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Mind of a portfolio investor: Which strategies should I use as a basis for my investment decisions
It is smart for investors to plan for a drop that may be accompanied by a recession in the late stages of a bull market. The authors examine a variety of passive and active strategies, as well as their success in different crises. However, while choosing the best of strategies in the worst of circumstances, investors must be cautious in defining 'best.' It's critical to comprehend not only the long-term performance but also the whole cost of putting various preventive measures in place. The authors analyse popular strategies like technical analysis, fundamental analysis, relying on financial news, seeking professional advice, tips from trade experts, and self-intuition while making portfolios. Our findings indicate that every investment is unique. Some defensive methods will be more effective than others in each case. As a result, diversification across several viable strategies may be the wisest course of action. 2023, IGI Global. All rights reserved. -
Why haven't you bought a solar panel? Consumerism at its "Green" best with the Myers-Briggs type indicator
This research study examined the relationships between Myers-Briggs' four personality dimensions and environmentally conscious consumption patterns. The study found that different personality types have different green purchase habits. The authors also propose five strategies for businesses who plan to venture into sustainable consumption. These strategies include leveraging the power of belongingness, forming addictive healthy habits, capitalizing on the domino effect, communicating with either the heart or the head, and prioritizing having experiences above owning things. The study's findings suggest that businesses need to take a more holistic approach to promoting sustainable consumption. Authors use discriminant analysis to evaluate whether each Myers-Briggs personality type had a distinct green purchase habit. For each of the four dichotomies associated with five kinds of green buying behaviors, SPSS was used to construct discriminant analysis indicators and outputs. 2023, IGI Global. All rights reserved. -
Securing end user computing environments using blockchain
COVID-19's phenomenal effect has expedited the adoption of digital technologies over several years, indicating that several of these breakthroughs are here to stay. Several enabling technologies are currently being implemented as major solutions for improving and responding to the pandemic's numerous issues, with blockchain being one of the preferred solution options. Blockchain can be used to re-structure processes, resulting in most effective operational and business models (e.g., democratising quality cancer detection with advanced artificial intelligence based radiomics technologies). The authors posit here that while there is a lot of anticipation about using blockchain to improve business capabilities, the lessons learned from the many pilots and proofs of concept so far should be considered. The necessity for a structured, formal decision-making process, based on good business logic and an awareness of the problem's process lifecycle, is however critical. Blockchain is a means to an end, not an end in and of itself. 2024, IGI Global. All rights reserved. -
Constructing an e-commerce model using the framework of blockchain
Blockchain technology is gaining immense popularity in the realm of online e-commerce, advertising, and consumerism. This study aims to develop a model that comprehends how blockchain can act as a significant disruptor for various functions and applications related to e-commerce. The research explores the possibility that this technological advancement has the potential to revolutionize the core operations of e-commerce industry by providing trade connections devoid of trust or need for specialized intermediaries or central authority in case of permission less blockchains. Furthermore, it could facilitate equitable access to immutable data across supply chains which may result in a major reorganization pertaining to information and value exchanged between businesses involved in electronic commerce and their customers. 2024, IGI Global. All rights reserved. -
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. -
Knowledge or Personality: An Empirical Analysis of Behavioural Finance and Investor Cognitive Biases
This research attempts to analyze to what extent knowledge and tactics or enduring personality traits predict investor behaviour and cognitive biases in portfolio investment. This study is based on exploring a wide-ranging dataset: responses to a questionnaire survey together with transactional data of the same individual customers of an Indian stock company. From the questionnaire survey, the authors estimate measures of domain-general personality traits, such as the big five, as compared to the knowledge, financial literacy, competency, and attitude specific to investor equity trading. The results show the dominance of knowledge and tactics measures over personality-related measures when predicting nine different dependent variables of investment performance, investor cognitive biases, and portfolio investment activity. This research concludes with the discussion of the findings and with insights into theory and managerial implications. Copyright 2022, IGI Global. -
Machine Learningcloud-Based Approach to Identify and Classify Disease
The term "Internet of Things"(IoT) describes the process of creating and modeling web-related physical objects across computing systems. IoT-based healthcare applications have offered multiple real-time products and benefits in recent years. For millions of people, these programmers provide hospitalization can get regular medical records and healthy lives. The introduction of IoT devices in the health sector has several technological developments. This study uses the IoT to construct a disease diagnostic system. Wearable sensors in this system initially monitor the patient's sympathy impulses. The impulses are then sent by a network environment to a server. In addition, a new hybrid approach to evaluation decision-making was presented as part of this research. This technique starts with the development of a set of features of the patient's pulses. Based on a learning approach qualifications are neglected. A fuzzy neural model was used as a diagnostic tool. A specific diagnosis of a particular ailment, such as the diagnosis of a patient's normal and abnormal pulse or the assessment of insulin issues, would be modeled to assess this technology. 2022 IEEE. -
