A hybrid parallel convolutional spiking neural network for enhanced skin cancer detection by K. Anup Kumar, C. Vanmathi

“…Furthermore, the system has a false positive rate (FPR) of 10.7% and a positive predictive value (PPV) of 90.8%, demonstrating its capacity to reduce wrong diagnosis while prioritizing true positive instances. …”
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Novel neoadjuvant therapies for muscle‐invasive bladder cancer: Systematic review and meta‐analysis by Shugo Yajima, Naoki Imasato, Hitoshi Masuda

“…Randomized trials are needed to establish optimal treatment algorithms and validate predictive biomarkers.…”
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Molecular biomarkers in salivary diagnostic materials: Point-of-Care solutions — PoC-Diagnostics and -Testing by Ziyad S. Haidar

“…Henceforth, ongoing advancements are expected to further integrate predictive analytics into our routine clinical practice, ultimately improving patient outcomes through personalized, cost-effective, and timely care, thereby enhancing overall healthcare quality and efficiency. …”
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Artificial Intelligence in Advancing Algal Bioactive Ingredients: Production, Characterization, and Application by Bingbing Guo, Xingyu Lu, Xiaoyu Jiang, Xiao-Li Shen, Zihao Wei, Yifeng Zhang

“…This review examines the multidimensional mechanisms by which AI enables and optimizes these processes: (1) AI-powered predictive models, integrated with machine learning algorithms (MLAs), Industry 4.0, and other advanced digital systems, support real-time monitoring and control of intelligent bioreactors, allowing for accurate forecasting of cultivation yields and market demand. (2) AI facilitates in-depth analysis of gene regulatory networks and key metabolic pathways, enabling precise control over the biosynthesis of targeted compounds. (3) AI-based spectral imaging and image recognition techniques enable rapid and reliable identification, classification, and quality assessment of active components. (4) AI accelerates the transition from mass production to the development of personalized medical and functional nutritional products. …”
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Artificial Intelligence and Smart Technologies in Safety Management: A Comprehensive Analysis Across Multiple Industries by Jiyoung Park, Dongheon Kang

“…AI-driven solutions, such as predictive analytics, machine learning algorithms, IoT sensor integration, and digital twin models, are shown to proactively identify and mitigate potential hazards, optimize energy consumption, and enhance operational efficiency. …”
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The Hydrodynamic Performance of a Vertical-Axis Hydro Turbine with an Airfoil Designed Based on the Outline of a Sailfish by Aiping Wu, Shiming Wang, Chenglin Ding

“…Response surface methodology was employed to establish predictive models for these critical performance indicators, effectively reducing computational resource consumption and experimental validation costs. …”
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Development of Electronic Nose as a Complementary Screening Tool for Breath Testing in Colorectal Cancer by Chih-Dao Chen, Yong-Xiang Zheng, Heng-Fu Lin, Hsiao-Yu Yang

“…We then used machine learning algorithms to develop predictive models and provided the estimated accuracy and reliability of the breath testing. (3) Results: We enrolled 77 patients, with 40 cases and 37 controls. …”
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Intratumoral Heterogeneity Scores as Predictors of Invasiveness in Lung Adenocarcinoma Presenting as Pure Ground-Glass Nodules: Insights from Explainable Machine Learning-Based Ter... by Wang Peng BS, Wanyin Qi BS, Yunhua Li BS, Sanhong Zhang BS, Juan Long BS

“…Among the 15 models, the light gradient boosting machine (LightGBM) exhibited the best predictive performance as a ternary classification model, achieving a macro-average AUC and an accuracy of 0.808 and 0.630, respectively. …”
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A Machine Learning and Remote Sensing‐Based Model for Algae Pigment and Dissolved Oxygen Retrieval on a Small Inland Lake by Maxwell R. W. Beal, Mutlu Özdoğan, Paul J. Block

“…Machine learning methods are implemented with existing algorithms to model chlorophyll‐a, phycocyanin, and Pc:Chla. …”
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Plasma sterols and vitamin D are correlates and predictors of ozone-induced inflammation in the lung: A pilot study. by Alexia N Perryman, Hye-Young H Kim, Alexis Payton, Julia E Rager, Erin E McNell, Meghan E Rebuli, Heather Wells, Martha Almond, Jamie Antinori, Neil E Alexis, Ned A Porter, Ilona Jaspers

“…We use computational analyses including machine learning to determine whether baseline plasma sterols are predictive of O3 responsiveness.<h4>Results</h4>We observed an overall decrease in the concentration of cholesterol precursors and derivatives (e.g. 27-hydroxycholesterol) and an increase in concentration of autooxidation products (e.g. secosterol-B) in sputum samples. …”
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Extracting diagnoses and investigation results from unstructured text in electronic health records by semi-supervised machine learning. by Zhuoran Wang, Anoop D Shah, A Rosemary Tate, Spiros Denaxas, John Shawe-Taylor, Harry Hemingway

