Total Classification Accuracy Research Articles

Prediction of deoxynivalenol contamination in spring oats in Sweden using explainable artificial intelligence

Weather conditions and agronomical factors are known to affect Fusarium spp. growth and ultimately deoxynivalenol (DON) contamination in oat. This study aimed to develop predictive models for the contamination of spring oat at harvest with DON on a regional basis in Sweden using machine-learning algorithms. Three models were developed as regional risk-assessment tools for farmers, crop collectors, and food safety inspectors, respectively. Data included: weather data from different oat growing periods, agronomical data, site-specific data, and DON contamination data from the previous year. Results showed that: (1) RF models were able to predict DON contamination at harvest with a total classification accuracy of minimal 0.72; (2) good predictions could already be made in June; (3) rainfall, relative humidity, and wind speed in different oat growing stages, followed by crop variety and elevation were the most important features for predicting DON contamination in spring oats at harvest.

Dual-path lightweight convolutional neural network for automatic sorting of olive fruit based on cultivar and maturity

Identifying olive cultivars and maturity stages is crucial in the olive industry, as these traits significantly impact the nutritional and sensory properties of olive products and extracted oil. For this purpose, this study presents a novel automatic computer vision system that applies state-of-the-art deep learning technology to sort and classify two Iranian olive cultivars, Zard and Roghani, in five maturity stages, resulting in a total of ten distinct classes. The model was developed by evaluating multiple user-defined and standard structures. It was based on a dual-path lightweight convolutional neural network that uses both regular and dilated convolution operators. Dilated convolutions were used to extract more information and capture different properties by providing larger receptive fields. With a significantly lower number of trainable parameters than standard architectures, the lightweight nature of the model would enhance its potential for delivering fast responses in on-the-go applications. Four optimizers (RMSProp, SGD, Adam, and Nadam) were tested on the developed model to enhance its performance, and Nadam exhibited the greatest accuracy. The proposed model achieved a total classification accuracy of 95.79 % and a loss of 0.2214. The proposed model was completely accurate for some classes, and the classification metrics for all categories were high, ranging from 88 % to 100 % for precision, 83–100 % for recall, and 86–100 % for F1-score. The accuracy of classification within the Roghani cultivar classes stood at 98.28 %, while for the Zard cultivar classes, it achieved 97.76 %. The study found that the proposed model can be efficiently incorporated into an olive sorting system, facilitating the identification of olives with different cultivars and varying levels of maturity, thereby enhancing the production of post-harvest products and superior quality of oil.

Validating the use of clinical MSCT scans for cranial nonmetric sex estimation in a contemporary Indonesian population

There is renewed interest in Asia for the development of forensic anthropological standards, partly due to the need for population-specific models to maintain high classification accuracies. At present, there are no known studies utilising morphoscopic standards specific to the Indonesian population. Craniometric analyses can often be time-consuming; morphoscopic assessments are quicker and are also known to be reliable and accurate. One of the most utilised morphoscopic standards for the estimation of skeletal sex is that of Walker (2008). Its application across population groups demonstrated reduced accuracies outside of the United States; population-specific predictive models would thus serve to improve the identification process of unknown skeletal remains. Digital imaging also allows for the validation of standards on a contemporary population and is an appropriate proxy to physical skeletal material.The present study quantifies the applicability of the Walker standard to a contemporary Indonesian population. A total of 200 cranial MSCT scans from a hospital in Makassar were analysed using OsiriX®. Scoring was performed in accordance with the Walker standard. Five univariate and nine multivariate predictive models were derived using single trait and multi-trait combinations. The best performing univariate model included the glabella, with a total classification accuracy of 82.0% and a sex bias of 14.6%. Classification accuracy with all traits considered was at 95.2% for females and 82.8% for males with a sex bias of 12.5%. These results provide forensic practitioners in Indonesia with an appropriate morphoscopic sex estimation standard, strengthening their capabilities in the field and improving judicial outcomes.

