Carcinogenic health risks and water quality assessment of groundwater around lead–zinc mining areas of Ebonyi state Nigeria: a data-driven machine learning approach by Obinna Chigoziem Akakuru, Moses Oghenenyoreme Eyankware, Godwin O. Aigbadon, Ayatu Ojonugwa Usman, Alexander Iheanyi Opara, Kizito Ojochenemi Musa, Micheal Akaninyene Okon, Okechukwu Pius Aghamelu, Gabriel Ehriga Odesa, Ifeyinwa Juliana Ofoh, Annabel U. Obinna-Akakuru

“…The novelty of this study lies in its application of machine learning models to accurately predict contamination levels in groundwater around lead–zinc mining areas in Ebonyi State, Nigeria, providing a data-driven approach to enhance environmental monitoring and public health protection in a highly impacted region.…”
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Deep Learning Hybrid Architecture Based on Vision Transformer for Phase Analysis of Moiré Fringes by Dajie Yu, Junbo Liu, Chuan Jin, Yuyang Li, Kairui Zhang, Ji Zhou

“…However, the alignment accuracy is significantly influenced by the Moiré fringe phase analysis algorithm. This paper proposes a hybrid deep learning architecture based on a Vision Transformer for Moiré fringe phase analysis. …”
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Spatial expansion of artisanal and small-scale gold mining nearby the Nile River, Sudan and its potential environmental impacts: Insights from Planetscope data and machine learning by Abdelmajeed A. Elrasheed, Yousif Y. Obaid, Szilárd Szabó

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Comparative Analysis of VGG16 and ResNet50 Model Performence in Cardiac ECG Image Classification by Hanif Rizaqi, Imam Tahyudin

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The utilization of mangrove forests in ESD-oriented science learning to improve environmental literacy and science literacy: A meta-analysis by Triyanti Marfiana, Fenny Roshayanti, Intan Indiati, Siti Patonah

“…The data obtained was analyzed using quantitative data analysis with percentage results and qualitative data analysis in the form of descriptions of the studies studied. …”
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Comparison of dynamic mode decomposition with other data-driven models for lung cancer incidence rate prediction by L. Raymond Guo, Jifu Tan, M. Courtney Hughes

“…Dynamic mode decomposition (DMD) is an alternative that offers efficient analysis with fewer resources. This study explores applying DMD in public health using lung cancer data and compares it with other machine learning models.MethodsWe analyzed lung cancer incidence data (2000–2021) from 1,013 US counties. …”
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Exploring the predictive values of CRP and lymphocytes in coronary artery disease based on a machine learning and Mendelian randomization by Yuan Liu, Yuan Liu, Xin Yuan, Xin Yuan, Yu-Chan He, Yu-Chan He, Zhong-Hai Bi, Zhong-Hai Bi, Si-Yao Li, Si-Yao Li, Ye Li, Ye Li, Yan-Li Liu, Yan-Li Liu, Liu Miao, Liu Miao

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Mapping Lithology with Hybrid Attention Mechanism–Long Short-Term Memory: A Hybrid Neural Network Approach Using Remote Sensing and Geophysical Data by Michael Appiah-Twum, Wenbo Xu, Emmanuel Daanoba Sunkari

“…Remote sensing provides an efficient roadmap in geological analysis and interpretation. However, some challenges arise when remote sensing techniques are integrated with machine learning in geological surveys. …”
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Advancing pearl millet yield forecasting: Comparative analysis of individual and ensemble machine learning approaches over Rajasthan, India. by Ahmad Alsaber, Parul Setiya, Anurag Satpathi, Abrar Aljamaan, Jiazhu Pan

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Applying genetic algorithm to extreme learning machine in prediction of tumbler index with principal component analysis for iron ore sintering by Senhui Wang

