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AI for Optimization of Farming Resources and their Management
The chapter explores the incorporation of artificial intelligence (AI) into framework strategies aimed at addressing the dynamic challenges confronting the agricultural industry. It focuses on issues like resource depletion, escalating labor costs, and the impacts of climate change, emphasizing the necessity for inventive solutions. The proposed framework adopts a comprehensive approach that integrates farm-to-fork strategies, smart agricultural practices, and advanced crop planning. Its primary objectives are to enhance crop yields, establish transparent supply chains, and optimize resource allocation. The chapter underscores the potential synergies associated with contextual understanding, efficient communication, and personalized user experiences, anticipating a transformative impact on agriculture. The integration of AI is anticipated to yield unprecedented benefits, paving the way for a more technologically advanced, sustainable, and productive future. Despite these promising prospects, challenges emerge during the integration process, manifesting as regulatory hurdles, infrastructure deficiencies, and inherent complexities. The chapter acknowledges these obstacles and asserts that overcoming them is crucial for realizing the full transformative potential of AI in agriculture. Looking ahead, the convergence of AI and framing strategies is poised to revolutionize the agricultural landscape, ushering in increased efficiency and sustainability. This innovative partnership holds the promise of building a resilient foundation for agriculture, ensuring its adaptability to changing needs and contributing to a greener and more productive future. 2025 selection and editorial matter, Sirisha Potluri, Suneeta Satpathy, Santi Swarup Basa, and Antonio Zuorro; individual chapters, the contributors. All rights reserved. -
AI in Data Recovery and Data Analysis
The use of artificial intelligence (AI) techniques for data collection and analysis is examined in this chapter. It also looks at the benefits, challenges, and future directions. It provides a broad overview of AI techniques and illustrates the use of generative adversarial networks (GANs), recurrent neural networks (RNNs), convolutional neural networks (CNNs), etc. in this area. Data recovery is an essential process when trying to recover lost or damaged data. For AI methods like CNN, the retrieval of image and video data has shown great promise. Using the power of deep learning, CNNs can search for patterns in data, assisting in the reconstruction and restoration of lost information. On the other hand, RNNs excel at retrieving serial data, such as text or time series data. These networks can efficiently learn dependencies and contexts, which makes it possible to precisely reconstruct missing or imperfect sequences. AI-based data analytics provides businesses with insightful information and opportunities. GANs, for example, are increasingly being used to generate and improve data, enabling organizations to expand the size of their datasets and improve the efficacy of their analytical models. Large amounts of data can also be divided up using A-based clustering algorithms, which are also well classified and provide insightful analysis and interpretation. In the gathering and analysis of data, AI has many benefits. Businesses can process and analyze enormous amounts of data in a fraction of the time thanks to this productivity-boosting automation of challenging and time-consuming tasks. By reducing bias and human error, AI techniques also increase accuracy, resulting in results that are more dependable and consistent. Additionally, AI-driven insights assist businesses in spotting trends, uncovering buried patterns, and coming to wise decisions that may not be apparent using traditional analytics methods. Due to privacy concerns, ethical considerations, interpretability, transparency, and accountability, AI deployment in data recovery and analysis is difficult. Future directions include collaboration between humans and AI, edge computing integration, and privacy-preserving methods. In conclusion, organizations looking to maximize their data assets stand to benefit greatly from the application of AI techniques to data analytics and data retrieval. 2024 selection and editorial matter, Kavita Saini, Swaroop S. Sonone, Mahipal Singh Sankhla, and Naveen Kumar. -
AI in e-learning
This current research chapter focuses on the different areas of e-learning where AI can be implemented to make e-learning a better experience. E-learning is a 24/7 platform where learners can gain knowledge at the convenience of their home and timeframe. AI can help such learners with different adaptive technologies in clarifying the doubt, identifying the problem area of the learner and providing them a customized learning solution. Adaptive learning suggested that the learning pace is different for different learners. It must be made sure that the educational supplies and amenities provided must fit the requirement of each learner; else, it will lose its essence. There are different AI features to enhance the learning experience of e-learning. The providers must keep this in mind that the acquired information about learners must be wisely used while implementing the AI technology to e-learning mode so that the blended model can provide an enriching experience to the end-user. Cognitive learning can be a key to constructive, collaborative and contextualized execution of AI-enabled learning processes. Maximization of AI effectiveness as a tool of e-learning can be brought only when it is implemented to overall program pedagogy and is monitored for continuous improvement. The Institution of Engineering and Technology 2021. -
