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A comprehensive view of artificial intelligence (ai)-based technologies for sustainable development goals (sdgs)
Agenda 2030, aimed at sustainable and inclusive development through seventeen SDGs formulated by the United Nations (UN), has become a massive challenge for most nations around the world. Many countries are setting a plan of action for achieving carbon neutrality by 2050. Due to this, industries are under immense pressure to mitigate harmful emissions and incorporate SD in their business activities. In the past decade, AI has grown as the dominating technology which influences nearly every aspect of human life, i.e., society, business, environment, etc. This chapter provides a comprehensive view of AI-driven technological applications in achieving SDGs. It provides a snapshot of the emerging relationship between AI applications and sustainable development and how AI could be used to create sustainable business models. Large-scale adoption of AI-driven technologies has enormous potential from the sustainable development perspective. The purpose of this chapter is to map the application of AI-based technological tools and solutions with the various SDGs. Further, this chapter also extends the discussion on AI-based technology as an enabler of or barrier to addressing sustainable development issues. It provides an important insight for policymakers, practitioners, investors, and other stakeholders about the conducive influence of AI on society, governance, and ecology in line with the priorities underlined in the UN SDGs. 2024 Walter de Gruyter GmbH, Berlin/Boston. -
A compression system for Unicode files using an enhanced Lzw method
Data compression plays a vital and pivotal role in the process of computing as it helps in space reduction occupied by a file as well as to reduce the time taken to access the file.This work relates to a method for compressing and decompressing a UTF-8 encoded stream of data pertaining to Lempel-Ziv-welch (LZW) method. It is worth to use an exclusive-purpose LZW compression scheme as many applications are utilizing Unicode text. The system of the present work comprises a compression module, configured to compress the Unicode data by creating the dictionary entries in Unicode format. This is accomplished with adaptive characteristic data compression tables built upon the data to be compressed reflecting the characteristics of the most recent input data. The decompression module is configured to decompress the compressed file with the help of unique Unicode character table obtained from the compression module and the encoded output. We can have remarkable gain in compression, wherein the knowledge that we gather from the source is used to explore the decompression process. Universiti Putra Malaysia Press. -
A computational approach for shallow water forced KortewegDe Vries equation on critical flow over a hole with three fractional operators
The KortewegDe Vries (KdV) equation has always provided a venue to study and generalizes diverse physical phenomena. The pivotal aim of the study is to analyze the behaviors of forced KdV equation describing the free surface critical flow over a hole by finding the solution with the help of q-homotopy analysis transform technique (q-HATT). he projected method is elegant amalgamations of q-homotopy analysis scheme and Laplace transform. Three fractional operators are hired in the present study to show their essence in generalizing the models associated with power-law distribution, kernel singular, non-local and non-singular. The fixed-point theorem employed to present the existence and uniqueness for the hired arbitrary-order model and convergence for the solution is derived with Banach space. The projected scheme springs the series solution rapidly towards convergence and it can guarantee the convergence associated with the homotopy parameter. Moreover, for diverse fractional order the physical nature have been captured in plots. The achieved consequences illuminates, the hired solution procedure is reliable and highly methodical in investigating the behaviours of the nonlinear models of both integer and fractional order. 2021 Balikesir University. All rights reserved. -
A computational approach for the generalised GenesioTesi systems using a novel fractional operator
This article presents the novel fractional-order GenesioTesi system, along with discussions of its boundedness, stability of the equilibrium points, Lyapunov stability, uniqueness of the solution and bifurcation. The efficient predictorcorrector approach is employed to quantitatively analyse the GenesioTesi system in fractional order. The findings enable conceptualisation and visualisation of the presented novel fractional-order GenesioTesi systems. The modified systems are proposed for future study on chaos control and applying the same for secure communication. Bifurcation analysis is carried out to see the variation in the systems behaviour from stability to chaos. The results of the bifurcation analysis support the results obtained for the stability of the equilibrium points. The system behaves chaotically since all the equilibrium points are unstable. The findings demonstrate a torus attractor for some of the suggested systems and a chaotic attractor for some of the novel fractional-order GenesioTesi systems. The systems torus attractor changes into a steady state when the order is reduced from integer to fractional. Changing the parameter values for one of the modified systems also shifts the systems behaviour, with the point attractor replacing the torus attractor. The point attractor of one of the systems changes into a steady character when the systems order is reduced from integer to fractional. The behaviour for one modified system is the same for fractional and integer orders. This discovery paves the way for the future study of the modified GenesioTesi system. This article gives a new direction to utilise these proposed GenesioTesi systems and study them extensively. The chaotic behaviour of the modified system can be used for secure communication. The synchronisation and chaos control of the modified system is recommended. 2024, Indian Academy of Sciences. -
