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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. -
A Brief Concept on Machine Learning
Machine learning is a subset of AI. Its a research project aimed at gathering computer programscapable of performing intelligent actions based on prior facts or experiences. Most of us utilize various machine learning techniques every day when we use Netflix, YouTube, Spotify recommendation algorithms, and Google and Yahoo search engines and voice assistants like Google Home and Amazon Alexa. All of the data is labeled, and algorithms learn to anticipate the output from the input. The algorithms learn from the datas underlying structure, which is unlabelled. Because some data is labeled, but not all are, a combination of supervised and unsupervised techniques can be used. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Real-Time Application with Data Mining and Machine Learning
Data mining and machine learning are the most expressive research and application domain. All real-time application directly or indirectly depends on data mining and machine learning. There are manyrelevantfields, like data analysis in finance,retail, telecommunications sector, analyzing biological data, otherscientific uses, and intrusiondetection.The most expressive research and application domain is data mining and machine learning. Data mining and machine learning are used in all real-time applications, whether directly or indirectly. Data analysis in finance, retail, telecommunications, biological data analysis, extra scientific applications, and intrusion detection are just a few exampleswhere it can be used. Because it captures a lot of data from sales, client purchase histories, product transportation, consumption, and services, DM has a lot of applications in the retail industry. It's only logical that the amount of data collected will continue to climb as the Internet's accessibility, cost, and popularity increase. In the retail industry, DM assists in the detection of customer buying behaviors and trends, resulting in improved customer service and increased customer retention and satisfaction. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Supervised Learning-Based Data Classification and Incremental Clustering
Using supervised learning-based data classification and incremental clustering, an unknown example can be classified using the most common class among K-nearest examples. The KNN classifier claims, Tell me who your neighbors are, and it will tell you who you are. The supervised learning-based data classification and incremental clustering technique is a simple yet powerful approach with applications in computer vision, pattern recognition, optical character recognition, facial recognition, genetic pattern recognition, and other fields. Its also known as a slacker learner because it doesnt develop a model to classify a given test tuple until the very last minute. When we say yes or no, there may be an element of chance involved. However, the fact that a diner can recognise an invisible food using his senses of taste, flavour, and smell is highly fascinating. At first, there can be a brief data collection phase: what are the most noticeable spices, aromas, and textures? Is the flavour of the food savoury or sweet? This information can then be used by the diner to compare the bite to other items he or she has had in the past. Earthy flavours may conjure up images of mushroom-based dishes, while briny flavours may conjure up images of fish. We view the discovery process through the lens of a slightly modified adage: if it smells like a duck and tastes like a chicken, youre probably eating chicken. This is a case of supervised learning in action. Machine learning can benefit from supervised learning, which is a concept that can be applied to it (ML). 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Yellow leaf disease, climate change, and its impact on the life of farmers in Sullia
Sullia taluk of Dakshina Kannada district has been receiving heavy rainfall for the past few years, having a huge impact on agriculture. Yellow leaf disease spreads from one fully grown tree to another and gradually to the adjacent trees. Arecanut plantations across the region developed the disease, reducing arecanut production. It has hit the livelihood of many farmers whose only source of income was the harvest of arecanuts. Apart from the fungal infection causing financial loss, farmers also face the brunt of gradually reducing agricultural yield in consecutive years. It is yet to be tackled with chemical treatment and hence stands tall as a problem causing an impact on the lives of farmers in Sullia. The current study explored the problem of climate change resulting in yellow disease and its impact on the well-being of plantation owners and workers in the context of Sullia whose culture is rooted in the land. A lack of awareness about mental health influencing the understanding of an epidemic interfering with the local way of life has been emphasized. 2025 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies. -
Business Intelligence in Action: Way of Successful Implementation of Automated Systems
This chapter presents an overview of the role of automated systems in Business Intelligence (BI). BI has emerged as a critical element for modern organizations in decision-making processes by analyzing large volumes of data. Automated BI systems offer several advantages over traditional manual systems, including increased efficiency, accuracy, and customized insights. Despite these benefits, there are several limitations and challenges associated with the implementation of automated BI systems. This chapter examines the benefits and limitations of automated BI systems and identifies common success factors for successful implementation. The chapter also explores different types of automated systems, including predictive analytics, machine learning, natural language processing, and robotics process automation. These systems can help organizations analyze and interpret large amounts of data more quickly and accurately, enabling them to make informed decisions. However, despite the potential benefits of automated BI systems, there are several challenges associated with their implementation, including technical expertise and integration issues. To address these challenges, careful planning, collaboration, and ongoing monitoring are essential. In conclusion, this chapter highlights the importance of automated BI systems in modern businesses and provides valuable insights into their benefits and limitations. The chapter also emphasizes the need for careful planning, collaboration, and monitoring for the successful implementation of automated BI systems. 2024 selection and editorial matter, Nidhi Sindhwani, Rohit Anand, A. Shaji George and Digvijay Pandey; individual chapters, the contributors. -
