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Bioremediation and Detoxification of Asbestos from Soil
Asbestos is referred to as magic mineral and used as excellent building material. It finds its application in wide range of products such as floor tiles, pipes, paper, rope, cloth, insulated partition board, etc. On average, India uses 3, 50, 000 tons of asbestos annually and asbestos fibers readily undergo weathering releasing them into soil, water and air. Occupational and environmental exposure to this asbestos is leading to asbestosis (asbestos-related disease), lung cancer, and heart failure. Considering the serious health risk, countries like Australia, Brazil, and Canada had banned the use of asbestos. As asbestos is extensively used in construction of buildings, the demolished materials are dumped in the soil and thus it finds its route in soil as pollutant. Soil borne microbes like bacteria, fungi and lichens are found to be best means to reduce the toxicity of asbestos. These microorganisms remove iron from asbestos and reduce its toxicity. Another most effective bioremediation approach is phytoremediation to clean up the soil wherein vegetative cover on contaminated soil can remove iron and breaks down asbestos as source of inorganic nutrient. The main advantage of phytoremediation is that it can be extended to any geographical area where plants can grow. This chapter emphasizes various means of use and disposal of asbestos, followed by various means of bioremediation using microbes and plants and as an alternate for the sustainable soil condition. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022, corrected publication 2022. -
Data visualization: Experiment to impose ddos attack and its recovery on software-defined networks
The entire network is doing paradigm shift towards the software-defined networks by separating forwarding plane from control plane. This gives a clear call to researchers for joining the ocean of software-defined networks for doing research considering its security aspects. The biggest advantage of SDN is programmability of the forwarding plane. By making the switches programmable, it can take live instructions from controllers. The versions of OpenFlow protocol and the compatibility of programmable switches with OpenFlow were the stepping stone making software-defined networks thrashed towards reality. The control plane has come up with multiple options of controllers such as NOX [2], Ryu [3], Floodlight [4], Open- DayLight [6], ONOS [7] and the list is big. The major players are Java based which keeps the doors open for enhancement of features by the contributors. However, more is expected from the practicality of P4Lang programmed switches by bringing skilled people to the industry who can actually implement programmable switches with ease. The obvious reason for delayed progress in the area of software-defined networks is the lack of awareness towards data visualization options existing as of now. The purpose of writing this chapter is to throw light upon the existing options available for data visualization in the area of SDN especially addressing the security aspect by analyzing the experiment of distributed denial of service (DDoS) attack on SDN with clarity on its usage, features, applicability and scopes for its adaptabilities in the world of networks which is going towards SDN. This chapter is a call to network researchers to join the train of SDN and push forward the SDN technology by proved results of data visualization of network and security matrices. The sections and subsections show clearly the experimental steps to implement DDoS attack on SDN and further provide solution to overcome the attack. Springer Nature Singapore Pte Ltd. 2020. -
Autoimmune diseases and an approach to type 1 diabetes analysis using PSO, K-means, and silhouette values
An estimated 50 million Americans suffer from autoimmune diseases, as per the report from AARDA (American Autoimmune Related Diseases Association). More than 30 million people suffer in India from type 1 diabetes. More than $100 billion is spent on healthcare for autoimmune diseases in America, more than for cancer healthcare. Host genes and environmental factors control autoimmune diseases, and typically they do not have any specific cure. This paper proposes an artificial intelligence-based framework for the initial prediction of autoimmune diseases. This work attempts to identify characteristics of autoimmune diseases, and it lists the commonly occurring autoimmune diseases, the organs attacked by them, and the different stages involved. It also seeks to identify ways to prioritize the severity of the patient's disease, for providing treatments based on the severity, with the goal of reducing the pressure on the healthcare sector. Type 1 diabetes is an autoimmune disease and identifying the risk associated with diabetes and other related health problems could help to improve health worldwide. This work proposes a framework while exploring autoimmune disease prediction using machine learning techniques. The autoimmune disease considered is type 1 diabetes. The usage of machine learning techniques can help to enhance patient care and early prediction. This research is an attempt to explore the possibilities and also to propose a framework for early prediction of type 1 diabetes. Clustering is performed using K-means and PSO K-means. Validation of the clusters is carried out using silhouette coefficient. 2024 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies. -
