Automation Of Tasks Research Articles

The Role of Artificial Intelligence in Website Creation

This article discusses the role of artificial intelligence (AI) in website creation, highlighting its impact on development processes and user interaction. Modern technologies such as generative models, recommendation systems, and chatbots are becoming integral to web development, significantly simplifying and accelerating site creation. Generative models like ChatGPT and DALL-E enable developers to automatically generate textual and visual content, saving time and enhancing creative processes. Recommendation systems analyze user behavior and offer personalized solutions, improving user experience and increasing engagement. Chatbots used for automating user communication provide round-the-clock support and instant responses to frequently asked questions, greatly enhancing service levels. Additionally, AI assists in data analysis and content optimization for search engines, improving site visibility online. The article also discusses the advantages of applying AI in web development, including automation of routine tasks, improvement of content quality, and enhancement of marketing strategy effectiveness. In conclusion, it emphasizes that the integration of AI into web development opens new opportunities for businesses and creates more dynamic and adaptive web applications that meet user needs in the modern digital world.

A Unified Resource Allocation Framework and Impact Evaluation for NGSO Satellite Constellations

ABSTRACTThe new era of satellite communications will rely on thousands of highly flexible spacecraft capable of autonomously managing constellation resources, such as power or frequency. Previous work has focused on the automation of the individual tasks that compose the resource allocation problem (RAP). However, two aspects remain unaddressed: (1) A unified method that autonomously solves the RAP under nongeosynchronous conditions is still to be developed, and (2) the cost–benefit of using optimization methods remains to be studied. Note that these studies are critical for satellite operators to take appropriate decisions concerning the automation of communications constellations operations. To close this gap, this work proposes an adaptive framework to solve the RAP for high‐dimensional nongeosynchronous satellite constellations. The proposed framework uses a divide‐and‐conquer approach that solves each step of the RAP, leveraging different optimization algorithms at the subproblem level to produce a valid and efficient allocation of resources over long time horizons. When comparing the proposed method against scalable greedy solutions, the former achieves up to four times more constellation capacity and reduces the overall consumed power by up to a factor of 3. The cost–benefit analysis reveals which RAP subproblems should be prioritized depending on the operator's objectives. Studying diverse operational conditions, we find that optimization methods enhance capacity consistently yet might raise power consumption due to trade‐offs in the routing algorithms.

Opportunities and Challenges for Information Technology and Business Educators in Implementing Generative Artificial Intelligence in Instruction

Generative artificial intelligence (AI) has the potential to revolutionize teaching and learning in the field of IT and business education. This research aims to explore the opportunities and challenges faced by IT and business educators in the utilization of generative AI in teaching and provide insights for educators seeking to harness the power of these tools while mitigating potential risks. The opportunities for IT and business educators in utilizing generative AI include enhanced learning experiences, automation of administrative tasks, exposure to real-world applications, and adaptability and personalization. Generative Artificial Intelligence allows educators to create interactive and personalized learning content, streamline administrative tasks, prepare students for AI-driven careers, and cater to individual learning needs. However, there are several challenges that educators need to navigate to effectively utilize generative AI. Ethical considerations, including data privacy, bias, and the impact of AI-driven decision-making, must be addressed to ensure ethical use of AI technology. Information technology and business educators must also develop the necessary skills and knowledge in AI to integrate it into the curriculum effectively. This requires staying current with advancements in the field. Additionally, the utilization of generative AI may require significant investments in infrastructure, software, and training. Adequate resources must be allocated to support educators in implementing and utilizing generative AI effectively. Lastly, educators must find a balance between AI-assisted teaching and maintaining a meaningful teacher-student relationship. By understanding and addressing these opportunities and challenges, information technology and business educators can harness the potential of generative AI to enhance teaching and learning experiences, prepare students for the AI-driven workforce, and create personalized and engaging educational content. The result of the study serves as basis by the faculty and administrators in strengthening the implementation of generative artificial intelligence in teaching.

