Food Security, poverty reduction and socio-economic well being can be considered as backbone of development.This can be achieved by enhanced education, employment generation, access to infrastructural facilities and increased purchasing power.However, to-date getting a required nutritious diet, clothing and shelter is challenging task and to curtail this MGNREGS was introduced with enormous objectives and policies.Thus, this paper makes an attempt to understand the impact of MGNREGS on food security and employment.This paper is a part of the major research project funded by UGC.The study is based on Primary study conducted in the districts of Bellary, Davangere, Gulbarga, Mandya and Shimoga in Karnataka state through a structured interview schedule comprising of 2500 samples.It is said that the development of a nation depends on the development of rural areas and this strategy can be achieved through MGNREGS.Thus, this paper makes an attempt to realize the intensity of the scheme in addressing food security issues.
In recent era of information technology and big data, a huge volume of data explosion on web makes it difficult to choose any information or item which users really interested about it.To handling such challenges, recommender systems (RS) emerged as helpful tool for recommending such information or item to potentially interested users.Content based recommendation (CBR) and collaborative filtering is two most popular classes of recommendation.Collaborative filtering (CF) approach analyzes user interest while CBR recommends items by object analysis.The main challenges of CF include scalability, synonymy and sparsity which creates obstacle in effective recommendation.In this work, we propose hybrid model of recommender system based on singular value decomposition (SVD), which combines with CF and content based recommendation approach.Experimental evaluation is carried out via prediction accuracy metrics root mean squared error (RMSE) and mean absolute Error (MAE).Empirical results using real dataset demonstrates the effectiveness of proposed approach in comparison with others baseline traditional rating RS.Hybrid recommendation model by combined features shows improved quality and better accuracy in rating prediction, introduced a diversity factors in recommendation.
Introduction:Fatty liver, also referred to as hepatic steatosis, has increased in prevalence during the past 20 years.Due to the fact that fatty liver patients usually have no symptoms when they are diagnosed, this is related to an increase in the prevalence of fatty liver combined with the increasing use of radiologic tests that identify fatty liver.[1] Increased liver fat content (LFC) is linked to factors like obesity, type 2 diabetes, insulin resistance, dietary problems, metabolic and genetic abnormalities, infections, and binge drinking.[2].Hepatic steatosis is typically painless, although it may progress into a more serious liver condition.Regardless of the patient's BMI, type 2 DM increases both the likelihood and the severity of NAFLD.[3].Histologic analysis, ultrasonography, computed tomography, and magnetic resonance imaging can all be used to assess the LFC [4].Histologic analysis regarded to be the most accurate approach for staging and grading fatty liver disease; however, it is an invasive procedure making it less desirable.Although histologic analysis has been shown to be the most reliable
India is a democratic country where it is "ruled by the people for the people."Althoughsome circumstances attract attention whether the above statement is true.We can only see the 2014 Loksabha elections where Narendra Modi became the Prime Minister of the country with 31% votes.In this paper we will discuss Borda count method in Indian voting system.So far plurality method has been applied in India's politics.
In this article, we examine the structural, electronic, and elastic characteristics of transition metal carbides ZnC and NbC as well as their ternary alloy ZnxNb1-xC (x =0, 0.25, 0.50 and 0.75, 1).Our analysis utilized a Generalized Gradient Approximation along with the modified Becke-Johnson potential (mBJ) using the Full Potential Linearized Augmented Plane Wave (FP-LAPW) approach.We calculated lattice parameters, bulk modulus, pressure derivative, and elastic constants.Additionally, we derived Young's modulus, shear modulus Poisson's ratio anisotropy factor from our obtained elastic constants.Furthermore, discussion is presented on total and partial densities of states as well as charge densities.
