A Novel Approach of Optimal Signal Streaming Analysis Implicated Supervised Feedforward Neural Networks by Farhan Ali, He Yigang

“…The dataset consists of artificially generated radio waves to train signals through neural networks (NNs) and machine learning algorithms to detect errors properly. The primary objective is to achieve optimal signal performance. …”
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Early Diagnosis of Knee Osteoarthritis With a Natural Language Processing–Driven Approach Based on Clinician Notes: Development and Validation Study by Narathip Thanyakunsajja, Kulsawasd Jitkajornwanich, Shan Xu, Donghee Shin, Pattama Charoenporn

“…Previous studies mostly focused on using approaches, such as those involving artificial intelligence, to automatically detect knee OA through such data. However, these studies did not incorporate clinicians’ or doctors’ notes (text data) into the analysis, although these data involving reported symptoms and behaviors are already available and easier to collect and access than laboratory data and image data. …”
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An Efficient and Fast Model Reduced Kernel KNN for Human Activity Recognition by Zongying Liu, Shaoxi Li, Jiangling Hao, Jingfeng Hu, Mingyang Pan

“…Many classic machine learning algorithms, such as artificial neural network, feed forward neural network, K-nearest neighbors, and support vector machine, achieve good performance for detecting human activity. …”
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Digital Transition as a Driver for Sustainable Tailor-Made Farm Management: An Up-to-Date Overview on Precision Livestock Farming by Caterina Losacco, Gianluca Pugliese, Lucrezia Forte, Vincenzo Tufarelli, Aristide Maggiolino, Pasquale De Palo

“…The increasing integration of sensing devices with smart technologies, deep learning algorithms, and robotics is profoundly transforming the agricultural sector in the context of Farming 4.0. …”
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Mapping Vegetation Dynamics in Wyoming: A Multi-Temporal Analysis using Landsat NDVI and Clustering by N. Kuppala, C. Navneet Krishna, V. V. Sajith Variyar, R. Sivanpillai

“…As part of this study, we compared the outputs generated by two unsupervised machine learning algorithms with a conventional image clustering technique. …”
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ARAN: Age-Restricted Anonymized Dataset of Children Images and Body Measurements by Hezha H. MohammedKhan, Cascha Van Wanrooij, Eric O. Postma, Çiçek Güven, Marleen Balvert, Heersh Raof Saeed, Chenar Omer Ali Al Jaf

“…To create a suitable reference, we trained state-of-the-art deep learning algorithms on the ARAN dataset to predict body measurements from the images. …”
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3Mont: A multi-omics integrative tool for breast cancer subtype stratification. by Miray Unlu Yazici, J S Marron, Burcu Bakir-Gungor, Fei Zou, Malik Yousef

“…In this study, we propose a novel approach called 3-Multi-Omics Network and Integration Tool (3Mont), which integrates various -omics data by applying a grouping function, detecting pro-groups, and assigning scores to each pro-group using Feature importance scoring (FIS) component. …”
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Early Warning of Nerve Agent Release in Large Indoor Environments Based on Encoder-Decoder Coupling Physics-Informed Neural Network by Shuobei Sun, Yang Peng, An Wang, Yiwen Xie, Yang Hu, Zhongyu Hou

“…Therefore, in this work, a new model called Encoder-Decoder Coupling Physics-Informed Neural Network is proposed, which learns from concentration data generated by experimentally validated CFD simulations and the spatio-temporal information to solve high-dimensional partial differential equations and provide predictions of nerve agents distribution that more closely align with objective physical laws. …”
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THE USE OF REMOTE SENSING TECHNIQUES FOR MODELING AND ANALYSIS OF THE URBAN EXPANSION OF AIN SALAH CITY IN THE ALGERIAN SAHARA BETWEEN 2000- 2023 by Ali SAIDOU, Saida MEFTAH

“…The study's use of machine learning algorithms and high-resolution satellite imagery provides valuable insights into the dynamics of urbanization in arid environments and can inform future urban planning and sustainable development strategies in similar regions.…”
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Deep sea spy: An online citizen science annotation platform for science and ocean literacy by Marjolaine Matabos, Pierre Cottais, Riwan Leroux, Yannick Cenatiempo, Charlotte Gasne-Destaville, Nicolas Roullet, Jozée Sarrazin, Julie Tourolle, Catherine Borremans

“…Although deep learning offers an alternative to human processing, training algorithms requires large annotated reference datasets. …”
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Bioconvergence: advancing biosensors with nanotechnology for real-time health monitoring by Rosaline Mishra, Sakshi Minocha, Radha Goel, Praveen Kumar Gaur, Kanak Lata, Rashmi Singh

