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Journal articles on the topic 'Accuracy function (AF)'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Zhao, Lina, Chengyu Liu, Shoushui Wei, Qin Shen, Fan Zhou, and Jianqing Li. "A New Entropy-Based Atrial Fibrillation Detection Method for Scanning Wearable ECG Recordings." Entropy 20, no. 12 (2018): 904. http://dx.doi.org/10.3390/e20120904.

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Entropy-based atrial fibrillation (AF) detectors have been applied for short-term electrocardiogram (ECG) analysis. However, existing methods suffer from several limitations. To enhance the performance of entropy-based AF detectors, we have developed a new entropy measure, named EntropyAF, which includes the following improvements: (1) use of a ranged function rather than the Chebyshev function to define vector distance, (2) use of a fuzzy function to determine vector similarity, (3) replacement of the probability estimation with density estimation for entropy calculation, (4) use of a flexibl

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Kalinina, E. S., O. Hanjykova, I. B. Begidova, and A. V. Zagatina. "Evaluation of left atrial function and left ventricular diastolic function during exercise stress echocardiography in patients with different types of atrial fibrillation." Ultrasound & Functional Diagnostics, no. 4 (November 1, 2024): 82–92. https://doi.org/10.24835/1607-0771-281.

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Objective: To determine cut-off values for left atrial function parameters at rest and during physical exercise that are specific to patients with atrial fibrillation (AF).Materials and Methods. The prospective single-center study with screening tests of 300 patients was carried out; 297 of them were included in the final analysis. The main cohort of patients was divided into three groups: patients with sinus rhythm without a history of AF, a group with paroxysmal/persistent AF, and patients with permanent AF. Stress echocardiography was performed in all patients according to the ABCDE protoco

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Nur, Farahaina Idris, Arfian Ismail Mohd, Saberi Mohamad Mohd, Kasim Shahreen, Zakaria Zalmiyah, and Sutikno Tole. "Breast cancer disease classification using fuzzy-ID3 algorithm based on association function." International Journal of Artificial Intelligence (IJ-AI) 11, no. 2 (2022): 448–61. https://doi.org/10.11591/ijai.v11.i2.pp448-461.

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Breast cancer is the second leading cause of mortality among female cancer patients worldwide. Early detection of breast cancer is considerd as one of the most effective ways to prevent the disease from spreading and enable human can make correct decision on the next process. Automatic diagnostic methods were frequently used to conduct breast cancer diagnoses in order to increase the accuracy and speed of detection. The fuzzy-ID3 algorithm with association function implementation (FID3-AF) is proposed as a classification technique for breast cancer detection. The FID3-AF algorithm is a hybridi

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Mandala, Satria, Annisa Rizki Pratiwi Wibowo, Adiwijaya, Suyanto, Mohd Soperi Mohd Zahid, and Ardian Rizal. "The Effects of Daubechies Wavelet Basis Function (DWBF) and Decomposition Level on the Performance of Artificial Intelligence-Based Atrial Fibrillation (AF) Detection Based on Electrocardiogram (ECG) Signals." Applied Sciences 13, no. 5 (2023): 3036. http://dx.doi.org/10.3390/app13053036.

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This research studies the effects of both Daubechies wavelet basis function (DWBF) and decomposition level (DL) on the performance of detecting atrial fibrillation (AF) based on electrocardiograms (ECGs). ECG signals (consisting of 23 AF data and 18 normal data from MIT-BIH) were decomposed at various levels using several types of DWBF to obtain four wavelet coefficient features (WCFs), namely, minimum (min), maximum (max), mean, and standard deviation (stdev). These features were then classified to detect the presence of AF using a support vector machine (SVM) classifier. Distribution of trai

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Idris, Nur Farahaina, Mohd Arfian Ismail, Mohd Saberi Mohamad, Shahreen Kasim, Zalmiyah Zakaria, and Tole Sutikno. "Breast cancer disease classification using fuzzy-ID3 algorithm based on association function." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 2 (2022): 448. http://dx.doi.org/10.11591/ijai.v11.i2.pp448-461.

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Breast cancer is the second leading cause of mortality among female cancer patients worldwide. Early detection of breast cancer is considerd as one of the most effective ways to prevent the disease from spreading and enable human can make correct decision on the next process. Automatic diagnostic methods were frequently used to conduct breast cancer diagnoses in order to increase the accuracy and speed of detection. The fuzzy-ID3 algorithm with association function implementation (FID3-AF) is proposed as a classification technique for breast cancer detection. The FID3-AF algorithm is a hybridi

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Dicorato, Marco Maria, Paolo Basile, Maria Ludovica Naccarati, et al. "Predicting New-Onset Atrial Fibrillation in Hypertrophic Cardiomyopathy: A Review." Journal of Clinical Medicine 14, no. 6 (2025): 2018. https://doi.org/10.3390/jcm14062018.