Ethical AI in Humanitarian Contexts: Challenges, Transparency, and Safety
This chapter elaborates on how emerging technologies for artificial intelligence (AI) can help create social change and solve worldwide problems. The chapter brings to light the issue of ethical matters and responsible AI practices that should be considered to avoid technology usage by the vulnerable population to harden already present inequalities. This chapter also examines the role of AI in ensuring that quality education is accessible to all, in addressing poverty through innovative approaches, and in the amplification quest of human rights advocacy by marginalized groups. This chapter presents a complete picture of the impact of AI on humanitarianism, exemplifying the devices of new horizons and emphasizing the necessity of responsible and inclusive applications. This chapter provides findings and advice for researchers, practitioners, policymakers, and all interested parties who are involved in using the new technologies to make their world fairer and well-sustained. The chapter aims to comprehend the AI-humanitarianism nexus and simultaneously proclaim safety measures and transparency for the sake of social upheaval. 2025 selection and editorial matter, Adeyemi Abel Ajibesin and Narasimha Rao Vajjhala; individual chapters, the contributors. -
Applications of artificial intelligence to neurological disorders: Current technologies and open problems
Neurological disorders are caused by structural, biochemical, and electrical abnormalities involving the central and peripheral nervous system. These disorders may be congenital, developmental, or acute onset in nature. Some of the conditions respond to surgical interventions while most require pharmacological intervention and management, and are also likely to be progressive in nature. Owing to a high global burden of the most common neurological disorders, such as dementia, stroke, epilepsy, Parkinsons disease, multiple sclerosis, migraine, and tension-type headache, there exist multiple challenges in early diagnosis, management, and prevention domains, which are further amplified in regions with inadequate medical services. In such situations, technology ought to play an inevitable role. In this chapter, we review artificial intelligence (AI) and machine learning (ML) technologies for mitigating the challenges posed by neurological disorders. To that end, we follow three steps. First, we present the taxonomy of neurological disorders, derived from well-established findings in the medical literature. Second, we identify challenges posed by each of the common disorders in the taxonomy that can be defined as computational problems. Finally, we review AI/ML algorithms that have either stood the test of time or shown the promise to solve each of these problems. We also discuss open problems that are yet to have an effective solution for the challenges posed by neurological disorders. This chapter covers a wide range of disorders and AI/ML techniques with the goal to expose researchers and practitioners in neurological disorders and AI/ML to each others field, leading to fruitful collaborations and effective solutions. 2022 Elsevier Inc. All rights reserved. -
Implementation of biological fuel cells in treating pharmaceutical effluents
Mankind suffers from a wide variety of infections, diseases, and lifestyle problems. To overcome, several industries worldwide aim to achieve their main objective as the synthesis of an enormous number of diverse drugs that neutralize problems. With the production of tones-to-tones pharmaceutical products these industries also generate extreme good amount of waste, pharmaceutical waste which is now concerned as it contains massive quantity of high organic load of toxic and non-toxic elements. However, the industrial sector adopts anaerobic wastewater treatment strategies to overcome this. As pharmaceutical waste owes highly varied and complexed recalcitrant elements in their complex drug molecules it is not ideal to treat only with anaerobic treatment. Hence, several biotreatments are becoming popularized because they employ MFCs, which are known for the generation of electricity directly from biodegradable organic compounds. The new bio electrochemical technology promises to be inexpensive in comparison to conventional ones. MFC holds the process of both oxidation and reduction permitting the degradation of a wide range of compounds to easily degradable and generates concurrent renewable energy. 2022 by Nova Science Publishers, Inc. -
Unleashing human potential: Integrating cognitive behavioral neuroscience into HR strategies
The world of work is transforming, driven by insights from the frontiers of science. Human resource (HR) practices are no longer limited to traditional methods and increasingly incorporate knowledge from disciplines like Cognitive Behavioral Neuroscience (CBN). By understanding how our brains work, we can design HR practices that enhance employee well-being, engagement, and, ultimately, performance. Drawing from neuroscientific research on decision-making, communication, stress, learning, motivation, and workplace design, this chapter delves into the intersection of CBN and HR, offering evidence-based practices that support a thriving workforce. This interdisciplinary approach holds promise for maximizing human potential in the context of the modern workplace. 2024 by IGI Global. All rights reserved. -
Applications of artificial intelligence in Echo Global Logistics
