Compressed 'CMB-lite' Likelihoods Using Automatic Differentiation by Lennart Balkenhol

“…In this work, we present an implementation of the CMB-lite framework relying on automatic differentiation. …”
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Medical Relevancy of Cancer-Related Tweets and Its Relation to Misinformation by Melanie McCord, Fahmida Hamid

“…We ran logistic regression and support vector machine models on them. The highest proportion of correctly identified “medically relevant” tweets, i.e., accuracy, was 0.795. …”
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Explainable Two-Layer Mode Machine Learning Method for Hyperspectral Image Classification by Wenjia Chen, Junwei Cheng, Song Yang, Li Sun

“…Explainable machine learning methods with a specific mathematical model provide insights into how the model works. …”
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A Novel LSTM Architecture for Automatic Modulation Recognition: Comparative Analysis With Conventional Machine Learning and RNN-Based Approaches by Sam Ansari, Soliman Mahmoud, Sohaib Majzoub, Eqab Almajali, Anwar Jarndal, Talal Bonny

“…Experimental results demonstrate that the model achieves a recognition accuracy of 99.87% at an SNR of -5 dB, outperforming several conventional machine learning techniques, including multi-layer perceptron (MLP), radial basis function (RBF) networks, adaptive neuro-fuzzy inference systems (ANFIS), decision trees (DT), naïve Bayes (NB), support vector machines (SVM), probabilistic neural networks (PNN), k-nearest neighbors (KNN), and ensemble learning models, as well as recurrent neural networks (RNNs). …”
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Advanced Methods for Identifying Counterfeit Currency: Using Deep Learning and Machine Learning by Nama'a Hamed, Fadwa Al Azzo

“…Using machine learning algorithms like Random Forest, Decision Tree Classifier, XGBoost, CatBoost, and Support Vector Machine (SVM) in addition to deep learning techniques like Convolutional Neural Networks (CNNs), VGG16, MobileNetV2, and InceptionV3, we examine the security characteristics of Iraqi dinar banknotes and build robust models. …”
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AI Painting Effect Evaluation of Artistic Improvement with Cross-Entropy and Attention by Yihuan Tian, Shiwen Lai, Zuling Cheng, Tao Yu

“…However, the effects of works created by different users using AI painting tools vary. …”
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Life Prediction Method of Remanufactured Machinery Equipment Based on Vibration Signal Feature Extraction by Bin Li, Le Kui, Jingdong Luo, Shiyong Chen

“…In order to reduce the influence of irregular characteristics in the vibration signal and simplify the complexity of the vibration signal, the wavelet transform and the support vector machine model are combined, according to the degradation after decomposition. …”
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The Persistent Threat of Chronic Inflammation on the Mortality Among Cervical Cancer Survivors: A Mendelian Randomization and Machine Learning Analysis Using UK Biobank and Chinese... by Wang J, Chen Z, Guan M, Ma Z, Peng L, Chen J, Fiori PL, Carru C, Capobianco G, Coradduzza D, Zhou L

“…After feature selection with 3 algorithms (LASSO regression, Boruta and Support vector machines), the gradient boosting machine (GBM) model outperformed other models by achieving an area under the curve (AUC) of 0.930 and a Brier score of 0.027 in 1-year overall survival (OS) prediction. …”
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Evaluating the impact of waste marble on the compressive strength of traditional concrete using machine learning by Kennedy C. Onyelowe, Viroon Kamchoom, Ahmed M. Ebid, Shadi Hanandeh, Susana Monserrat Zurita Polo, Rolando Fabián Zabala Vizuete, Rodney Orlando Santillán Murillo, Rolando Marcel Torres Castillo, Siva Avudaiappan

“…Advanced ML techniques such as the Group Methods Data Handling Neural Network (GMDH-NN), Support Vector Regression (SVR), K-Nearest Neighbors (kNN) and Adaptive Boosting (AdaBoost) have been applied in this research work. …”
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Using deep learning artificial intelligence for sex identification and taxonomy of sand fly species. by Mohammad Fraiwan, Rami Mukbel, Dania Kanaan

“…Using locally field-caught and prepared samples over a period of two years, and based on convolutional neural networks, transfer learning, and early fusion of genital and pharynx images, we achieved exceptional classification accuracy (greater than 95%) across multiple performance metrics and using a wide range of pre-trained convolutional neural network models. This study not only contributes to the field of medical entomology by providing an automated and accurate solution for sandfly gender identification and taxonomy, but also establishes a framework for leveraging deep learning techniques in similar vector-borne disease research and control efforts.…”
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Impact of lightweight clay aggregate with slag and biomedical waste ash on self-compacting concrete using machine learning approach by Kennedy C. Onyelowe, Viroon Kamchoom, Shadi Hanandeh, Ahmed M. Ebid, Janneth Alejandra Viñan Villagran, Raúl Gregorio Martínez Pérez, Fausto Ulpiano Caicedo Benavides, Paul Awoyera, Siva Avudaiappan

