Optimasi Pertanian Padi: Peramalan Curah Hujan Berbasis Arima Untuk Penentuan Waktu Tanam Yang Tepat by Sofi Defiyanti, Betha Nurina Sari, Tesa Nur Padilah

“…Forecasting using the ARIMA method can be used to predict future rainfall, helping to identify the best time to start planting. …”
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A deep learning-based method for the intelligent identification of the quantity of coals flushed out during borehole hydraulic flushing by Xiaojun LI, Mingyang ZHAO, Miao LI

“…The CIoU loss function was replaced with the SIoU loss function to accelerate the matching between prediction and ground truth boxes. Finally, the improved YOLOv8n algorithm was validated using a self-built dataset. …”
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Modelación con elementos finitos del agrietamiento en el hormigón por corrosión localizada en la armadura Finite element modeling of cracking in concrete due to localized corrosion... by J Castorena, J.L. Pérez, A Borunda, C Gaona, A Torres-Acosta, I Velázquez, A Martínez, F Almeraya

“…The results show the great influence that has the localized corrosion (small-size anode vs large-size cathode) on the prediction of rcrit, and that effect is only possible to analyze it in a three-dimensional way. …”
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Steering conservation biocontrol at the frontlines: A fuzzy logic approach unleashing potentials of climate-smart intercropping as a component within the integrated management of f... by Komi Mensah Agboka, Henri E.Z. Tonnang, Emily Kimathi, Elfatih M. Abdel-Rahman, John Odindi, Onisimo Mutanga, Saliou Niassy

“…Farms in the eastern and southern regions are predicted to be highly suitable, while the suitability of farms in West Africa is expected to improve over time due to the perennial nature and agronomic benefits of companion plants. …”
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ECG-LM: Understanding Electrocardiogram with a Large Language Model by Kai Yang, Massimo Hong, Jiahuan Zhang, Yizhen Luo, Suyuan Zhao, Ou Zhang, Xiaomao Yu, Jiawen Zhou, Liuqing Yang, Ping Zhang, Mu Qiao, Zaiqing Nie

“…Conclusions: The results across various tasks demonstrate that ECG-LM effectively captures the intricate features of ECGs, showcasing its versatility in applications such as disease prediction and advanced question answering.…”
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Prediksi Interaksi Drug Target pada Gen Kanker Menggunakan Metode Lasso-XGBoost by Muh Fadhil Al-Haaq Ginoga, Wisnu Ananta Kusuma, Mushthofa Mushthofa

“…In this study, a DTI prediction model is built by selecting features on the data set using Least Absolute Shrinkage and Selection Operator (LASSO) then data balancing performed with Synthetic Minority Oversampling Technique (SMOTE) and Extreme Gradient Boosting (XGBoost) performed to predict the interaction. …”
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Model Klasifikasi Dengan Logistic Regression Dan Recursive Feature Elimination Pada Data Tidak Seimbang by Sutarman, Rimbun Siringoringo, Dedy Arisandi, Edi Kurniawan, Erna Budhiarti Nababan

“…Logistic regression can yield good results in classification and prediction problems. The extensive features of the dataset can lead to computational burdens and reduced classification performance. …”
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Stage deformation characteristics of natural and saturated crushed gangue with large particle size by LU Wei, LU Yao, LIAO Changlong, LI Qinghai, LI Weiyu, JIANG Ning, MU Wenqiang, WANG Changxiang

“…The present study could provide reference for the design, stability monitoring and prediction of coal gangue filling.…”
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An Intelligent System for Classifying Patient Complaints Using Machine Learning and Natural Language Processing: Development and Validation Study by Xiadong Li, Qiang Shu, Canhong Kong, Jinhu Wang, Gang Li, Xin Fang, Xiaomin Lou, Gang Yu

“…The SVM algorithm performed best in prediction, achieving an average accuracy of 0.91 on the external test set with a 95% CI of 0.87-0.97. …”
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Unveiling the inhibitory effect of hydrogen-decorated voids and dislocation loops on the glide of edge dislocation in tungsten by Qing-Yuan Ren, Yu-Hao Li, Yu-Chen Du, Tian-Ren Yang, Dmitry Terentyev, Wei-Zhong Han, Hong-Bo Zhou, Guang-Hong Lu

“…Our findings advocate that the presence of interstitial impurities can dramatically modify the mechanical properties of materials underirradiation, and provide an important reference for the prediction of W performance and the development of advanced nuclear materials.…”
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Fuzzy Logic-Based Arrival Time Estimation for Indoor Navigation Using Augmented Reality by Muhammad Hidayat Mohammed Sohif, Muhammad Imran Mardzuki, Azhar Mohd Ibrahim, Ahmad Jazlan

“…The experimental results indicate the feasibility of using fuzzy logic to estimate arrival time for indoor navigation, with an average prediction error of 5.82%. ABSTRAK: Beberapa tahun ini, Realiti Terimbuh (AR) telah menjadi popular dalam pelbagai industri kerana keupayaannya meningkatkan kecekapan, menyediakan maklumat dan data secara masa nyata, dan mengekalkan kesedaran pengguna terhadap persekitaran sekeliling. …”
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Pengaruh Prediksi Missing Value pada Klasifikasi Decision Tree C4.5 by Aji Seto Arifianto, Kursita Dewi Safitri, Khafidurrohman Agustianto, I Gede Wiryawan

