Melanoma risk prediction models by Nikolić Jelena, Lončar-Turukalo Tatjana, Sladojević Srđan, Marinković Marija, Janjić Zlata

“…The aim of this study was to identify most significant factors for melanoma prediction in our population and to create prognostic models for identification and differentiation of individuals at risk. …”
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Forecasting returns volatility of cryptocurrency by applying various deep learning algorithms by Farman Ullah Khan, Faridoon Khan, Parvez Ahmed Shaikh

“…Abstract The study aims at forecasting the return volatility of the cryptocurrencies using several machine learning algorithms, like neural network autoregressive (NNETAR), cubic smoothing spline (CSS), and group method of data handling neural network (GMDH-NN) algorithm. …”
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Machine Learning Models to Predict Google Stock Prices by Cosmina Elena Bucura, Paolo Giudici

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A hybrid BOA-SVR approach for predicting aerobic organic and nitrogen removal in a gas-liquid-solid circulating fluidized bed bioreactor by Shaikh Abdur Razzak, Nahid Sultana, S.M. Zakir Hossain, Muhammad Muhitur Rahman, Yue Yuan, Mohammad Mozahar Hossain, Jesse Zhu

“…This study introduces the hybrid of the Bayesian optimization algorithm and support vector regression (BOA-SVR) models to predict the removal of aerobic organic (total chemical oxygen demand, COD) and nitrogen compounds such as total Kjeldahl Nitrogen (TKN), ammonium nitrogen (NH4-N), and nitrate nitrogen (NO3-N) from municipal wastewater in a gas-liquid-solid circulating fluidized bed (GLSCFB) bioreactor. …”
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Accurate estimation of permeability reduction resulted from low salinity water flooding in clay-rich sandstones by Xiaojuan Zhang, Muntadher Abed Hussein, Tarak Vora, Anupam Yadav, Asha Rajiv, Aman Shankhyan, Sachin Jaidka, Mehul Manu, Farzona Alimova, Issa Mohammed Kadhim, Zainab Jamal Hamoodah, Fadhil Faez, Ahmad Khalid

“…The results show that random forest and ensemble learning algorithms delivered the highest predictive accuracy, evidenced by the most substantial coefficient of determination (R2) and minimal error metrics. …”
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Optimizing Renewable Energy Integration Using IoT and Machine Learning Algorithms by Orken Mamyrbayev, Ainur Akhmediyarova, Dina Oralbekova, Janna Alimkulova, Zhibek Alibiyeva

“…Results showed significant improvements in forecasting accuracy, with the LSTM model achieving a 59.1% reduction in Mean Absolute Percentage Error compared to the persistence model. …”
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Battery State of Health Estimation Methods: Implementation and Comparison of 11 Algorithms by Eojin Kim, Sunghun Jung

“…Unlike previous research, this work emphasizes empirical validation using real-world battery datasets and evaluates each algorithm based on predictive accuracy, computational complexity, and practical applicability. …”
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Logatom articulation index evaluation of speech enhanced by blind source separation and single-channel noise reduction by Szymon DRGAS, Jędrzej KOCIŃSKI, Aleksander P. SĘK

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Enhancing power transformer health assessment through dimensional reduction and ensemble approaches in Dissolved Gas Analysis by Abdelmoumene Hechifa, Saurabh Dutta, Abdelaziz Lakehal, Hazlee Azil Illias, Arnaud Nanfak, Chouaib Labiod

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Adaptive algorithms for enhancement of speech subject to a high-level noise by Mariusz LATOS, Marek PAWEŁCZYK

“…One is the line enhancer, where the predictive realisation of the Wiener approach is used. …”
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Quantification of MODIS Land Surface Temperature Downscaled by Machine Learning Algorithms by Qi Su, Xiangchen Meng, Lin Sun, Zhongqiang Guo

“…By establishing non-linear relationships between LST and predictive variables through eXtreme Gradient Boosting (XGBoost) and Random Forest (RF) algorithms, the proposed framework was rigorously validated using in situ measurements across China’s Heihe River Basin. …”
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A Review on Automated Detection and Identification Algorithms for Highway Pavement Distress by Zhenglong Lv, Zhexin Hao, Yuhan Zhu, Cong Lu

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Automated Input Variable Selection for Analog Methods Using Genetic Algorithms by P. Horton, O. Martius, S. L. Grimm