A transfer learning approach for the classification of liver cancer

Abstract Problem The frequency of liver cancer is rising worldwide, and it is a common, deadly condition. For successful treatment and patient survival, early and precise diagnosis is essential. The automated classification of liver cancer using medical imaging data has shown potential outcome when employing machine and deep learning (DL) approaches. To train deep neural networks, it is still quite difficult to obtain a large and diverse dataset, especially in the medical field. Aim This article classifies liver tumors and identifies whether they are malignant, benign tumor, or normal liver. Methods This study mainly focuses on computed tomography scans from the Radiology Institute in Baghdad Medical City, Iraq, and provides a novel transfer learning (TL) approach for the categorization of liver cancer using medical images. Our findings show that the TL-based model performs better at classifying data, as in our method, high-level characteristics from liver images are extracted using pre-trained convolutional neural networks compared to conventional techniques and DL models that do not use TL. Results The proposed method using models of TL technology (VGG-16, ResNet-50, and MobileNetV2) successfully achieves high accuracy, sensitivity, and specificity in identifying liver cancer, making it an important tool for radiologists and other healthcare professionals. The experiment results show that the diagnostic accuracy in the VGG-16 model is up to 99%, ResNet-50 model 100%, and 99% total classification accuracy was attained with the MobileNetV2 model. Conclusion This proves the improvement of models when working on a small dataset. The use of new layers also showed an improvement in the performance of the classifiers, which accelerated the process.

Respiratory event index underestimates severity of sleep apnea compared to apnea-hypopnea index.

Polygraphy (PG) is often used to diagnose obstructive sleep apnea (OSA). However, it does not use electroencephalography, and therefore cannot estimate sleep time or score arousals and related hypopneas. Consequently, the PG-derived respiratory event index (REI) differs from the polysomnography (PSG)-derived apnea-hypopnea index (AHI). In this study, we comprehensively analyzed the differences between AHI and REI. Conventional AHI and REI were calculated based on total sleep time (TST) and total analyzed time (TAT), respectively, from two different PSG datasets (n = 1561). Moreover, TAT-based AHI (AHITAT) and TST-based REI (REITST) were calculated. These indices were compared keeping AHI as the gold standard. The REI, AHITAT, and REITST were significantly lower than AHI (p < 0.0001, p ≤ 0.002, and p ≤ 0.01, respectively). The total classification accuracy of OSA severity based on REI was 42.1% and 72.8% for two datasets. Based on AHITAT, the accuracies were 68.4% and 85.9%, and based on REITST, they were 65.9% and 88.5% compared to AHI. AHI was most correlated with REITST (r = 0.98 and r = 0.99 for the datasets) and least with REI (r = 0.92 and r = 0.97). Compared to AHI, REI had the largest mean absolute errors (13.9 and 6.7) and REITST the lowest (5.9 and 1.9). REI had the lowest sensitivities (42.1% and 72.8%) and specificities (80.7% and 90.9%) in both datasets. Based on these present results, REI underestimates AHI. Furthermore, these results indicate that arousal-related hypopneas are an important measure for accurately classifying OSA severity.

On the Use of Geometric Deep Learning for the Iterative Classification and Down-Selection of Analog Electric Circuits

Abstract Many complex engineering systems can be represented in a topological form, such as graphs. This paper utilizes a machine learning technique called Geometric Deep Learning (GDL) to aid designers with challenging, graph-centric design problems. The strategy presented here is to take the graph data and apply GDL to seek the best realizable performing solution effectively and efficiently with lower computational costs. This case study used here is the synthesis of analog electrical circuits that attempt to match a specific frequency response within a particular frequency range. Previous studies utilized an enumeration technique to generate 43,249 unique undirected graphs presenting valid potential circuits. Unfortunately, determining the sizing and performance of many circuits can be too expensive. To reduce computational costs with a quantified trade-off in accuracy, the fraction of the circuit graphs and their performance are used as input data to a classification-focused GDL model. Then, the GDL model can be used to predict the remainder cheaply, thus, aiding decision-makers in the search for the best graph solutions. The results discussed in this paper show that additional graph-based features are useful, favorable total set classification accuracy of 80% in using only 10% of the graphs, and iteratively built GDL models can further subdivide the graphs into targeted groups with medians significantly closer to the best and containing 88.2 of the top 100 best-performing graphs on average using 25% of the graphs.

