Intelligent Technology Research Articles

Examining the impact of artificial intelligence on employee performance in the digital era: An analysis and future research direction

A new era of digital transformation has begun with the rapid integration of Artificial Intelligence (AI) technology with human management, changing traditional methods to people management. Due to the unprecedented digital transitions that businesses are seeing, AI integration in Human resource management (HRM) has become a significant area of focus. This systematic literature critically examines how AI is altering the landscape of its impact on employee performance in the HRM area in the era of digital transformation. The Scopus database, where 73 peer-reviewed articles are found These are subjected to integrated methodology of Bibliometric analysis, Content analysis, Epistemological analysis and Substantive analysis to identify the emerging themes, trends and contribution of theory in the AI impact on the employee performance in digital transformation era. The results identifies the major themes a) Digital Transformation in HRM for enhancing Efficiency b) Deep learning and neural networks in HRM c) Innovation and Technology adoption in HRM and d) Human resource investments. From the epistemological analysis results “Resource Based theory,” has been mentioned highest (4 times) and shows how HRM is evolving in the context of digital change. With 3 instances each, institutional theory with empirical study is being the most common method. Substantive analysis shows the Digital Transformation, Employee Performance, Work life balance, and Organisational change are the significant criteria, whereas sub-criteria included Innovation, performance management, and Techno stress. Additionally, it investigates the challenges and opportunities arising from how AI is incorporated into HRM procedures and its implications for workforce dynamics. The study has proposed a model of 3 major criteria and 14 sub criteria for future research actions based on substantive analysis. AI integration in HRM requires strategic planning, ethical considerations, and employee well-being. Organisations that recognise these consequences and handle issues will be more prepared for AI-driven HRM.

Human Factors Considerations in Artificial Intelligence Applications for Nuclear Power Plants

In recent years, there has been a wave of artificial intelligence (AI) technologies that offer to solve problems from shopping habits to mortgage approvals to critical systems operations. The rapidity of the development of these systems has led to both excitement and apprehension about the roles these systems should play in our modern societies. This paper focuses on the critical infrastructure industry, in general, and nuclear power generation, in particular, and seeks to scrutinize how we can leverage these novel technologies in human-centered ways to maintain or enhance the established high levels of reliability and resilience in these industries. First, we discuss the broader aspects of cognitive systems and activities that are critical to understanding the human-AI space. Then we explore different approaches to explainability in AI and the notions of trust. We then move on to discuss several human factors concepts and methods and how they can support the design of human-AI teams. We then explore recent research related to nuclear power that has been undertaken and evaluate the current industry and regulatory landscapes. Finally, we discuss identified research gaps and recommendations for solving these for the critical infrastructure space.

Pedagogical support for the use of artificial intelligence at university

Introduction. In the modern world, artificial intelligence is becoming more widespread and it is widely used in many areas of life, including education. Readiness to use artificial intelligence in professional activities is becoming one of the important conditions for a successful career.Purpose setting. This study is aimed at identifying the possibilities of pedagogical support for students when studying and using artificial intelligence at a university, including, first of all, creating motivation for the meaningful use of this technology.Methodology and methods of the study. During the research process, Russian and foreign scientific and methodological literature was analyzed, observations and surveys were conducted in groups of students.Results. As a result of the study, factors in the development of students’ motivation to study and use artificial intelligence in educational and professional activities were identified and described. The stages, principles and approaches, content and methods of pedagogical support for students in the process of studying and using artificial intelligence are proposed. The results of the work on this topic allowed the authors to propose possible directions for research into the integration of artificial intelligence technologies in the educational field.Conclusion. Artificial intelligence technology, already familiar to students as part of the educational process should acquire the features of not only a teaching tool, but also a means of self-development and more effective professional activity. Expanding the meaning of artificial intelligence from exclusively everyday to educational and professional is the main task of pedagogical support.