“…Previous work showed the potential of genetic algorithms (GAs) to optimize most of the AM parameters. …”
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Bandit Algorithms for Efficient Toxicity Detection in Competitive Online Video Games by Jacob Morrier, Rafal Kocielnik, R. Michael Alvarez

“…When no pre-existing predictive model of toxic behavior is available, one must be estimated in real-time. …”
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PAPR optimization based on SLM and PTS algorithms in NC-OFDM systems by Jie ZHOU, Bernardo Esono Esono Mikue, Xueying WANG, Huiting ZHOU, Hong LUO

“…Based on the non-continuous orthogonal frequency division multiplexing (NC-OFDM) model, a fusion optimization technology based on selected mapping (SLM) algorithm and partial transmit sequence (PTS) algorithm was proposed, and a system model of fusion technology was designed.Through simulation comparison with other literature methods, it was verified that the SLM-PTS fusion technology had excellent peak to average power ratio (PAPR) reduction ability, but the algorithm implementation complexity was too high.Therefore, a complementary SLM-Clipping fusion solution was proposed, and the deep learning method PAPRnet model was construted.The simulation results verif that prove the effectiveness of the method, the algorithm has an excellent PAPR suppressed effect on the NC-OFDM system, and greatly improves the computational efficiency.…”
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PAPR optimization based on SLM and PTS algorithms in NC-OFDM systems by Jie ZHOU, Bernardo Esono Esono Mikue, Xueying WANG, Huiting ZHOU, Hong LUO

“…Based on the non-continuous orthogonal frequency division multiplexing (NC-OFDM) model, a fusion optimization technology based on selected mapping (SLM) algorithm and partial transmit sequence (PTS) algorithm was proposed, and a system model of fusion technology was designed.Through simulation comparison with other literature methods, it was verified that the SLM-PTS fusion technology had excellent peak to average power ratio (PAPR) reduction ability, but the algorithm implementation complexity was too high.Therefore, a complementary SLM-Clipping fusion solution was proposed, and the deep learning method PAPRnet model was construted.The simulation results verif that prove the effectiveness of the method, the algorithm has an excellent PAPR suppressed effect on the NC-OFDM system, and greatly improves the computational efficiency.…”
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The Accuracy of the Uganda National Tuberculosis and Leprosy Program diagnostic algorithm and the World Health Organisation treatment decision algorithms for childhood tuberculosis... by Peter J Kitonsa, Bernard Kikaire, Peter Wambi, Annet Nalutaaya, Jascent Nakafeero, Gertrude Nanyonga, Emma Kiconco, Deus Atwiine, Robert Castro, Ernest A Oumo, Hellen T Aanyu, Mary N Mudiope, Ezekiel Mupere, Moorine P Sekadde, Swomitra Mohanty, Adithya Cattamanchi, Eric Wobudeya, Devan Jaganath

“…We applied the 2017 Uganda NTLP and 2022 WHO algorithms (A with chest x-ray [CXR], B without CXR) to make a decision to treat for TB or not, and calculated the sensitivity, specificity and predictive values in reference to Confirmed vs. …”
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Meta-transformer: leveraging metaheuristic algorithms for agricultural commodity price forecasting by G. H. Harish Nayak, Md. Wasi Alam, B. Samuel Naik, B. S. Varshini, G. Avinash, Rajeev Ranjan Kumar, Mrinmoy Ray, K. N. Singh

“…Abstract Predicting agricultural commodity prices is inherently complex due to factors such as perishability, seasonality, and market volatility. …”
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Machine learning algorithms of riverbed change and environments of the Lower Apalachicola River by Ali R. Alruzuq, Joann Mossa

“…The study identifies potential drivers of riverbed changes using machine learning algorithms. The RF model has outperformed the XGBoost model with an R-square of 0.95 and 0.93 for the validation and testing sets, respectively, for RF, indicating high predictive accuracy while slightly less accurate with an R-square of 0.75 and 0.74 for the validation and testing sets. …”
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Allocation algorithms for multicore partitioned mixed-criticality real-time systems by Luis Ortiz, Ana Guasque, Patricia Balbastre, José Simó

“…This is achieved through the implementation of a Mixed-Integer Linear Programming (MILP) algorithm. The second phase involves the allocation of tasks to cores, employing both, an additional MILP algorithm and a modified worst fit decrease utilisation approach. …”
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