IMPLEMENTATION OF ARTIFICIAL INTELLIGENCE IN RESTAURANT CHAINS

The article examines the issue of introducing artificial intelligence in restaurant chains. The authors have studied the development of the restaurant chains market in Ukraine and the world, and identified significant differences in the scale and duration of the chains' development. The world's restaurant chains consist of thousands of establishments, while the largest Ukrainian chain is only 200 establishments. In addition, the vast majority of Ukrainian chains operate in the local market. The reasons for this are the short duration of network development, limited resources and difficulties in entering global markets. The article assesses the opportunities and risks of introducing artificial intelligence in restaurant enterprises, including demand forecasting, optimization and automation of processes and tasks, improving guest service and expanding the audience. Risks include a decrease in the role of staff, data privacy and security, management difficulties, and high implementation costs. The article analyzes the use of artificial intelligence technologies by restaurant chains in Ukraine and the world. The use of artificial intelligence technologies by Ukrainian restaurant chains is currently at the stage of experiments and individual implementations. At the same time, global networks are already making full use of all the possibilities of innovative technology. The reasons for this are the short development time of Ukrainian restaurant chains compared to the global average, limited resources, and the difficulty of entering global markets. The article also presents a study that was conducted in the form of a survey: “What do you know about artificial intelligence and the use of artificial intelligence in the restaurant business?”. The study has shown that people are well aware of what artificial intelligence is, but most of them do not know and have not encountered it during service in restaurant establishments. This study suggests that Ukrainian restaurant chains have great potential and prospects for implementing artificial intelligence technologies.

Artificial Intelligence in the Service Industry: Transforming Operations and Enhancing Customer Experience

Artificial Intelligence (AI) has become a driving force in reshaping the service industry, fundamentally altering how businesses operate and deliver value. Through the automation of tasks, AI enhances decision-making, streamlines operations, and provides personalized customer experiences. AI’s ability to analyze vast datasets, recognize patterns, and automate tasks previously handled by humans leads to significant improvements across industries like hospitality, retail, healthcare, and customer service. For example, in hospitality, AI-driven dynamic pricing and virtual concierges improve guest satisfaction and optimize revenue. In retail, AI powers recommendation engines, driving significant revenue growth. In healthcare, AI reduces diagnostic errors and accelerates decision-making. Similarly, AI-driven chatbots in customer service improve operational efficiency while ensuring high customer satisfaction. This paper explores these applications in depth, discussing benefits, challenges, and ethical considerations associated with AI integration in the service sector.

Artificial Intelligence in Academic Research at Bugema University: Transforming Methodologies and Ethical Considerations

This study explored the transformative impact of Artificial Intelligence (AI) on research methodologies at Bugema University, focusing on ethical considerations associated with AI's integration. The problem stems from AI's ability to enhance data analysis, predictive modelling, and task automation, aligning with Sustainable Development Goals (SDGs). However, it raises concerns such as algorithmic bias, data privacy, and the erosion of traditional research skills. Using a qualitative case study approach, the research examines AI adoption across various departments, involving in-depth interviews with academic staff. Findings indicate that AI improves research efficiency and quality but requires ongoing training to address technical challenges and ethical concerns. AI's integration highlights the need for continuous skill development, robust ethical guidelines, and interdisciplinary collaboration to ensure the responsible and effective use of AI in academic research. Recommendations include comprehensive AI training, the establishment of ethical guidelines, and the promotion of collaborative approaches for sustainable AI adoption in research practices

The role of automation etiquette and task-criticality on performance, workload, automation reliance, and user confidence

Previous research suggests good automation etiquette can yield positive effects on user performance, trust, automation reliance, and user confidence – especially in personified or anthropomorphized technologies. The current study examined the impact of automation etiquette and task-criticality in non-personified technology. The study used a computer-based automation task to examine good and bad automation etiquette models and different domain-based task-criticality levels (between-subjects) that contained various stages of automation (stage 2 and stage 3) and automation reliability levels (60% and 80%) (within-subjects). The study found that bad automation etiquette can increase automation bias and automation reliance which improved performance in the most capable automation condition (stage 3; 80% reliable) but also heightened user subjective workload and decreased user self-confidence. The study also demonstrated task-criticality can be successfully manipulated through domain and instructions. Overall, automation etiquette influenced performance and user confidence more in highly capable automation (stage 3) and in a low-criticality task domain.

