Maximum Prediction Accuracy Research Articles

Integrated Deep Auto-Encoder and Q-Learning-Based Scheme to Detect Anomalies and Supporting Forensics in Cloud Computing Environments

The cloud computing environment when deployed correctly is responsible for delivering scalability, cost efficiency, reliability, security and interoperability to the end users. Log analysis is considered to be an indispensable component of security regulations and framework, since these computer-generated records help the organizations, businesses and networks to respond to different kinds of risks that are possible to cloud environment in a reactive and proactive manner. In this paper, an Integrated Deep Auto-Encoder and Q-learning-based Deep Learning (IDEA-QLDL) Scheme is proposed for attaining maximum prediction accuracy during the process of exploring log data and classifying them into genuine and anomalous. It initiates the process of acceptance or denial based on the continuous investigation of behavioral patterns that are highly applicable for classification. The results of the proposed IDEA-QLDL Scheme confirmed its predominance in improving the classification accuracy, precision, recall and detection time compared to the benchmarked schemes considered for investigation.

An Optimization of Feature Selection for Classification using Modified Bat Algorithm

Data mining is the action of searching the large existing database in order to get new and best information. It plays a major and vital role now-a-days in all sorts of fields like Medical, Engineering, Banking, Education and Fraud detection. In this paper Feature selection which is a part of Data mining is performed to do classification. The role of feature selection is in the context of deep learning and how it is related to feature engineering. Feature selection is a preprocessing technique which selects the appropriate features from the data set to get the accurate result and outcome for the classification. Nature-inspired Optimization algorithms like Ant colony, Firefly, Cuckoo Search and Harmony Search showed better performance by giving the best accuracy rate with less number of features selected and also fine f-Measure value is noted. These algorithms are used to perform classification that accurately predicts the target class for each case in the data set. We propose a technique to get the optimized feature selection to perform classification using Meta Heuristic algorithms. We applied new and recent advanced optimized algorithm named Modified Bat algorithm on University of California Irvine datasets that showed comparatively equal results with best performed existing firefly but with less number of features selected. The work is implemented using JAVA and the Medical dataset has been used. These datasets were chosen due to nominal class features. The number of attributes, instances and classes varies from chosen dataset to represent different combinations. Classification is done using J48 classifier in WEKA tool. We demonstrate the comparative results of the presently used algorithms with the existing algorithms thoroughly. The significance of this research is it will show a great impact in selecting the best features out of all the existing features which gives best accuracy rates which helps in extracting the information from raw data in Data Mining Domain. The Value of this research is it will manage main fields like medical and banking which gives exact and proper results in their respective field. The best quality of the research is to optimize the selection of features to achieve maximum predictive accuracy of the data sets which solves both single variable and multi-variable functions through the generation of binary structuring of features in the dataset and to increase the performance of classification by using nature inspired and Meta Heuristic algorithms.

Neuron-specific enolase in cerebrospinal fluid as a biomarker of brain damage in infants with hypoxic-ischemic encephalopathy.

Neuron-specific enolase in cerebrospinal fluid as a biomarker of brain damage in infants with hypoxic-ischemic encephalopathy.

Evaluation of RGB and Multispectral Unmanned Aerial Vehicle (UAV) Imagery for High-Throughput Phenotyping and Yield Prediction in Barley Breeding

With advances in plant genomics, plant phenotyping has become a new bottleneck in plant breeding and the need for reliable high-throughput plant phenotyping techniques has emerged. In the face of future climatic challenges, it does not seem appropriate to continue to solely select for grain yield and a few agronomically important traits. Therefore, new sensor-based high-throughput phenotyping has been increasingly used in plant breeding research, with the potential to provide non-destructive, objective and continuous plant characterization that reveals the formation of the final grain yield and provides insights into the physiology of the plant during the growth phase. In this context, we present the comparison of two sensor systems, Red-Green-Blue (RGB) and multispectral cameras, attached to unmanned aerial vehicles (UAV), and investigate their suitability for yield prediction using different modelling approaches in a segregating barley introgression population at three environments with weekly data collection during the entire vegetation period. In addition to vegetation indices, morphological traits such as canopy height, vegetation cover and growth dynamics traits were used for yield prediction. Repeatability analyses and genotype association studies of sensor-based traits were compared with reference values from ground-based phenotyping to test the use of conventional and new traits for barley breeding. The relative height estimation of the canopy by UAV achieved high precision (up to r = 0.93) and repeatability (up to R2 = 0.98). In addition, we found a great overlap of detected significant genotypes between the reference heights and sensor-based heights. The yield prediction accuracy of both sensor systems was at the same level and reached a maximum prediction accuracy of r2 = 0.82 with a continuous increase in precision throughout the entire vegetation period. Due to the lower costs and the consumer-friendly handling of image acquisition and processing, the RGB imagery seems to be more suitable for yield prediction in this study.

