Automation Of Tasks Research Articles

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.

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.

Experimental assessment of IMU data processing techniques for a backpack mobile laser scanning system

Abstract. Positioning techniques are fundamental in many automation tasks with several applications. In GNSS-denied environments like in dense forests, other alternatives are required, such as inertial and visual navigation. However, Inertial Measurement Units (IMUs) data, mainly those from microelectromechanical-system (MEMS), are noisy, which affects the orientation estimation. MEMS IMUs have been employed in mobile laser scanning systems due to their compact design and low-cost solutions for short-term navigation. In this paper, we have compared three IMU processing techniques freely available: MAH (Mahony et al., 2009), MAD (Madgwick et al., 2011) and DCM (Hyyti and Visala, 2015). These techniques implemented different approaches to estimate the attitude. They were experimentally assessed with data from a backpack mobile laser scanning system, which is composed of an OS0-128 Ouster LiDAR equipped with an internal IMU. We have used data from a 5-second trajectory segment aiming to evaluate the attitude and position estimation for a local path. The results showed that the DCM algorithm maintained a consistent velocity for 5 seconds, achieving a positional error of 1.4 m, 0.06 m, and 1.05 m along the X-, Y- and Z-axis, respectively. In contrast, MAD and MAH showed a position error over 20 m, 7 m and 3 m along the X-, Y- and Z-axis, respectively, which was affected by the velocity drift.

What drives economics students to use generative artificial intelligence?

The increasing integration of Artificial Intelligence (AI) into education requires studying the motives for its use among students. This study aims to identify the key motivations for economics students to use AI and compare these motivations by grade level and gender. The study examines satisfaction with the use of AI and analyzes the number of AI tools used. An anonymous empirical study was conducted among 264 students from the Faculty of Economics at Taras Shevchenko National University of Kyiv, Ukraine. Data analysis included descriptive statistical methods, non-parametric statistical methods, and exploratory factor analysis. The study found that students’ main motivations for using AI are the automation of routine tasks (34.2%) and the need to save time (21.5%), while 18.7% use AI to compensate for lack of experience. Among Bachelor’s students, motivations such as automating routine tasks and saving time increased from 53% to 58% over the course of their studies, while lack of experience decreased from 22% to 15%. In contrast, Master’s students showed a decrease in routine automation (from 36% to 28%) but an increase in the need to compensate for lack of experience (from 15% to 28%) and to save time (from 18% to 25%). In terms of gender, men are more likely to use AI for learning and personal development, while women are slightly more likely to use AI for work. More than 38% of respondents say they need to use at least 2 AIs to achieve their goals. Acknowledgment This publication is based upon work from 24-PKVV-UM-002, ‘Strengthening the Resilience of Universities: Czech-Ukrainian Partnership for Digital Education, Research Cooperation, and Diversity Management,’ supported by the Czech Development Agency and the Ministry of Foreign Affairs under the initiative ‘Capacity Building of Public Universities in Ukraine 2024.’

Machine learning, banking, industry, digitalization, innovation

Aim: The application of artificial intelligence (AI) offers an encouraging avenue for developments in the banking industry. Methods: The study adopted a scoping review of methodology to provide a comprehensive overview of relevant research regarding the potential uses and challenges of AI in banking. Consumer expectations have evolved, emphasizing features such as payment gateways, swift transactions, and resilient banking services. Results: Major utilization of the AI in banking industry includes enhanced customer engagement, satisfaction, and relationships, personalized financial guidance, smart digital wallets, regulation compliance and collaborative efforts, forecasting, tracking market trends, decision-making and problem-solving, anti-money laundering and fraud detection, automation of repetitive, large and time-intensive tasks, enhanced cybersecurity, and opportunity for digital financial inclusion. Conclusion: Users encounter challenges involving security issues, privacy concerns, and the complexity of technology in AI-driven banking services. Recommendation: While AI holds a crucial role in the banking industry, successful implementation demands ample allocation and consistent, predictable utilization of AI practices.

COMPUTATIONALLY EFFICIENT DEEP FEDERATED LEARNING WITH OPTIMIZED FEATURE SELECTION FOR IOT BOTNET DETECTION

Internet of Things (IoT) is a technology that has revolutionized various fields, offering numerous benefits, such as remote patient monitoring, enhanced energy efficiency, and automation of routine tasks in homes. However, unsecured IoT devices are susceptible to botnet-based attacks such as distributed denial of Service (DDoS). Conventional machine learning models used for detecting these attacks compromise data privacy, prompting the adoption of federated learning (FL) to improve privacy. Yet, most FL-based cyberattack detection models proposed for IoT environments do not address computational complexity to suit their deployment on resource-constrained IoT edge devices. This paper introduces an FL model with low computational complexity, designed for detecting IoT botnet attacks. The study employs feature selection and dimensionality reduction to minimize computational complexity while maintaining high accuracy. First, an extreme gradient boosting model, trained with repeated stratified k-fold cross-validation, is used to select the optimal features of the botnet dataset based on feature importance. Principal component analysis is then used to reduce the dimensionality of these features. Finally, a differentially private multi-layer perceptron is trained locally by four FL clients and aggregated through federated averaging (FedAvg) to form a global Mirai botnet attack detection model. The model achieved an accuracy, precision, recall, and F1-score of 99.93%, an area under the curve of 1.0, and 8,612 floating-point operations, contributing to 87.34% reduction in computational complexity compared to the previous work. The proposed model is well-suited for detecting botnet attacks in smart homes, smart grids, and environments where resource-constrained IoT edge devices are deployed.

