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Application of Hydrogel in Paddy Field for Soil Moisture Retention and Yield Optimization
Agricultural sustainability is essential to enhance food and water security, particularly in the context of climate change. To ensure food security and to protect water resources, agricultural and irrigation practices need to be amended with innovative technology that conserves water and increases productivity. In the recent past, applications of hydrogels in agriculture have received substantial attention among researchers as well as among farmers. Paddy is the core crop for the vast newlineparts of the world. The present study elaborates on various aspects of hydrogels such as classifications, ideal properties for agricultural application, analysis of soil characteristic changes for pre and post crop newlineseason, irrigation water quality analysis for crop season. BPT 5204 and NDLR 07 varities of paddy had been experimented in this study. newlineVerification of hydrogel degradation was conducted using Fourier transform infrared (ft-ir) spectroscopy. The experimental methods for determining hydrogel properties were given specific attention to properties such as swelling, retention, slow release, and degradation which are vital for agricultural sustainability. Hydrogel experiments have demonstrate significant improvement in water consumption, water use newlineefficiency, crop growth and yield parameters. The reduction in water footprint in major crops such as paddy and wheat through hydrogel might establish a shift towards sustainable irrigation practices if adopted on a large scale. Integrating innovative solutions with environmental-friendly newlinehydrogels in the coming decades will contribute to the pursuit of achieving newlinesustainable development goals. The application of hydrogel as soil conditioners was identified as a possible solution. to increase water use efficiency in irrigation and optimization of crop yield. The study points towards developing a framework for the evaluation of the suitability of hydrogel for agricultural applications when get scaled up to regional level. -
Application of LSTM Model for Western Music Composition
Music is one of the innate creative expressions of human beings. Music composition approaches have always been a focal point of music-based research and there has been an increasing interest in Artificial Intelligence (AI) based music composition methods in recent times. Developing an accurate algorithm and neural network architecture is imperative to the success of an AI-based approach to music composition. The present work explores the composition of western music through neural network using a Long Short-Term Memory (LSTM) algorithm. Compositions from seminal western composers such as J.S. Bach, W.A. Mozart, L.V. Beethoven, and F. Chopin were used as the dataset to train the neural network. Seven compositions were generated by the LSTM model and these outputs were presented to a group of thirty volunteers between 18-24 years of age. They were surveyed to identify the music piece as composed by a human or AI and how interesting they found the melodies of each piece. It was found that the LSTM model generated compositions that were thought to be made by a human and create melodies of interest from the perception of the volunteers. It is expected that through this study, more AI-based composition approaches can be developed which encompass more and more of the musical phenomenon. 2022 IEEE. -
Application of machine intelligence-based knowledge graphs for software engineering
This chapter focuses on knowledge graphs application in software engineering. It starts with a general exploration of artificial intelligence for software engineering and then funnels down to the area where knowledge graphs can be a good fit. The focus is to put together work done in this area and call out key learning and future aspirations. The knowledge management system's architecture, specific application of the knowledge graph in software engineering like automation of test case creation and aspiring to build a continuous learning system are explored. Understanding the semantics of the knowledge, developing an intelligent development environment, defect prediction with network analysis, and clustering of the graph data are exciting explorations. 2021, IGI Global. -
Application of Machine Learning in Customer Churn Prediction
Retaining customers is the central component of a company's growth strategy. It is evident that several industries are experiencing a surge in customer churn due to the global pandemic. As a result, customer retention that lies at the core of customer relationship management, has become the foundation for every industry to plan for future growth. By reducing customer churn, a company can maximize its profit. Studies suggest that significant advancements are made in the field of customer churn prediction in domains like telecom, banking, e-commerce and energy sector. The focus of the paper is to present a detailed review of the various machine learning techniques applied to address churn. Fifty-five papers related to churn classification published between 2004 and 2020 are collected and analyzed. The reviewed papers are categorized into five main themes. These themes are feature selection techniques, methods to handle class imbalance, experimentation with machine learning algorithms, hybrid models and ensemble models respectively. Finally, few suggestions are presented as direction for future research. 2021 IEEE. -
Application of Nanomaterials in Fuel Cell and Photovoltaic System
