Browse Items (1422 total)
Sort by:
-
Business Intelligence in Action: Way of Successful Implementation of Automated Systems
This chapter presents an overview of the role of automated systems in Business Intelligence (BI). BI has emerged as a critical element for modern organizations in decision-making processes by analyzing large volumes of data. Automated BI systems offer several advantages over traditional manual systems, including increased efficiency, accuracy, and customized insights. Despite these benefits, there are several limitations and challenges associated with the implementation of automated BI systems. This chapter examines the benefits and limitations of automated BI systems and identifies common success factors for successful implementation. The chapter also explores different types of automated systems, including predictive analytics, machine learning, natural language processing, and robotics process automation. These systems can help organizations analyze and interpret large amounts of data more quickly and accurately, enabling them to make informed decisions. However, despite the potential benefits of automated BI systems, there are several challenges associated with their implementation, including technical expertise and integration issues. To address these challenges, careful planning, collaboration, and ongoing monitoring are essential. In conclusion, this chapter highlights the importance of automated BI systems in modern businesses and provides valuable insights into their benefits and limitations. The chapter also emphasizes the need for careful planning, collaboration, and monitoring for the successful implementation of automated BI systems. 2024 selection and editorial matter, Nidhi Sindhwani, Rohit Anand, A. Shaji George and Digvijay Pandey; individual chapters, the contributors. -
A Smart Internet of Things (IoT) Enabled Agricultural Farming System
Industry 4.0 has brought about a profound revolution in recent times. This advancement profoundly impacted technology usage in every aspect and has significantly improved businesses. Agriculture is one of the evergreen economic contributors to Indias GDP. With improvements in adaptability in this sector, the time is ripe for instituting IoT (Internet of Things)-based smart agriculture. Water scarcity and drastic climate change are real issues affecting crop yields, leading to the failure in the timely fulfillment of market demand (Nawandar 2019). The authors have collaborated to address these concerns by creating a system comprising a functional hardware prototype and an android application for regulating irrigation and temperature. The introduction of IoT (Internet of Things) automates crop monitoring and reduces labor costs. By using IoT, (Internet of Things) an earmarked agricultural field is covered with sensors. The sensors are concealed so as not to be affected by the bleakness of the external environment. These sensors work in tandem with drip irrigation following the sensed climatic conditions. The water is pumped directly to the root zone in an optimally sensed manner. The authors developed and tested the system successfully in a greenhouse system. The process initially aims to extract the values of soil parameters by using IoT (Internet of Things) sensors and appropriately control the watering of crops, thus enabling the cultivation of crops even in a hot and dry climate. Crops can be irrigated from a remote location and their temperature can be meticulously regulated to ensure they remain within an optimal range. Water utilization for agricultural crops is optimized with the use of automated irrigation systems that use W.S.N (Wireless - Sensor-Networks) and G.P.R.S (General-Packet-Radio-Service) modules. The algorithm employed in the system to control water usage is based on the needs of the crop and the terrain. The entire system is powered by photovoltaic panels, which are useful in rural and isolated areas without electricity (Raut and Shere 2014). A cellular network is used for duplex communication. Continuous monitoring and irrigation schedule programming are used by web apps to manage irrigation. This is also possible using a browser and web pages. A system with three identical automatic irrigation systems can save water use by up to 90%. 2024 by Nova Science Publishers, Inc. All rights reserved. -
Edge Computing in Aerial Imaging A Research Perspective
Internet of Drones (IoD) is a field that has a vast scope for improvement due to its high adaptability and complex problem statements. Aerial vehicles have been employed in various applications such as rescue operations, agriculture, crop productivity analysis, disaster management, etc. As computing and storage power have increased, satellite imaging and drone imaging have become possible, with vast datasets available for study and experiments. The recent work lies in the edge computing sector, where the captured aerial images are processed at the edge. Our paper focuses on the algorithms and technologies that easily facilitate aerial image processing. The applications and their architectures are focused on which can efficiently function using aerial processing. The various research perspectives in aerial imaging are concentrated on paving the way for further research. 2024 Scrivener Publishing LLC. All rights reserved. -
Leveraging Financial Data to Optimize Automation: An Industry 4.0 approach
