19 research outputs found
Numerical Simulation and Design of COVID-19 Disease Detection System Based on Improved Computing Techniques
The high demand for testing the sickness has led to a lack of resources at emergency clinics as the coronavirus epidemic continues. PC vision-based frameworks can be used to increase the productivity of Coronavirus localization. However, a significant amount of information preparation is needed to create an accurate and reliable model, which is currently impractical given the peculiar nature of the illness. One such model is for differentiating pneumonia cases by using radiographs, and it has achieved sufficiently high exactness to be used on patients. Various models are currently being used inside the medical services sector to order different illnesses. This proposal evaluates the benefit of using motion learning to broaden the presentation of the Coronavirus location model, starting from the premise that there is limited information available for Coronavirus ID. Infections that affect the human lungs include viral pneumonia caused by the coronavirus and other viruses. The World Health Organization (W.H.O.) proclaimed Covid a pandemic in 2020; the sickness originated in China and quickly spread to other countries. Early diagnosis of infected patients aids in saving the patient's life and prevents the infection's further spread. As one of the quickest and least expensive methods for diagnosing the condition, the convolutional neural organization (CNN) model is suggested in this research study to assist in the early detection of the infection using chest X-Beam images. Two convolutional brain organizations (CNN) models were created using two different datasets. The primary model was created for double characterization using one of the datasets that only included pneumonia cases and common chest X-Beam images. The second model made use of the information advanced by the primary model using move learning and was created for three class divisions on chest X-Beam images of cases with the coronavirus, pneumonia, and regular cases
Numerical Modeling and Design of Machine Learning Based Paddy Leaf Disease Detection System for Agricultural Applications
In order to satisfy the insatiable need for ever more bountiful harvests on the global market, the majority of countries deploy cutting-edge technologies to increase agricultural output. Only the most cutting-edge technologies can ensure an appropriate pace of food production. Abiotic stress factors that can affect plants at any stage of development include insects, diseases, drought, nutrient deficiencies, and weeds. On the amount and quality of agricultural production, this has a minimal effect. Identification of plant diseases is therefore essential but challenging and complicated. Paddy leaves must thus be closely watched in order to assess their health and look for disease symptoms. The productivity and production of the post-harvest period are significantly impacted by these illnesses. To gauge the severity of plant disease in the past, only visual examination (bare eye observation) methods have been employed. The skill of the analyst doing this analysis is essential to the caliber of the outcomes. Due to the large growing area and need for ongoing human monitoring, visual crop inspection takes a long time. Therefore, a system is required to replace human inspection. In order to identify the kind and severity of plant disease, image processing techniques are used in agriculture. This dissertation goes into great length regarding the many ailments that may be detected in rice fields using image processing. Identification and classification of the four rice plant diseases bacterial blight, sheath rot, blast, and brown spot are important to enhance yield. The other communicable diseases, such as stem rot, leaf scald, red stripe, and false smut, are not discussed in this paper. Despite the increased accuracy they offer, the categorization and optimization strategies utilized in this work lead it to take longer than typical to finish. It was evident that employing SVM techniques enabled superior performance results, but at a cost of substantial effort. K-means clustering is used in this paper segmentation process, which makes figuring out the cluster size, or K-value, more challenging. This clustering method operates best when used with images that are comparable in size and brightness. However, when the images have complicated sizes and intensity values, clustering is not particularly effective
Numerical Simulation and Design of Machine Learning Based Real Time Fatigue Detection System
The proposed research is a step to implement real time image segmentation and drowsiness with help of machine learning methodologies. Image segmentation has been implemented in real time in which the segments of mouth and eyes have been segmented using image processing. Input can be provided by the help of real time image acquisition system such as webcam or internet of things based camera. From the video input, image frames has been extracted and processed to obtain real time features and using clustering algorithms segmentation has been achieved in real time. In the proposed work a Support Vector Machine (SVM) based machine learning method has been implemented emotion detection using facial expressions. The algorithm has been tested under variable luminance conditions and performed well with optimum accuracy as compared to contemporary research
Design and Modeling of Stock Market Forecasting Using Hybrid Optimization Techniques
In this paper, an artificial neural network-based stock market prediction model was developed. Today, a lot of individuals are making predictions about the direction of the bond, currency, equity, and stock markets. Forecasting fluctuations in stock market values is quite difficult for businesspeople and industries. Forecasting future value changes on the stock markets is exceedingly difficult since there are so many different economic, political, and psychological factors at play. Stock market forecasting is also a difficult endeavour since it depends on so many various known and unknown variables. There are several ways used to try to anticipate the share price, including technical analysis, fundamental analysis, time series analysis, and statistical analysis; however, none of these approaches has been shown to be a consistently reliable prediction tool.
