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Student engagement and learning during COVID-19: an empirical analysis
COVID-19 pandemic brought along with it a widespread disruption of education system around the world. Schools, colleges and universities were shut all over the world. In order to maintain the continuity of education, educators and students alike adopted the online mode of teaching and learning. While mainstream education was mostly face-to-face; a sudden shift to the online mode of teaching and learning required teachers and students to get acquainted with the platform and tools. This study attempts to test a model to understand the impact of online education on students engagement levels in the context of higher education and the COVID-19 pandemic. Results indicate that access to digital resources and teacher effectiveness has positive impact on engagement and student engagement in turn has positive impact on learning outcomes. Stress has negative impact on student learning. The paper also discusses implications of the study and future direction for research. Copyright 2022 Inderscience Enterprises Ltd. -
Geo-spatial crime analysis using newsfeed data in indian context
Social media is the platforms where users communicate, interact, share ideas, career interest, pictures, video, etc. Social media gives an opportunity to analyze the human behavior. Crime analysis using data from social media such as Newsfeeds, Facebook, Twitter, etc., is becoming one of the emerging areas of research for law enforcement organizations across the world. The intelligence gathered through data is used for identifying future attacks and plan for reinforcements. This article focuses on the implementation of textual data analytics by collecting the data from different newsfeeds and provides an optimized visualization. This article establishes a framework for better prediction of 16 types of crime in India and the Bangalore area by providing the coordinates of the crime area, along with the crime which might happen there. 2019, IGI Global. -
Spatio-temporal crime analysis using KDE and ARIMA models in the Indian context
In developing countries like India, crime plays a detrimental role in economic growth and prosperity. With the increase in delinquencies, law enforcement needs to deploy limited resources optimally to protect citizens. Data mining and predictive analytics provide the best options for the same. This paper examines the news feed data collected from various sources regarding crime in India and Bangalore city. The crimes are then classified on the geographic density and the crime patterns such as time of day to identify and visualize the distribution of national and regional crime such as theft, murder, alcoholism, assault, etc. In total, 68 types of crime-related dictionary keywords are classified into six classes based on the news feed data collected for one year. Kernel density estimation method is used to identify the hotspots of crime. With the help of the ARIMA model, time series prediction is performed on the data. The diversity of crime patterns is visualized in a customizable way with the help of a data mining platform. Copyright 2020, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. -
Indias environmental policy paradox: dissecting Indias budgetary allocations for environment
This paper examines Indias environmental policies and budget allocations from 20162024, revealing a focus on infrastructure that may overshadow environmental conservation. Significant discrepancies between policy rhetoric and budgetary commitments suggest that there is a need for realignment. Advocating an environment-centric approach, the study calls for increased budgetary commitments to environmental protection, a strategic shift away from fossil fuels, and stringent regulatory oversight, all essential to ensure sustainable development in India. 2023 Informa UK Limited, trading as Taylor & Francis Group. -
Media Framing of Indian Green Fiscal Policy: A Survey of Environmental Policies Across Online News Portals
This research undertakes a framing analysis of news coverage from five leading Indian news portals, focusing on the environmental provisions of India's fiscal budgets for 2022-23 and 2023-24. These budgets marked a significant shift, being the first to prioritize "Energy Transition and Climate Action" and "Green Growth" as central themes. The analysis uncovers a spectrum of portrayals: from highlighting government initiatives and the potential economic windfalls of green policies to critical evaluations and concerns about their real-world implications. Specifically, the Times of India, with its 27 stories, leans heavily towards business and economic perspectives. The Hindu, through its five stories, both praises the government's green initiatives and critiques certain infrastructure projects. Hindustan Times offers a balanced view in its nine stories, juxtaposing government action plans against critiques of infrastructure spending. In contrast, The Scroll and The Wire, each with three stories, delve deeper, providing incisive, critical analyses of the government's environmental commitments. The study underscores that while independent news portals present nuanced insights, their narratives often stand in the shadow of mainstream portals that echo the government's perspective. Given the escalating global importance of environmental challenges, the findings strongly advocate for media outlets to establish dedicated environmental news sections. Such focused coverage could enhance public awareness and pressurize effective governmental action in the domain of green fiscal policies. 2023 by authors, all rights reserved. -
