Intelligent System Research Articles

Towards Linking Local and Global Explanations for AI Assessments with Concept Explanation Clusters

Understanding the inner workings of artificial intelligence (AI) systems is important both in the light of regulation (e.g., the EU AI Act), but also to uncover hidden weaknesses. Although local and global explanation methods can support this, a scalable and human-centered combination is required to combine the detail of the former with the latter's efficiency. Therefore, we present our method concept explanation clusters as a step towards explaining (sub-)strategies of the model through human-understandable concepts by identifying clusters in the input data while accounting for model predictions by local explanations. In this way, all the benefits of local explanations can be retained while allowing contextualisation on a larger (i.e., data-global) scale.

Optimizing embedded AI systems for autonomous driving: Challenges and solutions using bayesian networks

Abstract. Embedded Artificial Intelligence (AI) systems are important components of autonomous vehicles. However, incorporating AI into autonomous vehicles is technically complex, due to the constraints of computation, real-time processing of data, uncertainty handling, and hardware limitations. Bayesian Networks (BNs) are a promising method that allows probabilistic modelling in adaptive learning and environment perception. Here we report on an overview of the application of BNs on autonomous driving, with an emphasis on how BNs can be optimized for embedded system resource constraints, including both computational and energy. Various optimization techniques are discussed, such as model pruning, approximation, acceleration using hardware accelerators, such as Field-Programmable Gate Arrays (FPGA) and Application Specific Integrated Circuit (ASICs), and advanced cooling and power management to ensure AI reliability under high computational load. By reviewing these approaches, we aim to contribute to the development of more robust and green AI systems for autonomous driving.

Application and optimisation of intelligent control system for home robots

Abstract. As a significant part of the smart home, intelligent home robots can provide a variety of convenient home services such as automatic cleaning, elderly care, entertainment interaction, etc., which are highly valued and favoured by the majority of home users, and the market scale is expanding. However, the design and optimization of the control system represents a crucial technical challenge to truly realize the intelligence and autonomy of home robots. This paper presents a comprehensive analysis of the design and optimization of automatic control systems for intelligent home robots, which aims to improve the performance and user experience of intelligent home robots and better meet the needs of families. Through collection and analysis of the research on domestic robots and their control technology ,the core technical elements of the control system are summarized and refined, and the hardware structure and software framework of the control system are analyzed. The research results provide valuable insights for improving the performance and capabilities of home robot control systems to better meet the diverse needs and expectations of home users. These innovations provide a solid foundation for advancing the development of intelligent robots in the home. Future trends in control systems may include enhanced perceptual capabilities and decision-making intelligence to enable robots to better understand and meet the needs of home users.

Ethics dumping in artificial intelligence

Artificial Intelligence (AI) systems encode not just statistical models and complex algorithms designed to process and analyze data, but also significant normative baggage. This ethical dimension, derived from the underlying code and training data, shapes the recommendations given, behaviors exhibited, and perceptions had by AI. These factors influence how AI is regulated, used, misused, and impacts end-users. The multifaceted nature of AI’s influence has sparked extensive discussions across disciplines like Science and Technology Studies (STS), Ethical, Legal and Social Implications (ELSI) studies, public policy analysis, and responsible innovation—underscoring the need to examine AI’s ethical ramifications. While the initial wave of AI ethics focused on articulating principles and guidelines, recent scholarship increasingly emphasizes the practical implementation of ethical principles, regulatory oversight, and mitigating unforeseen negative consequences. Drawing from the concept of “ethics dumping” in research ethics, this paper argues that practices surrounding AI development and deployment can, unduly and in a very concerning way, offload ethical responsibilities from developers and regulators to ill-equipped users and host environments. Four key trends illustrating such ethics dumping are identified: (1) AI developers embedding ethics through coded value assumptions, (2) AI ethics guidelines promoting broad or unactionable principles disconnected from local contexts, (3) institutions implementing AI systems without evaluating ethical implications, and (4) decision-makers enacting ethical governance frameworks disconnected from practice. Mitigating AI ethics dumping requires empowering users, fostering stakeholder engagement in norm-setting, harmonizing ethical guidelines while allowing flexibility for local variation, and establishing clear accountability mechanisms across the AI ecosystem.