“…First, to decrease the data dimensionality, the sintering production data is addressed through principal component analysis (PCA) and the principal components with the accumulated contribution rate no more than 95% are extracted as the inputs of the predictive model based on Extreme Learning Machine (ELM). …”
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Enhanced Multi-Criteria DNA Sequence Analysis Using Neutrosophic Logic and Deep Learning: An Integrated Approach for Comparison and Classification by Romany M. Farag, Mahmoud Y. Shams, Dalia A. Aldawody, Hazem M. El-Bakry, Huda E. Khalid, Ahmed K. Essa, A. A. Salama

“…The approach we propose combines neutrosophic logic which primarily deals with data confusion and uncertainty with modern machine learning techniques to achieve more realistic and flexible analysis. …”
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Learning Modalities Shaped by Digital Transformation in Distance Education: A Bibliometric Analysis of Trends and Reflections (2017–2023) by Gizem Dilan Boztaş, Muhammet Berigel, Fahriye Altınay, Zehra Altınay

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Corpus-driven language learning: A scientometric analysis of contemporary trends and their trajectories for pedagogical innovation within the Indonesian context by Danang Satria Nugraha

“…This study investigates contemporary trends and trajectories in corpus-driven language learning (CDLL) through a scientometric analysis of 1,118 journal articles indexed in Scopus from 2014 to 2024. …”
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Evaluation of a learning collaborative on team-based care: qualitative analysis of coaching calls using normalisation process theory by Kathleen Thies, Amanda Schiessl, Nashwa Khalid, Kasey Harding, Deborah Ward, Meaghan Angers

“…A separate analysis indicated that prominent topics of discussion were interactions within the team, its relationship with the larger organisation, and difficulties accessing data and determining its reliability. …”
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Heavy metal adsorption efficiency prediction using biochar properties: a comparative analysis for ensemble machine learning models by Zaher Mundher Yaseen, Farah Loui Alhalimi

“…Data were collected from open-source literature review including 353 samples. …”
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Advancing Interstitial Cystitis/Bladder Pain Syndrome (IC/BPS) Diagnosis: A Comparative Analysis of Machine Learning Methodologies by Joseph J. Janicki, Bernadette M. M. Zwaans, Sarah N. Bartolone, Elijah P. Ward, Michael B. Chancellor

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Analysis of Passenger Flow Characteristics and Origin–Destination Passenger Flow Prediction in Urban Rail Transit Based on Deep Learning by Zhongwei Hou, Jin Han, Guang Yang

“…To simultaneously consider the spatiotemporal characteristics of passenger flow distribution and achieve high precision estimation of origin and destination (OD) passenger flow quickly, a predictive model based on a temporal convolutional network and a long short-term memory network (TCN–LSTM) combined with an attention mechanism was established to process passenger flow data in urban rail transit. Firstly, according to the passenger flow data of the urban rail transit section, the existing data characteristics were summarized, and the impact of external factors on section passenger flow was studied. …”
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Life Time Prediction of an Electromagnet Relay using Clustering based Principal Component Analysis with Hybrid Deep Learning Model by T. Maris Murugan, C. Reeda Lenus, S. Sridharan, A. Malligarjun

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Pengembangan Auto-AI Model Generatif Analisis Kompleksitas Waktu Algoritma Untuk Data Multi-Sensor IoT Pada Node-RED Menggunakan Extreme Learning Machine by Imam Cholissodin, Dahnial Syauqy, Dwi Ady Firmanda, Ibrahim Aji, Edy Rahman, Syazwandy Harahap, Fernando Septino

“…The results of the comparison of T(n) values from ELM revealed that the fastest or smallest computational time is used as the algorithm recommendations for multi-sensor data processing in the Internet of Things (IoT) simulators and non-simulators by utilizing Node-RED with additional platforms i.e., flespi and Heroku, as a general solution for the entire types of cases and analysis of their algorithms. …”
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Leveraging machine learning models to evaluate immune infiltration in the ovarian cancer microenvironment: a single-cell analysis approach by Jie Liu, Baoguo Xia, Bingxin Li, Hui Liang

“…This study aimed to establish an objective assessment model based on machine learning and single-cell RNA sequencing data to provide a basis for the individualized immunotherapy of ovarian cancer. …”
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