A Computational Data-Granular Model Highlighting the Evolving Fintech Landscape in India
The Fintech sector in India has undergone remarkable development, complementing the significant progress in financial technology designed to simplify financial services and provide innovative solutions. This study aims to discover and analyze two significant knowledge gaps in the Indian Fintech sector. It seeks to identify and examine the evolving patterns in web searches for potential career opportunities in the Fintech sector, providing perspectives into the trendline data from the country. Secondly, the study will assess employment in the Fintech Sector in India, emphasizing Position Titles, the geographical distribution of opportunities, and market trends from 2015 to 2023. Furthermore, it will examine the motivation and strategies essential for supporting and developing the Fintech sector in India. It performs a trend analysis on Fintech, Finance, and Accountancy searches and how they have changed over the years. By addressing these gaps, the research aims to provide valuable insights into the Fintech industry's dynamics and development in the Fintech job market over the years in the Indian context. To complement the trend analysis conducted in the paper, a computational modeling approach is used to predict future job trends in the Indian Fintech sector. The model relies on data from the years 2015 to 2023 on job openings, web searches, and geographical distribution. Therefore, the Autoregressive Integrated Moving Average (ARIMA) model has been used to understand the future patterns of job opportunities and skill requirements accordingly. This research will be helpful for companies and business owners to improve their financial operations in the long run. 2025, Bentham Books imprint. -
A COMPUTATIONAL MODEL FOR TEA LEAF PRICE PREDICTION BASED ON QUALITY FACTORS USING HYBRID MACHINE LEARNING TECHNIQUES
This document reflects the effort made to calculate and identify the grade of the tea leaves based on the assessment of the leaves' size and color. The leaves were classified based on their severity with the help of HSV. The leaves were further classified using the k prototypes clustering once their length and width were established. The leaves were then further categorized in line with that. Light, medium, and dark are the three-color categories into which it belongs. The leaves were further sorted according to their quality so that the farmer could sell the produce at a better price. With the machine learning method for the categorization part, we were able to show its values. All of the healthy leaves were considered in a different dataset, and the images were obtained using the feature selection method. The length and width of each individual leaf, along with its color and shape, were then measured using those leaves. We were able to differentiate between the various leaf grades based on the findings. The healthy leaves were separated from the diseased leaves using the textual features. Additionally, we were able to use the other criteria to obtain higher-grade leaves. Little Lion Scientific. -
A Computing Assisted Test Method in Healthcare Industry Using Artificial Intelligence
New and improved methods of diagnosis are needed because breast cancer is still the leading cancer-related killer worldwide. Updates to the methods used to categorize breast cancer have emerged because of recent advances in DL and ML. This research goal in conducting this research is to bring together existing breast cancer diagnostic and classification methods that make use of deep learning and machine learning techniques. Early detection and accurate prediction of breast cancer are crucial for improving outcomes and reducing the impact of this disease. The creation of prediction models and tools to assess risk has become an important field of research as it can assist healthcare workers in identifying individuals who are more likely to acquire breast cancer and adapting screening and preventative programs accordingly. This introduction provides an overview of breast cancer prediction, highlighting the importance of the topic and the motivation behind predictive models. It sets the stage for a more in-depth exploration of the subject and the various technologies, methods and factors involved in breast cancer prediction. 2025 IEEE. -
A Conception of Blockchain Platform for Milk and Dairy Products Supply Chain in an Indian Context
The potential for adulteration in the Indian dairy supply chain process is immense. The possibility of incorrect information recorded by middlemen cannot be ruled out and found to be rampant. The reality is that the data required to assess the safety and quality of milk produced is inadequate in the existing setup. The current set of checks and balances to fight adulteration of milk and dairy products in India is studied and articulated. An elaborate and daunting set of procedures marks these checks and is still significantly found wanting. To increase the product's safety and traceability of the product an alternate pathway to deploy Blockchain technology in the milk and dairy product supply chain has been proposed. Despite the proposal requiring drastic changes in the milk and dairy industry, the authors believe the benefits of implementing a Blockchain platform far outweigh the challenges involved. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Conceptual Framework for Agile as HR Operational Strategy
