Monitoring Gypsiferous Soils by Leveraging Advanced Spaceborne Hyperspectral Imagery via Spectral Indices and a Machine Learning Approach by Najmeh Rasooli, Saham Mirzaei, Stefano Pignatti

“…The results showcased that the difference soil index (DSI) achieved the highest R² scores of 0.96 (ASD), 0.79 (PRISMA), and 0.84 (EnMAP), slightly outperforming the normalized difference gypsum ratio (NDGI) and ratio soil index (RSI). …”
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Machine learning allows robust classification of lung neoplasm tissue using an electronic biopsy through minimally-invasive electrical impedance spectroscopy by Georgina Company-Se, Virginia Pajares, Albert Rafecas-Codern, Pere J. Riu, Javier Rosell-Ferrer, Ramon Bragós, Lexa Nescolarde

“…All the frequencies used to train and test the algorithms obtained high significant differences between neoplasm and the other types of tissues (P < 0.001). …”
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Biomedical Data Analysis Systems for the Diagnosis of Skin Neoplasms by O. O. Myakinin

“…One of the factors of the increase can be considered the introduction of a personalized mode - the addition of comparative features evaluating a difference between a tumor and a normal tissue in the software analysis module. …”
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Enhancing Sustainable Manufacturing in Industry 4.0: A Zero-Defect Approach Leveraging Effective Dynamic Quality Factors by Rouhollah Khakpour, Ahmad Ebrahimi, Seyed Mohammad Seyed Hosseini

“…In addition to eliminating waste in manufacturing resources, it also evaluates the impact of these improvements on sustainability. …”
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Enhanced Homomorphic Encryption for Cloud Security Through Individual Optimization by Radha Marepalli, Sunitha Vanamala, Manoj Kumar Konda, Venkata Rama Lakshmi Gundala, Prasanthi B.

“…These algorithms are also often referred to as PSO strategies. …”
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An efficient retrieval method on Google Earth Engine and comparison with hybrid methods: a case study of leaf area index retrieval by Sijia Li, Zhiguang Tang, Kaisen Ma, Zhenyi Wang, Wenjuan Li

“…However, these algorithms cannot quantify the uncertainty of estimates. …”
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Resolving multi-image spatial lipidomic responses to inhaled toxicants by machine learning by Nathanial C. Stevens, Tong Shen, Joshua Martinez, Veneese J. B. Evans, Morgan C. Domanico, Elizabeth K. Neumann, Laura S. Van Winkle, Oliver Fiehn

“…Abstract Regional responses to inhaled toxicants are essential to understand the pathogenesis of lung disease under exposure to air pollution. We evaluate the effect of combined allergen sensitization and ozone exposure on eliciting spatial differences in lipid distribution in the mouse lung that may contribute to ozone-induced exacerbations in asthma. …”
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A computer-based method for the automatic identification of the dimensional features of human cervical vertebrae by Nicola Cappetti, Luca Di Angelo, Carlotta Fontana, Antonio Marzola

“…The manual method demonstrated poor repeatability and reproducibility, with substantial differences between the minimum and maximum values for the measured features in intra- and inter-operator evaluations. …”
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Do We Need to Add the Type of Treatment Planning System, Dose Calculation Grid Size, and CT Density Curve to Predictive Models? by Reza Reiazi, Surendra Prajapati, Leonardo Che Fru, Dongyeon Lee, Mohammad Salehpour

“…<b>Methods:</b> This study evaluated dose calculation differences in the head and neck cancer treatment plans of 19 patients using two treatment planning systems, Pinnacle 9.10 and RayStation 11, with similar dose calculation algorithms. …”
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Development and Validation of a Clinical Risk Model for Predicting Malignancy in Patients with Thyroid Nodules by Shiva Borzouei, Ali Safdari, Erfan Ayubi

“…Generally, there was no difference among the six ML algorithms in terms of prognostic performance; however, the prognostic parameters of GLM and LDA algorithms were higher than the others. …”
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Combined Use of Multiple Cloud‐Free Snow Cover Products in China and Its High‐Mountain Region: Implications From Snow Cover Identification to Snow Phenology Detection by Longhui Zhang, Hongbo Zhang, Xueyan Sun, Lun Luo