Recent Advancements in the Application of Artificial Intelligence in Drug Molecular Generation and Synthesis Planning

AbstractThe design and synthesis of drug molecules is a pivotal stage in drug development that traditionally requires significant investment in time and finances. However, the integration of artificial intelligence (AI) in drug design accelerates the identification of potential drug candidates, optimizes the drug development process, and contributes to more informed decision-making. The application of AI in molecular generation is changing the way researchers explore the chemical space and design novel compounds. It accelerates the process of drug discovery and materials science, enabling rapid exploration of the vast chemical landscapes for the identification of promising candidates for further experimental validation. The application of AI in predicting reaction products accelerates the synthesis planning process, contributes to the automation of synthetic chemistry tasks, and supports chemists in making informed decisions during drug discovery. This paper reviewed the recent advances in two interrelated areas: the application of AI in molecular generation and synthesis routes. It will provide insights into the innovative ways in which AI is transforming traditional approaches in drug development and predict its future progress in these key fields.

Automation and augmentation: a process study of how robotization shapes tasks of operational employees

Robotization shapes organizational structures and jobs, which may bring benefits or negative consequences. Yet, to date, it remains unknown how robotization projects in firms shape automation and augmentation and how exactly employees’ tasks evolve while being automated and augmented. Our findings from a process study of five robotization projects within one organization in the chemical industry show that the automation of tasks is inevitably intertwined with their augmentation. We observe a transformation of the operational tasks, temporarily increasing workload and affecting employees’ motivation levels while influencing potential automation bias. We present these findings as a theorized model of how robotization projects drive the automation and augmentation of operational tasks, and how this evolves. We advance the literature on digital transformation and robotization in three ways. First, we show how automation and augmentation affect operational employees’ tasks. We theorize that employees’ workloads and skill requirements first expand to implement robotization and then contract once workable configurations have been found. Second, we explain a temporal and causal link between automation and augmentation, where the beneficial outcomes are dependent on their temporal entwinement. Third, we posit that active inclusion of operational employees in defining task automation and augmentation safeguards their feeling of accountability in later stages.

Autonomy and Use of Flexible Endoscopy Robots in Gastroenterology – A Review

Abstract The integration of mechatronic systems to improve the control and maneuverability of endoscopes in complex gastroenterology procedures is necessary since manual handling often lacks precision. The result are flexible endoscopy robots. Therefore, the question arises as to how far these robots have evolved. This basically depends on their degree of autonomy and the typical interventions they are designed to perform. In this review, we provide an overview of existing flexible endoscopy robotic systems, categorizing their levels of autonomy and intended applications. Of the 28 systems reviewed, most operate at autonomy levels 0 (no autonomy) and level 1 (robotic assistance), with some progress toward level 2 (task automation) in colonoscopy. For more complex procedures, efforts are made to establish master-slave systems. Advances in flexible endoscopic robots are aimed at increasing autonomy, particularly for autonomous navigation within hollow organs. The integration of advanced sensor technologies is critical to achieving precise control and steering in future developments, and thus higher levels of autonomy for these systems.

How i do it: Leveraging AutoHotkey and programmable peripheral devices for high efficiency diagnostic radiology.

This paper discusses the use of AutoHotkey (AHK) and programmable peripheral computing devices to enhance the workflow of diagnostic radiologists. Multiple features designed and coded by an emergency teleradiologist to optimize efficiency and complete redundant tasks with ease are presented. The full AutoHotkey script, which currently supports Visage PACS, PowerScribe 360, and Epic EHR, is available in the article appendix. Recommended peripheral devices and schematics for easy integration with the AutoHotkey script are provided. Downloadable peripheral device profiles for the recommended devices are available in the appendix. The combination of task automation, achieved with AutoHotkey, and the thoughtful configuration of programmable peripheral devices, providing easy access to task automations, can lead to improved ergonomics, increased efficiency, productivity, and job satisfaction.