Evaluation of robotic process automation tasks in large-scale environments: success rates for performance and Z scoring for non-conformity

Abstract Automation software is utilized to enhance efficiency and precision in various industries. Nonetheless, the performance and consistency of these software programs require careful monitoring through measurements. At the Finnish Government Shared Services Centre for Finance and HR (Palkeet), robotic process automation logging systems and experiments have been conducted to evaluate these measures. Success rate calculations are based on the logging system’s information and the findings (100 107 robot runs and 69 657 084 log levels during 2021-2022 and 2023/1-6). Through control charts and Z scoring, the experiments assess the proportion of non-conformity purchase invoices (987253 invoices, and 263822 defectives during 2020-2022) that the software cannot handle due to certain reasons, and the findings support an implementation of the Z scoring to observe out-of-control robot runs and analyze the causes. These results can be used to adapt and control the automation software’s processing capabilities.

The Evolution of Angular CLI and Schematics : Enhancing Developer Productivity in Modern Web Applications

This article examines recent advancements in Angular tooling and developer environments, focusing on their impact on productivity and code quality. We analyze Angular CLI's evolution, Schematics's introduction for task automation, and the transition from TSLint to ESLint for improved code linting. We demonstrate how these optimizations streamline the development process, reduce boilerplate code, and enhance overall project maintainability through literature reviews, case studies, and developer surveys. Our findings indicate a significant reduction in development time and increased code consistency across projects. The article also explores potential areas for further tooling enhancements, providing insights for Angular core developers and the broader web development community. This article contributes to the ongoing discourse on developer experience and tooling optimization in modern web application frameworks.

THE INNOVATIVE IMPACT OF GENERATIVE ARTIFICIAL INTELLIGENCE ON DIGITAL BUSINESS TRANSFORMATION

In the context of intensifying competition and evolving market dynamics, the deployment of cutting-edge technologies has become not merely a discretionary choice, but an indispensable imperative for any enterprise aspiring to achieve successful growth. Generative artificial intelligence, with its substantial potential for automation, personalisation and optimisation of business processes, is emerging as a highly promising avenue of digital transformation. This study is dedicated to investigating approaches and delineating strategies for aligning generative artificial intelligence with the requirements of digital business transformation. The research examines the development of artificial intelligence, with a focus on symbolic artificial intelligence, machine learning, deep learning and generative artificial intelligence. In addition, it considers the impact of these developments on business processes. The article identifies the potential benefits and challenges associated with the adaptation of generative artificial intelligence to the needs of modern business, in the areas of marketing, sales and data analysis. The utilisation of diverse methodologies and techniques, including prompts, fine-tuning, and the incorporation of interactive guidance systems, can enhance the efficacy and precision of generative AI in a business setting, thereby facilitating optimal outcomes in a multitude of tasks. The authors put forth the proposition of employing generative artificial intelligence technology in conjunction with Retrieval-Augmented Generation, with the objective of enhancing the quality and relevance of responses to user queries. Additionally, they advocate for the utilisation of agents or orchestration tools to provide guidance to models. The successful implementation of generative artificial intelligence hinges on three key factors: the clear definition of objectives, the selection of suitable tools and technologies, and the assurance of managerial and staff support. The implementation of generative artificial intelligence will contribute to increased efficiency through the automation of routine tasks, enhanced competitiveness through personalisation and innovation, optimised cost structures that increase profitability, and expanded opportunities for research and development.

Integrating Generative AI into the Software Development Lifecycle: Impacts on Code Quality and Maintenance

Recent advances in generative AI have depicted it as a revolutionary approach in the software development technologies pioneered to improve the codes' reliability and sustain their quality and performance. Generative AI tools can help develop code independently, suggest intelligent solutions and ideas, and enhance several development procedures thanks to superior algorithms and machine learning features. This paper discusses how generative AI can/has been applied within software development to achieve the following three goals: First, to increase the code quality using automated code generation/review. Second, the code maintainability should be improved through standards and documentation. Third, to increase the up-to-speed development productivity due to AI-based automation, namely the automation of repetitive tasks and fast prototyping. The paper also considers issues and difficulties that can be tied to AI in this context: problems of dependence on AI, ethical and security issues, and technical imperfections. Finally, the implications of generative AI in software development in the future are presented, which can open a new direction in the development of software products while primarily pointing to the processes of managing the introduction of generative AI. Using the evaluation of the current possibilities and future perspectives of generative AI presented in this paper, one can conclude its impact on the future of software engineering.