Wheat Crop Yield Forecasting Using Various Regression Models

The prediction of crop yield, particularly paddy production is a challenging task and researchers are familiar with forecasting the paddy yield using statistical methods, but they have struggled to do so with greater accuracy for a variety of factors. Therefore, machine learning methods such as Elastic Net, Ridge Regression, Lasso and Polynomial Regression are demonstrated to predict and forecast the wheat yield accurately for all India-level data. Assessment metrics such as coefficient of determination ($R^{2}$ ), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) are used to evaluate the performance of each developed model. Finally, while evaluating the prediction accuracy using evaluation metrics, the performance of the Polynomial Regression model is shown to be high when compared to other models that are already accessible from various research in the literature

MINIMIZATION OF MOBILE AD HOC NETWORKS ROUTING ATTACKS USING DS MATHEMATICAL THEORY

Mobile Ad hoc Networks (MANET) have been highly vulnerable to attacks due to the dynamic nature of its network infrastructure. Among these attacks, routing attacks have received considerable attention since it could cause the most devastating damage to MANET. Even though there exist several intrusion response techniques to mitigate such critical attacks, existing solutions typically attempt to isolate malicious nodes based on binary or naı¨ve fuzzy response decisions. However, binary responses may result in the unexpected network partition, causing additional damages to the network infrastructure, and naı¨ve fuzzy responses could lead to uncertainty in countering routing attacks in MANET. In this paper, we propose a risk-aware response mechanism to systematically cope with the identified routing attacks. Our risk-aware approach is based on an extended Dempster-Shafer mathematical theory of evidence introducing a notion of importance factors. In addition, our experiments demonstrate the effectiveness of our approach with the consideration of several performance metric

AERIAL SURVEILLANCE FOR VEHICLE DETECTION USING DBN AND CANNY EDGE DETECTOR

We present an automatic vehicle detection system for aerial surveillance in this paper. In this system, we escape from the stereotype and existing frameworks of vehicle detection in aerial surveillance, which are either region based or sliding window based. We design a pixel wise classification method for vehicle detection. The novelty lies in the fact that, in spite of performing pixel wise classification, relations among neighboring pixels in a region are preserved in the feature extraction process. We consider features including vehicle colors and local features. For vehicle color extraction, we utilize a color transform to separate vehicle colors and non-vehicle colors effectively. For edge detection, we apply moment preserving to adjust the thresholds of the Canny edge detector automatically, which increases the adaptability and the accuracy for detection in various aerial images. Afterward, a dynamic Bayesian network (DBN) is constructed for the classification purpose. We convert regional local features into quantitative observations that can be referenced when applying pixel wise classification via DBN. Experiments were conducted on a wide variety of aerial videos. The results demonstrate flexibility and good generalization abilities of the proposed method on a challenging data set with aerial surveillance images taken at different heights and under different camera angles

THE SEAL OF SEARCH WORDS IN MANY DESCRIPTIVE DATASETS

Unlike the tree indicators used in existing companies, our index is less receptive when it comes to increasing dimensions and metrics with multidimensional data. The unwanted candidates are cut in line with the distances between the MBR of the points or keywords and also with the best diameter. NKS queries are useful for many applications, for example, to analyze images in social systems, search for graphics patterns, perform geographic searches in GIS systems, etc. We produce exactly as well as the approximate form of formula. In this document, we consider that objects marked with keywords are baked in a vector space. Keyword-based search in rich, multidimensional data sets helps with many new applications and tools. From these data sets, we observe the queries that request the most precise categories of points that comply with the set of confirmed keywords. Our experimental results in real and synthetic datasets reveal that ProMiSH has up to 60 times more acceleration in tree-based art techniques. We recommend a unique method known as ProMiSH that uses random index structures and random fragmentation and achieves high scalability and acceleration. We carry out extensive experimental studies to demonstrate the performance of the proposed techniques

