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An Empirical Study of the Factors Influencing the Adoption of Electric Vehicles
Get PDFThe adoption of electric vehicles (EVs) has become increasingly important in recent years due to concerns about climate change and the need to reduce greenhouse gas emissions. The widespread adoption of EVs is critical to achieving global climate goals and reducing air pollution. Therefore, there is a need for empirical research to understand the factors that affect the adoption of EVs. The purpose of this study was to empirically investigate the factors affecting the adoption of EVs. The study used a sample of 425 individuals, and multiple regression analysis was conducted to analyze the data. The independent variables in the study were Economic Factors, Technological Factors, Social Factors, and Regulatory Factors. All the variables were found to be significant in explaining the adoption of EVs. The results of the study show that Economic Factors were the most important factor affecting the adoption of EVs, followed by Technological Factors, Social Factors, and Regulatory Factors. The findings suggest that cost and financial incentives play a significant role in the decision to adopt EVs. Technological factors, such as the availability and performance of charging infrastructure and battery technology, also influence the adoption of EVs. Additionally, social factors, such as social norms and attitudes towards EVs, and regulatory factors, such as government policies and regulations, also affect the adoption of EVs. The study\u27s findings have important implications for policymakers, industry leaders, and other stakeholders in the transportation sector. The results suggest that policies aimed at reducing the cost of EVs and providing financial incentives can encourage greater adoption of EVs. Additionally, efforts to improve charging infrastructure and battery technology can increase the attractiveness of EVs. Social and regulatory factors should also be considered in efforts to promote the adoption of EVs
The Impact of Artificial Intelligence Integration on Minimizing Patient Wait Time in Hospitals
Get PDFReduced patient wait-times benefit not just patients\u27 health but also the overall efficiency of the healthcare system, which is particularly crucial given the aging population and rising demand for medical services in recent decades. Reducing the time that outpatients have to wait is one of the most crucial actions that must be taken to improve the patient experience. Artificial intelligence and machine learning may be applied in health care and medicine to enhance insights, reduce waste and wait time, and increase speed, service efficiency, accuracy, and efficiency. The purpose of this research is to determine whether or not the deployment of AI in hospital management system help reduce the amount of time that patients have to wait for their appointments. The Random Forest Regression, Pairwise multiple regression, and the pairwise Pearson correlation have been performed. This research also included additional features such as the number of the office personnel, the number of doctors, the quantity of equipment, and the health expenses in order to eliminate any potential omitted variable biases. According to the findings of the Random Forest Regression, the integration of AI and ML seems to be required to cut down on the amount of time that patients have to wait. The size of the office personnel, the number of doctors, and the number of pieces of equipment are found to be significant factors in lowering the amount of time spent waiting. It was determined that the aspect of the cost was the least significant in terms of reducing the amount of time spent waiting. According to the findings of our study, the healthcare care center needs to expand the integration of AI in order to cut down on the waiting time for patients and to improve the overall experience they provide for them. The findings also suggest that wait times depend on many factors. Thus, focusing on a few factors does not significantly reduce wait time
Leveraging Predictive Modeling, Machine Learning Personalization, NLP Customer Support, and AI Chatbots to Increase Customer Loyalty
Get PDFAI, ML, and NLP are profoundly altering the way organizations work. With the increasing influx of data and the development of AI systems to understand it in order to solve business challenges, the excitement surrounding AI has grown. Massive datasets, computer capacity, improved algorithms, accessible algorithm libraries, and frameworks have compelled today\u27s organizations to use AI to enhance their operations and profits. These technologies aid every kind of industry, from agriculture to finance. More specifically, AI and ML, and NLP are assisting organizations in areas such as customer service, predictive modeling, customer personalization, picture identification, sentiment analysis, offline and online document processing. The purpose of this study was twofold. We first review the several applications of AI in business and then empirically test whether these applications increase customer loyalty using the datasets of 910 firms around the world. The datasets include the integration scores of four different AI features, namely, AI-powered customer service, predictive modeling, ML-powered personalization, and natural language processing integration. The target is the customer loyalty measure as binary. All the features are measured on a 5-pint Likert scale. We applied six different supervised machine learning algorithms, namely, Logistic regression, KNN, SVM, Decision Tree, Random Forest, and Ada boost Classifiers. the performance of each algorithm was evaluated using confusion matrices and ROC curves. The Ada boost and logistic classifiers performed better with test accuracies of 0.639 and 0.631, respectively. The decision tree and KNN had the performance with accuracies of 0.532 and 0.570, respectively. The findings of this study highlight that by incorporating AI, ML, and NLP, businesses may analyze data to uncover what\u27s useful, gaining valuable insights that can be used to automate processes and drive business strategies. As a result, firms that wish to remain competitive and increase customer loyalty should adopt them