МОЖЛИВОСТІ ТА ОБМЕЖЕННЯ ВЕЛИКИХ МОВНИХ МОДЕЛЕЙ

The work is dedicated to the study of large language models (LLMs) and approaches to improving their efficiency in a new service. The rapid development of LLMs based on transformer architecture has opened up new possibilities in natural language processing and the automation of various tasks. However, fully utilizing the potential of these models requires a thorough approach and consideration of numerous factors. A review of the evolution of large language models was conducted, highlighting leading companies engaged in the research and development of efficient systems. The structure of these models and ways of representing internal knowledge were examined. Key approaches to training were described, including data collection, preprocessing, and selecting appropriate neural network architectures used in large language models. It was noted that the greatest breakthrough was achieved with the Transformer neural network, which is based on the attention mechanism. A comparison of popular transformer-based chatbots was presented, namely: ChatGPT, Claude AI, and Gemini AI. Their metrics, capabilities, and limitations were identified. The relevance of the topic lies in the rapid development of natural language processing technologies and the growing demand for large language models across various industries. The effective use of these models has tremendous potential to improve productivity and the quality of work with textual data. However, due to the complexity of the architecture and the large amounts of data required for training, selecting and configuring the optimal model for a specific task is a challenging process. As a result of the study, recommendations for developers were provided on the use of popular open-source models in the new service or integration with third-party programs. The characteristics of the models, their strengths, limitations, and certain caveats regarding trust in the generated results were indicated. Keywords: large language models, transformer architecture, neural networks, chatbot, content generation.

Стратегічні аспекти цифрової трансформації бізнесу

The article formulates the essence of the concept of "digital transformation of business". The main strategic aspects of digitalization in the modern conditions of the functioning of international companies are substantiated. Based on the results of the research, the challenges of digital transformation were identified, and various types of digital business models were analyzed. The reasons that exacerbate problems in the field of digital transformation of business are formulated. Stages of the process of digital business transformation based on change management are recommended. The differences between digital marketing and digital branding are identified. Digital business transformation is the process of implementing digital technologies to improve all aspects of the company's activities, including optimization of business processes. Among the reasons that exacerbate problems in the field of digital transformation of business can be named: lack of a clear strategy and vision; employee unpreparedness; insufficient budget; cultural barriers; insufficient integration with existing systems; insufficient attention to cyber security; inadequate monitoring and evaluation. Digital business models have great potential to revolutionize almost all sectors of the economy. For companies that want to remain competitive, it is important to understand how to use digital technologies to create new value for customers and develop new business models. The use of digital marketing and branding tools is about strategic, customer-driven business transformation that requires end-to-end organizational change as well as digital adoption. Digital transformation drives the organization to better manage change as a whole, essentially making change a core competency as the enterprise becomes customer-centric system. Digital transformation has become an integral part of modern business. It allows companies to adapt to rapidly changing market conditions, improve operational efficiency and create new opportunities for growth and scaling. Companies that do not adopt digital technologies risk being left behind by their competitors. Digital tools allow to optimize business processes, improve the quality of products and services, as well as offer customers new opportunities, improve communications with them, and create a more personalized experience for customers. Automation of routine tasks, optimization of business processes, use of cloud technologies – all this allows to reduce costs and increase productivity. At the same time, digital technologies (AI, Internet of Things) open up new opportunities for creating innovative products and services.