The Application, Challenges, and Reflection of Generative Artificial Intelligence in the Field of Education

The rapid development of Generative Artificial Intelligence technology has brought new opportunities to the field of education, giving rise to a large number of intelligent teaching applications and improving the quality and efficiency of teaching. However, the education industry has also encountered unprecedented challenges and tests, including new adjustments and changes in educational concepts, curriculum content, teaching methods, allocation of educational resources, educational evaluation systems, and teacher career transformation. These challenges urgently need to be faced and overcome by educators together. Therefore, while facing the heat of artificial intelligence technology, it is necessary to maintain rational thinking and reflect on some of the problems exposed in the application process of artificial intelligence, ensuring that artificial intelligence technology is applied reasonably and truly serving the essence and goals of education, achieving a dynamic balance between technology and humanistic education.

Discrimination Associated with Artificial Intelligence Technologies

As is known, there has been a significant increase in the use of AI technologies in various fields related to the work of both public and private sectors. Despite its importance in economic, health, security, and educational fields, the use of AI technologies has led to several ethical and legal risks. For example, there are risks of bias or discrimination when building AI systems, challenges related to privacy and data protection, including issues that arise from errors in health protocol procedures and their impact on patient health. This paper aims to highlight a number of practical applications of bias or discrimination associated with the use of AI technologies, concluding with the importance of having clear ethical governance to address the emerging risks of using AI applications.

Advances in deep learning-based intelligent receivers

Abstract. This paper presents a summary of the progress of deep learning in the research of intelligent receivers and predicts its future development direction. First, the background of the era of intelligent receivers is introduced. With the rapid development of communication technology, intelligent receivers show insufficient flexibility, low accuracy, and spectral efficiency in detecting current radio signals. This is a challenge that is difficult to overcome in order to meet the needs of modern signal recovery. With the advent of artificial intelligence technologies such as deep learning, academia and industry began to explore the potential of applying these techniques to the field of communication receivers, leading to the emergence of intelligent receivers. This paper then introduces the fundamentals of wireless communication systems and deep learning neural networks, laying the groundwork for a deeper understanding of the subsequent content. Chapter 3 provides a comprehensive overview of the application of intelligent receivers in signal reception, including channel estimation, signal detection, modulation identification, demodulation and decoding. Finally, the paper considers the practical applications of intelligent receivers and speculates on their future development.

Opportunities and challenges in higher education arising from AI: A systematic literature review (2020–2024)

With society’s continuous development and progress, artificial intelligence (AI) technology is increasingly utilized in higher education, garnering increased attention. The current application of AI in higher education impacts teachers’ instructional methods and students’ learning processes. While acknowledging that AI advancements offers numerous advantages and contribute significantly to societal progress, excessive reliance on AI within education may give rise to various issues, students’ over-dependence on AI can have particularly severe consequences. Although many scholars have recently conducted research on artificial intelligence, there is insufficient analysis of the positive and negative effects on higher education. In this paper, researchers examine the existing literature on AI’s impact on higher education to explore the opportunities and challenges presented by this super technology for teaching and learning in higher educational institutions. To address our research questions, we conducted literature searches using two major databases—Scopus and Web of Science—and we selected articles using the PRISMA method. Findings indicate that AI plays a significant role in enhancing student efficiency in academic tasks and homework; However, when considering this issue from an ethical standpoint, it becomes apparent that excessive use of AI hinders the development of learners’ knowledge systems while also impairing their cognitive abilities due to an over-reliance on artificial technology. Therefore, our research provides essential guidance for stakeholders on the wise use of artificial intelligence technology.

