Comparison of Raman spectroscopy with mass spectrometry for sequence typing of Acinetobacter baumannii strains: a single-center study by Suling Liu, Ni Zhang, Jiawei Tang, Chong Chen, Weisha Wang, Jingfang Zhou, Long Ye, Xiaoli Chen, ZhengKang Li, Liang Wang

“…This study developed a novel approach, combining surface-enhanced Raman spectroscopy (SERS) with machine-learning (ML) algorithms, to construct predictive models for A. baumannii sequence typing based on SERS spectra. …”
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Efficient Explainable Models for Alzheimer’s Disease Classification with Feature Selection and Data Balancing Approach Using Ensemble Learning by Yogita Dubey, Aditya Bhongade, Prachi Palsodkar, Punit Fulzele

“…Explainable AI tools, such as SHAP, LIME, ALE, and ELI5 are integrated to provide transparency into the model’s decision-making process, highlighting key features influencing the classification and allowing clinicians to understand and trust the key features driving the predictions. <b>Results:</b> This approach results in a robust, interpretable, and clinically relevant framework for Alzheimer’s disease diagnosis. …”
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An Evaluation of Mine Water Inrush Based on Data Expansion and Machine Learning by Ye Zhang, Shoufeng Tang

“…Additionally, it performs well in the coal mine floor water inrush dataset, increasing the water inrush prediction algorithm’s accuracy.…”
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Single-cell transcriptomics and machine learning unveil ferroptosis features in tumor-associated macrophages: Prognostic model and therapeutic strategies for lung adenocarcinoma by Ting Ji, Ting Ji, Juanli Jiang, Juanli Jiang, Xin Wang, Xin Wang, Kai Yang, Kai Yang, Shaojin Wang, Shaojin Wang, Bin Pan, Bin Pan

“…The functional roles of key genes were validated through immune infiltration analysis, drug sensitivity prediction, and Western blot analysis.ResultsSingle-cell analysis revealed that macrophages in LUAD lead intercellular communication through the MIF (CD74+CXCR4) ligand-receptor interaction, with ferroptosis-related genes (FRGs) highly expressed in macrophages. 73 macrophage FRGs were identified, and through multi-algorithm cross-validation, HLF, HPCAL1, and NUPR1 were determined as core genes. …”
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The complexity of learning (pseudo)random dynamics of black holes and other chaotic systems by Lisa Yang, Netta Engelhardt

“…In this work, we prove that such bounded quantum algorithms cannot accurately predict (pseudo)random unitary dynamics, even if they are given access to an arbitrary set of polynomially complex observables under this time evolution; this shows that “learning” a (pseudo)random unitary is computationally hard. …”
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Machine learning–guided single-cell multiomics uncovers GDF15-driven immunosuppressive niches in NSCLC: A translational framework for overcoming anti-PD-1 resistance by Xianfei Zhang, Zhengxin Yin, Xueyu Chen, Nengchong Zhang, Shengjia Yu, Congcong Zhu, Lianggang Zhu, Liulan Shao, Bin Li, Runsen Jin, Hecheng Li

“…Comparative evaluation of 22 survival algorithms across four NSCLC cohorts (n=156) led to the development of an Accelerated Oblique Random Survival Forest model, which outperformed conventional Cox regression and deep learning methods in predictive accuracy (training C-index=0.864; test C-index=0.748). …”
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Disease activity and treatment response in early rheumatoid arthritis: an exploratory metabolomic profiling in the NORD-STAR cohort by Tahzeeb Fatima, Yuan Zhang, Georgios K. Vasileiadis, Araz Rawshani, Ronald van Vollenhoven, Jon Lampa, Bjorn Gudbjornsson, Espen A. Haavardsholm, Dan Nordström, Gerdur Gröndal, Kim Hørslev-Petersen, Kristina Lend, Marte S. Heiberg, Merete Lund Hetland, Michael Nurmohamed, Mikkel Østergaard, Till Uhlig, Tuulikki Sokka-Isler, Anna Rudin, Cristina Maglio

“…Abstract Background The variability in treatment response in people with rheumatoid arthritis (RA) warrants the prediction of patients at high risk of treatment failure. …”
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The role of neurovisualization in monitoring stroke risk among athletes: a review by Bauyrzhan Omarov, Akbayan Aliyeva

“…Results: the main results indicated that imaging biomarkers such as microbleeds, perfusion deficits, and white matter disruptions could be effectively detected and interpreted using artificial intelligence models. wearable data integrated with neuroimaging further enhanced the precision of predictive assessments. Discussion: the findings were consistent with previous studies that supported the use of multimodal imaging and computational tools in stroke risk evaluation. however, data heterogeneity and algorithmic transparency were identified as persistent challenges across the reviewed literature. …”
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Feature selection‐based android malware adversarial sample generation and detection method by Xiangjun Li, Ke Kong, Su Xu, Pengtao Qin, Daojing He

“…Prediction results obtained by the two classification algorithms are integrated based on certain rules. …”
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Integrating particle swarm optimization with backtracking search optimization feature extraction with two-dimensional convolutional neural network and attention-based stacked bidir... by Jyotirmayee Rautaray, Sangram Panigrahi, Ajit Kumar Nayak