AI in Forensics A Data Analytics Perspective
Artificial intelligence (AI) is rapidly becoming the most significant science in all areas of life, and forensic science is one of the fields benefiting from it. Forensics can be defined as a study of crime via the use of scientific methods and techniques. Around the globe, governments invest a large amount of money in developing forensics techniques to prove criminal activities and track criminals effectively. It is now becoming a practice to involve artificial intelligence in supporting the forensic application. It involves a smart and intelligent examination of massive volumes of very complicated data. As a result, AI is becoming an excellent solution for addressing many of the complicated issues that now exist in forensics. For example, AI proves more effective in skeleton-based human identification compared to the traditional skull/skeleton superimposition method. AI can be used to pool meta-data generated from multiple sources connected to forensic science and do a meta-analysis on it to simplify complex data. AI finds patterns and uses them to identify/recognize/predict something that is required in crime tracking or criminal/victim recognition. Complex analytics and probabilistic reasoning are used to recognize patterns. Among the most crucial things to forensic science is the identification of specific sorts of patterns in enormous amounts of data. This could include image pattern recognition, in which the program attempts to distinguish between distinct components of an image or a person. Other types of pattern recognition, such as finding patterns in text, may also exist. Artificial intelligence aids in the more accurate recognition of such patterns in complex data. This chapter introduces the reader to several aspects of artificial intelligence that can be used in forensics. 2024 selection and editorial matter, S. Vijayalakshmi, P. Durgadevi, Lija Jacob, Balamurugan Balusamy, and Parma Nand; individual chapters, the contributors. -
AI in IA: Impact of Artificial Intelligence in Internal Audit: A Qualitative Study
Internal auditing is becoming more crucial as businesses become more complex and extensive. Artificial intelligence (AI) in internal auditing is a trend change that promises to revolutionize how internal auditing functions are performed and delivered through significant improvements in audit quality and operational discipline. This paper reflects on many of the multifaceted impacts of AI on internal auditing functions. This paper intends to investigate how this AI will impact the audit profession. By interviewing ten individual internal audit experts qualitatively, the study shows that AIs implementation will impact the following six critical levels. AI makes it possible for an auditor to (1) spend less time and make the audit more productive, (2) increase coverage, (3) real-time auditing, (4) enhance decision-making, (5) risk assessment and management, and (6) create new advisory services. The findings thus imply a need for a well-defined and consistent audit structure that is flexible enough for auditors to improve their audits. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
AI vs. traditional portfolio management: A study on Indian investors
This research chapter investigates the dynamics between artificial intelligence (AI) and traditional portfolio management strategies, specifically focusing on the attitudes and preferences of investors in the Indian market. The study aims to elucidate the comparative performance, risk-adjusted returns, and behavioral aspects associated with AI-driven portfolio management as opposed to traditional methods. Utilizing a methodology tailored to the unique characteristics of the Indian investment landscape, this research engages investors with varying degrees of experience in the stock market. Through a meticulous collection of data during October and November 2023, employing convenience sampling, the authors explore the factors influencing investor perceptions and decisions in adopting AI-based portfolio management strategies. These findings contribute to the existing discourse by shedding light on the role of trust, subjective norms, perceived usefulness, perceived ease of use, and attitudes as critical variables shaping the adoption of AI in portfolio management. 2024, IGI Global. All rights reserved. -
Ai-assisted models for dyslexia and dysgraphia: Revolutionizing language learning for children
Dyslexia and dysgraphia are two common learning disabilities that affect children's ability to read, write, and spell accurately. These disabilities can significantly impede a child's academic performance, leading to lack of self-confidence, anxiety, and frustration. Traditional approaches to address these disabilities often involve one-on-one sessions with a tutor or special education teacher, which can be time-consuming and expensive. Artificial intelligence (AI) language learning models have shown tremendous potential in assisting children with dyslexia and dysgraphia. These models can provide real-time feedback and personalized instruction to help children overcome learning difficulties. This chapter highlights the importance of addressing these challenges and proposes a solution that leverages AI language learning models to assist children with dyslexia and dysgraphia. By embracing AI language learning models, educators and parents can empower children with dyslexia and dysgraphia, providing them with the necessary tools and support to overcome their learning challenges. 2023, IGI Global. All rights reserved. -