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Hypertrophic cardiomyopathy (HCM) is a condition characterized by left ventricular hypertrophy, with physiopathological remodeling that predisposes patients to atrial fibrillation (AF). The electrocardiogram is a basic diagnostic tool for evaluating heart electrical activity. Key electrocardiographic features that correlate with AF onset are P-wave duration, P-wave dispersion, and electromechanical delay in left atrium (LA). Clinical markers, including age, body mass index, New York Heart Association functional class, and heart failure symptoms, are also strong predictors of AF in HCM. Risk sc

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Kaufmann, Reinhard, Richard Rezar, Bernhard Strohmer, et al. "Left Atrial Ejection Fraction Assessed by Prior Cardiac CT Predicts Recurrence of Atrial Fibrillation after Pulmonary Vein Isolation." Journal of Clinical Medicine 10, no. 4 (2021): 752. http://dx.doi.org/10.3390/jcm10040752.

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Assuming that atrial fibrillation (AF) is associated with left atrial remodeling and dysfunction, we hypothesize that left atrial and left atrial appendage ejection fractions (LAEF and LAAEF) are useful and may be more sensitive outcome predictors of pulmonary vein isolation (PVI). Fifty patients who underwent PVI at our institution with available pre-interventional cardiac computed tomography (CT) for procedure planning were included in this retrospective study. The patients were separated into two groups by recurrence and non-recurrence of AF and subgroups of paroxysmal and persistent AF. Se

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Ahmed, Anwer, and Rabah Nori Farhan. "Autofocus Vision System Enhancement for UAVs via Autoencoder Generative Algorithm." Engineering, Technology & Applied Science Research 14, no. 6 (2024): 18867–72. https://doi.org/10.48084/etasr.8519.

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The Autofocus (AF) technology has become well-known over the past four decades. When attached to a camera, it eliminates the need to manually focus by giving the viewer a perfectly focused image in a matter of seconds. Modern AF systems are needed to achieve high-resolution images with optimal focus, and AF has become very important for many fields, possessing advantages such as high efficiency and autonomously interacting with Fenvironmental conditions. The proposed AF vision system for Unmanned Aerial Vehicle (UAV) navigation uses an autoencoder technique to extract important features from i

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Isakozawa, Shigeto, Taishi Fuse, Junpei Amano, and Norio Baba. "Spot auto-focusing and spot auto-stigmation methods with high-definition auto-correlation function in high-resolution TEM." Microscopy 67, no. 2 (2018): 75–88. http://dx.doi.org/10.1093/jmicro/dfy001.

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Abstract As alternatives to the diffractogram-based method in high-resolution transmission electron microscopy, a spot auto-focusing (AF) method and a spot auto-stigmation (AS) method are presented with a unique high-definition auto-correlation function (HD-ACF). The HD-ACF clearly resolves the ACF central peak region in small amorphous-thin-film images, reflecting the phase contrast transfer function. At a 300-k magnification for a 120-kV transmission electron microscope, the smallest areas used are 64 × 64 pixels (~3 nm2) for the AF and 256 × 256 pixels for the AS. A useful advantage of thes

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Inui, Tomohiko, Hiroki Kohno, Yohei Kawasaki, et al. "Use of a Smart Watch for Early Detection of Paroxysmal Atrial Fibrillation: Validation Study." JMIR Cardio 4, no. 1 (2020): e14857. http://dx.doi.org/10.2196/14857.

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Background Wearable devices with photoplethysmography (PPG) technology can be useful for detecting paroxysmal atrial fibrillation (AF), which often goes uncaptured despite being a leading cause of stroke. Objective This study is the first part of a 2-phase study that aimed at developing a method for immediate detection of paroxysmal AF using PPG-integrated wearable devices. In this study, the diagnostic performance of 2 major smart watches, Apple Watch Series 3 and Fitbit (FBT) Charge HR Wireless Activity Wristband, each equipped with a PPG sensor, was compared, and the pulse rate data outputt

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De Vito, Rafaella, Antonio Di Giovanni, Rosanna Reccia, et al. "Left atrial strain predicts postoperative atrial fibrillation in patient undergoing major orthopaedic surgery." International Cardiovascular Forum Journal 1, no. 4 (2015): 189. http://dx.doi.org/10.17987/icfj.v1i4.48.

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Background: Atrial fibrillation (AF) is an arrhythmia that often occurred in patients after orthopaedic surgery; the presence<br />of AF affects prognosis and prolongs hospitalization of these patients. Speckle tracking echocardiography (STE) is a<br />new echocardiographic technique that has recently shown to predict the occurrence of AF. The aim of this study was<br />to investigate left atrial (LA) function by STE in patients undergoing orthopaedic surgery and to correlate results with the<br />incidence of post-operative AF.<br />Methods: One-hundred and fifte

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Liu, Kaizheng, and Chengjie Liu. "Construction of A Prediction Model for Atrial Fibrillation in Patients with Dilated Cardiomyopathy and Heart Failure." Journal of Clinical and Nursing Research 8, no. 1 (2024): 228–32. http://dx.doi.org/10.26689/jcnr.v8i1.5871.

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Dilated cardiomyopathy (DCM) is a common myocardial disease characterized by enlargement of the heart cavity and decreased systolic function, often leading to heart failure (HF) and arrhythmia. The occurrence of atrial fibrillation (AF) is closely related to the progression and prognosis of the disease. In recent years, with the advancement of medical imaging and biomarkers, models for predicting the occurrence of AF in DCM patients have gradually become a research hotspot. This article aims to review the current situation of AF in DCM patients and explore the importance and possible methods o

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Pandey, Saroj Kumar, Gaurav Kumar, Shubham Shukla, Ankit Kumar, Kamred Udham Singh, and Shambhu Mahato. "Automatic Detection of Atrial Fibrillation from ECG Signal Using Hybrid Deep Learning Techniques." Journal of Sensors 2022 (September 22, 2022): 1–11. http://dx.doi.org/10.1155/2022/6732150.

