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Nifty index: Integrating deep learning models for future predictions and investments
The Indian stock market, led by the NSE and BSE, has witnessed remarkable growth, exemplified by the NIFTY 50 index surpassing INR 176 trillion in market capitalization. Post the transformative New Economic Policy reforms in 1991, the market underwent significant expansion due to increased accessibility. This chapter focuses on predicting Nifty index prices for the upcoming 10-day period, aiming to provide valuable insights for investment decisions. Despite the markets inherent complexity, exacerbated by various factors like economic conditions and investor sentiment, the objective of the research study is clear: to boost profitability, mitigate risk, and safeguard traders capital. Leveraging Long Short-Term Memory (LSTM) and Vector Autoregression (VAR) models, the research study rigorously evaluates prediction accuracy using the Root Mean Square Error (RMSE) metric. The study underscores the potential of deep learning techniques in achieving reasonable accuracy, especially for short-term forecasts, while acknowledging the markets inherent unpredictability. Notably, the findings demonstrate that the LSTM model excels in predicting Nifty Bank prices, with an impressive RMSE score of 242.55 compared to VAR models. Furthermore, optimal data splitting, at an 8:2 ratio, significantly enhances prediction accuracy across all models, emphasizing the critical role of high-quality data in training. In conclusion, this study unequivocally recommends LSTM as the preferred model for Nifty index price prediction, providing practitioners with a robust tool to navigate the complexities of the Indian stock market with enhanced precision and confidence. 2025 selection and editorial matter, Vivek S. Sharma, Shubham Mahajan, Anand Nayyar and Amit Kant Pandit; individual chapters, the contributors. -
Brain Tumor Localization Using Deep Ensemble Classification and Fast Marching Segmentation
A brain tumor is an unusual and excessive growth of brain cells, which can be cancerous (malignant) or noncancerous (benign). These growths can be risky as they press on healthy brain tissue or expand in the brain. Detecting brain tumors early is tough for radiologists. A typical brain tumor can double in size in just 25days, and without the right treatment, patients often have limited chances of survival, about six months. Initial symptoms can be confused with other illnesses, and brain cancer is difficult to diagnose because of the complex nature of the brain and tumor locations. In this study, we propose a strategy where we first sort medical images based on the presence of a brain tumor. Then, we pinpoint the part of the image containing the tumor through segmentation. We use a combined model of MobileNet-V3 and EfficientNetV2 for image classification. To segment the tumor in the image, we use a fast marching method. The combined model's classification accuracy is 98%, and the segmentation accuracy is 99.6%. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
A Implementation of Integration of AI and IOT Along with Metaverse Technology in the Field of Healthcare Industry
In the evolving panorama of healthcare, the appearance of Metaverse technology emerges as a transformative pressure, redefining traditional paradigms of healthcare shipping and education. This systematic assessment delves into the multifaceted impact of Metaverse technology, encapsulating their role in revolutionizing healthcare through modern-day academic frameworks, patient care interventions, and groundbreaking enhancements in medical imaging. Through an in-depth assessment of present-day literature, this observe illuminates the Metaverse's potential to facilitate immersive mastering tales, allow far flung interventions, and enhance the pleasant of scientific diagnostics and treatment making plans with its 3 -dimensional virtual environments. The findings underscore a burgeoning growth in Metaverse packages inner healthcare, highlighting its capability to noticeably beautify healthcare outcomes, affected person engagement, and expert abilities. Consequently, this evaluate advocates for the prolonged integration of Metaverse generation in healthcare, urging stakeholders to embody the ones enhancements and adapt to the following digital transformation in healthcare services and education. 2024 IEEE. -
GLANCEGuided Language Through Autoregression Establishing Natural and Classifier-Free Editing
In this study, researchers aimed to simplify text conversion into images using the latest text-to-image generation methods. While these methods have improved the quality and relevance of generated images, certain crucial questions remained unanswered, limiting their practicality and overall quality. To address these issues, the researchers introduced a novel text-to-image method. This method allows for better control of the scene depicted in the image through text, enhances the tokenization process by incorporating specific knowledge about key image regions such as faces and important objects, and provides guidance to the transformer model without needing a classifier. The outcome of this work was a model that achieved state-of-the-art results in terms of image quality and human evaluation, enabling the generation of high-fidelity 512?512-pixel images. Moreover, this method introduced new capabilities, including scene editing, text editing with reference scenes, handling out-of-distribution text prompts, and generating story illustrations. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
EFFECTIVENESS OF COGNITIVE BEHAVIOURAL THERAPY FOR ADULTS WITH DEPRESSION AND ANXIETY DURING COVID-19: A Systematic Review of Randomised Controlled Trials
Introduction: The COVID-19 pandemic has forced the administration of Cognitive Behavioural Therapy (CBT) either face-to-face or online. This systematic review aims to assess the effectiveness of CBT and Internet-Delivered CBT (iCBT) in treating depression and anxiety disorders during the COVID-19 outbreak. Methods: Three independent reviewers searched the Web of Science, PubMed, Cochrane Library, and Clinical Trial Databases using specific search phrases. PubMed searches included Cognitive Behavioural Therapy/Intervention and COVID-19 and 2019 Coronavirus Disease or 2019-nCoV, internet-administered/internet-based cognitive behavioural therapy, CBT, cognitive behavioural treatment. Two independent reviewers evaluated the risk of bias at the study level, with disagreements settled through discussion with other research team members. The study findings were reported as per the PRISMA guidelines. Results: Thirty-one studies met the inclusion criteria, and 17 were randomised controlled trials. The studies demonstrated that CBT and iCBT effectively treated depression and anxiety disorders during the COVID-19 pandemic. However, a hybrid CBT modality was more beneficial from a long-term perspective. Conclusion: The findings suggest that CBT and iCBT effectively treat depression and anxiety disorders during the COVID-19 pandemic. However, further research is needed to establish these interventions long-term effectiveness and identify the optimal mode of delivery for different populations. 2024 selection and editorial matter, Dr Rajesh Verma, Dr Uzaina, Dr Tushar Singh, Dr Gyanesh Kumar Tiwari, and Prof Leister Sam Sudheer Manickam. -
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.