“…At the end of the model work, AdaBoost and KNN excel in predictive accuracy with 97.5%, reducing the margin of error and ensuring precise mix designs for SCC. …”
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Application of machine learning algorithm for prediction of abortion among reproductive age women in Ethiopia by Angwach Abrham Asnake, Alemayehu Kasu Gebrehana, Hiwot Altaye Asebe, Beminate Lemma Seifu, Bezawit Melak Fente, Meklit Melaku Bezie, Mamaru Melkam, Sintayehu Simie Tsega, Yohannes Mekuria Negussie, Zufan Alamrie Asmare

“…This study underscores the value of integrating machine learning into public health research and practice. Future work should focus on refining these models with larger and more diverse datasets, as well as exploring their applicability in other contexts and regions to further global maternal health initiatives. …”
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Leveraging Neural Trojan Side-Channels for Output Exfiltration by Vincent Meyers, Michael Hefenbrock, Dennis Gnad, Mehdi Tahoori

“…In this expanded study, we introduced a broader range of experiments to evaluate the robustness and effectiveness of this attack vector. We detail a novel training methodology that enhanced the correlation between power consumption and network output, achieving up to a 33% improvement in reconstruction accuracy over benign models. …”
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On the Determination of the Output Power in Mono/Multicrystalline Photovoltaic Cells by Xia Liu, Yongqiu Liu, Mohammad Eslami

“…In the present work, two artificial intelligence-based models were proposed to determine the output power of two types of photovoltaic cells including multicrystalline (multi-) and monocrystalline (mono-). …”
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Deep Learning-Enhanced Portable Chemiluminescence Biosensor: 3D-Printed, Smartphone-Integrated Platform for Glucose Detection by Chirag M. Singhal, Vani Kaushik, Abhijeet Awasthi, Jitendra B. Zalke, Sangeeta Palekar, Prakash Rewatkar, Sanjeet Kumar Srivastava, Madhusudan B. Kulkarni, Manish L. Bhaiyya

“…Comparative analysis was conducted using multiple deep learning models, including Random Forest, the Support Vector Machine (SVM), InceptionV3, VGG16, and ResNet-50, to identify the optimal architecture for accurate glucose detection. …”
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SCIENTIFIC PARADIGM SHIFT AS THE BACKGROUND OF LANGUAGE EDUCATION by V. D. Tabanakova

“…Linguistic education takes up nearly the last position in the new model of the higher school dictated by economic interests. …”
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City2Twin: an open urban digital twin from data integration to visualization and analysis by B. P. Rafamatanantsoa, I. Jeddoub, A. Yarroudh, R. Hajji, R. Billen

“…Our approach integrates various data types, including 3D city models, dynamic air quality data, and external data imported from the client side, such as vector data, 3D city models, and point clouds. …”
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Modern Problems of Pedagogical Education by A. V. Lubkov

“…The methodological framework of the research is based on: current theoretical models and modernisation conceptions; the key provisions of problem-based axiological, hermeneutical and anthropological approaches; the methods of structural-functional and comparative analysis.Results and scientific novelty. …”
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Surface-Enhanced Raman Scattering Combined with Machine Learning for Rapid and Sensitive Detection of Anti-SARS-CoV-2 IgG by Thais de Andrade Silva, Gabriel Fernandes Souza dos Santos, Adilson Ribeiro Prado, Daniel Cruz Cavalieri, Arnaldo Gomes Leal Junior, Flávio Garcia Pereira, Camilo A. R. Díaz, Marco Cesar Cunegundes Guimarães, Servio Túlio Alves Cassini, Jairo Pinto de Oliveira

“…The machine learning models used were k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Decision Trees (DTs), logistic regression (LR), and Light Gradient Boosting Machine (LightGBM). …”
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Improving the accuracy of remotely sensed TSS and turbidity using quality enhanced water reflectance by a statistical resampling technique by Kunwar Abhishek Singh, Dongryeol Ryu, Meenakshi Arora, Manoj Kumar Tiwari, Bhabagrahi Sahoo

“…The resampled spectral data and in-situ TSS and turbidity measurements were used to train four ML models: Partial Least Squares Regression (PLSR), Random Forest Regression (RFR), Extreme Gradient Boosting (XGBoost), and Support Vector Regression (SVR). …”
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