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Unpacking the burden of hypertension and diabetes in Karnataka: implications for policy and practice based on NFHS-5 findings
Objective: To investigate the prevalence, risk factors, and healthcare-seeking patterns of hypertension and diabetes in Karnataka, India, and to offer knowledge that might guide public health initiatives intended to lessen the burden of these illnesses. Methods: In order to examine the prevalence, risk factors, and healthcare-seeking behaviour of hypertension and diabetes in Karnataka, India, a cross-sectional study is carried out using the information gathered from 26,574 households on 30,455 women and 4516 men (who were in their reproductive period) from the National Family Health Survey (201920). The information was summarised using descriptive statistics, which included frequencies and percentages. The association between different risk variables and the likelihood of getting diabetes and hypertension was examined using the chi-squared test and a logistic regression model. Data were analysed using STATA software version 16. Results: The study found that age, gender, education level, religion, and BMI are all significantly associated with hypertension and diabetes (p < 0.001). Tobacco use and alcohol consumption were not significantly associated with hypertension, but tobacco use was significantly associated with diabetes (p < 0.001). However, alcohol consumption was not found to be significantly associated with diabetes whereas the older age groups, males, underweight, overweight and obese, and tobacco use were all associated with increased odds of diabetes. On the other hand, females, secondary education or higher, and alcohol consumption were associated with decreased odds of diabetes. Conclusion: In conclusion, the study found a high prevalence of hypertension and diabetes in Karnataka, India, and identified several risk factors associated with these diseases. The study also highlighted the need for improved healthcare-seeking behaviour among people with hypertension and diabetes. The findings can inform public health interventions aimed at reducing the burden of these diseases in Karnataka and similar settings. The Author(s), under exclusive licence to Research Society for Study of Diabetes in India 2023. -
On the quick estimation of probability of recovery from COVID-19 during first wave of epidemic in India: a logistic regression approach
The COVID-19 pandemic has recently become a threat all across the globe with the rising cases every day and many countries experiencing its outbreak. According to the WHO, the virus is capable of spreading at an exponential rate across countries, and India is now one of the worst-affected country in the world. Researchers all around the world are racing to come up with a cure or treatment for COVID-19, and this is creating extreme pressure on the policy makers and epidemiologists. However, in India the recovery rate has been far better than in other countries, and is steadily improving. Still in such a difficult situation with no effective medicine, it is essential to know if a patient with the COVID-19 is going to recover or die. To meet this end, a model has been developed in this article to estimate the probability of a recovery of a patient based on the demographic characteristics. The study used data published by the Ministry of Health and Family Welfare of India for the empirical analysis. Hemlata Joshi, S. Azarudheen, M. S. Nagaraja, Singh Chandraketu. -
Factors Influencing Online Shopping Behaviour: An Empirical Study of Bangalore
Online shopping is growing rapidly in India, predominantly driven by tremendous and substantial divulgatory activities among millennial consumers. Online shopping is becoming more popular and attracts significant attention because it has excellent potential for both consumers and vendors. The convenience of online shopping makes it more successful and makes it an emerging trend among consumers. When all the companies are striving against one another, certain factors influence the behavior of customers. This paper analyses the relationship between the critical, independent variables, including consumer behavior, cultural, social, personal, psychological, and marketing mix factors. The results revealed that the influence of Brand as a factor had positively influenced the customers decisions in shopping online and evaluates the customers level of satisfaction with Online shopping. Results provided in this research could be employed as reference information for Ecommerce app builders and marketers regarding such issues in the city. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Enhancing Medical Decision Support Systems withtheTwo-Parameter Logistic Regression Model
