Utilization of Data Mining Techniques in SIMPUS towards Smart Library at IAIN Kendari by Samrin, Badarwan, Raehang, Obaid Moh. Yahya, Syahrul

“…This study aims to analyze the application of data mining techniques in SIMPUS at IAIN Kendari, in order to improve the efficiency of library management and provide smarter services to users. By using algorithms such as association rules, clustering, and classification, historical data on borrowing and collection management can be processed to produce valuable insights, such as user behavior patterns, book need predictions, and collection recommendations. …”
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Dysregulation of innate and adaptive lymphoid immunity may have implications for symptom attribution and predict responses to targeted therapies in neuropsychiatric systemic lupus... by Julius Lindblom, Guillermo Barturen, Lorenzo Beretta, Daniel Toro-Domínguez, Elena Carnero-Montoro, Maria Orietta Borghi, Jessica Castillo, Ellen Iacobaeus, Yvonne Enman, Chandra Mohan, Marta E. Alarcón-Riquelme, Dionysis Nikolopoulos, Ioannis Parodis, Jacques-Olivier Pers, Alain Saraux, Valérie Devauchelle-Pensec, Sandrine Jousse-Joulin, Bernard Lauwerys, Julie Ducreux, Anne-Lise Maudoux, Ana Tavares, Isabel Almeida, Miguel Angel Gonzalez-Gay Mantecón, Ricardo Blanco Alonso, Alfonso Corrales Martínez, Ignasi Rodríguez-Pintó, Gerard Espinosa, Rik Lories, Nicolas Hunzelmann, Doreen Belz, Niklas Baerlecken, Michael Zauner, Michaela Lehner, Eduardo Collantes, Ma Angeles Aguirre-Zamorano, Alejandro Escudero-Contreras, Ma Carmen Castro-Villegas, Norberto Ortego, María Concepción Fernández Roldán, Enrique Raya, Inmaculada Jiménez Moleón, Enrique de Ramon, Isabel Díaz Quintero, Pier Luigi Meroni, Tommaso Schioppo, Carolina Artusi, Carlo Chizzolini, Aleksandra Zuber, Donatienne Wynar, Attila Balog, Magdolna Deák, Márta Bocskai, Sonja Dulic, Gabriella Kádár, Falk Hiepe, Silvia Thiel, Manuel Rodriguez Maresca, Antonio López-Berrio, Rocío Aguilar-Quesada, Héctor Navarro-Linares

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Heterogeneity in willingness to share personal health information: a nationwide cluster analysis of 20,000 adults in Japan by Miho Sassa, Akifumi Eguchi, Keiko Maruyama-Sakurai, Takanori Fujita, Yumi Kawamura, Takayuki Kawashima, Yuta Tanoue, Daisuke Yoneoka, Hiroaki Miyata, Takanori Yamashita, Naoki Nakashima, Shuhei Nomura

“…Clustering analysis using Uniform Manifold Approximation and Projection (UMAP) and Ordering Points to Identify the Clustering Structure (OPTICS) algorithms was performed to identify distinct patterns in sharing preferences. …”
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Artificial intelligence for diagnosing rare bone diseases: a global survey of healthcare professionals by Behnam Javanmardi, Rebekah L. Waikel, Tinatin Tkemaladze, Shahida Moosa, Alexander Küsshauer, Jean Tori Pantel, Minu Fardipour, Peter Krawitz, Benjamin D. Solomon, Klaus Mohnike

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Lipid-Metabolism-Related Gene Signature Predicts Prognosis and Immune Microenvironment Alterations in Endometrial Cancer by Zhangxin Wu, Yufei Nie, Deshui Kong, Lixiang Xue, Tianhui He, Kuaile Zhang, Jie Zhang, Chunliang Shang, Hongyan Guo

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Dynamic Network Plasticity and Sample Efficiency in Biological Neural Cultures: A Comparative Study with Deep Reinforcement Learning by Moein Khajehnejad, Forough Habibollahi, Alon Loeffler, Aswin Paul, Adeel Razi, Brett J. Kagan

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Prescribing the Future: The Role of Artificial Intelligence in Pharmacy by Hesham Allam