The SDG conundrum in India: navigating economic development and environmental preservation
The paper explores the complex interplay between economic development and environmental sustainability in the context of Indias pursuit of the Sustainable Development Goals (SDGs). It examines the inherent contradictions and trade-offs involved, particularly in agriculture, industrialisation, and infrastructure sectors. The paper highlights how economic growth, essential for improving living standards, often conflicts with environmental objectives. The paper underscores the importance of integrating economic, environmental, and social objectives to achieve a sustainable and inclusive future for India. 2024 Informa UK Limited, trading as Taylor & Francis Group. -
Contradictions in conservation: Indias forest (Conservation) Amendment Bill, 2023
[No abstract available] -
Efficient one-pot green synthesis of carboxymethyl cellulose/folic acid embedded ultrafine CeO2 nanocomposite and its superior multi-drug resistant antibacterial activity and anticancer activity
Due to the prevalence of drug-resistant bacteria and the ongoing shortage of novel antibiotics as well as the challenge of treating breast cancer, the therapeutic and clinical sectors are consistently seeking effective nanomedicines. The incorporation of metal oxide nanoparticles with biological macromolecules and an organic compound emerges as a promising strategy to enhance breast cancer treatment and antibacterial activity against drug-resistant bacteria in various biomedical applications. This study aims to synthesize a unique nanocomposite consisting of CeO2 embedded with folic acid and carboxymethyl cellulose (CFC NC) via a green precipitation method using Moringa oleifera. Various spectroscopic and microscopic analyses are utilized to decipher the physicochemical characteristics of CFC NC and active phytocompounds of Moringa oleifera. Antibacterial study against MRSA (Methicillin-resistant Staphylococcus aureus) demonstrated a higher activity (95.6%) for CFC NC compared to its counterparts. The impact is attributed to reactive oxygen species (ROS), which induces a strong photo-oxidative stress, leading to the destruction of bacteria. The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of CFC NC are determined as 600g/mL and 1000g/mL, respectively. The anticancer activity against breast cancer cell resulted in the IC50 concentration of 10.8?g/mL and 8.2?g/mL for CeO2 and CFC NC respectively.The biocompatibility test was conducted against fibroblast cells and found 85% of the cells viable, with less toxicity. Therefore, the newly synthesized CFC NC has potential applications in healthcare and industry, enhancing human health conditions. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. -
HEDP-WBAN: Homomorphic Encryption and Differential Privacy for Secure Edge-Based WBANs
In the current era, healthcare is precious for all, so recent technology helps monitor the patients health via sensors and nodes, collect the same data, and analyze the patients condition. This is a technology known as a wireless body area network (WBAN). Nano-sensor devices are attached to the human body as part of WBAN to collect and monitor patient data, but have limited processing power and battery life. So, it cannot perform heavy computations within the node. Edge computing addresses this issue by processing data and reducing response times (in a near-edge node), which also helps mitigate delays and offloads work from WBAN nodes, but creates a privacy risk. Sensitive patient health information can be exposed through cyberattacks, unauthorized access, or profiling from edge nodes. This research proposes that PrivEdge-WBAN (Privacy-Preserving Edge Computing for WBANs) is integrated with edge computing, creates a framework for authentication and secure data processing, and supports new privacy-preserving and energy-efficient techniques. The proposed model combines lightweight Homomorphic Encryption (HE) and Differential Privacy (DP) to enable privacy-preserving computations and security at the edge node while maintaining energy efficiency. Moreover, the proposed framework combines an adaptive security engine that dynamically regulates cryptographic processes and authentication complexity derived from real-time energy levels and device workload. PrivEdge-WBAN aims to provide a comprehensive solution for security, privacy, and battery conservation in the real-world applications of WBAN. The outcome of this research can significantly influence the design of sustainable and secure surveillance systems for the healthcare sector, particularly for chronic illnesses, aging care, and other emergencies. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026. -
Psidium guajava-mediated green synthesis of Fe-doped ZnO and Co-doped ZnO nanoparticles: a comprehensive study on characterization and biological applications
