OBJECT DETECTION IN COMPUTER VISION SYSTEMS: A VISUAL SALIENCY BASED APPROACH by V. A. Kachurka, K. .. Madani, C. .. Sabourin, V. A. Golovko, P. A. Kachurka

“…Experimental results show viability and efficiency of this approach as compared with state-of-art algorithms, and predict its usability on the broader class of tasks - applied variations of eye fixation problem.…”
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A Cascade of Encoder–Decoder with Atrous Convolution and Ensemble Deep Convolutional Neural Networks for Tuberculosis Detection by Noppadol Maneerat, Athasart Narkthewan, Kazuhiko Hamamoto

“…The ensemble classifier was designed to predict the presence of TB by fusing DCNNs from the winning combination via weighted averaging. …”
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Genome-wide association between branch point properties and alternative splicing. by André Corvelo, Martina Hallegger, Christopher W J Smith, Eduardo Eyras

“…Using a Support Vector Machine algorithm, we created a model complemented with polypyrimidine tract features, which considerably improves the prediction accuracy over previously published methods. …”
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Big data and data science in global governance: anticipating future needs and applications in the UN and beyond by Lanxin Li, Jiarou Wang, Xi Wang, Peng Peng, Jiaying Shen, Haining Zhu, Ziyang Zhang

“…The research identifies the “4Vs” of big data (Volume, Velocity, Variety, and Veracity) as fundamental characteristics reshaping governance approaches while highlighting innovative applications like UN Global Pulse, SDG tracking systems, and AI-driven predictive analytics in crisis prevention. We assess technical, ethical, and organizational challenges, including data quality inconsistencies, interoperability issues, privacy concerns, algorithmic bias, and resource constraints that impede the full integration of big data into governance systems. …”
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Construction of a prognostic model based on memory CD4+ T cell–associated genes for lung adenocarcinoma and its applications in immunotherapy by Yong Li, Xiangli Ye, Huiqin Huang, Rongxiang Cao, Feijian Huang, Limin Chen

“…The constructed nomogram results demonstrated that the predictive performance of the nomogram was superior to the prognostic model and outperformed individual clinical factors. …”
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From data to nutrition: the impact of computing infrastructure and artificial intelligence by Pierpaolo Di Bitonto, Michele Magarelli, Pierfrancesco Novielli, Donato Romano, Domenico Diacono, Lorenzo de Trizio, Angelo Mariano, Claudia Zoani, Riccardo Ferrero, Alessandra Manzin, Maria De Angelis, Roberto Bellotti, Sabina Tangaro

“…This article explores the significant impact that artificial intelligence (AI) could have on food safety and nutrition, with a specific focus on the use of machine learning and neural networks for disease risk prediction, diet personalization, and food product development. …”
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The diagnostic performance evaluation of the SD BIOLINE HIV/syphilis Duo rapid test in southern Ethiopia: a cross-sectional study by Techalew Shimelis, Endale Tadesse

“…Syphilis serostatus was determined using the Treponema pallidum haemagglutination assay (TPHA).Results The respective sensitivity, specificity, positive predictive value and negative predictive value of the SD BIOLINE HIV/syphilis Duo test were 100, 99.5, 99.5 and 100% for HIV and 97.6, 96, 95.4 and 98% for syphilis testing, respectively. …”
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P-68 LIVGUARD, A DEEP NEURAL NETWORK FOR CIRRHOSIS DETECTION IN LIVER ULTRASOUND (USD) IMAGES by DIEGO ARUFE, Pablo Gomez del Campo, Ezequiel Demirdjian, Carlos Galmarini

“…Sensitivity, specificity, positive (P) and negative (N) predictive values (PV) were 88.8%, 88.5%, 85.5% and 92.2%, respectively. …”
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Innovative Dombi Aggregation Operators in Linguistic Intuitionistic Fuzzy Environments for Optimizing Telecommunication Networks by Dilshad Alghazzawi, Misbah Hayat, Ghaliah Alhamzi, Abdul Wakil Baidar

“…These technologies utilize live and predictive network data to advance the network to its full potential, proactively resolving performance issues prior to the impact on subscribers. …”
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People counting using IR-UWB radar sensors and machine learning techniques by Ange Joel Nounga Njanda, Jocelyn Edinio Zacko Gbadoubissa, Emanuel Radoi, Ado Adamou Abba Ari, Roua Youssef, Aminou Halidou

