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Reliable monitoring security system to prevent MAC spoofing in ubiquitous wireless network
Ubiquitous computing is a new paradigm in the world of information technology. Security plays a vital role in such networking environments. However, there are various methods available to generate different Media Access Control (MAC) addresses for the same system, which enables an attacker to spoof into the network. MAC spoofing is one of the major concerns in such an environment where MAC address can be spoofed using a wide range of tools and methods. Different methods can be prioritized to get cache table and attributes of ARP spoofing while targeting the identification of the attack. The routing trace-based technique is the predominant method to analyse MAC spoofing. In this paper, a detailed survey has been done on different methods to detect and prevent such risks. Based on the survey, a new proposal of security architecture has been proposed. This architecture makes use of Monitoring System (MS) that generates frequent network traces into MS table, server data and MS cache which ensures that the MAC spoofing is identified and blocked from the same environment. 2019, Springer Nature Singapore Pte Ltd. -
A Data Mining approach on the Performance of Machine Learning Methods for Share Price Forecasting using the Weka Environment
It is widely agreed that the share price is too volatile to be reliably predicted. Several experts have worked to improve the likelihood of generating a profit from share investing using various approaches and methods. When used in reality, these methods and algorithms often have too low of a success rate to be helpful. The extreme volatility of the marketplace is a significant contributor. This article demonstrates the use of data mining methods like WEKA to study share prices. For this research's sake, we have selected a HCL Tech share. Multilayer perceptron's, Gaussian Process and Sequential minimal optimization have been employed as the three prediction methods. These algorithms that develop optimal rules for share market analysis have been incorporated into Weka. We have transformed the attributes of open, high, low, close and adj-close prices forecasted share for the next 30 days. Compare actual and predicted values of three models' side by side. We have visualized 1step ahead and the future forecast of three models. The Evaluation metrics of RMSE, MAPE, MSE, and MAE are calculated. The outcomes achieved by the three methods have been contrasted. Our experimental findings show that Sequential minimal optimization provided more precise results than the other method on this dataset. 2023 IEEE. -
Investigation of Cervical Cancer Detection from Whole Slide Imaging
Early cancer detection is critical in enhancing a patient's clinical results. Cervical cancer detection from a large number of whole slide images generated regularly in a clinical setting is a complex and time-consuming task. As a result, we require an efficient and accurate model for early cancer diagnosis, especially cervical cancer as it can be fully prevented if detected in an early stage. This study focuses on in-depth writing on current methodologies for cervical cancer segmentation and characterization from the whole cervical slide. It combines the state of their specialty's performance measurement with the quantitative evaluation of cutting-edge techniques. Numerous publications over the last eleven years (2011-2022) clearly outline various cervical imaging methods over multiple blocks. And this review shows different types of algorithms used in each processing stage of detection. The study clearly indicates the advancements in the automation field and the necessity of the same. Published under licence by IOP Publishing Ltd. -
Overt dependence of health insurance industry on healthcare system
A vast majority of the population in the developing economies remains uninsured. Moreover, the informal sector that employs a larger section of the society is untouched by any of the government scheme. In this study, we use health belief model to examine the factors that induce willingness to buy health insurance among the illness and the non-illness group. A cross-sectional study was conducted on 1,339 participants above 20 years of age of which 351 had contracted illness in the past and 988 had not. Data was collected using questionnaire from four highly populated districts in India. The questionnaire was developed based on the constructs of health belief model. The data was statistically analysed. Kendalls Tau-b correlation technique was used to explore the relationship between perceived vulnerability and product aversion. Logistic regression was used to find out the odds at which each independent variable, categorised based on the health belief model, contributes to willingness to buy. The model was able to predict 15% of the variance for willingness-to-buy among the illness and 27% among the non-illness groups. Findings suggest that the perceived vulnerability reduced product aversion among the illness group. Mere presence of primary and super-specialty hospitals was not sufficient for the illness group to subscribe for health insurance. Income perceptions emerged as a significant predictor among the illness group. Presence of well-established hospital, income perceptions, and subjective norms were significant predictors among the non-illness group. The growth of the health insurance industry largely depends upon the presence of well-established hospitals. In the absence of adequate healthcare facilities, attempts by the insurers to promote insurance covers will become futile. Insurers should also consider alternate segmentation patterns albeit the present socio-demographic pattern, as the health risk experience differs among individuals. Asian Academy of Management and Penerbit Universiti Sains Malaysia, 2021. -
