High-performance glass classification using advanced machine learning and deep learning algorithms with a comprehensive feature analysis by Mohammed Bouziane, Abdelghani Bouziane, Samia Larguech, Khatir Naima, Mohammad Salman Haque, Younes Menni

“…Exploratory Data Analysis provides significant patterns and verifies pre-determined glass classes through clustering techniques like Gaussian Mixture Models. Advanced learning algorithms like Random Forest (RF), XGBoost, Support Vector Machines, and Bidirectional Long Short-Term Memory (BiLSTM) networks are applied for classification. …”
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Neural signals, machine learning, and the future of inner speech recognition by Adiba Tabassum Chowdhury, Ahmed Hassanein, Aous N. Al Shibli, Youssuf Khanafer, Mohannad Natheef AbuHaweeleh, Shona Pedersen, Muhammad E. H. Chowdhury

“…Building on prior literature, this work synthesizes and organizes existing ISR methodologies within a structured mathematical framework, reviews cognitive models of inner speech, and presents a detailed comparative analysis of existing ML approaches, thereby offering new insights into advancing the field.…”
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Investigation of aerodynamic processes in porous materials based on triply periodic minimal surfaces by D. M. Bragin, R. M. Mustafin, A. I. Popov, S. A. Zinina, A. V. Eremin

“…RELEVANCE: The relevance of this work lies in the study of new porous materials for use in compact, highly efficient heat exchange devices. …”
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A Human-Centered Approach to Academic Performance Prediction Using Personality Factors in Educational AI by Muhammad Adnan Aslam, Fiza Murtaza, Muhammad Ehatisham Ul Haq, Amanullah Yasin, Muhammad Awais Azam

“…We employed advanced regression models, including Gradient Boosting Regressor, K-Nearest Neighbors Regressor, Linear Regression, and Support Vector Regression, to predict students’ Cumulative Grade Point Average (CGPA). …”
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PROACTIVE MITIGATION OF DDoS IoT-RELATED ATTACK USING MACHINE LEARNING AND SOFTWARE DEFINED NETWORKING TECHNIQUES by Emmanuel J. Ebong, Samuel N. John, Dominic S. Nyitamen, Samuel F. Kolawole

“…Four (4) ML algorithms namely, Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT) and Random Forest (RF) were used to classify the models. …”
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Robust fault detection in electrochemical energy storage systems under label noise: applications to lithium-ion batteries and transformer windings by Tao He, Wei Liu, Xin Wu, Yu Wei

“…However, the performance of machine learning-based fault diagnosis models is often degraded in practice due to label noise in training data, caused by sensor inaccuracies, ambiguous fault transitions, and imperfect labeling processes. …”
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Seismic risk assessment of typical bridges in Northeastern Brazil through the application of Machine Learning methods by Lucas Filipe Da Ronch, Gustavo Henrique Ferreira Cavalcante, Eduardo Marques Vieira Pereira, Isabela Durci Rodrigues, Thiago Dias dos Santos, Gustavo Henrique Siqueira

“…Traditional approaches to evaluating fragility curves are time-consuming, mainly because of the relatively high computational cost of running several finite element-based models. Here, we present a computationally efficient scheme based on Machine Learning techniques to evaluate the application of artificial intelligence tools, such as Support Vector Machine, Decision Tree and Random Forest, for seismic vulnerability for structures situated in the northeastern region of Brazil. …”
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A systematic mapping to investigate the application of machine learning techniques in requirement engineering activities by Shoaib Hassan, Qianmu Li, Khursheed Aurangzeb, Affan Yasin, Javed Ali Khan, Muhammad Shahid Anwar

“…The authors focus on the systematic mapping of past work to investigate those studies that focused on the application of supervised learning techniques in RE activities between the period of 2002–2023. …”
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An EEG Dataset for Alzheimer’s Disease Patients in Iraq: Electrophysiological Recordings Across Cognitive Stages by Nigar M. Shafiq Surameery, Abdulbasit Alazzawi, Aras T. Asaad, Abas Nariman Sidiq Tilako, Sarwer Jamal Al-Bajalan

“…Benchmark classification was conducted with a wide variety of ML and DL models, including Random Forest (RF), Gradient Boosting methods, Support Vector Machines (SVM), Convolutional Neural Networks (CNNs), and Long Short-Term Memory networks (LSTMs), showing promising results-a maximum of 96.85% by Random Forest within ML techniques and a maximum of 96.05% by DNNs in DL techniques. …”
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COMPARATIVE MACHINE LEARNING ALGORITHM FOR CARDIOVASCULAR DISEASE PREDICTION by Ashish Mishra, Jyoti Mishra, Victor Hugo, Aloísio Vieira Lira Neto

“…The technique uses an ML model and key parameters to predict cardiovascular illness in patients. …”
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Improved Alzheimer Disease Diagnosis With a Machine Learning Approach and Neuroimaging: Case Study Development by Lilia Lazli