МОЖЛИВОСТІ ТА ОБМЕЖЕННЯ ВЕЛИКИХ МОВНИХ МОДЕЛЕЙ

The work is dedicated to the study of large language models (LLMs) and approaches to improving their efficiency in a new service. The rapid development of LLMs based on transformer architecture has opened up new possibilities in natural language processing and the automation of various tasks. However, fully utilizing the potential of these models requires a thorough approach and consideration of numerous factors. A review of the evolution of large language models was conducted, highlighting leading companies engaged in the research and development of efficient systems. The structure of these models and ways of representing internal knowledge were examined. Key approaches to training were described, including data collection, preprocessing, and selecting appropriate neural network architectures used in large language models. It was noted that the greatest breakthrough was achieved with the Transformer neural network, which is based on the attention mechanism. A comparison of popular transformer-based chatbots was presented, namely: ChatGPT, Claude AI, and Gemini AI. Their metrics, capabilities, and limitations were identified. The relevance of the topic lies in the rapid development of natural language processing technologies and the growing demand for large language models across various industries. The effective use of these models has tremendous potential to improve productivity and the quality of work with textual data. However, due to the complexity of the architecture and the large amounts of data required for training, selecting and configuring the optimal model for a specific task is a challenging process. As a result of the study, recommendations for developers were provided on the use of popular open-source models in the new service or integration with third-party programs. The characteristics of the models, their strengths, limitations, and certain caveats regarding trust in the generated results were indicated. Keywords: large language models, transformer architecture, neural networks, chatbot, content generation.

Perancaangan Sistem Informasi Apotik Berbasis Web: Optimalisasi Layanan Pelanggan

This research discusses the development and implementation of a "Web-Based Pharmacy Information System" which aims to improve the efficiency of customer service and drug inventory management. By using web technology, PHP programming language, and MySQL database, this system allows pharmacies to monitor drug stocks in real-time, manage patient information, and provide integrated services. The use of PHP and MySQL in developing this application allows the automation of administrative tasks, including drug stock tracking, orders, and patient data management. This system also facilitates customer service with easy access to drug information and online drug ordering services. The research results show that the implementation of the Web-Based Pharmacy Information System has succeeded in increasing pharmacy efficiency, reducing drug stock shortages, and improving customer service. This system supports integrated drug management by integrating patient data, drug stock and customer service in one platform. Overall, this system illustrates how the use of web, PHP, and MySQL technologies can optimize customer service and medication inventory management in the pharmacy context, providing major benefits to pharmacies and patients.

DISCOVERING THE FUTURE AND IMPACT OF ARTIFICIAL INTELLIGENCE: A QUALITATIVE EXPLORATION

This study explores public perceptions of Artificial Intelligence (AI), focusing on its perceived capabilities, potential risks, and societal implications. A questionnaire consisting of 15 questions was distributed to five respondents, yielding valuable insights into how individuals view AI's role in the future. The results indicate a general understanding of AI as a sophisticated, robot-like technology capable of mimicking human behavior, though not fully replicating human consciousness. While most respondents acknowledged AI’s potential to advance technology and improve daily life, there were concerns regarding its safety and the threat it poses to human employment. Specifically, three respondents expressed fear that AI could lead to global job displacement, highlighting the ongoing debate surrounding the automation of routine tasks. Despite these concerns, all respondents agreed that AI is essential for human advancement and should continue to evolve. Additionally, the study revealed that AI is already integrated into participants' lives, through gadgets and search engines like Google, demonstrating its growing presence in everyday technology. However, there was a divide on the level of intimidation AI causes, with four respondents feeling uneasy about its impact, while one remained unperturbed. The study concludes that while AI offers significant benefits, such as improving productivity and efficiency, its development must be approached with caution, ensuring ethical considerations and regulatory oversight to mitigate potential risks. These findings underscore the need for ongoing dialogue and responsible AI governance to balance innovation with societal well-being.

A Review on Multipurpose Agri Robot

A Multitasking robot for the field of Agriculture has been studied in this research. Now a day, precision agriculture by agricultural robots is the newly emerging technology in agriculture sector to save the time and energy that is wasted in repetitive farming tasks automation in farming processes is quite helpful. To design these sorts of robots there should be certain considerations and particular approach considering the agriculture environment in which it will be working. The working of an autonomous robot is based on field parameters i.e. length and width. Prototype of an agricultural robot “Agri-Bot” is modeled for multitasking such as seeding, ploughing and harvesting with a separate irrigation system. It is a four wheeled vehicle which is controlled by ATMEGA328 microcontroller as master controller, Humidity sensor for irrigation, power supply is provided by solar panel which is eco-friendly to the environment. It will also help in decreasing the use of nonrenewable sources of energy and will not pollute the environment. Other accessories are slaves performing specific operations.