Examining women's purchase pattern of casual footwear in accordance with their attitudes and interests
Purpose: The present study examines the association between the choices of casual footwear attributes of women in accordance with their behavioral pattern. Design/Methodology/Approach: Data was collected from 2365 women through a questionnaire that comprised of two sections. The first section comprised of 50 AIO statements based on which the respondents were profiled according to their behavioural patterns. The second section comprised of selected footwear and store attributes. The consumers were profiled into eleven clusters using factor analysis. The regression scores were used to assign the respondents to the respective components that were extracted through factor analysis. Reliability Test and KMO Test were conducted to check the reliability and adequacy of the sample size. Further, only those variables that qualified the collinearity test were alone subject to regression analysis. Through ANOVA test, it was observed that significant differences existed among the consumers within the clusters. Therefore, the AIO statements were considered as independent variables that were regressed against ten selected footwear attributes. Findings: The Results indicated that consumers with different behaviors had varied preferences towards footwear attributes. Practical Implications: The results of the study indicate that the manufacturers in the footwear sector should revisit their existing strategies and target the consumers on the basis of their behavior as the proliferation of the unorganized sector is very high in this sector. Original Value: There are innumerable literatures that focus on trade policies followed in the footwear market in international countries, treatment of workers in the footwear industry, therapeutic use of footwear, supply chain patterns etc., but hardly any significant study that explores the consumers' behaviour and their association towards their footwear preferences has been conducted. Behavioral segmentation has been used in many other products like apparels, insurance, real estate etc., but not in the footwear sector. The present study is an attempt to fill this gap. -
Recruitment Analytics: Hiring in the Era of Artificial Intelligence
Introduction: Traditional recruitment system relied heavily on the applicants curriculum vitae (CV). This system, besides becoming redundant, has proved to be a futile exercise leading to the hiring of candidates that eventually turn out to be misfits. CVs were the only source of candidates data available for the recruiters a few years back. Face-to-face interviews was considered to be the ultimate solution for hiring suitable candidates. However, evidence suggests that interview scores and job performances do not complement each other. Advancement in artificial intelligence (AI) has introduced several techniques in the recruitment process. Purpose: This chapter underscores the drawbacks of the traditional recruitment process. Evidence suggests that the traditional recruitment process is prone to subjectivity and is time-consuming. Surprisingly, despite the disadvantages, the integration of AI into the recruitment process is still slow. This chapter highlights the need to harness AI and the advantage technology could bring to the recruitment process. Some of the techniques that are garnering attention and widely used by organisations, such as chatbots, gamification, virtual employment interviews, and resume screening are described to enable the readers to understand with less effort. Chatbots and gamification techniques are described through process flow charts. We also describe the various types of interviews that could be conducted through virtual platforms and the modality by which the resume screening technique operates. Today, we are at a juncture wherein it is pertinent to acknowledge the superiority of technology-driven processes over traditional ones. This chapter will help the readers to understand the modus operandi to implement chatbots, gamification, virtual interviews and online resume screening techniques besides their advantages. Scope: Although chatbots, resume screening, virtual interviews, and gamification are used in other areas, too, such as training and development, marketing, etc., in this chapter, we restrict solely to employee recruitment processes. Methodology: Scoping review is used to examine the existing literature from various databases such as Google Scholar, IEEE, Proquest, Emerald, Elsevier, and JSTOR databases are used for extracting relevant articles. Findings: Automation and analytics in recruitment and selection remove bias which is otherwise increasingly found in manual hiring processes. Also, previous studies have observed that candidates engage in impression management tactics in traditional face-to-face interviews. However, through automated recruitment processes, the influence of these tactics can be eliminated. AI-based virtual interviews reduce human bias. It also helps recruiters to hire talents across the globe. Gamification improves the candidates perception of the work and work environments. Through gamified techniques, the recruiters can understand whether a candidate possesses the required job skills. Chatbots are an interactive technique that can respond to interviewees queries. Resume screening techniques can save the recruiters time by screening and selecting the most appropriate candidates from a large pool. Hence, the chosen candidates alone can be referred to the next stage of the recruitment cycle. AI improves the efficiency of the recruitment process. It reduces mundane tasks. It saves time for the human resources (HR) team. 2023 by V. R. Uma, Ilango Velchamy and Deepika Upadhyay. -