Dynamic Data Security Assurance In Cloud Computing

We create cloud-helped remote detecting systems for empowering dispersed agreement estimation of obscure parameters in a given geographic range. We first propose an appropriated sensor system virtualization calculation that looks for, chooses, and directions Internet-available sensors to perform a detecting undertaking in a particular locale. The emergence of yet more cloud offerings from a multitude of service providers calls for a meta cloud to smoothen the edges of the jagged cloud landscape. This meta cloud could solve the vendor lock-in problems that current public and hybrid cloud users face.    The cloud computing paradigm has achieved widespread adoption in recent years. Its success is due largely to customers’ ability to use services on demand with a pay-as-you go pricing model, which has proved convenient in many respects. Low costs and high flexibility make migrating to the cloud compelling. Despite its obvious advantages, however, many  companies hesitate to  “move  to  the  cloud,” mainly because of concerns related to service availability,  data  lock-in,  and  legal  uncertainties. Lock in is particularly problematic Our reproduction results demonstrate that the proposed calculation, when contrasted with traditional ADMM (Alternating Direction Method of Multipliers), diminishes correspondence overhead essentially without trading off the estimation mistake. Furthermore, the joining time, however builds somewhat, is still straight as on account of ordinary ADMM

Himalayan P waves in COPD - A Rare Feature

Himalayan or giant P-waves (amplitude =5 mm) are often known to be classically associated with congenital heart diseases with right to left shunt like tricuspid atresia, Ebstein anomaly, combined tricuspid and pulmonic stenosis, etc, where they indicate a dilated right atrium and tend to be persistent. These type P waves are rarely seen in chronic obstructive pulmonary disease (COPD) and in this condition it may be due to structural right atrial changes or hypoxemia or combination of both. Here we report a case of COPD with Himalayan P waves which is a rare entity

Hirayama Disease - A Variant of Motor Neuron Disease and Role of Flexion MRI in Diagnosis

Hirayama disease is a monomyelic variant of motor neuron disease (MND) and has distinctive features of male predominance, asymmetric involvement of upper extremities with a self limiting course. Flexion MRI (magnetic resonance imaging) forms the main stay for diagnosis of this condition. Here we report such an unusual case of Hirayama disease in a male patient of 20 years who presented with weakness and atrophy in right upper limb. Careful clinical examination will help to use the flexion MRI studies for the diagnosis of this condition as done in our case

Evolutionary computing based QoS oriented energy efficient VM consolidation scheme for large scale cloud data centers using random work load bench

In order to assess the performance of an approach, it is unavoidable to inspect the performance with distinct datasets with diverse characteristics. In this paper we had assessed the system performance with random workbench datasets. A-GA (Adaptive Genetic Algorithm) based consolidation technique has been compared with other consolidation techniques including dynamic CPU utilization techniques, VM (Virtual Machine) selection and placement policies. The proposed consolidation system had exhibited better results in terms of energy conservation, minimal Service Level Agreement (SLA) violation and Quality of Service (QoS) assurance

Review of Journal of Cardiovascular Magnetic Resonance 2015

There were 116 articles published in the Journal of Cardiovascular Magnetic Resonance (JCMR) in 2015, which is a 14 % increase on the 102 articles published in 2014. The quality of the submissions continues to increase. The 2015 JCMR Impact Factor (which is published in June 2016) rose to 5.75 from 4.72 for 2014 (as published in June 2015), which is the highest impact factor ever recorded for JCMR. The 2015 impact factor means that the JCMR papers that were published in 2013 and 2014 were cited on average 5.75 times in 2015. The impact factor undergoes natural variation according to citation rates of papers in the 2 years following publication, and is significantly influenced by highly cited papers such as official reports. However, the progress of the journal's impact over the last 5 years has been impressive. Our acceptance rate is <25 % and has been falling because the number of articles being submitted has been increasing. In accordance with Open-Access publishing, the JCMR articles go on-line as they are accepted with no collating of the articles into sections or special thematic issues. For this reason, the Editors have felt that it is useful once per calendar year to summarize the papers for the readership into broad areas of interest or theme, so that areas of interest can be reviewed in a single article in relation to each other and other recent JCMR articles. The papers are presented in broad themes and set in context with related literature and previously published JCMR papers to guide continuity of thought in the journal. We hope that you find the open-access system increases wider reading and citation of your papers, and that you will continue to send your quality papers to JCMR for publication