Self-supervised denoising of grating-based phase-contrast computed tomography by Sami Wirtensohn, Clemens Schmid, Daniel Berthe, Dominik John, Lisa Heck, Kirsten Taphorn, Silja Flenner, Julia Herzen

“…Therefore, the application of machine learning-based denoisers shifts the dose-normalized image quality in favor of gbPC-CT, bringing it one step closer to medical application.…”
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Genetic analysis of scab disease resistance in common bean (Phaseolus vulgaris) varieties using GWAS and functional genomics approaches by Shadrack Odikara Oriama, Benard W. Kulohoma, Evans Nyaboga, Y. O. Masheti, Reuben Otsyula

“…Annotation of genes proteins with significant association values was conducted using a machine learning algorithm of support vector machine on prPred using python3 on Linux Ubuntu 18.04 computing platform with an accuracy of 0.935. …”
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CFD modelling and simulation of anaerobic digestion reactors for energy generation from organic wastes: A comprehensive review by Muhammad Usman Farid, Indiana A. Olbert, Andreas Bück, Abdul Ghafoor, Guangxue Wu

“…Research gaps and critical challenges are identified in different aspects such as reactor design, and configuration, mixing, multiphase flow, heat transfer, biokinetics as well as machine learning approaches.…”
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IMCMK-CNN: A lightweight convolutional neural network with Multi-scale Kernels for Image-based Malware Classification by Dandan Zhang, Yafei Song, Qian Xiang, Yang Wang

“…The research into the direction of malware detection is dedicated to surmounting the limitations of conventional detection methodologies, and delves deeply into the application of cutting-edge technologies such as data visualization, machine learning, and hybrid detection within the realm of malware detection. …”
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Missing Risk Factor Prediction in Cardiovascular Disease Using a Blended Dataset and Optimizing Classification With a Stacking Algorithm by Jannatul Mauya, Saad Sahriar, Sanjida Akther, Ruhul Amin, Sabba Ruhi, Md. Shamim Reza

“…ABSTRACT Machine learning is important in the treatment of heart disease because it is capable of analyzing large amounts of patient data, such as medical records, imaging tests, and genetic information, in order to identify patterns and predict the risk of developing heart disease. …”
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Decoding cyanide toxicity: Integrating Quantitative Structure-Toxicity Relationships (QSTR) with species sensitivity distributions and q-RASTR modeling by Kabiruddin Khan, Ramin Abdullayev, Gopala Krishna Jillella, Varun Gopalakrishnan Nair, Mahmoud Bousily, Supratik Kar, Agnieszka Gajewicz-Skretna

“…Key molecular descriptors, including topological, geometrical, and electronic properties, were computed using ALOGPS 2.1, ChemAxon, and Elemental-Descriptor 1.0. Three machine learning methods MLR, PLS, and kNN were employed to develop predictive models. …”
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Artificial Neural Networks as a Tool for High-Accuracy Prediction of In-Cylinder Pressure and Equivalent Flame Radius in Hydrogen-Fueled Internal Combustion Engines by Federico Ricci, Massimiliano Avana, Francesco Mariani

“…Furthermore, this methodology could subsequently be applied to conventional road engines exhibiting characteristics and performance similar to those of a specific optical engine used as the basis for the machine learning analysis, offering a practical advantage in real-time diagnostics.…”
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Artificial intelligence empowered voice generation for amyotrophic lateral sclerosis patients by Stefano Regondi, Giordana Donvito, Emanuele Frontoni, Milutin Kostovic, Fabio Minazzi, Sébastien Bratières, Massimiliano Filosto, Raffaele Pugliese

“…In light of these challenges, this study aims to assess the effectiveness and the perceptual impact of AI-generated voices on ALS patients with preserved speech, utilizing a personalized voice synthesis system based on machine learning. The AI-generated patient-specific voice is achieved through voice recording, followed by fine-tuning using a Generative Adversarial Network for Efficient and High Fidelity Speech Synthesis (HiFi-GAN), resulting in a model capable of producing speech highly similar to the patient’s own voice, with exceptional expressive and audio quality. …”
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Modeling of wave-induced drift based on stepwise parameter calibration by Kui Zhu, Xueyao Chen, Lin Mu, Lin Mu, Lin Mu, Dingfeng Yu, Dingfeng Yu, Runze Yu, Zhaolong Sun, Tong Zhou, Tong Zhou

“…This study examined the wave-induced drift’s influence on field-observation experiments involving two common, differently sized SAR targets—an offshore fishing vessel (OFV) and a person in the water (PIW)—using parameter stepwise calibration and machine-learning (ML) methods. The sample of wave-induced drift velocity was obtained by gradually separating current-induced (CI) drift’s and wind-induced (WI) drift’s influence from the target-drift velocity using the least-square method and AP98 model. …”
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Distinguishing benign and malignant myxoid soft tissue tumors: Performance of radiomics vs. radiologists. by Joshua M Lawrenz, Can Cui, Samuel R Johnson, Katherine S Hajdu, Stephen W Chenard, Akhil Rekulapelli, Cullen P Moran, John J Block, Nicholson S Chadwick, Joanna L Shechtel, Brian Bingham, Leo Y Luo, Jennifer L Halpern, Herbert S Schwartz, Ginger E Holt, David S Smith, Benoit Dawant, Hakmook Kang