The emerging appliances and components of nanotechnology facilitate pioneering and cost-efficient strategies to meet the ever-growing energy demands. Employment of nanomaterials fetched innovative approaches for processing, storing, and exchange of energy owing to its nanosized and well-defined structure. This review presents an overview of the involvement of nanomaterials that made breakthroughs in the field of fuel cell and photovoltaic technologies. While the morphologies and unique dimensions of nanostructures offered novel electrolytes and high surface area for fuel cell catalysts; the probability of quick separation and collection of photogenerated charge carriers was enhanced in solar cells. This book chapter will focus on the recent research and developments for improving efficiency and lower device fabrication cost in nano-enabled fuel and solar cells. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Application of neuroscience methods in HRDM for brain-based human capital optimization
For years, human resource development and management (HRDM) has used behavioral assessments to gauge employee potential. However, advancements in cognitive behavioral neuroscience (CBN) have opened up new possibilities for understanding how the human mind works. This chapter explores the practical applications of neuroscience methods like EEG, ERP, MRI, and fMRI, as well as neurofeedback and biofeedback, in talent identification, leadership development, and employee well-being. Importantly, these insights can be directly applied in HRDM practices, leading to more effective talent management, leadership development, and improved employee well-being. While recognizing the ethical considerations involved with these technologies, the chapter presents a compelling vision for a future where HRDM practices are informed by a deeper understanding of the brain, enabling the workforce to reach its full potential. 2024 by IGI Global. All rights reserved. -
Application of normal wiggly dual hesitant fuzzy sets to site selection for hydrogen underground storage
The hesitant fuzzy set is a mathematical tool to express multiple values in decision making. If they could not give a resolution, it is important to give priority and importance to a number of different values. Here, we propose normal wiggly dual hesitant fuzzy set (NWDHFS), as an extension of normal wiggly hesitant fuzzy set. We define a new score function of normal wiggly dual hesitant fuzzy information. The NWDHFS can express deep ideas of membership and non-membership information. In this work, we use hesitant fuzzy set to expose the deepest ideas hidden in the thought-level of the decision makers. We show that the NWDHFS can handle the hesitant fuzzy information. It expresses the deeper ideas of hesitant fuzzy set. An illustration is provided to demonstrate the practicality and effectiveness to the application of site selection of the underground storage of hydrogen. We are compelled to look for alternating fuels to suits changing weather conditions and increasing number of vehicles. This alternative fuel is necessary to control global warming and to be economically viable. Based on this, hydrogen gas is selected as a good alternative fuel. The most important statement is the saving of the selected hydrogen gas. Thus, when saving hydrogen fuel, a safe storage space must be selected. Here, we use the MCDM ideas by use of proposed NWDHFV method is to select the appropriate hydrogen underground storage location. 2019 Hydrogen Energy Publications LLC -
Application of phase change material in asphalt mixture A review
The use of latent heat storage capacity from phase change material (PCM) to regulate asphalt pavement temperature is an innovative way to mitigate temperature-related pavement distresses, such as thermal cracking and rutting. In this review, a detailed discussion on the classification and incorporation methods of PCM in asphalt mixture is presented. Further, the physical and chemical performances of PCM modified asphalts were reviewed, followed by their thermal and mechanical properties. It was observed that temperature related performance of asphalt mixtures can be well controlled in the phase change temperature range. Polyethylene glycol (PEG) and n-Tetradecane have been generally used to regulate the high and low temperatures of asphalt pavement, respectively. Mixed results were obtained on rheological properties of binders with PCMs as well as on road performance properties of asphalt mixtures with PCMs. It is important to note that PCMs with high latent heat and thermal conductivity are preferred for effective thermal regulation. 2020 Elsevier Ltd -
Application of Regression Analysis of Student Failure Rate
The education sector has been rapidly growing and is currently facing several challenges. One such challenge is identifying students who are at risk of failing, as this can help educators provide targeted interventions to improve student performance. Machine learning models have been developed to predict the probability of student failure based on various student performance metrics to address this issue. In this paper, we present a regression-based model that predicts the probability of student failure using student performance metrics such as attendance, previous academic performance, and demographic information. The model was trained on a dataset of students and achieved high accuracy in predicting the probability of student failure. While the model performs well in predicting the probability of student failure, there is always room for improvement. Possible enhancements to the model include feature engineering, ensemble learning, hyperparameter tuning, deep learning, and interpretability. These enhancements can improve the models accuracy, stability, and transparency, leading to better predictions and targeted interventions for at-risk students. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Application of response surface methodology to optimize lead(Ii) ion adsorption by activated carbon fabricated from de oiled soya
