Browse Items (11810 total)
Sort by:
-
Green Data Centers: A Review of Current Trends and Practices
A green data center is a facility that makes use of eco-friendly techniques and technologies to lessen its carbon footprint and environmental impact. A data center can consume as much electricity as a small city and contains thousands of servers. These server farms require an enormous amount of processing power to operate, which presents numerous difficulties, including high energy costs, greenhouse gas emissions, backups, and recovery. This paper clarifies the various green data center best practices, including energy efficiency, cooling systems, renewable energy, sustainable building techniques, and carbon footprint. The need for green data centers in todays internet, commercial, financial, and business applications is also covered in the paper. The reality and myths of green data centers are alsoexamined. The paper delves into the metrics for each characteristic used to gauge how green and effective data centers are. The discussion has concluded with case studies of companies that have implemented green data centers. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Modeling the Intention to Use AI Healthcare Chabots in the Indian Context
Covid-19 has accelerated the need and use of artificial Intelligence-based healthcare Chabots. Penetration of the internet, smartphone, computational capability and machine learning technology brings healthcare services close to the patients. The penetration of AI healthcare Chatbot technology worldwide is on the rise. However, the healthcare ecosystem in India is unique and poses challenges in the adoption of healthcare chatbots. The demographic characteristics, economic conditions, diversity, belief systems on health-seeking, and alternative medical practices play a role in accepting and using chatbots. In this study, we attempt to model the factors influencing the intention and the purpose of using the chatbot. Through a literature review, we identify the variables related to the adoption of healthcare chatbots. We then focus on the more relevant concepts to the Indian context and develop a conceptual model. Through cases and literature, we frame the propositions of the study. We look at the awareness of chatbot features, perception towards the chatbot, trust and mistrust of the healthcare system, the doctors and the chatbots, health-seeking behavior, and the belief in traditional, complementary, and alternative medicine prevalent in India. This study contributes by developing an initial conceptual model for healthcare chatbots adoption in the Indian context. In the future, we plan to operationalize the study and test the propositions through an elaborate survey to validate the model empirically. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Strategic Data Analytics for Sustainable Competitive Advantage
Data and analytics have become major assets for all organizations to leverage into superior strategic positions in this cut-throat competitive world with buzzwords like data crunch, metrics, and dark data. This chapter discusses the structural and economic reasons of why business analytics is necessary for organizations. The ability to collect different resources and entities such as talent, process, data, and information technology to bring out a valuable output is crucial for business analytics success. The most common difficulty of big data begins when organizations are in the journey of business analytics. Since a number of organizations are still in the baby steps of basic, tackling data challenges is humongous for them. This situation calls for the need to foster a business analytics ecosystem by every organization. This paper discusses how optimizing analytics could lead to a sustainable competitive advantage, building data strategy, and setting Key Performance Indicators (KPI) for business analytics. The chapter further explores how analytics is used across business domains and the challenges in crafting a business analytics strategy. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Analyzing blockchain-based supply chain resilience strategies: resource-based perspective
Purpose: This research tries to find the blockchain-based resilience strategies that can help the supply chains of micro, small, and medium-sized enterprises (MSMEs) to recover from the disruptions and work effectively in a resource-based view perspective. Design/methodology/approach: Eight broad strategies and 32 sub-strategies are identified from the literature review. Delphi study was carried out, and detailed discussion with 16 experts helped in finalizing these strategies. Further, the best-worst method (BWM) prioritized these strategies. Findings: The findings suggests that building social capital, improving coordination capabilities, sensitivity towards market, flexibility in process and production, reduction in process and lead time,and having a resource efficiency and redundancy are the top strategies on which the top management should focus to overcome the situations of disruptions and enhance performance of MSMEs. Practical implications: The blockchain-based strategies will enable the companies in tracing the products from the company to customers. Further, the customers will be able to identify their manufacturers, the raw materials used in manufacturing, and the life and quality of raw used materials. Altogether the textile industry will become more sensitive toward environmental practices. Originality/value: The previous research has not identified and evaluated the blockchain-based resilience strategies, and therefore this study tries to fill this gap. This study used a smaller sample from the experts, so the results may vary if the larger data set is used and hypothesis testing can be done. 2023, Emerald Publishing Limited. -