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. -
Predictive Modeling for Uber Ride Cancellation and Price Estimation: An Integrated Approach
In the realm of ridesharing services, exemplified by Uber, two formidable challenges have surfaced: ride cancellations and precise fare estimation. This research introduces an innovative, integrated approach that leverages predictive modeling to address both issues. By analyzing historical ride data, we identify the intricate factors influencing cancellations, and through machine learning techniques, we develop predictive models to forecast cancellation likelihood. Additionally, we pioneer a dynamic approach to fare estimation by considering historical data alongside real-time variables. By unifying these strategies, we aim to enhance user satisfaction, optimize driver allocation, and promote trust and transparency within the ridesharing ecosystem. 2024 IEEE. -
Interplay between personality and attitude towards emotions with creative self concept among young adults
Creative self-concept, intimately intertwined with the personality traits and plays a pivotal role in shaping individuals behavioral tendencies. Personality traits are largely responsible to influence how people perceive and navigate their creative abilities and self-expression. Moreover, attitudes towards emotions are another key facet of ones psychological landscape, impacting their inclination to perceive, process, and manage emotional experiences. Keeping this view, the present research attempts to explore the interconnectedness of creative self-concept, personality traits, and attitudes towards emotions among young adults, as well as focuses on exploring the predictors of creative self concept. For this purpose participants consisted of 200 young adults with a mean age of 21.20 years. Statistical outcomes revealed that creative self concept is a significant positive correlate of openness, conscientiousness, extraversion, agreeableness, attitude towards sadness, and attitude towards fear. Additionally, stepwise multiple regression analysis confirmed that openness (R2 = 27%), neuroticism (R2 = 2%) and attitude towards sadness (R2 = 2%) emerged as the significant predictors of creative self concept. Findings from the current research concludes that for young adults to have self-perception in the realm of creativity, personality traits and attitude towards emotions are significant contributing factors. By recognizing and employing these connections, individuals, educators, counselors, and practitioners can contribute to the cultivation of creativity and personal development. The Author(s) 2024. -
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. -
Workplace spirituality in the Indian IT sector: development and validation of the scale
The Indian information technology (IT) sector faces a unique challenge of managing their knowledge workers. Workplace spirituality, defined as recognising employees as a spiritual being, is seen as a new solution to the challenges faced by the IT sector. There are many conceptual models, but very few empirical ones to measure spirituality at work in the Indian context. The present study aims to develop an instrument that measures workplace spirituality. In-depth interviews were conducted with 20 IT professionals and seven themes emerged from these interviews, based on which a 66-item questionnaire was developed which was further reduced to 33 items as per recommendations from experts. The questionnaire was administered to 172 Indian IT professionals and its reliability and construct validity were determined using convergent and discriminant validity. As a future scope, the questionnaire could be tested in other sectors and suitable changes be generalised in the Indian context. Copyright 2022 Inderscience Enterprises Ltd. -
A method to secure FIR system using blockchain
In India, we can see that technology has touched in every aspect of our life. There exist technology in all the fields e.g. education, agricultural, business, government etc. and we can also understand how beneficial it is, as it saves the time, money and human power. In spite of being technologically advanced, the system lacks in security perspective. When we talk about today, India has moved to the era of digitalization after the launch of the campaign Digital India, the Indian Police Department has replaced the manual system with the centralized online process to register the complaint. The main objective of this paper is to provide a method to secure the FIR system using blockchain technology. This introduces to the essential principal of blockchain technology and its future in the police department of India. Blockchain technology will also explain to protect the FIR from the malfeasance. BEIESP. -
Identification of Student Programming Patterns through Clickstream Data
In present educational era, teaching programming to the undergraduates is challenging. For an instructor, focusing on each of the aspect of programming like coding language, logical reasoning, debugging errors, troubleshooting code and problem solving is very daunting task. So, educational researchers are identifying ways to easily identify the student's struggles during programming so that timely assistance can be provided. Using programming platforms or software, a lot of programming data is generated in the form of activity logs or clickstream data. Using machine learning along with data analytics over this programming data can reveal programming patterns of students that may help in early interventions. This study focusses on identifying programming patterns of the students through clustering and groups the students into three major categories namely low performers, strugglers, and high scorers. Further, relevant features like test case success, code compile success and failure, finish test etc. that majorly contribute towards the student programming scores are identified through regression analysis. Through this research, educators can early categorize the students based on their programming patterns and provide timely intervention when necessary, ensuring that no student gets left behind in the fast-paced world of programming education. 2024 IEEE. -