Applications of artificial intelligence in obstetrics

Background: Artificial intelligence (AI), particularly chatbot technologies such as OpenAI’s ChatGPT, is increasingly influencing healthcare by improving quality, efficiency, and accessibility. ChatGPT is already useful in clinical practice, research, and education across various medical fields. However, its use raises concerns regarding accuracy and reliability, and ethics. This article discusses the potential and challenges of ChatGPT application in obstetrics.Current Concepts: In obstetrics, AI is being applied in several key areas. ChatGPT is useful for providing easy-to-understand information during unexpected birth situations and has been applied in diagnosis and patient management. Furthermore, it aids in patient education, clinical assessment, and research. In drug counseling, ChatGPT helps guide safe medication use during pregnancy and breastfeeding, although it also emphasizes consultations with healthcare professionals. AI has improved the interpretation of fetal monitoring and ultrasound accuracy and reduced the time required for diagnosis and potential errors. The recent launch of OpenAI’s “SearchGPT” could further revolutionize healthcare by providing real-time, tailored information and improving diagnostic precision.Discussion and Conclusion: ChatGPT, although useful, has limitations such as potential misinformation and variability in responses. It can assist with patient inquiries; however, it cannot replace clinical judgment. AI holds significant promise for application in obstetrics; nevertheless, careful attention to ethical and regulatory issues, including patient privacy, is necessary. Future applications of AI may include personalized treatment plans, real-time monitoring, and the automation of administrative tasks, allowing clinicians to focus more on patient care. AI also has the potential to improve medical education, assist with complex diagnoses, and improve patient engagement and outcomes.

DIGITAL TRANSFORMATION IN SUPPLY CHAINS: BENEFITS, CHALLENGES AND TECHNOLOGICAL INNOVATIONS

Digitalization is revolutionizing supply chains, resulting in significant gains in efficiency and competitiveness. This study investigates the role of digital technologies, such as the Internet of Things (IoT), artificial intelligence (AI), big data, and blockchain, in transforming supply chain management. The research presents a review of the current literature and analyzes case studies of companies that have adopted these innovations. The results show that the integration of these technologies allows for better visibility into processes, facilitating informed and agile decision-making. The automation of routine tasks and demand prediction through AI algorithms contribute to cost reduction and waste minimization. In addition, the use of digital platforms improves collaboration between the various actors in the chain, optimizing the flow of information. However, the study also highlights the challenges associated with digitalization, such as resistance to change, the need for significant investments, and the shortage of technical skills. The analysis concludes that, despite the barriers, the benefits of digitalization outweigh the challenges, making it essential for the sustainability and growth of supply chains.

Flight-Based Control Allocation: Towards Human–Autonomy Teaming in Air Traffic Control

It is widely recognized that airspace capacity must increase over the coming years. It is also commonly accepted that meeting this challenge while balancing concerns around safety, efficiency, and workforce issues will drive greater reliance on automation. However, if automation is not properly developed and deployed, it represents something of a double-edged sword, and has been linked to several human–machine system performance issues. In this article, we argue that human–automation function and task allocation may not be the way forward, as it invokes serialized interactions that ultimately push the human into a problematic supervisory role. In contrast, we propose a flight-based allocation strategy in which a human controller and digital colleague each have full control authority over different flights in the airspace, thereby creating a parallel system. In an exploratory human-in-the-loop simulation exercise involving six operational en route controllers, it was found that the proposed system was considered acceptable after the users gained experience with it during simulation trials. However, almost all controllers did not follow the initial flight allocations, suggesting that allocation schemes need to remain flexible and/or be based on criteria capturing interactions between flights. In addition, the limited capability of and feedback from the automation contributed to this result. To advance this concept, future work should focus on substantiating flight-centric complexity in driving flight allocation schemes, increasing automation capabilities, and facilitating common ground between humans and automation.