Utilização de redes neurais artificiais na classificação de níveis de degradação em pastagens Use of artificial neural networks in the classification of degradation levels of pastu... by César S. Chagas, Carlos A. O. Vieira, Elpídio I. Fernandes Filho, Waldir de C. Júnior

“…In this study, three different levels of pasture degradation have been identified (moderate, strong and very strong) and an image composition of 3 bands was tested (covering the visible and the near infra-red) with 15 m of spatial resolution. …”
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Optimasi Klasifikasi Sentimen Komentar Pengguna Game Bergerak Menggunakan Svm, Grid Search Dan Kombinasi N-Gram by Syahroni Wahyu Iriananda, Renaldi Widi Budiawan, Aviv Yuniar Rahman, Istiadi Istiadi

“…Grid Search (GS) was utilized for hyperparameter optimization to achieve the highest possible accuracy. To evaluate the impact of these methods, experiments were conducted across various scenarios, including different data quantities, hyperparameter settings, training and testing dataset ratios, and N-Gram configurations. …”
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Assessing the Impact of Social Media Usage and Performance of the Selected Small and Medium Businesses in Ntungamo District. by Nyamwija, Bonitah

“…The study included different retail shop owners such as; clothing, cosmetics, electronic accessories, make-up services, home appliances, and food stuffs. …”
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Feature Selection and Hyper-parameter Tuning Technique using Neural Network for Stock Market Prediction by Karanveer Singh, Rahul Tiwari, Prashant Johri, Ahmed A. Elngar

“…The existing stock market prediction focused on forecasting the regular stock market by using various machine learning algorithms and in-depth methodologies. The proposed work we have implemented describes the new NN model with the help of different learning techniques like hyperparameter tuning which includes batch normalization and fitting it with the help of random-search-cv. …”
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Hybrid Machine-Learning Model for Accurate Prediction of Filtration Volume in Water-Based Drilling Fluids by Shadfar Davoodi, Mohammed Al-Rubaii, David A. Wood, Mohammed Al-Shargabi, Mohammad Mehrad, Valeriy S. Rukavishnikov

“…Traditional FV measurement relies on human-centric experimental evaluation, which is time-consuming. Recently, machine learning (ML) proved itself as a promising approach for FV prediction. …”
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USE OF ARTIFICIAL INTELLIGENCE TO IDENTIFY AND CORRECT MISCONCEPTIONS ABOUT RADIATION by Oleksandr Tymoshchuk

“…The experiment involved presenting students with a series of statements designed to identify misconceptions related to factual knowledge (e.g., radiation units, background levels), conceptual understanding (e.g., the difference between radiation and radioactivity, effects of low-dose exposure), and application/evaluation (e.g., risk assessment, protective measures). …”
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Machine learning approaches for predicting energy and exergy efficiency in solar still by Somayeh Davoodabadi Farahani, Mohammad Javad Zarei, Hakan F. Oztop

“…This study further explores the energy efficiency and exergy of solar panels through machine learning algorithms aimed at enhancing their performance. A comprehensive database was created by solving thermodynamic equations, followed by an evaluation of five machine learning models: multilayer perceptron (MLP), MLP BAGGING, EXTRATREES, KNEIGHBORS, and RANDOM FOREST, along with a deep neural network model for regression tasks. …”
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Fine-Tuned Machine Learning Classifiers for Diagnosing Parkinson’s Disease Using Vocal Characteristics: A Comparative Analysis by Mehmet Meral, Ferdi Ozbilgin, Fatih Durmus

“…This study seeks to assess the effectiveness of machine learning algorithms optimized to classify PD based on vocal characteristics to serve as a non-invasive and easily accessible diagnostic tool. …”
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Exploiting K-Space in Magnetic Resonance Imaging Diagnosis: Dual-Path Attention Fusion for K-Space Global and Image Local Features by Congchao Bian, Can Hu, Ning Cao

“…The findings indicate robust performance across different datasets, highlighting strong generalizability and favorable algorithmic complexity. …”
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New Predictive Models for the Computation of Reinforced Concrete Columns Shear Strength by Anthos I. Ioannou, David Galbraith, Nikolaos Bakas, George Markou, John Bellos

“…Significantly improved predictive models are proposed herein through the implementation of machine learning (ML) algorithms on refined datasets. Three ML models, LREGR, POLYREG-HYT, and XGBoost-HYT-CV, were used to develop different predictive models that were able to compute the shear strength of RC columns. …”
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Advancing Alzheimer’s disease detection: a novel convolutional neural network based framework leveraging EEG data and segment length analysis by Md Nurul Ahad Tawhid, Siuly Siuly, Enamul Kabir, Yan Li