Human risk recognition and prediction in manned submersible diving tasks driven by deep learning models

The complexity of human cognition is increased by the multiple and interactive information clusters brought about by the application of advanced intelligent information technologies. Especially in systems operating in extreme regions far from society, human errors are more pronounced than ever before. Prolonged social isolation, extreme weightless or overweight environments, stressful atmospheres, and lack of situational awareness are all added potential elements contributing to human risk. Although the development of human reliability analysis methods and their variants continues to mature, accurately predicting the potential risk of dynamic human behavior from sparse and discrete events remains a great challenge. We focus on deep learning computational architectures that are similar to the cognitive processes and mechanisms of the brain, and build neural networks that match the perceptual activation and memory cycling of the cognitive features of the brain. This study focuses on investigating the ability of the joint SNN-ITLSTM network to predict human error behavior and the clusters of performance shaping factors that effectively characterize the far-social nature. Combining the bionic properties of SNN and the temporal update mechanism of LSTM in the form of hierarchical events constitutes a computationally efficient network architecture. Our results show that the joint model proposed in this study has the performance to strengthen temporal influences and characterize cognitive features of the brain.

Personalized and predictive strategies for diabetic foot ulcer prevention and therapeutic management: potential improvements through introducing Artificial Intelligence and wearable technology

Diabetic foot ulcers represent a serious and costly complication of diabetes, with significant morbidity and mortality. The purpose of this study was to explore advancements in Artificial Intelligence, and wearable technologies for the prevention and management of diabetic foot ulcers. Key findings indicate that Artificial Intelligence-driven predictive analytics can identify early signs of diabetic foot ulcers, enabling timely interventions. Wearable technologies, such as continuous glucose monitors, smart insoles, and temperature sensors, provide real-time monitoring and early warnings. These technologies promise to revolutionize diabetic foot ulcer prevention by offering personalized care plans and fostering a participatory healthcare model. However, the review also highlights challenges such as patient adherence, socioeconomic barriers, and the need for further research to validate these technologies’ effectiveness. The integration of artificial intelligence and wearable technologies holds the potential to significantly improve diabetic foot ulcer outcomes, reduce healthcare costs, and provide a more proactive and personalized approach to diabetic care. Further investments in digital infrastructure, healthcare provider training, and addressing ethical considerations are essential for successful implementation.

Phishing URL detection with neural networks: an empirical study

Cybercriminals create phishing websites that mimic legitimate websites to get sensitive information from companies, individuals, or governments. Therefore, using state-of-the-art artificial intelligence and machine learning technologies to correctly classify phishing and legitimate URLs is imperative. We report the results of applying deterministic and probabilistic neural network models to URL classification. Key achievements of this work are: (1) The development of a unique approach based on probabilistic neural networks that improves classification accuracy. (2) We show for the first time in URL phishing research that a machine learning model trained on a combination of open source and private datasets is successful in production. The dataset is constructed from open sources like Alexa, PhishTank, or OpenPhish and, most importantly, real-world production data from EasyDMARC. The daily validation of the model using daily reported URL data and corresponding labels, both from open-source platforms and private production, reach on average a 97% accuracy on the validation dataset, labeled by PhishTank, OpenPhish and EasdDMARC where possible mislabeled data can not be excluded and was not possible to check due to large number of URLs. Feature engineering was done without third-party dependencies. Lastly, the evaluation of both deterministic and probabilistic models shows high accuracy on short and long URLs, where short URLs are defined as having less than 50 characters.

Smart integration in agriculture: an Arduino-driven rice grain dryer for optimal post-harvest management

In the field of agriculture, the post-harvest stage often presents a multitude of obstacles that have the potential to undermine the quality and longevity of the crop. One of the primary obstacles in rice production is the effective drying of rice grains, which is crucial for preventing problems such as mold growth, discoloration, and unintentional germination. To tackle this issue, we propose an innovative solution: a rice grain drying system driven by an Arduino microcontroller. This device effectively combines technology accuracy with agricultural needs. The system is equipped with a very precise temperature sensor to maintain a uniform drying environment, fluctuating within the range of 50–60 °C. The use of a nichrome heater, selected for its consistent and dependable heat production, is complemented by an air blower to ensure the even distribution of heat throughout the drying chamber. One notable characteristic of this innovation is its significant drying capacity of 50 kg in each operation. The effectiveness of the product was verified by a rigorous testing process. A batch of glutinous rice weighing 25 kg, initially containing 25.5% moisture, was successfully reduced to 13.5% moisture content within a time frame of 125 min. In a similar vein, a batch of grains weighing 40 kg, which had just undergone threshing, had a moisture content of 22% initially. Remarkably, this batch achieved the desirable moisture level of 14% within a mere two-hour timeframe. This innovative technology not only provides a resolution to the difficulties faced in post-harvest processes but also signifies a fundamental change in thinking, introducing a fresh era where intelligent technology seamlessly integrates with traditional agricultural methods.