Neighborhood multigranulation rough sets for cost-sensitive feature selection on hybrid data

Feature selection is a vital preprocessing step in real applications of data mining and machine learning. With the prevalence of high-dimensional hybrid data sets in real-world scenarios, along with the presence of test costs and misclassification costs, the need for effective feature selection methods has become more prominent. However, existing feature selection approaches mainly focus on cost-sensitive data from a single granularity perspective, and they are primarily applicable to single-typed data sets. To address these limitations, this paper presents a novel feature selection approach specifically designed for hybrid data, considering variable test costs and misclassification costs. The proposed method is based on neighborhood multigranulation rough sets (NMRS), which provides more comprehensive and multi-angle data analysis for cost-sensitive hybrid data. First, a novel multigranulation model is developed to effectively process cost-sensitive hybrid data. Building upon this model, a cost-based multi-criteria measure is proposed to evaluate the significance of features. This measure takes into account the comprehensive information of candidate features, including their power in algebraic view, information view, and associated costs. Furthermore, a heuristic feature selection algorithm based on NMRS is proposed to handle hybrid data with the test costs and misclassification costs. This algorithm leverages the benefits of the proposed multigranulation model and cost-based measure to identify the most discriminative features efficiently. Finally, the experimental results on twelve different datasets show that the proposed heuristic algorithm outperforms other compared algorithms, especially in total cost and classification accuracy.

DSRSS-Net: Improved-Resolution Snow Cover Mapping from FY-4A Satellite Images Using the Dual-Branch Super-Resolution Semantic Segmentation Network

The Qinghai–Tibet Plateau is one of the regions with the highest snow accumulation in China. Although the Fengyun-4A (FY4A) satellite is capable of monitoring snow-covered areas in real time and on a wide scale at high temporal resolution, its spatial resolution is low. In this study, the Qinghai–Tibet Plateau, which has a harsh climate with few meteorological stations, was selected as the study area. We propose a deep learning model called the Dual-Branch Super-Resolution Semantic Segmentation Network (DSRSS-Net), in which one branch focuses with super resolution to obtain high-resolution snow distributions and the other branch carries out semantic segmentation to achieve accurate snow recognition. An edge enhancement module and coordinated attention mechanism were introduced into the network to improve the classification performance and edge segmentation effect for cloud versus snow. Multi-task loss is also used for optimization, including feature affinity loss and edge loss, to obtain fine structural information and improve edge segmentation. The 1 km resolution image obtained by coupling bands 1, 2, and 3; the 2 km resolution image obtained by coupling bands 4, 5, and 6; and the 500 m resolution image for a single channel, band 2, were inputted into the model for training. The accuracy of this model was verified using ground-based meteorological station data. Snow classification accuracy, false detection rate, and total classification accuracy were compared with the MOD10A1 snow product. The results show that, compared with MOD10A1, the snow classification accuracy and the average total accuracy of DSRSS-Net improved by 4.45% and 5.1%, respectively. The proposed method effectively reduces the misidentification of clouds and snow, has higher classification accuracy, and effectively improves the spatial resolution of FY-4A satellite snow cover products.

