Interpretable prediction model for hand-foot-and-mouth disease incidence based on improved LSTM and XGBoost by Xiao LI, Shuyu HE, Yan PENG, Rongxin YANG, Lu TAO, Tingqi LOU, Wenqi HE

“…To further enhance the LSTM performance, the GWO is employed to adaptively optimize the key parameters of the LSTM. Thirdly, to fully leverage the advantages of XGBoost in handling nonlinear relationships and high-dimensional data while overcoming its complexities in parameter tuning and slower convergence, the GA is used to optimize the parameters of XGBoost. …”
Get full text

Synthetic Data-Enhanced Classification of Prevalent Osteoporotic Fractures Using Dual-Energy X-Ray Absorptiometry-Based Geometric and Material Parameters by Luca Quagliato, Jiin Seo, Jiheun Hong, Taeyong Lee, Yoon-Sok Chung

Get full text

Role of Allium cepa and Allium sativum extracts in oxidative stress, sperm parameters and histological abnormalities induced by deep-frying oil in testis by Mohamed Anwer Mohamed, Safa Hamdy Alkalash, Samah Baleegh Meawad, Mohamed Nafea Azab, Marwa A. Ahmed, Ahmad Gadalla, Muhammed Abdelbaeth Hassan Elfiky, Abdel Rahman Z. Abdel Rahman, Hatem Ali Ahmed Abdelmottaleb, Ayman Sabry Yassin Al Sayed, Wafaa Yahia Alghonemy, Amira Osman, Abeer A. Almowafy, Ahmed Mohamed GadAllah

“…The study evaluated the beneficial role of Allium cepa L(AcL) and Allium sativum L (AsL) extracts in oxidative stress, histopathological, and sperm parameters abnormalities induced by deep-frying palm olein oil in rat testis. …”
Get full text

Antioxidant activity and mechanism of action of phycocyanin in bulk sunflower oil and respective oil-in-water emulsion by Elham Ehsandoost, Mohammad Hadi Eskandari, Malihe Keramat, Mohammad-Taghi Golmakani

Get full text

AN EVALUATION OF PHOTOVOLTAIC SYSTEMS MPPT TECHNIQUES UNDER THE CHARACTERISTICS OF OPERATIONAL CONDITIONS by I. A. Elzein, Yu. N. Petrenko

Get full text

Research on the evaluation method of cooperative jamming effectiveness based on IPSO-ELM by A. Tianjian Yang, B. Xing Wang, C. Siyi Cheng, D. You Chen, E. Xi Zhang

“…First, based on the working parameters of the group network radar and the information fusion rules, the cooperative jamming effectiveness evaluation function is established. …”
Get full text

Application of machine learning to optimized design of layer structured particles by Hiroki GONOME, Hirotake SATO, Tatsuro HIRAI

“…The accuracy of the machine learning was evaluated by predicting the absorption property from the particle parameters. …”
Get full text

Artificial Neural Network and Ensemble Models for Flood Prediction in North-Central Region of Nigeria by Sikiru Abdulganiyu Siyanbola, Aisha Olabisi Sowemimo, Zaid Habibu, Timothy Ebuka Eberechukwu

“…The collected data are the input parameters in training the machine learning models: Artificial Neural Networks (ANN), Adaptive Boosting (AdaBoost), Stochastic Gradient Boosting (GBM), Extreme Gradient Boosting (XGBoost), and Random Forest (RF) models, for predicting flood occurrence in the region. …”
Get full text

Effect of Light Conditions on Growth and Antioxidant Parameters of Two Hydroponically Grown Lettuce Cultivars (Green and Purple) in a Vertical Farm System by Cristian Hernández-Adasme, María José Guevara, María Auxiliadora Faicán-Benenaula, Rodrigo Neira, Dakary Delgadillo, Violeta Muñoz, Carolina Salazar-Parra, Bo Sun, Xiao Yang, Víctor Hugo Escalona

“…Chlorophyll concentration increased under PAR intensity of 180 µmol m⁻² s⁻¹, and leaf color varied with spectrum, with RW producing lighter leaves. Antioxidant parameters declined over time, but a PAR intensity of 180 µmol m⁻² s⁻¹, particularly under RW, boosted TPC and TFC contents in both lettuce cultivars during early stages (days 0 and 15). …”
Get full text

Predicting the compressive strength of polymer-infused bricks: A machine learning approach with SHAP interpretability by Sathvik Sharath Chandra, Rakesh Kumar, Archudha Arjunasamy, Sakshi Galagali, Adithya Tantri, Sujay Raghavendra Naganna

“…The polymer bricks’ compressive strength was recorded as the output parameter, with cement, fly ash, M sand, PP waste, and age serving as the input parameters. …”
Get full text

Eco-Driving Level Evaluation Model for Electric Buses Entering and Leaving Stops by Aihong Lyu, Huiming Zhang, Yubo Shen, Yali Zhang

