optimization algorithm (optimizer) for a Neural Network Model is among the most important ventures in Deep Learning and all classes of Neural Networks. It’s a case of trial and error experimentation. In this paper, we will experiment with seven of the most popular optimization algorithms namely: sgd, rmsprop, adagrad, adadelta, adam, adamax and nadam on four unrelated datasets discretely, to conclude which one dispenses the best accuracy, efficiency and performance to our deep neural network. This work will provide insightful analysis to a data scientist in choosing

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Yaqub, Muhammad, Jinchao Feng, M. Sultan Zia, et al. "State-of-the-Art CNN Optimizer for Brain Tumor Segmentation in Magnetic Resonance Images." Brain Sciences 10, no. 7 (2020): 427. http://dx.doi.org/10.3390/brainsci10070427.

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Brain tumors have become a leading cause of death around the globe. The main reason for this epidemic is the difficulty conducting a timely diagnosis of the tumor. Fortunately, magnetic resonance images (MRI) are utilized to diagnose tumors in most cases. The performance of a Convolutional Neural Network (CNN) depends on many factors (i.e., weight initialization, optimization, batches and epochs, learning rate, activation function, loss function, and network topology), data quality, and specific combinations of these model attributes. When we deal with a segmentation or classification problem,

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Putra, Ivan Pratama, Rusbandi Rusbandi, and Derry Alamsyah. "Klasifikasi Penyakit Daun Jagung Menggunakan Metode Convolutional Neural Network." Jurnal Algoritme 2, no. 2 (2022): 102–12. http://dx.doi.org/10.35957/algoritme.v2i2.2360.

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Jagung merupakan tanaman pangan utama ketiga setelah padi dan terigu di dunia dan menempati posisi kedua setelah padi di Indonesia. Penyakit tanaman sering kali disebabkan oleh aktifitas atau serangan organism di dalam bagian tubuh tanaman, di luar tubuh, atau di sekitarnya. Penelitian ini bertujuan untuk mengklasifikasikan penyakit daun jagung menggunakan metode convolutional neural network (CNN) dengan arsitektur Resnet 50 dengan optimizer Adam, Nadam dan SGD. Dataset terdapat 4225 citra di pisahkan menjadi 3380 data train, 845 data test. Citra yang digunakan di resize menjadi ukuran 224x224

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Hoang, Nhat-Duc. "Automatic Impervious Surface Area Detection Using Image Texture Analysis and Neural Computing Models with Advanced Optimizers." Computational Intelligence and Neuroscience 2021 (February 16, 2021): 1–17. http://dx.doi.org/10.1155/2021/8820116.

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Up-to-date information regarding impervious surface is valuable for urban planning and management. The objective of this study is to develop neural computing models used for automatic impervious surface area detection at a regional scale. To achieve this task, advanced optimizers of adaptive moment estimation (Adam), a variation of Adam called Adamax, Nesterov-accelerated adaptive moment estimation (Nadam), Adam with decoupled weight decay (AdamW), and a new exponential moving average variant (AMSGrad) are used to train the artificial neural network models employed for impervious surface detec

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Manguri, Kamaran, and Aree A. Mohammed. "SMART OPTIMIZER SELECTION TECHNIQUE: A COMPARATIVE STUDY OF MODIFIED DENSNET201 WITH OTHER DEEP LEARNING MODELS." Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska 13, no. 4 (2023): 39–43. http://dx.doi.org/10.35784/iapgos.5332.

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The rapid growth and development of AI-based applications introduce a wide range of deep and transfer learning model architectures. Selecting an optimal optimizer is still challenging to improve any classification type's performance efficiency and accuracy. This paper proposes an intelligent optimizer selection technique using a new search algorithm to overcome this difficulty. A dataset used in this work was collected and customized for controlling and monitoring roads, especially when emergency vehicles are approaching. In this regard, several deep and transfer learning models have been comp

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Habie, Khairul Fathan, Murinto Murinto, and Sunardi Sunardi. "Impact of Optimizer Selection on MobileNetV1 Performance for Skin Disease Detection Using Digital Images." Jurnal Teknik Informatika (Jutif) 6, no. 3 (2025): 1589–604. https://doi.org/10.52436/1.jutif.2025.6.3.4685.