Integrated Method for Classifying Medium Resolution Satellite Remotely Sensed Imagery into Land Use Map

There are several remotely sensed images of varied resolutions available. As a result, several classification techniques exist, which are roughly classified as pixel-based and object-based classification methods. Based on the foregoing, this study provided an integrated method of deriving land use from a coarse satellite image. Location coordinates of the land uses were acquired with a handheld Global Positioning System (GPS) instrument as primary data. The study classified the image quantitatively (pixel-based) into built-up, water, riparian, cultivated, and uncultivated land cover classes with no mixed pixels, and then qualitatively into educational, commercial, health, residential, and security land use classes that were conflicting due to spectral similarity. The total accuracy and kappa coefficient of the pixel-based land cover classification were 92.5% and 94% respectively. The results showed that residential land use occupied an area of 5500.01ha, followed by educational (2800.69ha); security (411.27ha); health (133.88ha); and commercial (109.01ha) respectively. The integrated method produces a crisp-appearance like the object-based image classification method. It eliminates the "salt and pepper" appearance that a traditional pixel-based classification would have. The output can be a vector or raster model depending on the purpose for which it is created.

Rapid Correction of Turbidity and CDOM Interference on Three-Dimensional Fluorescence Spectra of Live Algae Based on Deep Learning

In natural aquatic environments, the existence of colored dissolved organic matter (CDOM), suspended particles, and colloids can cause scattering and reflection of light and even emit fluorescence itself. Such interference negatively impacts algal fluorescence, further making it unreliable to measure the algal concentration using three-dimensional excitation–emission matrix (3D-EEM) fluorescence spectroscopy. In this study, we proposed a novel algal fluorescence anti-interference network (AFAI-Net) based on a convolutional neural network. The main procedure of this model can be divided into two parts: (1) to quickly determine if there is an interference of CDOM or turbidity in the detected algal samples; (2) to correct the interfered samples and output the fluorescent components of the algae. We trained the model using the 3D-EEMs of pure algal samples (non-interfered) and mixed samples of algae and CDOM or turbidity (interfered); as a result, the well-trained model achieved a total classification accuracy of 96.82%, and the RMSE of CDOM and turbidity removal fitting effects were 0.2274 and 0.3423, respectively. Compared with the non-negative weighted least squares (NNLS) regression analysis method, using the CNN model for CDOM correction resulted in 13.11%, 0.65%, and 5.69% reductions in the average deviation rate for PD, PG, and CM, respectively. Furthermore, the spectra corrected by the model predicted algal densities that were closer to the true algal densities. This study provides a new way to remove non-algal factors that affect algal fluorescence spectra in water bodies, which is beneficial to monitoring eutrophication and red tide in aquatic systems.

Energy-Efficient Audio Processing at the Edge for Biologging Applications

Biologging refers to the use of animal-borne recording devices to study wildlife behavior. In the case of audio recording, such devices generate large amounts of data over several months, and thus require some level of processing automation for the raw data collected. Academics have widely adopted offline deep-learning-classification algorithms to extract meaningful information from large datasets, mainly using time-frequency signal representations such as spectrograms. Because of the high deployment costs of animal-borne devices, the autonomy/weight ratio remains by far the fundamental concern. Basically, power consumption is addressed using onboard mass storage (no wireless transmission), yet the energy cost associated with data storage activity is far from negligible. In this paper, we evaluate various strategies to reduce the amount of stored data, making the fair assumption that audio will be categorized using a deep-learning classifier at some point of the process. This assumption opens up several scenarios, from straightforward raw audio storage paired with further offline classification on one side, to a fully embedded AI engine on the other side, with embedded audio compression or feature extraction in between. This paper investigates three approaches focusing on data-dimension reduction: (i) traditional inline audio compression, namely ADPCM and MP3, (ii) full deep-learning classification at the edge, and (iii) embedded pre-processing that only computes and stores spectrograms for later offline classification. We characterized each approach in terms of total (sensor + CPU + mass-storage) edge power consumption (i.e., recorder autonomy) and classification accuracy. Our results demonstrate that ADPCM encoding brings 17.6% energy savings compared to the baseline system (i.e., uncompressed raw audio samples). Using such compressed data, a state-of-the-art spectrogram-based classification model still achieves 91.25% accuracy on open speech datasets. Performing inline data-preparation can significantly reduce the amount of stored data allowing for a 19.8% energy saving compared to the baseline system, while still achieving 89% accuracy during classification. These results show that while massive data reduction can be achieved through the use of inline computation of spectrograms, it translates to little benefit on device autonomy when compared to ADPCM encoding, with the added downside of losing original audio information.