We built three alternative Adaptive Neuro-Fuzzy Inference System (ANFIS) models to compare the outcomes. The average of the tuned models is used to create an ensemble model. Although comparable applications have been attempted in the literature, the data set is extremely difficult to work with because it only contains sharp peaks and falls with no seasonality. In this study, fuzzy c-means clustering, subtractive clustering, and grid partitioning are all used. The experiments we ran were designed to assess the effectiveness of various construction techniques used to our ANFIS models. When evaluating the outcomes, the metrics of R-squared and mean standard error are mostly taken into consideration. In the experiments, R-squared values of over.90 are attained
A Novel DWT-CT approach in Digital Watermarking using PSO
The importance of watermarking is dramatically enhanced due to the promising technologies like Internet of Things (IoT), Data analysis, and automation of identification in many sectors. Due to these reasons, systems are inter-connected through networking and internet and huge amounts of information is generated, distributed and transmitted over the World Wide Web. Thus authentication of the information is a challenging task. The algorithm developed for the watermarking needs to be robust against various attack such as salt & peppers, filtering, compression and cropping etc. This paper focuses on the robustness of the algorithm by using a hybrid approach of two transforms such as Contourlet, Discrete Wavelet Transform (DWT). Also, the Particle Swarm Optimization (PSO) is used to optimize the embedding strength factor. The proposed digital watermarking algorithm has been tested against common types of image attacks. Experiment results for the proposed algorithm gives better performance by using similarity metrics such as NCC (Normalized Cross Correlation value) and PSNR (Peak Signal to Noise Ratio)
A Novel Approach for Workflow Scheduling in Hybrid Cloud with Dynamic Datacenter
Work process is used to speak to variety of use which requires huge data figuring and limit. To vanquish this need of data computation and limit dispersed processing has created as one of the best responses for on asks for resource provider. Nevertheless, on occasion the benefits open to us may not be satisfactory, so the need develops to gather more sources from various fogs. This is done by using the Hybrid cloud. Half and half cloud is mix of open and private cloud. The private cloud is guaranteed by the customer consequently there are no extra charges for using the benefits available in it, however open cloud is controlled by others so we have to pay for the using the advantage as indicated by the businesses. The USAge of the cream cloud offers adaptability to the customer. While using the half breed cloud, two most basic inquiries rises. The first is the best approach to segment the work procedure. In addition, the second one is the thing that benefit we need to get from individuals as a rule cloud so it can meet our essential inside the foreordained due date. The changed booking organization work handle for hybrid cloud give the less make span for the DAG than the main figuring and give us the best resources that we need to secure from open cloud to have enough planning vitality to arrange the work procedure inside given due date.