AI-based online interview bot with an interactive dashboard
In recent years, video interviews have become increasingly popular in the recruitment process due to their convenience and efficiency. However, evaluating a candidates communication skills and perceived personality traits from a video interview can be challenging. The agent utilizes natural language processing and computer vision techniques to analyze the candidates verbal and nonverbal behavior during the interview. Specifically, the agent focuses on linguistic features such as fluency, grammar, and vocabulary, as well as nonverbal cues such as facial expressions and body language. Based on these features, the agent predicts the candidates communication skills and perceived personality traits. To validate the effectiveness of the agent, a Talk was conducted with a group of participants who completed video interviews with and without the agent. The results show that the agents predictions of communication skills and perceived personality traits are highly correlated with the ratings given by human evaluators. Additionally, the agent is able to provide valuable insights into the candidates performance that may not be immediately apparent to human evaluators. Overall, the intelligent video interview agent proposed here has the potential to improve the recruitment process by providing more accurate and objective evaluations of candidates communication skills and perceived personality traits. 2025 selection and editorial matter, A. Vadivel, K. Meena, P. Sumathy, Henry Selvaraj, P. Shanmugavadivu and Shaila S. G.; individual chapters, the contributors. -
AI-driven decision-making and optimization in modern agriculture sectors
AI-driven decision-making tools have emerged as a novel technology poised to replace traditional agricultural practices. In this chapter, AI's pivotal role in steering the agricultural sector towards sustainability is highlighted, primarily through the utilization of AI techniques such as robotics, deep learning, the internet of things, image processing, and more. This chapter offers insights into the application of AI techniques in various functional areas of agriculture, including weed management, crop management, and soil management. Additionally, it underlines both the challenges and advantages presented by AIdriven applications in agriculture. In conclusion, the potential of AI in agriculture is vast, but it faces various impediments that, when properly identified and addressed, can expand its scope. This chapter serves as a valuable resource for government authorities, policymakers, and scientists seeking to explore the untapped potential of AI's significance in agriculture. 2024, IGI Global. All rights reserved. -
AI-powered marketing strategies in the tourism and hospitality sector
A highly competitive environment with increased demand for personalized services drives the tourism and hospitality industry to embrace immersive and intelligent technologies. Smart technologies like artificial intelligence (AI) and virtual reality (VR) assist in promotions, marketing brands, customer analysis, and ultimately leading to sustainable businesses. Marketing research is an inevitable element for any businesses that helps in understanding their customers, catering their needs, and turning them into loyal customers. Marketing strategies incorporated with smart technologies are gaining high importance in the tourism and hospitality industries due to three major outcomes such as experience enhancement, revenue improvement and effective operations. Artificial intelligence revamped the hospitality industry with customized services and tailored recommendations based on a wholesome of customer data. Virtual reality technology provides high immersive experience to boost tourism, to enhance customer experience, to influence positive travel decisions. 2024, IGI Global. All rights reserved. -
AI, mindfulness, and emotional well-being: Nurturing awareness and compassionate balance
This chapter examines the intricate relationship between artificial intelligence (AI), mindfulness, and emotional health. It explored the synergistic potential of AI and mindfulness in enhancing emotional awareness and the function of AI in promoting emotional well-being in educational, occupational, and mental health settings. The discussion addressed emerging trends and ethical considerations. It emphasized the transformative potential of AI and mindfulness in promoting emotional well-being, focusing on maintaining a compassionate balance in the AI-driven world. 2024, IGI Global. All rights reserved. -
Aiming at digital health via mHealth application for generation Y post-pandemic scenario
