Convergence Of Artificial Intelligence Research Articles

AI and Cloud Computing in Healthcare: Transforming Patient Care with AWS and Java

A new era in healthcare is being ushered in by the convergence of artificial intelligence (AI) and cloud computing, which will significantly improve patient care through improved diagnosis, individualized treatment planning, and effective patient management. This article explores the potent combination of Java and Amazon Web Services (AWS) and how these technologies may be used to develop cutting-edge AI-driven healthcare solutions that optimize healthcare operations and dramatically enhance patient outcomes. Together with Java's robust and flexible programming environment, healthcare providers can leverage AWS's scalable infrastructure and advanced machine learning capabilities to create and implement sophisticated AI models that analyze massive amounts of medical data at a speed and accuracy never seen before. These developments are making it possible to make more accurate diagnoses, customized treatment plans, and proactive patient monitoring, which can result in better clinical judgment and possibly life-saving measures. But even as we consider the enormous positive effects these technologies have on society, we also have to face the ethical problems they raise, such as issues with data privacy, potential biases in algorithms, and unequal access to cutting-edge medical treatments.

AI and Cloud Computing in Healthcare: Transforming Patient Care with AWS and Java

A new era in healthcare is being ushered in by the convergence of artificial intelligence (AI) and cloud computing, which will significantly improve patient care through improved diagnosis, individualized treatment planning, and effective patient management. This article explores the potent combination of Java and Amazon Web Services (AWS) and how these technologies may be used to develop cutting-edge AI-driven healthcare solutions that optimize healthcare operations and dramatically enhance patient outcomes. Together with Java's robust and flexible programming environment, healthcare providers can leverage AWS's scalable infrastructure and advanced machine learning capabilities to create and implement sophisticated AI models that analyze massive amounts of medical data at a speed and accuracy never seen before. These developments are making it possible to make more accurate diagnoses, customized treatment plans, and proactive patient monitoring, which can result in better clinical judgment and possibly life-saving measures. But even as we consider the enormous positive effects these technologies have on society, we also have to face the ethical problems they raise, such as issues with data privacy, potential biases in algorithms, and unequal access to cutting-edge medical treatments. The goal of this thorough investigation is to present a fair assessment of the revolutionary potential of artificial intelligence (AI) and cloud computing in the healthcare industry, while also discussing the wider ramifications and obligations associated with incorporating these formidable technologies into our healthcare systems.

The Convergence of Artificial Intelligence and Privacy: Navigating Innovation with Ethical Considerations

This article explores the complex relationship between artificial intelligence (AI) and privacy. While acknowledging AI's potential benefits, the authors emphasize the ethical implications of its data-driven nature. The article begins by outlining the privacy risks inherent in AI systems, including data breaches, surveillance, and the potential for bias and discrimination. It then delves into ethical considerations surrounding AI development, such as transparency, accountability, and the need to prioritize human values. Various frameworks for balancing innovation with privacy protection are discussed, including Privacy by Design principles and the General Data Protection Regulation (GDPR). It also examine case studies of privacy violations in AI systems, highlighting the real-world consequences of inadequate safeguards. Looking towards the future, the article identifies advancements in privacy-preserving AI technologies as a crucial area of research. It concludes by advocating for a comprehensive approach to AI governance that combines technological innovation with ethical and regulatory strategies, by stressing the importance of proactive measures to mitigate privacy risks and ensure that AI technologies are developed and deployed in a manner that respects.

Dependency-aware online task offloading based on deep reinforcement learning for IoV

The convergence of artificial intelligence and in-vehicle wireless communication technologies, promises to fulfill the pressing communication needs of the Internet of Vehicles (IoV) while promoting the development of vehicle applications. However, making real-time dependency-aware task offloading decisions is difficult due to the high mobility of vehicles and the dynamic nature of the network environment. This leads to additional application computation time and energy consumption, increasing the risk of offloading failures for computationally intensive and latency-sensitive applications. In this paper, an offloading strategy for vehicle applications that jointly considers latency and energy consumption in the base station cooperative computing model is proposed. Firstly, we establish a collaborative offloading model involving multiple vehicles, multiple base stations, and multiple edge servers. Transferring vehicular applications to the application queue of edge servers and prioritizing them based on their completion deadlines. Secondly, each vehicular application is modeled as a directed acyclic graph (DAG) task with data dependency relationships. Subsequently, we propose a task offloading method based on task dependency awareness in deep reinforcement learning (DAG-DQN). Tasks are assigned to edge servers at different base stations, and edge servers collaborate to process tasks, minimizing vehicle application completion time and reducing edge server energy consumption. Finally, simulation results show that compared with the heuristic method, our proposed DAG-DQN method reduces task completion time by 16%, reduces system energy consumption by 19%, and improves decision-making efficiency by 70%.