We have gone through with two different approaches after scheduling has been performed to check the successful transaction between dynamic and static data center. We contemplated a hybrid approach which will reduce the complexity of the network but at the same time it also perform successful transaction between dynamic datacenters .Each time the transaction has been performed between different centers or clusters the consequences are successful
A new critical social science research agenda on pesticides
The global pesticide complex has transformed over the past two decades, but social science research has not kept pace. The rise of an enormous generics sector, shifts in geographies of pesticide production, and dynamics of agrarian change have led to more pesticide use, expanding to farm systems that hitherto used few such inputs. Declining effectiveness due to pesticide resistance and anemic institutional support for non-chemical alternatives also have driven intensification in conventional systems. As an inter-disciplinary network of pesticide scholars, we seek to renew the social science research agenda on pesticides to better understand this suite of contemporary changes. To identify research priorities, challenges, and opportunities, we develop the pesticide complex as a heuristic device to highlight the reciprocal and iterative interactions among agricultural practice, the agrochemical industry, civil society-shaped regulatory actions, and contested knowledge of toxicity. Ultimately, collaborations among social scientists and across the social and biophysical sciences can illuminate recent transformations and their uneven socioecological effects. A reinvigorated critical scholarship that embraces the multifaceted nature of pesticides can identify the social and ecological constraints that drive pesticide use and support alternatives to chemically driven industrial agriculture
A new critical social science research agenda on pesticides
Unidad de excelencia María de Maeztu CEX2019-000940-MThe global pesticide complex has transformed over the past two decades, but social science research has not kept pace. The rise of an enormous generics sector, shifts in geographies of pesticide production, and dynamics of agrarian change have led to more pesticide use, expanding to farm systems that hitherto used few such inputs. Declining effectiveness due to pesticide resistance and anemic institutional support for non-chemical alternatives also have driven intensification in conventional systems. As an inter-disciplinary network of pesticide scholars, we seek to renew the social science research agenda on pesticides to better understand this suite of contemporary changes. To identify research priorities, challenges, and opportunities, we develop the pesticide complex as a heuristic device to highlight the reciprocal and iterative interactions among agricultural practice, the agrochemical industry, civil society-shaped regulatory actions, and contested knowledge of toxicity. Ultimately, collaborations among social scientists and across the social and biophysical sciences can illuminate recent transformations and their uneven socioecological effects. A reinvigorated critical scholarship that embraces the multifaceted nature of pesticides can identify the social and ecological constraints that drive pesticide use and support alternatives to chemically driven industrial agriculture
international Epidemiology of Carbapenemase-Producing Escherichia Coli
BACKGROUND: Carbapenemase-producing (CP) Escherichia coli (CP-Ec) are a global public health threat. We aimed to describe the clinical and molecular epidemiology and outcomes of patients from several countries with CP-Ec isolates obtained from a prospective cohort.
METHODS: Patients with CP-Ec were enrolled from 26 hospitals in 6 countries. Clinical data were collected, and isolates underwent whole-genome sequencing. Clinical and molecular features and outcomes associated with isolates with or without metallo-β-lactamases (MBLs) were compared. The primary outcome was desirability of outcome ranking (DOOR) at 30 days after the index culture.
RESULTS: Of the 114 CP-Ec isolates in Consortium on resistance against carbapenems in Klebsiella and other Enterobacterales-2 (CRACKLE-2), 49 harbored an MBL, most commonly blaNDM-5 (38/49, 78%). Strong regional variations were noted with MBL-Ec predominantly found among patients in China (23/49). Clinically, MBL-Ec were more often from urine sources (49% vs 29%), less often met criteria for infection (39% vs 58%, P = .04), and had lower acuity of illness when compared with non-MBL-Ec. Among patients with infection, the probability of a better DOOR outcome for a randomly selected patient with MBL-Ec as compared with non-MBL-Ec was 62% (95% CI: 48.2-74.3%). Among infected patients, non-MBL-Ec had increased 30-day (26% vs 0%; P = .02) and 90-day (39% vs 0%; P = .001) mortality compared with MBL-Ec.
CONCLUSIONS: Emergence of CP-Ec was observed with important geographic variations. Bacterial characteristics, clinical presentations, and outcomes differed between MBL-Ec and non-MBL-Ec. Mortality was higher among non-MBL isolates, which were more frequently isolated from blood, but these findings may be confounded by regional variations