A Trust Based Anomaly Detection Scheme Using a Hybrid Deep Learning Model for IoT Routing Attacks Mitigation by Khatereh Ahmadi, Reza Javidan

“…Several security solutions have been proposed to address routing attacks detection in RPL–based IoT, most of which are based on machine learning techniques, intrusion detection systems and trust-based approaches. …”
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Predicting lymphovascular space invasion in early-stage cervical squamous cell carcinoma using heart rate variability by Junlong Fang, Ming Liu, Zhijing Song, Yifang Zhang, Bo Shi, Jian Liu, Sai Zhang

“…However, current LVSI assessment of ECSCC mainly relies on the invasive method of pathological biopsy, which needs to be further improved in terms of convenience. …”
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Transformer-Based Decomposition of Electrodermal Activity for Real-World Mental Health Applications by Charalampos Tsirmpas, Stasinos Konstantopoulos, Dimitris Andrikopoulos, Konstantina Kyriakouli, Panagiotis Fatouros

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Fault Diagnosis for Rotating Machinery Based on Convolutional Neural Network and Empirical Mode Decomposition by Yuan Xie, Tao Zhang

“…Classic fault diagnosis methods are mainly based on traditional signal features such as mean value, standard derivation, and kurtosis. …”
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Deep Learning-Based Fault Diagnosis via Multisensor-Aware Data for Incipient Inter-Turn Short Circuits (ITSC) in Wind Turbine Generators by Qinglong Wang, Shihao Cui, Entuo Li, Jianhua Du, Na Li, Jie Sun

“…Although significant progress has been made in condition monitoring, the current methods still fall short of the robustness required for early fault diagnosis in complex operational settings. …”
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Outdoor and indoor ozone level: A potential impact on human health by Valuntaitė Vaida, Girgždienė Raselė

“…The aim of this study was to assess the ozone level in ambient air and working premises in terms of its possible influence on human health. Methods. The study was based on the results obtained in Lithuanian conditions. …”
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Blockchain-Enabled VANET for Smart Solid Waste Management by Muhammad Saad, Maaz Bin Ahmad, Muhammad Asif, Muhammad Khalid Khan, Toqeer Mahmood, Muhammad Tariq Mahmood

“…Lastly, blockchain technology is applied in the proposed solution to make machine-to-machine (M2M) communication more secure, reliable and trustworthy across IoT devices. …”
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Emerging trends in Alzheimer's disease diagnosis and prediction using artificial intelligence: A bibliometric analysis of the top cited 100 articles by Guangheng Zhang, Weijie Zhao, Shimeng Lv, Ziyue Wang, Yunhao Yi, Haoteng Ma, Yitong Lu, Wei Yan, Jing Teng

“…Background AD is a significant public health challenge, and AI technologies, including deep learning and machine learning, offer the potential to dramatically improve diagnostic and predictive accuracy. …”
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Approach to prototyping a technical device for fire detection based on the principles of artificial intelligence by Andreev A. V., Doronin A. S., Mirkhasilova Z. K., Norov B. Kh., Abduganiev A. S.

“…The aim of the study is to justify the developed prototype of a technical device that uses machine learning methods for early detection of fires in order to minimise damage and save human lives. …”
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Vulnerability-Based Economic Loss Rate Assessment of a Frame Structure Under Stochastic Sequence Ground Motions by Zheng Zhang, Yunmu Jiang, Zixin Liu

“…While effective at capturing nonlinear correlations, these methods are typically black box in nature, data-dependent, and difficult to generalize across tectonic settings. …”
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Public Medical Appeals and Government Online Responses: Big Data Analysis Based on Chinese Digital Governance Platforms by Hebin Li, Zhihan Liu, Ziyan Zhang, Lu Ping, Wenxin Gu, Yuan Yao