The efficacy of nanoparticles (NPs) in healthcare applications hinges on their biocidal activity and biocompatibility. This research is dedicated to green-synthesized NPs with potent biocidal properties, aiming for high inhibition rates in bacterial infections and offering a multifunctional application, including potential use in anticancer therapy, in comparison to traditional antibiotics. The present study focuses on synthesis of zinc oxide (ZnO) nanoparticles (NPs), including iron-doped ZnO (GZF) and cobalt-doped ZnO (GZC), using the green co-precipitation method involving Psidium guajava (P. guajava) leaf extract. The physicochemical properties of the synthesized NPs were analyzed using various characterization techniques. The antibacterial and anticancer activity depends on the generation of reactive oxygen species (ROS), particle size, surface area, oxygen vacancy, Zn2+ release, and diffusion ability. The antibacterial activity of the synthesized NPs was tested against various Gram-positive (Streptococcus pneumoniae (S. pneumoniae), Bacillus subtilis (B. subtilis) and Gram-negative (Klebsiella pneumoniae (K. pneumoniae), and Pseudomonas aeruginosa (P. aeruginosa) bacterial strains. The zone of inhibition showed higher activity of GZC (1820mm) compared to GZF (1619mm) and GZO (1115mm) NPs. Moreover, anticancer studies against blood cancer cell line (MOLT-4) showed half-maximal inhibitory concentration of 11.3?g/mL for GZC compared to GZF and GZO NPs with 12.1?g/mL and 12.5?g/mL, respectively. Cytotoxicity assessments carried out on the fibroblast L929 cell line indicated that GZO, GZF, and GZC NPs demonstrated cell viabilities of 85.43%, 86.66%, and 88.14%, respectively. Thus, green-synthesized GZC NPs hold promise as multifunctional agents in the biomedical sector. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. -
Efficient one-pot green synthesis of carboxymethyl cellulose/folic acid embedded ultrafine CeO2 nanocomposite and its superior multi-drug resistant antibacterial activity and anticancer activity
Due to the prevalence of drug-resistant bacteria and the ongoing shortage of novel antibiotics as well as the challenge of treating breast cancer, the therapeutic and clinical sectors are consistently seeking effective nanomedicines. The incorporation of metal oxide nanoparticles with biological macromolecules and an organic compound emerges as a promising strategy to enhance breast cancer treatment and antibacterial activity against drug-resistant bacteria in various biomedical applications. This study aims to synthesize a unique nanocomposite consisting of CeO2 embedded with folic acid and carboxymethyl cellulose (CFC NC) via a green precipitation method using Moringa oleifera. Various spectroscopic and microscopic analyses are utilized to decipher the physicochemical characteristics of CFC NC and active phytocompounds of Moringa oleifera. Antibacterial study against MRSA (Methicillin-resistant Staphylococcus aureus) demonstrated a higher activity (95.6%) for CFC NC compared to its counterparts. The impact is attributed to reactive oxygen species (ROS), which induces a strong photo-oxidative stress, leading to the destruction of bacteria. The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of CFC NC are determined as 600g/mL and 1000g/mL, respectively. The anticancer activity against breast cancer cell resulted in the IC50 concentration of 10.8?g/mL and 8.2?g/mL for CeO2 and CFC NC respectively.The biocompatibility test was conducted against fibroblast cells and found 85% of the cells viable, with less toxicity. Therefore, the newly synthesized CFC NC has potential applications in healthcare and industry, enhancing human health conditions. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. -
The Impact of Technological Advancements in Financial Markets
The technological advancements have transformed the financial markets by increasing the efficiency, transparency and accessibility. Emerging technologies like Artificial Intelligence, Blockchain technology, Machine learning and Big data analytics have reshaped the investment strategies and enhance risk management techniques. However, concerns cybersecurity and regulatory challenges still persist. This chapter mainly discuss about how the technological advancements is changing financial markets, while taking into account both the advantages and disadvantages of these advancements. 2026, IGI Global Scientific Publishing. All rights reserved. -
Data Security-Based Routing in MANETs Using Key Management Mechanism
A Mobile Ad Hoc Network (MANET) is an autonomous network developed using wireless mobile nodes without the support of any kind of infrastructure. In a MANET, nodes can communicate with each other freely and dynamically. However, MANETs are prone to serious security threats that are difficult to resist using the existing security approaches. Therefore, various secure routing protocols have been developed to strengthen the security of MANETs. In this paper, a secure and energy-efficient routing protocol is proposed by using group key management. Asymmetric key cryptography is used, which involves two specialized nodes, labeled the Calculator Key (CK) and the Distribution Key (DK). These two nodes are responsible for the generation, verification, and distribution of secret keys. As a result, other nodes need not perform any kind of additional computation for building the secret keys. These nodes are selected using the energy consumption and trust values of nodes. In most of the existing routing protocols, each node is responsible for the generation and distribution of its own secret keys, which results in more energy dissemination. Moreover, if any node is compromised, security breaches should occur. When nodes other than the CK and DK are compromised, the entire networks security is not jeopardized. Extensive experiments are performed by considering the existing and the proposed protocols. Performance analyses reveal that the proposed protocol outperforms the competitive protocols. 2022 by the authors. Licensee MDPI, Basel, Switzerland. -
Efficient neighbour feedback based trusted multi authenticated node routing model for secure data transmission