The logistic regression model is an invaluable tool for predicting binary response variables, yet it faces a significant challenge in scenarios where explanatory variables exhibit multicollinearity. Multicollinearity hinders the models ability to provide accurate and reliable predictions. To address this critical issue, this study introduces innovative combinations of Ridge and Liu estimators tailored for the two-parameter logistic regression model. To evaluate the effectiveness of the combination of ridge and Liu estimators under the two-parameter logistic regression, a real-world dataset from the medical domain is utilized, and Mean Squared Errors are employed as a performance metric. The findings of our investigation revealed that the ridge estimator, denoted as k4, outperforms other Liu estimators when multicollinearity is present in the data. The significance of this research lies in its potential to enhance the reliability of predictions for binary outcome variables in the medical domain. These novel estimators offer a promising solution to the multicollinearity challenge, contributing to more accurate and trustworthy results, ultimately benefiting medical practitioners and researchers alike. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Unraveling the complexity of thyroid cancer prediction: A comparative examination of imputation methods and ML algorithms
Despite being relatively rare, thyroid cancer is being identified more often as a result of improved awareness and detection. Even if it has a high survival rate, it is crucial to comprehend its forms, risk factors, and therapies. Better results and prompt intervention are made possible by the early detection of thyroid cellular alterations made possible by evolving machine learning (ML) techniques. The USA Cancer Data Access System's Thyroid Cancer Factor Data, gathered from patient questionnaires, are used in this study. Missing values and imbalance in the dataset are addressed using resampling techniques (SMOTE, under-sampling) and imputation techniques (Median, KNN). To increase the accuracy of thyroid cancer prediction and improve early identification and prognoses for improved patient care, a comparative analysis of machine learning algorithms (ML) (Logistic Regression, LDA, KNN, Decision Tree, SVM, Naive Bayes) with imputation and resampling techniques is being conducted. 2024, IGI Global. All rights reserved. -
AN ANALYSIS OF PERCEPTION AND AWARENESS OF UNDERGRADUATE YOUTH TOWARDS CYBERCRIME
The perception of a situation or reality determines how one responds and awareness is the first step towards understanding, knowing or recognizing it. The majority of the public and the police may be familiar with the phrase cybercrime, but all of the mare fully informed ofthe nature and scope of these crimes, as well as of the cybercriminals and cyber victims, which has an impact on how they see these issues. This studys main goal was to examine the perception and awareness of cybercrime among undergraduate youth studying in BBA or BCA courses. In this study, we discovered that young peoples responses to cybercrime mostly depend on their perceptions of it and their awareness level. To accomplish the studys objective, a thorough examination of existing literature was undertaken. Primary data of200 students were collected through Google Forms. Percentile analysis, correlation analysis and t-test are done to test the hypotheses. The results of this study may help college administrators better comprehend the mind set of todays youth as they develop laws and policies aimed at reducing cybercrime among students. The results of this study show that the youngsters surveyed have high levels of awareness and a good perception. 2024 Kiran Joshi and Priyanka Kaushik. -
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Impact of Corporate Announcement of Green Innovation on Automaker's Market Value -An Event Study Methodology
The aim of this paper is to analyse the effects of the Green Innovation event and corporate announcements regarding green innovation on the stock price of the Automobile Industry and the performance of firms. The authors also aim to assess the impact of these events on business performance and identify the effective innovation strategy influenced by the type of corporate announcement. The study focuses on the corporate announcements made by the automobile industry and their impact on company performance, specifically in relation to the application of green innovation methods. Furthermore, there is no universally agreed upon standard for defining and categorising corporate announcements. The writers also exclude the impact of media and other events that occur during the event window when categorising these announcements. The findings of this study have important practical consequences. They suggest that the release of green innovations, which aim to protect the environment, can have an impact on an organization's stock market success. Specifically, the type of innovation and the trade segment in which the organisation operates can influence its stock market performance. Grenze Scientific Society, 2024. -
P-energy of generalized Petersen graphs
For a given graph G, its P-energy is the sum of the absolute values of the eigenvalues of the P-matrix of G. In this article, we explore the P-energy of generalized Petersen graphs G(p; k) for various vertex partitions such as independent, domatic, total domatic and k-ply domatic partitions and partition containing a perfect matching in G(p; k). Further, we present a python program to obtain the P-energy of G(p; k) for the vertex partitions under consideration and examine the relation between them. 2022 The authors. -
Machine Learning Model for Depression Prediction during COVID-19 Pandemic