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Automatic detection of skin diseases using digital images is a growing field in the application of deep learning in the medical world, especially to help the early diagnosis process. One of the most widely used models is MobileNetV1 because it is lightweight and efficient in image processing. However, the performance of the model is greatly affected by the training configuration, including the type of optimizer used. This study aims to compare the effectiveness of six types of optimizers, namely SGD, RMSprop, Adam, Adadelta, Adagrad, Adamax, and Nadam in training MobileNetV1 models for human s

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Ezgi, Ahmet, and Aytuğ Onan. "Automatic Knee Osteoarthritis Severity Grading using Deep Neural Networks: Comparative Analysis of Network Architectures and Optimization Functions." International Conference on Applied Engineering and Natural Sciences 1, no. 1 (2023): 197–203. http://dx.doi.org/10.59287/icaens.992.

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Knee osteoarthritis (OA) is a prevalent degenerative joint disease that requires accurate assessment of its severity for effective treatment planning. In this study, we propose an automatic knee OA severity-grading system based on deep neural networks. Specifically, we explore various network architectures, including VGG-16, VGG-19, ResNet-101, EfficientNet-B7, and EfficientNet-B6, along with different optimization functions such as SGD, ADAM, Nadam, AdamW, and AdaDelta. Furthermore, we investigate two loss functions, namely, the novel ordinal loss and the cross-entropy loss. The proposed syst

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Ramachandran, Prabhakar, Tamma Eswarlal, Margot Lehman, and Zachery Colbert. "Assessment of Optimizers and their Performance in Autosegmenting Lung Tumors." Journal of Medical Physics 48, no. 2 (2023): 129–35. http://dx.doi.org/10.4103/jmp.jmp_54_23.

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Purpose: Optimizers are widely utilized across various domains to enhance desired outcomes by either maximizing or minimizing objective functions. In the context of deep learning, they help to minimize the loss function and improve model’s performance. This study aims to evaluate the accuracy of different optimizers employed for autosegmentation of non-small cell lung cancer (NSCLC) target volumes on thoracic computed tomography images utilized in oncology. Materials and Methods: The study utilized 112 patients, comprising 92 patients from “The Cancer Imaging Archive” (TCIA) and 20 of our loca

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Masruroh, Fitriana, Bayu Surarso, and Budi Warsito. "Perbandingan Kinerja Inception- Resnetv2, Xception, Inception-v3, dan Resnet50 pada Gambar Bentuk Wajah." Jurnal Teknologi Informasi dan Ilmu Komputer 10, no. 1 (2023): 11. http://dx.doi.org/10.25126/jtiik.20231014941.

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<p class="Abstrak">Saat ini, klasifikasi bentuk wajah banyak diterapkan dalam berbagai bidang. Dalam bidang industri fashion dapat digunakan untuk pemilihan gaya rambut, pemilihan bingkai kacamata, tata rias, dan mode lainnya. Selain itu, dalam bidang medis bentuk wajah digunakan untuk bedah plastik. Identifikasi bentuk wajah adalah tugas yang menantang karena kompleksitas wajah, ukuran, pencahayaan, usia dan ekspresi. Banyak metode yang dikembangkan untuk memberikan hasil akurasi terbaik dalam klasifikasi bentuk wajah. Deep learning menjadi tren dibidang komputer vision karena memberika

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Podder, Prajoy, Fatema Binte Alam, M. Rubaiyat Hossain Mondal, Md Junayed Hasan, Ali Rohan, and Subrato Bharati. "Rethinking Densely Connected Convolutional Networks for Diagnosing Infectious Diseases." Computers 12, no. 5 (2023): 95. http://dx.doi.org/10.3390/computers12050095.

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Due to its high transmissibility, the COVID-19 pandemic has placed an unprecedented burden on healthcare systems worldwide. X-ray imaging of the chest has emerged as a valuable and cost-effective tool for detecting and diagnosing COVID-19 patients. In this study, we developed a deep learning model using transfer learning with optimized DenseNet-169 and DenseNet-201 models for three-class classification, utilizing the Nadam optimizer. We modified the traditional DenseNet architecture and tuned the hyperparameters to improve the model’s performance. The model was evaluated on a novel dataset of

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Mustaqim, Tanzilal, Pima Hani Safitri, and Daud Muhajir. "A Deep Learning Model Comparation for Diabetic Retinopathy Image Classification." Scientific Journal of Informatics 12, no. 1 (2025): 21–30. https://doi.org/10.15294/sji.v12i1.20939.

