1,171 research outputs found
A comparison of statistical machine learning methods in heartbeat detection and classification
In health care, patients with heart problems require quick responsiveness in a clinical setting or in the operating theatre. Towards that end, automated classification of heartbeats is vital as some heartbeat irregularities are time consuming to detect. Therefore, analysis of electro-cardiogram (ECG) signals is an active area of research. The methods proposed in the literature depend on the structure of a heartbeat cycle. In this paper, we use interval and amplitude based features together with a few samples from the ECG signal as a feature vector. We studied a variety of classification algorithms focused especially on a type of arrhythmia known as the ventricular ectopic fibrillation (VEB). We compare the performance of the classifiers against algorithms proposed in the literature and make recommendations regarding features, sampling rate, and choice of the classifier to apply in a real-time clinical setting. The extensive study is based on the MIT-BIH arrhythmia database. Our main contribution is the evaluation of existing classifiers over a range sampling rates, recommendation of a detection methodology to employ in a practical setting, and extend the notion of a mixture of experts to a larger class of algorithms
A Novel Approach for Crop Selection and Water Management using Mamdani’s Fuzzy Inference & IOT
In the modern world, technology is always evolving to replace more human labour with artificial intelligence. Moreover, farmers are under constant pressure to irrigate their farms at regular intervals without even a rudimentary grasp of the rainfall pattern and soil humidity, since it is extremely difficult to cultivate any agricultural food in regions with irregular rainfall patterns and high mean temperatures.
This paper proposes a crop predictor and smart irrigation system using Mamdani’s fuzzy inference and IoT. The system aims to optimize water usage and crop yield by considering various factors such as soil moisture, temperature, humidity, rainfall, crop type and season. The system consists of three modules: a crop predictor module that uses fuzzy logic to suggest the best crop for a given location and season, an IOT module that collects and transmits the environmental data from sensors to a cloud server, and a smart irrigation module that uses fuzzy logic to control the water flow to the crops based on the data and the crop predictor module. The system is implemented and tested on a NodeMCU and MATLAB platform and shows promising results in terms of water conservation and crop productivity
AgRISTARS: Foreign commodity production forecasting. Country summary report, Australia
Australia is one of the world's major growers and exporters of wheat and as such is one of the countries of interest in the AgRISTARS program which endeavors to develop technology to estimate crop production using aerospace remote sensing. A compilation of geographic, political, and agricultural information on Australia is presented. Also included is a summary of the aerospace remote sensing, meteorological, and ground-observed data which were collected with respect to Australia, as well as a summary of contacts between AgRISTARS and Australia personnel
Measuring Annual Sedimentation through High Accuracy UAV-Photogrammetry Data and Comparison with RUSLE and PESERA Erosion Models
Model-based soil erosion studies have increased in number, given the availability of geodata and the recent technological advances. However, their accuracy remains rather questionable since the scarcity of field records hinders the validation of simulated values. In this context, this study aims to present a method for measuring sediment deposition at a typical Mediterranean catchment (870 ha) in Greece through high spatial resolution field measurements acquired by an Unmanned Aerial Vehicle (UAV) survey. Three-dimensional modeling is considered to be an emerging technique for surface change detection. The UAV-derived point cloud comparison, applying the Structure-from-Motion (SfM) technique at the Platana sediment retention dam test site, quantified annual topsoil change in cm-scale accuracy (0.02–0.03 m), delivering mean sediment yield of 1620 m3 ± 180 m3 or 6.05 t ha−1yr−1 and 3500 m3 ± 194 m3 or 13 t ha−1yr−1 for the 2020–2021 and 2021–2022 estimation. Moreover, the widely applied PESERA and RUSLE models estimated the 2020–2021 mean sediment yield at 1.12 t ha−1yr−1 and 3.51 t ha−1yr−1, respectively, while an increase was evident during the 2021–2022 simulation (2.49 t ha−1yr−1 and 3.56 t ha−1yr−1, respectively). Both applications appear to underestimate the net soil loss rate, with RUSLE being closer to the measured results. The difference is mostly attributed to the model’s limitation to simulate gully erosion or to a C-factor misinterpretation. To the authors’ better knowledge, this study is among the few UAV applications employed to acquire high-accuracy soil loss measurements. The results proved extremely useful in our attempt to measure sediment yield at the cm scale through UAV-SfM and decipher the regional soil erosion and sediment transport pattern, also offering a direct assessment of the retention dams’ life expectancy.Greece and the European UnionPeer Reviewe
Requirements of a global information system for corn production and distribution
There are no author-identified significant results in this report
Persistence of a Geographically-Stable Hybrid Zone in Puerto Rican Dwarf Geckos
Determining the mechanisms that create and maintain biodiversity is a central question in ecology and evolution. Speciation is the process that creates biodiversity. Speciation is mediated by incompatibilities that lead to reproductive isolation between divergent populations and these incompatibilities can be observed in hybrid zones. Gecko lizards are a speciose clade possessing an impressive diversity of behavioral and morphological traits. In geckos, however, our understanding of the speciation process is negligible. To address this gap, we used genetic sequence data (both mitochondrial and nuclear markers) to revisit a putative hybrid zone between Sphaerodactylus nicholsi and Sphaerodactylus townsendi in Puerto Rico, initially described in 1984. First, we addressed discrepancies in the literature on the validity of both species. Second, we sampled a 10-km-wide transect across the putative hybrid zone and tested explicit predictions about its dynamics using cline models. Third, we investigated potential causes for the hybrid zone using species distribution modeling and simulations; namely, whether unique climatic variables within the hybrid zone might elicit selection for intermediate phenotypes. We find strong support for the species-level status of each species and no evidence of movement, or unique climatic variables near the hybrid zone. We suggest that this narrow hybrid zone is geographically stable and is maintained by a combination of dispersal and selection. Thus, this work has identified an extant model system within geckos that that can be used for future investigations detailing genetic mechanisms of reproductive isolation in an understudied vertebrate group