Purpose: This paper examined Agile human resource (HR) as an operational strategy, emphasizing the relationships between operational, HR, and organizational strategies. It develops a collaborative culture, establishes learning organizations, supports agile team design, and improves agile strategic behavior. Agile HR has been underutilized in academic literature despite its potential, highlighting the disconnect between practitioner objectives and HR research. Methodology: A conceptual framework for Agile HR was developed using qualitative secondary research methods. Secondary sources included books, journal articles, research papers, reports, and whitepapers. A thematic analysis was used to code the data and identify themes relevant to Agile HR, and concept mapping was used to illustrate the relationships between the key concepts. Findings: A conceptual framework for Agile HR strategies was developed to foster an agile organizational culture and equip employees with agile strategic behaviors. Organizations will be able to establish and preserve a durable competitive edge in quickly changing marketplaces by using these tactics. Practical Implications: This paper provided insights into implementing agile HR operational strategies. Continuous iteration was used to enhance processes, boost employee experiences, and improve organizational agility to implement these strategies. Originality: While existing literature explored the relationship between organizational agility and dynamic capabilities, it largely overlooked the concept of agile behavior. This research addressed this gap by proposing a framework for flexible adjustments to human and organizational capabilities. It was a targeted approach for agile management aligned with organizational, HR, and agile strategies, emphasizing scalability. 2024, Associated Management Consultants Pvt. Ltd.. All rights reserved. -
A Conceptual Framework for AI Governance in Public Administration - A Smart Governance Perspective
With the public governance lagging behind the fast evolving of AI in their attempts to yield sufficient governance, corresponding principles are necessary to be in par with this dynamic advancement. As AI becomes more pervasive and integrated into various domains, there is a growing need for AI governance models that can ensure that the development and deployment of AI systems align with ethical, legal, and social standards. There are some answers that literature puts forward to the question onthe way the government and public administration has to react to the huge concerns related to AI and usage of policies to avoid the emerging challenges. In this survey, AI problems and the prior AI regulation techniques are analyzed. In this research study, a governance model for AI is proposed by combining all the facets and also implements a new procedure for governing AI. This study will help the decision makers to make smart government a reality by using AI governance framework. 2023 IEEE. -
A conceptual framework for consumer engagement in social media influencer posts
Influencer marketing has received significant attention and is considered as the best way to build consumer engagement with the brand. However, research on Influencer marketing is burgeoning, and it is important to study the consumer behaviour associated with influencer marketing. Therefore, this study proposes a logical conceptual framework by integrating various construct such as ad recognition, informativeness, deceptiveness, irritation, entertainment, ad content value, and consumer engagement from various theories and provides implications for marketers to frame an effective marketing campaign and policymakers to formulate policies to protect consumers from deceptive advertising practice. 2024, IGI Global. All rights reserved. -
A conceptual framework for the worklife balance of police officers: a post-COVID-19 perspective
This study has undertaken a comprehensive review of literature from 2019 to 2021, encompassing work/life balance review articles to identify the research gap in the work/life balance area. Employing the PRISMA framework for systematic literature review, the study identified prospective areas for future research on work/life balance and variables associated with work/life balance in the police sector. The articles published between 2013 and 2023 relating to the police force over the past decade were reviewed to frame a conceptual framework for the work/life balance of police officers. Further literature review attests that there is a research gap in the police sector. The primary goal is to identify the area (work/life balance in the police sector) that lacked research and establish a conceptual framework addressing the work/life balance of police officers, even in challenging situations like COVID-19. The articles related to police from 2013 to 2023 were scrutinised through the PRISMA framework to identify variables necessary for constructing the conceptual framework for police officers. This article, set in the post-COVID-19 era, delves into the factors influencing police officers capacity to uphold a healthy worklife balance. By pinpointing those research gaps, the article proposes a conceptual framework designed to help police officers balance their professional and personal lives. Such a framework can aid organisations in formulating effective strategies for employee well-being, facilitating worklife balance even in challenging circumstances, such as those imposed by the COVID-19 pandemic. 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. -