Predictive potential of cardiovascular risk factors and their associations with arterial stiffness in people of European and Korean ethnic groups

Aim. To compare the effect of cardiovascular risk factors on aortic stiffness in people of European and East Asian ethnic groups.Material and methods. A total of 266 patients aged 18-60 years of European (n=133) and Korean (n=133) ethnic groups were examined. Clinical assessment was carried, Also, following blood parameters was evaluated: total cholesterol (TC), low- (LDL-C) and high- (HDL-C) density lipoprotein cholesterol, apolipoproteins A (apo-A) and B (apo-B), triglycerides (TG), uric acid, creatinine, glucose, adiponectin, resistin. The aortic pulse wave velocity (PWV) and central blood pressure (CBP) were determined using a Tensiomed arteriograph (Hungary). The study design included 3 stages. The first stage included statistical analysis using Mann-Whitney, χ2, Fisher tests, while the second one — determination of weighing coefficients of individual risk factors on aortic PWV. The third stage consists of verification of the relationship between ethnicity and aortic PWV using multivariate logistic regression and stochastic gradient boosting (SGB).Results. In Europeans, the median values of growth, body mass index (BMI), waist circumference (WC) and waist-to-height ratio were significantly higher, while the levels of apo-B, TC, HDL-C, LDL-C, TG was significantly lower than in Asians. Koreans had higher blood concentrations of UA, creatinine, glucose, while the resistin concentration was 1,8 times lower. Among Europeans, the odds ratio of developing hypertension (HTN) was significantly higher. The level of aortic PWV in people of different ethnic groups did not differ significantly. Univariate logistic regression showed a dominant influence of age, CPP and waist-to-height ratio on aortic PWV. A less noticeable significant relationship with aortic PWV had HTN, female sex, BMI, levels of systolic, diastolic and pulse BP. Multivariate logistic regression and SGB showed the maximum prediction accuracy when 5 predictors were combined in one model: age, height, HTN, LDL-C, and ethnicity. Comparable accuracy was demonstrated by a model where glucose level was used instead of LDL-C. The results indicate a nonlinear relationship between the ethnic factor and aortic PWV. Its predictive potential was realized only in combination with functional and metabolic status parameters of patients. In Koreans, the threshold values of these factors can be significantly higher than in Europeans.Conclusion. Developed using modern machine learning technologies, the assessment aortic PWV models taking into account the ethnic factor can be a useful tool for processing and analyzing data in predictive studies.

Sensitivity and specificity of the CRAFFT to identify heavy cannabis use: Evidence from a large statewide adolescent sample

Background and AimScreening adolescents at risk for cannabis use disorders is critical. The CRAFFT is a screening tool designed to address both alcohol and drug use among youth. Current study tests the psychometric properties of the CRAFFT and attempts to modify one of the screening items to compare the efficiency. DesignWe examined the ideal cut-off point of the CRAFFT for identifying persons with heavy cannabis use and compared the utility of the original and revised CRAFFT. ParticipantsSample (N = 132,555) averaged 16.19 (±1.21) years of age; 51.0% were female, 59.7% were White, 15.2% were Latino/Latina, and 6.7% were African-American. Majority resided in non-rural area and 34.5% were receiving free or reduced lunch at school. MeasurementsHeavy cannabis use was defined as using cannabis 10 or more times in the past 30 days. Sensitivity, specificity, the area under the receiver operating characteristic curve, and Youden value were analyzed to determine the ideal cut-off point. FindingsMaximum overall predictive accuracy was at a cutoff score of 2 or higher when using the original CRAFFT questions. At a cutoff score of 2, sensitivity was 82.0%, specificity was 83.7%, with an AUC of 0.880. On the contrary, when an alternative CAR question was used, maximum predictive accuracy was at a cutoff score of 1 or higher when predicting heavy cannabis use. At a cutoff score of 1, sensitivity was 92.7%, specificity was 75.5%, with an AUC of 0.900. ConclusionsThe results provide evidence that the CRAFFT is a promising brief diagnostic instrument for heavy cannabis use among youth. Modification to Car item may have potential in reducing disparities in sensitivity among different racial ethnic groups, as well as those who with low socioeconomic status.