“…Next, we create features based on statistical and entropic properties of the signal and apply several classification algorithms, including ANN, Random Forest, KNN, XGBOOST, and multiple linear regression, to predict the number of people present. …”
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A multi‐objective feature optimization strategy for developing high‐entropy alloys with optimal strength and ductility by Yan Zhang, Shewei Xin, Wei Zhou, Xiao Wang, Yangyang Xu, Yanjing Su

“…Here, we propose a multi‐objective feature optimization strategy that identifies feature subsets to improve both prediction accuracy and active learning efficiency for iterative experimentation. …”
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Forecasting Stock Market Volatility Using Housing Market Indicators: A Reinforcement Learning-Based Feature Selection Approach by Pourya Zareeihemat, Samira Mohamadi, Jamal Valipour, Seyed Vahid Moravvej

“…This study tackles the complex challenge of accurately predicting stock market volatility through indicators from the housing market. …”
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Multilevel determinants of racial/ethnic disparities in severe maternal morbidity and mortality in the context of the COVID-19 pandemic in the USA: protocol for a concurrent triang... by Bankole Olatosi, Jiajia Zhang, Xiaoming Li, Chen Liang, Jihong Liu, Peiyin Hung, Shan Qiao, Berry A Campbell, Myriam E Torres, Neset Hikmet

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A lightweight knowledge graph-driven question answering system for field-based mineral resource survey by Mingguo Wang, Chengbin Wang, Jianguo Chen, Bo Wang, Wei Wang, Xiaogang Ma, Jiangtao Ren, Zichen Li, Yicai Ye, Jiakai Zhang, Yue Wang

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Targeted urinary metabolomics combined with machine learning to identify biomarkers related to central carbon metabolism for IBD by Miao-Lin Lei, Guan-Wei Bi, Xiao-Lin Yin, Xiao-Lin Yin, Yue Wang, Yue Wang, Yue Wang, Zi-Ru Sun, Zi-Ru Sun, Zi-Ru Sun, Xin-rui Guo, Hui-peng Zhang, Xiao-han Zhao, Feng Li, Feng Li, Yan-Bo Yu

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Machine learning based calculation of refractive index of polyethylene glycol polymer by Walid Abdelfattah, Munthar Kadhim Abosaoda, Hardik Doshi, H.S. Shreenidhi, Manoranjan Parhi, Devendra Singh, Prabhjot Singh, Bilakshan Purohit, Kamal Kant Joshi, Ahmad Abumalek

“…This study develops advanced machine learning algorithms to accurately predict the refractive index of polyethylene glycol (PEG) polymers using temperature and molecular weight as key input variables. …”
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AI Innovations in Liver Transplantation: From Big Data to Better Outcomes by Eleni Avramidou, Dominik Todorov, Georgios Katsanos, Nikolaos Antoniadis, Athanasios Kofinas, Stella Vasileiadou, Konstantina-Eleni Karakasi, Georgios Tsoulfas

“…As a result, algorithms are being developed to assess steatosis in pre-implantation biopsies and predict liver graft function, with AI applications displaying great accuracy across various studies included in this review. …”
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Opportunities and limitations of introducing artificial intelligence technologies into reproductive medicine by V. A. Lebina, O. Kh. Shikhalakhova, A. A. Kokhan, I. Yu. Rashidov, K. A. Tazhev, A. V. Filippova, E. P. Myshinskaya, Yu. V. Symolkina, Yu. I. Ibuev, A. A. Mataeva, A. N. Sirotenko, T. T. Gabaraeva, A. I. Askerova

“…AI can analyze vast amounts of data, including medical histories and research results, to more accurately predict pregnancy outcomes. This enables doctors to make more justified clinical decisions. …”
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Development and validation of a population pharmacokinetic model to guide perioperative tacrolimus dosing after lung transplantation by Todd A. Miano, PharmD, PhD, Athena F. Zuppa, MD, MSCE, Rui Feng, PhD, Stephen Griffiths, BS, Laurel Kalman, BA, Michelle Oyster, MS, Edward Cantu, MD, MSCE, Wei Yang, PhD, Joshua M. Diamond, MD, MSCE, Jason D. Christie, MD, MSCE, Marc H. Scheetz, PharmD, MSC, Michael G.S. Shashaty, MD, MSCE

“…The strongest predictor of tacrolimus clearance was postoperative day, with median predicted clearance increasing more than 3-fold over the 14-day study period. …”
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QoS Routing in Telecommunications Networks by N. I. Listopad, O. A. Lavshuk

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