Industry 4.0 is a transformative approach that leverages advanced technologies to enhance business efficiency and productivity. Automation is a crucial aspect of next-generation industry, and leveraging financial data is essential to optimizing the automation process. This chapter discusses the role of financial data in optimizing automation processes using an I-4.0 approach. Financial data is derived from various sources and can be collected through different methods, such as automated data collection, manual entry, or using sensors and Internet of Things (IoT) devices. The integration of these sources can pose challenges for businesses. The chapter outlines techniques for automation optimization, such as machine learning, predictive analytics, and business process reengineering. Optimizing automation using financial data offers various benefits for businesses, including cost savings, improved quality, and increased profitability. However, there are challenges that businesses face in leveraging financial data, including the integration of various data sources and formats and the need for skilled personnel to analyze and interpret the data. The successful implementation of automation and optimization of processes can lead to sustainable growth and enhanced operations, making it crucial for businesses to remain competitive in the I-4.0 era. By leveraging financial data to optimize automation processes, businesses can maximize their potential and drive growth. Overall, this chapter highlights the significance of financial data in automation optimization and provides insights into the benefits and challenges that businesses must consider when leveraging financial data for optimization. 2024 selection and editorial matter, Nidhi Sindhwani, Rohit Anand, A. Shaji George and Digvijay Pandey; individual chapters, the contributors. -
Revolutionizing Healthcare with IoT: Connecting the Dots for Better Patient Outcomes
Healthcare enhances ones physical and emotional well-being via the detection, treatment, and eventual cure of disease, illness, injuries, and other debilitating conditions. The importance of information systems has increased everywhere, particularly in the healthcare sector. Information technology has long benefitted the health business, from electronic health records to cloud-based platforms. Information systems are becoming increasingly important in advancing healthcare and healthcare administration. The pandemic brought virtual space and services to all sectors of the economy, especially healthcare, which was predominantly supported through face-to-face services earlier, but due to the requirement of social distancing, hospitals started offering services in virtual mode. Also, evolution in the information system and the Internet has paved the way for the Healthcare Internet of Things (HIoT). The Healthcare Internet of Things (HIoT) is the interconnection of intelligent objects or devices that enables the development of new healthcare services and applications. HIoT can take many forms, namely medical devices, public health services, innovative technology, medication refills, and remote monitoring. This healthcare data is a new treasure for healthcare stakeholders to improve patients health and experiences while creating revenue opportunities and improving healthcare operations. Thus, HIoT is redefining healthcare by ensuring better care, improved treatment outcomes, and reduced patient costs, as well as better processes and workflows, improved performance, and patient experience for healthcare providers. HIoT devices can also be useful for asset management tasks like controlling inventory at the pharmacy, checking refrigerator temperatures, and controlling humidity and temperature in the environment. Having said the advantages, one cannot deny the challenges it has brought to safety, security, privacy, and scalability aspects. Hence, this chapter will explore the evolution of IoT in healthcare, its elements, applications, and challenges. 2024 selection and editorial matter, Alex Khang. -
Bioactive Compounds and Biological Activities of Taro (Colocasia esculenta (L.). Schott)
Plants are said to be the finest source of food and phytochemicals. Along with aerial plant components, subterranean tuberous, stems, and roots were often consumed for their phytochemical and nutritional worth. Colocasia esculenta(L.). Schott is an essential plant that is utilized for its nutritional and phytochemical properties. It is commonly called taro, which is very rich in plant secondary metabolites and their respective pharmacological properties. Taro is consumed by people worldwide and serves as a staple food in Asian and African countries, leading to its abundant production. Extensive studies has explored the nutritional composition of taro, which has been identified as a promising source of dietary fiber. Moreover, taro exhibits a wealth of minerals and phytochemicals, including phenols, flavonoids, and various derivatives, which contribute to its diverse pharmacological activities, such as antioxidant, antimicrobial, antidiabetic, anti-inflammatory, and anticancer effects. This chapter provides a comprehensive overview of taro, encompassing its nutritional profile, phytochemistry, and numerous pharmacological properties. Additionally, it addresses the important aspects of biosafety in relation to taro consumption and highlights potential prospects for sustainable production of this remarkable tuber crop. Springer Nature Switzerland AG 2024. -
Designing Artificial Intelligence-Enabled Training Approaches and Models for Physical Disabilities Individuals
