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Modern Technology Usage for Education Field during COVID-19: Statistical Analysis
The COVID-19 pandemic has had vast effects on the concept of education as a whole. During the pandemic, students had no access to physical teaching practices, which had been adapted worldwide as the principal way of education since the 1800's. Due to the restrictions imposed to garner safety from the spread of the virus, this methodology had to be modified based on the situation at hand. Alternatives through the usage of Virtual Learning Platforms (VLP), Online Tutoring Platforms (OTP), Web Conferencing Platforms (WCP) and multiple assessment tools like plagiarism checker, poll sites, quiz platforms, online proctored examinations (OPE) started gaining popularity among all institutes to cope with the limitations levied. The technologies molded a path for student-teacher interaction, performance assessments, document sharing and online tutoring. This research highlights the lack of online tutoring equipment, educators' limited expertise with online learning, the knowledge gap, a inimical atmosphere for independent study, equity, and academic success in postsecondary learning. The goal of this review is to present an overview of available technologies for online teaching that can be used to improve the quality of education during COVID-19. 2022 IEEE. -
A study on key determinants of economic growth during pre and post reform periods in india
Schumpeter says that economic life is a process of growth and change, meaning newlinedevelopment . The process involves interplay of forces or factors of production, viz. land, labour and capital. The understanding of economic growth has improved enormously in recent years. There has been a much greater understanding of the interrelated laws governing the growth of population, the pace of capital accumulation, the rate of technological innovation in an environment characterised by relative scarcity of natural newlineresources. The major determinants of Indian economic growth, identified in this study in terms of their influence on productivity of different sectors are physical capital formation, technological progress, human capital formation, increase in labour force, foreign investment and trade openness. newlineThere are some notable features associated with economic growth in India. One, it is found that the structural adjustments leading to foreign capital inflow and trade openness have fuelled the economic growth in India after economic reforms in 1991. Two, it is observed that exports and imports play a significant role in determining economic growth in India in the post reform period. Three, despite the new growth there are issues like imbalances with regard to employment, manufacturing base, social newlineindicators etc., even as India strives to enhance competitiveness, competence and global relevance. newlineIt is in this context that the present study looks into the major issues and challenges related to the changes in the sectoral composition of economic growth in India over time. It also attempts to identify and discuss the influence of key determinants of economic growth in the pre and post reform periods in India. In this context, it is very crucial to study how different sectors influence the overall growth of a country at various points of its growth trajectory. newlineInterestingly, the analysis shows that the services sector growth has become the highlight of India s changing growth pattern in the post reform era. -
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. -
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. -
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. -
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. -
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. -
An Intelligent Cognitive Framework for Crime Prediction in Smart Cities using Video Mining
Booming development in cities with dense population have led to urban policing and public safety emerging as urgent concerns in city environments.current monitoring practices including CCTV'S and other IOT sensors generate a vast amount of data ,thus making them inadequate for the task. However a combination of video mining,computer vision,artificial intelligence and data mining techniques,do offer us a better framework for monitoring and real-time detection of crime in Smart city"s environment. This paper proposes an intelligent and Cognitive framework for prediction of crime. by combining various advanced modals such as YOLO (You took Only Look Once) for detecting objects, 3D Convolutional Neural Networks (CNN) for recognizing actions, deep SORT for tracking multiple objects, One-class SVM for detecting anomaly and LSTM for behavioral analysis. These modals can organized to function in a coherent system which can be organized to distinguish examine and trail illegal activities such of mugging, robbery, pick pocketing, violence, utilizing available live video feeds. By efficient date processing, and overcoming shortcomings such of limited labeled datasets and real-time feed detection, this framework can provide practical conclusion making tool for law enforcement in urban smart city environments which can enhance urban safety.Besides effective crime detection,this tool compiles with established ethical standards such as upholding privacy and legal compliance. 2025 IEEE. -
Advancing Brain Tumor Detection with Deep Learning and Machine Learning: A Performance Analysis of Different Deep Learning Models
The current study examines the difficulty of employing a deep learning architecture to diagnose brain tumors quickly and effectively. Our study is built upon a dataset of 253 MRI pictures that have been carefully categorized by medical experts as either positive (Yes) or negative (No) for brain tumors. To guarantee the robustness of model performance, the dataset is carefully divided into training and validation subsets, with 70% set aside for training and 30% for validation. We analyze the diagnostic performance of several machine learning models, including K-Nearest Neighbors (KNNs), Recurrent Neural Networks (RNNs), Support Vector Machines (SVMs), Convolutional Neural Networks (CNNs), and Artificial Neural Networks (ANNs). When these algorithms are applied to MRI scans, brain tumors can be quickly detected, and the increased accuracy makes patient treatment easier. The findings of this study could lead to a rapid and accurate diagnosis of brain tumors, which would greatly enhance patient care and treatment. The results also show how deep learning frameworks can transform medical image processing and diagnosis. This work offers a thorough review of recent findings and techniques for MRI scan-based deep learning-based brain tumor detection. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
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. -
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. -
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. -
Advancing Climate Finance for Sustainable Future: Integrating Human Capital, Climate Neutrality, and Emerging Technologies
The Intergovernmental Panel on Climate Change (IPCC) laid the scientific groundwork for the widespread agreement that, in the medium and long terms, development and inclusive growth are seriously threatened by climate change caused by humans. The importance of sustainable development and the pressing need to address climate change cannot be underscored in the rapidly evolving global setting. A key tool for reaching carbon neutrality and greening sustainable economic growth is Climate Finance, which is centered on eco-friendly investments and activities. By supporting renewable energy sources like solar and wind power, climate finance assists nations in lowering their greenhouse gas emissions. It also aids in community adaptation to the effects of climate change. Combining human capital and emerging technologies such as blockchain, Artificial Intelligence (AI) with climate finance is an additional opportunity to boost migration prospects and ease transitions in adaptation processes. Furthermore, in addition to addressing concerns about the potentially negative effects of some climate policies on development prospects and economic growth, an integrated policy package incorporating the scaling up of low-carbon and climate-resilient infrastructure, sustainable finance, and carbon pricing could also help achieve the goals of the UN Sustainable Development Goals (SDGs) and the Paris Agreement. In order to solve urgent environmental issues and achieve sustainable development goals, climate finance must improve. This chapter explores the need of climate finance and human capital with evolving technologies. Further, it analyzes the synergy of climate finance with climate neutrality and challenges and barriers in the achievement of sustainability. Furthermore, a need for a regulatory framework for achieving climate neutrality in terms of climate finance is discussed. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
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. -
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. -
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. -
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. -
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. -
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. -