“…This framework contains EEG data collection, pre-processing for noise removal, temporal segmentation, convolutional neural network (CNN) model training and classification, and finally, evaluation. We have tested different segment lengths to test the impact on AD detection. …”
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Relationship Between Weight Status and Health-Related Quality of Life in School-age Children in China by Mandana Zanganeh, Peymané Adab, Bai Li, Miranda Pallan, Wei J. Liu, Lin Rong, Wei Liu, James Martin, Kar K. Cheng, Emma Frew

“…However, the relationship between weight status and HRQOL is not well established in China, where obesity trends follow a different pattern compared with high-income countries. …”
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Robust EEG Characteristics for Predicting Neurological Recovery from Coma After Cardiac Arrest by Meitong Zhu, Meng Xu, Meng Gao, Rui Yu, Guangyu Bin

“…Our evaluation revealed that functional connectivity features contribute the most to classification at 70%. …”
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Significance of Chest Computed Tomography Scan Findings at Time of Diagnosis in Patients with COVID-19 Pneumonia by Rajaa Suhail Najim, Ahmed Diaa Abdulwahab, Dina Nasih Tawfeeq

“…The aim of this study was to summarize the significance of certain radiological features in evaluating Covid-19 pneumonia severity in Iraqi patients. …”
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Utilizing Machine Learning Techniques for Cancer Prediction and Classification based on Gene Expression Data by Mariwan Mahmood Hama Aziz, Sozan Abdullah Mahmood

“…It holds the promise of delivering systematic, precise, and scientifically backed diagnoses for different types of cancer. Lately, several studies have delved into cancer classification by leveraging data mining techniques, machine learning algorithms, and statistical methods to thoroughly analyze high-dimensional datasets. …”
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A Review of Approaches for Rapid Data Clustering: Challenges, Opportunities, and Future Directions by Mahnoor, Imran Shafi, Mahnoor Chaudhry, Elizabeth Caro Montero, Eduardo Silva Alvarado, Isabel de la Torre Diez, Md Abdus Samad, Imran Ashraf

“…The paper includes a brief introduction to clustering, discussing various clustering algorithms, improvements in handling various data types, and appropriate evaluation metrics. …”
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Meta-Features Extracted from Use of kNN Regressor to Improve Sugarcane Crop Yield Prediction by Luiz Antonio Falaguasta Barbosa, Ivan Rizzo Guilherme, Daniel Carlos Guimarães Pedronette, Bruno Tisseyre

“…The predictive performance of models utilizing multispectral features, LiDAR-derived features, and a fusion of both modalities was evaluated against a benchmark model based on the Normalized Difference Vegetation Index (NDVI). …”
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Comparative Effectiveness of Early and Delayed Surgical Interventions in Patients with Acute Adhesive Intestinal Obstruction: A Multicenter Controlled Randomized Trial Prospective... by B. V. Sigua, A. E. Demko, S. V. Petrov, P. A. Kotkov, I. A. Soloviev, A. V. Vovk, A. V. Osipov, V. I. Serdyuk, D. A. Chernyshev, M. A. Protchenkov, I. I. Gubkov, M. G. Zaitsev, S. Yu. Puzanov, A. A. Kozobin, O. V. Babkov, A. V. Glebova, D. S. Sakhno, D. V. Gurzhiy, V. P. Zemlyanoy

“…During the implementation of the algorithm of actions, it was possible to achieve non-surgical resolution of AAIO phenomena in 86 patients (73.5%) of the main group, which, although statistically insignificant, exceeded the similar indicator of the comparison group, where successful conservative measures were carried out in 61 patients (61.6%) (χ2=3.48, p=0.06). …”
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A study on plant root apex morphology as a model for soft robots moving in soil. by Anand Kumar Mishra, Francesca Tramacere, Roberto Guarino, Nicola Maria Pugno, Barbara Mazzolai

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A novel model for predicting immunotherapy response and prognosis in NSCLC patients by Ting Zang, Xiaorong Luo, Yangyu Mo, Jietao Lin, Weiguo Lu, Zhiling Li, Yingchun Zhou, Shulin Chen

“…Methods Patients were randomly divided into training cohort and validation cohort at a ratio of 2:1. The random forest algorithm was applied to select important variables based on routine blood tests, and a random forest (RF) model was constructed to predict the efficacy and prognosis of ICIs treatment. …”
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