Predicting the Soft Error Vulnerability of Parallel Applications Using Machine Learning

With the widespread use of the multicore systems having smaller transistor sizes, soft errors become an important issue for parallel program execution. Fault injection is a prevalent method to quantify the soft error rates of the applications. However, it is very time consuming to perform detailed fault injection experiments. Therefore, prediction-based techniques have been proposed to evaluate the soft error vulnerability in a faster way. In this work, we present a soft error vulnerability prediction approach for parallel applications using machine learning algorithms. We define a set of features including thread communication, data sharing, parallel programming, and performance characteristics; and train our models based on three ML algorithms. This study uses the parallel programming features, as well as the combination of all features for the first time in vulnerability prediction of parallel programs. We propose two models for the soft error vulnerability prediction: (1) A regression model with rigorous feature selection analysis that estimates correct execution rates, (2) A novel classification model that predicts the vulnerability level of the target programs. We get maximum prediction accuracy rate of 73.2% for the regression-based model, and achieve 89% F-score for our classification model.

A polar motion prediction method considering the polar coordinates

Conventional polar motion (PM) parameter modelling and forecasting research is directly based on observation data for PM parameters. For any given PM, its periodic characteristics are basically the same since it is affected by the same excitation source. However, the time-varying patterns of PM in different coordinates or with different parameterizations are generally different. Therefore, it is possible for one parameterization to show better regularity than another. Notably, for the first time, daily polar displacement and the angle of polar displacement with respect to the X axis of the terrestrial frame display better regularity than the observed polar motion value. Therefore, improved accuracy could be expected in modelling and forecasting using the above two parameters. Different approach was tested to verify the effectiveness and the superiority of the proposed method based on multiple sets of PM prediction experiments with the data of IERS 14 C04 series which spanned from 1 January 2018 to 26 September 2020. Compared with typical conventional approach, the maximum prediction accuracy of the proposed method in the PMX direction and the PMY direction is increased by 59.9% and 63.2%, respectively, and the corresponding 1–500-day forecasting accuracies are improved by 25.1% and 36.4%, respectively, on average.

Optimal Length of Heart Rate Variability Data and Forecasting Time for Ventricular Fibrillation Prediction Using Machine Learning.

Ventricular fibrillation (VF) is a cardiovascular disease that is one of the major causes of mortality worldwide, according to the World Health Organization. Heart rate variability (HRV) is a biomarker that is used for detecting and predicting life-threatening arrhythmias. Predicting the occurrence of VF in advance is important for saving patients from sudden death. We extracted features from seven HRV data lengths to predict the onset of VF before nine different forecast times and observed the prediction accuracies. By using only five features, an artificial neural network classifier was trained and validated based on 10-fold cross-validation. Maximum prediction accuracies of 88.18% and 88.64% were observed at HRV data lengths of 10 and 20 s, respectively, at a forecast time of 0 s. The worst prediction accuracy was recorded at an HRV data length of 70 s and a forecast time of 80 s. Our results showed that features extracted from HRV signals near the VF onset could yield relatively high VF prediction accuracies.