The convergence of artificial intelligence in toxinology and venomics

The convergence of artificial intelligence in toxinology and venomics

Reviewing the impact of artificial intelligence and machine learning in enhancing IoT applications performance

The convergence of Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT) has revolutionized various industries by enhancing IoT applications' efficiency and performance. This paper presents a comprehensive review of the impact of AI and ML in optimizing IoT systems. This study investigates how AI/ML algorithms contribute to real-time decision-making, energy efficiency, data management, and security in IoT networks through a qualitative research methodology. Additionally, it identifies the limitations of current research and provides directions for future work.

Application of Artificial Intelligence to Improve Production Process Efficiency in Manufacturing Industry

The rapid advancement of Artificial Intelligence (AI) has profoundly impacted the manufacturing industry, offering transformative potential to enhance production process efficiency. This paper presents a systematic literature review of AI applications in the manufacturing sector, focusing on key AI technologies such as machine learning, robotics, predictive analytics, and natural language processing. The review highlights how these technologies have improved quality control, resource management, and overall operational performance. However, the adoption of AI also presents challenges, including significant investment costs, the need for a skilled workforce, and concerns over data security and privacy. Despite these challenges, the integration of AI in manufacturing presents numerous opportunities for future research and innovation, particularly in the areas of sustainable manufacturing and the convergence of AI with other emerging technologies. This study concludes that while AI offers substantial benefits for production efficiency, its successful implementation requires careful strategic planning and investment in both technology and human resources.

Sentiment-based predictive models for online purchases in the era of marketing 5.0: a systematic review

The convergence of artificial intelligence (AI), big data (DB), and Internet of Things (IoT) in Society 5.0, has given rise to Marketing 5.0, revolutionizing personalized customer experiences. In this study, a systematic literature review was conducted to examine the integration of predictive modelling and sentiment analysis within the Marketing 5.0 domain. Unlike previous research, this study addresses both aspects within a single context, emphasizing the need for a sentiment-based predictive approach to the buyers’ journey. This review explores how predictive and sentiment models enhance customer experience, inform business decisions, and optimize marketing processes. This study contributes to the literature by identifying areas of improvement in predictive modelling and emphasizes the role of a sentiment-based approach in Marketing 5.0. The sentiment-based model assists businesses in understanding customer preferences, offering personalized products, and enabling customers to receive relevant advertisements during their purchase journey. The paper’s structure covers the evolution of traditional marketing to digital marketing, AI’s role in digital marketing, predictive modelling in marketing, and the significance of analyzing customer sentiments in their reviews. The Prisma-P methodology, research questions, and suggestions for future work and limitations provide a comprehensive overview of the scope and contributions of this review.

Leveraging AI-powered mobile learning: A pedagogically informed framework

The convergence of Artificial Intelligence (AI) and mobile learning (mLearning) has revolutionized digital education, heralding a transformative paradigm shift in learning methodologies. AI-powered mLearning holds promise but requires deep understanding of pedagogical principles. Concerns over smartphone overuse have heightened discussions about mLearning integration. In this context, with the rising penetration of AI in various educational settings, it is crucial that research delves into the pedagogically sound integration of AI into mLearning environments. A systematic literature review of forty-two articles has yielded a framework summarizing mLearning attributes, functionalities, and their impact on transversal learning principles highlighting the profound impact of AI.