Investigating the Impacts of AR, AI, and Website Optimization on Ecommerce Sales Growth
Get PDFE-commerce has evolved into a vital element of modern life by giving customers a quick and easy way to buy products and services online. Businesses increasingly focus on building their online presence in order to remain competitive, which represents a huge change as a result of the growth of e-commerce. Utilizing artificial intelligence (AI), augmented reality (AR), and website optimization is one of the primary ways firms are aiming to improve their e-commerce operations at the moment. While AR can improve product recommendations and the visual component of online shopping by giving customers a more immersive experience, AI can be used to tailor the user experience and boost personalization. On the other side, website optimization can assist companies in enhancing the user experience and raising conversion rates. Businesses can make better choices about how to implement these variables into their operations by knowing how they affect e-commerce sales. This study used data from 190 global e-commerce sites to empirically examine the effects of using AI, AR, and website optimization on the increase of e-commerce sales. The study used a multiple regression analysis to look at how these factors and the rise of e-commerce relate to one another. The study\u27s findings demonstrated that every element had a favorable and significant impact on the increase of e-commerce sales. This suggests that companies investing in artificial intelligence, augmented reality, and website optimization can anticipate a comparable rise in revenue. These results suggest that companies wishing to enhance their e-commerce operations should think about investing in AI, AR, and website optimization. They may improve client satisfaction this way, boost conversion rates, and eventually boost sales.
 
Determining the Drivers and Barriers to the Adoption of Smart Vending Machine
Get PDFThe Internet of Things (IoT) revolution is revolutionizing numerous industries, including the vending machine industry. Smart vending machines are one example of how the Internet of Things is altering the way vending machines operate. Smart vending machines can do functions other than merely dispatching products in exchange for payment by combining modern technologies such as internet connectivity and touch screens. They can make purchasing more convenient for customers, track inventories in real-time, and even take mobile payments using a smartphone app. By surveying 412 business owners, this study employed a stacking classifier to analyze the determinants of smart vending machine adoption. The findings indicate that improved security and safety, as well as the decrease in operational costs, are the primary drivers of adoption among firms that have adopted the smart vending machine. Smart vending machines can be equipped with security cameras and alarms to deter theft and vandalism, as well as to prevent contamination or tampering. This can help to improve the vending machine\u27s general security and safety, as well as the products it dispenses. By automating processes such as inventory management and refilling, smart vending machines can also help to minimize operating expenses. This can save the operator time and money. This study\u27s findings also revealed that the primary barriers to adoption are upfront costs and technological challenges. The initial cost of purchasing and installing a Smart vending machine might be too expensive, particularly for SMEs. Operators may be required to spend on technical help and training in order to successfully use and maintain this equipment. The future of vending machines is expected to witness a steady move toward Smart vending machines. As sophisticated technology becomes more widely accessible and inexpensive, more operators are likely to realize it and make the switch
The Determinants of Cloud Computing Adoption in Healthcare
Get PDFCloud computing is useful for the healthcare sector since it reduces complexity, enables efficientadministration, and facilitates collaboration between the systems in healthcare sectors. This research seeksto examine the factors affecting the adoption of cloud computing in healthcare. It used three robust leastsquare estimation techniques such as S-estimation, M-estimation, and MM-estimation. The findings suggestthat the determinants of adoption of cloud computing are similar to other business institutions such ascompatibility, technological preparedness, complexity, security, competitive constraints, savings on costs,assistance to senior management, vendor assistance
Ethical Considerations in the Advent of 3D Printing Technology in Healthcare
Get PDFThe emergence of 3D printing technology in healthcare has ushered in a new era of personalized medical solutions. However, alongside its promises, this technology also introduces several critical challenges that demand attention. This research investigates the implications of 3D printing on patient safety, intellectual property, equity, data security, informed consent, and the roles of healthcare professionals. 