A Document Clustering Approach Using Shared Nearest Neighbour Affinity, TF-IDF and Angular Similarity
Quantum of data is increasing in an exponential order. Clustering is a major task in many text mining applications. Organizing text documents automatically, extracting topics from documents, retrieval of information and information filtering are considered as the applications of clustering. This task reveals identical patterns from a collection of documents. Understanding of the documents, representation of them and categorization of documents require various techniques. Text clustering process requires both natural language processing and machine learning techniques. An unsupervised spatial pattern identification approach is proposed for text data. A new algorithm for finding coherent patterns from a huge collection of text data is proposed, which is based on the shared nearest neighbour. The implementation followed by validation confirms that the proposed algorithm can cluster the text data for the identification of coherent patterns. The results are visualized using a graph. The results show the methodology works well for different text datasets. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Discovering patterns using feature selection techniques and correlation
Term Frequency and inverse document frequency is reported to have a significant contribution for various text categorization, document clustering and many other text mining related tasks. A collection of the applications and the enhancements of the Term Frequency and Inverse Document Frequency based document representation technique is examined in this work. The document representation algorithm is essential in the field of text - script mining. In this algorithm, unstructured data is converted into a vector space model where each related document is considered as a point in the vector space. Related documents come in proximity to the other related documents while the documents that are very far away from being coherent remain different from each other. In this paper, four feature selection techniques are implemented to discover the patterns from a repository of unstructured data by using correlation similarity measure. Analysis and comparison with other existing technique is also included. The validation of the patterns formed is performed by using silhouette values. Experiments are conducted to compare performance. Results indicate that TDMp1 performance is poor compared to others. Springer Nature Switzerland AG 2020. -
Performance Analysis of Logistic Regression, KNN, SVM, Nae Bayes Classifier for Healthcare Application During COVID-19
Heart disease is one of the main causes of mortality in India and the USA. According to statistics, a person dies out of a heart-related disease every 36s. COVID-19 has introduced several problems that have intensified the issue, resulting in increased deaths associated to heart disease and diabetes. The entire world is searching for new technology to address thesechallenges. Artificial intelligence [AI] and machine learning [ML] are considered as the technologies, which are capable of implementing a remarkable change in the lives of common people. Health care is the domain, which is expected to get the desirable benefit to implement a positive change in the lives of common people and the society at large. Previous pandemics have given enough evidence for the utilization of AI-ML algorithm as an effective tool to fight against and control the pandemic. The present epidemic, which is caused by Sars-Cov-2, has created several challenges that necessitate the rapid use of cutting-edge technology and healthcare domain expertise in order to save lives. AI-ML is used for various tasks during pandemic like tracing contacts, managing healthcare-related emergencies, automatic bed allocation, recommending nearby hospitals, recommending vaccine centers nearby, drug-related information sharing, recommending locations by utilizing their mobile location. Prediction techniques are used to save lives as early detections help to save lives. One of the problems that might make a person suffering from COVID-19 extremely sick is heart disease. In this research, four distinct machine learning algorithms are used to try to detect heart disease earlier. Many lives can be saved if heart disease can be predicted earlier. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Towards sustainable business: Review of sentiment analysis to promote business and well-being
Sustainability in business is expected considering the growth in the long run. Sustainable development goals are important for our sustainability on this planet. In case of a business, it is essential to ensure sustainable processes and sustainability of the existing customers. Sustainable customers can in turn contribute to improving the process by providing constructive suggestions to the business. This paper is an attempt to review sentiment analysis techniques to improve the customer experience of a business. 2024 Srinesh Thakur, Anvita Electronics, 16-11-762, Vijetha Golden Empire, Hyderabad. -
