Prediction of Linear Cationic Antimicrobial Peptides Active against Gram-Negative and Gram-Positive Bacteria Based on Machine Learning Models by Ümmü Gülsüm Söylemez, Malik Yousef, Zülal Kesmen, Mine Erdem Büyükkiraz, Burcu Bakir-Gungor

“…Following data exploration and data preprocessing steps, a variety of classification algorithms are used with 100-fold Monte Carlo Cross-Validation to build models and to predict the antimicrobial activity of the peptides. Among the generated models, Random Forest has resulted in the best performance metrics for both Gram-negative dataset (Accuracy: 0.98, Recall: 0.99, Specificity: 0.97, Precision: 0.97, AUC: 0.99, F1: 0.98) and Gram-positive dataset (Accuracy: 0.95, Recall: 0.95, Specificity: 0.95, Precision: 0.90, AUC: 0.97, F1: 0.92) after outlier elimination is applied. …”
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PEMBELAJARAN REMEDIAL TERPADU DENGAN MENERAPKAN MODEL PEMBELAJARAN GENERATIF UNTUK MENGUBAH MISKONSEPSI SISWA TERHADAP KONSEP USAHA DAN ENERGI by Nur Rosuli, Irwan Koto, Nyoman Rohadi

“…The result showed that there was a significant effect on remedial learning related to applying generative models to change in students’ conceptions with t hitung > t tabel (16,33 ). …”
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A Hybrid CNN-Based Review Helpfulness Filtering Model for Improving E-Commerce Recommendation Service by Qinglong Li, Xinzhe Li, Byunghyun Lee, Jaekyeong Kim

“…The user/item recommendation generator models the user’s preference on items based on their past interactions. …”
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The feasibility of pragmatic influenza vaccine randomized controlled real-world trials in Denmark and England by Joshua Nealon, Daniel Modin, Rebecca E. Ghosh, Deborah Rudin, Gunnar Gislason, Helen P. Booth, Jens Ulrik Stæhr Jensen, Rachael Williams, Hilary Shepherd, Eleanor Yelland, Helene Bricout, Sandra S. Chaves, Tor Biering-Sørensen

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Os hotéis como pólos geradores de viagens by Lenise Grando Goldner, Diego Westphal, Jorge Augusto Martins Gonçalves, Ronaldo Balassiano

“…In the case of Brazil, the studies of Feitosa (2003) and Goldner et al. (2006) assessing trip patterns and also those of Goldner and Inocencio (2007), who analyzed rates and trip generation models to hotels located in Florianopolis. A comparative analysis among the main models is also carried out, aiming at the verification of the adequacy of ITE models to the Brazilian reality. …”
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CCD-BSMG: Composite-Curve-Dilation-Based Brush Stroke Model Generator for Robotic Chinese Calligraphy by Dongmei Guo, Liang Ye, Guang Yan

“…Compared with other stroke generative models based on disordered pixels, our generator is based on parameterized brush strokes and provides a better foundation for robotic writing in deep learning or other fields. …”
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Reliability of Cone Bean Weight-Bearing Measurements of the Postoperative Outcome of Total Ankle Arthroplasties and Inter-Method Reliability with Respect to the Weight Bearing x-ra... by Agustin Barbero MD, Ben Efrima MD, Camilla Maccario MD, Cristian Indino MD, Joshua Ovadia MD, Federico Giuseppe Usuelli MD

“…The purpose of this study was (1) to measure the surgical outcome of TAA using semiautomatic generated models, (2) to evaluate the agreement levels between two raters, and (3) to evaluate the inter-method reliability with respect to WBXR. …”
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ViolenceNet: Dense Multi-Head Self-Attention with Bidirectional Convolutional LSTM for Detecting Violence by Fernando J. Rendón-Segador, Juan A. Álvarez-García, Fernando Enríquez, Oscar Deniz

“…Finally, to check if the generated model is able to generalize violence, a cross-dataset analysis is performed, which shows the complexity of this approach: using three datasets to train and testing on the remaining one the accuracy drops in the worst case to 70.08% and in the best case to 81.51%, which points to future work oriented towards anomaly detection in new datasets.…”
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Repurposing existing skeletal spatial structure (SkS) system designs using the Field Information Modeling (FIM) framework for generative decision-support in future construction pro... by Reza Maalek, Shahrokh Maalek