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In cardiac rhythm disorders, atrial fibrillation (AF) is among the most deadly. So, ECG signals play a crucial role in preventing CVD by promptly detecting atrial fibrillation in a patient. Unfortunately, locating trustworthy automatic AF in clinical settings remains difficult. Today, deep learning is a potent tool for complex data analysis since it requires little pre and postprocessing. As a result, several machine learning and deep learning approaches have recently been applied to ECG data to diagnose AF automatically. This study analyses electrocardiogram (ECG) data from the PhysioNet/Comp

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Zhang, Tianyang, Hailong Chen, Yanyu Shi, et al. "Mitochondrial biological function and risk of atrial fibrillation and atrial flutter: A 2-sample Mendelian randomization study." Medicine 103, no. 27 (2024): e38631. http://dx.doi.org/10.1097/md.0000000000038631.

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Current research suggests that mitochondrial dysfunction can be a contributing factor in the development of cardiac arrhythmias. In pursuit of elucidating the causal link between the biological functions of mitochondria and the occurrence of atrial fibrillation/flutter, we conducted a 2-sample Mendelian randomization (MR) study. Mitochondrial proteins were selected for exposure in this study. To enhance the accuracy of our study, we selected data on AF/AFL from the FinnGen study and the UK Biobank for MR analysis, respectively. The inverse variance-weighted method was utilized as the primary a

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Aini, Hijratul, and Haviluddin Haviluddin. "Crude Palm Oil Prediction Based on Backpropagation Neural Network Approach." Knowledge Engineering and Data Science 2, no. 1 (2019): 1. http://dx.doi.org/10.17977/um018v2i12019p1-9.

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Crude palm oil (CPO) production at PT. Perkebunan Nusantara (PTPN) XIII from January 2015 to January 2018 have been treated. This paper aims to predict CPO production using intelligent algorithms called Backpropagation Neural Network (BPNN). The accuracy of prediction algorithms have been measured by mean square error (MSE). The experiment showed that the best hidden layer architecture (HLA) is 5-10-11-12-13-1 with learning function (LF) of trainlm, activation function (AF) of logsig and purelin, and learning rate (LR) of 0.5. This architecture has a good accuracy with MSE of 0.0643. The resul

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Guan, Shan, Kai Zhao, and Fuwang Wang. "Multiclass Motor Imagery Recognition of Single Joint in Upper Limb Based on NSGA- II OVO TWSVM." Computational Intelligence and Neuroscience 2018 (June 28, 2018): 1–11. http://dx.doi.org/10.1155/2018/6265108.

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In the study of the brain computer interface (BCI) system, electroencephalogram (EEG) signals induced by different movements of the same joint are hard to distinguish. This paper proposes a novel scheme that combined amplitude-frequency (AF) information of intrinsic mode function (IMF) with common spatial pattern (CSP), namely, AF-CSP to extract motor imagery (MI) features, and to improve classification performance, the second generation nondominated sorting evolutionary algorithm (NSGA-II) is used to tune hyperparameters for linear and nonlinear kernel one versus one twin support vector machi

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Kšela, Juš, Jan Kafol, Viktor Avbelj, and Jurij Matija Kališnik. "Predictive Value of Heart Rate Variability for Postoperative Atrial Fibrillation in Off-Pump Coronary Artery Bypass Patients." Medicina 61, no. 6 (2025): 984. https://doi.org/10.3390/medicina61060984.

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Background and Objectives: Postoperative atrial fibrillation (AF) is a frequent complication after coronary artery bypass grafting (CABG), and is particularly associated with poor outcomes. Heart rate variability (HRV), a non-invasive marker of autonomic function, has been proposed as a tool to predict AF risk, but its utility in off-pump CABG remains unclear. This study aimed to evaluate the predictive value of preoperative HRV parameters, including nonlinear metrics, for postoperative AF in patients undergoing off-pump CABG. Materials and Methods: We prospectively enrolled 67 patients underg

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Ribeiro, Andre, Bruce L. Golden, and Matthew L. Spangler. "21 President Oral Presentation Pick: Using Deep Neural Networks to determine birth weight data quality for genetic evaluations in beef cattle." Journal of Animal Science 98, Supplement_4 (2020): 7. http://dx.doi.org/10.1093/jas/skaa278.013.

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Abstract The objective of this work was to evaluate the use of deep neural networks (DNN) for classifying contemporary groups based on the method used to generate birth weight (BWT) phenotypes. Contemporary groups (CG; n = 120,000) ranging between 10 and 500 animals were simulated assuming 12 data collection and CG formation scenarios that could impact CG phenotypic variance, including weights recorded with a digital scale (REAL), hoof tape (TAPE), and those that were fabricated (FAB). The performance of 6 activation functions (AF; ReLu, sigmoid, exponential, ReLu6, Softmax, Softplus) were eva

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Osorio, Diego, Aikaterini Vraka, Aurelio Quesada, Fernando Hornero, Raúl Alcaraz, and José J. Rieta. "An Efficient Hybrid Methodology for Local Activation Waves Detection under Complex Fractionated Atrial Electrograms of Atrial Fibrillation." Sensors 22, no. 14 (2022): 5345. http://dx.doi.org/10.3390/s22145345.

