Browse Items · CHRIST (Deemed To Be University) Institutional Repository

Patent Number: 202111036684, Applicant: Dr. Akhilesh Tiwari.
The present inventions is about a method and system by which the entity can interact with each other in a manufacturing channel by using a messaging system (11). The said messaging system to perform status inquiry and functional processing steps with respect to data stored (1) at the resource management system of the other. That information is transmitted through a messaging system (11) to the other party such as the seller.

Article

Meta AI features on users flow experience and acceptance in the beauty and cosmetics industry: moderating role of Meta AI literacy

Purpose This study aims to integrate Artificial Intelligence Device Use Acceptance, diffusion of innovation and flow theory to explore the impact of innovation dimensions (primary appraisal) on user flow experience (UFEx) (secondary appraisal) and users willingness to accept Meta artificial intelligence (AI) features (Outcome). The moderating role of Meta AI literacy in the relationships among primary appraisal, secondary appraisal and the outcome were also examined. Design/methodology/approach This study used covariance-based structured equation modelling to analyse the data collected from 383 respondents who use Meta AI features for beauty and cosmetic products on social media. Findings The findings of the study indicates that compatibility and observability positively influence users flow experience, which subsequently impacts users willingness to accept Meta AI features on social media. Furthermore, Meta AI literacy positively moderates the relationship between relative advantage, compatibility, trialability, observability and users flow experience. Originality/value This study introduces a unique framework grounded on Meta AI features in social media, particularly in beauty and cosmetics, contributing to the literature on Meta AI, UFEx and willingness to accept. This research makes a novel attempt to explore how Meta AI literacy shapes the links among primary appraisal, secondary appraisal and outcomes. 2025 Emerald Publishing Limited

Article

Meta-analysis of EMF-induced pollution by COVID-19 in virtual teaching and learning with an artificial intelligence perspective

Concerns about the health effects of frequent exposure to electromagnetic fields (EMF) emitted from mobile towers and handsets have been raised because of the gradual increase in usage of cell phones and frequent setting up of mobile towers. The present study is targeted to detrimental effects of EMF radiation on various biological systems mainly due to online teaching and learning processes by suppressing the immune system. During the COVID-19 pandemic, the increased usage of internet due to online education and online office leads to more detrimental effects of EMF radiation. Further inculcation of soft computing techniques in EMF radiation has been presented. A literature review focusing on the usage of soft computing techniques in the domain of EMF radiation has been presented in the article. An online survey has been conducted targeting Indian academic stakeholders (specially teachers, students, and parents termed as population in the paper) for analyzing the awareness towards the biohazards of EMF exposure. 2022 IGI Global. All rights reserved.

Book Chapter

Meta-Teaching on Leveraging the Metaverse for Definitive Efficiency in Learning in Higher Education

One of the most intriguing results of the technology revolution over the past ten years has been virtual reality (VR). This experience is set to be enhanced by the metaverse, the next major technological revolution of our time. The metaverse delivers a fully immersive 3D digital experience that blends virtual and real worlds. The idea is interpreted as the future of the internet, it will allow users to interact with one another in a 3D virtual environment, through gaming or collaborating on projects. In the education sector, metaverse will play a vital role in overcoming learning limitations. Activities that occur in remote locations in the real world can now take place virtually. With VR, students are fully immersed in a simulated environment, free from distractions which enhances the student's ability to learn. Scientific studies show that VR improves spatial memory and cognition. Visual learning can boost student's understanding of more complicated subjects, concepts and languages by allowing them to learn from a first-person perspective and observe everything happening around them. 2025 selection and editorial matter, Kennedy Andrew Thomas, Joseph Chacko Chennattuserry and Joseph Varghese Kureethara; individual chapters, the contributors.