The focus of this research is on investigating AI-based strategies and models that can be used to develop workforce training systems specifically for individuals with physical disabilities. The goal is to leverage the advancements in artificial intelligence (AI) and its potential impact on workplace learning and development. There is an increasing demand for utilizing AI capabilities to design comprehensive training programs that are both inclusive and effective for people who face physical challenges. The research will examine effective strategies, real-life examples, and current AI-based training platforms for people with physical disabilities. Additionally, it aims to tackle the obstacles and ethical matters linked to incorporating AI in workforce training. These concerns include mitigating biases, ensuring accessibility, and safeguarding privacy. The outcomes of this study will assist in creating progressive approaches and frameworks driven by AI that can empower individuals with physical disabilities by improving their employability prospects while simultaneously fostering inclusivity within workforce training. The chapter will also explore the integration of AI-powered solutions in training programs for physically challenged individuals. By utilizing AI technologies like personalized learning algorithms, predictive analytics, and adaptive content delivery systems, training can be customized to cater to the unique requirements and learning needs of everyone. The implementation of AI has the potential to automate processes, analyze data effectively, and generate personalized learning pathways for improved accessibility. 2024 selection and editorial matter, Alex Khang; individual chapters, the contributors. -
Assessing Climate Change through Artificial Intelligence An Ethico-Legal Study
IPCC (The Intergovernmental Panel on climate change) [1], in the 6th Assessment Report released in 2022, reports that the net anthropogenic GHGs (greenhouse gases) continued to rise during the period 2010-19. It shows that GHG emission in the last decade is the highest in human history. According to the World Inequality Report, 2022, carbon dioxide concentration level in the atmosphere across the globe is the highest in millions of years. Consistent rise in the global emission level leading to alarming rise in atmospheric temperature has been a cause of concern for mankind. Rising atmospheric temperature leading to climate change has severely affected weather patterns; led to melting of glaciers; caused natural disaster and extinction of species, and severely impacted the ground water table. It has put the human race at a crossroads and thrown open an existential question for the world. Attempts have been made, both international and national, to reverse the impact of the rising scenario concerning climate change but have yet to be successful. The technological revolutions arising in recent times, especially in the domain of Artificial Intelligence (AI), offer hope to give a new shape to human civilization. With the aid of human intelligence, AI can perform assessment and predictive work as well which may help in mitigating the effect of adversely affecting climate change and help improve the environment. As per UNESCO (United Nations Educational, Scientific and Cultural Organisation), AI can perform assessment and prediction of climate change, which may assist in the protection of the environment. The Council identifies three priority areas relating to use of AI which includes improved understanding and predictions of climate change and geohazards [2]. This chapter aims at exploring the contribution of AI in assessing the behavioral pattern of climate change and the ethico-legal challenges involved therein. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. -
Predicting Stock Market Indexes with Artificial Intelligence
The forecasting of the Share market has been a popular research area, involving the analysis of input and output stock data using computer technology and algorithmic knowledge. This involves building unpredictable relationships among the data and analyzing the stock market trends to provide a reference for investors. The inception of artificial intelligence (AI) technology, blended with the web, immense data, and cloud computing has provided technical support for various industries. AI technology is employed to scrutinize and predict the equity market, exploring curvilinear associations amid stock market information, and furnishing a foundation for investors to formulate investment determinations. Predicting equity prices is a demanding undertaking due to diverse factors like governmental happenings, fiscal circumstances, business resolutions, investor mentality, and overseas currency hazards. The securities exchange is a vastly active and disordered framework, and producing precise projections of the securities exchange is of paramount significance. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. -
Effective Temperature Prediction for An Enhanced Climate Forecast System
Ever since the first industrial revolution, there has been a subtle temperature change. The transition to new manufacturing processes in conjunction with the surge in population has a negative consequence on the earths atmosphere. Climate change has been identified as the most crucial environmental issue of this century, and it has sparked heated discussions [1]. Temperature is the most common metric to evaluate the change in climate/global warming. It is anticipated that climate change will result in an adverse and enduring impact on the ecosystem. Weather forecasting today extensively depends on conventional methodologies and requires complex and complicated infrastructure [2]. Prime problems concern quality of acquired data, timeliness, availability, reliability, and usability constraints on forecast preparation. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. -
Data Modeling and Analysis for the Internet of Medical Things