Refining the Scrum Paradigm: A Comprehensive Study of Software Development Practices (2020-2023)

This article presents a complex vision on software production practices aimed at enhancing the Scrum methodology within software project management. The recommended best practices are closely aligned with contemporary trends in the IT sector, including the total digitalization and virtualization of production processes, the transition to fully remote software development models, the incorporation of artificial intelligence technologies, and the implementation of cost-effective models in team organization. The modifications to software production processes discussed herein are grounded in research conducted between 2020 and 2023, encompassing insights from leading Russian, European, and global IT companies. This research underscores a significant transformation in IT business practices, reflecting a shift towards new benchmarks for Scrum team efficiency. These benchmarks emphasize the data-driven formalization of production models, automation facilitated by AI tools, and noticeable cost optimization within engineering teams, which collectively align with the evolving demands of the modern IT landscape. The article details a series of management strategies designed to adapt to these shifts, including the formalization of sprint goals and processes within Scrum, management of technical debt, and the cost-efficient organization of development teams.

Artificial intelligence technologies in Emirati private universities: challenges and effectiveness in improving the quality of education

Artificial intelligence technologies in Emirati private universities: challenges and effectiveness in improving the quality of education

Exploring the Practical Teaching Mode of Language Laboratory based on Artificial Intelligence

With the rapid development of artificial intelligence (AI) technology, its application in the field of education is becoming more and more extensive, especially in the teaching mode of language laboratory, AI technology shows great potential and innovation space. The purpose of this paper is to explore the practical teaching mode of language lab based on artificial intelligence, and to reveal its advantages and challenges in language learning by analysing the current status of the application of AI technology in teaching. This study aims to explore the in-depth analysis of the research on the practical teaching mode of language labs based on artificial intelligence. Firstly, it introduces the problems of language labs and analyses the limitations and problems of traditional labs. Secondly, the language laboratory solution measures are elaborated in detail to construct the practical teaching mode of language laboratory based on artificial intelligence. Then, the practical teaching mode of language laboratory based on artificial intelligence is explored. Finally, the application value of the language laboratory of artificial intelligence in the aspects of linguistic research and teaching practicability is discussed. As an innovative application of educational technology, the language laboratory of artificial intelligence has a positive role in promoting the transformation of the modern language teaching mode, and provides new perspectives and methods for foreign language teaching. This paper focuses on the design and construction of the practical language teaching mode of artificial intelligence. With the rapid development of artificial intelligence technology, its application in the field of education has become increasingly widespread. In language teaching, the construction of an AI-based practical teaching model can help to improve teaching efficiency and quality and stimulate students’ interest in learning. This paper elaborates the design principles, construction elements, implementation process and effect evaluation of this teaching mode from various aspects.

