Detection of Malicious Office Open Documents (OOXML) Using Large Language Models: A Static Analysis Approach by Jonas Heß , Kalman Graffi

“…The extensive knowledge base and rapid analytical abilities of a large language model enable not only the assessment of extracted evidence but also the contextualisation and referencing of information to support the final decision. We demonstrate that Claude 3.5 Sonnet by Anthropic, provided with a substantial quantity of raw data, equivalent to several hundred pages, can identify individual malicious indicators within an average of five to nine seconds and generate a comprehensive static analysis report, with an average cost of USD 0.19 per request and an F1-score of 0.929.…”
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Leveraging Digital Twins for Stratification of Patients with Breast Cancer and Treatment Optimization in Geriatric Oncology: Multivariate Clustering Analysis by Pierre Heudel, Mashal Ahmed, Felix Renard, Arnaud Attye

“…Manifold learning and machine learning algorithms were applied to uncover complex data relationships and develop predictive models. …”
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High-precision prediction of non-resonant high-order harmonics energetic particle modes via stacking ensemble strategies by Sheng Liu, Zhenzhen Ren, Weihua Wang, Kai Zhong, Jinhong Yang, Hongwei Ning

“…The evaluation results indicate that the performance of the proposed model surpasses most supervised learning algorithms. Specifically, in comparison with the SVR and Bagging algorithms, the growth rate predictions of stacking model reduces Root mean squared error (RMSE) by 45% and 33%, mean absolute error (MAE) by 47% and 32%, and increases the R -squared coefficient ( R ^2 ) by 5% and 3%, respectively. …”
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Prognostic tools or clinical predictions: Which are better in palliative care? by P Stone, V Vickerstaff, A Kalpakidou, C Todd, J Griffiths, V Keeley, K Spencer, P Buckle, D Finlay, R Z Omar

“…Future studies should therefore assess the impact of prognostic tools on clinical practice and decision-making.…”
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CHANGING STATUS OF GLOBAL COVID-19 OUTBREAK IN THE WORLD AND IN TURKEY AND CLUSTERING ANALYSIS by Elif Kartal, Mehmet Erdal Balaban, Bülent Bayraktar

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Integrated Bidding and Battery Scheduling in a Microgrid for Sealed-Bid Double Auction Power Trading With Peer Microgrids Under Uncertainty and Its Blockchain-Based Implementation by Zubin J. B., Sunitha R., Gopakumar Pathirikkat

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Estimating the potential risks of developing clusters in medical industry using various ranking criteria by Y. G. Gertsik

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The future of critical care: AI-powered mortality prediction for acute variceal gastrointestinal bleeding and acute non-variceal gastrointestinal bleeding patients by Zhou Liu, Guijun Jiang, Liang Zhang, Palpasa Shrestha, Yugang Hu, Yi Zhu, Guang Li, Yuanguo Xiong, Liying Zhan

“…As many as 12 machine learning (ML) algorithms, namely, logistic regression (LR), decision tree (DT), random forest (RF), gradient boosting (GB), AdaBoost, XGBoost, Naive Bayes (NB), support vector machine (SVM), light gradient-boosting machine (LightGBM), K-nearest neighbors (KNN), extremely randomized trees (ET), and voting classifier (VC), were performed. …”
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Machine learning-based detection of DDoS attacks on IoT devices in multi-energy systems by Hesham A. Sakr, Mostafa M. Fouda, Ahmed F. Ashour, Ahmed Abdelhafeez, Magda I. El-Afifi, Mohamed Refaat Abdellah

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Clinical use of measurable residual disease in adult ALL: recommendations from a panel of US experts by Nicholas J. Short, Ibrahim Aldoss, Daniel J. DeAngelo, Marina Konopleva, Jessica Leonard, Aaron C. Logan, Jae Park, Bijal Shah, Wendy Stock, Elias Jabbour

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The Heart of Transformation: Exploring Artificial Intelligence in Cardiovascular Disease by Mohammed A. Chowdhury, Rodrigue Rizk, Conroy Chiu, Jing J. Zhang, Jamie L. Scholl, Taylor J. Bosch, Arun Singh, Lee A. Baugh, Jeffrey S. McGough, KC Santosh, William C.W. Chen