Bionanomaterials in Improving Food Quality and Safety
Current inventions in the area of nanotechnology opened several transformations in scientific and industrial sectors. One such rapidly developing technology gets a lot of application in the food industrys changing the culture of food cultivation to its several branches, like production, processing, packaging, preservation, detection of foodborne pathogens, transportation, shelf life and bioavailability of its valuable nutrients. Far smaller in size and in surface area is strongly related to its stability in terms of chemical and biological activities. Hence, food nanotechnology empowers advancement in several novel bio-nanomaterials with an extensive choice towards potential applications. Nanotechnology benefits the food industry in several ways: to extend and predictable for the growth due to recent and swiftly developing technology influences the characteristic of the food products, which should not get exposed to human and microbial activities. Therefore, implication of bio-nanomaterials in food-related industries pose a significant contribution for economy and also a key community concern. The involvement of nanotechnology throughout the life cycle of food processing, storage, transportation, safety, and potential benefits to mankind are also briefly reviewed in this chapter. Acceptance of nano-based ingredients by the public in various phases of the food business and their associated safety and regulatory measures pertaining to food items can be improved by many methods of nanotechnology. 2025 selection and editorial matter, Shakeel Ahmed; individual chapters, the contributors. -
Blending of Knowledge Management with Industry 4.0: A New Formula for Success!
The convergence of Industry 4.0 and knowledge management presents a transformative opportunity for organizations seeking enhanced efficiency and sustainable growth. In the context of organizational processes, the amalgamation of technological advancements and effective knowledge management practices can lead to a reduction in costs and an overall improvement in operational efficiency. Understanding the intricacies of knowledge management procedures is crucial, encompassing the production, transfer, acquisition, storage, and utilization of knowledge resources across the organizational spectrum. The advent of the fourth industrial revolution, commonly referred to as Industry 4.0, has significantly reshaped traditional knowledge management systems. Industry 4.0 introduces the interconnectivity of machines and their autonomous capacity to learn and share data. While both knowledge management and Industry 4.0 offer distinct benefits individually, a strategic approach that combines the strengths of both can unlock new opportunities for efficient business growth and success in the external environment. This article delves into the symbiotic relationship between Industry 4.0 and knowledge management, emphasizing their combined potential. Industry 4.0 generates vast volumes of data, and by leveraging knowledge management, organizations can derive valuable insights to inform decision-making processes. Historical data and best practices, accessible through knowledge management, contribute to process optimization. Integration with Industry 4.0 technologies, such as automation and the Internet of Things, further enhances process efficiency. The marriage of knowledge management and Industry 4.0 extends beyond process optimization to workforce development. Recognizing employees as the building blocks of an organization, this integration enables better management by upgrading knowledge and skills. Consequently, it enhances the overall productivity of the workforce, contributing to organizational success. In the dynamic landscape of globalization, technology, and competition, this chapter serves as a guide for organizations aiming to harness the collective power of knowledge management and Industry 4.0. By exploring their complementary benefits, it seeks to facilitate the informed utilization of these tools for the betterment and sustainability of businesses in the contemporary world. 2024 Scrivener Publishing LLC. -
Smart car - accident detection and notification using amazon alexa
The high demand for automobiles has increased traffic hazards and road accidents. Life of the people is under high risk. This is because of the lack of the best emergency facilities available in the country. The proposed system can detect accidents in significantly less time and sends the basic information first to aid center and relatives of the victim on mobile and Amazon Alexa within a few seconds covering geographical coordinates. Various devices like Arduino UNO for car movement demonstration, Arduino Mega for accident detection and Raspberry Pi 3B for internet services gateway, accelerometer and impact sensor working together to detect an accident. All connected over the internet to generate a huge amount of data which holds a lot of information about the occurrence of the accident based on the speed and location and can be used to detect accident hotspots. The system also focuses on the safety of pedestrians where a safety band is programmed to perform the notification services using an emergency push button. The ESP8266 NodeMCU invokes the same services using a button on the module. The data generated may be used for the prediction, analysis to prevent future accidents and contribute to future road safety. Springer Nature Switzerland AG 2020. -
