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Data Classification and Incremental Clustering Using Unsupervised Learning
Data modelling, which is based on mathematics, statistics, and numerical analysis, is used to look at clustering. Clusters in machine learning allude to hidden patterns; unsupervised learning is used to find clusters, and the resulting system is a data concept. As a result, clustering is the unsupervised discovery of a hidden data concept. The computing needs of clustering analysis are increased becausedata mining deals with massive databases. As a result of these challenges, data mining clustering algorithms that are both powerful and widely applicable have emerged. Clustering is also known as data segmentation in some applications because it splits large datasets into categories based on their similarities. Outliers (values that are far away from any cluster) can be more interesting than typical examples; hence outlier detection can be done using clustering. Outlier detection applications include the identification of credit card fraud and monitoring unlawful activities in Internet commerce.As a result, multiple runs with alternative initial cluster center placements must be scheduled to identify near-optimal solutions using the K-means method. A global K-means algorithm is used to solve this problem, which is a deterministic global optimization approach that uses the K-means algorithm as a local search strategy and does not require any initial parameter values. Insteadof selecting initial values for all cluster centers at random, as most global clustering algorithms do, the proposed technique operates in stages, preferably adding one new cluster center at a time. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Data Ethics
Ethics is all about living an ethical life. As rational beings, humans have always been in the pursuit of ethical life in spite of the contrary temptations. Society and engagement in society are helpful for a person to be ethical. However, it is the choice one makes in critical situations that define the ethical nature of a person. Swarmed by a vast pool of data, the complex nature of ethical decision-making is getting far more complex for human beings with autonomous cognitive faculty. One needs to be conscious and focused to face any dilemma in ones life. Dealing prudently with private and public data and understanding the science of data would help the homo sapiens to prove her/his relevance in this data-driven world. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Data Mining-Based Variant Subset Features
A subset of accessible variants data is chosen for the learning approaches during the variant selection procedure. Itincludes the important one with the fewest dimensions and contributes the most to learner accuracy. The benefit of variant selection would be that essential information about a particular variant isnt lost, but if just a limited number of variants are needed,and the original variants are extremely varied, there tends to be a risk of information being lost since certain variants must be ignored. Dimensional reduction, also based on variant extraction, on the other hand, allows the size of the variant space to be reduced without losing information from the original variant space.Filters, wrappers, and entrenched approaches are the three categories of variant selection procedures. Wrapper strategies outperform filter methods because the variation selection procedure is suited for the classifier to be used. Wrapper techniques, on the other hand, are too expensive to use for large variant spaces due to their high computational cost;therefore each variant set must be evaluated using the trained classifier, which slows down the variant selection process. Filter techniques have a lower computing cost and are faster than wrapper procedures, but they have worse classification reliability and are better suited to high-dimensional datasets. Hybrid techniques, which combine the benefits of both filters and wrappers approaches, are now being organized. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Data Modeling and Analysis for the Internet of Medical Things
Smart biomedical technology greatly assists in rapid disease screening and diagnosis within hospitals. One innovative device, a smart inhaler, incorporates sensors to track medication doses, usage patterns, and effectiveness. These inhalers provide valuable support to asthma sufferers, allowing for improved condition management and better patient outcomes. Asthma, a chronic respiratory disease affecting millions worldwide, causes airway constriction and swelling, resulting in breathing difficulties. Typically, medication such as inhaled corticosteroids and bronchodilators is used for management. However, medication adherence is often inadequate, leading to worsened outcomes and exacerbations. Smart inhalers aim to address this challenge by enabling users to monitor medication usage and compliance. Equipped with sensors, the inhalers track when, how much, and how frequently the prescribed medication is taken. The collected data is then transmitted to a mobile app or web portal, accessible to patients and healthcare providers. This integration facilitates medication tracking and provides personalized coaching for improved asthma control. The gathered data serves multiple purposes, including helping patients monitor their medication use and adherence. Patients can receive feedback on their treatment plan adherence and utilize the app to set medication reminders, promoting adherence and enhancing outcomes. 2024 CRC Press. -
Data Privacy