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Local activation waves (LAWs) detection in complex fractionated atrial electrograms (CFAEs) during catheter ablation (CA) of atrial fibrillation (AF), the commonest cardiac arrhythmia, is a complicated task due to their extreme variability and heterogeneity in amplitude and morphology. There are few published works on reliable LAWs detectors, which are efficient for regular or low fractionated bipolar electrograms (EGMs) but lack satisfactory results when CFAEs are analyzed. The aim of the present work is the development of a novel optimized method for LAWs detection in CFAEs in order to assis

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He, Pan, Lu Shen, Benjamin Henson, and Yuriy V. Zakharov. "Coarse-to-Fine Localization of Underwater Acoustic Communication Receivers." Sensors 22, no. 18 (2022): 6968. http://dx.doi.org/10.3390/s22186968.

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For underwater acoustic (UWA) communication in sensor networks, the sensing information can only be interpreted meaningfully when the location of the sensor node is known. However, node localization is a challenging problem. Global Navigation Satellite Systems (GNSS) used in terrestrial applications do not work underwater. In this paper, we propose and investigate techniques based on matched field processing for localization of a single-antenna UWA communication receiver relative to one or more transmit antennas. Firstly, we demonstrate that a non-coherent ambiguity function (AF) allows signif

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Raza, Ali, Akhtar Ali, Sami Ullah, Yasir Nadeem Anjum, and Basit Rehman. "Optimizing skin cancer screening with convolutional neural networks in smart healthcare systems." PLOS ONE 20, no. 3 (2025): e0317181. https://doi.org/10.1371/journal.pone.0317181.

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Skin cancer is among the most prevalent types of malignancy all over the global and is strongly associated with the patient’s prognosis and the accuracy of the initial diagnosis. Clinical examination of skin lesions is a key aspect that is important in the assessment of skin disease but comes with some drawbacks mainly with interpretational aspects, time-consuming and healthare expenditure. Skin cancer if detected early and treated in time can be controlled and its deadly impacts arrested completely. Algorithms applied in convolutional neural network (CNN) could lead to an enhanced speed of id

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Patil, Rutuja Rajendra, and Sumit Kumar. "Predicting rice diseases across diverse agro-meteorological conditions using an artificial intelligence approach." PeerJ Computer Science 7 (September 3, 2021): e687. http://dx.doi.org/10.7717/peerj-cs.687.

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With the aid of a plant disease forecasting model, the emergence of plant diseases in a given region can be predicted ahead of time. This makes it easier to take proactive steps to reduce losses before they occur. The proposed model attempts to find an association between agrometeorological parameters and the occurrence of the four types of rice diseases. Rice is the staple food of people in Maharashtra. The four major diseases that occur on rice crops are focused on this paper (namely Rice Blast, False Smut, Bacterial Blight and Brown Spot) as these diseases spread rapidly and lead to economi

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Zhang, Heng, Faming Shao, Xiaohui He, et al. "ATS-YOLOv7: A Real-Time Multi-Scale Object Detection Method for UAV Aerial Images Based on Improved YOLOv7." Electronics 12, no. 23 (2023): 4886. http://dx.doi.org/10.3390/electronics12234886.

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The objects in UAV aerial images have multiple scales, dense distribution, and occlusion, posing considerable challenges for object detection. In order to address this problem, this paper proposes a real-time multi-scale object detection method based on an improved YOLOv7 model (ATS-YOLOv7) for UAV aerial images. First, this paper introduces a feature pyramid network, AF-FPN, which is composed of an adaptive attention module (AAM) and a feature enhancement module (FEM). AF-FPN reduces the loss of deep feature information due to the reduction of feature channels in the convolution process throu

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González-Díaz_Conti, Griselda, Javier Vázquez-Castillo, Omar Longoria-Gandara, et al. "Hardware-Based Activation Function-Core for Neural Network Implementations." Electronics 11, no. 1 (2021): 14. http://dx.doi.org/10.3390/electronics11010014.

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Today, embedded systems (ES) tend towards miniaturization and the carrying out of complex tasks in applications such as the Internet of Things, medical systems, telecommunications, among others. Currently, ES structures based on artificial intelligence using hardware neural networks (HNNs) are becoming more common. In the design of HNN, the activation function (AF) requires special attention due to its impact on the HNN performance. Therefore, implementing activation functions (AFs) with good performance, low power consumption, and reduced hardware resources is critical for HNNs. In light of t

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Hoyer, Ingo, Alexander Utz, André Lüdecke, et al. "Design of Hardware Accelerators for Optimized and Quantized Neural Networks to Detect Atrial Fibrillation in Patch ECG Device with RISC-V." Sensors 23, no. 5 (2023): 2703. http://dx.doi.org/10.3390/s23052703.