The Challenges of Blockchain Technology Adoption in the Agro-based Industries
Blockchain is one of the latest innovations in information technology, bringing a digital revolution to many industries by increasing transparency. But this technology needs to be explored a lot as of now. Agriculture supply chain management distributes agro-based products like vegetables, fruits, pulses, and cereals. This research is conducted to identify the agro-based industries' adoption of blockchain in their supply chain for achieving sustainability. The next step towards sustainable agriculture is primarily seen as blockchain-enabled agriculture. By making supply chains transparent, technology can follow products from the point of manufacture and prevent waste and inefficiency. A structured literature review helped determine the barriers to blockchain adoption in agro-based industries. This research is unique as no survey-based research on blockchain in the agriculture supply chain using structural equation modeling has been found. The seven proposed hypotheses support the blockchain challenges for adoption in agro-based industries. The findings of this study suggest that the blockchain can bring transparency and traceability and will remove the agro-industry inefficiencies. 2022 International Journal of Mathematical, Engineering and Management Sciences. All rights reserved. -
Exploring challenges in online higher education for AI integration using MICMAC analysis
The consequence of Covid-19 has affected the traditional higher education system. Acknowledging the significant role of online education in national development for accessibility and quality education, countries around the world have understood its importance in current digital era. Indian policymakers have been giving due importance to enhancing the education quality, however the progress made by the country in higher education is not adequate. Amidst all the inadequacies of traditional education system, artificial intelligence (AI) technologies are bringing new ray of hope to democratize education system. This chapter is subjected to identify the challenges in online education and suggest specific ways to address each of them. The challenges are categorized into internal and external challenges/barriers. These challenges have been modeled with the expertise of educationalist's opinions and interpretive structural modeling to create a hierarchy of the barriers using MICMAC analysis and categorize these barriers into four clusters. 2024, IGI Global. All rights reserved. -
Prefabricated Houses - A Model to Sustainable Housing Market
Effective spatial planning in an urban center is the need of the hour especially for offering affordable and sustainable houses. separate sheet. Spatial planning may help in achieving Sustainable Development goals (SDG) (09) Industry, Innovation and Infrastructure, (11) Sustainable cities and communities, and (15) Life on Land. The prefabricated house model can be used as a strategy in achieving above mentioned SDGs. It is important to study the prefabricated housing market for a country like India, considering its growing population and the necessity of access to affordable and sustainable houses. The main objective of this study is to identify the determinant factors of prefabricated houses and its impact on preference among urban consumers. The study is quantitative in nature and adopts a survey method. SEM model is used to analyze the data. A structured questionnaire is developed based on the objectives of the study. The questionnaire majorly focused on the perception of Sustainability, Affordability, Durability, Barriers, Opportunities, and Quality. The Electrochemical Society -
Relationship Between Sustainable Tourism Indicators and the Operational Challenges of the Tourism Business: Empirical Evidence from the Wildlife Resorts of Karnataka, India