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Purpose: This study compares the performance of various deep learning models for diabetic retinopathy (DR) classification, emphasizing the impact of different optimization functions. Early detection of DR is vital for preventing blindness, and the research investigates how optimization functions influence the classification accuracy and efficiency of several convolutional neural networks (CNNs). This study fills a gap in the existing literature by examining how optimization functions affect model performance in conjunction with architectural considerations. Methods: This paper uses the APTOS 2

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Tinaliah, Tinaliah. "Penerapan Convolutional Neural Network Untuk Klasifikasi Citra Ekspresi Wajah Manusia Pada MMA Facial Expression Dataset." JATISI (Jurnal Teknik Informatika dan Sistem Informasi) 8, no. 4 (2021): 2051–59. http://dx.doi.org/10.35957/jatisi.v8i4.1437.

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Ekspresi wajah manusia secara umum mewakili emosi atau perasaan yang sedang dirasakannya saat itu. Klasifikasi citra ekspresi wajah dapat membantu untuk mengetahui apakah emosi yang sedang dirasakan seseorang. CNN adalah jenis neural network yang digunakan untuk mengekstrak fitur – fitur dari sebuah citra dan sangat unggul apabila diterapkan pada data citra. Pada penelitian ini akan dilakukan klasifikasi citra ekspresi wajah dengan menerapkan Convolution Neural Network pada dataset MMA Facial Expression. Dimana data akan dibagi menjadi 2 kelas, yaitu happy dan sad. Pengujian dilakukan mengguna

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Riyadi, Willy, and Jasmir Jasmir. "Comparative Analysis of Optimizer Effectiveness in GRU and CNN-GRU Models for Airport Traffic Prediction." Jurnal Ilmiah Teknik Elektro Komputer dan Informatika 10, no. 3 (2024): 580–93. https://doi.org/10.26555/jiteki.v10i3.29659.

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The COVID-19 pandemic has posed significant challenges to airport traffic management, necessitating accurate predictive models. This research evaluates the effectiveness of various optimizers in enhancing airport traffic prediction using Deep Learning models, specifically Gated Recurrent Units (GRU) and Convolutional Neural Network-Gated Recurrent Units (CNN-GRU). We compare the performance of optimizers including RMSprop, Adam, Nadam, AdamW, Adamax, and Lion, and analyze the impact of their parameter tuning on model accuracy. Time series data from airports in the United States, Canada, Chile,

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Teo, Min-Er, Lee-Ying Chong, Siew-Chin Chong, and Pey-Yun Goh. "2.5D Face Recognition System using EfficientNet with Various Optimizers." JOIV : International Journal on Informatics Visualization 8, no. 4 (2025): 2388. https://doi.org/10.62527/joiv.8.4.3030.

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Face recognition has emerged as the most common biometric technique for checking a person's authenticity in various applications. The depth characteristic that exists in 2.5D data, also known as depth image, is utilized by the 2.5D facial recognition algorithm to supply additional details, strengthening the system's precision and durability. A deep learning approach-based 2.5D facial recognition system is proposed in this research. The accuracy of 2.5D face recognition could be enhanced by integrating depth data with deep learning approaches. Besides, optimizers in the deep learning approach a

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USMAN, KOREDIANTO, NOR KUMALASARI CAECAR PRATIWI, NUR IBRAHIM, HERI SYAHRIAN, and VITRIA PUSPITASARI RAHADI. "Evaluasi Optimizer pada Residual Network untuk Klasifikasi Klon Teh Seri GMB Berbasis Citra Daun." ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika 9, no. 4 (2021): 841. http://dx.doi.org/10.26760/elkomika.v9i4.841.