In recent years, as smart phones have grown in popularity, problematic cell phone use has gotten more attention among students. Due to the quarantine period, loneliness, and online classes, students' use of smartphones rose during the COVID-19 epidemic, affecting their physical and psychological well-being. Aim: To assess the health impact on excessive usage of smart phone and their effects among students during the COVID-19 pandemic wave-2.0 in India. Settings and Design: Data was acquired using a semi-structured Google Forms and responses were collected via social media via Google form among students across India during COVID-19. Results: Our research study shows that students were experiencing health problems like headache (86%), vision problems (68%), sleep pattern disturbance (79%), lack of attention (66%). The average usage of smart phone usage among the students were found to be greater than 4 hours per day for 85% of the students. Smartphone usages were more prevalent in males (56%) than in females (44%). The online classes cause increased usage of smartphone and affects their health more and 83% of the participants felt that they were addicted to smartphone. Concusion: Smartphone usage is becoming a necessity, even though students should be educated regarding proper usage and handling of smartphones and school/college should follow Government Guidelines for minimizing health hazards
The stock market prediction process is carried out to forecast the future price movement of a stock.It involves using various analytical techniques and procedures.The process utilized in forecasting the future price movement of stocks involves using various techniques and methods.It is very challenging to predict the stock market's direction due to the multitude of factors that can affect its performance, such as economic indicators, corporate events, and investor sentiments.Data mining techniques have gained popularity in the field of forecasting the stock market, as they can extract valuable information from vast amounts of data.This paper presents an empirical study on the use of these techniques to predict the stock market trends.The study utilizes four popular mining algorithms SVM, Linear Regression, Random Forest (RF) and Naïve Bayes.The objective of the study was to analyze the effects of various factors on stock prices of major technology companies.These included the volume of trading, the priceto earnings ratio, and the news sentiment score.The results of the tests revealed that the RF performed better than the others in terms of accuracy.The former performed well when all of the available factors were utilized, while the latter performed even better when only a single factor was used.Although the Naive Bayes and linear regression algorithms performed well, their accuracy rates were not as high as those of the two others.The findings of the study show that data mining techniques can effectively predict the stock market's direction.
The climate, topography, soil and biology are highly dependent on agriculture.In which land plays a significant role.Despite considerable progress in the service sector, agriculture remains India's primary source of employment and income, and price fluctuations in agricultural commodities have a significant impact on people's daily lives as well as agricultural inputs and outputs.In order to overcome all this, soil test values are used in the current study to classify several important soil characteristics, such as the Available Phosphorus (P), Potassium (K), Organic Carbon (OC) and Boron (B) village-wise soil fertility indices, as well as the Soil Reaction parameter (pH).We have addressed various algorithms related to data mining and machine learning (ML) classification techniques in this paper along with IOT hardware that used in agriculture.These algorithms are developed on a data set for yield prediction of crops that have been collected over the years.In addition, a comparative study is carried out to show which classification algorithm is better suited for classification techniques success prediction.
Social media data mining can be very challenging to manage due to the various factors that affect its quality and reliability.Some of these include the volume of information, the complexity of the data, and the ethical and privacy issues that arise.The rapid pace of social media also makes it hard to keep up with the changes in user behavior and trends.Due to the massive amount of information that social media platforms collect, data mining has become an increasingly important tool for analyzing and improving marketing strategies.This process can help businesses identify potential customers and develop effective marketing campaigns.Despite the various challenges that social media data mining can face, it has been successfully used by many organizations to improve their competitive advantage.For instance, by analyzing the sentiment data of their customers, they were able to identify key opinion leaders and influencers.This paper explores the various methods that are used in social media data mining.These include unsupervised and supervised learning, network analysis, and text mining.We will also talk about the applications of these techniques in various areas, such as brand management, social network analysis, and sentiment analysis.Through case studies, we will explore the various advantages and challenges of data mining on social media.We will also identify the potential directions for this technology in the future.
Wireless Sensor Networks (WSNs) have gained significant attention in recent years due to their ability to monitor and detect structural damage.This has led to the development of a wireless sensor network for structural health monitoring.The system is designed to collect and analyze data from sensors placed on structures, such as bridges and buildings, to assess their health status.The implementation of the system involved deploying the sensors on a bridge and collecting data over an extended period.The collected data was analyzed using the algorithms developed during the design phase.The results of the analysis provided insights into the health status of the bridge and enabled the identification of potential areas of concern.