Lead(II) ion a heavy metal is known for its toxicity. An initiative has been taken in this study, to adsorb toxic lead(II) ion using activated carbon made of de oiled soya, by an aqueous solution through batch adsorption methodology. Adsorption process variables such as adsorbent dose, contact time, solution pH, and lead(II) ion concentration were optimized by central composite design (CCD). To find the interaction between process variables, response surface plots were utilized using response surface methodology. Design-Expert software version 7 was resorted to in this experiment. It was observed that the components from the analysis of variance of the CCD revealed that the selective process independent variables had significant control over adsorption capacity. Desirability function was used to appraise the factors and response in adsorption experiments to find an optimum point where the preferred adsorption could be obtained. Adsorption process with the application of activated carbon developed from de-oiled soya meritoriously removed lead(II) ion with an optimum adsorption capacity of 26.279 mg/g for an initial concentration of lead(II) at 60 mg/L. 2021 Desalination Publications. All rights reserved. -
Application of smart manufacturing in business
The application of machine learning to production is becoming a chief objective for businesses all around the world. Smart product-service systems enable digital business model innovation by merging digitized product and service components. The life cycle that comes with the realization of customer value is a critical component of these industrial solutions and manufacturing industry is undergoing significant changes as a result of digitalization and automation. As a result, smart services, or digital services that generate value from product data, are gaining popularity. Customers may now contribute in greater numbers in product design during the design process. Giving more people access, on the other hand, increases the security vulnerabilities associated with cloud manufacturing. Smart Manufacturing is one of the technology-driven approach to manufacturing that uses network-connected machines to monitor the process. Smart manufacturing has the ability to be used in a variety of ways, including putting sensors in manufacturing machines and collecting data on their operating state and performance. Thus, the main purpose here is to find ways to improve and automate production performance. This conceptual paper attempts to give a view of how a smart intelligence system may be used in business and how individuals and organizations can produce value. 2023 Author(s). -
Application of Spray Drying process to convert Beneficial Compounds extracted from Plants into free-flowing powder
The use of herbal tablets has been rapidly growing and significant research work is being carried out worldwide with the goal to reap the benefits of the many useful plants that are available with medicinal values. Many of these plants go largely underutilized either due to lack of information on not only just the medicinal properties but simple and effective extraction methodologies as well, without sacrificing the properties of the extracts. Once extracted, the concentrates also must be converted into a suitable form that can be loaded in a capsule etc., ready to be consumed. While there many process methodologies being used worldwide to extract the useful resources from the plant, focus also must be on the process methodology that is being practiced to convert the extract (liquid or semi solid) into a solid free flowing powder form. Thus, in an herbal tablet, there many factors concerned with the manufacturing. They are (i) Identifying the most suitable plant for a particular immunity boosting purpose (ii) extraction of the useful contents, mostly in a liquid or slurry form (iii) transform the extract into a user-friendly product such as powder and finally (iv) encapsulation of the powder for ease of human consumption. This paper brings in a review of the several useful plants available around us across the world. In addition, the paper also highlights the suitable experimental results of the usefulness of spray-drying technology, which is a highly versatile process methodology to transform the extracts into free-flowing powder. Published under licence by IOP Publishing Ltd. -
Application of the Taguchi method and RSM for process parameter optimization in AWSJ machining of CFRP composite-based orthopedic implants
Abrasive water suspension jet (AWSJ) machining on carbon fiber-reinforced polymer (CFRP) composite-based orthopedic implants yielded insightful results based on experimental data and subsequent statistical validations. Underwater AWSJ cutting consistently outperformed free air cutting, with numerical findings demonstrating its superiority. For instance, at #100 abrasive size and 5 mm standoff distance (SOD), the material removal rate (MRR) peaked at 2.44 g/min with a kerf width of 0.89 mm and a surface roughness (SR) of 9.25 ?m. Notably, the increase in abrasive size correlated with higher MRR values, such as achieving 2.15 g/min at #120 grit and 3 mm SOD. Furthermore, optimization techniques like the Taguchi method and response surface methodology (RSM) were applied to refine machining parameters. These methodologies enhanced MRR, exemplified by achieving 2.10 g/min with #120 abrasive size and 5 mm SOD in underwater cutting conditions. The research explored the impact of key process parameters, namely, the speed, feed, and SOD on the MRR, kerf width, and SR in both free air cutting and underwater cutting conditions, which is one of the novel research endeavors in the domain of abrasive jet machining of composites. 2024 the author(s) -
Application of XAI in Integrating Democratic and Servant Leadership to Enhance the Performance of Manufacturing Industries in Ethiopia