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Atrial Fibrillation (AF) is one of the most common heart arrhythmias. It is known to cause up to 15% of all strokes. In current times, modern detection systems for arrhythmias, such as single-use patch electrocardiogram (ECG) devices, have to be energy efficient, small, and affordable. In this work, specialized hardware accelerators were developed. First, an artificial neural network (NN) for the detection of AF was optimized. Special attention was paid to the minimum requirements for the inference on a RISC-V-based microcontroller. Hence, a 32-bit floating-point-based NN was analyzed. To redu

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Qin, Dong, Yuhao Wang, and Tianqing Zhou. "Performance Analysis of AF Relays with Maximal Ratio Combining in Nakagami-m Fading Environments." Wireless Communications and Mobile Computing 2019 (February 24, 2019): 1–11. http://dx.doi.org/10.1155/2019/1613690.

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This paper investigates the maximal ratio combining (MRC) performance of an amplify and forward (AF) relay system in Nakagami-m fading environments. The study considers a general scenario with distinct m fading parameters for the following three links, source to relay link, and source to destination link and relay to destination link. We derive new closed form expressions for the statistics of important performance metrics, including the moment generating function, outage probability, higher order moments of equivalent signal to noise ratio (SNR), ergodic capacity, and average symbol error pro

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Vijayakumar, K., Vinod J. Kadam, and Sudhir Kumar Sharma. "Breast cancer diagnosis using multiple activation deep neural network." Concurrent Engineering 29, no. 3 (2021): 275–84. http://dx.doi.org/10.1177/1063293x211025105.

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Deep Neural Network (DNN) stands for multilayered Neural Network (NN) that is capable of progressively learn the more abstract and composite representations of the raw features of the input data received, with no need for any feature engineering. They are advanced NNs having repetitious hidden layers between the initial input and the final layer. The working principle of such a standard deep classifier is based on a hierarchy formed by the composition of linear functions and a defined nonlinear Activation Function (AF). It remains uncertain (not clear) how the DNN classifier can function so we

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Sembhi, Ramanpreet, Tuneesh Ranota, Matthew Fox, et al. "Feasibility of Dynamic Inhaled Gas MRI-Based Measurements Using Acceleration Combined with the Stretched Exponential Model." Diagnostics 13, no. 3 (2023): 506. http://dx.doi.org/10.3390/diagnostics13030506.

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Dynamic inhaled gas (3He/129Xe/19F) MRI permits the acquisition of regional fractional-ventilation which is useful for detecting gas-trapping in lung-diseases such as lung fibrosis and COPD. Deninger’s approach used for analyzing the wash-out data can be substituted with the stretched-exponential-model (SEM) because signal-intensity is attenuated as a function of wash-out-breath in 19F lung imaging. Thirteen normal-rats were studied using 3He/129Xe and 19F MRI and the ventilation measurements were performed using two 3T clinical-scanners. Two Cartesian-sampling-schemes (Fast-Gradient-Recalled-

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Acevedo-Haro, Juan G., Waddah Mohamed, Prebashan Moodley, et al. "Sensitivity of diagnosis of spontaneous bacterial peritonitis is higher with the automated cell count method." World Journal of Hepatology 16, no. 11 (2024): 1265–81. http://dx.doi.org/10.4254/wjh.v16.i11.1265.

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BACKGROUND Spontaneous bacterial peritonitis (SBP) is one of the most important complications of patients with liver cirrhosis entailing high morbidity and mortality. Making an accurate early diagnosis of this infection is key in the outcome of these patients. The current definition of SBP is based on studies performed more than 40 years ago using a manual technique to count the number of polymorphs in ascitic fluid (AF). There is a lack of data comparing the traditional cell count method with a current automated cell counter. Moreover, current international guidelines do not mention the type

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Duangburong, Suttirak, Busaba Phruksaphanrat, and Sombat Muengtaweepongsa. "Comparison of ANN and ANFIS Models for AF Diagnosis Using RR Irregularities." Applied Sciences 13, no. 3 (2023): 1712. http://dx.doi.org/10.3390/app13031712.

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Classification of normal sinus rhythm (NSR), paroxysmal atrial fibrillation (PAF), and persistent atrial fibrillation (AF) is crucial in order to diagnose and effectively plan treatment for patients. Current classification models were primarily developed by electrocardiogram (ECG) signal databases, which may be unsuitable for local patients. Therefore, this research collected ECG signals from 60 local Thai patients (age 52.53 ± 23.92) to create a classification model. The coefficient of variance (CV), the median absolute deviation (MAD), and the root mean square of the successive differences (

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Escribano, Pilar, Juan Ródenas, Manuel García, et al. "Novel Entropy-Based Metrics for Long-Term Atrial Fibrillation Recurrence Prediction Following Surgical Ablation: Insights from Preoperative Electrocardiographic Analysis." Entropy 26, no. 1 (2023): 28. http://dx.doi.org/10.3390/e26010028.