LSTM-MGTO: a novel early breast cancer detection using long short term memory based modified gorilla troops optimization algorithm
One of the most prevalent and severe tumors in women, breast cancer, remains a major global health issue despite a notable increase in incidence over the last ten years. It is the second leading cause of cancer-related death among women. Identifying breast cancer in its early stages has the potential to save lives; however, current screening techniques for the illness require several laboratory procedures involving medical experts. Automated solutions with rapid and reliable diagnostic capabilities are needed to minimize human error and expedite breast cancer diagnosis. The projected accuracy of cancer diagnosis remains far from matching the precision offered by existing approaches, even with the research on automated systems for the disease being studied. This work suggests a long short-term memory-based modified Gorilla troop optimization (LSTM-MGTO) method for breast cancer classification in order to address these issues. The Mastectomy Koibra Dataset (BCCD) and Wisconsin Diagnostic Mastectomy (WDBC) datasets were used to test the suggested methods. First, the proposed system employs contrast-limited adaptive histogram equalization (CLAHE) to enhance the quality of digital mammograms. Furthermore, employ a semantic deep learning (SDL) model to extract features. After the feature selection process, a recursive feature elimination technique was implemented to determine the crucial WDBC and BCCD characteristics that are relevant to breast cancer detection. Moreover, recommend a modified U-Net architecture for partitioning in both unmapped and guided contexts. The experimental findings indicate that the newly developed partitioning model surpasses existing advanced techniques, yielding superior results in both Dice and IoU score evaluations. On the WDBC and BCCD datasets, the suggested U-Net segmentation produces maximum Dice scores of 97.65% and 96.24%, respectively. Additionally, the model obtained the greatest IoU scores of 95.43% and 90.65% on the WDBC and BCCD datasets, respectively, according to the experimental findings. The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2025. -
Exploration of digital image tampering detection using CNN with modified particle swarm optimization in deep learning
The field of image processing is crucial for many different applications, including forensic evidence, insurance claims, medical imaging, bio-informatics, artifact collection and more. In many sectors nowadays, digital photographs are regarded as a trustworthy source of information. The manipulation of such photographs leads to a variety of issues. The study presents a method using convolutional neural networks (CNN) combined with modified particle swarm optimization (MPSO) to improve the accuracy of tampering detection. This advancement contributes to improved reliability in fields requiring image authenticity verification, such as forensics and media. The design includes the collection of a dataset comprising both original and tampered images for training and testing the model. A dataset, such as the Media Integration and Communication Center (MICC) dataset, is utilized, which includes various images that have been altered through different tampering techniques. This dataset serves as the foundation for training the CNN and evaluating its performance The findings indicate that the proposed MPSO_CNN method outperforms traditional techniques in terms of precision, accuracy, recall, and F-measure, demonstrating its effectiveness in identifying tampered images. The results highlight the significance of using advanced deep learning techniques for reliable image authenticity verification. 2025, Institute of Advanced Engineering and Science. All rights reserved. -
Insuretech: Saviour of insurance sector in India
Technology in finance has propelled financial literacy and inclusiveness and may give the insurance sector an edge to reach its potential consumers. The current study aimed to identify the role of Fintech in transforming the insurance sector and improving the penetration rate in India. With the descriptive research design, the study collects the primary data through a survey technique targeting the general public and personnel in the insurance sector as a study population. A conceptual model is proposed to understand the interlink between consumer attitude towards Insurance, factors influencing their decision, and the role of Fintech in bridging the gap in insurance penetration. This study focuses on three areas, namely health insurance, life insurance, and vehicle insurance. The study's findings reveal that the insurtech will significantly improve the efficiency of the insurance sector which will result in significant financial performance. 2024 Srinesh Thakur, Anvita Electronics, 16-11-762, Vijetha Golden Empire, Hyderabad. -
Ban or boon: Consumer attitude towards plastic bags ban