The Mobile Ad Hoc Network (MANET) is a network that does not have a fixed infrastruc-ture. Migratory routes and related hosts that are connected via wireless networks self-configure it. Routers and hosts are free to wander, and nodes can change the topology fast and unexpectedly. In emergencies, such as natural/human disasters, armed conflicts, and emergencies, the lowest configuration will ensure ad hoc network applicability. Due to the rapidly rising cellular service requirements and deployment demands, mobile ad-hoc networks have been established in numerous places in recent decades. These applications include topics such as environmental surveillance and others. The underlying routing protocol in a given context has a significant impact on the ad hoc network deployment power. To satisfy the needs of the service level and efficiently meet the deployment requirements, developing a practical and secure MANET routing protocol is a critical task. However, owing to the intrinsic characteristics of ad hoc networks, such as frequent topology changes, open wireless media and limited resources, developing a safe routing protocol is difficult. Therefore, it is vital to develop stable and dependable routing protocols for MANET to provide a better packet delivery relationship, fewer delays, and lower overheads. Because the stability of nodes along this trail is variable, the route discovered cannot be trusted. This paper proposes an efficient Neighbour Feedback-based Trusted Multi Authenticated Node (NFbTMAN) Routing Model. The proposed model is compared to traditional models, and the findings reveal that the proposed model is superior in terms of data security. 2021 by the authors. Licensee MDPI, Basel, Switzerland. -
Group Key Management Techniques forSecure Load Balanced Routing Model
Remote sensor organizations (WSNs) assume a vital part in giving ongoing information admittance to IoT applications. Be that as it may, open organization, energy limitation, and absence of brought together organization make WSNs entirely defenseless against different sorts of pernicious assaults. In WSNs, recognizing vindictive sensor gadgets and dispensing with their detected data assume a vital part for strategic applications. Standard cryptography and confirmation plans cannot be straightforwardly utilized in WSNs on account of the asset imperative nature of sensor gadgets. In this manner, energy productive and low idleness procedure is needed for limiting the effect of malignant sensor gadgets. In this research work presents a secured and burden balanced controlling contrive for heterogeneous bunch-based WSNs. SLBR shows a predominant trust-based security metric that beats the issue when sensors proceed to influence from extraordinary to terrible state and the other way around; besides, SLBR alters stack among CH. In this way, underpins fulfilling superior security, allocate transmission, and vitality efficiency execution. Trials are driven to calculate this presentation of developed SLBR demonstrate over existing trust-based controlling show, particularly ECSO. The result accomplished appears SLBR demonstrate fulfills favored execution over ECSO as distant as vitality capability (i.e., arrange lifetime considering to begin with sensor contraption downfall and total sensor contraption passing), correspondence overhead, throughput, allocate planning idleness, and harmful sensor contraption mis-classification rate and recognizable verification. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Genetics Analysis and Identification of SSR Makers Linked to Downy Mildew Resistance Through BSA in Cucumber (Cucumis sativus L.)
The research was carried out to divulge the genetics of inheritance, nature of gene action, correspondingly to identify the SSR markers linked to downy mildew resistance in cucumber. The parents Swarna Agethi and IIHR-438 were used and developed six generations (P1, P2, F1, F2, BC1 and BC2), the parents, filial generations and backcross populations were deployed for genetic studies. The Mendelian segregation suggested that the downy mildew resistance was governed by two pairs of recessive genes with inhibitory recessive epistasis (3 resistant: 13 susceptible) gene interaction. IIHR-438 possessed a higher degree of resistance; and epistatic interaction (additive dominance) was of greater importance than the main effect. Bulk segregant analysis (BSA) aided in identifying the two SSR markers (SSR 35 and SSR 413), which had polymorphism between resistant and susceptible bulk, and the markers among backcross population segregated in an equal (1:1) ratio. Genetic distance between identified markers was found to be 16.6 cM from the QTL IciMapping V3.2 software, which depicted two new quantitative trait loci (QTLs) on chromosome number 3. These findings on genetics of downy mildew resistance provide an implication to advance the reciprocal recurrent selection followed by pedigree selection to aid in the development of resistant varieties in cucumber. 2025 Wiley-VCH GmbH. Published by John Wiley & Sons Ltd. -
Artificial Butterfly Optimizer Based Two-Layer Convolutional Neural Network with Polarized Attention Mechanism for Human Activity Recognition
