Corn Leaf Disease Classification Optimization Using Resnet50 Architecture Utilizing Bayesian Optimization by Yahya Auliya Abdillah, Kusrini Kusrini

“…This research aims to optimize the classification of diseases on corn leaves using Convolutional Neural Network (CNN) architecture, ResNet50, combined with hyperparameter optimization techniques using Bayesian Optimization. …”
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Seleksi Fitur dengan Particle Swarm Optimization pada Klasifikasi Penyakit Parkinson Menggunakan XGBoost by Deni Kurnia, Muhammad Itqan Mazdadi, Dwi Kartini, Radityo Adi Nugroho, Friska Abadi

“…Selain itu model juga akan diterapkan SMOTE untuk mengatasi masalah ketidakseimbangan kelas data dan hyperparameter tuning pada XGBoost untuk mendapatkan hyperparameter yang optimal. …”
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Congruent Learning for Self-Regulated Federated Learning in 6G by Jalil Taghia, Farnaz Moradi, Hannes Larsson, Xiaoyu Lan, Adam Orucu, Masoumeh Ebrahimi, Andreas Johnsson

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Performance Evaluation of Long Short-Term Memory for Chili Price Prediction by Fata Nabil Fikri, Nurochman Nurochman

“…For this reason, the experimental method is used by testing several predetermined variables to obtain the right architecture and hyperparameter configuration. The results of this research show that the LSTM network can be applied in this case and the architecture and best hyperparameter configuration obtained are the same for both types of chilies, namely red chilies and rawit chilies. …”
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Classification of psychiatry clinical notes by diagnosis: a deep learning and machine learning approach by Sergio Rubio-Martín, María Teresa García-Ordás, Antonio Serrano-García, Clara Margarita Franch-Pato, Arturo Crespo-Álvaro, José Alberto Benítez-Andrades

“…These findings underscore the importance of hyperparameter tuning in maximizing model performance. …”
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Enhanced CO₂ Emissions Prediction Using Temporal Fusion Transformer Optimized by Football Optimization Algorithm by Amel Ali Alhussan, Marwa Metwally

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Using Generative Adversarial Networks to Translate Microresistivity Image Logs of Carbonates Into Synthetic Core Images With Accurate Dunham Textures by Saira Baharuddin, Cédric M. John

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Impact of computing platforms on classifier performance in heart disease prediction by Beenish Ayesha Akram, Muhammad Irfan, Amna Zafar, Sidra Khan, Rubina Shaheen

“…However, when using WEKA, both logistic regression and SVM demonstrated nearly 91% accuracy using the exact same hyperparameters. This research demonstrated the significance of platform selection in influencing classifier performance, offering valuable insights on how results reported in research can be impacted by the selection of the software and tools, using heart disease prediction as a use case scenario.…”
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Utilization of Machine Learning for Predicting Corrosion Inhibition by Quinoxaline Compounds by Muhamad Fadil, Muhamad Akrom, Wise Herowati

“…By conducting a comparative analysis among three algorithms: AdaBoost Regressor (ADB), Gradient Boosting Regressor (GBR), and Extreme Gradient Boosting Regressor (XGBR), and optimizing parameters through hyperparameter tuning using Grid Search and Random Search, this research demonstrates that the XGBR model yields the most superior prediction results. …”
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Enhanced Credit Card Fraud Detection Using Deep Hybrid CLST Model by Madiha Jabeen, Shabana Ramzan, Ali Raza, Norma Latif Fitriyani, Muhammad Syafrudin, Seung Won Lee

“…In the case of hyperparameter tuning, the detection rate is greatly enhanced. …”
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Urban flood hazard assessment using FLA-optimized boost algorithms in Ankara, Türkiye by Enes Gul

“…By applying advanced boosting algorithms—specifically, XGBoost, GradientBoost, and CatBoost—along with hyperparameter optimization through the Fick’s law algorithm (FLA), this research introduces an innovative methodology aimed at improving the reliability and accuracy of flood hazard assessments in Ankara’s urban landscape. …”
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Ensemble Voting Method for Phonocardiogram Heart Signal Classification Using FFT Features by Adisaputra Zidha Noorizki, Heri Pratikno, Weny Indah Kusumawati

“…Hyperparameter tuning, particularly learning rate adjustment, is applied to optimize the performance of the models. …”
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Hybrid genetic algorithm and deep learning techniques for advanced side-channel attacks by Faisal Hameed, Hoda Alkhzaimi