“…Real-time Monitoring: For ongoing, non-invasive health monitoring, include biosensors with wearable technology or point-of-care diagnostics. Data analysis: To interpret and analyse biosensor signals for precise and rapid health evaluations, apply machine learning techniques and sophisticated data analysis algorithms. …”
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AI for rapid identification of major butyrate-producing bacteria in rhesus macaques (Macaca mulatta) by Annemiek Maaskant, Donghyeok Lee, Huy Ngo, Roy C. Montijn, Jaco Bakker, Jan A. M. Langermans, Evgeni Levin

“…Results By integrating fecal image data with corresponding metagenomic sequencing information, the deep learning (DL) and machine learning (ML) algorithms successfully predicted 16 individual bacterial genera (area under the curve (AUC) > 0.7) among the 50 most abundant genera in rhesus macaques (Macaca mulatta). …”
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Towards a Digital Twin for Gas Turbines: Thermodynamic Modeling, Critical Parameter Estimation, and Performance Optimization Using PINN and PSO by Jian Tiong Lim, Achnaf Habibullah, Eddie Yin Kwee Ng

“…Ultimately, the proposed methods aim to enhance GT operations via a data-driven digital twin concept combination of deep learning and optimization algorithms.…”
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Analysis of Acoustic Signals of Footsteps from the Piezoelectric Sensor by Bilge Çiğdem Çiftçi, Gamze Kaya, Mustafa Kurt

“…The source of the acoustic signal can be determined by matching it with the existing database using machine learning algorithms like face recognition systems for future goals.…”
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Modeling the Relationship Between Radon Anomalies and Seismic Activity Using Artificial Neural Networks and Statistical Methods by Kostadin Yotov, Emil Hadzhikolev, Stanka Hadzhikoleva

“…Large datasets from three radon measurement stations in Bulgaria—Yambol, Dimitrovgrad, and Krupnik—were used. Data from seismic periods were excluded during the training of the neural networks to ensure that the models learn only the natural radon variations under non-seismic conditions. …”
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Diagnosis of knee meniscal injuries using artificial intelligence: A systematic review and meta-analysis of diagnostic performance. by Soheil Mohammadi, Ali Jahanshahi, Mohammad Shahrabi Farahani, Mohammad Amin Salehi, Negin Frounchi, Ali Guermazi

“…<h4>Aim of the study</h4>The aim was to systematically review the literature and perform a meta-analysis to estimate the performance of artificial intelligence (AI) algorithms in detecting meniscal injuries.<h4>Materials and methods</h4>A systematic search was performed in the Scopus, PubMed, EBSCO, Cinahl, Web of Science, IEEE Xplore, and Cochrane Central databases on July, 2024. …”
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Artificial intelligence-enabled non-invasive ubiquitous anemia screening: The HEMO-AI pilot study on pediatric population by Daniel Gordon, Jason Hoffman, Keren Gamrasni, Yotam Barlev, Alex Levine, Tamar Landau, Ronen Shpiegel, Avishai Lahad, Ariel Koren, Carina Levin, Osnat Naor, Hannah Lee, Xin Liu, Shwetak Patel, Gilad Chayen, Michael Brandwein

“…It identifies important sample collection parameters and design, provides critical algorithms for the pre-processing of fingernail data, and reports an initial capability to detect anemia with 87% sensitivity and 84% specificity. …”
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Support Vector Machines and Model Selection for Control Chart Pattern Recognition by Chih-Jen Su, I-Fei Chen, Tzong-Ru Tsai, Tzu-Hsuan Wang, Yuhlong Lio

“…To conquer these downsides, algorithms for control chart pattern recognition (CCPR) leverage machine learning models to detect non-normality or normality and ensure product quality is established, and novel approaches that integrate the support vector machine (SVM), random forest (RF), and K-nearest neighbors (KNN) methods with the model selection criterion, named SVM-, RF-, and KNN-CCPR, respectively, are proposed. …”
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System architecture and key technologies of intelligent, safe and efficient mining for rockburst coal seams by Zhigang DENG, Yunpeng LI, Shankun ZHAO, Yin WANG, Shaogang LI, Linghai KONG, Junjun JIANG, Zhenguo SU, Yizhe LI, Kai QIN

“…Thirdly, with the help of deep learning and AI technology, the computer can independently learn the holographic data of the mine, make independent decisions on mining and prevention engineering measures according to the real-time changes of monitoring data and engineering activities, and build a standardized communication technology system to realize the interconnection of various equipment in the mine and create an intelligent mine ecosystem. …”
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Recent Advances in Flexible and Wearable Gas Sensors Harnessing the Potential of 2D Materials by Azmira Jannat, Md Mehdi Masud Talukder, Zhong Li, Jian Zhen Ou

“…Furthermore, it explores innovative solutions like self‐healing sensors, artificial intelligence‐driven sensor arrays, in situ surface passivation, and multisensor platforms coupled with machine learning algorithms, offering valuable insights for advancing next‐generation wearable gas‐sensing technologies.…”
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