Wildlife resorts are one of the most prominent and attractive segments of the accommodation sector. They face many socio-economic and environmental challenges to implement sustainable tourism practices in their daily operations. This study aims at investigating whether there is any relationship between socio-economic and environmental issues, and challenges faced by wildlife resorts in implementing sustainable tourism practices, and the indicators used by the resorts to measure their sustainable tourism practices. The study employs triangulation design to conduct the research. Survey method was employed for identifying the sustainable tourism indicators and personal interviews with resort managers were conducted to identify the operational issues and challenges of wildlife resorts for the implementation of sustainable practices. Based on purposive sampling, sixteen wildlife resorts from the Indian state of Karnataka are selected for the study. Dedoose software is used to conduct mixed method analysis, to compare and analyse the data obtained from both qualitative and quantitative methods. Copyright 2022, IGI Global. -
Local community involvement in wildlife resorts: Issues and Challenges
The Global Code of Ethics for Tourism Article 5 states that tourism should be a beneficial activity for host countries and communities (UNWTO). The code also emphasises on equitable distribution (between host countries and communities) of the economic and sociocultural benefits generated by tourism activities. The tourism resorts and accommodation sector have to involve local communities in socio-economic activities and priority should be given to local manpower. A wildlife resort has vast opportunities to involve local communities in their day to day operation by purchasing local products, promoting local festivals, providing employment opportunities to locals, and involving local communities in decision-making. Wildlife resorts can also promote local culture, create environment awareness among local people, provide educational support to the local children, and support development of infrastructure and medical facilities for the locals. Though local communities can be involved in various activities of wildlife resorts, it is essential to address the issues and challenges that hinder wildlife resorts from doing so. This paper attempts to determine the issues and challenges faced by wildlife resorts in involving local communities in their day to day operations and suggests ways and means to overcome those challenges. The scope of the study covered selected wildlife resorts in Karnataka. The targeted respondents of the research survey were resort managers and data were collected using open-ended questions to understand real-time issues and challenges involving local communities in resort activities. The data were then analysed using thematic text analysis. The findings from the study will help explore means of providing a better framework which will help wildlife resorts overcome issues and challenges involving local communities. The Author(s) 2017. -
Sustainable tourism management : Issues and challenges of eco wildlife resorts in karnataka
Sustainable tourism principles comprise of visitor satisfaction, the economic newlinesustainability of the industry, environment conservation, socio-cultural and economic newlinedevelopment of local communities. The tourism industry has to consider all these elements while developing any form of tourism for its long-term sustainability. Eco and wildlife resorts are one of the prominent and attractive segments of the accommodation sector. It has a major implication for implementing sustainable tourism practices in their daily operations since they are set close to nature and reside in pristine wildlife regions. It is inevitable for eco and wildlife tourism, to consider all newlinethe elements of sustainable tourism practices and to implement it in their day to day newlineactivities. At the same time, they might have to face issues and challenges in newlineimplementing sustainable practices. So, the main objective of the research is to newlineunderstand operational issues and challenges of eco and wildlife resorts in achieving newlinesustainable tourism principles. The purpose of the study is to investigate the issues and challenges faced by eco and wildlife resorts in implementing sustainable tourism practices and understanding the indicators used by the resorts to measure their sustainable tourism newlinepractices. The scope of the study covers selected eco and wildlife resorts in Karnataka because most eco and wildlife reserve areas are shared by pristine natural areas and are located near villages. The targeted respondents of the study are eco and wildlife resort managers and visitors of eco and wildlife resorts. Based on purposive sampling, 30 resorts are selected for the study, and 410 visitors are selected based on convenient sampling technique. The study employs a mixed-method research design. newlineTriangulation design is used in the study. The study adopted a tool to identify the newlinesustainable tourism indicators used by the resorts to measuring their sustainable newlinetourism practices. -
Business Forecasting and Error Handling Using AI