Data privacy is a private and public phenomenon and its operations have implications for the individual and the society. This understanding of privacy ceases it from being viewed as a simple technological process and highlights different factors that are associated with it. While on one hand, the right to privacy is seen as integral to the freedom of the individual, on the other hand, it is also seen as the ability to hide certain information for malpractice. This chapter delves into this existing dichotomy of data privacy and simplifies various terms and operations that have emerged in the field of study. A discussion is facilitated on the concept and its associated areas. The chapter looks at privacy regimes in different countries to note emerging developments and also presents a critique of the practice to bring forth shortcomings and enable change. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Data science: the Artificial Intelligence (AI) algorithms-inspired use cases
The data science field is growing fast with the faster maturity and stability of its implementation technologies. We had been fiddling with traditional data analytics methods. But now, with Artificial Intelligence (AI), it is possible to embark on predictive and prescriptive insights generation in time. There are several data science (DS) use cases emerging with the wider adoption and adaptation of AI technologies and tools. This chapter is dedicated to illustrate various AI-inspired use cases. The Institution of Engineering and Technology 2022. -
Data Security
Corporates, industries, and governments have completely digitized their infrastructure, processes, and data or running towards completed digitalization. This data could be text files, different types of databases, accounts, and networks. The data living in the digital format needs to be preserved and also protected from unauthorized access. If this data remains open for access, any unauthorized user can destroy, encrypt, or corrupt the data, making the data unusable. There are implications of data security threats such as data breaches and data spills, beyond cost and can spell doom for the business. Hence the data needs to be protected from such threats. Data security is a mechanism through which data is protected and prevented from loss due to unauthorized access. It is a mix of practices and processes to ensure data remains protected from unauthorized use and readily accessible for authorized use. Data Security is essential for achieving data privacy in general. To define appropriate security measures, we must define the difference between a data breach and a data leak. Data security mechanisms could be data-centric such as identity and access management, encryption and tokenization, and backup and recovery. A defined data governance and compliance can also ensure data security. This chapter will explain why there is a need for data security, methods and processes to achieve data security, and touch upon some of the data security laws and regulations. We will also see a case study on how hackers exploited a vulnerability to mount a data security attack worldwide and how data security mechanisms could have prevented it. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
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. -
Data-driven behaviour finance for mutual fund investment decision making
When it comes to money and investing funds, the individual portfolio investor isn't always as logical as he feels he is, which is why there's a whole school of thought dedicated to explaining why people behave in irrational and weird ways. The primary objective of this research is to investigate the effects of five major behavioural biases on individual investor decisions in a metro city India, with a focus on mutual funds, as well as to examine how individuals make decisions to ensure that their investments generate greater returns for a better future. The statistical evidence shows that a variety of behavioural elements have a significant part in people' investment decision-making patterns, which has an impact on the population's economic situation. The purpose of this study is to illustrate how an individual's perspective, attitude, and conduct affect mutual fund investments. 2023, IGI Global. -
Data-Driven Decision Making in the VUCA Context: Harnessing Data for Informed Decisions
Data-driven decision making (DDDM) has evolved from being a strategic advantage to a necessity for organizations aiming to thrive in the dynamic business contexts. It is about using data as a tool to enhance strategic thinking, scenario planning, and adaptation in rapidly changing environments. It involves leveraging data and analytics to navigate the challenges of volatility, uncertainty, complexity, and ambiguity. By embracing DDDM, organizations can enhance their decision-making processes, gain a competitive edge, and navigate the challenges of volatility, uncertainty, complexity, and ambiguity with greater confidence. However, successful implementation requires addressing challenges, fostering a data-driven culture, and continually adapting best practices to meet the evolving demands of the VUCA environment. This chapter discusses how organizations leverage DDDM in VUCA context to support effective and rapid decision making aligned with organizations vision. Particularly, it would offer insights to transit from volatility to vision, uncertainty to understanding, complexity to clarity, and ambiguity to agility. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Data: An Anchor for Decision- Making to Build the Future Workforce Management System
In this digital era, the change in business environments and the nature of work lead to skill gaps. Training the workforce on desired skill sets must fill these skill gaps. Data play a crucial role in identifying the skills needed and helping organizations to plan the future workforce. Data is essential for any organizations growth and success in the dynamic market. Knowing the skill set in advance allows organizations and individuals to plan the business and skill requirements well. The way work is done may be impacted by these structural changes as the world is changing swiftly. Building the abilities necessary for the uncertain environments of the present and future environments is also crucial for training the employees. However, such skills must first be acknowledged and appreciated before being developed. Empirical data must support the methodology for valuing such abilities and skills. This chapter outlines the significance of data in skill identification for individuals to be future-ready. Finding the most relevant abilities in a given environment is the first step toward their formalization and acceptance at the systems level. It also presents the importance of creating skill matrices for students and organizations. The skill matrix objectively quantifies skill value for specific occupations and the possible trajectories to acquire those skill sets. This metric will allow policymakers to navigate this fast-changing workforce landscape and focus resources to ensure that skills are needed as students transition into the workforce and have skills that enable them to transition. 2024 selection and editorial matter, Alex Khang, Sita Rani, Rashmi Gujrati, Hayri Uygun, and Shashi Kant Gupta; individual chapters, the contributors. -