A CONCEPTUAL MODEL FOR SKILL DEVELOPMENT: A KEY DRIVER FOR INCLUSIVE GROWTH AND SUSTAINABLE DEVELOPMENT
Purpose: This chapter explores the two major schemes applicable to skill development in India: Skill Acquisition and Knowledge Awareness for Livelihood Promotion (SANKALP) and Pradhan Mantri Kaushal Vikas Yojana (PMKVY). Need for the Study: The primary objective of this research is to check the role of these schemes in enhancing the skills of socio-economically stressed community members for their livelihoods. The secondary aim is to analyse the outcomes of these schemes through a qualitative inquiry. Methodology: A survey was conducted, and the data was collected from trainees of the skill development programmes. Based on the responses, a qualitative content analysis was performed, which showed that most trainees have the thirst and urge to enhance their life skills for a minimalistic livelihood. Findings: The study concluded that though there are many schemes, only PMKVY is active. They focus on more than just youth communities. Instead, they consider individuals in different age categories. Practical Implications: The Government of India (GOI) is progressing towards a healthy economy to compete with other countries. For this mission to be achieved, skill and labour development is paramount. Appropriate training must be provided and administrated through government schemes. 2024 by P. S. Anuradha, L. Mynavathi and M. Anand Shankar Raja Published under exclusive licence by Emerald Publishing Limited. -
A conceptual study on the impact of COVID-19 awareness campaigns by social media influencers on brand awareness
Covid 19 has severally affected various people across the world. Undoubtedly, a campaign on forming awareness of the pandemic among people is very important in order to curb the transmission from individual to individual. In this regard, social media influencers have played a significant role in many awareness campaigns organized by companies and the World Health Organization (WHO) because of their popularity and influence among the audience. Social media influencer campaigns on awareness of the pandemic conducted during this period have generated audience engagement. Moreover, it is vital to study the crucial factors that determine the creation of brand awareness among consumers related to influencer marketing campaigns on covid 19 awareness. This study has proposed a conceptual framework by integrating the variables from various theories. This research has incorporated variables such as awareness of covid 19, consumer engagement, physical health, mental health, and brand awareness. This study provides both theoretical and managerial implications. 2024, IGI Global. -
A concise and effectual method for neutral pitch identification in stuttered speech
Researchers have studied that human-computer interactions (HCIs) can be more effective only when machines understand the emotions conveyed in speech. Speech emotion recognition has seen growing interest in research due to its usefulness in different applications. Building a neutral speech model becomes an important and challenging task as it can help in identifying different emotions from stuttered speech. This paper suggests two different approaches for identifying neutral pitch from stuttered speech. The implementation has proved through its accuracy the best model that can be adopted for neutral speech pitch identification. 2017 Walter de Gruyter GmbH, Berlin/Boston. -
A concise route to fused tetrazolo scaffolds through 10-camphor sulfonic acid auto-tandem homogeneous catalysis and mechanistic investigation
10-Camphor sulfonic acid (10-CSA) as an organo-catalyst has gained interest due to its versatile solubility and easiness of handling. This work reports a simple synthetic method through non-classical Biginelli for the construction of tetrazolo pyrimidine (4a-m) and quinazolines (4a?-o?). Azolopyrimidines and quinazolines are of great pharmaceutical importance. Numerous compounds are currently in use for the treatment of different diseases. Therefore their synthesis is industrially inevitable. Employing aldehydes, 1,3-dicarbonyls, and 5-Aminotetrazole, we report eco-friendly, cost-effective catalysis through a tandem reaction catalyzed by the 10-CSA that gave excellent yields, 7095 % for tetrazolo quinazoline and 4576 % tetrazolo pyrimidines respectively. The homonuclear NOESY analysis confirms the selective formation of one isomer. All the compounds are characterised by 1H NMR, 13C NMR, and MS. Investigation of the reaction mechanism by both experimental and theoretical studies provides evidence. Mechanism of the reaction was also explained utilizing the information from mass spectrometry monitoring. DFT calculation carried out at PBEPBE (Perdew-Burke-Ernzerhof) functional and 6-31G (d,p) basis set level of theory of the various intermediates observed supports the experimental evidence. 2023 Elsevier B.V. -
A concise study on the phytochemistry and antimicrobial efficiency of Artemisia absinthium L.: Phytochemical analysis of plant
