adm: An R package for constructing abundance‐based species distribution models by Admir Cesar deOliveira Junior, Santiago José Elías Velazco

“…Here, we present the adm R package developed to support the construction of ADM, including data preparation, model fitting, prediction and model exploration. This package offers several modelling approaches (i.e. algorithms) that can be fine‐tuned and customized. …”
Get full text

Yield Response of Different Rice Ecotypes to Meteorological, Agro-Chemical, and Soil Physiographic Factors for Interpretable Precision Agriculture Using Extreme Gradient Boosting a... by Md. Sabbir Ahmed, Md. Tasin Tazwar, Haseen Khan, Swadhin Roy, Junaed Iqbal, Md. Golam Rabiul Alam, Md. Rafiul Hassan, Mohammad Mehedi Hassan

“…Moreover, this study found a different set of those factors with respect to the yield response of different rice ecotypes. Machine learning algorithms named Extreme Gradient Boosting (XGBoost) and Support Vector Regression (SVR) have been used for predicting the yield response. …”
Get full text

A New Approach to Characterize Superplastic Materials from Free-Forming Test and Inverse Analysis by Gillo Giuliano, Wilma Polini

“…In this work, a new procedure is presented that implies the two material parameters vary with strain. It allows for a reduction in the number of constants needed to determine the material constitutive equation, thus requiring low simulation time compared to models that adopt the multiple-objective optimization based on genetic algorithms (GAs). …”
Get full text

Quantitative Analysis of Sulfur Elements in Mars-like Rocks Based on Multimodal Data by Yuhang Dong, Zhengfeng Shi, Junsheng Yao, Li Zhang, Yongkang Chen, Junyan Jia

“…Considering that sulfur exhibits distinct absorption bands in infrared spectra but demonstrates weak characteristic lines in LIBS spectra due to its low ionization energy, the combination of both spectral techniques enables the model to capture complementary sample features, thereby effectively improving prediction accuracy and robustness. To validate the advantages of the multimodal approach, comparative analyses were conducted against unimodal methods. …”
Get full text

Parallel Simplex Method Improvements by Bashir Salih, Muhammad Ali

Get full text

A novel mechanism-guided residual network for accurate modelling of scroll expander under noisy and sparse data conditions by Xiaoshuang Lv, Xin Ma, Wei Peng, Ke Li, Chengdong Li

“…Finally, comparative prediction experiments are carried out to validate the proposed MGResNet. …”
Get full text

Color-Coded Compressive Spectral Imager Based on Focus Transformer Network by Jinshan Li, Xu Ma, Aanish Paruchuri, Abdullah Alrushud, Gonzalo R. Arce

Get full text

Analyzing Fairness of Computer Vision and Natural Language Processing Models by Ahmed Rashed, Abdelkrim Kallich, Mohamed Eltayeb

“…Machine learning (ML) algorithms play a critical role in decision-making across various domains, such as healthcare, finance, education, and law enforcement. …”
Get full text

Rapid and Nondestructive Identification of Origin and Index Component Contents of Tiegun Yam Based on Hyperspectral Imaging and Chemometric Method by Yue Zhang, Yuan Li, Cong Zhou, Junhui Zhou, Tiegui Nan, Jian Yang, Luqi Huang

“…The optimal residual predictive deviation (RPD) values of starch, polysaccharide, and protein prediction models selected in this study were 5.21, 3.21, and 2.94, respectively. …”
Get full text

Processing of Polymers Stress Relaxation Curves Using Machine Learning Methods by Anton S. Chepurnenko, Tatiana N. Kondratieva, Ebrahim Al-Wali

“… Currently, one of the topical areas of application of machine learning methods is the prediction of material characteristics. The aim of this work is to develop machine learning models for determining the rheological properties of polymers from experimental stress relaxation curves. …”
Get full text

Integrated multiomics analysis and machine learning refine neutrophil extracellular trap-related molecular subtypes and prognostic models for acute myeloid leukemia by Fangmin Zhong, Fangyi Yao, Zihao Wang, Jing Liu, Bo Huang, Xiaozhong Wang

“…Additionally, patients with a high risk score were also predicted to exhibit a favorable response to anti-PD-1 therapy, suggesting that these individuals may derive greater benefits from immunotherapy.ConclusionThe NET-related signature, derived from a combination of diverse machine learning algorithms, has promising potential as a valuable tool for prognostic prediction, preventive measures, and personalized medicine in patients with AML.…”
Get full text