Date Palm: Genomic Designing for Improved Nutritional Quality
Date palm (Phoenix dactylifera L.) is one of the oldest fruit trees known where a significant amount of breeding has been carried out to improve various agronomic and nutritional characteristics. Numerous studies have been done to improve the nutritional composition and quality of the fruit due to its significant biological properties. Various strategies have been formulated for improving the agronomic characters through biofortification as well as preserving through postharvesting techniques. Modern breeding practices using molecular markers have significantly helped to identify the phenotypic, as well as genotypic, diversity for the selection of superior date palm cultivars, advanced agronomic characters like nutritional quality, disease resistance, and yield. Availability of the whole genome sequence, organellar sequence, and genetic map of date palm has helped breeders in modification and improvement of the characteristics. With the availability of bioinformatic tools and gene editing knowledge, the nutritional composition of date palm can be effectively manipulated to develop better crops along with good agronomic characters and resistance to diseases. The authors have compiled the nutritional composition of date palm fruits and detail the strategies to edit the genome and improve nutritional quality. Springer Nature Singapore Pte Ltd. 2023. -
Dealing with missing values in a relation dataset using the DROPNA function in python
Python provides a rich data structure library called PANDAS, which provides fast and efficient data transformation and analysis. The word PANDAS is an abbreviation of Python Data Analysis Library. PANDAS facilitate optimized and dynamic data structure designs work with "relational" or "labeled" data. Python's approach is meant to provide a high-level, high-performance building block that can be used to do real-world analysis of data. PANDAS Library is allowing users to import data from different file formats, such as CSV, SQL, Microsoft Excel etc. [1]. It helps in data preparation, as well as in data modeling, for those projects, which aims data analysis for the extraction of information. Python's future will be built on this layer for statistical computing. In addition to discussing future areas of work and growth opportunities for statistics and data analytics applications built on Python, the study provides details about the language's design and features [2]. In this research paper, we intend to solve the problem of missing values in a dataset using the DROPNA function in Python using PANDAS library. 2023 Scrivener Publishing LLC. -
Decent Work Deficit: A Challenge on the Women Empowerment in Indian Agricultural Sector
Women play a crucial role in Indian agriculture, but they also confront several obstacles that reduce their productivity and prevent them from fully engaging in the sectors development. The majority of women in India are employed in agriculture, which is one of the sectors that contributes most to the GDP and is essential to the economic development of the nation. Although women continue to have a significant and recognized role in agriculture, their function is frequently overlooked. Women make up about 75% of the full-time labor force on Indian farms. The nation wont develop unless its women farmers are empowered. Only through decent work labour the agriculture sector will be developed which will help in the empowermentof women agricultural Labourers in India. So the government should take all steps to implement the decent work concept of ILO in the Indian agricultural sector. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Decoding the alchemy of employee retention: A case of the manufacturing sector of the National Capital Region, India
The ability of a company to retain its staff is referred to as employee retention. It may also be referred to as a decrease in employee attrition or employee turnover rate. Employee retention is one such mechanism which ensures that the human capital stays with the organisation for a longer duration. The study focusses on identifying the drivers of employee retention in the manufacturing industry with respect to certain factors such as mentoring, career development, work environment, job autonomy, and compensation. This research has used the descriptive research design with some elements of exploratory research design. The sample size for the study was 122. Primary data has been collected with the help of a prevalidated questionnaire with multiple-choice closed-ended questions on a five-point Likert scale. The collected data was analysed using Excel and SPSS with statistical tools like T-test, ANOVA, multiple linear regression, etc. A direct positive relation has been found between mentoring, work environment and compensation, and the employees' intention to stay. 2024, IGI Global. -
Decolonizing the Mind: Invoking the Vernacular Experience in a Postcolonial Language Classroom