This study is meant to elucidate the phytochemical and antibacterial characteristics of wormwood Artemisia absinthium L, a perennial herb from Asteraceae family that has been used extensively in traditional medicine. It has diverse phytochemical composition, including bitter sesquiterpenoid lactones like absinthin, as well as essential oil constituents including camphene, ?-cadinene, guaiazulene, ?-thujone, ?-thujone, and thujyl alcohol esters. Applications of A. absinthium in the past include its ability to treat a wide range of illnesses, from fever to gastrointestinal problems. This study highlights the presence of various phytochemical compounds in plant extract of A. absinthium, such as tannins, saponins, and terpenoids through standardised protocols. Remarkably, this study also reveals its antibacterial capabilities using agar well diffusion method against five different pathogenic bacterial strains, including Escherichia coli (MTCC 443), Salmonella typhi (not sequenced, procured from Chettinad Hospital, Chennai), Staphylococcus aureus (MTCC 3160), Enterococcus faecalis (MTCC 439), and Klebsiella pneumonia (MTCC 109). Testing it against these strains of bacteria highlighted its effectiveness in this area. A. absinthium presents a compelling topic for continued scientific investigation due to its complex phytochemical composition and antimicrobial efficiency. 2026, ScienceIn Publishing. All rights reserved. -
A Congruent Approach to Normal Wiggly Interval-Valued Hesitant Pythagorean Fuzzy Set for Thermal Energy Storage Technique Selection Applications
Thermal energy is the energy from a substance in which molecules and atoms vibrate faster because of an increase in temperature. Thermal energy storage (TES) is an available energy resource for renewable energy platforms that enables them to meet sustainable technical requirements. The TES technique is divided into three categories; sensible TES, latent-heat TES, and thermo-chemical TES. The best of these techniques is selected in this research paper. Here the Interval-Valued Hesitant Pythagorean Fuzzy Set (IVPHFS) under the Normal Wiggly Mathematical Methodology is proposed and described for application to multi-criteria decision making (MCDM) technology. The MCDM methods, the Step-wise Weight Assessment Ratio Analysis (SWARA) method for determining weight values, and the Weighted Aggregated Sum Product Assessment (WASPAS) method for ranking alternative values are used employed here. The alternative values are selected based on the following criteria: capacity, efficiency, storage period, charging and discharging times, and cost 2021, Taiwan Fuzzy Systems Association. -
A constrained multi-period portfolio optimization model based on quantum-inspired optimization
Multi-period portfolio optimization (MPO) is one of the most important problems to be solved to help investors select optimal portfolios for investment plans. The portfolios are influenced by the risk factors in the market and it is important to select optimal portfolios that can maximize the returns with minimum risk values. Other than the risk factor, there are several other influential factors that reduce the optimality of the portfolios. Therefore, by considering all possible constraints, this study proposes a multi-constraint MPO model that selects the optimal portfolio based on the asset returns. To solve the multi-constrained problem, a novel quantum-inspired whale optimization algorithm (QWOA) is introduced in this paper. The proposed algorithm enhances the traditional optimization model to work in a multi-constrained scenario. Here, quantum entanglement is adapted to reduce the slow convergence issue of whale optimization. Apart from considering only the risk factors, this paper also considers certain higher-order moments (HOM), such as skewness, kurtosis, transaction cost, diversification, boundary and budget constraints. These factors affect the portfolios as the market is dynamic, and timely changes are always seen. Thus, optimizing the mentioned factors aids in attaining an optimal portfolio. Empirical evaluations are performed, and the results suggested that the proposed model provided beneficial outcomes as compared with other algorithms like whale optimization algorithm (WOA), gray wolf optimization (GWO), fruitfly optimization algorithm (FOA), particle swarm optimization (PSO) and fruitfly algorithm (FA). The overall net return rate of the proposed model is always above 0.85% for different values of upper bounds, and the obtained Sharpe ratio, Sortino ratio, STARR ratio, information ratio, Shannon entropy, and downside deviation values of the proposed algorithm are 5.016254, 0.89327, ? 0.01987, 0.103826, 3.04452 and 0.2854. Hence, the proposed approach is highly effective for optimizing the constrained MPO. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
A Context-Aware Finite State Machine for Gesture-Driven UAV Control
Gesture based Unmanned Aerial Vehicles (UAVs) is a very intuitive way to control drones (UAVs). Current methods tend to associate one gesture to one action, a practice which is rigid and inflexible. In this paper, we propose the Finite State Machine (FSM)-based gesture control framework, which allows triggering several actions of the UAV with a single gesture depending on the current state of the drone. MediaPipe hand gestures recognition and integration with ROS2 and PX4 allows the system to automatically takeoff, land, hover, automated ascents, and directional speed variation. Experiments in a ROS2 simulation environment test the system in terms of gesture-to-state latency, the rate of successful commands, the extent of the FSM that the system is capable of controlling, and the rate of false positives (spurious transitions). The results indicate that the suggested method has robust and responsive control of the UAV, which forms the basis to establish more intuitive and adaptive human-UAV interaction in limited spaces. 2025 IEEE.