“…A comprehensive literature review was conducted using platforms such as PubMed, Semantic Scholar, and multidisciplinary databases, with AI-driven algorithms refining the retrieval of relevant and up-to-date studies. …”
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Integrating AI-generated content tools in higher education: a comparative analysis of interdisciplinary learning outcomes by Zhang Yan, Tang Qianjun

“…Using a mixed-methods approach, we analyzed implementation patterns and learning outcomes across humanities, STEM, and social sciences programs at multiple institutions. …”
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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

“…We provide formal definitions and algorithms for each module and demonstrate their integration in a unified architecture. …”
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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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3-Dimensional Spatial Analysis of Parking Lot Wall Scratch Using Mobile Point Cloud Data by Y. Li, C. Ye, Y. Zhang, Z. Kang

“…This study investigates the application of point cloud data for identifying and analyzing scratch patterns on walls within underground parking lots. …”
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Rethinking the Paradigm of Using Ps for Diagnosing Compartment Syndrome by Yasser Bouklouch, BSc, MPH, July Agel, MA, ATC, William T. Obremskey, MD, MPH, MMHC, Andrew H. Schmidt, MD, Kathy Liu, MB, ChB, Jerald R. Westberg, MPH, Matthew Zakariah, BSc, Eli Bunzel, MD, Greer Henry, MSc, Andres Fidel Diaz, MD, Thierry Bégué, MD, Mitchell Bernstein, MD, Edward J. Harvey, MDCM, MSc

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ADPO: Adaptive DRAM Controller for Performance Optimization by Zhuorui Liu, Yan Li, Xiaoyang Zeng

“…The research methodology involves implementing a rank-level timing aware read/write turnaround arbiter and setting read/write queue thresholds and read/write turnaround settings based on observed patterns. …”
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Transcriptomic exploration yields novel perspectives on the regulatory network underlying trichome initiation in Gossypium arboreum hypocotyl by Yuxing Xie, Luying Yang, Zewei Zhao, Mingquan Ding, Yuefen Cao, Xin Hu, Junkang Rong

“…RT-qPCR validation confirmed their stage-specific expression patterns, which were consistent with the RNA-Seq data. …”
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A systematic review on sleep stage classification and sleep disorder detection using artificial intelligence by Tayab Uddin Wara, Ababil Hossain Fahad, Adri Shankar Das, Md Mehedi Hasan Shawon

“…Brain waves are the most commonly used body signals for studying sleep patterns and disorders. Almost 36 % of the research exclusively used brain activity signals, and 80 % combined them with other body parameters in sleep staging. …”
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The role of artificial intelligence in promoting health and developing preventive strategies for diabetes by Ameneh Marzban

“…Dear Editor Diabetes remains a significant public health challenge, and the integration of artificial intelligence (AI) presents remarkable opportunities to enhance early diagnosis, personalized treatment, and effective prevention strategies.1 AI algorithms, including supervised learning and convolutional neural networks, can efficiently analyze large datasets to identify patterns and risk factors associated with diabetes, surpassing the capabilities of traditional methods.2 This advanced analysis enables healthcare providers to predict the likelihood of diabetes in individuals and populations, facilitating timely interventions and customized prevention strategies. …”
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Employing Data Mining Techniques and Machine Learning Models in Classification of Students’ Academic Performance. by Hussein, Alkattan, Alhumaima, Ali Subhi, Oluwaseun, Adelaja A., Abotaleb, Mostafa, Mijwil, Maad M., Pradeep, Mishra, Sekiwu, Denis, Bamwerinde, Wilson, Turyasingura, Benson

“…The study deals with the use of data mining techniques to build a classification model to predict students' academic performance. The research indicates that the use of machine learning models and data mining methods can reveal hidden patterns and relationships in big data, making them indispensable tools in the field of education analysis. …”
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AI Driven Fraud Detection Models in Financial Networks: A Comprehensive Systematic Review by Nusrat Jahan Sarna, Farzana Ahmed Rithen, Umme Salma Jui, Sayma Belal, Al Amin, Tasnim Kabir Oishee, A. K. M. Muzahidul Islam

“…By analyzing vast datasets, AI can uncover hidden fraud patterns and dynamically adapt to emerging threats. …”
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