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ABSTRAKKomoditas teh berperan strategis terhadap pertumbuhan perekonomian Indonesia, salah satunya dari teh klon Gambung (GMB). Klon GMB memiliki beberapa karakter khas, dengan tingkat kemiripan morfologi yang sangat tinggi. Hal ini berdampak pada proses pengenalan klon GMB dilakukan melalui pengamatan visual oleh tenaga ahli sangat rentan terhadap kesalahan identifikasi. Sehingga, dalam penelitian ini dirancang suatu sistem identifikasi terhadap 11 klon teh seri GMB (GMB-1 hingga GMB-11) dengan menggunakan arsitektur ResNet101. Evaluasi sistem akan dilakukan dengan membandingkan tujuh algorit

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Prayoga, Kresna, Syariful Alam, and Imay Kurniawan. "IMPLEMENTASI LONG SHORT-TERM MEMORY (LSTM) UNTUK FORECASTING HARGA CRYPTOCURRENCY." JATI (Jurnal Mahasiswa Teknik Informatika) 9, no. 5 (2025): 8336–42. https://doi.org/10.36040/jati.v9i5.15048.

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Penelitian ini bertujuan mengimplementasikan model Long Short-Term Memory (LSTM) untuk memprediksi harga Bitcoin dengan memanfaatkan data historis dari Yahoo Finance. Metode yang digunakan meliputi pra-pemrosesan data (normalisasi Min-Max, pembagian dataset 95:5), pelatihan model LSTM (2 lapis LSTM dengan 128 dan 64 neuron, optimizer Nadam, 10 epoch), dan evaluasi menggunakan metrik MAE, RMSE, dan MAPE. Hasil menunjukkan model mencapai akurasi tinggi dengan MAE 1403.3701, RMSE 1983.4696, dan MAPE 1.5192%. Model ini diimplementasikan dalam aplikasi web berbasis Streamlit untuk prediksi interakt

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Kurdthongmee, Wattanapong. "Comprehensive Evaluation of Deep Neural Network Architectures for Parawood Pith Estimation." HighTech and Innovation Journal 4, no. 3 (2023): 543–59. http://dx.doi.org/10.28991/hij-2023-04-03-06.

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Accurate pith estimation is crucial for maintaining the quality of wood products. This study delves into deep learning techniques for precise Parawood pith estimation, employing popular convolutional neural networks (ResNet50, MobileNet, and Xception) with adapted regression heads. Through variations in regression functions, optimizers, and training epochs, the most effective models were pinpointed. Xception, coupled with Huber Loss regression, Nadam optimizer, and 200 epochs, showcased superior performance, achieving a 4.48 mm mean error (with a standard deviation of 3.69 mm) in Parawood. Not

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Arshad, Belal, and Atin Mukherjee. "Comparative Analysis of Deep Learning Models for the Detection of Epileptic Seizure." American Journal of Advanced Computing 2, no. 1 (2023): 29–35. http://dx.doi.org/10.15864/ajac.21016.

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Electroencephalogram (EEG) is used to detect epilepsy, a common neurological disorder. Neurologists visually examine EEG results to make the diagnosis. Researchers have suggested automated technologies to diagnose the seizure because traditional method are lengthy and there is a dearth of professionals everywhere. The common symptoms of seizures, which are characterized by aberrant brain activity brought on by an epileptic disease, include bewilderment, loss of awareness, and strange behaviour. Sometimes it becomes very difficult to identify the seizure in persons. So, for determining seizures

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Khan, Shahrukh, Saiaf Bin Rayhan, Md Mazedur Rahman, Jakiya Sultana, and Gyula Varga. "Optimized ANN Model for Predicting Buckling Strength of Metallic Aerospace Panels Under Compressive Loading." Metals 15, no. 6 (2025): 666. https://doi.org/10.3390/met15060666.

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The present research proposes an Artificial Neural Network (ANN) model to predict the critical buckling load of six different types of metallic aerospace grid-stiffened panels: isogrid type I, isogrid type II, bi-grid, X-grid, anisogrid, and waffle, all subjected to compressive loading. Six thousand samples (one thousand per panel type) were generated using the Latin Hypercube Sampling method to ensure a diverse and comprehensive dataset. The ANN model was systematically fine-tuned by testing various batch sizes, learning rates, optimizers, dense layer configurations, and activation functions.