“…Finally, the Random Forest (RF), Gradient-Boosted Decision Trees (GBDT), and Light Gradient Boosting Machine (LightGBM) algorithms are applied to develop the evaluation models of eco-driving level for entering and leaving stops. …”
Get full text

Evaluation of machine learning-based regression techniques for prediction of diabetes levels fluctuations by Badriah Alkalifah, Muhammad Tariq Shaheen, Johrah Alotibi, Tahani Alsubait, Hosam Alhakami

Get full text

Mucosal adjuvanticity and mucosal booster effect of colibactin-depleted probiotic Escherichia coli membrane vesicles by Hiroki Uchiyama, Toshifumi Kudo, Takehiro Yamaguchi, Nozomu Obana, Kenji Watanabe, Kimihiro Abe, Hidetaka Miyazaki, Masanori Toyofuku, Nobuhiko Nomura, Yukihiro Akeda, Ryoma Nakao

“…In addition, glycoengineered ΔflhDΔclbP-MVs displaying serotype-14 pneumococcal capsular polysaccharide (CPS14+MVs) were well-characterized based on biological and physicochemical parameters. Subcutaneous (SC) and intranasal (IN) booster effects of CPS14+MVs on systemic and mucosal immunity were evaluated in mice that have already been subcutaneously prime-immunized with the same MVs. …”
Get full text

Effect of process parameters and optimization of photocatalytic removal of lead from wastewater over CuZn oxide nanocomposite using response surface methodology by Hiba Abduladheem Shakir, Alyaa K. Mageed, May Ali Alsaffar, Mohamed Abdel Rahman Abdel Ghany

“…The study underlines the requirement of enhancing process parameters to boost photocatalytic performance and shows the capacity of CuZn oxide nanocomposites in effectively eliminating Pb2+. …”
Get full text

A Short-Term Carbon Emission Accounting Method for Power Industry Using Electricity Data Based on a Combined Model of CNN and LightGBM by ZENG Jincan, HE Gengsheng, LI Yaowang, DU Ershun, ZHANG Ning, ZHU Haojun

“…The results indicate that the proposed model outperforms other models in both performance evaluation and the consistency between estimated and target values.…”
Get full text

Evaluation on nature-connected environment in building embedded landscape: theory, detection, and case design by Shu Zhong, Jiao Ren

“…Self-reported data were used to evaluate positive and negative emotion scores (Cronbach α: 0.803) and the emotional nonparametric relation index (ENRI) (Pearson correlation in retest: R square = 66.37%).ResultsThree machine-learning algorithms (random forest, AdaBoost, and gradient boosting trees [GBT]) were compared, with GBT being selected (R square: 76.49 ‒ 88.64 %) for further comparison with multivariate linear regression (MLR). …”
Get full text

Development and evaluation of a machine learning model for osteoporosis risk prediction in Korean women by Minkyung Je, Seunghyeon Hwang, Suwon Lee, Yoona Kim

“…The six classification models were developed using ML techniques, including decision tree, random forest, multilayer perceptron, support vector machine, light gradient boosting machine, and extreme gradient boosting (XGBoost). …”
Get full text

Comprehensive Evaluation of Bankruptcy Prediction in Taiwanese Firms Using Multiple Machine Learning Models by Hung V. Pham, Tuan Chu, Tuan M. Le, Hieu M. Tran, Huong T.K. Tran, Khanh N. Yen, Son V. T. Dao

“…From the results measured by evaluation metrics, the proposed model ANN with the combination of parameter tuning, feature selection algorithm, SMOTE-ENN, and optimal hyper-parameters demonstrates superior performance compared to traditional methods, achieving an F1 Score of 98.5% and an accuracy of 98.6%. …”
Get full text

Application of Machine Learning Techniques for Predicting Students’ Acoustic Evaluation in a University Library by Dadi Zhang, Kwok-Wai Mui, Massimiliano Masullo, Ling-Tim Wong

“…Using the collected personal information, room-related parameters, and sound pressure levels as input, six machine learning models (Support Vector Machine–Radial Basis Function (SVM (RBF)), Support Vector Machine–Sigmoid (SVM (Sigmoid)), Gradient Boosting Machine (GBM), Logistic Regression (LR), Random Forest (RF), and Naïve Bayes (NB)) were trained to predict students’ acoustic acceptance/satisfaction. …”
Get full text

Evaluating groundwater potential with the synergistic use of geospatial methods and advanced machine learning approaches by Vicky Anand, Vishnu D. Rajput, Tatiana Minkina, Saglara Mandzhieva, Aastha Sharma, Deepak Kumar, Sunil Kumar

“…Abstract The rapid increase in population, urbanization, and industrial activity in developing countries is intensifying pressure on groundwater resources, leading to severe water shortages. This study aims to evaluate and compare the predictive capabilities of six ensemble machine learning (ML) models; i.e., Random Forest (RF), AdaBoost, Neural Network, Decision Tree, k-Nearest Neighbors and Extreme Gradient Boosting. …”
Get full text