An algorithm for piece-wise indefinite quadratic programming problem

An indefinite quadratic programming problem is a mathematical programming problem which is a product of two linear factors. In this paper, the piecewise indefinite quadratic programming problem (PIQPP) is considered. Here, the objective function is a product of two continuous piecewise linear functions defined on a non-empty and compact feasible region. In the present paper, the optimality criterion is derived and explained in order to solve PIQPP. While solving PIQPP, we will come across certain variables which will not satisfy the optimality condition. For these variables, cases have been elaborated so as to move from one basic feasible solution to another till we reach the optimality. An algorithmic approach is proposed and discussed for the PIQPP problem. A numerical example is presented to decipher the tendered method

TOWARDS SUSTAINABLE COMMUNITY AND INSTITUTIONAL RESPONSE TO CLIMATE EXTREMES: A SITUATIONAL ANALYSIS OF INSTITUTIONS, COMMUNITIES AND THEIR RESPONSE TO CLIMATE CHANGE INDUCED DISASTERS IN UTTARAKHAND

This paper presents a situational analysis of disaster in 2013 and issues related to the preparation of mountain communities in Uttarakhand with specific reference to Bhatwari Block in Uttarkashi District and their adaptation and response. It also anlayses the response of the institutions in the light of needs of the communities. The paper argues that the determinants for such a response are located in the information flow and access, resource access and governance. The need for looking at these responses in the light of Mountain vulnerability frameworks and sensitivity aspects which include both gender and social marginality issues are emphasized. The issues of gendered vulnerability of the mountain communities located in a fragile eco system and inequitious social system with specific reference to agricultural and forest based livelihoods are discussed. The paper also focuses on how these vulnerabilities are impacted by climate change. The paper argues that institutional contexts also influence the responses of adaptation and mitigation. The paper concludes with suggesting some mechanisms for preparation of the communities and institutions for a sustainable response

TOWARDS SUSTAINABLE COMMUNITY AND INSTITUTIONAL RESPONSE TO CLIMATE EXTREMES: A SITUATIONAL ANALYSIS OF INSTITUTIONS, COMMUNITIES AND THEIR RESPONSE TO CLIMATE CHANGE INDUCED DISASTERS IN UTTARAKHAND

This paper presents a situational analysis of disaster in 2013 and issues related to the preparation of mountain communities in Uttarakhand with specific reference to Bhatwari Block in Uttarkashi District and their adaptation and response. It also anlayses the response of the institutions in the light of needs of the communities. The paper argues that the determinants for such a response are located in the information flow and access, resource access and governance. The need for looking at these responses in the light of Mountain vulnerability frameworks and sensitivity aspects which include both gender and social marginality issues are emphasized. The issues of gendered vulnerability of the mountain communities located in a fragile eco system and inequitious social system with specific reference to agricultural and forest based livelihoods are discussed. The paper also focuses on how these vulnerabilities are impacted by climate change. The paper argues that institutional contexts also influence the responses of adaptation and mitigation. The paper concludes with suggesting some mechanisms for preparation of the communities and institutions for a sustainable response

Análisis comparativo de K-NN, Naïve-Bayes y regresión logística para la detección de fraude con tarjetas de crédito