Business forecasting is the technique of accurately predicting the future of a business and outcomes using historical data and present trends. To evaluate historical data and find patterns, trends, and other elements that might be used to forecast future events, a variety of analytical tools and techniques are used. Business forecasting is a crucial component of strategic planning because it enables businesses to foresee market changes, spot possible risks and opportunities that may arise in the future, and make wise resource allocation and investment decisions. Businesses that use effective business forecasting can plan and carry out their programs that help them stay competitive, expand their operations, and meet their objectives. According to Glueck [1], Forecasting is a formal process of predicting future events that will significantly affect the functioning of an enterprise.. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. -
-
Machine Learning Technology-Based Heart Disease Detection Models
At present, a multifaceted clinical disease known as heart failure disease can affect a greater number of people in the world. In the early stages, to evaluate and diagnose the disease of heart failure, cardiac centers and hospitals are heavily based on ECG. The ECG can be considered as a regular tool. Heart disease early detection is a critical concern in healthcare services (HCS). This paper presents the different machine learning technologies based on heart disease detection brief analysis. Firstly, Nae Bayes with a weighted approach is used for predicting heart disease. The second one, according to the features of frequency domain, time domain, and information theory, is automatic and analyze ischemic heart disease localization/detection. Two classifiers such as support vector machine (SVM) with XGBoost with the best performance are selected for the classification in this method. The third one is the heart failure automatic identification method by using an improved SVM based on the duality optimization scheme also analyzed. Finally, for a clinical decision support system (CDSS), an effective heart disease prediction model (HDPM) is used, which includes density-based spatial clustering of applications with noise (DBSCAN) for outlier detection and elimination, a hybrid synthetic minority over-sampling technique-edited nearest neighbor (SMOTE-ENN) for balancing the training data distribution, and XGBoost for heart disease prediction. Machine learning can be applied in the medical industry for disease diagnosis, detection, and prediction. The major purpose of this paper is to give clinicians a tool to help them diagnose heart problems early on. As a result, it will be easier to treat patients effectively and avoid serious repercussions. This study uses XGBoost to test alternative decision tree classification algorithms in the hopes of improving the accuracy of heart disease diagnosis. In terms of precision, accuracy, f1-measure, and recall as performance parameters above mentioned, four types of machine learning (ML) models are compared. Copyright 2022 Umarani Nagavelli et al. -
ML-Based Prediction Model for Cardiovascular Disease
In this paper, the prediction of cardiovascular disease model based on the machine learning algorithm is implemented. In medical system applications, data mining and machine learning play an important role. Machine learning algorithms will predict heart disease or cardiovascular disease. Initially, online datasets are applied to preprocessing stage. Preprocessing stage will divide the data from baseline data. In the same way, CVD events are collected from data follow-ups. After that, data will be screened using the regression model. The regression model consists of logistic regression, support vector machine, nae Bayes, random forest, and K-nearest neighbors. Based on the techniques, the disease will be classified. Before classification, a testing procedure will be performed. At last from results, it can observe that accuracy, misclassification, and reliability will be increased in a very effective way. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Bio-Inspired Energy Storage Electrode: Utilizing Co3O4 Hollow Spheres Derived from Sugarcane Bagasse Extract Synthesis Via Hydrothermal Route
Recent research has explored the utilization of sugarcane bagasse, a bio-industrial waste, to fabricate energy storage devices due to ecofriendly nature, low cost with industrial scale production. In this investigation, cobalt oxide hollow spheres (Co3O4 HSs) were synthesized from waste sugarcane bagasse extract with the carbon spheres (CSs) act as template. The main component of sucrose (C12H22O11) linked with cellulose fibers and other oxygenic functional groups were used to prepare CSs. Previously, a metal precursor (Co(NO3)2.6H2O) was mixed with sugarcane bagasse extract and subjected to a hydrothermal process, resulting in uniform-sized metal CSs. The uniform sized Co3O4 HSs were formed by calcined metal CSs. The calcination temperature plays a crucial role to eliminating implanted carbon material on inter surface area of the metal oxide, shaping the Co3O4 HSs. Structural, vibrational, morphology and elemental analyses were confirmed by X-ray diffraction (XRD), Fourier transformed infrared spectroscopy (FTIR), Scanning electron microscopy (SEM), Energy Dispersive X-ray Spectroscopy (EDX), respectively. Electrochemical tests show improved ion transport and low resistance, leading to high capacitance in asymmetric supercapacitor (ASC) devices. Subsequently, for asymmetric supercapacitor (ASC) devices, using with Co3O4 HSs has function of cathode and activated carbon (AC) as anode, the devices demonstrated impressive results of 33.1 Fg? 1 at 1 Ag? 1, 86.8% retention after 4,000 cycles, as well as the energy density and power density of 5.9W h kg? 1 at 1500W kg? 1. The Co3O4 HSs||AC device exhibits promising energy storage properties for future applications. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Comparative electrochemical investigation for scheelite structured metals tungstate (MWO4 (M = Ni, Cu and Co)) nanocubes for high dense supercapacitors application