“…The purpose of this study is comparison of the diagnostic performance of a radiomic based machine learning (ML) model to musculoskeletal radiologists.…”
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Prediksi Rating Film IMDb Menggunakan Decision Tree by Rifqy Rosdiyah Ilmi, Fachrul Kurniawan, Sri Harini

“…Prediction of movie ratings values can be modeled through machine learning using the decision tree model. From this research, it can be concluded that the popularity of the film and the value of user votes on the IMDb page have an effect on the film rating value. …”
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Precision Adverse Drug Reactions Prediction with Heterogeneous Graph Neural Network by Yang Gao, Xiang Zhang, Zhongquan Sun, Payal Chandak, Jiajun Bu, Haishuai Wang

“…Abstract Accurate prediction of Adverse Drug Reactions (ADRs) at the patient level is essential for ensuring patient safety and optimizing healthcare outcomes. Traditional machine learning‐based methods primarily focus on predicting potential ADRs for drugs, but they often fall short of capturing the complexity of individual demographics and the variations in ADRs experienced by different people. …”
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Neural network quantification for solar radiation prediction: An approach for low power devices by Brenda Alejandra Villamizar-Medina, Angelo Joseph Soto Vergel, Byron Medina-Delgado, Darwin Orlando Cardozo-Sarmiento, Dinael Guevara-Ibarra, Oriana Alexandra Lopez-Bustamante

“… Accurate solar radiation prediction leverages various machine learning techniques, with artificial neural networks (ANN) being the most common and precise due to their ability to detect and learn relationships between meteorological variables and solar radiation. …”
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Mapping the Landscape of AI-Driven Human Resource Management: A Social Network Analysis of Research Collaboration by Mehrdad Maghsoudi, Motahareh Kamrani Shahri, Mehrdad Agha Mohammad Ali Kermani, Rahim Khanizad

“…The findings identify four primary research themes: AI for System Identification and Control, focusing on workforce planning and adaptive management; HR Analytics and Performance Management, emphasizing data-driven decision making; Machine Learning for Classification and Prediction, addressing talent acquisition and retention; and AI-Driven HR Decision-Making, exploring strategic planning and unbiased evaluation systems. …”
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How to Coordinate Urban Ecological Networks and Street Green Space Construction? Insights from a Multi-Scale Perspective by Shujun Hou, Ying Yu, Taeyeol Jung, Xin Han

“…This study examines the area within Chengdu’s Third Ring Road, employing the following methodologies: (1) constructing the regional ecological network using Morphological Spatial Pattern Analysis (MSPA), the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, and circuit theory; (2) analyzing the street green view index (GVI) through machine learning semantic segmentation techniques; and (3) identifying key areas for the coordinated development of urban ecological networks and street green spaces using bivariate spatial correlation analysis. …”
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An Efficient CNN Model for COVID-19 Disease Detection Based on X-Ray Image Classification by Aijaz Ahmad Reshi, Furqan Rustam, Arif Mehmood, Abdulaziz Alhossan, Ziyad Alrabiah, Ajaz Ahmad, Hessa Alsuwailem, Gyu Sang Choi

“…To further prove that the proposed model outperforms other models, a comparative analysis has been done with some of the machine learning algorithms. The proposed model has outperformed all the models generally and specifically when the model testing was done using an independent testing set.…”
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Development of metastasis and survival prediction model of luminal and non-luminal breast cancer with weakly supervised learning based on pathomics by Hui Liu, Linlin Ying, Xing Song, Xueping Xiang, Shumei Wei

“…These features served as the foundational input for developing a machine learning algorithm for metastasis analysis and a Cox regression model for survival analysis. …”
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5‐Hydroxymethylcytosine Profiles of cfDNA in Urine as Diagnostic, Differential Diagnosis and Prognostic Markers for Multiple Myeloma by Weiwei Xie, Xuehui Li, Hangyu Chen, Jinlin Chu, Lei Zhang, Bo Tang, Wenrong Huang, Linlin Li, Jian Lin, Yujun Dong

“…Then, we applied a machine learning algorithm to develop diagnostic and differential diagnosis model. …”
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Wearable Regionally Trained AI-Enabled Bruxism-Detection System by Anusha Ishtiaq, Jahanzeb Gul, Zia Mohy Ud Din, Azhar Imran, Khalil El Hindi

“…The augmented data has been trained, validated, and tested on six machine-learning classifiers and three deep-learning models. …”
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Can Radiomics of Dynamic PET Imaging with 11C-methionine Predict EGFR Amplification Status in Glioblastoma? by Gleb DANILOV, Andrey POSTNOV, Diana KALAEVA, Nina VIKHROVA, Tatyana KOBYAKOVA

“…Three datasets were used to predict EGFR amplification status via machine learning: 1) Radiomic features calculated as time series for each image biomarker; 2) Dynamic tumor-to-normal brain ratio (T/N) of radiopharmaceutical uptake - time series of T/N peak for 26 frames; 3) Static T/N - peak, max, and average T/N for static images. …”
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