CS-ABC: Cuckoo Search based adaptive boosting classifier for malaria infected erythrocyte detection

The contribution of this manuscript is a supervised learning technique that enables to detect the malaria scope in given erythrocyte. Contrast to former efforts in this region, this study utilises multiple observable phase images of the unspotted cells. Automatically, erythrocytes are segmented utilising optical phase thresholds and redeployed to allow quantitative association of the phase images. Redeployed images are examined to extract manifold morphological descriptors on the basis of phase information. When the entire individual descriptors are statistically diverse among uninfected and infected cells, every descriptor will not allow parting of the populations at the satisfactory level aimed at clinical service. The experimental study carried on the proposal and other contemporary models evincing that the proposed CS-ABC (Cuckoo Search based Adaptive Boosting Classifier) is considerably significant with maximal prediction accuracy and minimal misclassification rate that compared to other contemporary models.

CS-ABC: Cuckoo Search based adaptive boosting classifier for malaria infected erythrocyte detection

The contribution of this manuscript is a supervised learning technique that enables to detect the malaria scope in given erythrocyte. Contrast to former efforts in this region, this study utilises multiple observable phase images of the unspotted cells. Automatically, erythrocytes are segmented utilising optical phase thresholds and redeployed to allow quantitative association of the phase images. Redeployed images are examined to extract manifold morphological descriptors on the basis of phase information. When the entire individual descriptors are statistically diverse among uninfected and infected cells, every descriptor will not allow parting of the populations at the satisfactory level aimed at clinical service. The experimental study carried on the proposal and other contemporary models evincing that the proposed CS-ABC (Cuckoo Search based Adaptive Boosting Classifier) is considerably significant with maximal prediction accuracy and minimal misclassification rate that compared to other contemporary models.

1229. Volatile Biomarkers of Influenza Infection in the Breath

BackgroundAnnual influenza epidemics cause significant morbidity and mortality. New, emerging strains threaten to cause catastrophic pandemics. Assay of exhaled breath for volatile organic compounds (VOCs) via gas chromatography-mass spectroscopy (GC-MS) is an emerging diagnostic modality ideally suited to fill the gap in influenza diagnostics.MethodsPatients with influenza like illness (ILI) presenting to the Troop Medical Clinic on JBSA Fort Sam Houston, TX, from 3/2017 to 3/2019 submitted a 2-minute breath sample in addition to a nasopharyngeal swab collected for polymerase chain reaction (PCR) assay for influenza virus. ILI was defined as temperature > 100.40F AND respiratory symptoms like cough, sputum production, chest pain and/or sore throat. Breath VOCs were assayed with GC-MS and data were analyzed in order to identify the significant breath VOC biomarkers that discriminated between ILI patients with and without a PCR assay positive for influenza with greater than random accuracy.ResultsDemographic, clinical, PCR and breath data were available for 237 episodes of ILI. PCR was positive for influenza for 32 episodes (30 influenza A and 2 B). The median age of participants was 21 (IQR 19, 23) and 69% were male. There were no differences in age, gender, education, race, or smoking, between the influenza positive and negative groups. Likewise, there was no difference in days of limited activity or missed work, or symptoms at presentation between the groups. The algorithm achieved near maximal predictive accuracy of 78% with four biomarkers (74% sensitivity and 70% specificity). Based on their mass spectra, these biomarker VOCs were tentatively identified as 2-amino-1-propanol, 2-butanamine, n-nitro, 3-methyl-hexanal, and heptane, which are consistent with products of oxidative stress.Figure. Accuracy, Sensitivity, and Specificity of Influenza Breath Test. Receiver operating characteristic (ROC) of the breath test (sensitivity versus 1-specificity). The accuracy of the breath test was 78%. With a cutoff point at the “shoulder” of the ROC curve, the test had 74% sensitivity and 70% specificity. ConclusionOur findings bolster available benchtop and clinical data suggesting that breath testing may be a useful diagnostic modality for influenza infection. The next step will be to study the predictive algorithm developed in this protocol in a blinded validation cohort. If the predictive algorithm performs well in a validation study, adaptation for its use in a portable, tabletop GC would be warranted to allow for a rapid, accurate, universal point-of-care influenza diagnostic test.DisclosuresMichael Phillips, MD, Menssana Research, Inc (Grant/Research Support)

Geographic Information System Technology Combined with Back Propagation Neural Network in Groundwater Quality Monitoring