Scheelite structured metal tungstate MWO4 (M = Ni, Cu and Co) nanocubes were synthesized through the chemical reflux for supercapacitors application and ceyltrimethylammonium bromide (C-TAB) as surfactant. In X-ray diffraction (XRD) result are fit with relevant JCPDS cards, synthesized materials are closely matched with monoclinic and triclinic crystal phase corresponding to NiWO4, CoWO4 and CuWO4 with Scheelite type structure. To resist the growth of the particles and succeeding nanocubes morphology were achieving by using PEG-400 and C-TAB act as a surfactant. The prepared modified electrodes were examined electrochemical analysis after successive coating of working material in empty Ni foil. From the galvanostatic charge-discharge (GCD) comparative analysis, fast ions movements are interacts through the aqueous electrolyte medium with nanocubes NiWO4 electrode are achieving specific capacitance of 1185 Fg?1 at 0.5 Ag?1 and cyclic stability 93.084 % (retentivity) formerly compare to CuWO4 and CoWO4 electrodes. 2023 -
An Empirical and Statistical Analysis of Fetal Health Classification Using Different Machine Learning Algorithm
The health of both the mother and the baby is affected by how well the fetus is doing during pregnancy, making it a matter of utmost importance. To achieve the best results possible, it is essential to regularly monitor and intervene when needed. While there are many ways to observe the wellbeing of the fetus in the mother's womb, using artificial intelligence (AI) has the potential to enhance accuracy, efficiency, and speed when it comes to diagnosing any issues. This study focuses on developing a machine learning-driven system for accurate fetal health classification. The dataset comprises detailed information on the signs and symptoms of pregnant individuals, particularly those at risk or with emerging fetal health issues. Employing a set of ten machine learning models namely Nae Bayes, Logistic Regression, Decision Tree, Random Forest, KNN, SVM, Gradient Boosting, Linear Discriminant Analysis, Quadratic Discriminant Analysis Light Gradient Boosting Machine (LGBM) along with ensemble-based processes, the Light Gradient Boosting Machine (LGBM) has been identified as a standout performer, accomplishing an accuracy of 96.9%. Furthermore, our exploration demonstrates overall performance like character fashions, signaling promising prospects for sturdy and correct fetal fitness class systems. This study highlights the power of machine learning that could revolutionize prenatal care by identifying fetal health problems early. 2024 IEEE. -
Production, Delivery, and Regulatory Aspects for Application of Plant-Based Anti-microbial Peptides: a Comprehensive Review
Antimicrobial peptides (AMPs) are small, positively charged biomolecules produced by various organisms such as animals, microbes, and plants. These AMPs play a significant role in defense mechanisms and protect from adverse conditions. The emerging problem of drug resistance in microbes poses a global health challenge in treating diseases. This plant-based antimicrobial peptide is a promising candidate for fighting against drug-resistant microbes. The PAMPs process specific key properties, proving their efficacy as antimicrobial agents against a broad spectrum of microbes such as Gram-positive, Gram-negative, and fungi. Extensive research on PAMPs has explored their potential as plant growth regulators and therapeutic agents. Their diverse mode of action on microbes encouraged their application in food industries. ThePAMPs are isolated and purified from various plant species organs such as roots, shoots, leaves, flowers, and seeds. These are bioactive molecules with significant stability, and low toxicity has encouraged their application as food additives. Furthermore, to meet the consumer demand, mass production of AMPs was possible with recombinant DNA technology. The advanced and nanotechnology-based delivery system has significantly improved the efficacy and bioavailability of PAMPs as food preservatives for improved shelf-life and prevent spoilage of food products. ThePAMPs are of green origin and can be used as natural bio preservatives that do not alter the sensory properties of food and are harmless to consumers. Plants being the rich resource of AMPs to support their quick identification, and retrieval for commercial applications there is a need to integrate the omics