In Tamil Nadu, the state government has imposed a ban on plastic bags two years ago. This has created a major impact of the day to day life of common people. Though it has positive effect on the environment, the common public had different perception as a consumer. This paper aimed at studying the consumer attitude towards the ban on plastic bags. A descriptive research design adopted to address the various dimension of consumer perception towards the ban on plastic ban. A sample size of 400 respondents was selected on the basis of systematic random sampling technique to collect data through structured questionnaire. For conducting the survey, consumers of retail shops in urban and rural places were chosen as target respondents. The collected data were analyzed with the help of statistical tools such as ANOVA, t-Test, Correlation, Linear Regression and Structural equation modelling and the interpretation reported. The result revealed that only 34 percentage of respondent were aware the environmental impact of plastic bags. About 71 percentage of consumers reported that they have faced difficulties in their day to day life due to plastic ban. 2021 American Institute of Physics Inc.. All rights reserved. -
Influence of manufacturing process on distribution of MWCNT in aluminium alloy matrix and its effect on microhardness
Nano composites are finding increased focus and their influence on improving the matrix properties are very attractive. But the success is fully dependent on the uniform distribution and dispersion of nano reinforcements in the matrix. Manufacturing process was found to have greater role in distribution of the reinforcements. The liquid processing and solid processing like SPS and hot coining found to have different effect on the matrix due to the nature of reinforcements. Current study focussed on the microstructure study using Back scattered images and the microhardness with and without reinforcements. MWCNT was occupying the particle boundary. Hot coining was found to distribute MWCNT on the particle surface as well as on the particle boundary. Clustering was absent and resulted in improved hardness in comparison with casting as well as spark plasma sintering. 2018 Trans Tech Publications, Switzerland. -
Study of multilayer flow of two immiscible nanofluids in a duct with viscous dissipation
Numerical simulations for the mixed convective multilayer flow of two different immiscible nanofluids in a duct with viscous heating effects were performed in this study. The left and right faces of the duct are maintained to be isothermal, while other side faces are insulated. The mathematical governing system for each layer consists of an incompressibility condition equation, the Navier-Stokes momentum equation, and the conservation of energy equation. At the interface of the immiscible layer, the continuity of velocity, shear stress, temperature, and heat flux are considered. The dimensionless equations governing each layer were numerically integrated using the finite difference method and the Southwell-over-relaxation method. A mesh independence test is conducted. Furthermore, a parametric study is performed to analyze how the different nanoparticle volume fractions and viscous heating affect the transport characteristics of engine oil-copper and mineral oil-silver nanofluids. The study also examined the effects of various types of nanoparticles and base fluids. The results demonstrated that heat transport could be efficiently controlled by considering the viscous heating aspect. Moreover, the effects of different nanoparticles on heat transport were found to be more significant than those of base fluids. Finally, a point-wise comparison of our numerical results demonstrates a good agreement with existing studies in the literature. 2023 Author(s). -
Unsteady squeezing flow of a magnetized nano-lubricant between parallel disks with Robin boundary conditions
The aim of the present work is to examine the impact of magnetized nanoparticles (NPs) in enhancement of heat transport in a tribological system subjected to convective type heating (Robin) boundary conditions. The regime examined comprises the squeezing transition of a magnetic (smart) Newtonian nano-lubricant between two analogous disks under an axial magnetism. The lower disk is permeable whereas the upper disk is solid. The mechanisms of haphazard motion of NPs and thermophoresis are simulated. The non-dimensional problem is solved numerically using a finite difference method in the MATLAB bvp4c solver based on Lobotto quadrature, to scrutinize the significance of thermophoresis parameter, squeezing number, Hartmann number, Prandtl number, and Brownian motion parameter on velocity, temperature, nanoparticle concentration, Nusselt number, factor of friction, and Sherwood number distributions. The obtained results for the friction factor are validated against previously published results. It is found that friction factor at the disk increases with intensity in applied magnetic field. The haphazard (Brownian) motion of nanoparticles causes an enhancement in thermal field. Suction and injection are found to induce different effects on transport characteristics depending on the specification of equal or unequal Biot numbers at the disks. The main quantitative outcome is that, unequal Biot numbers produce significant cooling of the regime for both cases of disk suction or injection, indicating that Robin boundary conditions yield substantial deviation from conventional thermal boundary conditions. Higher thermophoretic parameter also elevates temperatures in the regime. The nanoparticles concentration at the disk is boosted with higher values of Brownian motion parameter. The response of temperature is similar in both suction and injection cases; however, this tendency is quite opposite for nanoparticle concentrations. In the core zone, the resistive magnetic body force dominates and this manifests in a significant reduction in velocity, that is damping. The heat build-up in squeeze films (which can lead to corrosion and degradation of surfaces) can be successfully removed with magnetic nanoparticles leading to prolonged serviceability of lubrication systems and the need for less maintenance. IMechE 2021. -
Waste Management for Waste Entrepreneurship: An Emerging Concept