“…The algorithms were tested and validated on four real-world point cloud datasets to solve two generative modeling problems, namely, engineering design optimization and facility location optimization. …”
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Multispectral Satellite Image Generation Using StyleGAN3 by Michael Alibani, Nicola Acito, Giovanni Corsini

“…Recently, the field of image generation has developed increasingly sophisticated techniques, such as generative neural models, usually known as generative adversarial networks (GANs), to create synthetic images from scratch that appear almost real. Generative models have traditionally been applied to RGB or grayscale images and have been used for generating fake images of faces, animals, or objects. …”
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An influent generator for WRRF design and operation based on a recurrent neural network with multi-objective optimization using a genetic algorithm by Feiyi Li, Peter A. Vanrolleghem

“…Hence, the influent generator model is able to generate a time series with a probability distribution that better represents reality, thus giving a better influent description for WRRF design and operation. …”
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Quality Control for Distantly-Supervised Data-to-Text Generation via Meta Learning by Heng Gong, Xiaocheng Feng, Bing Qin

“…The evaluation results show that our model can improve the state-of-the-art DSG (abbreviation for Distant Supervision Generation) model across all automatic evaluation metrics, with an improvement of 3.72% on the WITA dataset and 3.82% on the DIST-ToTTo dataset in terms of the widely used metric BLEU (abbreviation for BiLingual Evaluation Understudy). …”
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Automatic Radiology Report Generator Using Transformer With Contrast-Based Image Enhancement by Hilya Tsaniya, Chastine Fatichah, Nanik Suciati

“…This study constructed a medical report generator model using the transformer approach and image enhancement implementation. …”
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Exploring Spatial-Based Position Encoding for Image Captioning by Xiaobao Yang, Shuai He, Junsheng Wu, Yang Yang, Zhiqiang Hou, Sugang Ma

“…In addition, the robustness and plug-and-play ability of the proposed method are validated based on a medical captioning generation model.…”
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A Methodology and an Empirical Analysis to Determine the Most Suitable Synthetic Data Generator by A. Kiran, S. Saravana Kumar

“…To achieve this, we utilize the Synthetic Data Vault, Data Synthesizer, and Smartnoise-synth, which are three synthetic data generation packages that are accessible via Python. Different data generation models available within the package are presented with 13 tabular datasets iteratively as sample inputs to generate synthetic data. …”
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Method for accuracy assessment of topo-bathymetric surface models based on geospatial data recorded by UAV and USV vehicles by Oktawia Lewicka

“…The conducted study does not clearly indicate any of the methods, as the selection of the method is also affected by the visualization of the generated model. However, having compared the accuracy measures of the charts and models obtained, it was concluded that for this type of data, the kriging (linear model) method was the best. …”
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Pedestrian Trajectory Prediction Based on SOPD-GAN Used for the Trajectory Planning and Motion Control of Mobile Robot by Hao Li, Dong-Hai Qian, Guang-Yin Liu, Ze Cui, Jing-Tao Lei

“…Additionally, we incorporate pedestrian historical trajectory information using LSTM, and combine it with SOPD to form the generator model. The training of this model is carried out using a generative adversarial network (GAN), which is referred to as SOPD-GAN. …”
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Smart Core and Surface Temperature Estimation Techniques for Health-Conscious Lithium-Ion Battery Management Systems: A Model-to-Model Comparison by Sumukh Surya, Akash Samanta, Vinicius Marcis, Sheldon Williamson

“…Typically, a temperature estimation scheme consists of a heat generation model and a heat transfer model. Several researchers have already proposed ranges of thermal models with different levels of accuracy and complexity. …”
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Spatio-Temporal Hydrological Model Structure and Parametrization Analysis by Mostafa Farrag, Gerald Corzo Perez, Dimitri Solomatine

“…By changing the runoff generation model description, we explore the responses to events. …”
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Deepfake Detection on Social Media: Leveraging Deep Learning and FastText Embeddings for Identifying Machine-Generated Tweets by Saima Sadiq, Turki Aljrees, Saleem Ullah

“…Consequently, text-generative models have become increasingly powerful allowing the adversaries to use these remarkable abilities to boost social bots, allowing them to generate realistic deepfake posts and influence the discourse among the general public. …”
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