3D printing has opened up remarkable opportunities in the creation of medical devices, implants, and prosthetics. Nevertheless, the potential for errors during the manufacturing process poses a significant concern. Ensuring the safety and reliability of 3D-printed medical products becomes paramount, as any defects or inaccuracies could have severe consequences on patient health and well-being. The accessibility of 3D printing technology raises apprehensions regarding intellectual property rights and regulatory standards. The possibility of replicating medical devices and pharmaceuticals may lead to patent infringements and pose difficulties in enforcing regulatory compliance. Striking a balance between innovation and protection of intellectual property becomes crucial in fostering a thriving 3D printing healthcare ecosystem. While 3D printing holds to democratize healthcare by offering personalized medical solutions, it also has the potential to exacerbate existing disparities in healthcare access. The cost of 3D printing technology and related services might prove prohibitive for certain communities, thereby widening the gap in access to advanced medical treatments. Addressing these disparities and ensuring equitable access to 3D printing healthcare solutions must be a priority for healthcare policymakers and stakeholders. The integration of 3D printing in healthcare necessitates the utilization and storage of sensitive patient data. However, ethical concerns emerge around the security and privacy of this data. Any breaches or misuse of patient information could not only compromise patient confidentiality but also erode trust in healthcare systems. Implementing robust data security measures and respecting patient privacy rights are essential to maintain public trust in 3D printing healthcare applications. As 3D printing enables the production of custom medical devices and implants, obtaining informed consent from patients becomes increasingly complex. Patients must comprehend the risks, benefits, and uncertainties associated with these personalized treatments to make autonomous decisions about their healthcare. Healthcare providers must develop comprehensive strategies to ensure adequate patient education and empowerment during the informed consent process
The Costs Of Psychosis And The Rationale For Early Intervention
Get PDFThere is often a delay of months or years between the onset of psychotic symptoms and the initiation of appropriate treatment. Early intervention is required to mitigate the negative consequences of prolonged periods of untreated symptoms. Phase-specific medicines are associated with improved outcomes, at least in the near future. Massive economic and societal expenses complement the terrible personal and family repercussions. Multiple studies have examined the long-term outcomes for schizophrenia patients experiencing their first episode. After five years, at least half of patients still have moderate-to-severe functional and/or social. Early psychosis may be a "critical period" for determining a patient\u27s long-term prognosis, and course-influencing variables may provide a large treatment window of opportunity. One of the few ways to improve long-term outcomes is by reducing the time to treatment success. Typically, psychosis occurs in late adolescence or early adulthood. Patients with psychosis are more susceptible to anxiety problems, depression, aggression, drug addiction, and suicide. Early management has been associated with enhanced functional result, decreased recurrence rates, enhanced treatment adherence, and increased patient satisfaction
Examining the Impact of EdTech Integration on Academic Performance Using Random Forest Regression
Get PDFEdTech has the ability to revolutionize the learning process by enhancing student involvement, tailoring lessons to the unique needs of each student, facilitating distance and online education, enhancing evaluation and feedback, streamlining communication among classmates, and opening up a wealth of new resources for teachers. The goal of this study is to see whether deploying EdTech in schools enhances students\u27 academic performance. The Random Forest Regression has been carried out with primary datasets of 479 high school students. In order to avoid any possible omitted variable biases, this study incorporated other aspects such as student involvement, family income, teacher-student ratio, and students\u27 healthy lifestyle. According to the results of the Random Forest Regression, the integration of EdTech in schools seems to be essential to boost academic performance levels. Student involvement, teacher-student ratio, and a healthy lifestyle are determined to be important variables in enhancing academic achievement levels. It was also discovered that the factor of family income was the least important. The findings of this study imply that academic achievement is dependent on numerous variables, and hence concentrating on a few aspects may not substantially help students with low academic performance. While technology may provide numerous advantages to education, it is crucial to recognize that it must be utilized in combination with a well-designed curriculum and competent pedagogy in order to be genuinely successful
Architectural Strategies for Implementing and Automating Service Function Chaining (SFC) in Multi-Cloud Environments
Get PDFService Function Chaining (SFC) represents a paradigm shift in the deployment, management, and automation of network services, enabling a dynamic approach to connecting virtual network functions (VNFs) in a prescribed sequence. Businesses adopting multi-cloud environments to leverage different cloud providers face significant challenges in implementing and automating SFC across these distributed infrastructures. These include managing the complexities of orchestrating SFC across heterogeneous cloud platforms, ensuring consistent performance, optimizing resource allocation, and maintaining security. This paper explores architectural strategies for implementing and automating SFC in multi-cloud environments, focusing on optimizing deployment, orchestration, and scalability. It examines the components and frameworks required to achieve seamless SFC automation, such as Network Function Virtualization (NFV), Software-Defined Networking (SDN), and cloud-native technologies like Kubernetes. The paper also discusses the importance of policy-driven orchestration, dynamic scaling, and integration of AI/ML techniques for performance optimization. This research also proposes the use of cross-layer coordination and programmable data planes to enhance SFC deployment in multi-cloud environments. The goal of the paper is to demonstrate how to create a robust and adaptive SFC architecture that efficiently operates in multi-cloud setups for enhancing service delivery, reducing operational costs, and improving network agility