A Framework of Faster CRNN and VGG16-Enhanced Region Proposal Network for Detection and Grade Classification of Knee RA

We developed a framework to detect and grade knee RA using digital X-radiation images and used it to demonstrate the ability of deep learning approaches to detect knee RA using a consensus-based decision (CBD) grading system. The study aimed to evaluate the efficiency with which a deep learning approach based on artificial intelligence (AI) can find and determine the severity of knee RA in digital X-radiation images. The study comprised people over 50 years with RA symptoms, such as knee joint pain, stiffness, crepitus, and functional impairments. The digitized X-radiation images of the people were obtained from the BioGPS database repository. We used 3172 digital X-radiation images of the knee joint from an anterior-posterior perspective. The trained Faster-CRNN architecture was used to identify the knee joint space narrowing (JSN) area in digital X-radiation images and extract the features using ResNet-101 with domain adaptation. In addition, we employed another well-trained model (VGG16 with domain adaptation) for knee RA severity classification. Medical experts graded the X-radiation images of the knee joint using a consensus-based decision score. We trained the enhanced-region proposal network (ERPN) using this manually extracted knee area as the test dataset image. An X-radiation image was fed into the final model, and a consensus decision was used to grade the outcome. The presented model correctly identified the marginal knee JSN region with 98.97% of accuracy, with a total knee RA intensity classification accuracy of 99.10%, with a sensitivity of 97.3%, a specificity of 98.2%, a precision of 98.1%, and a dice score of 90.1% compared with other conventional models.

Coral Reefs Detecting with Artificial Neural Network Classification and PlanetScope Imagery in Cu Lao Xanh Island, Binh Dinh province

Coral reefs are a vital component of coastal and marine ecosystems. They are now under strong environmental challenges and are being harmed by human activities and rising sea surface temperatures, which are reducing the living coral cover. The purpose of this research was to evaluate the mapping accuracy of coral covers using PlanetScope satellite pictures with the Artificial Neural Network (ANN) method surrounding Cu Lao Xanh Island in Binh Dinh province. To adjust for the sunglint effect, the bands were corrected using the Hedley technique. After that, the Depth-Invariant Index technique was utilized to reduce the influence of the water column, and the ANN algorithm was employed for mapping. Hard coral, soft coral, seagrass, deep water, and bare bottom were identified as the five kinds of benthic habitat. The accuracy of the classification results was assessed using field data collected on May 10 and 11, 2022. The results indicated that the artificial neural network (ANN) technique had a higher accuracy, with a total classification accuracy of 89.55% and a kappa value of 0.87. Cu Lao Xanh’s coral area is around 68 hectares, with soft corals mostly found west and southwest of the island and hard corals in the east. This finding demonstrates that Planetscope satellite imagery is effective at monitoring shallow coral reefs on small islands, providing a scientific foundation and reliable data for the development of a more comprehensive coral reef ecological monitoring and management.

Point Cloud Data Processing Optimization in Spectral and Spatial Dimensions Based on Multispectral Lidar for Urban Single-Wood Extraction

Lidar can effectively obtain three-dimensional information on ground objects. In recent years, lidar has developed rapidly from single-wavelength to multispectral hyperspectral imaging. The multispectral airborne lidar Optech Titan is the first commercial system that can collect point cloud data on 1550, 1064, and 532 nm channels. This study proposes a method of point cloud segmentation in the preprocessed intensity interpolation process to solve the problem of inaccurate intensity at the boundary during point cloud interpolation. The entire experiment consists of three steps. First, a multispectral lidar point cloud is obtained using point cloud segmentation and intensity interpolation; the spatial dimension advantage of the multispectral point cloud is used to improve the accuracy of spectral information interpolation. Second, point clouds are divided into eight categories by constructing geometric information, spectral reflectance information, and spectral characteristics. Accuracy evaluation and contribution analysis are also conducted through point cloud truth value and classification results. Lastly, the spatial dimension information is enhanced by point cloud drop sampling, the method is used to solve the error caused by airborne scanning and single-tree extraction of urban trees. Classification results showed that point cloud segmentation before intensity interpolation can effectively improve the interpolation and classification accuracies. The total classification accuracy of the data is improved by 3.7%. Compared with the extraction result (377) of single wood without subsampling treatment, the result of the urban tree extraction proved the effectiveness of the proposed method with a subsampling algorithm in improving the accuracy. Accordingly, the problem of over-segmentation is solved, and the final single-wood extraction result (329) is markedly consistent with the real situation of the region.