Article

Metabolite profiling and bioactivity assessment of diverse endophytic fungi from the endangered plant, Nilgirianthus ciliatus

Endophytic fungi are potential sources of bioactive compounds with therapeutic properties. This study investigated the fungal endophytes associated with Nilgirianthus ciliatus, an endangered medicinal plant, to discover its secondary metabolites and bioactivities. Molecular analysis revealed the prominent species to be Aspergillus niger, Didymella sp., Trichoderma viride, Bipolaris zeicola and Nigrospora sphaerica. Alkaloids, flavonoids, phenolics, terpenes and saponins were detected in ethyl acetate extracts employing phytochemical screening. Didymella sp. has showed the highest level of antioxidant activity, demonstrating strong DPPH radical scavenging and reduction capability. T. viride had strong antibacterial action against Klebsiella pneumoniae and Escherichia coli, meanwhile Didymella sp. and N. sphaerica were most effective against E. coli. GC-MS analysis uncovered many bioactive chemicals, including trans-farnesol and pentadecanoic acid, which are renowned for their antibacterial and antioxidant properties. These findings highlight the presence of the rich variety of diverse endophytic fungi harboring such medicinal plants, which offer promising applications in medicine, biotechnology and agriculture as sources of novel bioactive compounds. Further exploration and characterization of these strains could unlock valuable sustainable resources for various industries. The Author(s).

Article

Metabolomics Pathway Prediction Using Enhanced-Graph Convolutional Networks with Graph Attention Networks

Metabolomics, the comprehensive study of small molecules in biological systems, has a central role to play in the diagnosis of diseases, biomarker detection, and the design of new drugs. Although there have been major breakthroughs in analytical toolsets such as mass spectrometry (MS) coupled with chromatography, it is hard to predict metabolomics pathways because biochemical interactions are inherently complex. To meet this end, the current research suggests a deep learning-based approach using graph neural networks (GNN), which have shown high efficiency for graph-structured biological data. We specifically propose an enhanced graph convolutional network integrated with graph attention networks (EGCNGAT) to enhance pathway prediction performance. The hybrid framework employs graph convolutional networks (GCN) to represent molecular structural data and graph attention networks (GAT) to provide context-sensitive feature importance, thus improving the models capacity for learning complex pathway patterns. Comparative experiments against current deep learning approaches show that the introduced EGCN-GAT model obtains an accuracy of 98.90 percent, which is a 0.26 percent increase compared to the baseline MLGL-MP model. In addition, it demonstrates a 0.94 percent gain in precision as well as a slight gain in recall. The findings validate the performance of the proposed method and highlight its utility for developing pathway-level predictions in metabolomics studies. 2025 by the authors of this article. Published under CC-BY.

Article

Metaheuristic Machine Learning Algorithms for Liver Disease Prediction

In machine learning, optimizing solutions is critical for improving performance. This study explores the use of metaheuristic algorithms to enhance key processes such as hyperparameter tuning, feature selection, and model optimization. Specifically, we integrate the Artificial Bee Colony (ABC) algorithm with Random Forest and Decision Tree models to improve the accuracy and efficiency of disease prediction. Machine learning has the potential to uncover complex patterns in medical data, offering transformative capabilities in disease diagnosis. However, selecting the optimal algorithm for model optimization presents a significant challenge. In this work, we employ Random Forest, Decision Tree models, and the ABC algorithmbased on the foraging behaviours of honeybeesto predict liver disease using a dataset from Indian medical records. Our experiments demonstrate that the Random Forest model achieves an accuracy of 85.12%, the Decision Tree model 76.89%, and the ABC algorithm 80.45%. These findings underscore the promise of metaheuristic approaches in machine learning, with the ABC algorithm proving to be a valuable tool in improving predictive accuracy. In conclusion, the integration of machine learning models with metaheuristic techniques, such as the ABC algorithm, represents a significant advancement in disease prediction, driving progress in data-driven healthcare. 2024, Iquz Galaxy Publisher. All rights reserved.