Depression is an unfamous mental health disorder that has affected half the population worldwide. In December 2019, the break of the COVID-19 pandemic was first spotted in Wuhan, China, and later spread to 212 countries and territories worldwide, impacting half the population. It took a significant toll on their physical health and their mental health. Many among the population lost their loved ones, businesses, and being in quarantine for years, completely shifted to the online mode made everyone's life miserable. Many may be dealing with escalated levels of alcohol and drug use, sleeplessness, and an anxious state of mind. So, the need to address this and help the severely affected ones is significant. Self-quarantine also causes additional stress and challenges the mental health of citizens. This paper intends to identify the people who were mentally affected by the pandemic using machine learning techniques. A survey was conducted among college-going students and professionals. The paper used classification techniques such as Naive Bayes, KNN, Random Forest, Logistic Regression, k-fold cross-validation to get results. Support Vector Machine gave the maximum accuracy of 99.35%. 2022 IEEE. -
The Nainital-Cape Survey: IV. A search for pulsational variability in 108 chemically peculiar stars
The Nainital-Cape Survey is a dedicated ongoing survey program to search for and study pulsational variability in chemically peculiar (CP) stars to understand their internal structure and evolution. Aims. The main aims of this survey are to find new pulsating Ap and Am stars in the northern and southern hemisphere and to perform asteroseismic studies of these new pulsators. Methods. The survey is conducted using high-speed photometry. The candidate stars were selected on the basis of having Stromgren photometric indices similar to those of known pulsating CP stars. Results. Over the last decade a total of 337 candidate pulsating CP stars were observed for the Nainital-Cape Survey, making it one of the longest ground-based surveys for pulsation in CP stars in terms of time span and sample size. The previous papers of this series presented seven new pulsating variables and 229 null results. In this paper we present the light curves, frequency spectra and various astrophysical parameters of the 108 additional CP stars observed since the last reported results. We also tabulated the basic physical parameters of the known roAp stars. As a part of establishing the detection limits in the Nainital-Cape Survey, we investigated the scintillation noise level at the two observing sites used in this survey, Sutherland and Nainital, by comparing the combined frequency spectra stars observed from each location. Our analysis shows that both the sites permit the detection of variations of the order of 0.6 milli-magnitude (mmag) in the frequency range 1-4 mHz, Sutherland is on average marginally better. 2016 ESO. -
Optical characterization of oxadiazoles analogues doped PMMA film for photonic application
In the present study, newly synthesized nitrobenzene derivatives (PBT and PBF) doped poly(methyl methacrylate) films were prepared using spin coating techniques, and their optical properties were analyzed. The absorption spectra of various weight percentages (0.02%, 0.1%, 0.2%, and 0.3%) of nitrobenzene derivative-doped polymer films were recorded using a UVvisible spectrometer. From the absorption spectra, optical properties such as refractive index, band gap energy, extinction coefficient, and dielectric constant were calculated. The effect of doping on the optical properties of PMMA was investigated, with results revealing normal dispersive behavior from the refractive index and extinction coefficient. Atomic force microscopy and scanning electron microscopy images indicated that the synthesized films have a low degree of roughness and a smooth surface. Additionally, the nonlinear optical properties of the PBF-doped polymer film were investigated, and the ? value was determined to be 7.403cm/W. Overall, the findings suggest that PBF-doped polymer films are promising candidates for photonic applications. Indian Association for the Cultivation of Science 2024. -
Algae-Based Nanoparticles for Contaminated Environs Nanoremediation
Currently, the rapidly growing human interference has increased the percentage of pollutants that include organic and inorganic and this has been threatening the ecosystems. Remediation by conventional physicochemical methods, bioremediation has gained immense acceptance due to their ecofriendly, economical, and sustainable approach. Microbial-based nanoparticles act as facilitators in remediating contaminants by microbial growth and immobilization of remediating agents, by inducing microbial remediating enzymes or enhanced biosurfactants that helps to improve solubility of hydrophobic hydrocarbons to create a conducive milieu for remediation. Algal-NPs can be produced easily using low-cost medium and simple scaling up process which is economically feasible. Silver nanoparticles (AgNPs) and gold nanoparticles (AuNPs) have been synthesized using Nannochloropsis sps (NN) and Chlorella vulgaris (CV), while, brown seaweeds Petalonia fascia, Colpomenia sinuosa, and Padina pavonica were used with iron oxide NPs along with their aqueous extracts. These applications have shown to be promising alternative bioremediating methods that are safe. Algal-based NPs can act as a pollution abatement device that can help to effectively target the pollutants for efficient nanobioremediation and helps to promote environmental clean-up for eliminating heavy metals, dyes, and other organic and inorganic waste from the environment. 2025 by Apple Academic Press, Inc. -
Model between mind share branding factors and trustworthiness /
Patent Number: 202111055024, Applicant: Dr.Vikas Singla.