Sexual Dimorphism and Discrimination of Barbel Steed (Hemibarbus labeo) in the Jinhe River, China: An Indicator of Habitat Status

Sex identification is linked to sexual dimorphism and is an important study issue in fish biology and aquaculture. However, owing to the unmarked sexual heteromorphism between adult Hemibarbus labeo, it is often difficult to distinguish their sex by visual observation. This study aimed to find a simple and reliable morphometric criterion for the sex identification of H. labeo using discriminant models. Forty-two morphometric traits of sixty-eight H. labeo individuals collected from the Jinhe River were measured, and 41 standardized features were calculated and analyzed. Eight trait variables from 41 standardized attributes were screened using stepwise discriminant analysis. The total classification accuracy of the model was 95.59%. Twelve standardized features significantly differed between male and female H. labeo individuals (p &lt; 0.05). The condition factor, body height, dorsal fin coxal length, the distance between the pelvic and anal fins, and body length were significantly greater in females than in males (p &lt; 0.05), suggesting that females of H. labeo in the Jinhe River were plumper than males, with a larger body size, but a smaller caudal peduncle. These results implied that the sex identification of H. labeo can be performed using the discriminant equation established in this study. This study provides a theoretical basis for endangered fish species protection and their artificial propagation.

Automatic Detection of Outliers in Multi-Channel EMG Signals Using MFCC and SVM

The automatic detection of noisy channels in surface Electromyogram (sEMG) signals, at the time of recording, is very critical in making a noise-free EMG dataset. If an EMG signal contaminated by high-level noise is recorded, then it will be useless and can’t be used for any healthcare application. In this research work, a new machine learning-based paradigm is proposed to automate the detection of low-level and high-level noises occurring in different channels of high density and multi-channel sEMG signals. A modified version of mel frequency cepstral coefficients (mMFCC) is proposed for the extraction of features from sEMG channels along with other statistical parameters i-e complexity coefficient, hurst exponent, and root mean square. Several state-of-the-art classifiers such as Support Vector Machine (SVM), Ensemble Bagged Trees, Ensemble Subspace Discriminant, and Logistic Regression are used to automatically identify an EMG channel either bad or good based on these extracted features. Comparison-based analyses of these classifiers have also been considered based on total classification accuracy, prediction speed (observations/sec), and processing time. The proposed method is tested on 320 simulated EMG channels as well as 640 experimental EMG channels. SVM is used as our main classifier for the detection of noisy channels which gives a total classification accuracy of 99.4% for simulated EMG channels whereas accuracy of 98.9% is achieved for experimental EMG channels.

