Survey on Backdoor Attacks on Deep Learning: Current Trends, Categorization, Applications, Research Challenges, and Future Prospects by Muhammad Abdullah Hanif, Nandish Chattopadhyay, Bassem Ouni, Muhammad Shafique

“…On the other hand, it opens up avenues for a new set of vulnerabilities, where an adversary (someone from a third party) can infect the model with malicious functionality that is triggered only with specific input patterns. Such attacks are usually referred to as Trojan or backdoor attacks and are very stealthy and hard to detect. …”
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Locomotion Joint Angle and Moment Estimation With Soft Wearable Sensors for Personalized Exosuit Control by Luying Feng, Lianghong Gui, Wenzhu Xu, Xiang Wang, Canjun Yang, Yaochu Jin, Wei Yang

“…Recent advancements in flexible sensing and machine learning have positioned soft sensors as promising alternatives to traditional methods for human posture detection. …”
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Identification of atrial fibrillation using heart rate variability: a meta-analysis by Ziwei Yin, Changxin Liu, Chenggong Xie, Zixing Nie, Jiaming Wei, Wen Zhang, Hao Liang

“…Recently, artificial intelligence (AI) algorithms have leveraged heart rate variability (HRV) patterns to enhance the accuracy of AF identification.MethodsWe conducted a systematic review of the literature by searching four major biomedical databases—PubMed, Web of Science, Embase, and Cochrane Library—spanning from their inception to December 13, 2024, following the PRISMA guidelines. …”
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Transcriptomic signatures of severe acute mountain sickness during rapid ascent to 4,300 m by Ruoting Yang, Aarti Gautam, Rasha Hammamieh, Robert C. Roach, Beth A. Beidleman

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Explaining drivers of housing prices with nonlinear hedonic regressions by Heng Wan, Pranab K. Roy Chowdhury, Jim Yoon, Parin Bhaduri, Vivek Srikrishnan, David Judi, Brent Daniel

“…In this study, we employ interpretable ML to analyze the patterns driving the housing market in Baltimore, Maryland, USA. …”
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Traffic flow modelling of vehicles on a six lane freeway: Comparative analysis of improved group method of data handling and artificial neural network model by Isaac Oyeyemi Olayode, Alessandro Severino, Frimpong Justice Alex, Elmira Jamei

“…The primary objective of this study was to evaluate the predictive accuracy and efficacy of both models in replicating complex traffic patterns and to provide insights into their suitability for real-time traffic flow applications. …”
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A century of Sudd wetland’s water storage dynamics using reconstructed and downscaled GRACE/GRACE-FO Data by Dinuka Kudagama, Joseph Awange, Jielong Wang, Ayalsew Zerihun, Leidy Luna

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New Forecasting Metrics Evaluated in Prophet, Random Forest, and Long Short-Term Memory Models for Load Forecasting by Prajowal Manandhar, Hasan Rafiq, Edwin Rodriguez-Ubinas, Themis Palpanas

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Identification of gene signatures and potential pharmaceutical candidates linked to COVID-19-related depression based on gene expression profiles by Shaojun Chen, Yiyuan Luo, Lihua Zhang

“…Finally, molecular dynamics (MD) simulations were conducted to investigate the binding interactions between molecules and the target proteins.ResultsWe uncovered 60 DEGs that overlapped between the two datasets but exhibited distinct patterns of expression in each dataset. Subsequent machine learning analyses revealed EMILIN3, OPA3, and TFCP2 as potential shared hub genes underlying both diseases. …”
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The relationship between mixed exposure to blood metal and serum neurofilament light chain levels in the general U.S. population: an unsupervised clustering approach by Jiyu Nie, Jiyu Nie, Lin Wen, Zhentian Lai, Zhentian Lai, Chuhang Lin, Chuhang Lin, Haiyin Li, Haiyin Li, Jing Zhang, Jing Zhang, Shen Xie, Shen Xie, Xiaosong Ben, Chunxia Jing, Chunxia Jing

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Heart disease prediction using autoencoder and DenseNet architecture by Norah Saleh Alghamdi, Mohammed Zakariah, Achyut Shankar, Wattana Viriyasitavat

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Color QR Codes for Smartphone-Based Analysis of Free Chlorine in Drinking Water by María González-Gómez, Ismael Benito-Altamirano, Hanna Lizarzaburu-Aguilar, David Martínez-Carpena, Joan Daniel Prades, Cristian Fàbrega