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Atrial fibrillation (AF) is a prevalent cardiac arrhythmia often treated concomitantly with other cardiac interventions through the Cox–Maze procedure. This highly invasive intervention is still linked to a long-term recurrence rate of approximately 35% in permanent AF patients. The aim of this study is to preoperatively predict long-term AF recurrence post-surgery through the analysis of atrial activity (AA) organization from non-invasive electrocardiographic (ECG) recordings. A dataset comprising ECGs from 53 patients with permanent AF who had undergone Cox–Maze concomitant surgery was analy

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Juan, Ródenas García. "Novel Entropy-Based Metrics for Long-Term Atrial Fibrillation Recurrence Prediction Following Surgical Ablation: Insights from Preoperative Electrocardiographic Analysis." Entropy 2024, 26(1) (December 27, 2023): 28. https://doi.org/10.3390/e26010028.

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Atrial fibrillation (AF) is a prevalent cardiac arrhythmia often treated concomitantly with other cardiac interventions through the Cox–Maze procedure. This highly invasive intervention is still linked to a long-term recurrence rate of approximately 35% in permanent AF patients. The aim of this study is to preoperatively predict long-term AF recurrence post-surgery through the analysis of atrial activity (AA) organization from non-invasive electrocardiographic (ECG) recordings. A dataset comprising ECGs from 53 patients with permanent AF who had undergone Cox–Maze concomitant surge

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Park, Changsik, Euntack Han, Ikjae Kim, and Dongkyoo Shin. "A Study on the High Reliability Audio Target Frequency Generator for Electronics Industry." Electronics 12, no. 24 (2023): 4918. http://dx.doi.org/10.3390/electronics12244918.

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The frequency synthesizer performs a simple function of generating a desired frequency by manipulating a reference frequency signal, but stable and precise frequency generation is essential for reliable operation in mechanical equipment such as communication, control, surveillance, medical, and commercial fields. Frequency synthesis, which is commonly used in various contexts, has been used in analog and digital methods or hybrid methods. Especially in the field of communication, a precise frequency synthesizer is required for each frequency band, from very low-frequency AF (audio frequency) t

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Shrubkovskyi, O. V. "Review of data assimilation methods for refining the results of pollution dispersion models af-ter accidental releases." Mathematical machines and systems 1 (2025): 113–23. https://doi.org/10.34121/1028-9763-2025-1-113-123.

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Data assimilation (DA) is a crucial task in pollution forecasting, as it enables the integration of observations with models, improving the accuracy of pollutant dispersion predictions in the atmosphere, ocean, and land. This is particularly important for assessing the impact of accidental emissions and managing environmental risks. This paper reviews and compares four different data assimilation methods for pollution dispersion following accidental releases. The methods include ensemble-based approaches: the ensemble Kalman filter (EnKF), Ensemble Smoother (ES), and two novel approaches based

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Gizatulina, T. P., L. U. Martyanova, T. I. Petelina, et al. "The association of growth differentiation factor 15 (GDF-15) level with extent of left atrial fibrosis in patients with nonvalvular atrial fibrillation." Kardiologiia 60, no. 9 (2020): 22–29. http://dx.doi.org/10.18087/cardio.2020.9.n1144.

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Aim To study the relationship between the serum level of growth differentiation factor 15 (GDF-15) and clinical and functional characteristics and severity of left atrial (LA) fibrosis in patients with nonvalvular atrial fibrillation (AF).Material and methods The study included 87 patients with nonvalvular AF (62 patients with paroxysmal AF and 25 patients with persistent AF) aged 27 to 72 years (mean age, 56.9±9.2 years, 32 women). 85 % of these patients had arterial hypertension (AH), 33 % had AH and ischemic heart disease, and 12.6 % had isolated AF and were hospitalized for primary cathete

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Cao, Hui, Tingting Wang, Min Yuan, Jingsong Yu, and Fei Xu. "Growth and Modeling of Staphylococcus aureus in Flour Products under Isothermal and Nonisothermal Conditions." Journal of Food Protection 80, no. 3 (2017): 523–31. http://dx.doi.org/10.4315/0362-028x.jfp-16-248.

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ABSTRACT This study was conducted to investigate the growth of Staphylococcus aureus in traditional Chinese flour products under isothermal (10, 15, 20, 25, 30, and 37°C) and nonisothermal (10 to 20, 20 to 30, and 25 to 37°C) conditions. Then, models for the growth of S. aureus in flour products as a function of storage temperature, pH, and water activity (aw) were developed, and the goodness of fit of models was evaluated using the determination coefficient (R2), root mean square error (RMSE), bias factor (Bf), and accuracy factor (Af). Based on the above information, S. aureus growth in stea

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Jaman, Khairina, Syazwani Idrus, Abdul Malek Abdul Wahab, et al. "Influence of Molasses Residue on Treatment of Cow Manure in an Anaerobic Filter with Perforated Weed Membrane and a Conventional Reactor: Variations of Organic Loading and a Machine Learning Application." Membranes 13, no. 2 (2023): 159. http://dx.doi.org/10.3390/membranes13020159.

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This study highlighted the influence of molasses residue (MR) on the anaerobic treatment of cow manure (CM) at various organic loading and mixing ratios of these two substrates. Further investigation was conducted on a model-fitting comparison between a kinetic study and an artificial neural network (ANN) using biomethane potential (BMP) test data. A continuous stirred tank reactor (CSTR) and an anaerobic filter with a perforated membrane (AF) were fed with similar substrate at the organic loading rates of (OLR) 1 to OLR 7 g/L/day. Following the inhibition signs at OLR 7 (50:50 mixing ratio),

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Liu, Yunpeng, Haoran Wang, Kaiwen Song, et al. "CroReLU: Cross-Crossing Space-Based Visual Activation Function for Lung Cancer Pathology Image Recognition." Cancers 14, no. 21 (2022): 5181. http://dx.doi.org/10.3390/cancers14215181.