“…A critical challenge in training effective neural network models lies in hyperparameter optimization. This research introduces a genetic algorithm (GA) framework that efficiently navigates complex hyperparameter search spaces, overcoming limitations of conventional methods: grid search’s poor scalability and Bayesian optimization’s challenges with high-dimensional spaces. …”
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Pengembangan Deep Learning untuk Sistem Deteksi Dini Komplikasi Kaki Diabetik Menggunakan Citra Termogram by Medycha Emhandyksa, Indah Soesanti, Rina Susilowati

“…In addition, the research conducted can be an opening or preliminary research on the design of an early detection system for diabetic foot complications using deep learning artificial intelligence-based thermography in Indonesia. …”
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An empirical study of the naïve REINFORCE algorithm for predictive maintenance by Rajesh Siraskar, Satish Kumar, Shruti Patil, Arunkumar Bongale, Ketan Kotecha, Ambarish Kulkarni

“…For AutoRL frameworks, this research encourages seeking new design approaches to automatically identify optimum algorithm-hyperparameter combinations.…”
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Enhancing phishing detection with dynamic optimization and character-level deep learning in cloud environments by Vishnukumar Ravula, Mangayarkarasi Ramaiah

“…To improve precision, hyperparameter tuning has been done using DAOA. The proposed method offers a feasible solution for identifying the phishing URLs, and the method achieves computational efficiency through the attention mechanism and dynamic hyperparameter optimization. …”
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Carrier-independent screen-shooting resistant watermarking based on information overlay superimposition by Xiaomeng LI, Daidou GUO, Xunfang ZHUO, Heng YAO, Chuan QIN

“…Financial security, an important part of national security, is critical for the stable and healthy development of the economy.Digital image watermarking technology plays a crucial role in the field of financial information security, and the anti-screen watermarking algorithm has become a new research focus of digital image watermarking technology.The common way to achieve an invisible watermark in existing watermarking schemes is to modify the carrier image, which is not suitable for all types of images.To solve this problem, an end-to-end robust watermarking scheme based on deep learning was proposed.The algorithm achieved both visual quality and robustness of the watermark image.A random binary string served as the input of the encoder network in the proposed end-to-end network architecture.The encoder can generate the watermark information overlay, which can be attached to any carrier image after training.The ability to resist screen shooting noise was learned by the model through mathematical methods incorporated in the network to simulate the distortion generated during screen shooting.The visual quality of the watermark image was further improved by adding the image JND loss based on just perceptible difference.Moreover, an embedding hyperparameter was introduced in the training phase to balance the visual quality and robustness of the watermarked image adaptively.A watermark model suitable for different scenarios can be obtained by changing the size of the embedding hyperparameter.The visual quality and robustness performance of the proposed scheme and the current state-of-the-art algorithms were evaluated to verify the effectiveness of the proposed scheme.The results show that the watermark image generated by the proposed scheme has better visual quality and can accurately restore the embedded watermark information in robustness experiments under different distances, angles, lighting conditions, display devices, and shooting devices.…”
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Insights into landslide susceptibility: a comparative evaluation of multi-criteria analysis and machine learning techniques by Zuleide Ferreira, Bruna Almeida, Ana Cristina Costa, Manoel do Couto Fernandes, Pedro Cabral

“…Although some studies have employed machine learning (ML) algorithms and multi-criteria analysis (MCA) for landslide susceptibility mapping (LSM), comparative evaluations of these methods remain scarce, particularly regarding predictor importance, performance metrics, and hyperparameter optimization. This research addresses these gaps by comparing logistic regression (LR), random forest (RF), support vector machines (SVM), and MCA, focusing on landslide susceptibility in Petrópolis, Brazil. …”
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Predicting Atmospheric Dispersion of Industrial Chemicals Using Machine Learning Approaches by Maria Valle, Jairo A. Cardona, Cesar Viloria-Nunez, Christian G. Quintero M.

“…This study presents an intelligent framework for assessing atmospheric dispersion in industrial accident scenarios involving chemical substances. The research focuses on modeling the dispersion of key chemicals, such as chlorine, methanol, and propane, under various accident conditions, including leaks, fires, and explosions. …”
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Performance prediction of sintered NdFeB magnet using multi-head attention regression models by Qichao Liang, Qiang Ma, Hao Wu, Rongshun Lai, Yangyang Zhang, Ping Liu, Tao Qi

“…This study offers new insights into machine learning-based modeling of structure-property relationships in materials and has potential to advance the research of multimodal NdFeB magnet models.…”
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