“…However, comprehensive analysis of differences among products and their impact on snow phenology detection is lacking. …”
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Predicting the generalization of computer aided detection (CADe) models for colonoscopy by Joel Shor, Carson McNeil, Yotam Intrator, Joseph R. Ledsam, Hiro-o Yamano, Daisuke Tsurumaru, Hiroki Kayama, Atsushi Hamabe, Koji Ando, Mitsuhiko Ota, Haruei Ogino, Hiroshi Nakase, Kaho Kobayashi, Masaaki Miyo, Eiji Oki, Ichiro Takemasa, Ehud Rivlin, Roman Goldenberg

“…Abstract Generalizability of AI colonoscopy algorithms is important for wider adoption in clinical practice. …”
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Subtypes detection of papillary thyroid cancer from methylation assay via Deep Neural Network by Andrea Colacino, Andrea Soricelli, Michele Ceccarelli, Ornella Affinito, Monica Franzese

“…When applied to the thyroid independent dataset, the proposed Neural Net architecture successfully discriminates tumor versus normal samples (AUC = 0.91 +/- 0.05) and follicular versus classical PTC subtypes (AUC = 0.80 +/- 0.05), outperforming traditional machine learning algorithms. Conclusions: In conclusion, the study highlights the effectiveness of CNNs in the methylation based classification of thyroid tumors and their subtypes, demonstrating its ability to capture subtle epigenetic differences with minimal preprocessing.This versatility makes the model adaptable for classifying other tumor types. …”
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The incremental value of tuberculosis detecting African giant pouched rats over Smear microscopy and Xpert MTB/RIF for Tanzanian TB detection. by Tefera B Agizew, Joseph Soka, Stephen Mwimanzi, Cynthia D Fast, Gilbert Mwesiga, Nashon Edward, Marygiven Stephen, Rehema Kondo, Christophe Cox, Negussie Beyene

“…Sputum-smear microscopy (smear) is being replaced by Xpert MTB/RIF (Xpert)-based diagnostic algorithms in many countries. We evaluated the incremental values of rat-based case detection over smear and Xpert.…”
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Balancing accuracy and cost in machine learning models for detecting medial vascular calcification in chronic kidney disease: a pilot study by Urszula Bialonczyk, Malgorzata Debowska, Lu Dai, Abdul Rashid Qureshi, Leon Bobrowski, Magnus Soderberg, Bengt Lindholm, Peter Stenvinkel, Tomasz Lukaszuk, Jan Poleszczuk

“…Abstract Machine learning algorithms that integrate multiple biomarkers are increasingly used in disease detection, yet economic considerations are often overlooked. …”
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StructuRly: A novel shiny app to produce comprehensive, detailed and interactive plots for population genetic analysis. by Nicola G Criscuolo, Claudia Angelini

“…Population genetics focuses on the analysis of genetic differences within and between-group of individuals and the inference of the populations' structure. …”
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ASVBM: Structural variant benchmarking with local joint analysis for multiple callsets by Peizheng Mu, Xiangyan Feng, Lanxin Tong, Jie Huang, Chaoqun Zhu, Fei Wang, Wei Quan, Yuanjun Ma, Yucui Dong, Xiao Zhu

“…A fundamental challenge in benchmarking SV detection results is determining whether two SVs represent the same event. Differences in the variation-awareness and strategic implementation of aligners inherently constrain SV detection algorithms that rely on alignment-based approaches. …”
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Comparison of 46 Cytokines in Peripheral Blood Between Patients with Papillary Thyroid Cancer and Healthy Individuals with AI-Driven Analysis to Distinguish Between the Two Groups by Kyung-Jin Bae, Jun-Hyung Bae, Ae-Chin Oh, Chi-Hyun Cho

“…Among the five classification algorithms evaluated, XGBoost demonstrated superior performance in terms of accuracy, precision, sensitivity (recall), specificity, F1-score, and ROC-AUC score. …”
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Machine learning applications to classify and monitor medication adherence in patients with type 2 diabetes in Ethiopia by Ewunate Assaye Kassaw, Ewunate Assaye Kassaw, Ashenafi Kibret Sendekie, Ashenafi Kibret Sendekie, Bekele Mulat Enyew, Biruk Beletew Abate, Biruk Beletew Abate

“…Eight widely used ML algorithms were employed to develop the models, and their performance was evaluated using metrics such as accuracy, precision, recall, and F1 score. …”
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Parallel Multi-Scale Semantic-Depth Interactive Fusion Network for Depth Estimation by Chenchen Fu, Sujunjie Sun, Ning Wei, Vincent Chau, Xueyong Xu, Weiwei Wu

“…Although self-supervised algorithms can reduce the dependence on labeled data, the performance is still affected by scene occlusions, lighting differences, and sparse textures. …”
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