“…Then in the second treatment, the prediction of missing value was applied using the mean and mode formula before the formation of the rule tree, obtained 24 rules. …”
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Development of improved deep learning models for multi-step ahead forecasting of daily river water temperature by Mehdi Gheisari, Jana Shafi, Saeed Kosari, Samaneh Amanabadi, Saeid Mehdizadeh, Christian Fernandez Campusano, Hemn Barzan Abdalla

“…This study addresses the limited use of signal decomposition in hybrid WT prediction models by proposing three methods: namely ensemble empirical mode decomposition (EEMD) on AdaBoost, long short-term memory (LSTM), and gated recurrent unit (GRU). …”
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A Reinforcement-Learning Based Approach for Designing High-Voltage SiC MOSFET Guard Rings by Tejender Singh Rawat, Chia-Lung Hung, Yi-Kai Hsiao, Wei-Chen Yu, Surya Elangovan, Wei-Ting Lin, Yi-Rong Lin, Kai-Lin Yang, Nien-Yi Jan, Yung-Hui Li, Hao-Chung Kuo

“…In this work, the reinforcement learning method has been successfully implemented on the 1.7 kV SiC guard ring device TCAD simulated data for the prediction of parameters. Our work has predicted the parameters successfully for the 2.5 kV guard ring design. …”
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SDUST2023BCO: a global seafloor model determined from a multi-layer perceptron neural network using multi-source differential marine geodetic data by S. Zhou, S. Zhou, J. Guo, H. Zhang, Y. Jia, H. Sun, X. Liu, D. An

“…Second, the input data at interesting points are fed into the MLP model to obtain prediction bathymetry. Finally, a high-precision bathymetric model with a resolution of <span class="inline-formula">1^′</span> <span class="inline-formula">×</span> <span class="inline-formula">1^′</span> has been constructed for the global marine area. …”
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Maternal health risk factors dataset: Clinical parameters and insights from rural BangladeshMendeley Data by Mayen Uddin Mojumdar, Dhiman Sarker, Md Assaduzzaman, Hasin Arman Shifa, Md. Anisul Haque Sajeeb, Oahidul Islam, Md Shadikul Bari, Mohammad Jahangir Alam, Narayan Ranjan Chakraborty

“…It will aid in generating high-risk pregnancy evaluation and prediction models to support clinical management. This dataset is valuable for its potential to serve as a benchmark for comparing maternal health responses across different clinical conditions of patients, thereby contributing to a broader understanding of pregnancy-related complications. …”
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Genome-Wide Identification and Expression Analysis of the <i>CAMTA</i> Gene Family in Roses (<i>Rosa chinensis</i> Jacq.) by Wanyi Su, Yuzheng Deng, Xuejuan Pan, Ailing Li, Yongjie Zhu, Jitao Zhang, Siting Lu, Weibiao Liao

“…The cis-acting element prediction results show that the rose <i>CAMTA</i> gene family contains phytohormone-signaling response elements, abiotic stress responses, light responses, and other elements, most of which are hormone-signaling response elements. …”
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HyQ2:&#x2009;A&#x2009;Hybrid&#x2009;Quantum&#x2009;Neural&#x2009;Network for&#x2009;NextG&#x2009;Vulnerability&#x2009;Detection by Yifeng Peng, Xinyi Li, Zhiding Liang, Ying Wang

“…The results show that the prediction accuracy and receiver operating characteristic AUC value fluctuate around 0.2&#x0025;, indicating HyQ2&#x2019;s robustness in noisy quantum environments. …”
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A joint three-plane physics-constrained deep learning based polynomial fitting approach for MR electrical properties tomography by Kyu-Jin Jung, Thierry G. Meerbothe, Chuanjiang Cui, Mina Park, Cornelis A.T. van den Berg, Stefano Mandija, Dong-Hyun Kim

“…Within this framework, deep learning is used to discern the optimal polynomial fitting weights for a physics based polynomial fitting reconstruction on the complex B1+ data. For the prediction of optimal fitting coefficients, three neural networks were separately trained on simulated heterogeneous brain models to predict optimal polynomial weighting parameters in three orthogonal planes. …”
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Analysis of Inflammatory Mediator Profiles in Sepsis Patients Reveals That Extracellular Histones Are Strongly Elevated in Nonsurvivors by Tanja Eichhorn, Ingrid Linsberger, Lucia Lauková, Carla Tripisciano, Birgit Fendl, René Weiss, Franz König, Gerhard Valicek, Georg Miestinger, Christoph Hörmann, Viktoria Weber

“…The timely recognition of sepsis and the prediction of its clinical course are challenging due to the complex molecular mechanisms leading to organ failure and to the heterogeneity of sepsis patients. …”
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