159 research outputs found
The Role of AI-Enhanced Personalization in Customer Experiences
No full textThe purpose of this research study is to investigate how AI-driven-personalization chatbots and virtual assistants might improve customer experiences across different types of businesses. It investigates if artificial intelligence is able to cater goods, services, and marketing to the preferences of customers. The areas of retail and hospitality, together with finance, are the primary emphasis of this article. This study investigates the ways in which artificial intelligence can improve retail virtual shopping assistants and product recommendations. This article investigates the use of artificial intelligence (AI) chatbots in the hotel industry to give individualized booking experiences and recommendations. This study investigates the ways in which artificial intelligence-driven communications and individualized financial advice can improve customer service. Through the use of case studies and data analysis, the author of this study analyzes the practical uses of AI-powered personalization as well as the benefits to the customer experience. The findings are an attempt to illustrate that AI is capable of personalizing their experiences and engaging customers across a variety of industries
The Role of AI-Enhanced Personalization in Customer Experiences
No full textThe purpose of this research study is to investigate how AI-driven-personalization chatbots and virtual assistants might improve customer experiences across different types of businesses. It investigates if artificial intelligence is able to cater goods, services, and marketing to the preferences of customers. The areas of retail and hospitality, together with finance, are the primary emphasis of this article. This study investigates the ways in which artificial intelligence can improve retail virtual shopping assistants and product recommendations. This article investigates the use of artificial intelligence (AI) chatbots in the hotel industry to give individualized booking experiences and recommendations. This study investigates the ways in which artificial intelligence-driven communications and individualized financial advice can improve customer service. Through the use of case studies and data analysis, the author of this study analyzes the practical uses of AI-powered personalization as well as the benefits to the customer experience. The findings are an attempt to illustrate that AI is capable of personalizing their experiences and engaging customers across a variety of industries
Federated TON_IoT Windows Datasets for Evaluating AI-based Security Applications
No full textExisting cyber security solutions have been basically developed using knowledge-based models that often cannot trigger new cyber-attack families. With the boom of Artificial Intelligence (AI), especially Deep Learning (DL) algorithms, those security solutions have been plugged-in with AI models to discover, trace, mitigate or respond to incidents of new security events. The algorithms demand a large number of heterogeneous data sources to train and validate new security systems. This paper presents the description of new datasets, the so-called ToN_IoT, which involve federated data sources collected from Telemetry datasets of IoT services, Operating system datasets of Windows and Linux, and datasets of Network traffic. The paper introduces the testbed and description of TON_IoT datasets for Windows operating systems. The testbed was implemented in three layers: edge, fog and cloud. The edge layer involves IoT and network devices, the fog layer contains virtual machines and gateways, and the cloud layer involves cloud services, such as data analytics, linked to the other two layers. These layers were dynamically managed using the platforms of software-Defined Network (SDN) and Network-Function Virtualization (NFV) using the VMware NSX and vCloud NFV platform. The Windows datasets were collected from audit traces of memories, processors, networks, processes and hard disks. The datasets would be used to evaluate various AI-based cyber security solutions, including intrusion detection, threat intelligence and hunting, privacy preservation and digital forensics. This is because the datasets have a wide range of recent normal and attack features and observations, as well as authentic ground truth events. The datasets can be publicly accessed from this link [1]
Gen-Optimizer: A Generative AI Framework for Strategic Business Cost Optimization
No full textStrategic cost optimization is a critical challenge for businesses aiming to maintain competitiveness in dynamic markets. This paper introduces Gen-Optimizer, a Generative AI-based framework designed to analyze and optimize business costs through intelligent decision support. The framework employs a transformer-based model with over 140 million parameters, fine-tuned using a diverse dataset of cost-related business scenarios. By leveraging generative capabilities, Gen-Optimizer minimizes inefficiencies, automates cost analysis tasks, and provides actionable insights to decision-makers. The proposed framework achieves exceptional performance metrics, including a prediction accuracy of 93.2%, precision of 93.5%, recall of 93.1%, and an F1-score of 93.3%. The perplexity score of 20.17 demonstrates the model’s superior language understanding and generative abilities. Gen-Optimizer was tested in real-world scenarios, demonstrating its ability to reduce operational costs by 4.11% across key business functions. Furthermore, it aligns with sustainability objectives, promoting resource efficiency and reducing waste. This paper highlights the transformative potential of Generative AI in business cost management, paving the way for scalable, intelligent, and cost-effective solutions
A framework of AI-Powered Engineering Technology to aid Altair Data Intelligence Start-up Benefits; speeding up Data-Driven Solution
No full textToday, software instruments support all parts of engineering work, from design to creation. Many engineering processes call for tedious routine appointments and torments with manual handoffs and data storehouses. AI designers train profound brain networks and incorporate them into software structures
Adoption of ChatGPT for students' learning effectiveness