Since the very beginning of civilization, waste has always been an incessant problem and their management remains burdensome till date, as the rate of waste generation is increasing with the increase in population, land use and development of economy. Waste is generally considered as an unavoidable trash/nuisance with zero value and concerns which can be overruled by the waste management system. It is a well-organized holistic expensive process that includes segregation at sources, on-time collection, transportation, reuse, recycle, reprocess and disposal of the leftover materials into the landfills, which usually receives inadequate attention as public get easily acclimatized to live along with the generated wastes. Managing waste in an environmentally favorable, culturally acceptable and techno-economically feasible manner is a need in recent times. Society is in a need to think of ways to minimize and utilize waste for other uses. Understanding waste management in terms of its challenges involves knowledge dissemination to the public, waste prevention, valorization, responsible material production and packaging, maximum recycling, conservation of resources, enhancement of sustainability and reduction of greenhouse gasses. Opportunities in waste management could be achieved by exercising circular economy practices which reinforce environmental, societal and economic benefits. Role of entrepreneurs in the waste management system encompasses a cluster of skilled as well as unskilled workers, as it is a labor-intensive system. Entrepreneurs may invest money as well as infuse novel skills and technologies to transform trash into treasures. The efficacy and significance of waste management will eventually increase with the active participation of entrepreneurs. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Eggshells biowaste for hydroxyapatite green synthesis using extract piper betel leaf - Evaluation of antibacterial and antibiofilm activity
The present research work reports the biosynthesis of hydroxyapatite (HAp) from eggshells and green synthesis of HAp from eggshells with incorporation of Piper betel leaf extract (PBL-HAp) using microwave conversion method. Although there are several works on synthesis of HAp from eggshells and other calcium and phosphorus rich substrates, the incorporation of herbal extract with HAp to promote antimicrobial and antibiofilm activity is less explored and reported. This research work highlights a simple and cost-effective method for development of antimicrobial biomaterials by combining the concepts of waste management, biomaterial science, and herbal medicine. In the present study, characterization of synthesized HAp was applied by X-ray Diffraction (XRD), Fourier Transform Infrared (FTIR) spectroscopy, Proton Nuclear Magnetic Resonance (1H NMR) spectroscopy, and morphological analysis using Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM). The characterization results indicated that the prepared HAp and PBL-HAp were pure b-type carbonated HAp. The PBL-HAp was checked for its antibacterial activity using the well diffusion method and biofilm inhibitory activity by crystal violet assay against some common pathogens. The antibacterial activities against Staphylococcus aureus and biofilm inhibitory activities against Escherichia coli, Vibrio harveyi, Pseudomonas aeruginosa, and Staphylococcus aureus of Piper betel leaf extract coated HAp (PBL-HAp) were showed to be significant and offered a promising role for the development of potent dental biomaterials. 2021 Elsevier Inc. -
Microbial Fuel Cells: The Microbial Route for Bioelectricity
The quest for sustainable energy sources serves as the essential pillar for development of humans since the dawn of civilization. The alarming increase in demand of energy, especially electricity propelled the need to screen for alternative sources of energy over the conventional fossil based non-renewable counterparts. Electricity generation through microbial route functions by the fundamental phenomena of electron transport chain and the microbes operate as the source of energy production utilizing the substrate. Since its initiation, microbial fuel cell has gained a lot of research focus from all over the world. The integration of waste treatment with power generation was highlighted as the most productive and sustainable part of microbial fuel cells. Over the past few decades, a lot of research and development was done on improving the design of fuel cells, searching for cost-effective electrodes and membranes for commercialization. Despite tremendous research done on this domain, its commercialization still faces a lot of hurdles especially once it comes to the overall maintenance and production cost. This chapter summarizes the basic architecture of different microbial fuel cells and the challenges that need to be addressed for making microbial fuel cells a sustainable route for the bioelectricity generation from microorganisms. Springer Nature Singapore Pte Ltd. 2020. -
Fish waste valorisation through production of biodiesel and biopolymers for sustainable development: A mini review