“…QR Codes are powerful machine-readable patterns that are used worldwide to encode information (i.e., URLs or IDs), but their computer vision features allow QR Codes to act as carriers of other features for several applications. …”
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Identification of Exosome-Associated Biomarkers in Diabetic Foot Ulcers: A Bioinformatics Analysis and Experimental Validation by Tianbo Li, Lei Gao, Jiangning Wang

“…Finally, RT-qPCR validation in clinical DFU tissues (<i>n</i> = 5) recapitulated the bioinformatic expression patterns. <b>Conclusions:</b> We present five exosome-associated genes as novel DFU biomarkers with diagnostic potential and mechanistic links to immune modulation and vesicular transport. …”
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Identification of diagnostic genes for myocardial ischemia reperfusion injury associated with metabolic syndrome through the integration of bioinformatics analysis, molecular docki... by Shufang Liu, Shufang Liu, Yan Zhang, Yan Zhang, Yanan Zhao, Ping Wu, Ping Wu, Shouyuan Tian, Han-Qing Pang, Han-Qing Pang, Zhifang Wu, Zhifang Wu, Sijin Li, Sijin Li

“…Drug candidates were predicted via PPI networks, CTD, and molecular docking, followed by experimental evaluation of dexamethasone.ResultsFive hub genes—DAK, GTF3C5, KCNMB1, TRAF1, and ZNF692—were identified, with distinct expression patterns (DAK/GTF3C5 downregulated; KCNMB1/TRAF1/ZNF692 upregulated). …”
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Simulation of Net Ecosystem Carbon Flux in Rice Planting Area of Yangtze River Delta based on Multi-layer Perceptron Model by Wenyang XI, Jianjun HE, Zhilin WANG, Lifeng GUO, Yarong LI

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Metabolomics and nutrient intake reveal metabolite–nutrient interactions in metabolic syndrome: insights from the Korean Genome and Epidemiology Study by Minyeong Kim, Suyeon Lee, Junguk Hur, Dayeon Shin

“…Conclusions This comprehensive metabolomic analysis of the KoGES Ansan-Ansung cohort revealed distinct metabolic profiles and nutrient intake patterns associated with MetS, highlighting altered metabolite–nutrient relationships and disrupted metabolic pathways. …”
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A benchmark for computational analysis of animal behavior, using animal-borne tags by Benjamin Hoffman, Maddie Cusimano, Vittorio Baglione, Daniela Canestrari, Damien Chevallier, Dominic L. DeSantis, Lorène Jeantet, Monique A. Ladds, Takuya Maekawa, Vicente Mata-Silva, Víctor Moreno-González, Anthony M. Pagano, Eva Trapote, Outi Vainio, Antti Vehkaoja, Ken Yoda, Katherine Zacarian, Ari Friedlaender

“…This makes it difficult to, for example, identify patterns in what works well for machine learning-based analysis of bio-logger data. …”
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A mobile hybrid deep learning approach for classifying 3D-like representations of Amazonian lizards by Arthur Gonsales da Silva, Arthur Gonsales da Silva, Roger Pinho de Oliveira, Caio de Oliveira Bastos, Caio de Oliveira Bastos, Elena Almeida de Carvalho, Bruno Duarte Gomes

“…Additionally, we evaluated five classical ML models for classifying the extracted patterns: (a) Support Vector Machine (SVM); (b) GaussianNB (GNB); (c) AdaBoost (ADB); (d) K-Nearest Neighbors (KNN); and (e) Random Forest (RF). …”
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Exploring potential methylation markers for ovarian cancer from cervical scraping samples by Ju-Yin Lien, Lu Ann Hii, Po-Hsuan Su, Lin-Yu Chen, Kuo-Chang Wen, Hung-Cheng Lai, Yu-Chao Wang

“…Since aberrant DNA methylation patterns are linked to cancer progression, DNA methylation offers a promising avenue for early diagnosis. …”
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Decoding per- and polyfluoroalkyl substances (PFAS) in hepatocellular carcinoma: a multi-omics and computational toxicology approach by Yanggang Hong, Deqi Wang, Zeyu Liu, Yuxin Chen, Yi Wang, Jiajun Li

“…These targets were further validated via bulk RNA-seq, single-cell RNA-seq, and spatial transcriptomics, which revealed differential expression patterns across various cell types in the HCC tumor microenvironment. …”
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