Entropy of frequency domain of heart rate variability

Introduction. The heart rate variability (HRV) is based on measuring (time) intervals between R-peaks (of RR-intervals) of an electrocardiogram (ECG) and plotting a rhythmogram on their basis with its subsequent analysis by various mathematical methods which are classified as Time-Domain (TD), Frequency-Domain (FD) and Nonlinear [1, 2]. There are a number of popular Nonlinear methods used in HRV analysis, such as entropy-based measures that mostly applied for TD. Spectral Entropy (SE) is using for Frequency-Domain: it is defined to be the Shannon entropy of the power spectral density (PSD) of the data. An important characteristic of Frequency-Domain studies is sympatho-vagal balance, which has been overlooked by entropy-based analysis. This is due to the fact that good entropy analysis restricted the number of existing HRV data, which is shrinking in FD and also in total spectrum parts. Aim of the research. The goal of this paper is to provide a reliable formula for calculating entropy accurately for Frequency-domain of standard 5-min. HRV records and to show the advantages of such approach for analyzing of sympatho-vagal balance for healthy subjects (NSR), Congestive Heart Failure (CHF) and Atrial Fibrillation (AF) patients. Materials and Methods. We used MIT-BIH long-term HRV records for Normal Sinus Rhythm (NSR), Congestive Heart Failure (CHF) and Atrial Fibrillation (AF). The generalized form of the Robust Entropy Estimator (EnRE) for Frequency-domain of standard 5-min. HRV records was proposed and the key EnRE futures was shown. The difference between means of the two independent selections (NSR and CHF, before and after AF) has been determined by a t-test for independent samples; discriminant analysis and statistical calculations have been done by using the statistical package IBM SPSS 27. The results of the study. We calculate entropy for all valuable for HRV spectral interval, namely 0–0.4 Hz and to compare with existing results for Spectral Entropy: qualitatively we receive the same distribution number as [14] and significant difference (p &lt; 0.001) between entropy averages for NSR and CHF or AF patients. We define low-frequencies (LF) power spectrum components in the range of 0.04–0.15 Hz and high-frequencies (HF) power spectrum components in the range of 0.15–0.4 Hz [1]. The sympatho-vagal balance is a simple ratio LF/HF [1]. Then, we define an entropy eLF of the LF power spectrum components, an entropy eHF of the HF power spectrum components and entropy based sympatho-vagal balance as a ratio eLF/eHF. The difference between NSR and CHF groups are significant in both cases LF/HF and eLF/eHF with p &lt; 0.001, but in case of eLF/eHF the results are quite better (t = -4.8, compared to LF/HF where t = -4.4). The discriminant analysis shows total classification accuracy for eLF/eHF in 79.3 % (χ2 = 19.4, p &lt; 0.001) and for LF/HF in 72.4 % (χ2 = 16.6, p &lt; 0.001). We applied entropy-based Frequencies-domain analyzing for AF patients and showed that ratio eLF/eHF is significantly higher during AF than before AF (p &lt; 0.001). This is opposite to ordinary LF/HF where difference is insignificant due to high variation of this ratio. Conclusion. Proposed in the article is generalized form for Robust Entropy Estimator EnRE for Frequencies-domain, which allows, for time series of a limited length (standard 5-min. records), to find entropy value of HRV power spectrum (total spectrum, low- and high- frequencies bands). Using the proposed EnRE for MIT-BIH database of HRV records, we show for standard 5 min. HRV records the usage of EnRE of HRV power spectrum and entropy-based sympatho-vagal balance of Normal Sinus Rhythm (NSR) and Congestive Heart Failure (CHF) cases. It is demonstrated, that, entropy-based Frequencies-domain analyzing is applicable for case of Atrial Fibrillation (AF) even during AF episodes. We showed the significant difference (p &lt; 0.001) before and during AF for entropy of total spectrum, as well as for sympatho-vagal balance in form of eLF/eHF.

Spatiotemporal dynamics analysis of aquaculture zones and its impact on green tide disaster in Haizhou Bay, China