Medical and health products have become a part of our lives. A health-conscious .society is the aftermath of the pandemic. The increasing role of technology has pushed people to online alternatives for medical services, progressing towards digital health. This research thus contributes to the nascent literature on the impact of mHealth apps and the consumption pattern in Bangalore in the post-pandemic scenario. This research investigates from the perspective of usage, privacy, and affordability of the mHealth apps. Results suggest that usage is positively affected by the affordability and privacy of these apps. Firstly, app developers could use the findings for different digital health marketing strategies and implementations for the mHealth app. Secondly, academics can look at other aspects such as the knowledge people possess regarding apps and their proficiency in accepting technology. Finally, the policy discussion makers can work on concerns of affordability and privacy to cater to the more significant population segment. 2023 by IGI Global. All rights reserved. -
AIoT concepts and integration: Exploring customer interaction, ethics, policy, and privacy
Integrating AIoT technologies provide businesses with increased productivity, cost savings, data-driven insights, and enhanced consumer interactions. Nevertheless, difficulties include data privacy, ethics, regulatory compliance, and technical complexities. The recommendations include transparent practices, accountability, bias mitigation, data minimization, informed consent, and ethical design. Policymakers must develop adaptable regulations, place a premium on privacy and security, and involve stakeholders. A user-centric approach and training in data ethics are essential. AIoT offers enormous potential but requires a delicate balance between innovation and responsibility, with ethics, privacy, and policy compliance at the forefront. 2024 by IGI Global. All rights reserved. -
Algae-Based Nanoparticles for Contaminated Environs Nanoremediation
Currently, the rapidly growing human interference has increased the percentage of pollutants that include organic and inorganic and this has been threatening the ecosystems. Remediation by conventional physicochemical methods, bioremediation has gained immense acceptance due to their ecofriendly, economical, and sustainable approach. Microbial-based nanoparticles act as facilitators in remediating contaminants by microbial growth and immobilization of remediating agents, by inducing microbial remediating enzymes or enhanced biosurfactants that helps to improve solubility of hydrophobic hydrocarbons to create a conducive milieu for remediation. Algal-NPs can be produced easily using low-cost medium and simple scaling up process which is economically feasible. Silver nanoparticles (AgNPs) and gold nanoparticles (AuNPs) have been synthesized using Nannochloropsis sps (NN) and Chlorella vulgaris (CV), while, brown seaweeds Petalonia fascia, Colpomenia sinuosa, and Padina pavonica were used with iron oxide NPs along with their aqueous extracts. These applications have shown to be promising alternative bioremediating methods that are safe. Algal-based NPs can act as a pollution abatement device that can help to effectively target the pollutants for efficient nanobioremediation and helps to promote environmental clean-up for eliminating heavy metals, dyes, and other organic and inorganic waste from the environment. 2025 by Apple Academic Press, Inc. -
Algorithmic Strategies for Solving Complex Problems in Financial Cryptography
Cryptography is used in applications where subversion of the communication system could lead to financial loss, which is known as financial cryptography. In contrast to classical encryption, which has mostly been utilized for military and diplomatic purposes throughout recorded history, financial cryptography focuses on privacy and security. The techniques and algorithms required for the security of financial transfers as well as the development of new money types are included in financial cryptography. Financial cryptography includes proof of work and several auction mechanisms. Spam is being restricted by using hashcash. The applications of financial cryptography have been observed to be highly diverse. Financial cryptography is incredibly difficult and calls for knowledge from many different, incompatible, or at the very least, hostile disciplines. The higher risk factor that efforts to build financial cryptography systems will reduce or eliminate crucial strategies that they are trapped among financial application and cryptography, or between accountants and programmers. Digital finance is playing a big role in how financial services are organized globally. Digitalization, data analysis, and increased processing power enable a wide range of new financial services and transactions. The importance of economic development has attracted a lot of attention to this economic development enabled by digital financial technology (Fintech). Cryptography has begun to expand swiftly in the Fintech sector, and both investors and financial bankers are becoming more favorable toward digital assets. The observed market factors are directly related to how people behave when they engage in financial activity. The result analysis in this behavioral strategies of financial cryptography from a specific market analysis is still limited, despite the abundance of research and theories on the underlying motives of peoples behavior in financial frameworks. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023. -
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. -
