Explainable AI-based suicidal and non-suicidal ideations detection from social media text with enhanced ensemble technique by Daniyal Alghazzawi, Hayat Ullah, Naila Tabassum, Sahar K. Badri, Muhammad Zubair Asghar

“…Our methodology, along with an updated ensemble method, bridges the gap between Explainable AI and leverages a variety of machine learning algorithms to improve predictive accuracy. By leveraging Explainable AI’s interpretability to analyze the features, the model elucidates the reasoning behind its classifications leading to a comprehension of hidden patterns associated with suicidal ideations. …”
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Cheating Detection in Online Exams Using Deep Learning and Machine Learning by Bahaddin Erdem, Murat Karabatak

“…This study aims to identify the best deep learning and machine learning models to identify the unethical behavior patterns of learners using distance education exam data of an educational institution. …”
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Modeling Flood Susceptibility Utilizing Advanced Ensemble Machine Learning Techniques in the Marand Plain by Ali Asghar Rostami, Mohammad Taghi Sattari, Halit Apaydin, Adam Milewski

“…In this case study, flood susceptibility patterns in the Marand Plain, located in the East Azerbaijan Province in northwest Iran, were analyzed using five machine learning (ML) algorithms: M5P model tree, Random SubSpace (RSS), Random Forest (RF), Bagging, and Locally Weighted Linear (LWL). …”
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The effective landscape design parameters with high reflective hardscapes: guidelines for optimizing human thermal comfort in outdoor spaces by design -a case on hot arid climate w... by Reham A. Abdelwahab, Ahmed A. Fekry, Reham El-Dessuky Hamed

“…Findings indicate that smaller, scattered hardscape patterns with maximum 6% divided ratio, combined with light and dark surfaces, wood, and softscape areas, significantly reduce reflected solar energy. …”
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The role of epigenetic regulation in pancreatic ductal adenocarcinoma progression and drug response: an integrative genomic and pharmacological prognostic prediction model by Kang Fu, Junzhe Su, Yiming Zhou, Xiaotong Chen, Xiao Hu

“…High-risk patients exhibited distinct mutation patterns, including higher frequencies of KRAS and TP53 mutations. …”
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Human Clustering Based on Graph Embedding and Space Functions of Trajectory Stay Points on Campus by Ke Xie, Tao Wang, Pan Zhong, Zihao Zhao, Zixiang Wang

“…Available research on spatial clustering patterns of human activities has been investigated mainly based on similarities of locations and temporal attributes of spatial trajectories. …”
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Hip-spine syndrome: Diagnostic difficulties by A. A. Kargaltsev, M. A. Makarov, S. A. Makarov, A. M. Lila

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Content Creator Strategy as Tourism Journalism for Post-Covid-19 Pandemic Recovery by Wiki Angga Wiksana, Firmansyah, Dian Widya Putri, Doddy Iskandar Cakranegara

“…This study aims to get an overview of tourism journalism patterns carried out by content creators which can be  a strategy for recovery and increasing the attractiveness of tourist destinations. …”
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Integrating asthma care guidelines into primary care electronic medical records: a review focused on Canadian knowledge translation tools by Matheson McFarlane, Alison Morra, M. Diane Lougheed

“…Knowledge translation (KT) electronic tools (eTools) exist aiming to address these gaps, but their impact on practice patterns and patient outcomes is variable. We aimed to conduct a nonsystematic review of the literature for key asthma care gaps and identify limitations and future directions of KT eTools optimised for use in electronic medical records (EMRs). …”
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Artificial Intelligence−Driven Analysis Identifies Anterior Cruciate Ligament Reconstruction, Hip Arthroscopy and Femoroacetabular Impingement Syndrome, and Shoulder Instability as... by Henry B.G. Baird, B.S., William Allen, M.D., Mauricio Gallegos, M.D., Cody C. Ashy, M.D., Harris S. Slone, M.D., W. Michael Pullen, M.D.