Embarrassment in the Context of Negative Emotions and Its Effects on Information Processing
Negative emotions are feelings of sadness arising out of negative evaluation of oneself by self or others. Embarrassment is characterized as a negative emotion which is experienced as a threat to ones social identity. This chapter discusses the differences between embarrassment and related negative emotions, namely shame, guilt and humiliation and its effects on information processing. Around 45 articles have been reviewed in the process, which were selected based on their relation to either negative emotions in general or specifically to one or more of them. The study uses the interactional (bio-psycho-social) approach to determine the antecedents and consequences of experiencing embarrassment and how it affects information processing. It further explores gender differences in the experience of negative emotions. Given that the existing evidence reveals many contradictory findings in the experience of negative emotions, this chapter conceptualizes certain factors that might influence this experience. It also provides some reasons for variations in experience of embarrassment and related negative emotions, on the basis of gender. This chapter concludes by proposing the complexity of embarrassment as an emotion and a conceptual framework of a continuum on which the experiences of embarrassment may lie and the factors determining the placement of these experiences with their cognitive implications. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020, Corrected Publication 2020. -
Role of medicinal plants against cancer
Cancer is a fatal disease where uncontrolled multiplication of cells occurs in the body. Radiation therapy, Chemotherapy, and medications are some of the procedures for treating cancer infections, but they are expensive, and the cure is ineffective. Usage of plants for the treatment of cancer can be one of the effective processes as the phytochemical compounds in these plants have the potential of alleviating various malignancies that includes cancer. The phytochemical compounds found in the plants have the medicinal properties like anti-inflammation, apoptotic, anti-oxidative to treat various disease include the cancer. The following chapter will be about the Indian medicinal plants such as Carica papaya, Glycyrrhiza glabra, Morinda citrifolia, Azadirachta indica, Psidium guajava, and Annona reticulate, in treating the cancer and its future perspectives. 2024, IGI Global. -
Cybercrimes in the Associated World
Phrases that scarcely existed a decade ago are now a part of our day-to-day lifestyle, as criminals use malicious new technologies to commit cyber attacks against businesses, individuals, and governments. These crimes cause serious harm and impose real threats to victims worldwide either physically or virtually. There are no borders in cyberspace. Attacks can come from any place and at any time. Cybercrime can take many forms, but they all have a digital platform/environment in common. It can be done with both good and bad intentions. But, nowadays, the most common types of cybercrime activities such as phishing scams, identity theft, Internet frauds, online intellectual property or patent infringements, online harassment, and cyber stalking are sadly very widespread in todays associated world. Cyber bullying and online harassment activities spread casually in social media posts and comments or through direct messages and also via emails. The main motive of these messages is to threaten either an individual or a group. Such kinds of cybercrime activities are extremely damaging to the victims mental health. Government agencies working to investigate cybercrimes have reported multiple records of victims developing mental illnesses and even ending up committing suicide. On the other hand we have phishing scams, one of the widespread crime activities. Organizations have detected an increase in the ratio of phishing emails to professional emails from unknown or anonymous service providers appending fake attachments and invoices. These files and attachments may contain malicious payloads to scam people and to create a backdoor in that system, so the attacker can gain access to the system anytime and from anywhere without the victims knowledge. This has been considered as one of the major advantages for the attacker. Cybercrimes have not restricted to only these forms of criminal activities. A wide variety of new attacks have been created and have spread all over the world through commonly used platforms such as social media sites, blogs, and news portals. We are living in a digital world where all our activities are being monitored by someone, somewhere - even keystrokes are being monitored using keyloggers. Nothing seems to be secret and protected unless you are tech savvy. National agencies are keeping a close watch on all individual online activities to prevent illegal activities from happening. No longer the delete option is possible in this digital world; rather, only migration of data from one location to another or from a local server to a cloud server is possible. In our day-to-day lives, several new viruses and attack mechanisms are triggered by attackers by following very new tactics with the help of more complex algorithms. So, its time to advance our knowledge on protecting our valuable assets by spending time in learning and following proper online practices. 2024 selection and editorial matter, S. Vijayalakshmi, P. Durgadevi, Lija Jacob, Balamurugan Balusamy, and Parma Nand; individual chapters, the contributors. -