Smart biomedical technology greatly assists in rapid disease screening and diagnosis within hospitals. One innovative device, a smart inhaler, incorporates sensors to track medication doses, usage patterns, and effectiveness. These inhalers provide valuable support to asthma sufferers, allowing for improved condition management and better patient outcomes. Asthma, a chronic respiratory disease affecting millions worldwide, causes airway constriction and swelling, resulting in breathing difficulties. Typically, medication such as inhaled corticosteroids and bronchodilators is used for management. However, medication adherence is often inadequate, leading to worsened outcomes and exacerbations. Smart inhalers aim to address this challenge by enabling users to monitor medication usage and compliance. Equipped with sensors, the inhalers track when, how much, and how frequently the prescribed medication is taken. The collected data is then transmitted to a mobile app or web portal, accessible to patients and healthcare providers. This integration facilitates medication tracking and provides personalized coaching for improved asthma control. The gathered data serves multiple purposes, including helping patients monitor their medication use and adherence. Patients can receive feedback on their treatment plan adherence and utilize the app to set medication reminders, promoting adherence and enhancing outcomes. 2024 CRC Press. -
READING AND ENGAGING WITH KACEN CALLENDERS MOONFLOWER THROUGH INTERSECTIONAL PEDAGOGIES
This chapter argues that privileged perspectives can be decentered using intersectional pedagogies when engaging with literary texts such as Moonflower, a novel that engages children with vital topics relating to race, gender, and mental health. 2024 selection and editorial matter, KaaVonia Hinton and Karen Michele Chandler; individual chapters, the contributors. All rights reserved. -
Artificial Intelligence-Based Approaches for Anticipating Financial Market Index Trends
The stock market is an essential component of the world economy and significantly impacts how different countries handle their finances. Predicting stock prices has gained popularity recently since it can offer traders, investors, and policymakers useful information. Making informed financial decisions, lowering risk, and maximizing returns can all be facilitated by accurate stock price projections. Stock price prediction is a current research subject due to improvements in machine learning (ML) techniques, and several methodologies have been put forth in the literature. To increase the accuracy of stock price prediction, one method combines the feature extraction ability of convolutional neural networks (CNNs) with the classification strength of support vector machines (SVMs). CNNs are a subclass of neural networks that have excelled in voice and picture recognition. They can be taught to extract valuable features from the supplied data automatically. Contrarily, SVMs are a well-liked machine learning (ML) technique that has been applied for regression and classification tasks. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. -
Promoting Net-Zero Economy for Sustainable Development: Practice-Based View
The present research investigates the utilization of various resources, including tangible assets, human expertise, and intangible assets, in a cohesive set of established procedures, which impact the development and implementation of net-zero practices. It also explores the effect on the environmental performance of SME enterprises operating in business markets. Additionally, the study explores whether digitalization plays a moderating role in this relationship. The samples of 291 were used in the study. Data were analyzed using partial least square structural equation modeling. For a sustainable net-zero economy (SNZE), it is essential for managers to acknowledge the importance of resource and capabilities management. While the management of tangible assets and human skills is vital, greater emphasis should be placed on intangible resources like organizational culture and learning. Furthermore, the capacity of small-sized enterprises (SMEs) to process and implement knowledge could prove to be instrumental in accomplishing net-zero targets. Consequently, managers should leverage Industry-4.0-based technological solutions to enhance resource and capabilities management effectively. This research pioneers an exploration into the influence of human capital and various assets (tangible and intangible), on the development and implementation of a SNZE in organizations, underpinned by empirical data. The study broadens the understanding of the practice-based view (PBV) framework in realizing SNZE, particularly within SME B2B enterprises. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Customer Perspective through Artificial Intelligence: Forecasting Green Products Sustainable Development
The idea of planned behavior was developed in 1980 as a philosophy of deliberate action to interpret human behavior. The primary element of this theory is an individuals purpose, which is impacted by the attitude of expecting that the behavior will result in the desired result. This theory has helped in determining certain characteristics of an individual that includes smoking, drinking, services, and so on. The theory states that the behaviors are achieved through motivation and control. These characteristics developed are completely voluntary which can sometimes help in the betterment and improvement in any field. The name of the theory itself gives us a clarity that it is a well-planned formation of behaviors different from his or her normative and preconceived beliefs and norms. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. -