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. -
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. -
New Empirical Equation for Fundamental Time Period of RC Moment-Resisting Frame Buildings Using Machine Learning Algorithms
The fundamental time period of reinforced concrete (RC) buildings is a critical parameter in structural engineering, influencing their dynamic behavior and response to seismic and wind loads. This study aims to propose a new empirical formula for estimating the fundamental time period of RC buildings through regression analysis. Leveraging the SAP2000 API with VBA code, a dataset comprising 200 two-dimensional RC building models was rapidly generated, allowing for efficient exploration of various building configurations. Modal analysis was conducted for each model to determine the fundamental time period, and regression analysis was performed using both multiple linear regression and curve estimation regression techniques. The input parameters included total building height and base dimensions, while the output variable was the fundamental time period obtained from SAP2000 results. Multiple linear regression yielded two best-fit models, while curve estimation regression produced logarithmic and exponential models. The proposed models were compared with the fundamental time period values obtained from SAP2000 results and those calculated using the formula specified in the Indian Standards (IS) code. Further the results obtained are used to develop a machine learning model that can be used to estimate the time period of RC structures for a given height. The model is chosen after estimating the coefficient of regression for various individual machine learning algorithms and ensemble algorithms. This research contributes to the advancement of structural engineering by providing a systematic approach to developing empirical formulas tailored to RC buildings. The proposed formula, enabled by the automation capabilities of the SAP2000 API, offers a more accurate and reliable method for estimating the fundamental time period, facilitating improved seismic design and analysis practices. Further validation and verification of the formulas performance using additional datasets and real-world case studies are recommended to enhance its applicability and robustness. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Multiobjective portfolio optimization using multilevel quantum inspired optimization algorithms: a comparative study
The study of portfolio optimization has been a significant focus for computer science and finance researchers, with frequent publication of innovative methods. Numerous works have illustrated that conventional approaches like quadratic programming struggle with nonlinear constraints. This chapter compares ant colony optimization and particle swarm intelligence optimization within classical and quantum inspired frameworks, utilizing qubits and qutrits. This study analyzes benchmark datasets from the NASDAQ, Dow Jones, and BSE spanning over a decade. A pioneering effort has been made to develop a multiobjective portfolio optimization technique through a multilevel quantum inspired optimization algorithm. The experimental results demonstrate that the quantum inspired metaheuristic technique that utilizes qutrits slightly outperforms classical and qubit based quantum inspired methods. 2026 Elsevier Inc. All rights reserved. -
Explainable artificial intelligence enhanced quantum-inspired spider monkey optimization for a constrained portfolio optimization proble
Optimizing portfolios has consistently posed significant challenges while being an extensively researched subject in finance and accounting. This process requires selecting and distributing appropriate assets in alignment with a set of specified objectives. This nonlinear constraint issue is not effectively solvable using traditional methods. This paper investigates the use of spider monkey optimization, ageist spider monkey optimization, and a newly proposed enhanced spider monkey optimization technique for portfolio optimization problems. The explainability of the spider monkey optimization has been improved without compromising the optimization results. It has been observed that the proposed technique marginally enhances the results of spider monkey optimization and can improve trust and risk management in the portfolio optimization problem. Furthermore, a quantum-inspired version of the proposed method is also implemented, and the results are compared using three benchmarked datasets from Dow Jones, BSE, and NASDAQ. Experimental results obtained using these benchmark datasets demonstrate that the newly introduced technique within the quantum-inspired framework marginally outperforms all other methods in the classical and quantum-inspired domains. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025. -
Portfolio Optimization Using Quantum-Inspired Modified Genetic Algorithm
Optimization of portfolios has an additional level of complexity and has been an area of interest for both financial leaders and artificial intelligence experts. In this article, a quantum-inspired version of an improved genetic algorithm is proposed for the task of portfolio optimization. An effort is made to implement two different genetic versions along with their extension in the quantum-inspired space. Improvements to the popular crossover techniques, viz. (i) arithmetic and (ii) heuristic crossover are proposed to reduce computational time. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.