“…The evaluation metrics include ROUGE score, BLEU score, cohesion, sensitivity, positive predictive value, readability, and scenarios of best, worst, and average case performance to ensure coherence, non-redundancy, and grammatical correctness. …”
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Development of an artificial intelligence system for the forecasting of infectious diseases by A. A. Kuzin, R. I. Glushakov, S. A. Parfenov, K. V. Sapozhnikov, A. A. Lazarev

“…Here, we provided an overview of artificial intelligence (AI) approaches for developing a system for prediction of infectious diseases and designed a respective step-by-step protocol.Materials and Methods. …”
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Survey on explainable knowledge graph reasoning methods by Yi XIA, Mingjng LAN, Xiaohui CHEN, Junyong LUO, Gang ZHOU, Peng HE

“…In recent years, deep learning models have achieved remarkable progress in the prediction and classification tasks of artificial intelligence systems.However, most of the current deep learning models are black box, which means it is not conducive to human cognitive reasoning process.Meanwhile, with the continuous breakthroughs of artificial intelligence in the researches and applications, high-performance complex algorithms, models and systems generally lack the transparency and interpretability of decision making.This makes it difficult to apply the technologies in a wide range of fields requiring strict interpretability, such as national defense, medical care and cyber security.Therefore, the interpretability of artificial intelligence should be integrated into these algorithms and systems in the process of knowledge reasoning.By means of carrying out explicit explainable intelligence reasoning based on discrete symbolic representation and combining technologies in different fields, a behavior explanation mechanism can be formed which is an important way for artificial intelligence to realize data perception to intelligence perception.A comprehensive review of explainable knowledge graph reasoning was given.The concepts of explainable artificial intelligence and knowledge reasoning were introduced briefly.The latest research progress of explainable knowledge graph reasoning methods based on the three paradigms of artificial intelligence was introduced.Specifically, the ideas and improvement process of the algorithms in different scenarios of explainable knowledge graph reasoning were explained in detail.Moreover, the future research direction and the prospect of explainable knowledge graph reasoning were discussed.…”
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Enhanced Performance of New Scaling-Free CORDIC for Memory-Based Fast Fourier Transform Architecture by C. Paramasivam, Sandeep Singh Chauhan, Veerpratap Meena, A. Sreejagathi, B. A. V. N. Hasini, K. L. K. Kishore, T. V. N. G. Vamsikrishna, M. Durga Ananta Sai, Abdessamad Didi

“…The angle of convergence (AOC) of the algorithm is 57.1&#x00B0;, and it is extended to 180&#x00B0; using the pre-rotation operation and optimized shift value prediction technique. …”
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MSASGCN :  Multi-Head Self-Attention Spatiotemporal Graph Convolutional Network for Traffic Flow Forecasting by Yang Cao, Detian Liu, Qizheng Yin, Fei Xue, Hengliang Tang

“…Experiments on two real datasets verify the stability of our proposed model, obtaining a better prediction performance than the baseline algorithms. …”
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An Adaptive Motion Estimation Scheme for Video Coding by Pengyu Liu, Yuan Gao, Kebin Jia

“…Then, design a MV distribution prediction method, including prediction of the size of MV and the direction of MV. …”
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Automatic evaluation and optimization of map registration based on feature overlap area ratio by Jiqiu Deng, Xiaoyan Chen, Dong Zhang

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An Efficient Method for Diagnosing Brain Tumors Based on MRI Images Using Deep Convolutional Neural Networks by Thanh Han-Trong, Hinh Nguyen Van, Huong Nguyen Thi Thanh, Vu Tran Anh, Dung Nguyen Tuan, Luu Vu Dang

“…In the next step, Deep Convolutional Neural Networks are used and then we propose to apply ADAS optimization function to build predictive models based on that normalized dataset. …”
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Revolutionizing Drug Design with Artificial Intelligence: A Comprehensive Review of Techniques, Applications, and Case Studies by Varun Pareek, Lakshya Tuteja, Lokendra Sharma, Susheel Kumar, Noopur Verma

“…Results: AI techniques such as machine learning, deep learning, and reinforcement learning have been successfully used in virtual screening, de novo drug design, and prediction of ADME properties. Virtual screening involves the use of AI algorithms to identify promising compounds for further testing, while de novo drug design involves the generation of novel compounds using AI techniques. …”
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Pathway analysis of GWAS provides new insights into genetic susceptibility to 3 inflammatory diseases. by Hariklia Eleftherohorinou, Victoria Wright, Clive Hoggart, Anna-Liisa Hartikainen, Marjo-Riitta Jarvelin, David Balding, Lachlan Coin, Michael Levin

“…Using a variable selection algorithm, we identified variants responsible for the pathway association and evaluated their use for disease prediction using a 10 fold cross-validation framework in order to calculate out-of-sample area under the Receiver Operating Curve (AUC). …”
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Rapid Identification of Nine Easily Confused Mineral Traditional Chinese Medicines Using Raman Spectroscopy Based on Support Vector Machine by Jing Ming, Long Chen, Yan Cao, Chi Yu, Bi-Sheng Huang, Ke-Li Chen

“…The identification model was subsequently built by the SVM algorithm. The 3-fold cross validation (3-CV) accuracy of the SVM model established based on extracting characteristic intensity data from spectra pretreated by first derivation was 98.61%, and the prediction accuracies of the training set and validation set were 100%. …”
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