NEURAL NETWORK FORECASTING OF ENERGY CONSUMPTION OF A METALLURGICAL ENTERPRISE by Anna Bakurova, Olesia Yuskiv, Dima Shyrokorad, Anton Riabenko, Elina Tereschenko

“…The LSTM network turned out to be the most effective among the considered neural networks, for which the indicator of the maximum prediction error had the minimum value. Conclusions: analysis of forecasting results using the developed models showed that the chosen approach with experimentally selected architectures and learning algorithms meets the necessary requirements for forecast accuracy when developing a forecasting model based on artificial neural networks. …”
Get full text

Unveiling the effect of urinary xenoestrogens on chronic kidney disease in adults: A machine learning model by Bowen Zhang, Liang Chen, Tao Li

“…Four ML algorithms—random forest classifier (RF), XGBoost (XGB), k-nearest neighbors (KNN), and support vector machine (SVM)—were used alongside traditional logistic regression to predict CKD. …”
Get full text

A Comprehensive Review of Thermal Management Methods and Ideal System Design for Improved Electric Vehicle Battery Pack Performance and Safety by M. Murugan, P. V. Elumalai, KCK Vijayakumar, M. Babu, K. Suresh Kumar, M. Ganesh, Liu Kuang, S. Prabhakar

“…This is shown through the industry's constant pursuit to develop in this critical area through the discovery of novel technologies, including predictive control algorithms and superior thermal materials. …”
Get full text

Detection of Right and Left Ventricular Dysfunction in Pediatric Patients Using Artificial Intelligence–Enabled ECGs by Scott Anjewierden, Donnchadh O'Sullivan, Kathryn E. Mangold, Grace Greason, Itzhak Zachi Attia, Francisco Lopez‐Jimenez, Paul A. Friedman, Samuel J. Asirvatham, Jason Anderson, Benjamin W. Eidem, Jonathan N. Johnson, Shisheer Havangi Prakash, Talha Niaz, Malini Madhavan

“…The model to detect LVEF <50% had a sensitivity of 0.85, specificity of 0.80, positive predictive value of 0.095, and negative predictive value of 0.995. …”
Get full text

Identification of Spambots and Fake Followers on Social Network via Interpretable AI-Based Machine Learning by Danish Javed, Noor Zaman Zaman, Navid Ali Khan, Sayan Kumar Ray, Arafat Al-Dhaqm, Victor R. Kebande

“…LIME will help to comprehend the model&#x2019;s predictions, offering clarity regarding the traits or attributes that drive the classification conclusion. …”
Get full text

SPARCQ: Enhancing Scalability and Adaptability of Proactive Edge Caching Through Q-Learning by Shruti Lall, Johan de Clercq, Nelishia Pillay, Bodhaswar T. Maharaj

“…To overcome these challenges, we propose a novel framework, SPARCQ, that leverages Q-learning, a reinforcement learning algorithm, to automate hyperparameter tuning for LSTM-based prediction models. …”
Get full text

Machine learning-based analysis on pharmaceutical compounds interaction with polymer to estimate drug solubility in formulations by Ahmad J. Obaidullah, Wael A. Mahdi

“…Hyperparameter tuning is rigorously conducted utilizing the Harmony Search (HS) algorithm. For drug solubility prediction, the ADA-DT model demonstrates superior performance, achieving an R² score of 0.9738 on the test set, with a Mean Squared Error (MSE) of 5.4270E-04 and a Mean Absolute Error (MAE) of 2.10921E-02. …”
Get full text

A novel bias-alleviated hybrid ensemble model based on over-sampling and post-processing for fair classification by Fang He, Xiaoxia Wu, Wenyu Zhang, Xiaoling Huang

“…Three datasets with different sensitive attributes and four evaluation metrics were used to evaluate the prediction accuracy and fairness of the BAHEM. The experimental results verify the superior fairness of the BAHEM with little accuracy reduction.…”
Get full text

Artificial intelligence driven innovations in biochemistry: A review of emerging research frontiers by Mohammed Abdul Lateef Junaid

“… Artificial intelligence (AI) has become a powerful tool in biochemistry, greatly enhancing research capabilities by enabling the analysis of complex datasets, predicting molecular interactions, and accelerating drug discovery. …”
Get full text