“…An important difference was established between healthy patients and patients with AD (PF1F1F1 ConclusionsEffective ML models using neuroimaging data could help physicians working on AD diagnosis and will assist them in prescribing timely treatment to patients with AD. …”
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African Studies: The Chronotope of the Crossroads by Gavristova Tatiana Mikhaylovna

“…The author of the article focuses her research on the works of the renowned intellectuals who made a special contribution to the decolonization of education and of humanitarian knowledge. …”
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Multimodal framework for phishing attack detection and mitigation through behavior analysis using EM-BERT and SPCA-BASED EAI-SC-LSTM by Mahmoud Murhej, G. Nallasivan

“…Next, the phishing attack is detected using the Explainable AI SERF CoLU Long Short Term Memory (EAI-SC-LSTM) model. The recognized phishing data and URL are updated to the Blacklist; hence, any new URL, which is already on Blacklist, is reported to the user.ResultsAs per the experimental outcomes, the proposed EAI-SC-LSTM attains accuracies of 99.627% for SSC, 99.645% for PEC, and 99.541% for WPD in phishing attack detection, which are higher than the existing works. …”
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Manufacturing of CD19 Specific CAR T-Cells and Evaluation of their Functional Activity in Vitro by AV Petukhov, VA Markova, DV Motorin, AK Titov, NS Belozerova, PM Gershovich, AV Karabel’skii, RA Ivanov, EK Zaikova, EYu Smirnov, PA Butylin, AYu Zaritskey

“…Human T-lymphocytes were transduced by the lentiviral vector containing anti-CD19-CAR, RIAD, and GFP genes. …”
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TAE Predict: An Ensemble Methodology for Multivariate Time Series Forecasting of Climate Variables in the Context of Climate Change by Juan Frausto Solís, Erick Estrada-Patiño, Mirna Ponce Flores, Juan Paulo Sánchez-Hernández, Guadalupe Castilla-Valdez, Javier González-Barbosa

“…Mexico, with its diverse climate and geographic position, is particularly vulnerable, underscoring the need for robust strategies to predict atmospheric variables. This work presents TAE Predict (Time series Analysis and Ensemble-based Prediction with relevant feature selection) based on relevant feature selection and ensemble models of machine learning. …”
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Potential of Multi-Source Multispectral vs. Hyperspectral Remote Sensing for Winter Wheat Nitrogen Monitoring by Xiaokai Chen, Yuxin Miao, Krzysztof Kusnierek, Fenling Li, Chao Wang, Botai Shi, Fei Wu, Qingrui Chang, Kang Yu

“…These findings highlight the potential of low-cost multispectral platforms for practical crop N monitoring. Future work should validate these models using real satellite imagery and explore multi-source data fusion with advanced learning algorithms.…”
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A Systematic Literature Review of Concept Drift Mitigation in Time-Series Applications by Mujaheed Abdullahi, Hitham Alhussian, Norshakirah Aziz, Said Jadid Abdulkadir, Yahia Baashar, Abdussalam Ahmed Alashhab, Afroza Afrin

“…Moreover, this SLR presents a roadmap for detecting CDs using Artificial Intelligence (AI)-based learners, along with a comparative analysis of well-known baseline methods. Future work should focus on developing adaptive learning models capable of adapting to CD without explicit re-training, thereby, ensuring optimal performance.…”
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Peculiarities of using geoinformation systems in training of future geography specialists in higher education institutions by Olena Korol, Olesia Kornus, Anatolii Kornus

“…The possibilities of open programs are also presented; they can be used in the educational process (PhotoFiltre (mapping), XnView (viewing), Picasa (adding geotagging with which you can specify the capture area of any photo, using Google Earth and Google maps), Quantum GIS (can be used as the main platform for teaching students how to implement GIS and how to create the highest quality maps), Google Earth network services (electronic globe), ArcGIS Online (used to manage and share maps and geographic information), Golden SoftWare (Surfer) (universal mapping software that runs on Microsoft Windows and is used to quickly and accurately erase map surfaces and convert the provided data into contour, relief or post maps, three-dimensional surfaces, 3D frames or vector graphics). The main models of data presentation, due to which the work of GIS can be realized, are considered. …”
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Fall prediction in a quiet standing balance test via machine learning: Is it possible? by Juliana Pennone, Natasha Fioretto Aguero, Daniel Marczuk Martini, Luis Mochizuki, Alexandre Alarcon do Passo Suaide

“…All algorithm models were able to classify the participants into young or old, but our main goal was not achieved, no model identified participants at high risk of falling. …”
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Real-Time Detection of Internal Short Circuits in Lithium-Ion Batteries using an Extend Kalman Filter: A Novel Approach Combining Electrical and Thermal Measurements by Yiqi Jia, Lorenzo Brancato, Marco Giglio, Francesco Cadini

“…This technique is based on the augmentation of the model state vector in an extended Kalman filter (EKF), combining both classical voltage measurements to surface temperature observations. …”
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