Human activity recognition (HAR) is a focal point of study in the realms of human perception and computer vision due to its widespread applicability in various contexts, such as intelligent video surveillance, ambient assisted living, HCI, HRI, IR, entertainment, and intelligent driving. With the prevalence of deep learning techniques for image classification, researchers have shifted away from the labor-intensive practice of hand-crafting in favor of these methods in HAR. However, Convolutional Neural Networks (CNNs) face challenges such as the receptive field problem and limited sample issues that remain unsolved. This paper introduces a two-branch convolutional neural network for HAR classification, incorporating a polarized full attention method to address the aforementioned issues. The Artificial Butterfly Optimization (ABO) is employed for optimal hyper-parameter tuning. The proposed network utilizes twobranch CNNs to efficiently extract data, simplifying convolutional layers' kernel sizes to enhance network training and suitability for low-data settings. Feature extraction effectiveness is improved by implementing the one-shot assembly method. To amalgamate feature maps and provide global context, an enhanced full attention block called polarized full attention is utilized. Experimental results demonstrate the superiority of the proposed model in detecting human behaviors on the LoDVP Abnormal Behaviors dataset and the UCF50 dataset. Furthermore, the suggested model is adaptable to incorporate new sensor data, making it particularly valuable for real-time human activity identification applications. The Recall is 100 for the 1st dataset, 94 for the 2nd dataset, and 100 for the 3rd dataset, respectively. The F1-Score is 96.61836 for the 1st dataset, 96.90722 for the 2nd dataset, and 98.03922 for the 3rd dataset, respectively. 2024 The authors. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/). All Rights Reserved. -
Analyzing Deep Learning Architectures in Cotton Crop for Precision Disease Diagnosis
Cotton is an important cash crops worldwide, providing raw materials for the textile industry and is the basis of livelihood of millions of farmers. In India, it has an important place in the agricultural economy, which contributes significantly to both domestic consumption and export income. However, cotton production is highly sensitive to infection of various diseases and insects, such as bacterial scorching, powdery mildew and targeted spots, which can cause severe yield reduction and economic loss. Traditional disease management methods often depend on manual inspection, which is difficult to scale in time consuming, human error and large cultivated areas. Therefore, it is necessary to detect the initial and accurate detection of the disease to ensure plant health and maximize productivity. This study examines advanced intensive teaching methods for automatic cotton disease diagnosis, and compare the performance of VGG16 and ResNet18 architecture. Experimental results showed that the VGG16 model achieved verification accuracy of 99.69%, while ResNet18 achieved an accuracy of 99.58%. In addition, a real time forecasting interface was developed from the URL provided by the user to classify images of cotton leaves, making practical signs possible for use in the area. This research highlights effectiveness of deep learning in improving accurate agriculture, which helps in timely detection of diseases to reduce the loss of crops. 2025 IEEE. -
Time resolved spectroscopy of a GRS 1915 + 105 flare during its unusual low state using AstroSat
Since its disco v ery in 1992, GRS 1915 + 105 has been among the brightest sources in the X-ray sky. Ho we ver, in early 2018, it dimmed significantly and has stayed in this faint state ever since. We report on AstroSat and NuSTAR observation of GRS 1915 + 105 in its unusual low/hard state during 2019 May. We performed time-resolved spectroscopy of the X-ray flares observed in this state and found that the spectra can be fitted well using highly ionized absorption models. We further show that the spectra can also be fitted using a highly relativistic reflection dominated model, where for the lamp post geometry, the X-ray emitting source is al w ays very close to the central black hole. For both interpretations, the flare can be attributed to a change in the intrinsic flux, rather than dramatic variation in the absorption or geometry. These reflection dominated spectra are very similar to the reflection dominated spectra reported for active galactic nuclei in their low flux states. 2024 The Author(s). -
Research on Unmanned Artificial intelligence Based Financial Volatility Prediction in International Stock Market
This study digs into the area of unmanned artificial intelligence (AI) for financial volatility prediction in the worldwide stock market, delivering unique insights into the deployment of cutting-edge technology to handle the multifarious issues of market dynamics. Our research uses Long Short-Term Memory (LSTM) networks as the AI model of choice, showing its usefulness in capturing temporal relationships in financial data by analyzing past stock price data, trading volumes, and a variety of technical indicators. Our findings suggest a potential capacity to reliably predict financial market volatility after extensive data pretreatment, feature engineering, and model training. A powerful instrument for investors, fund managers, and financial institutions to make better informed and accurate investment choices, the model's low Root Mean Squared Error (RMSE) and high (R2) values highlight its practical usefulness. Beyond the purely technical, our study considers the ethical, regulatory, risk reduction, and optimization implications for the financial sector. Financial decision-making and risk management are being transformed by the increasingly globalized market environment, and the results given here provide a concrete roadmap towards the appropriate integration of unmanned AI systems. 2024 IEEE.