With the rapid marine economic development, the problem of the marine ecological environment has become progressively prominent. Mariculture monitoring plays an essential role in sustaining ecological stability, rational planning, and green economic development of sea areas. Using the Landsat image, the raft-mariculture area information of Haizhou Bay and its adjacent southern waters were extracted by the object-oriented classification method based on remote sensing techniques. Landscape pattern index and principal component analysis were used to analyze the spatiotemporal expansion and structural changes of mariculture areas, and to quantify the effects of natural, socio-economic factors on the spatiotemporal variations of mariculture areas. This study discusses the correlation between the mariculture area and the outbreak scale of Enteromorpha Enteromorpha green tide. Results show that the object-oriented classification method has the highest accuracy, with total classification accuracy and Kappa coefficient of more than 90% and 0.79, respectively. The total area, patch density, and landscape shape index of mariculture areas in Haizhou Bay increase yearly, which demonstrates that the heterogeneity and fragmentation increase with the expansion of the mariculture area. The landscape pattern changes in the mariculture area are predominantly impacted by annual mean sea surface temperature (SST), annual average wind speed, social development level, and population density, etc. The larger the area of raft-aquaculture, the wider the outbreak scale of the Enteromorpha prolifera disaster. Study results can provide scientific references for the further development of mariculture in Haizhou Bay and marine environmental protection.

Fourier transform infrared (FTIR) spectroscopy approach combined with discriminant analysis and prediction model for crude palm oil authentication of different geographical and temporal origins

The traceability of palm oil to determine its geographical origins within the palm oil supply chains is of importance to ensure quality and sustainable production. The current traceability systems are insufficient, thus requiring a more viable and effective approach to improve the traceability framework of the palm oil supply chain. In this study, the feasibility of Fourier transform infrared (FTIR) and chemometric methods for the traceability of crude palm oil (CPO) samples in Malaysia was investigated. The orthogonal partial least squares-discriminant analysis (OPLS-DA) was used to examine the FTIR spectral data of 354 CPO samples collected from four different mills in Malaysia from February until July 2019. The optimal OPLS-DA models were achieved with standard normal variate (SNV) preprocessing and Pareto (Par) scaling to discriminate the CPO according to its geographical provenance and time of sampling. OPLS-DA models using CPO fingerprints successfully discriminated CPO samples according to their geographical origin and production month, with a total accuracy of correct classification for the blind samples (test set) in the prediction models greater than 90% and with the area under the receiver operating characteristic (ROC) curve values exceeding 0.9. The OPLS-DA loading plot indicated that spectral regions associated with free fatty acid (FFA), iodine value (IV) as well as the saturated and unsaturated fatty acid content were responsible for the discrimination patterns and can be potentially used as spectral markers to differentiate CPO samples. Overall, the study shows that combination of FTIR fingerprinting and chemometric methods could serve as an effective tool to trace and discriminating CPO samples according to their geographical origins and production times. The approaches used in the study present an innovative strategy for monitoring quality of palm oil, and moving forward will also facilitate efforts at successful tracking of palm oil across supply chains, especially when combined with additional analytical techniques.

Prognostic factors in diabetes: Comparison of Chi-square automatic interaction detector (CHAID) decision tree technology and logistic regression.

This study aimed to develop a diabetes prediction model. The model performance was compared with logistic regression, and the decision tree Chi-square automatic interaction detection (CHAID) was used to predict diabetes.In total, 3233 patients were included in the analysis. Of these, 589 patients with diabetes and 2644 patients without diabetes were included after analyzing the study sample from the Korean Genome and Epidemiology Study (KoGES)-8 data. In this study, Diabetes Mellitus (DM) diagnosis prediction was compared with logistic regression and prediction through machine learning (ML) using the CHAID decision classification tree. We performed statistical analysis using the CHAID method with International Business Machine (IBM) statistical program SPSS®.We performed logistic regression analysis to predict the classification of diabetes accurately, and the total classification accuracy of the analysis was 81.7%, and the CHAID decision tree classification accuracy was 81.8%. A diabetes diagnosis decision tree was created, which included seven terminal nodes and three depth levels. This analysis showed that a blood pressure problem and hospital visits were the most decisive variables at the time of classification, and two risk levels were created for diabetes diagnosis.The suggested method is a valuable tool for predicting diabetes. Patients who visit the hospital because of blood pressure problems are more likely to develop diabetes than under-treating hyperlipidemia. The diabetes prediction model can help doctors make decisions by detecting the possibility of diabetes early; however, it is impossible to diagnose diabetes using only the model without the doctor’s opinion.