A New Era of Discovery: How Artificial Intelligence has Revolutionized the Biotechnology

In the growing field of biotechnology, artificial intelligence (AI) has emerged as a pivotal force of innovation, unveiling a new era of discovery and advancement. The convergence of AI with biotechnology has revolutionized the landscape of scientific research and development. The dynamic interplay between AI and biotechnology highlights the transformative power of AI techniques in accelerating advancements in drug discovery, development, personalized medicine, biomolecular engineering, bioprocessing, CRISPR technology, genome editing, genomics, proteomics, metabolomics, transcriptomic, and AI-enabled robotics in biotechnology. This integration of AI with biotechnology helps us combat global challenges and offers environmentally friendly and sustainable solutions like bioremediation, bioplastics, biodiesel, and biofiltration. Current examples of these problems include waste management, air pollution, healthcare, clean water, energy access, sustainable practices, conservation of biodiversity, and ecosystems. This review article provides a comprehensive analysis, drawing on current literature, case studies, and emerging trends, to highlight the transformative potential of AI in reshaping the biotechnological landscape. It also addresses the challenges and opportunities associated with this AI-powered transformation, discussing future directions, ethical considerations, and the need for human-AI collaboration to ensure responsible and sustainable progress for a brighter future.

Artificial Intelligence for Climate Change Biology: From Data Collection to Predictions.

In the era of big data, ecological research is experiencing a transformative shift, yet advancements in thermal ecology and the study of animal responses to climate conditions remain limited. This review discusses how big data analytics and artificial intelligence (AI) can significantly enhance our understanding of microclimates and animal behaviors under changing climatic conditions. We explore AI's potential to refine microclimate models and analyze data from advanced sensors and camera technologies, which capture detailed, high-resolution information. This integration allows researchers to dissect complex ecological and physiological processes with unprecedented precision. We describe how AI can enhance microclimate modeling through improved bias correction and downscaling techniques, providing more accurate estimates of the conditions that animals face under various climate scenarios. Additionally, we explore AI's capabilities in tracking animal responses to these conditions, particularly through innovative classification models that utilize sensors such as accelerometers and acoustic loggers. Moreover, the widespread usage of camera traps can benefit from AI-driven image classification models to accurately identify thermoregulatory responses, such as shade usage and panting. AI is therefore instrumental in monitoring how animals interact with their environments, offering vital insights into their adaptive behaviors. Finally, we discuss how these advanced data-driven approaches can inform and enhance conservation strategies. Detailed mapping of microhabitats essential for species survival under adverse conditions can guide the design of climate-resilient conservation and restoration programs that prioritize habitat features crucial for biodiversity resilience. In conclusion, the convergence of AI, big data, and ecological science heralds a new era of precision conservation, essential for addressing the global environmental challenges of the 21st century.

The Diversity of Artificial Intelligence Applications in Marine Pollution: A Systematic Literature Review

Marine pollution, a major disturbance to the sustainable use of oceans, is becoming more prevalent around the world. Multidimensional and sustainable ocean governance have become increasingly focused on managing, reducing, and eliminating marine pollution. Artificial intelligence has been used more and more in recent years to monitor and control marine pollution. This systematic literature review, encompassing studies from the Web of Science and Scopus databases, delineates the extensive role of artificial intelligence in marine pollution management, revealing a significant surge in research and application. This review aims to provide information and a better understanding of the application of artificial intelligence in marine pollution. In marine pollution, 57% of AI applications are used for monitoring, 24% for management, and 19% for prediction. Three areas are emphasized: (1) detecting and responding to oil pollution, (2) monitoring water quality and its practical application, and (3) monitoring and identifying plastic pollution. Each area benefits from the unique capabilities of artificial intelligence. If the scientific community continues to explore and refine these technologies, the convergence of artificial intelligence and marine pollution may yield more sophisticated solutions for environmental conservation. Although artificial intelligence offers powerful tools for the treatment of marine pollution, it does have some limitations. Future research recommendations include (1) transferring experimental outcomes to industrial applications in a broader sense; (2) highlighting the cost-effective advantages of AI in marine pollution control; and (3) promoting the use of AI in the legislation and policy-making about controlling marine pollution.

