In abstract algebra, a symmetric group defined over a set is the group for which the composition of functions is the group operation, and whose members are all the bijections from the set to itself. Galois theory, invariant theory, lie group representation theory, and combinatorics are just a few of the many branches of mathematics that rely on the symmetric group. The mathematical field of group representation theory studies the effects that groups have on predetermined structures. Particular attention is paid here to group operations on vector spaces. Yet, we also take into account groups that operate upon other groups or sets. The primary goal of this study is to offer an alternate approach of describing group elements of finite simple groups that is both brief and informative. In this research, we show how we found some new symmetric constructions for significant finite groups. As the orders of our photographs are becoming more and bigger, we've started using Magma to help us out with some of the math.
We consider the connectivity of fiber graphs with respect to Grobner basis and Graver basis moves. First, we present a sequence of fiber graphs using moves from a Grobner basis and prove that their edge-connectivity is lowest possible and can have an arbitrarily large distance from the minimal degree. We then show that graph-theoretic properties of fiber graphs do not depend on the size of the right-hand side. This provides a counterexample to a conjecture of Engstrom on the node-connectivity of fiber graphs. Our main result shows that the edge-connectivity in all fiber graphs of this counterexample is best possible if we use moves from Graver basis instead.
Purpose: In recent times, sugarcane production and area under cultivation have been fluctuating from year to year depending on climate and price policy, adversely affecting sugarcane growers' decisions to invest in cultivation and their livelihood. The declining trend of productivity may affect the future competitiveness, and therefore it needs to be investigated. Design/Methodology/Approach: In this study, prediction of sugarcane yielding through regression analysis is performed with the help of Multivariate Adaptive Regression Splines (MARS), Support Vector Regression (SVR), Partial Least Square Regression (PLSR), Elastic-Net Regression, and Multiple Linear Regression (MLR) on the basis of the historical data of sugarcane cultivation from 1971-72 to 2018-19. The prediction is done by training all the regression models with 80% of the data, by taking the overall Indian sugarcane productivity as a dependent variable and other major sugarcane producing states as independent variables. Findings: As a main result, the non-parametric regression model MARS is found to be much better than other well-fitted models. All of these models' performances are cross-validated using the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and the Wilcoxon Signed-Rank test. Also, the MARS model is found to be a more flexible and accurate model in predicting the behavior of sugarcane yielding in India. Practical Implications: The practitioners and farmers facilitate the model comparisons to achieve more profits through accurate estimation The research outcome implicates the agricultural industry to improve the sugarcane cultivation and productivity under uncertain environments. Originality/Value: The study suggests best management practices can be developed to increase the large potential of sugarcane production in India towards greater sustainability and food security modelling.
This study was undertaken to explore the determinants which affects the customer's intention to continue digital payment services. The ECM model was extended by using security and responsiveness. The conceptual model of the study was tested on 350 respondents using Partial Least Squares Structured Equation Modelling. The current study looked at how perceived security, responsiveness, and usefulness influenced the quality of digital payment services, digital payment customer satisfaction, and consumers' intentions to use these digital payment services. The findings indicate that perceived security, responsiveness, customer satisfaction and service quality have a significant impact on customers' willingness to continue using digital payments whereas perceived usefulness is not found to have a statistically significant impact on service quality towards digital payment services. Responsiveness found to be the most significant predictor of service quality while security is found to be the most significant predictor of customer satisfaction towards digital payment services. The present research provides a guide to the banking sector and helps them to identify the factors which contribute towards the intention of customers to continue digital payments services. The findings of the study also demonstrate that marketing managers and analysts have enormous potential to capitalize on these opportunities and plan for the long-term success of digital payment systems.
In this paper, an IVP for a system of two singularly perturbed delay differential equations with Robin initial conditions is considered. A Shishkin piecewise uniform mesh is implemented and combined with a classical finite difference method to create a numerical method for solving this problem. The numerical approximations obtained are essentially first order convergence uniformly with respect to the singular perturbation parameters. Numerical result is provided in support of the theory.
This paper focuses on providing economical methods for manufacturing of hot die steel inserts, for various optimized solutions developed the constraint of the existing manufacturing setup is also considered. The techniques are implemented on a pressure die casting moving die insert component. The present optimized solution is derived for manufacturing rope length minimization of the die casting inserts (moving and fixed die) to get optimized solutions at every stage. The implementation of combined electrode, combined assembly machining and the cluster plate machining techniques are the key development done in the research. Combined electrodes, assembly machining along with cluster machining of the electrodes reduce the total physical operational time of the die which will ultimately leads to saving the overall manufacturing cost and increase the profit The proposed solution opens up new avenues for similar automobile components by setting benchmark to decrease the rope length associated to manufacturing and increase the profit.