Modernization of Rural Electric Infrastructure
In the recent digital era, the energy sector in India is truly challenging. But some way or another digital technology has the potential to change the scenario of energy supply in industry. One of the important developments in this decade is the application of Artificial Intelligence (AI). This technology will help us to control smart software and optimize our decision-making and operations. We cannot ignore the need of energy to become sustainable after the introduction of the Internet of Things (IoT). Smart grid technology in IoT is used to detect even minute changes in electricity supply and demand. These two technologies (AI and IOT) jointly provide us a magical tool to improve operational performance in the energy industry. In rural areas, there is a lack of electricity infrastructure supply and demand technologies. A large portion electricity supply is shifting from manufacturing industry to rural areas. They are using grid technology to transform electricity and the load is highly variable. From the demand side, lack of infrastructure and industrial equipment affect consumer devices. An increasing need for electricity in all aspects presents a significant challenge to utilization and cost efficiency. An important issue for the delivery of electricity to rural areas is the infrastructure and administrative policies and regulations. Power plants need to be constructed in rural areas to supply the electricity. This is the modernization of a rural electricity infrastructure. In modernization techniques, smart grid technology can be used to meet low carbon emission and cost-efficiency. It will be interconnected with the traditional grid architecture of electricity energy. Based on recent research, the smart grid should be robust and agile and it might dynamically optimize the grid operations, energy-efficient resources, and so on. Without affecting the nature of village environments, an alternate technology, such as the consumption of solar energy, can also be mutually considered in order to utilize renewable energy. In this chapter we focus on the comparison of traditional and modern technology used for the supply and demand of electricity in rural areas, issues on the implementation of modern technologies, research and development in modernization of electric power systems, and so on. The Author(s), under exclusive license to Springer Nature Switzerland AG 2023. -
Portfolio optimization using simulated annealing and quantum-inspired simulated annealing: A comparative study
Portfolio optimization has been a highly studied problem in financial investment expert systems. The nonlinear constraint portfolio optimization problem cannot be efficiently solved using traditional approaches. This chapter presents a metaheuristic approach to portfolio optimization using simulated annealing (SA). Experiments have been conducted on over 10 years of NASDAQ stock price data. This first-of-its-kind effort is also made to implement the quantum-inspired version of SA (QiSA) for portfolio optimization, and the results are compared with the classical approach. The optimization parameters are chosen using sensitivity analysis, and the results are compared using different statistical measures. Preliminary results show that the QiSA approach is very promising and faster than SA when applied to the portfolio optimization domain. 2024 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies. -
An exploratory study of Python's role in the advancement of cryptocurrency and blockchain ecosystems
Blockchain is the foundation of cryptocurrency and enables decentralized transactions through its immutable ledger. The technology uses hashing to ensure secure transactions and is becoming increasingly popular due to its wide range of applications. Python is a performant, secure, scalable language well-suited for blockchain applications. It provides developers free tools for faster code writing and simplifies crypto analysis. Python allows developers to code blockchains quickly and efficiently as it is a completely scripted language that does not require compilation. Different models such as SVR, ARIMA, and LSTM can be used to predict cryptocurrency prices, and many Python packages are available for seamlessly pulling cryptocurrency data. Python can also create one's cryptocurrency version, as seen with Facebook's proposed cryptocurrency, Libra. Finally, a versatile and speedy language is needed for blockchain applications that enable chain addition without parallel processing, so Python is a suitable choice. 2023, IGI Global. All rights reserved. -
Bioremediation of Antibiotics as a Pollutant in Soil