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Lung cancer is one of the most common malignant tumors in human beings. It is highly fatal, as its early symptoms are not obvious. In clinical medicine, physicians rely on the information provided by pathology tests as an important reference for the final diagnosis of many diseases. Therefore, pathology diagnosis is known as the gold standard for disease diagnosis. However, the complexity of the information contained in pathology images and the increase in the number of patients far outpace the number of pathologists, especially for the treatment of lung cancer in less developed countries. To

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Khan, Muhammad Kamraz, Muhammad Sajjad Ali Khan, Kamran, and Ioan-Lucian Popa. "Intuitionistic Hesitant Fuzzy Rough Aggregation Operator-Based EDAS Method and Its Application to Multi-Criteria Decision-Making Problems." Axioms 14, no. 1 (2024): 21. https://doi.org/10.3390/axioms14010021.

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The fundamental notions of the intuitionistic hesitant fuzzy set (IHFS) and rough set (RS) are general mathematical tools that may easily manage imprecise and uncertain information. The EDAS (Evaluation based on Distance from Average Solution) approach has an important role in decision-making (DM) problems, particularly in multi-attribute group decision-making (MAGDM) scenarios, where there are many conflicting criteria. This paper aims to introduce the IHFR-EDAS approach, which utilizes the IHF rough averaging aggregation operator. The aggregation operator is crucial for aggregating intuition

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Dang, Cuong, Leonardo J. Rodr´ıguez, Nghi H. Tran, Forrest Sheng Bao, and Shivakumar Sastry. "Secrecy Rate and Optimal Power Allocation of the Amplify-and-Forward Relay Wire-Tap System." Journal of Science and Technology: Issue on Information and Communications Technology 2, no. 1 (2016): 1. http://dx.doi.org/10.31130/jst.2016.19.

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In this research work, we investigate the secrecy rate and optimal power allocation schemes for a half-duplex (HD) wire-tap Rayleigh fading channel in which a source wishes to communicate securely to a destination in the presence of an eavesdropper and under the aid of an amplify-and-forward (AF) relay. The secrecy capacity and the corresponding optimal power allocation schemes are examined under both individual and joint power constraints. Due to the absence of an insightful expression of the secrecy rate for a given power allocation scheme, determining such secrecy capacity is challenging. T

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PANAGOU, EFSTATHIOS Z., and GEORGE-JOHN E. NYCHAS. "Dynamic Modeling of Listeria monocytogenes Growth in Pasteurized Vanilla Cream after Postprocessing Contamination." Journal of Food Protection 71, no. 9 (2008): 1828–34. http://dx.doi.org/10.4315/0362-028x-71.9.1828.

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A product-specific model was developed and validated under dynamic temperature conditions for predicting the growth of Listeria monocytogenes in pasteurized vanilla cream, a traditional milk-based product. Model performance was also compared with Growth Predictor and Sym'Previus predictive microbiology software packages. Commercially prepared vanilla cream samples were artificially inoculated with a five-strain cocktail of L. monocytogenes, with an initial concentration of 102 CFU g−1, and stored at 3, 5, 10, and 15°C for 36 days. The growth kinetic parameters at each temperature were determin

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Poletaev, N. I. "Correlation method for measuring the combustion time of micro-sized metal particles in a dust flame." Physics of Aerodisperse Systems, no. 62 (December 25, 2024): 72–85. https://doi.org/10.18524/0367-1631.2024.62.318556.

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The paper considers the possibilities of experimental determination of the combustion time of metal particles using the correlation analysis of flame luminosity. Experiments were carried out for dust flames of micro-sized spherical particles (d10< 5 µm) of Fe, Zr and Al in an axisymmetric laminar diffusion dust flame. The number density of particles in the gas suspension (in nitrogen) was about 1012 m-3. The width of the combustion zone at temperature T = (2000÷3000) K in the flame was 1÷2 mm. Under these conditions, the combustion zone is optically thin. This ensures the additive contribut

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Kumar, Arvind, and Sartaj Singh Sodhi. "Neural network with NewSigmoid activation function." Journal of Intelligent & Fuzzy Systems, January 17, 2022, 1–15. http://dx.doi.org/10.3233/jifs-212333.

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We increase the power of the Artificial Neural Networks with the help of the Activation Function (AF). The tansig and logsig are widely used AF. But there is still requires some improvement in the AF. So, in this paper, we have proposed a NewSigmoid AF in the neural network. NewSigmoid is also as powerful as tansig and logsig. In multiple cases, the NewSigmoid function gives a better or equivalent performance as compared with both these AF. Like these AF, NewSigmoid is also a smooth S-shape, bounded, continuously differentiable, and zero-centered function. Therefore the NewSigmoid is also suit

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Akanksha, Singh, and Ahmad Bhat Shahid. "A novel score and accuracy function for neutrosophic sets and their real-world applications to multi-criteria decision-making process." March 21, 2021. https://doi.org/10.5281/zenodo.4625705.