A PCA-SMO Based Hybrid Classification Model for Predictions in Precision Agriculture
The human population is growing at an extremely rapid rate, the demand of food supplies for the survival and sustainability of life is a gleaming challenge. Each living being in the planet gets bestowed with the healthy food to remain active and healthy. Agriculture is a domain which is extremely important as it provides the fundamental resources for survival in terms of supplying food and thus the economy of the entire world is highly dependent on agricultural production. The agricultural production is often affected by various environmental and geographical factors which are difficult to avoid being part of nature. Thus, it requires proactive mitigation plans to reduce any detrimental effect caused by the imbalance of these factors. Precision agriculture is an approach that incorporates information technology in agriculture management, the needs of crops and farming fields are fulfilled to optimized crop health and resultant crop production. The proposed study involves an ambient intelligence-based implementation using machine learning to classify diseases in tomato plants based on the images of its leaf dataset. To analytically evaluate the performance of the framework, a publicly available plant-village dataset is used which is transformed to appropriate form using one-hot encoding technique to meet the needs of the machine learning algorithm. The transformed data is dimensionally reduced by Principal Component Analysis (PCA) technique and further the optimal parameters are selected using Spider Monkey Optimization (SMO) approach. The most relevant features as selected using the Hybrid PCA-SMO technique fed into a Deep Neural Networks (DNN) model to classify the tomato diseases. The optimal performance of the DNN model after implementing dimensionality reduction by Hybrid PCA-SMO technique reached at 99% accuracy was achieved in training and 94% accuracy was achieved after testing the model for 20 epochs. The proposed model is evaluated based on accuracy and loss rate metrics; it justifies the superiority of the approach
Climate Change and Land Management Impact Rangeland Condition and Sage-Grouse Habitat in Southeastern Oregon
Contemporary pressures on sagebrush steppe from climate change, exotic species, wildfire, and land use change threaten rangeland species such as the greater sage-grouse (Centrocercus urophasianus). To effectively manage sagebrush steppe landscapes for long-term goals, managers need information about the potential impacts of climate change, disturbances, and management activities. We integrated information from a dynamic global vegetation model, a sage-grouse habitat climate envelope model, and a state-and-transition simulation model to project broad-scale vegetation dynamics and potential sage-grouse habitat across 23.5 million acres in southeastern Oregon. We evaluated four climate scenarios, including continuing current climate and three scenarios of global climate change, and three management scenarios, including no management, current management and a sage-grouse habitat restoration scenario. All climate change scenarios projected expansion of moist shrub steppe and contraction of dry shrub steppe, but climate scenarios varied widely in the projected extent of xeric shrub steppe, where hot, dry summer conditions are unfavorable for sage-grouse. Wildfire increased by 26% over the century under current climate due to exotic grass encroachment, and by two- to four-fold across all climate change scenarios as extreme fire years became more frequent. Exotic grasses rapidly expanded in all scenarios as large areas of the landscape initially in semi-degraded condition converted to exotic-dominated systems. Due to the combination of exotic grass invasion, juniper encroachment, and climatic unsuitability for sage-grouse, projected sage-grouse habitat declined in the first several decades, but increased in area under the three climate change scenarios later in the century, as moist shrub steppe increased and rangeland condition improved. Management activities in the model were generally unsuccessful in controlling exotic grass invasion but were effective in slowing woodland expansion. Current levels of restoration treatments were insufficient to prevent some juniper expansion, but increased treatment rates under the restoration scenario maintained juniper near initial levels in priority treatment areas. Our simulations indicate that climate change may have both positive and negative implications for maintaining sage-grouse habitat
Brazilian Surface and Upper-level Wind Characteristics Based on Ground and Model Observations from 1980–2014
The examination of temporal changes in surface winds has been analyzed by scientists for a variety of physical, biological, climatological, and socioeconomic reasons. This research uses surface and upper-level wind data from historical in-situ and climate models to examine the geographical and climatological characteristics of wind across Brazil during 1980–2014. Overall linear and quantile regression shows that surface wind speed trends are changing regionally across Brazil. Wind speeds across northeastern Brazil are increasing, while a decreasing trend is documented for interior and southeastern Brazil. The spatial and temporal trends found are possibly related to alterations in the physical landscape (urbanization and land-cover change) and the seasonal relationship between the Intertropical Convergence Zone and the South Atlantic Anticyclone. To further examine the role of the South Atlantic Anticyclone, an additional analysis was performed to show how the position of high pressure system affects surface conditions across Brazil. Results show that surface winds across northern Brazil are affected by an equatorward shift of the semi-permanent high pressure, while southern Brazil is more influenced by migrating anticyclones that were passing through the South Atlantic Basin. A spatial and temporal analysis of upper-level wind speed trends was conducted to examine how surface and marco-scale features have evolved over Brazil. An overall vertical profile shows a decreasing trend in lower-level winds (1000–850 hPa) that switches to a positive trend in the upper portions of the atmosphere (400–200 hPa). A geographical interpretation of upper-level wind trends was performed based on a three-dimensional model. The model depicts that seasonal wind trend patterns across Brazil occur within the proximity of the Bolivian high and subtropical jet (400–200 hPa). A regional analysis confirms the role of these two synoptic features
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