Echo Global Logistics is a premier provider of business process outsourcing, using technology to meet its clients logistics and transportation needs. They deliver substantial transportation savings to clients while providing top-tier service, thanks to state-of-the-art web-based technologies, dedicated service teams, and significant purchasing power. The most significant business risk in 2023 will be supply chain interruptions, which can impact cash flow, growth, and shareholder value. Echo Global Logistics has introduced an innovative self-service website called Echo Ship, designed for shippers of less-than-truckload (LTL) shipments. Echo Ship simplifies LTL shipping with excellent visibility, outstanding functionality, and a quick, user-friendly design. Logistics is evolving at Echo Global Logistics, with patented technology incorporating the latest developments in the most flexible and reliable transport management system (TMS) currently available. This TMS is developed using Artificial Intelligence (AI), machine learning, and complex load-matching algorithms. Echos unique software is user-friendly, adaptable, and highly scalable, addressing the evolving needs of carriers and shippers regarding transportation management, enabling customers to move their goods swiftly, securely, and affordably. A transportation management company leverages AI to provide supply chain solutions that optimize transportation and logistics needs. The list of services also encompasses executive dashboard presentations, rate negotiation, transportation procurement, shipment execution and tracking, carrier management, carrier selection, reporting, compliance, and comprehensive shipment reports, Over the next five years, supply chain companies anticipate a twofold increase in the use of machine automation in their operations. Similarly, there is a projected 40% compound annual growth rate (CAGR) over the next seven years, going from $1.67 billion in 2018 to $12.44 billion in 2024. Supply chain executives are often time-constrained, making it challenging to attend numerous meetings for solution implementation. Actionable insights from integrated AI tools can remove bottlenecks and unlock real-time value. This is vital because supply chain businesses require more action rather than excessive analysis. This chapter delves into the AI and supply chain practices at Echo Global Logistics, illustrating how AI-based solutions reduce costs, enhance supply chains, boost productivity, and improve service quality. It aims to determine whether the company can transform its products and services, creating new value propositions for Echo Global Logistics customers with the aid of AI. 2024 by Elsevier Inc. All rights reserved, including those for text and data mining, AI training, and similar technologies. -
Atman's awakening: Bhagavad Gita's Path to Moksha through Karma Yoga and Atmabodha
Indian psychology is characterized by its diverse and rich traditions that have evolved over several centuries. This chapter tries to fulfill four objectives: 1) To provide a brief overview of the concept of self in Bhagavad Gita; 2) to give a brief overview of the two frameworks for moksha given in the Bhagavad Gita with the help of empirical evidence of current research; 3) to propose a conceptual model using Triguna Framework and Trimarg Framework; and 4) to provide the implications of the proposed model. The chapter begins with an explanation of the Indian philosophical understanding of self from the lens of Bhagavad Gita. In the second section, an effort has been made to compare and contrast the two frameworks given in Bhagavad Gita for Moksha. The last section introduces a conceptual model to enhance sattva guna and reduce the rajas and tamas gunas to attain atmabodha that can have positive psychological implications in modern times. 2024, IGI Global. All rights reserved. -
Navigating the dynamic interplay of fear of failure and social cognition in the digital era
This chapter delves into the intricate relationship between fear of failure and our ability to perceive, interpret, and respond to social cues (i.e., social cognition). This chapter will examine the theoretical foundations of fear of failure and how it manifests across cognitive, emotional, behavioral, and social dimensions. Drawing from empirical research, it will provide real-world insights into how this fear can profoundly affect social interactions. The chapter highlights interventions, such as CBT and mindfulness practices, designed to address the fear of failure and enhance social cognition. It will further explore the dynamic interplay between fear of failure, social cognition, and the evolving landscape of online interventions. As the digital realm shapes our social interactions, understanding how fear of failure influences social cognition in the online context and how online interventions can mitigate its impact is of paramount importance. This chapter seeks to present a thorough summary of these interconnected variables. 2024, IGI Global. -
Genetic Algorithms for Graph Theoretic Problems
[No abstract available] -
Algorithms for the metric dimension of a simple graph
Let G = (V, E) be a connected, simple graph with n vertices and m edges. Let v1, v2 $$\in$$ V, d(v1, v2) is the number of edges in the shortest path from v1 to v2. A vertex v is said to distinguish two vertices x and y if d(v, x) and d(v, y) are different. D(v) as the set of all vertex pairs which are distinguished by v. A subset of V, S is a metric generator of the graph G if every pair of vertices from V is distinguished by some element of S. Trivially, the whole vertex set V is a metric generator of G. A metric generator with minimum cardinality is called a metric basis of the graph G. The cardinality of metric basis is called the metric dimension of G. In this paper, we develop algorithms to find the metric dimension and a metric basis of a simple graph. These algorithms have the worst-case complexity of O(nm). The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
An Overview of Augmenting AI Application in Healthcare