Introduction: This paper highlights the outcome of the comparative study of “Various Machine learning algorithms namely K-NN, Naive Bayes, and Logistic Regression for Credit Card Fraud Detection” carried out based on a dataset taken from UCI.com in 2022-23 at Manav Rachna International Institute of Research and Studies. Problem: Credit card fraud is still rife today and the modes are increasingly varied. Quite often we hear of fraud cases that cause irreplaceable injury to banks and financial institutions which cannot be compensated in terms of costs. To avoid scams with various modes of credit cards, we must be able to identify and find out the modes often used by fraudsters. This scheme liberates such financial institutions and banks with complete and appropriate information using Machine Learning Techniques, not only about the modes that scammers or fraudsters often use but also ways to protect against such frauds. Objective: The present paper discusses the various machine learning models based on classification and regression, namely K-Nearest Neighbors, Naïve Bayes, and Logistic Regression, which are successfully able to achieve the classification accuracy of 80% using Logistic Regression with a Precision of 78%, Recall of 100%, and F1-Score of 88% for fraudulent credit card transactions. Methodology: The comparative analysis demonstrates that for Precision, Recall, and Accuracy parameters, the K-Nearest Neighbor is a better approach for detecting fraudulent transactions than the Logistic Regression and Naïve Bayes. Results: The accuracy is marginal high in Logistic Regression but the False Positive parameters are not able to identify the imbalanced data; therefore, they disguise the results and accuracy of Logistic Regression and K-Nearest Neighbor deems fit for such cases. Conclusion: This scheme depicts the automated fraud classification systems using machine learning techniques, namely K-Nearest Neighbor, Logistic Regression, and Naive Bayes, to produce a model that can distinguish valid and invalid credit card transactions. Originality: Through this research, the most relevant features are used to go through the visualization of accuracy with the confusion matrix, and accuracy calculations are obtained from the dataset used.Limitations: Deep learning techniques could have been used to fetch even better results.Introducción: este artículo muestra el resultado de un estudio comparativo de “varios algoritmos de machine learning, a saber, K-NN, Naïve-Bayes y regresión logística para la detección de fraudes con tarjetas de crédito”, realizado con base en un conjunto de datos tomado de UCI.com en 2022-23 en el Instituto Internacional de Investigaciones y Estudios Manav Rachna. Problema: el fraude con tarjetas de crédito está muy extendido hoy en día y las modalidades son cada vez más variadas. A menudo, se oye hablar de casos de fraude que causan daños irreparables a bancos e instituciones financieras, que no pueden ser compensados en términos de costos. Para evitar estafas con diversos modos de tarjetas de crédito, se debe poder identificar y descubrir los modos que suelen utilizar los estafadores. Este esquema proporciona a dichas instituciones financieras y bancos información completa y adecuada utilizando técnicas de machine learning, no solo sobre los modos que suelen utilizar los estafadores o defraudadores, sino también sobre las formas de protegerse contra dichos fraudes. Objetivo: el presente artículo analiza los diversos modelos de machine learning basados en clasificación y regresión, a saber, K-Nearest Neighbors (K-NN), Naïve Bayes y regresión logística, que pueden lograr con éxito una precisión de clasificación del 80% utilizando regresión logística con una precisión de 78%, Retiro del 100% y F1 Score del 88% para transacciones fraudulentas con tarjeta de crédito. Método: el análisis comparativo muestra que, para los parámetros de precisión, recuperación y exactitud, el K-NN es un mejor enfoque para detectar transacciones fraudulentas que la regresión logística y el Naïve Bayes.Resultados: la precisión es marginalmente alta en la regresión logística, pero los parámetros de falso positivo no pueden identificar los datos desequilibrados; por lo tanto, disfrazan los resultados y la precisión de la regresión logística y el K-NN se considera adecuado para tales casos. Conclusión: este esquema describe los sistemas automatizados de clasificación de fraude que utilizan técnicas de machine learning, a saber, K-NN, Regresión logística y Naïve Bayes, para producir un modelo que pueda distinguir transacciones con tarjetas de crédito válidas e inválidas. Originalidad: a través de esta investigación, se utilizan las características más relevantes para visualizar la precisión con la matriz de confusión y se obtienen cálculos de precisión a partir del conjunto de datos utilizado.Limitaciones: se podrían haber utilizado técnicas de Deep learning para obtener mejores resultados

A Novel Hybrid Approach for Fast Block Based Motion Estimation

The current work presents a novel hybrid approach for motion estimation of various video sequences with a purpose to speed up the entire process without affecting the accuracy. The method integrates the dynamic Zero motion pre-judgment (ZMP) technique with Initial search centers (ISC) along with half way search termination and Small diamond search pattern. Calculation of the initial search centers has been shifted after the process of zero motion pre-judgment unlike most the previous approaches so that the search centers for stationary blocks need not be identified. Proper identification of ISC dismisses the need to use any fast block matching algorithm (BMA) to find the motion vectors (MV), rather a fixed search pattern such as small diamond search pattern is sufficient to use. Half way search termination has also been incorporated into the algorithm which helps in deciding whether the predicted ISC is the actual MV or not which further reduced the number of computations. Simulation results of the complete hybrid approach have been compared to other standard methods in the field. The method presented in the manuscript ensures better video quality with fewer computations

To Accommodate or Not to Accommodate: (When) Should the State Regulate Religion to Protect the Rights of Children and Third Parties?

