Crop production is a vital aspect of human survival. Reducing global poverty heavily relies on increasing the robustness in crop yields. The quantity of crops harvested from a given agricultural land, also known as cr...
Crop production is a vital aspect of human survival. Reducing global poverty heavily relies on increasing the robustness in crop yields. The quantity of crops harvested from a given agricultural land, also known as crop yields of that land is the important parameter that helps to increase the smartness in adopting novel agricultural plans. Measuring this parameter can be done through supervised machine learning techniques which helps with accurate predictions given the historical data of crop yield. This paper proposes an ideation, that uses Support Vector Machines (SVMs) because of its strength being accuracy, robust and flexible. SVM can predict reliable crop yield estimates that help to allocate resources and smart planning for agricultural activities. Also, SVMs are good enough to handle the imperfections in historical crop yield datasets, outliers, and inconsistencies. Furthermore, SVMs can bring out the relationships and any nonlinear patterns in the crop yields which are influenced by uncertain environmental factors over various seasons. This helps to identify the anomalies in the historical data. The anomalies could be exceptionally high yield or the failures which happen to be pinpointed as abnormal years. The identified outliers are excluded by the SVM, thus bringing out a prediction model that focusses on “normal years”. By this, it is possible to represent more accurate inter-annual trends of crop yields avoiding the anomalies aiding the farmer to make better decisions for much more productivity in the crop yield. Based on the ideation, simulations are performed that showcase the success of the SVM in predicting good for the crop yields in inter-annual period.
A method of conducting continuous medical monitoring of human operators of human-machine systems is proposed. This method assumes building a decision support system that selects the control mode of the technological p...
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Background: Frailty has become a growing global concern and is associated with social determinants of health (SDoH). However, the relative importance and cumulative contribution of multidomain SDoH to frailty, and whe...
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Background: Frailty has become a growing global concern and is associated with social determinants of health (SDoH). However, the relative importance and cumulative contribution of multidomain SDoH to frailty, and whether these relationships differ across countries, remain ***: We included participants aged ≥45 years from the USA (N=5,792), England (N=3,773), and China (N=5,016). SDoH (n=121 for the USA, n=125 for England, and n=94 for China) were selected across seven domains. Frailty was assessed by the frailty index. We developed Extreme Gradient Boosting to predict frailty at the 4-year follow-up and used SHapley Additive exPlanations to quantify variable-wise and domain-wise contributions of SDoH, and to explore nonlinear relationships between SDoH and ***: Our models explained 0.242 (95% confidence interval [CI]: 0.203–0.281), 0.258 (95% CI: 0.191–0.325), and 0.173 (95% CI: 0.126–0.215) of the variance in FI among all participants from the USA, England, and China. Health behaviors and social connections or stressors were the most important domains in the USA and England, while material circumstances contributed largely in China. Several important SDoH predictors, such as body mass index, were consistent across countries, while country-specific risk factors, such as engagement in maintenance or gardening in the USA, were also identified. We observed nonlinear relationships between SDoH, such as sleep duration, and ***: Our findings reveal the priorities of SDoH domains for addressing aging disparities and promoting healthy aging, especially region-specific risk factors for tailored public health prevention ***: This work was supported by the National Natural sciencefoundation of China (grant no. 71972164).Declaration of Interest: No conflicting relationship exists for any *** Approval: All studies received ethical approval from relevant local research ethics committees, and participants were
Autonomous Vehicle (AV) decision-making in urban environments is inherently challenging due to the dynamic interactions with surrounding vehicles. For safe planning, AV/ego must understand the weightage of various spa...
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Nowadays, dietary issues are increasing around the world. Numerous problems, such as weight gain, obesity, diabetes, etc., can arise from an unbalanced diet. By integrating image processing, the system can assess food...
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This work focuses on diverse machine learning methods for big data analytics, which works to leverage predictive performance. The techniques like data preprocessing, dimensionality reduction, feature selection, model ...
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Brain tumors are a serious health concern caused by the abnormal growth of cells in or around the brain. These tumors can either be benign or malignant. Few of the causes for Brain tumors are genetic mutations, exposu...
Brain tumors are a serious health concern caused by the abnormal growth of cells in or around the brain. These tumors can either be benign or malignant. Few of the causes for Brain tumors are genetic mutations, exposure to radiation, or environmental factors. Early detection of brain tumors is essential for effective treatment and improved patient outcomes. To address this, a novel approach has been proposed for brain tumor detection using transfer learning with the VGG-16 convolutional neural network (CNN) architecture. By leveraging the pre-trained VGG-16 model on the ImageNet dataset, which provides a broad range of data for learning and representing complex features of various objects, meaningful features can be extracted from MRI scans and differentiate between tumor and normal tissue accurately. This refined VGG-16 model can provide a more precise and reliable diagnosis, which is crucial for successful treatment. This research study demonstrates that the utilization of VGG-16 model for brain tumor diagnosis can provide an efficient and accurate method for identifying brain tumors. Moreover, this approach can be applied to develop automated systems to assist medical professionals in diagnosing and treating brain tumors more effectively.
The proposed work suggests a novel approach for the cooperation of geographically scattered individuals and the blending of different points of view. This work ensures that the center on heterogeneous networks in whic...
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Existing research for argument representation learning mainly treats tokens in the sentence equally and ignores the implied structure information of argumentative context. In this paper, we propose to separate tokens ...
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Motor coordination is crucial for preschoolers’development and is a key factor in assessing childhood *** diagnostic methods often rely on subjective manual *** paper presents a machine vision-based approach aimed at...
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Motor coordination is crucial for preschoolers’development and is a key factor in assessing childhood *** diagnostic methods often rely on subjective manual *** paper presents a machine vision-based approach aimed at improving the objectivity and adaptability of *** method proposed involves the extraction of key points from the human skeleton through the utilization of a lightweight pose estimation network,thereby transforming video assessments into evaluations of keypoint *** study uses different methods to handle static and dynamic actions,including regularization and Dynamic Time Warping(DTW)for spatial alignment and temporal discrepancies.A penalty-adjusted single-frame pose similarity method is used to evaluate *** lightweight pose estimation model reduces parameters by 85%,uses only 6.6%of the original computational load,and has an average detection missing rate of less than 1%.The average error for static actions is 0.071 with a correlation coefficient of 0.766,and for dynamic actions it is 0.145 with a correlation coefficient of *** results confirm the proposed method’s effectiveness,which includes customized visual components like motion waveform graphs to improve accuracy in pediatric healthcare diagnoses.
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