Qualitative Analysis of Responses in Estimating Older Adults Cognitive Functioning in Spontaneous Speech: Comparison of Questions Asked by AI Agents and Humans

Background/Objectives: Artificial Intelligence (AI) technology is gaining attention for its potential in cognitive function assessment and intervention. AI robots and agents can offer continuous dialogue with the elderly, helping to prevent social isolation and support cognitive health. Speech-based evaluation methods are promising as they reduce the burden on elderly participants. AI agents could replace human questioners, offering efficient and consistent assessments. However, existing research lacks sufficient comparisons of elderly speech content when interacting with AI versus human partners, and detailed analyses of factors like cognitive function levels and dialogue partner effects on speech elements such as proper nouns and fillers. Methods: This study investigates how elderly individuals’ cognitive functions influence their communication patterns with both human and AI conversational partners. A total of 34 older people (12 men and 22 women) living in the community were selected from a silver human resource centre and day service centre in Tokyo. Cognitive function was assessed using the Mini-Mental State Examination (MMSE), and participants engaged in semi-structured daily conversations with both human and AI partners. Results: The study examined the frequency of fillers, proper nouns, and “listen back” in conversations with AI and humans. Results showed that participants used more fillers in human conversations, especially those with lower cognitive function. In contrast, proper nouns were used more in AI conversations, particularly by those with higher cognitive function. Participants also asked for explanations more often in AI conversations, especially those with lower cognitive function. These findings highlight differences in conversation patterns based on cognitive function and the conversation partner being either AI or human. Conclusions: These results suggest that there are differences in conversation patterns depending on the cognitive function of the participants and whether the conversation partner is a human or an AI. This study aims to provide new insights into the effective use of AI agents in dialogue with the elderly, contributing to the improvement of elderly welfare.

AIGD-PFT: the first AI-driven global daily gap-free 4 km phytoplankton functional type data product from 1998 to 2023

Abstract. Long time series of spatiotemporally continuous phytoplankton functional type (PFT) data are essential for understanding marine ecosystems and global biogeochemical cycles as well as for effective marine management. In this study, we integrated artificial intelligence (AI) technology with multisource marine big data to develop a spatial–temporal–ecological ensemble model based on deep learning (STEE-DL). This model generated the first AI-driven global daily gap-free 4 km PFT chlorophyll a concentration product from 1998 to 2023 (AIGD-PFT). The AIGD-PFT significantly enhances the accuracy and spatiotemporal coverage of quantifying eight major PFTs: diatoms, dinoflagellates, haptophytes, pelagophytes, cryptophytes, green algae, prokaryotes, and Prochlorococcus. The model input encompasses (1) physical oceanographic, biogeochemical, and spatiotemporal information and (2) ocean colour data (OC-CCI v6.0) that have been gap-filled using a discrete cosine transform–penalized least squares (DCT-PLS) approach. The STEE-DL model utilizes an ensemble strategy with 100 residual neural network (ResNet) models, applying Monte Carlo and bootstrapping methods to estimate the optimal PFT chlorophyll a concentration and assess the model uncertainty through ensemble means and standard deviations. The model's performance was validated using multiple cross-validation strategies – random, spatial-block, and temporal-block methods – combined with in situ data, demonstrating STEE-DL's robustness and generalization capability. The daily updates and seamless nature of the AIGD-PFT data product capture the complex dynamics of coastal regions effectively. Finally, through a comparative analysis using a triple-collocation analysis (TCA) approach, the competitive advantages of the AIGD-PFT data product over existing products were validated. The complete product dataset (1998–2023) can be freely downloaded from https://doi.org/10.11888/RemoteSen.tpdc.301164 (Zhang and Shen, 2024a).

The Adoption of Artificial Intelligence Technologies in Arab Newsrooms: Potentials and Challenges

Arab newsrooms and journalists are still attempting to process how to benefit from using artificial intelligence (AI) in their daily news production. Many have started experimenting with generative AI. However, in countries with strong economies, or in newsrooms funded by strong economies like in the UAE, Saudi Arabia, and Qatar, news organizations have endorsed AI technologies beyond ChatGPT and similar tools. The majority of Arab journalists, however, are yet to understand what is meant by AI. Very few news organizations in the region have strategically approached the question of AI. Arab newsrooms need to examine when and why they need to use AI technologies, whether they have the infrastructure or can afford the resources. Implementing AI in Arab newsrooms presents an additional set of challenges, including language complexities, cultural sensitivities, potential biases in algorithms, and the need for tailored solutions that resonate with local audiences. The ethical implications present its own set of challenges. ChatGPT has proven to generate inaccurate data in Arabic. Resources are to be put in place to ensure the model is trained on a diverse and comprehensive dataset of Arabic text. Implementing bias detection and mitigation techniques to ensure the models are free from biases and offensive content is crucial. Newsrooms operating in authoritarian regimes have to be aware of governments' attempts to use generative AI to whitewash and promote state-centered actions and policies. Maintaining editorial integrity remains an important consideration.