The importance of Mindshare branding (MB) strategy in building long-term and sustainable psychological links with consumers had been sufficiently highlighted in literature. However, very few research attempted to provide a structured tool for its measurement. This study proposed a 13-point four-factor multidimensional scale which could be used to measure MB formally. Dimensions measuring MB were derived from literature and then examined on different brands in order to achieve a reliable and valid scale. -
Studies on color energy and its variations in graphs
This thesis consists of studies on color energy and its variations in graphs. Apart from the exploration of color energy corresponding to various coloring schemes, the notion of P-energy as a generalization of color energy has been introduced. The computation of color energy and P-energy of graphs has been carried out using Python programs, while the general results are derived using research methods and proof techniques in linear algebra. The bounds of color energy for a graph G have been established in terms of several graph parameters such as chromatic number and#967;(G), domination number and#947;(G), maximum degree and#8710;(G) etc. It has been found out that the color energy of a graph G is greater than or equal to 1 n and#947;(G) q 2(m + mand#8242;c). Further, the bounds of color energy of a graph G in terms of extreme eigenvalues of color matrix of G have been obtained. The study on color energy with respect to the minimum number of colors and L(h, k)-coloring has been examined in detail for some families of graphs such as star graph, double star, crown graph and their color complements. We have also examined the variation of color energy in the specific cases of T-coloring and radio coloring for some families of graphs. The examination of color energy corresponding to some improper colorings such as Hamiltonian coloring, open neighborhood coloring and improper C-coloring has also been done. Moreover, the color equi-energetic families of graphs with respect to various coloring schemes have been investigated. The concept of P-energy has been introduced as a generalization of the concept of color energy. This stems from the fact that coloring problems in essence are vertex partition problems. For any vertex partition P having k elements, we define the P-matrix AP(G) having and#8722;1, 0, 1, 2 as off diagonal entries and diagonal entries represent the cardinality of the elements in partition P. Then, the P-energy EP(G) is defined as the sum of the absolute values of eigenvalues of P-matrix of G. -
Studies on color energy and its variations in graphs
This thesis consists of studies on color energy and its variations in graphs. Apart from the exploration of color energy corresponding to various coloring schemes, the notion of P-energy as a generalization of color energy has been introduced. The computation of color energy and P-energy of graphs has been carried out using Python programs, while the general results are derived using research methods and proof techniques in linear algebra. The bounds of color energy for a graph G have been established in terms of several graph parameters such as chromatic number χ(G), domination number γ(G), maximum degree ∆(G) etc. It has been found out that the color energy of a graph G is greater than or equal to 1 n γ(G) p 2(m+m′ c ). Further, the bounds of color energy of a graph G in terms of extreme eigenvalues of color matrix of G have been obtained. -
Improved dhoa-fuzzy based load scheduling in iot cloud environment
Internet of things (IoT) has been significantly raised owing to the development of broadband access network, machine learning (ML), big data analytics (BDA), cloud computing (CC), and so on. The development of IoT technologies has resulted in a massive quantity of data due to the existence of several people linking through distinct physical components, indicating the status of the CC environment. In the IoT, load scheduling is realistic technique in distinct data center to guarantee the network suitability by falling the computer hardware and software catastrophe and with right utilize of resource. The ideal load balancer improves many factors of Quality of Service (QoS) like resource performance, scalability, response time, error tolerance, and efficiency. The scholar is assumed as load scheduling a vital problem in IoT environment. There are many techniques