This chapter attempts to understand the teaching-learning practices, programmes, courses, and pedagogies of an English department that recently co-opted cultural studies as a means of decolonisation in a private university in India to understand how cultural diversity, learner diversity, teacher experiences, and learner interests became considered factors in language learning pedagogies and selection of learning content. The research will employ mixed methods of qualitative and quantitative techniques of course content analysis, student interviews to gauge the impact of the learning on the decolonisation process, teacher interviews to understand approaches to task design, and the intended outcome and the strategies and perception changes in material production and task development when the learning shifted to the online mode as a result of the pandemic disruption. 2023 by IGI Global. All rights reserved. -
Decrypting Free Expression: AMMA-WCC Conflict and Comment Culture Rattling the Malayalam Film Industry
The chapter examines the gender-power dynamics in the Malayalam film industry through an analysis of a skit, a YouTube video and trolls related to a recent controversy involving the Association of Malayalam Movies Artistes (AMMA) and the Women in Cinema Collective (WCC). This analysis is supported by an exploration of the historical roots of sexism in the industry and a discussion about how it continues to perpetuate sexism in the industry. The study also investigates the emergence of WCC as a response to the actresss molestation case and the subsequent division within the industry. The research focuses on the Sthree Shaktheekaranam skit performed at AMMAs cultural show, a YouTube video, Oru Feminichi Kadha and a sample of trolls which targeted the WCC and women who refuse to comply with AMMAs patriarchal bias. The chapter analyses the content of these representations, highlighting the power play structuring them. The study sheds light on the contradictions and hypocrisy within the industry and its portrayal of progressive values while perpetuating regressive gender norms. 2024 selection and editorial matter, Francis Philip Barclay and Kaifia Ancer Laskar; individual chapters, the contributors. -
Deducing Water Quality Index (WQI) by Comparative Supervised Machine Learning Regression Techniques for India Region
Water quality is of paramount importance for the wellbeing of the society at large. It plays avery important role in maintaining the health of the living being. Several attributes like biological oxygen demand (BOD), power of hydrogen (pH), dissolved oxygen (DO) content, nitrate content (NC) and so on help to identify the appropriateness of the water to be used for different purposes. In this research study, the focus is to deduce the Water Quality Index (WQI) by means of artificial intelligence (AI)-based machine learning (ML) models. Six parameters, namely BOD, DO, pH, NC, total coliform (CO) and electrical conductivity (EC) are used to measure, analyze and predict WQI using nine supervised regression machine learning techniques. Bayesian Ridge regression (BRR) and automatic relevance determination regression (ARD regression) yielded a low mean squared error (MSE) value when compared to other regression techniques. ARD regression model parameters as independent a priori so that non-zero coefficients do not exploit vectors that are not just sparse, but they are dependent. In the estimation process, BRR contains regularization parameters; regularization parameters are not set hard but are adjusted to the relevant data. Due to these reasons, ARD regression and BRR models performed better. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Deep learning approaches to understanding psychological impacts on vulnerable populations
This chapter investigates the psychological effects on vulnerable groups, with a particular emphasis on the relationship between deep learning techniques and the impact of climate. Vulnerable groups confront particular problems, which might lead to negative psychological results. Investigating this complexity is critical to designing effective intervention techniques. Using sophisticated deep learning techniques, this study seeks to find subtle patterns and correlations in a variety of datasets, including psychological markers, socioeconomic characteristics, and climatic variables. The work employs a comprehensive technique that includes deep learning models, feature extraction, and interpretability analysis to untangle complicated relationships. Preliminary findings imply that deep learning approaches might uncover previously unknown links between climate change and psychological effects on vulnerable groups. This insight adds to a more comprehensive understanding of the difficulties. This understanding contributes to a more holistic grasp of the challenges faced by these groups. By including climate-related factors into the deep learning framework, this study hopes to close the gap between environmental impacts and psychological 2024, IGI Global. All rights reserved. -
Deep learning based federated learning scheme for decentralized blockchain
Blockchain has the characteristics of immutability and decentralization, and its combination with federated learning has become a hot topic in the field of artificial intelligence. At present, decentralized, federated learning has the problem of performance degradation caused by non-independent and identical training data distribution. To solve this problem, a calculation method for model similarity is proposed, and then a decentralized, federated learning strategy based on the similarity of the model is designed and tested using five federated learning tasks: CNN model training fashion-mnist dataset, alexnet model training cifar10 dataset, TextRnn model training thusnews dataset, Resnet18 model training SVHN dataset and LSTM model training sentiment140 dataset. The experimental results show that the designed strategy performs decentralized, federated learning under the nonindependent and identically distributed data of five tasks, and the accuracy rates are increased by 2.51, 5.16, 17.58, 2.46 and 5.23 percentage points, respectively. 2024 selection and editorial matter, Arvind Dagur, Karan Singh, Pawan Singh Mehra & Dhirendra Kumar Shukla; individual chapters, the contributors.