This study tests the conceptual model theorizing democratic leadership, servant leadership, learning organization, and performance of manufacturing industries using Structural Equation Modeling (SEM). The impact of democratic and servant leadership on learning organizations and the performance of manufacturing industries in Ethiopia is analyzed, and the role of learning organizations as a mediating variable is examined. Confirmatory Factor Analysis was performed, which includes a well-established Chi-square test, the Chi-square ratio to degrees of freedom, the goodness-of-fit index, the TuckerLewis index, the comparative fit index, the adjusted goodness-of-fit, and the root mean square error of approximation. Further, the performance of manufacturing industries has been assessed using XAI which helps in having a higher clarity on understanding the complexities in production. Based on linear regression, two methods SHAP and LIME have been used for precise predictions and forecast for future production plans in the manufacturing industry. This research contributes to the existing body of knowledge by dissecting the nuanced relationships between the two leadership styles and learning organization and further, their implications for an organizations performance. The findings of the study would provide insights for policymakers and practitioners to improve the performance of manufacturing industries. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
APPLICATIONS OF ARTIFICIAL INTELLIGENCE IN BUSINESS AND FINANCE 5.0
AI has evolved as a burgeoning technology in many industries, including the financial and banking sectors, which are facing greater challenges in data management, identity theft, and fraud as transactions and other company processes shift online and gain popularity. As systems using deep learning technology are able to detect data patterns and spot suspicious activity and probable fraud, AI can advance many financial and business activities. This book, Applications of Artificial Intelligence in Business and Finance 5.0, provides a valuable overview of how artificial intelligence (AI) applications are transforming global businesses and financial organizations, looking at the newest artificial intelligence-based solutions for e-commerce, corporate management, finance, banking and trading, and more. Chapters look at using artificial intelligence and machine learning techniques to forecast and assess different financial risks such as liquidity risk, volatility risk, and credit risk. The book also describes the use of natural language processing and text mining paired with machine learning models to assist in guiding sophisticated investors and corporate managers in financial decision making. Other topics include cryptocurrency in emerging markets; the role of artificial intelligence in making a positive impact on sustainable development; the use of fintech for micro, small and medium enterprises; the role of AI in financial education; the application of artificial intelligence in cyber security; and more. With a cross-disciplinary theme, this volume will be helpful to those in the corporate world, including professionals in business, finance, the e-commerce, economic sociology, political science, public administration, mass media and communications, information systems, development studies, among others. 2025 by Apple Academic Press, Inc. -
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. -
Applications of artificial intelligence techniques in modern banking sectors
AI-powered decision-making instruments are cutting-edge technology that has the potential to displace conventional banking procedures. This chapter emphasizes the critical role artificial intelligence (AI) has played in guiding the banking industry toward expansion. AI techniques including robotics, deep learning, facial recognition, natural language processing, and more are used to achieve this goal. This chapter provides an overview of the use of AI approaches in several banking functional domains, such as loan approval, customer lifecycle management, customer services, alarm systems, and so on. It also highlights the benefits and difficulties that AI-driven financial apps provide. In summary, artificial intelligence (AI) has enormous promise in banking, but it also confronts several obstacles that, if correctly recognized and overcome, might broaden its use. This chapter is an invaluable tool for researchers, lawmakers, and bank officials who want to learn more about the unrealized potential of artificial intelligence in banking. 2024, IGI Global. All rights reserved. -
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. -
Applications of bioconvection for tiny particles due to two concentric cylinders when role of Lorentz force is significant
The bioconvection flow of tiny fluid conveying the nanoparticles has been investigated between two concentric cylinders. The contribution of Lorenz force is also focused to inspect the bioconvection thermal transport of tiny particles. The tiny particles are assumed to flow between two concentric cylinders of different radii. The first cylinder remains at rest while flow is induced due to second cylinder which rotates with uniform velocity. Furthermore, the movement of tiny particles follows the principle of thermophoresis and Brownian motion as a part of thermal and mass gradient. Similarly, the gyro-tactic microorganisms swim in the nanofluid as a response to the density gradient and constitute bio-convection. The problem is modeled by using the certain laws. The numerical outcomes are computed by using RKF-45 method. The graphical simulations are performed for flow parameters with specific range like 1?Re?5, 1?Ha?5, 0.5?Nt?2.5, 1?Nb?3, 0.2?Sc?1.8, 0.2?Pe?1.0 and 0.2???1.0. It is observed that the flow velocity decreases with the increase in the Hartmann number that signifies the magnetic field. This outcome indicates that the flow velocity can be controlled externally through the magnetic field. Also, the increase in the Schmidt numbers increases the nanoparticle concentration and the motile density. 2022 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.