Horti-IoT: a new data framework for Dutch horticulture

Abstract Consistency issues limit the sharing of horticultural data across multiple systems, resulting in challenges for users to analyze data effectively across various systems utilizing artificial intelligence technology. Introducing data governance principles can help standardize and unify data practices, making it easier for analysts to locate, comprehend, transfer and integrate data from diverse sources to enable data-driven horticulture. Implementing data governance and principles specific to horticulture can assist in standardizing the layout and format of data structures from different sources. This study aims to propose a new governance framework, Horti-IoT, based on the Data Management Body of Knowledge and several structured frameworks for the Internet of Things (IoT) governance that will lead to data-driven horticulture. This study is empirical in nature. The Dutch horticulture stakeholders are involved in this initiative, providing the data, knowledge, and experiences needed for this study. The data stream from various sources, including camera images, sap flow sensors, climate sensors and manually measured growth data. The key findings following the implementation of the Horti-IoT framework’s principles are reduced workload for data analysts, efficiency in plant monitoring, savings time in pre-processing, enhanced water resource management, reduced system administrator contacts and compliance with General Data Privacy Regulation. The new proposed Horti-IoT framework, compatible with Dutch horticulture, is presented. The data were obtained from the Lab greenhouse at the World Horti Centre in the Netherlands, in the framework of the Regionale SIA RAAK MKB call March 2022-September 2024 subsidy funds for project title ‘Gewasgroei Goed Gemeten (GeGoGe). This project is a collaboration between three educational institutions. Inholland University of Applied Science, the Hague University of Applied Science, Lentiz Vocational School, and stakeholders.

Integration of blockchain with artificial intelligence technologies in the energy sector: a systematic review

Recently, artificial intelligence (AI) and blockchain have become two of the most trending and disruptive technologies. Blockchain technology can automate payment in cryptocurrency and provide access to a shared ledger of data, transactions, and logs in a decentralized, secure, and trusted manner. In addition, with smart contracts, blockchain has the ability to govern interactions among participants with no intermediary or a trusted third party. AI, on the other hand, offers intelligence and decision-making capabilities to machines similar to humans. This review presents a detailed survey on blockchain and AI basics and features. This paper provides a review of the literature and a brief on the integration of blockchain and AI applications in multiple areas. We also identify some sole cases of blockchain–AI integration in the energy sector with current use cases. Eventually, we discuss research advantages and challenges associated with integrating blockchain with AI in the energy domain.

A review of load forecasting technology based on artificial intelligence

Abstract. Driven by the "dual carbon" goals and the construction of new-type power systems, the importance of power load forecasting technology has become increasingly prominent. Accurate power load forecasting can not only ensure the safe and stable operation of the power system, but also promote the efficient use of clean energy and reduce carbon emissions. Traditional power load forecasting methods have become insufficient in dealing with the complexity and nonlinear growth of power systems, and the development of artificial intelligence technology has brought new possibilities for power load forecasting. Power load forecasting methods based on machine learning and deep learning have gradually become a hot topic of research due to their powerful nonlinear mapping capabilities and advantages in processing big data. These methods not only perform well in short-term forecasting, but also show potential in medium- and long-term forecasting. This paper discusses the application of different artificial intelligence technologies in power load forecasting by sorting out the development status of related technologies, and provides a reference direction for future research.