Enhancing cross-border payments: The convergence of AI and blockchain for currency exchange optimization

The integration of Artificial Intelligence (AI) and blockchain technologies holds transformative potential for cross-border payment systems, particularly in the realm of currency exchange. This paper explores how AI's predictive capabilities, combined with blockchain's secure and transparent ledger, can significantly enhance the efficiency and security of currency exchanges in international transactions. Through the use of advanced predictive models like ARIMA and LSTM networks, AI enables the forecasting of currency trends, facilitating preemptive decision-making that can protect against exchange rate volatility and improve transaction value. Concurrently, blockchain technology ensures the integrity and trustworthiness of these transactions by providing a tamper-proof recording mechanism. This synergistic application not only minimizes the risks associated with currency fluctuations but also streamlines operational processes, yielding a more reliable and user-friendly cross-border payment experience. This study aims to quantify the benefits of integrating AI and blockchain in reducing exchange rate risks and improving operational efficiencies, offering a novel perspective on the future of financial transactions in the global economy.

AI Robots in Various Sector

The convergence of Artificial Intelligence (AI) and robotics is revolutionizing multiple sectors, enhancing productivity, safety, and quality. In manufacturing, AI-driven robots automate assembly lines, optimize logistics, and perform precision tasks. These robots adapt to changing production demands, improving efficiency and reducing errors. In agriculture, vision-based AI assists robots in weed detection, crop scouting, disease identification, and harvesting. By integrating AI algorithms with sensors, agricultural robots enhance yield prediction and resource management. In healthcare, surgical robots aid in minimally invasive procedures, while AI algorithms analyze medical images for early disease detection. The synergy between AI and robotics holds immense potential, reshaping industries and advancing human well-being

Artificial intelligence powered internet of vehicles: securing connected vehicles in 6G

<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p>The rapid advancements in automotive technology and the emergence of next-generation networks such as 5G and 6G are laying the foundation for the internet of vehicles (IoV), a revolutionary concept to transform transportation systems. The convergence of artificial intelligence (AI) and connected vehicles IoV is driving a paradigm shift in the transportation sector, especially in the dynamic framework of 5G and future 6G networks. This survey paper provides a thorough survey of the evolving AI-based IoV security landscape. We explore key areas of 5G/6G networks, focusing on the complex interplay of machine learning (ML) and deep learning (DL) in enhancing vehicle-to-everything (V2X) security and connected vehicles. Addressing the unique challenges of 6G, this paper outlines future directions for improving security and highlights open research issues. This comprehensive survey, which aims to provide information and guidance to both researchers and practitioners, contributes to a detailed understanding of the security issues associated with connected vehicles in the emerging 6G era.</p></div></div></div>

The transformative role of artificial intelligence in human resources

The article explores the landscape of Artificial Intelligence (AI) applications in Human Resources (HR) by highlighting current trends and providing some anticipations about future trends and developments. AI is revolutionary reshaping basic HR processes – from workforce planning, recruitment, to employee’s development and fostering diversity and inclusion. AI plays important role in addressing bias in recruitment, enhances objectivity and promotes equal opportunities. AI-driven tools (like chatbots and virtual assistants etc.) integration in HR processes enables seamless communication and propellers HR practices towards enhanced efficiency and strategic decision-making. Furthermore, the article provides a short analysis of some software solutions that serve organizations as AI HR tools. By taking a look towards the future, we can predicts that tools like predictive analytics, monitoring of employee well-being, and convergence of AI with augmented reality (AR) and virtual reality (VR) can be projected as some of the future key developments. In the conclusion we can stress out the transformative synergy between AI and HR, which allows organizations and HR professionals to embrace innovation in order to deliver better results for all of the stakeholders: employees, management, owners and broader society.

AI-Mediated Methods For Cancer Treatment

The integration of Artificial Intelligence (AI) into cancer treatment represents a major advancement in diagnostic, prognostic, and therapeutic methods. This article explores how AI enhances drug response predictions, optimizes treatment regimens, and improves diagnostic imaging precision, leading to early and accurate tumor detection. AI accelerates drug discovery by identifying novel compounds, optimizing dosages, and repurposing existing drugs, thereby enhancing treatment development. In clinical trials, AI improves trial design, patient selection, and monitoring, increasing efficiency and success rates. Its application in medical imaging, including tumor localization and organ segmentation, provides detailed insights for precise diagnostic and treatment planning. Despite challenges such as data privacy and model transparency, AI's continuous learning and integration into personalized medicine highlight its transformative potential in cancer care. This convergence of AI and medical science promises to revolutionize oncology, improve patient outcomes, and tackle complex challenges.