accessible to load scheduling in IoT environments.With this motivation, this paper presents an improved deer hunting optimization algorithm with Type II fuzzy logic (IDHOA-T2F) model for load scheduling in IoT environment. The goal of the IDHOA-T2F is to diminish the energy utilization of integrated circuit of IoT node and enhance the load scheduling in IoT environments. The IDHOA technique is derived by integrating the concepts of Nelder Mead (NM) with the DHOA. The proposed model also synthesized the T2L based on fuzzy logic (FL) systems to counterbalance the load distribution. The proposed model finds useful to improve the efficiency of IoT system. For validating the enhanced load scheduling performance of the IDHOA-T2F technique, a series of simulations take place to highlight the improved performance. The experimental outcomes demonstrate the capable outcome of the IDHOA-T2F technique over the recent techniques. 2022 Tech Science Press. All rights reserved. -
Triggers of Changes in Business Processes and Applications: A Systematic Review
Organizations must constantly adapt due to the rapid rate of technological development, market conditions, and customer expectations. The multidimensional world of catalysts that drive changes in corporate processes and applications is explored in this systematic review. Every business must adopt the changes if it wants to compete in the market and outlast its rivals. A wide range of factors, including internal and external forces, can cause applications and business processes to change. These changes are frequently necessary to stay current with the shifting demands of the market, technology advancements, organizational requirements, competitive pressures, legal compliance, environmental and sustainability programs, market trends, and consumer insights. Taking this into account, this chapter attempts to concentrate on the causes of changes in business processes and applications by analyzing the perspective. 2024, Iquz Galaxy Publisher. All rights reserved. -
An advanced machine learning framework for cybersecurity
The world is turning out to be progressively digitalized raising security concerns and the urgent requirement for strong and propelled security innovations and procedures to battle the expanding complex nature of digital assaults. This paper examines how AI is being utilized in digital security in both resistance and offense exercises, remembering exchanges for digital attacks focused on AI models. Digital security is the assortment of approaches, systems, advancements, and procedures that work together to ensure the confidentiality, trustworthiness, and accessibility of processing assets, systems, programming projects, and information from attacks. Machine learning-based examination for cybersecurity is the following rising pattern in digital security, planned for mining security information to reveal progressed focused on digital threats and limiting the operational overheads of keeping up static relationship rules. In this paper, we are mainly focusing on the detection and diagnosis of various cyber threats based on machine learning. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
Regression Analysis using Machine Learning Algorithms to Predict CO2 Emissions
Precise measurement of fuel consumption and emissions plays an important role in evaluating the environmental effects of materials and stringent emission control methods, especially within the transportation sector. This sector represents a substantial contributor to both global greenhouse gas emissions and the release of hazardous pollutants, making accurate assessment imperative for addressing climate change. The primary objective is to construct accurate predictive models that estimate CO2 emissions based on vehicle attributes, fostering a deeper understanding of the environmental impact of vehicular activities. Leveraging the 'CO2 Emissions-Canada.csv' dataset, the paper embarks on an extensive journey of data preprocessing, exploratory data analysis, and model training. These algorithms are meticulously fine-tuned and evaluated through metrics such as R-squared and mean absolute percentage error, rendering insights into their predictive accuracies. In essence, this paper pioneers a pathway towards environmentally responsible mobility solutions, capitalizing on the fusion of data science and environmental conservation. 2024 Bharati Vidyapeeth, New Delhi.