ALIGNING INVESTMENTS WITH VALUES: CREATING PORTFOLIOS BASED ON CORPORATE SOCIAL RESPONSIBILITY AND NIM
Purpose: This research discusses the importance of corporate social responsibility (CSR) and its link to a financial performance metric called net interest margin (NIM) in the context of non-banking financial companies (NBFCs). CSR initiatives can lead to long-term sustainability and improved financial performance, attracting investors seeking to align their investments with their values. Need for the Study: The research composes portfolios based on financial companies CSR performance and NIM ratios to help investors understand the difference between CSR and financial performance, making investment decisions based on their portfolio goals and values. Striking a balance between sustainability and the financial performance of financial companies, will help investors find a suitable balance between portfolios for investment purposes. Methodology: The authors used data from 55 financial companies for daily returns from 20142015 to 20212022 and used descriptive statistics to measure the performance of portfolios. Findings: The findings suggest that financial companies in India have improved their CSR scores over time, indicating an increased focus on integrating socially responsible practices into their operations. The data also show that NBFCs are catching up with banks regarding CSR scores, and some NBFC portfolios even outperform banks regarding returns. However, the study also highlights the need for some companies to focus more on CSR and business operations. Practical Implications: The results serve as a benchmark for financial companies to assess their relative CSR performance, highlighting the need for companies to focus on integrating socially responsible practices into their operations and guiding areas where companies can improve. 2024 by Ishfaq Hussain Bhat, Shilpi Gupta and Satinder Singh Published under exclusive licence by Emerald Publishing Limited. -
An advanced machine learning framework for cybersecurity
The world is turning out to be progressively digitalized raising security concerns and the urgent requirement for strong and propelled security innovations and procedures to battle the expanding complex nature of digital assaults. This paper examines how AI is being utilized in digital security in both resistance and offense exercises, remembering exchanges for digital attacks focused on AI models. Digital security is the assortment of approaches, systems, advancements, and procedures that work together to ensure the confidentiality, trustworthiness, and accessibility of processing assets, systems, programming projects, and information from attacks. Machine learning-based examination for cybersecurity is the following rising pattern in digital security, planned for mining security information to reveal progressed focused on digital threats and limiting the operational overheads of keeping up static relationship rules. In this paper, we are mainly focusing on the detection and diagnosis of various cyber threats based on machine learning. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
AN ANALYSIS OF PERCEPTION AND AWARENESS OF UNDERGRADUATE YOUTH TOWARDS CYBERCRIME
The perception of a situation or reality determines how one responds and awareness is the first step towards understanding, knowing or recognizing it. The majority of the public and the police may be familiar with the phrase cybercrime, but all of the mare fully informed ofthe nature and scope of these crimes, as well as of the cybercriminals and cyber victims, which has an impact on how they see these issues. This studys main goal was to examine the perception and awareness of cybercrime among undergraduate youth studying in BBA or BCA courses. In this study, we discovered that young peoples responses to cybercrime mostly depend on their perceptions of it and their awareness level. To accomplish the studys objective, a thorough examination of existing literature was undertaken. Primary data of200 students were collected through Google Forms. Percentile analysis, correlation analysis and t-test are done to test the hypotheses. The results of this study may help college administrators better comprehend the mind set of todays youth as they develop laws and policies aimed at reducing cybercrime among students. The results of this study show that the youngsters surveyed have high levels of awareness and a good perception. 2024 Kiran Joshi and Priyanka Kaushik. -
An Analysis of Sentiment Using Aspect-Based Perspective
Opinions play a major role in almost every human practice. Finding product and service reviews is made easy online. Product reviews are readily available in huge quantities. Considering each review and making a concise decision about a product is not feasible or even possible. Aspect-based sentiment analysis (ABSA) is one of the best solutions to this problem. Summary and online reviews analysis is delivered in this paper. ABSA has made extensive use of machine learning techniques. Recent years have seen deep learning take off due to the growth of computer processing power and digitalization. When applied to various deep learning techniques, numerous NLP tasks produced futuristic results. An overview of various deep learning models used in the field of ABSA is presented in this chapter after an introduction to ABSA. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.