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A Machine Learning-Based Intelligent Framework for Predicting Energy Efficiency in Next-Generation Residential Buildings by Hafiz Muhammad Shakeel, Shamaila Iram, Richard Hill, Hafiz Muhammad Athar Farid, Akbar Sheikh-Akbari, Farrukh Saleem

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The impact of virtual rheumatology care on patient outcomes and hospital admissions: an ambispective study by Ummugulsum Gazel, Tommy Han, Seyyid Bilal Acikgoz, Tara Swami, Ricardo Sabido-Sauri, Hart Goldhar, Nataliya Milman, Nancy Maltez, Catherine Ivory, Susan Humphrey-Murto, Sibel Aydin

“…Results Within 226 patients, the total number of rheumatology (median (IQR): 2 (2–3) vs. 3 [2, 3, 4], p < 0.001), emergency visits (19% vs. 29.3%, p:0.006) and hospital admissions (12.9% vs. 20.8%, p:0.015) due to any cause were increased during the pandemic, whereas there was no increased ER visit or admissions due to their rheumatological disease. …”
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Ultrasonic radiomics in predicting pathologic type for thyroid cancer: a preliminary study using radiomics features for predicting medullary thyroid carcinoma by Dai Zhang, Dai Zhang, Dai Zhang, Dai Zhang, Fan Yang, Fan Yang, Fan Yang, Fan Yang, Wenjing Hou, Wenjing Hou, Wenjing Hou, Wenjing Hou, Ying Wang, Ying Wang, Ying Wang, Ying Wang, Jiali Mu, Jiali Mu, Jiali Mu, Jiali Mu, Hailing Wang, Hailing Wang, Hailing Wang, Hailing Wang, Xi Wei, Xi Wei, Xi Wei, Xi Wei

“…Delong test results showed that the comprehensive model was significantly better than the clinical model (Z=-3.791, P&lt;0.001) and the radiomics model (Z=-2.017, P=0.044). …”
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A Survey of Data Stream-Based Intrusion Detection Systems by Rodrigo Sanches Miani, Gustavo Di Giovanni Bernardo, Guilherme Weigert Cassales, Hermes Senger, Elaine Ribeiro de Faria

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Enhancing Student Management Through Hybrid Machine Learning and Rough Set Models: A Framework for Positive Learning Environments by Ateeq Ur Rehman Butt, Hamid Ali, Muhammad Asif, Hessa Alfraihi, Mohamad Khairi Ishak, Khalid Ammar

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Improving resectable gastric cancer prognosis prediction: A machine learning analysis combining clinical features and body composition radiomics by Gianni S.S. Liveraro, Maria E.S. Takahashi, Fabiana Lascala, Luiz R. Lopes, Nelson A. Andreollo, Maria C.S. Mendes, Jun Takahashi, José B.C. Carvalheira

“…Body composition radiomics were integrated with clinicopathological factors using machine learning (ML) algorithms trained for patient outcome prediction. We compared results using Random Forest, Logistic Regression and Boosted Decision Tree algorithms. …”
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Machine learning in dentistry and oral surgery: charting the course with bibliometric insights by Shuangwei Liu, Yuquan Hao, Shijie Zhu, Liyao Wan, Zhe Yi, Zhichang Zhang

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On the Potential of Bayesian Neural Networks for Estimating Chlorophyll-a Concentration from Satellite Data by Mohamad Abed El Rahman Hammoud, Nikolaos Papagiannopoulos, George Krokos, Robert J. W. Brewin, Dionysios E. Raitsos, Omar Knio, Ibrahim Hoteit

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JUVENILE GRANULOSA CELL TUMOR IN A 5 YEARS-OLD BULGARIAN GIRL PRESENTING WITH PRECOCIOUS PUBERTY: A CASE REPORT by Diana Hristova, Ivan Kostov, Radko Tocev, Tanya Petkova, Ibryam Ibryam

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