Data mining in AI: Evolution, applications, and future directions

Abstract. This paper provides a comprehensive analysis of the evolution and impact of data mining in the field of artificial intelligence (AI), with a particular focus on its application within social and information networks. It traces the origins of AI back to the 1956 Dartmouth Conference, highlighting the subsequent advancements in technologies such as machine learning and data mining that have fueled AI's growth. The paper explores the multifaceted applications of data mining in various sectors including healthcare, transportation, and industrial manufacturing, and delves into the challenges and innovations in recommendation systems, matrix factorization, and intelligent control of autonomous vehicles in intelligent transportation systems. The study emphasizes the significance of distributed algorithms and big data processing frameworks in enhancing the efficiency and applicability of data mining techniques.

TinyML-powered ensemble modeling for greenhouse climate control using XGBoost and LightGBM

The cultivation of crops in smart greenhouses is experiencing a profound transformation, fueled by cutting-edge technological advancements in environmental control that significantly improve efficiency, sustainability, and productivity. Nonetheless, the intricate and ever-changing dynamics of microclimate conditions pose challenges in customizing environments to satisfy the specific requirements of various plants. Accurate prediction of these microclimate parameters emerges as a promising solution to this challenge. This study explores the integration of machine learning and TinyML platforms to create a groundbreaking ensemble approach for effectively forecasting microclimate conditions. We obtained exceptional prediction accuracy for temperature (R2 = 0.9972) and humidity (R2 = 0.9976) using a stacking ensemble of XGBoost and LightGBM models. We used Optuna for accurate hyperparameter optimization and thoroughly examined the best possible input variable combinations as part of our meticulous model construction approach. The results of this study demonstrate the revolutionary potential of machine learning in greenhouse climate management, opening the door for data-driven, intelligent agricultural systems that maximize crop yields while reducing energy consumption.

Exploration and Application Analysis of Autonomous Driving Technology

Abstract. In recent years, with the acceleration of urbanization and the increase in vehicle ownership, traffic congestion has become an increasingly prominent issue. As vehicle technology advances, autonomous driving vehicles have gradually entered the public eye. Autonomous driving technology, seen as an important solution to the problem of traffic congestion, has garnered widespread attention and application. This paper employs a literature review and case analysis method to comprehensively explore the development history, core components, and challenges faced in the practical application of autonomous driving technology. It details the practical examples of companies such as Tesla, Google Waymo, and Baidu Apollo, fully showcasing the key role and vast potential of autonomous driving technology in intelligent transportation systems. Through a comprehensive analysis of the application of autonomous driving technology, this paper provides an in-depth introduction to its current applications, aiming to offer a more systematic knowledge framework and address the limitations of a single-perspective approach.

Artificial Intelligence in SAP S/4HANA: Transforming Enterprise Resource Planning through Intelligent Automation

This comprehensive article investigates the transformative impact of AI-enhanced SAP S/4HANA Finance across healthcare, manufacturing, scientific research, auto, food & Oil &Gas sectors, focusing on human-AI collaboration patterns and implementation outcomes. Through a mixed-methods approach analyzing 15 organizations over 18 months, the research examines how AI integration transforms traditional ERP functionalities into intelligent financial management systems. The article collected data from 450 end-users and 45 key stakeholders, employing both quantitative metrics and qualitative assessments to evaluate implementation patterns, challenges, and success factors. The findings reveal significant improvements across all sectors: healthcare organizations achieved 40% reduction in billing processing time and 15% improvement in collection rates; manufacturing entities realized 35% reduction in unplanned downtime and 22% decrease in working capital requirements; while research institutions demonstrated 45% faster grant processing and 35% improved budget forecasting accuracy. The article introduces the Adaptive Financial Intelligence Framework (AFIF) for conceptualizing human-AI collaboration in financial management, contributing to both theoretical understanding and practical implementation strategies. The article concludes that successful AI integration depends on industry-specific adaptations, comprehensive training programs, and robust governance frameworks while highlighting the critical role of human expertise in maximizing system benefits. These findings provide valuable insights for organizations pursuing AI-enhanced financial management solutions while offering a roadmap for future developments in human-AI collaboration within enterprise systems.