No full textChatGPT represents a state-of-the-art progression in artificial intelligence (AI)-enabled technology, used extensively in various sectors to make human lives easier and more convenient. It may provide additional assistance with different pedagogical methods. This research examines the effectiveness of integrating ChatGPT as an educational resource in student learning. A total of 505 responses were collected from university students located in Malaysia. The data were processed in SmartPLS 4.0 using the partial least squares with structural equation modelling technique. Findings reveal that tech competency has a positive impact on ChatGPT literacy and transparency; however, it has no significant impact on the adoption of ChatGPT. Additionally, ChatGPT's literacy and transparency have a significant impact on its adoption. Transparency acts as a mediator in the relationship between tech competency and ChatGPT adoption. On the other hand, ChatGPT literacy does not mediate the influence of tech competency on the adoption of ChatGPT. The impact of user innovativeness acts as a moderator on the influence of tech competency on ChatGPT literacy, transparency, and its adoption for learners' effectiveness. These results are informative for educational stakeholders who aim to enhance instructional design and refine the quality of learners' educational experiences by revealing the impact and prospects of ChatGPT as an educational tool. These findings enable improved decision-making and the design of more effective technology-enhanced educational interventions
THE ROLE OF AI IN PROMOTING SUSTAINABILITY WITHIN THE MANUFACTURING SUPPLY CHAIN ACHIEVING LEAN AND GREEN OBJECTIVES
No full textThis research article explores the critical role of Artificial Intelligence (AI) in advancing sustainability within the manufacturing supply chain, with a focus on achieving lean and green objectives. The study emphasizes how AI technologies can optimize supply chain processes, reduce waste, and enhance environmental sustainability while simultaneously improving efficiency. Through a comprehensive literature review, the article examines existing AI applications in supply chain management, identifying key trends, challenges, and opportunities. The methodology section outlines the systematic approach used to gather and analyze relevant data, while the findings highlight the transformative potential of AI in fostering sustainable practices. The discussion delves into the implications of these findings for the manufacturing sector, suggesting that the integration of AI not only aligns with lean manufacturing principles but also supports broader sustainability goals. The article concludes by emphasizing the need for continued research and development in AI-driven supply chain solutions to fully realize their potential in promoting a greener, more efficient manufacturing industry
Reimagining Journalism: Exploring the AI Revolution - A Thorough Analysis of Potential Advantages and Challenges
No full textThe integration of Artificial Intelligence (AI) into journalism has caused a significant shift in the industry. AI-powered tools are now a part of the journalistic workflow, transforming how news is collected, reported, and shared. These tools can assist with fact-checking, research, and content generation. This technological advancement has the potential to revolutionize how we interact with news in the digital age. In this article, we explore the relationship between AI and journalism, examining its advantages and challenges. Through a comprehensive analysis of academic literature and interviews with experts in journalism and AI, this article embarks on a journey of discovery. It concludes that a balanced and ethical approach is crucial to the integration of AI in journalism. The article emphasizes the importance of ethical guidelines to govern the use of AI in newsrooms, with a focus on transparency, accountability, and eliminating bias. As AI-infused journalism continues to evolve, it is the responsibility of journalists to ensure that this technological marvel enhances human capabilities and does not undermine the fundamental principles of journalism.
Reimagining Journalism: Exploring the AI Revolution - A Thorough Analysis of Potential Advantages and Challenges
No full textThe integration of Artificial Intelligence (AI) into journalism has caused a significant shift in the industry. AI-powered tools are now a part of the journalistic workflow, transforming how news is collected, reported, and shared. These tools can assist with fact-checking, research, and content generation. This technological advancement has the potential to revolutionize how we interact with news in the digital age. In this article, we explore the relationship between AI and journalism, examining its advantages and challenges. Through a comprehensive analysis of academic literature and interviews with experts in journalism and AI, this article embarks on a journey of discovery. It concludes that a balanced and ethical approach is crucial to the integration of AI in journalism. The article emphasizes the importance of ethical guidelines to govern the use of AI in newsrooms, with a focus on transparency, accountability, and eliminating bias. As AI-infused journalism continues to evolve, it is the responsibility of journalists to ensure that this technological marvel enhances human capabilities and does not undermine the fundamental principles of journalism.
AI chatbots: A disguised enemy for academic integrity?
No full textThe widespread popularity of ChatGPT and other AI chatbots has sparked debate within the scientific community, particularly regarding their impact on academic integrity among students. While several studies have examined AI's role in education, a significant gap remains concerning how AI chatbot usage affects students’ perceptions of academic integrity. This study aims to address this gap through rigorous quantitative techniques to explore the dynamics of student interactions with AI chatbots and assess whether this engagement diminishes academic integrity in higher education. Using a non-experimental design, the research investigates the causal relationship between AI chatbot usage and academic integrity, focusing on eight latent variables identified in the literature. A stratified sampling technique was employed to collect a representative sample of 594 participants via a 5-point Likert scale survey from four Southern Asian countries. The dataset underwent extensive statistical analysis using Structural Equation Modeling (SEM) techniques. The findings establish significant links between motivations for using AI chatbots and a decline in academic integrity. The study identifies a behavioral link between academic integrity and pedagogical limitations, highlighting traditional classroom-based pedagogy as the most impactful factor influencing students’ motivation to engage with AI chatbots. This research not only quantitatively addresses ethical concerns related to AI in academia but also offers insights into user behavior by assigning distinct weights to post-usage behavioral factors, differentiating it from earlier studies that treated these factors equally
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