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Azar, Ahmad Taher, Mohamed Tounsi, Suliman Mohamed Fati, et al. "Automated System for Colon Cancer Detection and Segmentation Based on Deep Learning Techniques." International Journal of Sociotechnology and Knowledge Development 15, no. 1 (2023): 1–28. http://dx.doi.org/10.4018/ijskd.326629.

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Colon cancer is one of the world's three most deadly and severe cancers. As with any cancer, the key priority is early detection. Deep learning (DL) applications have recently gained popularity in medical image analysis due to the success they have achieved in the early detection and screening of cancerous tissues or organs. This paper aims to explore the potential of deep learning techniques for colon cancer classification. This research will aid in the early prediction of colon cancer in order to provide effective treatment in the most timely manner. In this exploratory study, many deep lear

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Riyadi, Willy, Jasmir, and Xaverius Sika. "Comparison Airport Traffic Prediction Performance Using BiGRU and CNN-BiGRU Models." Jurnal Online Informatika 10, no. 1 (2025): 12–21. https://doi.org/10.15575/join.v10i1.1362.

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COVID-19 pandemic has significantly disrupted the aviation industry, highlighting the critical need for accurate airport traffic predictions. This study compares the performance of BiGRU and CNN-BiGRU models to enhance airport traffic forecasting accuracy models from March to December 2020. Data preprocessing was performed using Python's Pandas library. This involved filtering, scaling using min-max normalization, and splitting the data into 80:20 training-testing split using Python's Pandas library. Various optimization techniques—RMSProp, Adam, Nadam, Adamax, AdamW, and Lion—were applied, al

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Sun, Hao. "Unpaired image neural style transfer based on Non-Local-Attention-Cycle-Consistent adversarial network." Theoretical and Natural Science 18, no. 1 (2023): 152–59. http://dx.doi.org/10.54254/2753-8818/18/20230364.

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Existing image translation methods already enable style transfer on unpaired data. Although these methods have yielded satisfactory results, they still result in changing the background while changing the object. One reason is that when using convolutional neural networks, global information is lost as the number of network layers increases, and the absence of an effective sensory field leads to the failure to generate high-quality results. This paper proposed a Non-Local-Attention-Cycle-Consistent Adversarial Networks for unpaired images style transfer. The no-local-attention can quickly capt

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Ilyas, Muhammad Nur, and Erwin Budi Setiawan. "Weight-Based Hybrid Filtering in a Movie Recommendation System Based on Twitter with LSTM Classification." JURNAL MEDIA INFORMATIKA BUDIDARMA 7, no. 4 (2023): 1838. http://dx.doi.org/10.30865/mib.v7i4.6668.

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With the era of digitalization, movie-watching has gained immense popularity, with platforms like Disney+ offering easy access to a variety of films. After watching, users frequently share their opinions on social media platforms such as Twitter, because of it is freedom of expression. With numerous movies available, users frequently encounter challenges in deciding what to watch. To address this, a recommendation system is proposed to streamline the decision-making process for users. Collaborative Filtering (CF), Content-Based Filtering (CBF), and Hybrid Filtering are common techniques used i

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Mambang and Finki Dona Marleny. "Image Prediction of Exact Science and Social Science Learning Content with Convolutional Neural Network." JOIV : International Journal on Informatics Visualization 6, no. 4 (2022): 749. http://dx.doi.org/10.30630/joiv.6.4.923.

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Learning content can be identified through text, images, and videos. This study aims to predict the learning content contained on YouTube. The images used are images contained in the learning content of the exact sciences, such as mathematics, and social science fields, such as culture. Prediction of images on learning content is done by creating a model on CNN. The collection of datasets carried out on learning content is found on YouTube. The first assessment was performed with an RMSProp optimizer with a learning rate of 0.001, which is used for all optimizers. Several other optimizers were

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Fayyad, Muhammad Fauzi, Viki Kurniawan, Muhammad Ridho Anugrah, Baihaqi Hilmi Estanto, and Tasnim Bilal. "Application of Recurrent Neural Network Bi-Long Short-Term Memory, Gated Recurrent Unit and Bi-Gated Recurrent Unit for Forecasting Rupiah Against Dollar (USD) Exchange Rate." Public Research Journal of Engineering, Data Technology and Computer Science 2, no. 1 (2024): 1–10. http://dx.doi.org/10.57152/predatecs.v2i1.1094.