Fish processing waste accounts for one of the major classes of food waste generated worldwide in terms of the high volume of waste generated. The presence of high amounts of organic compounds (proteins: 1530 %, lipids: 520 %) in fish waste makes them highly susceptible to autolysis which when not managed properly pose adverse effects on the environment like production of offensive odor, generation of hydrogen sulfide, higher biological oxygen demand (1000 mg/L to 12,000 mg/L or even higher) (BOD), and multiplication of pathogenic bacteria. Fish waste is rich in lipids and polysaccharides that can be channelized for biodiesel and biopolymer production respectively. Biodiesel refers to the biofuel produced from transesterification of plant and animal fats. Extraction of oils from fish waste followed by transesterification reactions can yield biodiesel through a biorefinery approach. Biorefinery concept emphasizes the conversion of biomass into commercially important byproducts. Biopolymers refers to the natural polymers that can be extracted from the natural sources or produced through microbial fermentation process. Furthermore, commercially important biopolymers like chitosan and polyhydroxyalkanoates (PHAs) can be used as biorefineries. This review work presents the sequential strategies for conversion of fish waste to biodiesel, PHA and chitosan through various physicochemical and biological methods. The review also presents the existing challenges and the future in the fish waste biorefinery concept. The scope of this review is to present a broader concept of integrating fish waste biorefinery for production of multiple value added products like biodiesel and biopolymers. 2025 Elsevier Ltd -
Fruit Waste as Sustainable Resources for Polyhydroxyalkanoate (PHA) Production
Production of polyhydroxyalkanoate (PHA) using commercially available carbon sources like glucose or sucrose makes the bioprocess economically nonviable, thereby hindering its commercialization. As an alternative to this issue, inexpensive and easily available agro-industrial wastes are now being exploited as feedstock for PHA production. Fruit wastes are generally discarded as they are considered to be the non-product leftovers which do not have any economic value when compared with the cost of their collection and recovery steps for reuse. But through the use of appropriate technological applications, these wastes can be converted to valuable by-products, which can increase the value of the products much higher than the cost associated with recovery steps. By recycling and reprocessing the fruit wastes, they can be channeled into many applications, and thereby the amount of fruit wastes discharged into the environment can be completely reduced along with their detrimental effects. Large amounts of fruit wastes are produced by fruit-based industries. The waste products can be both solids and liquids, and these wastes are of high nutritional and biomass values for microorganism; thus their addition to waterbodies can make them highly polluted (high BOD or COD). These fruit-based wastes still have a promising potential for bioconversion into products of commercial importance or can be successfully exploited as cheap raw materials for industrial production of commercially important metabolites. This chapter deals with the strategies for production of PHA from fruit waste substrates, extraction and characterization of PHA, and their applications in diverse sectors. The Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2021. -
Extraction, characterization, and fabrication of cellulose biopolymer sheets from Pistia stratiotes as a biodegradative coating material: an unique strategy for the conversion of invasive weeds into value-added products
This study explores the possibility of using Water lettuce (Pistia stratiotes) as a cost-effective substrate for the commercial extraction of cellulose biopolymer using a wide variety of physicochemical treatment methods to compare their efficiency in cellulose extraction. The extraction of cellulose from water lettuce, although promising due to their high cellulose content, was less explored as per the available literature. In this study, functional properties like bulk density-packed density, hydrated density, water retention capacity, oil retention capacity, emulsifying activity and setting volume of the extracted cellulose were studied. The cellulose content from water lettuce was found to be 38.94 0.10% by anthrone method. Preliminary confirmation of cellulose biopolymer was done using the study of functional groups using Fourier Transform Infrared (FT-IR) analysis. Further characterization studies like Scanning Electron Microscopy (SEM), X- Ray Diffraction (XRD), Differential Scanning Calorimetry (DSC) and thermogravimetric analysis (TGA) were conducted to understand the molecular architecture and purity of the cellulose extracted. Fabrication of cellulose sheets was carried out using starch as the plasticizer. Biodegradation studies were conducted in garden soil for four weeks and a high degradation rate of 78.22 0.71% was observed in the fourth week of soil burial. 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Sustainable Biodegradable and Bio-based Materials
The quest for sustainable biodegradable and bio-based materials is ever increasing due to their versatile properties and also their ability to serve as potential alternatives to their synthetic counterparts. The major types of bio-based materials of commercial importance can be derived majorly from plant, animal, and microbial sources through physical, chemical, or biological extraction methods. Despite their potential applications, biocompatibility, and biodegradability, these bio-based polymers still face hurdles in competing with conventional plastics. The major factors contributing to this involve the production and extraction cost. In recent years, the integration of waste valorization with biopolymer production and the development of eco-friendly green extraction protocols with minimum usage of chemicals were visualized as efficient strategies for the sustainable production of biopolymers. This study summarizes the important biodegradable and bio-based materials of commercial importance along with their production methods and application in diverse sectors. 2023 selection and editorial matter, Ajay, Parveen, Sharif Ahmad, Jyotsna Sharma, Victor Gambhir.