Artificial intelligence (AI) is showing a paradigm shift in all spheres of the world by mimicking human cognitive behavior. The application of AI in healthcare is noteworthy because of availability of voluminous data and mushrooming analytics techniques. The various applications of AI, especially, machine learning and neural networks are used across different areas in the healthcare industry. Healthcare disruptors are leveraging this opportunity and are innovating in various fields such as drug discovery, robotic surgery, medical imaging, and the like. The authors have discussed the application of AI techniques in a few areas like diagnosis, prediction, personal care, and surgeries. Usage of AI is noteworthy in this COVID-19 pandemic situation too where it assists physicians in resource allocation, predicting death rate, patient tracing, and life expectancy of patients. The other side of the coin is the ethical issues faced while using this technology like data transparency, bias, security, and privacy of data becomes unanswered. This can be handled better if strict policy measures are imposed for safe handling of data and educating the public about how treatment can be improved by using this technology which will tend to build trust factor in near future. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Inclusive innovation in tourism sector: Gap analysis through impact assessment
The travel and tourism industry is a major catalyst for global economic growth. Inclusivity and equity are indeed significant challenges faced by the service sector. The present study attempts to analyze the attributes of achieving true inclusivity and equity. This requires addressing systemic barriers and biases that can perpetuate unequal opportunities and outcomes towards building a pro-active tourism community. The chapter identifies the inequities already in place as well as untapped prospects for inclusive innovation in the tourism industry by performing an extensive gap analysis and impact assessment. A comprehensive and multi-faceted approach in practicing inclusiveness in the tourism sector demands an innovative pedagogy and unconventional business practices. The chapter highlights the bigger picture of inclusive innovation in tourism, which clarifies how business, society, and technology interact. A smart tourism approach with an integrated technological effort and socially-driven innovation builds the competency of the destinations. The study aims to assess the existing level of inclusion in tourism innovation, which determines significant representational and access gaps while assessing the potential economic and cultural effects of introducing more inclusive practices. As we navigate complex global challenges, from social inequality to environmental sustainability, inclusive innovation offers a path forward. The implications indicated in this chapter benefits the stakeholders with socially driven inclusive innovation. Towards inclusive innovation, equity takes center stage. An inclusive setting ensures collaboration and co-creation and integrating people from diverse backgrounds, including entrepreneurs, researchers, policymakers, and community members. Socially engaged stakeholders are aligned with inclusive innovation with insights and experiences, resulting in high relevance and sustainable orientation, thereby fostering growth and development. 2024 Nova Science Publishers, Inc. All rights reserved. -
Social tourism and sustainability spectrum: A theoretical evaluation of marginalized community and partnership
The economic and development policies that ignore marginalized communities make the rural front of the society more vulnerable. This chapter examines social engagement and sustainable outlook by emphasizing the linkage between tourism partnership and community engagement through tourism business. This chapter engages research paradigms to integrate varied discourses by assessing tourism with promarginalized community growth paradigm. The chapter is indicative on proactive community empowerment to appreciate an inclusive engagement from a sustainable outlook which aims to pinpoint the tourism business ventures within that aid business and support smallholders in improving their standard of living. The analogy of social inclusive ideology ideates a strategic consideration towards ecologically viable measures and responsible tourism approaches while integrating social inclusivity based on sustainable consciousness. 2024 Nova Science Publishers, Inc. All rights reserved. -
Volunteering-based student engagement: A model for student well-being in higher education institutions
Student well-being issues are rising alarmingly, and addressing these well-being concerns takes the predominant focus of higher education institutions, together with competency building. Understanding the importance of student engagement in student well-being, Christ University, Bengaluru, India, adopted a student engagement model based on volunteering. The current paper tries to understand the dynamics between volunteering and student well-being. It explores the relationship between volunteering and personal responsibility, social responsibility, meaning in life, and a helping attitude. It also tries to check whether the relationship between a helping attitude and student well-being is mediated by personal responsibility and social responsibility. Further, it explores whether volunteers and non-volunteers differ in their social responsibility, personal responsibility, and student well-being. A group of 350 students, of which 175 volunteers and 175 non-volunteers, were approached to participate in the study. Post data cleaning procedure, 327 data points qualified for analysis, and it was found that there exists a relationship between volunteering and personal responsibility, social responsibility and helping attitude, and personal responsibility and social responsibility mediated the relationship between helping attitude and student well-being. Results also revealed that volunteers and nonvolunteers differed in their helping attitude, personal responsibility, and student well-being. These findings point towards the fact that the student engagement model based on volunteering has a positive impact on student well-being. A detailed discussion of the application of these findings is provided in the full paper. 2024 Nova Science Publishers, Inc.