When should we accommodate religious practices? When should we demand that religious groups instead conform to social and legal norms? Who should make these decisions, and how? These questions lie at the very heart of our contemporary debates in the field of Law and Religion.Particularly thorny issues arise where religious practices may impose health-related harm to children within a religious group or to third parties. Unfortunately, legislators, scholars, courts, ethicists, and medical practitioners have not offered a consistent way to analyze such cases and the law is inconsistent. This Article suggests that the lack of consistency is a troubling artifact of our political system, and further, that it raises serious constitutional questions that lie at the intersection of the Free Exercise and Establishment clauses of the First Amendment.To resolve these problems, we propose and develop a test to determine whether such a religious practice should be accommodated by legislators, courts, and medical practitioners. Our test is sensitive to the institutional strengths and weaknesses of differently-situated decision-makers, and is designed to be flexible enough to account for these differences. Consequently, it has distinctive applications for legislators, administrative officials, judges, and medical practitioners. Further, although the test was developed specifically to address religious practices that may impose health-related harms to children and third-parties, it also has potential implications in other contexts as well, such as the debates over sexual orientation non-discrimination laws should accommodate religious dissent

A linear fractional bilevel programming problem with multichoice parameters

A bilevel programming problem (BLPP) is a hierarchical optimization problem where the constraint region of the upper level is implicitly determined by the lower level optimization problem. In this paper, a bilevel programming problem is considered in which the objective functions are linear fractional and the feasible region is a convex polyhedron. Linear fractional objectives in BLPP are useful in production planning, financial planning, corporate planning and so forth. Here, the cost coefficient of the objective functions are multi-choice parameters. The multi-choice parameters are replaced using interpolating polynomials. Then, fuzzy programming is used to find a compromise solution of the transformed BLPP. An algorithm is developed to find a compromise solution of BLPP. The method is illustrated with the help of an example

Literature Review of Omicron: A Grim Reality Amidst COVID-19

Coronavirus disease 2019 (COVID-19) first emerged in Wuhan city in December 2019, and became a grave global concern due to its highly infectious nature. The Severe Acute Respiratory Coronavirus-2, with its predecessors (i.e., MERS-CoV and SARS-CoV) belong to the family of Coronaviridae. Reportedly, COVID-19 has infected 344,710,576 people around the globe and killed nearly 5,598,511 persons in the short span of two years. On November 24, 2021, B.1.1.529 strain, later named Omicron, was classified as a Variant of Concern (VOC). SARS-CoV-2 has continuously undergone a series of unprecedented mutations and evolved to exhibit varying characteristics. These mutations have largely occurred in the spike (S) protein (site for antibody binding), which attribute high infectivity and transmissibility characteristics to the Omicron strain. Although many studies have attempted to understand this new challenge in the COVID-19 strains race, there is still a lot to be demystified. Therefore, the purpose of this review was to summarize the structural or virologic characteristics, burden, and epidemiology of the Omicron variant and its potential to evade the immune response

Assessing COVID-19 Booster Hesitancy and Its Correlates: An Early Evidence from India

The emergence of SARS-CoV-2 mutants, waning immunity, and breakthrough infections prompted the use of booster doses of the COVID-19 vaccine to fight against the pandemic. India started booster doses in January 2022 and it is critical to determine the intention of booster dose uptake and its correlates. Therefore, the current cross-sectional study aimed to investigate booster dose acceptability and associated predictors among the Indian population. A convenience sampling technique was utilized to recruit a sample of 687 Indian residents. A 55-item psychometric validated survey tool was used to assess booster dose acceptability, vaccine literacy and vaccine confidence. Univariate, bivariate, and multivariate statistical methods were used to analyze the data. Over 50% of participants reported their willingness to take the booster dose. Among the group not willing to take the booster dose (n = 303, 44.1%), a significantly larger proportion of respondents were unvaccinated with the primary series (12.2% vs. 5.2%, p \u3c 0.001), had an annual income below 2.96 lacs/annum (52.8% vs. 33.1, p \u3c 0.001), were residents of rural areas (38.0% vs. 23.2%, p \u3c 0.001), were not living with vulnerable individuals (78.5% vs. 65.2%, p \u3c 0.001) and did not have family/friends who had tested positive for COVID-19 (54.6% vs. 35.1%, p = 0.001). Demographic, vaccine variables and multi-theory model subscales to predict the initiation of booster dose among hesitant participants were statistically significant, R2 = 0.561, F (26, 244) = 11.978, p \u3c 0.001; adjusted R2 = 0.514. Findings of this study highlight the need to develop evidence-based interventions to promote vaccine uptake, particularly among hard-to-reach communities living in developing countries