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Foreign exchange rates have a crucial role in a country's economic development, influencing long-term investment decisions. This research aims to forecast the exchange rate of Rupiah to the United States Dollar (USD) by using deep learning models of Recurrent Neural Network (RNN) architecture, especially Bi-Long Short-Term Memory (Bi-LSTM), Gated Recurrent Unit (GRU), and Bi-Gated Recurrent Unit (Bi-GRU). Historical daily exchange rate data from January 1, 2013 to November 3, 2023, obtained from Yahoo Finance, was used as the dataset. The model training and evaluation process was performed bas

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Ch, Anusha, Rupa Ch, Samhitha Gadamsetty, Celestine Iwendi, Thippa Reddy Gadekallu, and Imed Ben Dhaou. "ECDSA-Based Water Bodies Prediction from Satellite Images with UNet." Water 14, no. 14 (2022): 2234. http://dx.doi.org/10.3390/w14142234.

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The detection of water bodies from satellite images plays a vital role in research development. It has a wide range of applications such as the prediction of natural disasters and detecting drought and flood conditions. There were few existing applications that focused on detecting water bodies that are becoming extinct in nature. The dataset to train this deep learning model is taken from Kaggle. It has two classes, namely water bodies and masks. There is a total of 2841 sentinel-2 satellite images with corresponding 2841 masks. Additionally, the present work focuses on using UNet, Tensorflow

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Kumar, S. Charan, Amit Kumar Thakur, J. Ronald Aseer, et al. "An Experimental Analysis and ANN Based Parameter Optimization of the Influence of Microalgae Spirulina Blends on CI Engine Attributes." Energies 15, no. 17 (2022): 6158. http://dx.doi.org/10.3390/en15176158.

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In this present investigation, emittance and performance attributes of a diesel engine using micro-algae spirulina blended biodiesel mixtures of various concentrations (20%, 35%, 50%, 65%, 80%, and 100%) were evaluated. An optimization model was also developed using an Artificial Neural Network (ANN) to characterize the experimental parameters. Experimental findings demonstrated significant improvement in brake specific fuel consumption (BSFC) using varied blends. Furthermore, brake thermal efficiency (BTE) is decreased gradually for biodiesel blends as compared to diesel. Micro-algae spirulin

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Rasywir, Errissya, Yovi Pratama, and Fachruddin Fachruddin. "Eksperimen Pengujian Optimizer dan Fungsi Aktivasi Pada Code Clone Detection dengan Pemanfaatan Deep Neural Network (DNN)." Building of Informatics, Technology and Science (BITS) 4, no. 2 (2022): 405–12. http://dx.doi.org/10.47065/bits.v4i2.1776.

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The problem of similarity (similarity) of program code can be one solution to the plagiarism detection approach. Plagiarism raises a form of action and consequences of plagiarism itself if the source used is not open source. Plagiarism is an act of deception of the work of others without the knowledge of the original author, which violates a Copyright and Moral Rights. With the increasing amount of data and data complexity, deep learning provides solutions for predictive analytics, with increased processing capabilities and optimal processor utilization. Deep learning shows success and improve

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Altaee, Mustafa, Talib A., M. A. Jalil, Ali J., and Thamer A. Alalwani. "Intelligent Multi-Level Feature Fusion Using Remote Sensing and CNN Image Classification Algorithm." Journal of Intelligent Systems and Internet of Things 9, no. 1 (2023): 53–70. http://dx.doi.org/10.54216/jisiot.090103.

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The collection of fetures in both multispectral and hyperspectral domains is possible with Hyperspectral Image (HSI). It comprises a vast array of multispectral bands with functional relationships. However, they become more complex when dealing with small samples. To tackle this issue, researchers employed a deep learning convolutionary neural network system (DL-CNN) and implemented a temporal abstraction strategy to grade HSI. This approach is an intelligent multi-level feature fusion that combines the temporal abstraction strategy and DL-CNN for HSI grading. Researchers have introduced the i

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