approach with databases. TheAMPs are small, positively charged biomolecules produced by various organisms such as animals, microbes, and plants. These AMPs play a significant role in defense mechanisms and protect from adverse conditions. The emerging problem of drug resistance in microbes poses a global health challenge in treating diseases. This plant-based antimicrobial peptide is a promising candidate for fighting against drug-resistant microbes. The PAMPs process specific key properties, proving their efficacy as antimicrobial agents against a broad spectrum of microbes such as Gram-positive, Gram-negative, and fungi. Extensive research on PAMPs has explored their potential as plant growth regulators and therapeutic agents. Their diverse mode of action on microbes encouraged their application in food industries. ThePAMPs are isolated and purified from various plant species organs such as roots, shoots, leaves, flowers, and seeds. These are bioactive molecules with significant stability, and low toxicity has encouraged their application as food additives. Furthermore, to meet the consumer demand, mass production of AMPs was possible with recombinant DNA technology. The advanced and nanotechnology-based delivery system has significantly improved the efficacy and bioavailability of PAMPs as food preservatives for improved shelf-life and prevent spoilage of food products. ThePAMPs are of green origin and can be used as natural bio preservatives that do not alter the sensory properties of food and are harmless to consumers. Plants being the rich resource of AMPs to support their quick identification, and retrieval for commercial applications there is a need to integrate the omics approach with databases. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
In vitro production of bacosides from Bacopa monnieri
Bacopa monnieri (L.) Wettst. (Plantaginaceae) is an important Ayurvedic medicinal herb commonly known as brahmi, growing in the region of Indian subcontinent. Bacosides are the major chemical component having the major role in the biological and pharmacological field. Bacopa cultivation is time-consuming, requires labor team, and needs great efforts to maintain the quality of bacosides as growths are affected by environmental factors such as soil, water, temperature, climate, pests, and pathogens. To solve these problems, organ and cell cultures have been adopted for swift and efficient production of Bacopa biomass and bacosides. In the current chapter, various parameters, such as types of media, media composition, elicitors, salinity, drought, types of vessels used, and effect of heavy metals, were investigated against the in vitro production of bacosides from Bacopa monnieri. Springer Nature Singapore Pte Ltd. 2018. -
In vitro propagation and secondary metabolite production from Withania Somnifera (L.) dunal
Withania somnifera (L.) Dunal, commonly known as ashwagandha or Indian ginseng, is an important medicinal plant that belongs to the family Solanaceae. Ashwagandha has been used from time immemorial in different systems of medicine and extensively used in the Indian system of medicine, and there is discussion of this plant in different ayurvedic scripts like Charaka samhita, Ashtanga sangraha, etc. The plant is extensively used for anti-aging and general well-being, and also has anti-cancer potential. Ashwagandha is also known for its antioxidant, anti-inflammatory, and other therapeutic activities. In the recent days of Covid-19, the plant has been extensively used as an immunostimulant. The plant has great potential for its raw materials, especially for the extraction of bioactive molecules like withanolide-A, withaferin-A, withasomniferin, withanone, etc. The conventional mode of propagation could not meet the required commercial demand for either the pharmaceutical industries or the traditional practitioners. The conventional method of obtaining biomass is influenced by a large number of environmental factors, where biomass quality and quantity of bioactive molecules have shown variation. To overcome this, biotechnological approaches such as plant tissue culture techniques have been established for large-scale cultivation using micropropagation and also other techniques like a callus and cell suspension culture, shoot culture, adventitious root culture, and hairy root culture have been extensively used for in vitro production of bioactive molecules from ashwagandha. With the advent of metabolic engineering, biosynthetic pathway editing has made it possible to obtain higher yields of desired metabolites. The present chapter focuses on the in vitro propagation, biosynthesis of withanolides, and tissue culture strategies for obtaining high biomass and metabolites. The chapter also focuses on different elicitation strategies, metabolic engineering approaches, and the development of elite germplasms for improved metabolite content. The chapter also identifies research lacunas that need to be addressed for the sustainable production of important bioactive molecules from ashwagandha. 2024 Bentham Science Publishers. All rights reserved.