Information retrieval is intended to help people who are constantly looking for information. Partitioning the video into frames is the first stage in video information retrieval. Most video frames are brief and do not provide much information about the image content. On the other hand, scene border recognition or video fragmentation into scenes provides a better understanding of the video scene by clustering images based on similar image content. This paper is about video scene identification, specifically video formation mining for template matching with deep characteristics. The study proposed and created a workflow that included phases for frame extraction, finding similarities between consecutive frames, grouping frames, identifying key frames, and seeing detection by merging the relevant frames. Python's OpenCV generates the frames. The process is evaluated using scene identification metrics. The results show that scene detection and quality are significant, as measured by several criteria. In addition, we examined and studied current recognition and analysis criteria. Furthermore, our proposed methodologies have been thoroughly tested on various public scene video datasets, and they outperform some state-of-the-art approaches. This work's findings can be used to create real-time conceptual video interpretations.
In this modern era cardiovascular diseases are the good number tedious one. This disorder physical attacks a human so in a flicker that it only just gets at anything time to get treated with. So identifying patients truthfully on suitable hypothesis is the most difficult obligation for the therapeutic society. A wrong prediction to find heart disease will be a big problem in patient's life. The medical errors were took place in India and also in other countries. The encouragement behind this paper is to append to an economical knowledge by utilizing the mining improvements for heartening in the sequence base choice sensitively compassionate network. This paper deliberated on the viewpoint which is dismissed in the development.
The emergence of neural networking designs with great performance in computer tasks which are vision-related has sparked interest in radiology artificial intelligence (AI). Radiologists could get advantage from a greater understanding the principles of AI as AI-based software systems become more incorporated into the clinical workflow. Machine learning (ML) is becoming increasingly popular in the domains of medical imaging, radiomics, and medical image analysis. Deep learning is a sort of machine learning that originated in the field of computer vision and has since expanded in popularity across a wideaspect range of sectors. Deep learning has demonstrated outstanding performance in a variety of fields, including picture classification, object detection, and segmentation. This work gives an overall overview of recent achievements in this area by surveying deep learning architectures and DL approaches used to diagnose disease based on medical images.
Rather than just focusing on improving Intelligence Quotient (IQ), more and more people are focusing on balancing IQ with Emotional Quotient (EQ) as a solid foundation for future success. Many studies have found that the Emotional Quotient is far more important than the Intelligence Quotient in determining success, relationship quality, and overall happiness. People of all ages can improve their Emotional Quotient and live happier lives. The benefits of Emotional Quotients are validated and evaluated in this study. According to the results of the comparison, all people, regardless of age or socioeconomic status, need to learn to manage their emotions in order to lead more meaningful lives.
More number of vehicles get problems these days due to engine faults, breakdown, Tyre problems. etc. In that situation people suffer a lot. If it is a well- known city or a place, they can find a mechanic or a workshop, but in new places and environment they don't know where to go and find mechanic. Then the user had to go in search of mechanic without any support and Knowledge. Considering these kind of vehicle problems an android app is the easy solution to handle this problem correctly because everyone has an android phone, they can easily go through a user-friendly interface to find help. So, this android app helps the user to find the nearby live available mechanics on his Surroundings and can call the mechanic so the user can call them for support and get their live location. This android application helps the people to connect with the mechanic within the nearest surroundings. This application contains two users which are Mechanic and vehicle user. Its acts as a bridge between user and a mechanic. whenever a problem is occurred in the vehicle, user can find the nearest mechanic at his surroundings.
This paper shows a new way to use a combination of filter feature selection algorithms for image classification. In pattern recognition, machine learning, and computer vision, feature selection is one of the most important problems. The main goal of feature selection is to classify images, improve how well they can be classified, and make the whole process easier to understand. The only method that is guaranteed to find the best subsets is the exhaustive search method, but it takes a lot of time to run. A new adaptive and hybrid approach to selecting features is proposed. This approach combines and uses different methods to make a more general solution. Several state-of-the-art feature selection methods are described in detail with examples of how they can be used, and a thorough evaluation is done to compare their performance to that of the proposed approach. The results show that the individual methods for selecting features perform very differently on the test cases, but the combined algorithm always gives a much better answer.