This study was conducted to explore the distribution and changes of groundwater resources in the research area, and to promote the application of geographic information system (GIS) technology and its deep learning methods in chemical type distribution and water quality prediction of groundwater. The Shiyang River Basin in Minqin County was selected as the research object for analyzing the natural components distribution and its preliminary forecast in partial areas. With the priority control of groundwater pollutants, the concentration changes of four indicators (including the permanganate index) in different spatial distributions were analyzed based on the GIS technology, so as to provide a basis for the groundwater quality prediction. Taking the permanganate as a benchmark, this study evaluated the prediction effects of the conventional back propagation (BP) neural network (BPNN) model and the optimized BPNN based on the golden section (GBPNN) and wavelet transform (WBPNN). The algorithm proposed in this study is compared with several classic prediction algorithms for analysis. Groundwater quality level and distribution rules in the research area are evaluated with the proposed algorithm and GIS technology. The results reveal that GIS technology can characterize the spatial concentration distribution of natural indicators and analyze the chemical distribution of groundwater quality based on it. In contrast, the WBPNN has the best prediction result. Its average error of the whole process is 3.66%, and the errors corresponding to the six predicated values are all below 10%, which is dramatically better than the values of the other two models. The maximal prediction accuracy of the proposed algorithm is 97.68%, with an average accuracy of 96.12%. The prediction results on the water quality level are consistent with the actual condition, and the spatial distribution rules of the groundwater water quality can be shown clearly with the GIS technology combined with the proposed algorithm. Therefore, it is of great significance to explore the distribution and changes of regional groundwater quality, and this studywill play a critical role in determining the groundwater quality.

A genomic signature for accurate classification and prediction of clinical outcomes in cancer patients treated with immune checkpoint blockade immunotherapy

Tumor mutational burden (TMB) is associated with clinical response to immunotherapy, but application has been limited to a subset of cancer patients. We hypothesized that advanced machine-learning and proper modeling could identify mutations that classify patients most likely to derive clinical benefits. Training data: Two sets of public whole-exome sequencing (WES) data for metastatic melanoma. Validation data: One set of public non-small cell lung cancer (NSCLC) data. Least Absolute Shrinkage and Selection Operator (LASSO) machine-learning and proper modeling were used to identify a set of mutations (biomarker) with maximum predictive accuracy (measured by AUROC). Kaplan–Meier and log-rank methods were used to test prediction of overall survival. The initial model considered 2139 mutations. After pruning, 161 mutations (11%) were retained. An optimal threshold of 0.41 divided patients into high-weight (HW) or low-weight (LW) TMB groups. Classification for HW-TMB was 100% (AUROC = 1.0) on melanoma learning/testing data; HW-TMB was a prognostic marker for longer overall survival. In validation data, HW-TMB was associated with survival (p = 0.0057) and predicted 6-month clinical benefit (AUROC = 0.83) in NSCLC. In conclusion, we developed and validated a 161-mutation genomic signature with “outstanding” 100% accuracy to classify melanoma patients by likelihood of response to immunotherapy. This biomarker can be adapted for clinical practice to improve cancer treatment and care.

Energy efficient compressive sensing with predictive model for IoT based medical data transmission

Internet of Things (IoT) systems tends to produce massive and diverse kinds of data which needs to be processes and responds in a smallperiod. A most important challenge exists in IoT devices is the amount of energy utilization while transmitting data into cloud. This paper presents a new energy efficient compressive technique with predictive model for IoT based medical data collection and transmission. The proposed model make use of Sensor-Lempel Ziv Welch (SLZW) technique is utilized to perform compressive sensing earlier to data transmission followed by particle swarm optimization (PSO) based deep neural network (DNN) based prediction. The PSO algorithm is applied for optimizing the node count of hidden layer in DNN due to the issue that the classical DNN got trapped into local minima and the node count in hidden layer have to select manually. The performance of the presented SLZW-PSO-DNN algorithm has been validated and the results are investigated under distinct scenarios. The obtained experimental outcome indicated that the SLZW-PSO-DNN algorithm is found to be effective under several aspects over the existing method. The experimental results stated that the PSO-DNN model has resulted in a maximum predictive average accuracy of 98.5% and 98.4% under original and compressed data respectively.