The COVID-19 pandemic has posed an unparalleled challenge to global public health.The precise prediction of the dissemination of the virus is of utmost importance as it empowers decision-makers to promptly implement measures to manage the propagation of the virus.This paper provides an evaluation and comparison of various models that employ data mining and machine learning methodologies for the purpose of predicting the propagation of COVID-19.The discourse encompasses diverse models, including the Long Short-Term Memory (LSTM) model, the Susceptible-Exposed-Infected-Recovered (SEIR) model, the Decision Tree algorithm, and the Random Forest algorithm.Each of the aforementioned models has exhibited efficacy in predicting the quantity of COVID-19 cases, hospitalisations, and fatalities.The results of these models illustrate the significance of utilising data mining and machine learning methodologies for forecasting the dissemination of COVID-19.Furthermore, these models have the potential to offer significant insights into the fundamental factors that impact the transmission of the virus.Conclusively, the investigation pertaining to COVID-19 prognostication models utilising data mining and machine learning holds substantial significance.Additional investigation is required to enhance the precision of said models and their capacity to anticipate the dissemination of novel virus strains.
The significance of wearing masks in mitigating the transmission of infectious diseases has been underscored by the persistent COVID-19 pandemic.The utilisation of machine learning algorithms for mask detection has emerged as a promising solution for detecting whether individuals are wearing masks in public spaces.This paper presents an analysis of recent studies on the detection of masks using machine learning techniques.The focus is on the utilisation of deep learning algorithms, including convolutional neural networks (CNNs), transfer learning, and object detection models such as YOLO and Faster R-CNN.This study involves a comparative analysis of model performance, an examination of the influence of training datasets on model accuracy, and an investigation of ensemble techniques as a means of enhancing model performance.The results of our study indicate that the employment of CNNbased models and transfer learning methods that utilise pre-existing networks have exhibited the most elevated levels of precision.Nevertheless, additional investigation is required to enhance these models for pragmatic applications and to assess their efficacy in authentic environments.In general, the utilisation of machine learning techniques for mask detection holds promise as a useful strategy for mitigating the transmission of contagious illnesses.The present review offers valuable perspectives for future investigations in this domain
It is possible to study the reviews-based dataset.The validity of this paradigm was demonstrated by our data analysis.The data taken from the Twitter reviews (Myer-Briggs) dataset is subjected to behaviour analysis.To determine the user's remark, the data is evaluated.We want to use data-driven marketing technologies including supervised machine learning models, natural language processing, along with information visualisation.Logistic Regression is one of the classification techniques on which the system was constructed.The current issues with each topic are examined before the most recent fixes are provided and debated.The findings from the experiment demonstrate the accuracy, precision, recall, and F1 score.Following that, we can use the Twitter API to forecast the personality.It illustrates how openness is compared.
Public key encryption can be replaced with Identity-Based Encryption (IBE), because of this handling public keys and certificates is easier at Public Key Infrastructure (PKI) simpler.Nevertheless, the overhead calculation at the Private Key Generator (PKG) One important efficiency factor when a person revokes their account limitations of IBE.In a typical PKI environment, effective revocation has been extensively investigated.Nevertheless, IBE aims to reduce the burdensome maintenance of certificates.We provide a revocable IBE scheme for the server-assisted environment after first including outsourced computing into IBE to address the critical issue of identity revocation.According to our design, a Key Update Cloud Service Provider will handle the majority of the duties connected to key generation that are involved with key issuance and key update operations, leaving PKG and users to handle a limited number of simple tasks locally.In order to do this, we develop a special collusion-resistant mechanism that deploys a hybrid private key for every user and uses an AND gate to link and tie the identity component and the time component.Moreover, we suggest alternative architecture that, according to the freshly developed Refereed Delegation of Computation paradigm, is probably safe.Lastly, we offer a wide variety of experimental findings that highlight the potency of our suggested structure.
The manufacturing processes used to create the finest bamboo fabric do not include the removal of cellulose.The fibers from bamboo wood are crushed, treated with a natural enzyme, cleaned, and then spun into yarn.This causes harm to the ecosystem and may pose risks to human skin.The problem motivated the research into the performance of herbal-finished cotton and bamboo fabric.