The discovery of antibiotics had been a major breakthrough in the field of medicine. Apart from its use in treating disease, it is been used extensively in agricul-tural fields and animal husbandry to improve livestock and crop yield. Improper and overuse of antibiotics have found a route in the food chain and has accumulated in environmental resources like water and soil. This is of serious concern as it leads to the development of drug-resistant microorganisms which is a global threat and also alters the microbial diversity as they are bacteriostatic and bactericidal. Bioaugmen-tation and Biostimulation approaches are effective in the degradation of antibiotics in soil. For enhanced degradation of antibiotics consortia, engineered microbes and enzyme-mediated methods are feasible methods for effective remediation of antibi-otics in soil. Currently, extensive research on the bioremediation of antibiotics is carried out as they are cost-effective and eco-friendly. The present chapter deals with various contamination sources of antibiotics in soil, adverse effects of antibiotics in soil, different bioremediation approaches, and mechanisms, and regulations in the use of antibiotics. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022. -
Demand and Supply Forecasts for Supply Chain and Retail
Demand and supply forecasts serve as the backbone of strategic decision-making in todays rapidly changing business environment, assisting organizations in optimizing inventory levels, production planning, and pricing strategies. The ability to forecast demand and supply accurately is critical for effective supply chain and retail management. This chapter provides a comprehensive overview of supply chain and retail demand and supply forecasts. It discusses various forecasting methods and techniques, as well as related concepts. In addition, the chapter emphasizes the significance of accurate forecasting in optimizing supply chain and retail operations, as well as emerging trends and future directions in demand and supply forecasting. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. -
Voices of the Future: Generation Zs Views on AIs Ethical and Social Impact
As artificial intelligence (AI) becomes increasingly integral to modern society, its profound implications are coming to the forefront of discussions. This research paper investigates the perspective of Generation Z on the multifaceted societal and ethical impacts of AI. Gen Z is the first generation to fully embrace AI across all facets of life. Therefore, understanding their attitudes, concerns, and expectations towards AI is imperative for cultivating a responsible, adaptable, and ethically conscious society in the AI-driven era. This study addresses a significant research gap by exploring Gen Zs perceptions of the challenges associated with AI, such as issues related to privacy, data security, transparency, bias, public fear and more. It also examines the impact of AI on employment dynamics, specifically on job displacement and the necessity for reskilling in the face of AI-driven automation. The paper adopts a global perspective, acknowledging the variations in perception influenced by cultural, economic, and historical factors. Leveraging a sample size of approximately 200250 respondents aged 1825years, the research aims to provide a comprehensive view of Gen Zs viewpoints on AIs ethical and societal ramifications. Findings emphasize the need for transparent and accountable AI systems, as Gen Z is uncomfortable with the ambiguity in AI algorithms. Concerns about privacy and data security highlight the necessity for robust safeguards. They also advocate for strategies to address job displacement and ensure harmonious coexistence between humans and AI. In education, Gen Z sees AI as transformative, endorsing personalized learning. They stress the importance of regulatory frameworks to combat AI bias. They recognize AIs potential to enhance human connections and combat social isolation. The studys findings contribute to policy discussions, educational strategies, and business practices, offering insights into how to harness AIs benefits while mitigating its potential pitfalls. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Crop management using predictive analytics
The term "predictive analytics" covers a number of different statistical methods, such as "data mining," "machine learning," and "predictive modelling," which examine past and present data in order to formulate hypotheses and predictions about future events. The use of predictive analytics may provide farmers with the ability to predict future environmental changes more correctly, as well as the demand for their commodities, and improve their ability to make decisions. While predictive analytics may seem like an effective way to forecast future events, it cannot account for unforeseeable changes or external factors that could impact the accuracy of its predictions. Furthermore, relying solely on past and present data can lead to biased outcomes and fail to consider alternative scenarios that may occur. In essence, predictive analytics should not be used as the sole basis for decision-making in any given situation for crop management. 2024, IGI Global. All rights reserved.