“…ObjectiveThis study aims to investigate the thematic structure, emotional tone, and underlying logic of governmental responses related to public medical appeals in China. MethodsWe collected messages posted on the “Message Board for Leaders” hosted by People’s Daily Online between January 2022 and November 2023 to identify valid medical appeals for analysis. (1) Key themes of public appeals were identified using the term frequency-inverse document frequency model for feature word extraction, followed by hierarchical cluster analysis. (2) Sentiment classification was conducted using supervised machine learning, with additional validation through sentiment scores derived from a lexicon-based approach. (3) A binary logistic regression model was employed to examine the influence of textual, transactional, and macro-environmental factors on the likelihood of receiving a government response. …”
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The hidden route: an exploratory study on autonomic influences in early phases of information processing by Elisa Cainelli, Stefano Vicentin, Giulia Stramucci, Sara Guglielmi, Maria Devita, Luca Vedovelli, Patrizia Bisiacchi

“…In the regression, LF, EDR, and smoke resulted in good predictors of the S2/S1 ratio (LF, Beta=-0.516, p < 0.001; EDR, Beta=-0.405, p = 0.002, smoke, Beta=-0.453, p < 0.001). Applying a machine learning approach showed that the LF HRV component was significantly influenced by frontocentral spectral EEG activity in theta and gamma frequencies. …”
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Development of metastasis and survival prediction model of luminal and non-luminal breast cancer with weakly supervised learning based on pathomics by Hui Liu, Linlin Ying, Xing Song, Xueping Xiang, Shumei Wei

“…Subsequently, we combined patch-level predictions, probability histograms, and Term Frequency-Inverse Document Frequency (TF-IDF) features to create comprehensive participants representations. …”
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Simulations of Microwave Land Surface Emissivity Using FengYun-3D Microwave Radiation Imager Data: A Case in the Tibetan Plateau by Yonghong Liu, Fuzhong Weng, Fei Tang, Rui Li, Yongming Xu, Yang Han, Jun Yang, Qingyang Liu

“…These results indicate that machine learning is likely to be an effective method for future all-sky simulation of MLSE.…”
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Robust Cross-Validation of Predictive Models Used in Credit Default Risk by Jose Vicente Alonso, Lorenzo Escot

“…Model validation is a challenging Machine Learning task, usually more difficult for consumer credit default models because of the availability of small datasets, the modeling of low-frequency events (imbalanced data), and the bias in the explanatory variables induced by the train/test sets split of the validation techniques (covariate shift). …”
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Predictors of Successful Testicular Sperm Extraction: A New Era for Men with Non-Obstructive Azoospermia by Aris Kaltsas, Sofoklis Stavros, Zisis Kratiras, Athanasios Zikopoulos, Nikolaos Machairiotis, Anastasios Potiris, Fotios Dimitriadis, Nikolaos Sofikitis, Michael Chrisofos, Athanasios Zachariou

“…The potential role of artificial intelligence and machine learning in enhancing predictive models was also explored. …”
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Bird Call Identification Using Ensemble Empirical Mode Decomposition by Jingxuan Liu, Hailan Chen

“…Finally, applying a support vector machine classification and recognition algorithm enables a comparative analysis of various calls. …”
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PredβTM: A Novel β-Transmembrane Region Prediction Algorithm. by Amrita Roy Choudhury, Marjana Novič

“…Using amino acid pair frequency information in known β-transmembrane protein sequences, we have trained a support vector machine classifier to predict β-transmembrane segments. …”
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Energy efficiency in ROS communication: a comparison across programming languages and workloads by Michel Albonico, Manuela Bechara Cannizza, Andreas Wortmann

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Explainable artificial intelligence to diagnose early Parkinson’s disease via voice analysis by Matthew Shen, Pouria Mortezaagha, Arya Rahgozar

“…Early diagnosis is essential for effective treatment, but traditional methods are often time-consuming and expensive. This study leverages Artificial Intelligence (AI) and Machine Learning (ML) techniques, using voice analysis to detect early signs of PD. …”
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