Optimizing whole-genomic prediction for autotetraploid blueberry breeding

Blueberry (Vaccinium spp.) is an important autopolyploid crop with significant benefits for human health. Apart from its genetic complexity, the feasibility of genomic prediction has been proven for blueberry, enabling a reduction in the breeding cycle time and increasing genetic gain. However, as for other polyploid crops, sequencing costs still hinder the implementation of genome-based breeding methods for blueberry. This motivated us to evaluate the effect of training population sizes and composition, as well as the impact of marker density and sequencing depth on phenotype prediction for the species. For this, data from a large real breeding population of 1804 individuals were used. Genotypic data from 86,930 markers and three traits with different genetic architecture (fruit firmness, fruit weight, and total yield) were evaluated. Herein, we suggested that marker density, sequencing depth, and training population size can be substantially reduced with no significant impact on model accuracy. Our results can help guide decisions toward resource allocation (e.g., genotyping and phenotyping) in order to maximize prediction accuracy. These findings have the potential to allow for a faster and more accurate release of varieties with a substantial reduction of resources for the application of genomic prediction in blueberry. We anticipate that the benefits and pipeline described in our study can be applied to optimize genomic prediction for other diploid and polyploid species.

A Novel Model to Estimate Key Obstructive Sleep Apnea Endotypes from Standard Polysomnography and Clinical Data and Their Contribution to Obstructive Sleep Apnea Severity.

Rationale: There are at least four key pathophysiological endotypes that contribute to obstructive sleep apnea (OSA) pathophysiology. These include 1) upper-airway collapsibility (Pcrit); 2) arousal threshold; 3) loop gain; and 4) pharyngeal muscle responsiveness. However, an easily interpretable model to examine the different ways and the extent to which these OSA endotypes contribute to conventional polysomnography-defined OSA severity (i.e., the apnea-hypopnea index) has not been investigated. In addition, clinically deployable approaches to estimate OSA endotypes to advance knowledge on OSA pathogenesis and targeted therapy at scale are not currently available.Objectives: Develop an interpretable data-driven model to 1) determine the different ways and the extent to which the four key OSA endotypes contribute to polysomnography-defined OSA severity and 2) gain insight into how standard polysomnographic and clinical variables contribute to OSA endotypes and whether they can be used to predict OSA endotypes.Methods: Age, body mass index, and eight polysomnography parameters from a standard diagnostic study were collected. OSA endotypes were also quantified in 52 participants (43 participants with OSA and nine control subjects) using gold-standard physiologic methodology on a separate night. Unsupervised multivariate principal component analyses and data-driven supervised machine learning (decision tree learner) were used to develop a predictive algorithm to address the study objectives.Results: Maximum predictive performance accuracy of the trained model to identify standard polysomnography-defined OSA severity levels (no OSA, mild to moderate, or severe) using the using the four OSA endotypes was approximately twice that of chance. Similarly, performance accuracy to predict OSA endotype categories ("good," "moderate," or "bad") from standard polysomnographic and clinical variables was approximately twice that of chance for Pcrit and slightly lower for arousal threshold.Conclusions: This novel approach provides new insights into the different ways in which OSA endotypes can contribute to polysomnography-defined OSA severity. Although further validation work is required, these findings also highlight the potential for routine sleep study and clinical data to estimate at least two of the key OSA endotypes using data-driven predictive analysis methodology as part of a clinical decision support system to inform scalable research studies to advance OSA pathophysiology and targeted therapy for OSA.