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  The purpose of this work is to understand the ranking order of the neutrosophic sets, where the uncertain or ambiguous information/data is stored in the terms of three independent variables i.e., degree of truthfulness, degree of indeterminacy, and degree of falseness. There exist many ranking tools in decision-making (DM) like score function (SF) and accuracy function (AF) that help to rank the single-valued neutrosophic set (SVNS) and the interval-valued neutrosophic set (IVNS) to make a better choice among all the available alternatives. An intensive study about all the existing scor

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Mc, Kellar Emil Arthur. "Enhancing GNSS Accuracy Through the Actualization Function: A Complete Framework for Signal Correction, Filtering, and Environmental Compensation." April 10, 2025. https://doi.org/10.5281/zenodo.15189742.

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This paper presents the finalized GNSS correction model using the Actualization Function (AF), offering a comprehensive framework for improving satellite signal accuracy across dynamic environmental conditions. Expanding upon the foundational AF theory, this version integrates mathematical derivations, real-world constraints, filtering optimization, and field-level adaptation strategies. Designed for immediate implementation, the model allows researchers and engineers to improve timing precision and signal stability in GPS and other GNSS systems. It complements and extends the initial GNSS AF

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Kassar, Ahmad, Nadia Chamoun, Yaacoub Chahine, and Nazem Akoum. "Abstract 4143300: Atrial Function Improves Stroke Prediction in Patients with Atrial Fibrillation." Circulation 150, Suppl_1 (2024). http://dx.doi.org/10.1161/circ.150.suppl_1.4143300.

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Background: Stroke contributes to significant morbidity and mortality in atrial fibrillation (AF). Clinical risk scores lack predictive accuracy and mechanistic links to thrombo-embolization. Hypothesis Adding MRI-derived parameters to stroke risk calculators improves their predictive accuracy. Aims Demonstrate an association between left atrial emptying fraction (LAEF), long axis strain (LAS) and stroke in AF patients. Incorporating the above-mentioned LARF and LAS into the CHA 2 DVASc score improves its predictive accuracy. Methods: AF patients undergoing cardiac MRI were identified from the

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Rotschild, Ofer, Brian Le, Elif Gokcal, et al. "Abstract WP57: The Effects of Atrial Fibrillation Burden on Cognitive Function and Brain Structure." Stroke 56, Suppl_1 (2025). https://doi.org/10.1161/str.56.suppl_1.wp57.

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Introduction: Atrial fibrillation (AF) has been linked to cognitive impairment and dementia (CID) even among patients without clinically obvious stroke, but the mechanism is not established. In this study, we aim to clarify the relationship between the burden of AF and CID in a cohort of AF patients without any history of stroke. We hypothesized that a higher AF burden would be associated with CID, possibly through more pronounced cerebral hypoperfusion and covert infarcts. Methods: We included 843 patients with AF and without a history of clinical stroke or neurodegenerative disease, who unde

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Zhong, Chuyu, Kun Liao, Tianxiang Dai, et al. "Graphene/silicon heterojunction for reconfigurable phase-relevant activation function in coherent optical neural networks." Nature Communications 14, no. 1 (2023). http://dx.doi.org/10.1038/s41467-023-42116-6.

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AbstractOptical neural networks (ONNs) herald a new era in information and communication technologies and have implemented various intelligent applications. In an ONN, the activation function (AF) is a crucial component determining the network performances and on-chip AF devices are still in development. Here, we first demonstrate on-chip reconfigurable AF devices with phase activation fulfilled by dual-functional graphene/silicon (Gra/Si) heterojunctions. With optical modulation and detection in one device, time delays are shorter, energy consumption is lower, reconfigurability is higher and

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Mandeş, Leonard, Monica Roşca, Daniela Ciupercă, et al. "Electrocardiographic and Echocardiographic Predictors of Atrial Fibrillation in Patients With Hypertrophic Cardiomyopathy." Frontiers in Cardiovascular Medicine 9 (May 27, 2022). http://dx.doi.org/10.3389/fcvm.2022.905128.

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BackgroundPatients with hypertrophic cardiomyopathy (HCM) have an increased prevalence of atrial fibrillation (AF) compared to the general population, and left atrium (LA) remodeling is strongly correlated with the risk of AF. This prospective, monocentric study aimed to assess the role of LA electrocardiographic and echocardiographic (structural and functional) parameters in predicting the risk for incident AF in patients with HCM.Methods and ResultsThe study population consisted of 126 HCM patients in sinus rhythm (52.6 ± 16.2 years, 54 men), 118 of them without documented AF. During a media

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Biryukova, T. K. "Signal processing algorithm for neural networks with integrodifferential splines as an activation function and its particular case of image classification." Highly available systems, 2021. http://dx.doi.org/10.18127/j20729472-202102-02.

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Classic neural networks suppose trainable parameters to include just weights of neurons. This paper proposes parabolic integrodifferential splines (ID-splines), developed by author, as a new kind of activation function (AF) for neural networks, where ID-splines coefficients are also trainable parameters. Parameters of ID-spline AF together with weights of neurons are vary during the training in order to minimize the loss function thus reducing the training time and increasing the operation speed of the neural network. The newly developed algorithm enables software implementation of the ID-spli

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