“…We evaluated the precision (positive predictive value) and recall (sensitivity) of S3CM in classifying unlabelled texts against the gold standard of manual review. …”
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Optimization design of cross border intelligent marketing management model based on multi layer perceptron-grey wolf optimization convolutional neural network by Zongping Lin, Jing Yang, Yabin Lian, Yanhong Chen, Zhonghui Huang, Kexin Ning

“…Next, the latent feature vectors generated by MLP and CNN are fused in the output layer to generate the final predictive marketing strategy last. Experiments were conducted using a real cross-border e-commerce dataset, and the results showed that compared with traditional recommendation algorithms, the MLP-GWO-CNN model proposed in this paper performs better in utilizing user tag information, effectively improving the accuracy and personalization of marketing recommendations. …”
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Tribological Performance Enhancement in FDM and SLA Additive Manufacturing: Materials, Mechanisms, Surface Engineering, and Hybrid Strategies—A Holistic Review by Raja Subramani, Ronit Rosario Leon, Rajeswari Nageswaren, Maher Ali Rusho, Karthik Venkitaraman Shankar

“…Further, the review highlights the growing use of finite element modeling, digital twins, and machine learning algorithms for predictive control of tribological performance at AM parts. …”
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Integrated analysis unraveling the immunologic and clinical prognostic values of synaptotagmin like 4 in pan-cancer by Yuehan Ren, Xiangbin Wu, Jinlei Li, Zhenhua Zhou, Shichang Ni, Jianhui Cai

“…This study systematically and comprehensively reveals the functions of SYTL4 and potential clinical diagnostic and therapeutic predictive values of SYTL4 in pan-cancer.…”
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Informational Approaches in Modelling Social and Economic Relations: Study on Migration and Access to Services in the European Union by Florentina-Loredana Dragomir-Constantin, Camelia Madalina Beldiman, Monica Laura Zlati

“…The applied methodology includes attribute distribution analysis, identification of hidden patterns through clustering algorithms (K-Means and Expectation-Maximisation) and training of classifiers using regression decision trees with linear leaf models (M5P) corresponding to interdependent data processing and integration modules, exploratory analysis module, machine learning and decision-making modules, oriented to support public policies through explainable scenarios and predictive-evaluative structures. …”
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MED-AGNeT: An attention-guided network of customized augmentation of samples based on conditional diffusion for textile defect detection by Jun Liu, Haolin Li, Hao Liu, Jiuzhen Liang

“…Ultimately, AGNet’s true positive rate (TPR), positive predictive value (PPV), and f-measure exceed those of the state-of-the-art (SOTA) algorithms by 1.88%, 0.05%, and 0.77%, respectively, and with a consistent model architecture, its parameter quantity is reduced by 56%.…”
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Data augmentation of time-series data in human movement biomechanics: A scoping review. by Christina Halmich, Lucas Höschler, Christoph Schranz, Christian Borgelt

“…Tailoring augmentation to data characteristics can enhance the performance and relevance of predictive models. However, understanding how different augmentation techniques impact data quality and downstream performance remains essential for developing better methods.…”
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Machine learning of whole-brain resting-state fMRI signatures for individualized grading of frontal gliomas by Yue Hu, Xin Cao, Hongyi Chen, Daoying Geng, Kun Lv

“…A total of 7134 features were extracted from the mean amplitude of low-frequency fluctuation (mALFF), mean fractional ALFF, mean percentage amplitude of fluctuation (mPerAF), mean regional homogeneity (mReHo) maps and resting-state functional connectivity (RSFC) matrix. Twelve predictive features were selected through Mann-Whitney U test, correlation analysis and least absolute shrinkage and selection operator method. …”
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Advanced strategies for the efficient optimization and control of industrial compressed air systems by D. Salomone-González

“…Real-time data transmission, powered by big data algorithms, enables continuous analysis to optimize the overall performance of the compressor plant. …”
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Linguistic Markers of Pain Communication on X (Formerly Twitter) in US States With High and Low Opioid Mortality: Machine Learning and Semantic Network Analysis by ShinYe Kim, Winson Fu Zun Yang, Zishan Jiwani, Emily Hamm, Shreya Singh

“…Six machine learning algorithms (random forest, k-nearest neighbor, decision tree, naive Bayes, logistic regression, and support vector machine) were applied to predict state-level opioid mortality risk based on linguistic features derived from Linguistic Inquiry and Word Count. …”
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