Burns Depth Assessment Using Deep Learning Features

PurposeBurns depth evaluation is a lifesaving task and very challenging that requires objective techniques to accomplish. While the visual assessment is the most commonly used by surgeons, its accuracy reliability ranges between 60 and 80% and subjective that lacks any standard guideline. Currently, the only standard adjunct to clinical evaluation of burn depth is Laser Doppler Imaging (LDI) which measures microcirculation within the dermal tissue, providing the burns potential healing time which correspond to the depth of the injury achieving up to 100% accuracy. However, the use of LDI is limited due to many factors including high affordability and diagnostic costs, its accuracy is affected by movement which makes it difficult to assess paediatric patients, high level of human expertise is required to operate the device, and 100% accuracy possible after 72 h. These shortfalls necessitate the need for objective and affordable technique.MethodIn this study, we leverage the use of deep transfer learning technique using two pretrained models ResNet50 and VGG16 for the extraction of image patterns (ResFeat50 and VggFeat16) from a a burn dataset of 2080 RGB images which composed of healthy skin, first degree, second degree and third-degree burns evenly distributed. We then use One-versus-One Support Vector Machines (SVM) for multi-class prediction and was trained using 10-folds cross validation to achieve optimum trade-off between bias and variance.ResultsThe proposed approach yields maximum prediction accuracy of 95.43% using ResFeat50 and 85.67% using VggFeat16. The average recall, precision and F1-score are 95.50%, 95.50%, 95.50% and 85.75%, 86.25%, 85.75% for both ResFeat50 and VggFeat16 respectively.ConclusionThe proposed pipeline achieved a state-of-the-art prediction accuracy and interestingly indicates that decision can be made in less than a minute whether the injury requires surgical intervention such as skin grafting or not.

GIS-based forest fire susceptibility modeling in Pauri Garhwal, India: a comparative assessment of frequency ratio, analytic hierarchy process and fuzzy modeling techniques

This study performs a comparative evaluation of Frequency Ratio (FR), Analytic Hierarchy Process (AHP), and Fuzzy AHP (FAHP) modeling techniques for forest fire susceptibility mapping in Pauri Garhwal, Uttarakhand, India. Locations of past forest fire events reported from November 2002 to July 2019 were collected from the Uttarakhand Forest Department and Forest Survey of India and combined with the ground observations obtained from the manual survey. Then, the locations were categorized into two groups of 70% (10,500 locations) and 30% (4500 locations), randomly, for training and validation purposes, respectively. Forest fire susceptibility mapping was performed on the basis of fourteen different topographic, biological, human-induced and climatic criteria such as Digital Elevation Model, Slope, Aspect, Curvature, Normalized Difference Vegetation Index, Normalized Difference Moisture Index, Topographic Wetness Index, Soil, Distance to Settlement, Distance to Road, Distance to Drainage, Rainfall, Temperature, and Wind Speed. The Receiver Operating Characteristic curve and the Area Under the Curve (AUC) were implemented for validation of the three achieved Forest Fire Susceptibility Maps. The AUC plot evaluation revealed that FAHP has a maximum prediction accuracy of 83.47%, followed by AHP (81.75%) and FR (77.21%). Thus, the map produced by FAHP exhibits the most satisfactory properties. Results and findings of this study will help in developing more efficient fire management strategies in both the open and the protected forest areas (Rajaji and Jim Corbett National Park) of the district.

Maximizing efficiency of genomic selection in CIMMYT\u2019s tropical maize breeding program

Key messageHistorical data from breeding programs can be efficiently used to improve genomic selection accuracy, especially when the training set is optimized to subset individuals most informative of the target testing set.The current strategy for large-scale implementation of genomic selection (GS) at the International Maize and Wheat Improvement Center (CIMMYT) global maize breeding program has been to train models using information from full-sibs in a “test-half-predict-half approach.” Although effective, this approach has limitations, as it requires large full-sib populations and limits the ability to shorten variety testing and breeding cycle times. The primary objective of this study was to identify optimal experimental and training set designs to maximize prediction accuracy of GS in CIMMYT’s maize breeding programs. Training set (TS) design strategies were evaluated to determine the most efficient use of phenotypic data collected on relatives for genomic prediction (GP) using datasets containing 849 (DS1) and 1389 (DS2) DH-lines evaluated as testcrosses in 2017 and 2018, respectively. Our results show there is merit in the use of multiple bi-parental populations as TS when selected using algorithms to maximize relatedness between the training and prediction sets. In a breeding program where relevant past breeding information is not readily available, the phenotyping expenditure can be spread across connected bi-parental populations by phenotyping only a small number of lines from each population. This significantly improves prediction accuracy compared to within-population prediction, especially when the TS for within full-sib prediction is small. Finally, we demonstrate that prediction accuracy in either sparse testing or “test-half-predict-half” can further be improved by optimizing which lines are planted for phenotyping and which lines are to be only genotyped for advancement based on GP.