Workforce Forecasting Using Artificial Intelligence
Workforce forecasting predicts an organizations future demand and supply of the workforce. Each organization has its strategies to manage and track the appropriate workforce. The adequate forecasting technique for the workforce involves data analysis and pattern mining from various data points. Some of the critical attributes considered for the analysis and the forecasting of the workforce requirement include the data such as demographics, economic trends, and labor market conditions; these help in calculating informed predictions about future workforce requirements [1,2]. The primary aim of workforce forecasting is to ensure that an organization has suitable employees with the appropriate skills to meet its business needs by helping organizations make informed decisions about staffing levels, employee training, and other workforce management strategies. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. -
Strategic Management Practices for Sustainability: A Study of Micro-entrepreneurs of Wellness Industry in Mysore District
The global wellness industry is seeing a major shift in its relevance and growth post COVID-19. The fast-growing wellness industry is driven by organizations of all sizes and scope: large, medium, small and micro enterprises offering a range of services from holistic wellness offerings to focused services, such as beauty, spa, alternative therapy, gym and physical fitness. While we have heterogeneous businesses on the supply side, we have the entire global population on the demand side. Due to the size of the market and growth potential, the competition in wellness service space is intense. In such a situation, it is a challenge to register growth and sustain the same. The challenge is more pronounced for micro-entrepreneurs due to their limited resources and reach. It calls for a strategic approach to managing the businesses to endure the competition and succeed. Hence, wellness businesses are adopting Strategic Management Practices (SMPs) in greater numbers. However, not all strategies work. The purpose of this study is to analyze the impact of significant SMPs adopted by micro-entrepreneurs on business sustainability in the wellness industry. Responses of 392 microentrepreneurs from the wellness industry are recorded and analyzed for the SMPs adopted by them for the economic, environmental and social sustainability of their businesses. The study identified various strategic approaches that are implemented by micro-entrepreneurs in Mysore District and studied the impact SMPs had on sustainability factors of the wellness industry. A model is proposed to support the study. The results conclude that the application of a good amount of SMPs in the form of strategic entrepreneurship enhances the sustainability of a venture as well as the industry, aiding transformation from an unorganized to an organized sector and better regulations. 2024 by World Scientific Publishing Co. Pte. Ltd. -
Agricultural Crop-Yield Prediction: Comparative Analysis Using Machine Learning Models
Machine learning (ML) is a crucial decision-support tool for predicting agricultural crop yields, enabling choices about which crops to grow and what to do while they are in the growing season. The research on agricultural production prediction has been supported by the application of several ML techniques. We employed a comparative analysis in this study to synthesize using three ML models, including linear regression, polynomial regression, and K-nearest neighbors (KNN), and extracted the results for the prediction of yield. Crop yield depends on a variety of aspects such as temperature, pesticide usage, rainfall, and even year due to changing climatic conditions. It is in our best interest to find out the crop yield based on these factors, as it will help in advancing the farming sector. These collected data have gone through preprocessing - i.e., cleaning, to ensure that no redundant or error data is used to train the ML models. Before we train the models, the dataset is divided into training and testing to provide the performance metrics of each model we use. The experimental results on predictions indicate KNN performs slightly better in comparison with linear regression and polynomial regression models. 2024 Taylor & Francis Group, LLC. -
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. -
Farming Futures: Leveraging Machine Language for Potato Leaf Disease Forecasting and Yield Optimization
Crop yield prediction is of paramount importance in modern agriculture. It serves as a linchpin for ensuring food security, efficient resource management, risk mitigation, environmental sustainability, and socioeconomic development. Accurate predictions enable us to maintain a stable food supply, optimize resource allocation, and manage the uncertainties associated with climate and market fluctuations. By fostering sustainable farming practices, crop yield prediction also plays a crucial role in reducing environmental impact and promoting rural development. Integrating artificial intelligence (AI) and machine learning (ML) in modern agricultural practices offers the potential to revolutionize the way we produce food, making it more sustainable, efficient, and resilient. This study has demonstrated the effectiveness of convolutional neural networks (CNNs) in the classification of potato leaf disease, achieving remarkable results with a test loss of 0.0757 and a test accuracy of 0.9741. 2024 Taylor & Francis Group, LLC.