“…Conclusions: Using BERTopic, the study showed an efficient way to analyze large amounts of data to establish patterns within orthopaedic sports medicine literature. …”
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Decoding bacterial methylomes in four public health-relevant microbial species: nanopore sequencing enables reproducible analysis of DNA modifications by Valentina Galeone, Johanna Dabernig-Heinz, Mara Lohde, Christian Brandt, Christian Kohler, Gabriel E. Wagner, Martin Hölzer

“…We investigated distinct methylation patterns within and between species, focusing on heavily modified genes or genomic regions. …”
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Artificial Intelligence in Primary Malignant Bone Tumor Imaging: A Narrative Review by Platon S. Papageorgiou, Rafail Christodoulou, Panagiotis Korfiatis, Dimitra P. Papagelopoulos, Olympia Papakonstantinou, Nancy Pham, Amanda Woodward, Panayiotis J. Papagelopoulos

“…Ethical considerations, including data privacy and the interpretability of complex AI algorithms, also warrant careful attention. Future research should prioritize multicenter collaborations, external validation of AI models, and the integration of explainable AI systems into clinical practice. …”
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GIS Analysis Model Integration and Service Composition Prospects by L. Ding, P. Cai, W. Huang, H. Zhang, F. Ding, W. Zhao, D. Tang, Z. Wang

“…These innovations promise self-learning agents capable of automated spatial pattern recognition, real-time alerts, and optimized resource allocation. …”
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Optimizing Remote Sensing Image Retrieval Through a Hybrid Methodology by Sujata Alegavi, Raghvendra Sedamkar

“…Low-level features are extracted using the modified Multiscale Multiangle Completed Local Binary Pattern (MSMA-CLBP) algorithm to capture local contexture features, while high-level features are obtained through a hybrid CNN structure combining pretrained networks (Alexnet, Caffenet, VGG-S, VGG-M, VGG-F, VGG-VDD-16, VGG-VDD-19) and a fully connected dense network. …”
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Computational intelligence investigations on evaluation of salicylic acid solubility in various solvents at different temperatures by Adel Alhowyan, Wael A. Mahdi, Ahmad J. Obaidullah

“…Abstract This research shows the utilization of various tree-based machine learning algorithms with a specific focus on predicting Salicylic acid solubility values in 13 solvents. …”
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What factors enhance students' achievement? A machine learning and interpretable methods approach. by Hui Mao, Ribesh Khanal, ChengZhang Qu, HuaFeng Kong, TingYao Jiang

“…Prior research on student achievement has typically examined isolated factors or bivariate correlations, failing to capture the complex interplay between learning behaviors, pedagogical environments, and instructional design. …”
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AI-Driven Framework for Evaluating Climate Misinformation and Data Quality on Social Media by Zeinab Shahbazi, Rezvan Jalali, Zahra Shahbazi

“…Data quality is defined using key dimensions of credibility, accuracy, relevance, and sentiment polarity, and a pipeline is developed using transformer-based NLP models, sentiment classifiers, and misinformation detection algorithms. The system processes user-generated content to detect sentiment drift, engagement patterns, and trustworthiness scores. …”
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Enhancing privacy in clustering and data mining: A novel approach for sensitive data protection by Haythem Hayouni

“… In the era of big data, clustering and data mining have become essential tools for uncovering patterns and insights from vast datasets. However, these processes often involve the use of sensitive data, raising significant concerns about privacy, security, and trustworthiness. …”
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Review of Methods and Models for Forecasting Electricity Consumption by Kamil Misiurek, Tadeusz Olkuski, Janusz Zyśk

“…The authors conducted a comparative analysis of various models, such as autoregressive models, neural networks, fuzzy logic systems, hybrid models, and evolutionary algorithms. Particular attention was paid to the effectiveness of these methods in the context of variable input data, such as weather conditions, seasonal fluctuations, and changes in energy consumption patterns. …”
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On the effect of sampling frequency on the electricity theft detection performance by Fatemeh Soleimani Nasab, Foad Ghaderi

“…Recently, machine and deep learning techniques are being used widely to detect thieves by analysing the consumption patterns. While the prediction accuracy of these methods depends on the number and quality of the existing samples used for training models, the majority of previous research work focussed on data with high sampling frequencies, for example, data from smart grids. …”
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