Conference Paper

Metaheuristic Optimization of Deep Learning Models for Land Cover Classification Using Remote Sensing Data

Deep learning techniques have greatly advanced land-cover classification from remote sensing imagery, but their performance depends critically on choosing optimal hyperparameters. Manually tuning hyperparameters (e.g., learning rate, network depth, dropout rate) is time-consuming and often suboptimal. Metaheuristic algorithms offer an automated approach to this problem. In this work, we compare five metaheuristic optimizersParticle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), African Vulture Optimization Algorithm (AVOA), and an Enhanced Dipper Throat Optimization Algorithm (EDTOA)for hyperparameter tuning of convolutional neural networks (CNNs), a ResNet-50, and a U-Net. We evaluate these methods on two benchmark land-cover datasets: EuroSAT (patch-level multispectral image classification) and DeepGlobe (pixel-wise satellite image segmentation). Our data preprocessing includes normalization, data augmentation, and computing spectral indices (e.g., NDVI) to enrich the feature set. Each metaheuristic searches the hyperparameter space to maximize validation accuracy (for EuroSAT) or mean Intersection-over Union (mIoU) (for DeepGlobe). In addition to predictive performance, we analyze the computational cost (wall-clock time, epochs to convergence, GPU usage) of each optimizer to assess the trade-off between efficiency and accuracy. AVOA and EDTOA achieve the best results on both datasets (e.g., up to 98.5% accuracy on EuroSAT and 56% mIoU on DeepGlobe), outperforming the PSO, GA, and DE baselines while offering favorable cost-performance balance. These findings demonstrate that advanced metaheuristics can significantly improve deep model performance in land-cover classification. Our contributions include a comprehensive experimental comparison of five optimizers, a detailed methodology integrating spectral index features, a cost performance analysis, and reference results to guide future research. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Conference Paper

Metaheuristicsbased Task Offloading Framework in Fog Computing for Latency-sensitive Internet of Things Applications

The Internet of Things (IoT) applications have tremendously increased its popularity within a short span of time due to the wide range of services it offers. In the present scenario, IoT applications rely on cloud computing platforms for data storage and task offloading. Since the IoT applications are latency-sensitive, depending on a remote cloud datacenter further increases the delay and response time. Most of the IoT applications shift from cloud to fog computing for improved performance and to lower the latency. Fog enhances the Quality of service (QoS) of the connected applications by providing low latency. Different task offloading schemes in fog computing are proposed in literature to enhance the performance of IoT-fog-cloud integration. The proposed methodology focuses on constructing a metaheuristic based task offloading framework in the three-tiered IoT-fog-cloud network to enable efficient execution of latency-sensitive IoT applications. The proposed work utilizes two effective optimization algorithms such as Flamingo search algorithm (FSA) and Honey badger algorithm (HBA). Initially, the FSA algorithm is executed in an iterative manner where the objective function is optimized in every iteration. The best solutions are taken in this algorithm and fine tuning is performed using the HBA algorithm to refine the solution. The output obtained from the HBA algorithm is termed as the optimized outcome of the proposed framework. Finally, evaluations are carried out separately based on different scenarios to prove the performance efficacy of the proposed framework. The proposed framework obtains the task offloading time of 71s and also obtains less degree of imbalance and lesser latency when compared over existing techniques. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Article

Metal and Ligand-Free Approach Towards the Efficient One-Pot Synthesis of Dipyridopyrimidinimine Derivatives

We report a facile, expeditious, user-friendly, and convenient metal-free synthesis employing base catalysis in a one-pot procedure to construct 11H-dipyrido[1,2-a : 3?,2?-d]pyrimidin-11-imine derivatives. This protocol involves a domino process leading to the formation of double C?N bonds utilising KOtBu as the base and DMAc as the superior solvent at 25 C for 2 h. The versatility of this methodology was demonstrated by its successful application to substrates with both electron-withdrawing and electron-donating functional groups, yielding novel functionalized stable 11H-dipyrido[1,2-a : 3?,2?-d]pyrimidin-11-imine derivatives in good to excellent yields. Additionally, we have discussed a plausible reaction pathway for the synthesis. 2024 Wiley-VCH GmbH.