The Influence of AI Chatbots in Fintech Services on Customer Loyalty within the Banking Industry

The convergence of Artificial Intelligence (AI) and FinTech services has brought about a transformative impact on customer engagement in institutions. AI-powered chatbots have emerged as indispensable tools for enhancing customer experiences within the banking sector. In this era of digital transformation, financial institutions have recognized the vital importance of exceptional service quality and the utilization of chatbots in attracting and retaining customers. Consequently, there is a growing interest in comprehending the role of chatbot service quality in shaping customer loyalty. This study adopts an empirical and descriptive approach, employing a quantitative research method. Data were obtained from 729 respondents through an online survey employing judgemental nonprobability sampling. The gathered data were analysed using Smart PLS- Structural Equation Model (SEM). The study delineates the attributes of AI chatbot service quality and identifies their significant influence on customer loyalty, taking into account the mediating role of customer satisfaction. The findings of this study contribute to the understanding of the relationship between chatbots, customer satisfaction, and loyalty. The study validates the significance of customer satisfaction, enhances the understanding of chatbot effectiveness, explores the unique characteristics of chatbot interactions, and demonstrates how technology-driven customer service can be integrated into loyalty models. Moreover, this study offers practical implications that can guide businesses in optimizing their chatbot strategies. By prioritizing the enhancement of chatbot performance, personalizing interactions, integrating across channels, and monitoring customer satisfaction and loyalty metrics, organizations can elevate customer satisfaction, cultivate loyalty, and achieve positive business outcomes.

AI-Enhanced Fintech communication: Leveraging Chatbots and NLP for efficient banking support

The convergence of artificial intelligence (AI) and financial technology (fintech) has revolutionized the way banks and financial institutions communicate with customers. This paper explores the use of AI-enhanced fintech communication, focusing on the utilization of chatbots and natural language processing (NLP) to provide efficient banking support. AI-powered chatbots have become indispensable tools for banks seeking to enhance customer service and streamline communication channels. By leveraging NLP algorithms, these chatbots can understand and respond to customer queries in real-time, providing personalized assistance round-the-clock. The integration of AI into fintech communication enables banks to offer seamless and efficient support, improving customer satisfaction and loyalty. The key to the effectiveness of AI-enhanced fintech communication lies in the ability of chatbots to interpret and respond to natural language input accurately. NLP algorithms enable chatbots to analyze and understand the intent behind customer queries, allowing them to provide relevant and contextually appropriate responses. This capability enhances the overall customer experience by reducing response times and ensuring that customers receive accurate and helpful information. Furthermore, AI-powered chatbots can handle a wide range of inquiries, from basic account inquiries to complex financial transactions. By automating routine tasks and inquiries, banks can free up human agents to focus on more complex and value-added activities. This not only improves operational efficiency but also allows banks to deliver faster and more responsive customer service. In addition to providing support to customers, AI-enhanced fintech communication can also help banks gather valuable insights into customer preferences and behavior. By analyzing interactions between customers and chatbots, banks can identify trends, anticipate customer needs, and tailor their products and services accordingly. This data-driven approach enables banks to offer more personalized and targeted offerings, leading to increased customer satisfaction and loyalty. In conclusion, AI-enhanced fintech communication, powered by chatbots and NLP, offers significant benefits for banks and financial institutions. By leveraging AI technology, banks can provide efficient and personalized support to customers, improve operational efficiency, and gain valuable insights into customer behavior. As AI continues to advance, the future of fintech communication promises even greater efficiency, personalization, and innovation in banking support. Keywords: Al- Enhanced, Fintech Communication, Leveraging, Chatbots, NLP.

The convergence of artificial intelligence, blockchain and fintech in energy, oil and gas trading: Increasing efficiency, transparency and automations

The convergence of artificial intelligence, blockchain and fintech in energy, oil and gas trading: Increasing efficiency, transparency and automations