Researchers have shown that broccoli benefits are growing tremendously due to its awareness among the people. People who are concerned about their health are moving forward to change their food habits. Even if the new products that are evolving into the market are suddenly dropped out due to the various health defects in human life. The optimization studies is the first and foremost analysis for the raw material because the processed product shows different characteristics while processing. In this paper work detailing the optimization of the process in the tray drying the broccoli powder at the two different temperatures namely 60 degrees C and 70 degrees C for the development of protein rich pizza base. The broccoli optimization studies are done with the working principle of the analysis and calculation are made with respect to the values. The dryer analysis are carried out to interpret the efficiency of three types of dryers: tray drier, cabinet -tray drier and microwave dryer. As a result of analysis and interpretation the broccoli powder which is dried at 60 degrees C gives the best results over the combination with the millet powder. Development of the product with the optimized parameters are giving the diet supplement with processed food like pizza.
As the Information communication technology advances, it has become crucial to ensure the originality of digital content. This paper focuses on using recursive equation approach of steganography to ensure authenticity of digital content. This is achieved by embedding hash value of the digital data in the same digital file. In this paper, we have taken 24 bit BMP files to implement the concept. We have used recursive equation of any order as key to find pixel location in the BMP file to embed the 128 bit MD5 value of the BMP file before its transmission. At the receiving end the MD5 is extracted from the zero bytes of BMP file and the original bits in extra bytes are restored before computing the MD5 value of the received and restored BMP. The two MD5 values are compared. Equality of the value authenticates the originality of BMP file.
Alzheimer's disease is a mind-issue sickness. The sickness is dealt with and attempted to direct the illness with different procedures. The goal is to foster a technique to observe likely amyloid-based biomarkers for early AD identification utilizing the ML approach. The principal focus is on supervised learning algorithms. This algorithm trains the machines with predefined training data and foresees the results. Linear Regression is utilized as the proposed calculation. Additionally, it has shown an extraordinary execution over conventional ML in distinguishing perplexing constructions in complex high-layered data. Our model portrays a specificity and sensitivity of 79% and 95% respectively in comparison with the Support Vector Mac hine ( SVM).
Blockchain is an emerging technology of the future. A wide range of transactions and applications uses the blockchain. Cryptocurrency and bitcoin applications were used to start of the block chain concept that was used for a secure and reliable network. Amalgam of different tools and methods like cryptography, mathematics, and networking is the Block chain technology. This paper enlightens the varied types of Blockchain algorithms. An insight of the real time applications of Blockchain in the recent era is discussed.
In this article, we are developing a new approach for solving a transportation problem in fuzzy environment to find out the lease fuzzy transportation cost. Here, we are solving trapezoidal fuzzy transportation problem with the help of ranking technique, whose parameters are trapezoidal fuzzy numbers. This new approach is well defined procedure and it can be utilized for all types of fuzzy transportation problem whether maximize or minimize objective function. At the end, this method is illustrated with a numerical example.
Objective: Agricultural play a major role in human life. The crop yield prediction is the needed one, because the investment and work process consume high but the yield output going low in every year. Methods: Here introduces machine learning (ML), which can be a key differentiator for obtaining real, estimated predictions for yield issues. In ML, we choose the random forest algorithm for the yield predictions. The classifier model used here includes logistic regression, naive Bayes, and random forests, of which extended random forests provide maximum accuracy Findings: Based on the dataset provided, we got the yields prediction by RF. The crop yield is different by the crops and usage of fertilizer. The fertilizer also depends upon the soil of the place. Novelty: The clustering method considers data-related environmental factors, soil factors and weather, soil fertility, and production over the past year, and recommends profitable plants that remain mature in the expected atmospheric conditions.
The significant challenges of wireless sensor networks include the connectivity and coverage, which is impacted by the node placement. Accordingly, the cost-effective deployment could be achieved with the optimal sensor node placement in the monitored area. The maximum coverage should be provided by the sensor nodes' positions with maximized network lifetimes. Researchers aim to achieve an optimal deployment that increases the coverage rate and network lifetime with minimization in energy consumption. Moth-Flame optimization algorithm is a bio-inspired optimization method that is used to solve k-coverage node deployment on target based WSN. However the conventional MFO algorithm suffers from premature convergence and stagnation problems. In this work, a new approach of MFO with mutation capability - MUMFO has been introduced to balance the exploration and exploitation capability of the traditional MFO and to increase coverage rate and connectivity. The moths are divided into three categories namely 'good', 'average' and 'bad' moths, based on the evaluated fitness values and mutation is performed among these categorized moths. The proposed strategy of MUMFO node deployment strategy has been compared with the existing node deployment strategies PSOIL & EDEM node placement methods. The MUMFO algorithm's effectiveness has been demonstrated in the simulation results that achieved better data delivery rate, minimal energy consumption rate, and maximum coverage.