Book

Metal and Metal Oxide Nanoparticles in Textile Applications

In today's consumer-driven market, textiles are valued not only for their aesthetic appeal but also for their functional protection properties. The use of nanoparticles for specialty finishing has emerged as a promising area in textile processing and engineering. Among these nanoparticles, inorganic metal and metal oxides are particularly significant due to their large surface area and high surface energy, allowing them to impart multifunctional properties to textiles. Metal and Metal Oxide Nanoparticles in Textile Applications serves as a comprehensive guide, offering authors a profound understanding of the application of various metal and metal oxide nanoparticles on textiles. The content is thoughtfully organized, beginning with the first five chapters providing insights into the introduction of metal nanoparticles, their conventional and advanced synthesis methods, and characterization techniques. Subsequently, the following eight chapters delve into the effects of different metal nanoparticles, such as Ag, Au, Cu, Al, Ti, and others, along with their oxides, on textiles. The final section of the book encompasses chapters covering metal nanocomposites, electrospinning techniques, toxicology considerations, branding applications, and the challenges and future prospects of metal nanoparticles on textiles. This title serves as an invaluable asset to academicians, scholars, researchers, and professionals in the industry. 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Article

Metal organic frameworks in biomedicine: Innovations in drug delivery

Metal-organic frameworks (MOFs) have emerged as a class of versatile materials, finding extensive applications in drug delivery because of their unique properties and flexible design. This comprehensive review aims to give a broad perspective on the recent advancements in the area of drug delivery applications using MOFs. The fundamental characteristics of MOFs, highlighting their exceptional porosity, high surface area, and tuneable framework structures, enable MOFs to serve as ideal drug carriers, allowing efficient drug loading and controlled release. The review delves into the various ligands and metal ions employed for drug encapsulation. These include physical encapsulation, covalent bonding, and host-guest interactions, each offering distinct advantages for diverse types of drugs and therapeutic applications. The importance of tailoring MOF properties to optimize drug loading capacity, stability, and release kinetics has been emphasized. Additionally, the explorations involve delving into the mechanisms of drug release from MOFs, with factors such as pH, temperature, and external stimuli that can be harnessed to trigger controlled drug release. The utilization of MOFs in combination therapies, such as co-delivery of multiple drugs or integrating imaging agents, has also been examined. Numerous examples of MOFs used for drug delivery, encompassing both in-vitro and in-vivo studies, covering a wide range of therapeutic areas, including cancer treatment, antimicrobial therapy, and targeted drug delivery, are included. Additionally, the review addresses the challenges and future perspectives in the development of MOFs for drug delivery. Strategies to improve MOF stability, biocompatibility, and scalability are discussed, along with the understanding of MOF-drug interaction and potential toxicity concerns. With their tuneable properties, high loading capacities, and controlled release capabilities, MOFs hold exceptional capabilities that promise to enhance the efficacy of therapeutic interventions. Continued research and development in this area can pave way for the translation of MOFs into clinical applications in the near future. 2024 The Author(s)

Book Chapter

Metal Organic Frameworks to Remove Arsenic Adsorption from Wastewater

Water is an integral part of life on earth. Rapid industrialization, urbanization, and population explosion have all contributed to the pollution of ground and surface water with, among other things, heavy metals. This has led to an acute shortage of clean drinking water. Arsenic is one of the most toxic heavy metals found in water, posing a serious threat to the environment, human beings, and aquatic life. Over the years, a considerable amount of research has been directed toward the elimination of arsenic from water via sustainable methodologies. Metal organic frameworks are a class of materials possessing exceptional features like chemical stability, high porosity, multiple functional groups, and large surface areas. These properties can be effectively channelized to make metal organic frameworks excellent adsorbents for the removal of arsenic from contaminated water and make it drinkable. We have reviewed herein, the problems of heavy metal contamination, specifically the different forms of arsenic that pollute water. The importance of metal organic frameworks and the progress made in the synthesis of materials having a metal oxide framework have been discussed. Significant properties like adsorption and mechanistic aspects of adsorption through metal organic frameworks have been described. Furthermore, the characterization of the electronic and geometric aspects of metal organic frameworks using density functional theory has been reviewed. Insight into proper scaling up and development of metal organic frameworks for practical applications have also been suggested. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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