Ethical Responsibility in the Design of Artificial Intelligence (AI) Systems

Ethical Responsibility in the Design of Artificial Intelligence (AI) Systems

Artificial intelligence in the workplace – challenges, opportunities and HRM framework: a critical review and research agenda for change

PurposeThis paper specifically aims to examine how (via which activities, methods and capabilities) organizations’ management deploy Artificial Intelligence (AI) systems to address underperformance. Five mitigation strategies/recommendations are introduced to manage the challenges and facilitate greater efficacies in changing organizations.Design/methodology/approachThis paper conceptually synthesizes 47 articles, thematically reports and critically analyzes the AI–HRM–managerial decision-making relationship in changing organizations and discusses the impacts.FindingsThe results highlight three significant challenges and opportunities for changing organizations: (1) job performance challenges, (2) organizational performance challenges and HR and (3) collaborative intelligence opportunities.Originality/valueThe paper’s originality lies in addressing the current lack of a theoretical framework guiding HRM and AI experts on the managerial and strategic capabilities needed to address underperformance and their impacts in facilitating collective efficacies in human–AI collaboration in changing organizations. By further capturing an innovative HR Framework’s (1) human, (2) AI, (3) employees’ well-being, (4) jobs and (5) organizational performance, and its five key managerial recommendations/strategies, this paper develops two concepts: “technological servitization” and “re-ontological in-securitization” to advance theory in Managerial Psychology regarding the unintended/paradoxical consequences of managements’ AI-driven organizational performance interventions, including meaninglessness in organizations.

QUARL: Quantifying Adversarial Risks in Language Models

It is well documented that artificial intelligence (AI) systems have various types of vulnerabilities and associated risks. As such systems are deployed in safety-critical domains, it has become necessary not only to identify and enumerate the vulnerabilities but also to quantify the resulting risks. In this position paper, we discuss approaches for the challenge of quantifying AI risks. The approach is based on a general framework for testing and evaluating language model systems that we have previously developed (called TEL'M). In particular, we extend TEL'M to deal with the problem of quantifying the effort required by an adversary to discover and exploit a language model vulnerability.

Analysis of features of electric energy quality management and voltage regulation in housing and communal services

Subject of research: methods of improving the quality of electric energy in electric networks used for power supply mainly to municipal consumers in urban areas. The purpose of the study is to determine the most relevant methods for improving the quality of electric energy of municipal consumers, their advantages and disadvantages, as well as determining the direction of modernization. Methods and objects of research: the object of research in this material is the housing and communal complex in urban areas, in particular the electric grid complex. The work is based on such scientific research methods as analysis, synthesis, modeling and generalization. The main results of the study: the analysis of the features of electric energy quality management and voltage regulation in modern conditions. The importance and necessity of using modern intelligent systems to improve reliability, energy efficiency and quality of electric energy i s highlighted. The key prerequisites for the introduction of smart systems for monitoring the state of the electrical network are formulated. The tasks, the solution of which is necessary for the modernization of the complex, have been identified. A proposal has been made to use artificial intelligence-based systems as the main tool for modernization.

Towards the Development of a Copyright Risk Checker Tool for Generative Artificial Intelligence Systems

Generative Artificial Intelligence (GAI) is fundamentally changing the ways of working and blurring the boundaries between human and machine-generated contents. While there is an increasing interest in the adoption of GAI systems, such as ChatGPT and DALL-E, there are also serious concerns about the copyright of the contents – the inputs or generated as outputs by the GAI systems. Such concerns need to be identified and assessed to ensure the ethical and responsible use of GAI systems. Thus, this paper aims to address the key research challenge: "how to identify and assess GAI system's copyright concerns"? In response, we propose the development of a Copyright Risk Checker (CRC) Tool. This tool has been formulated and evaluated using a recognised design science research methodology, drawing on an analysis of ten legal cases across Australia, the United Kingdom, the United States, and Europe. The CRC Tool has undergone evaluation through an experimental scenario, and the results suggest that it is suitable for conducting an indicative copyright risk check of GAI systems. The outcomes of this preliminary assessment can be further examined by expert legal advisors for an in-depth analysis. The development of the CRC Tool provides a foundation for continued research and advancement in this significant area of study.

Design and Application of Fuzzy PID Controller Based on MATLAB

In an era where the development of intelligent technology is a pivotal force propelling economic advancement, enhancing production efficiency, diminishing costs, and reshaping daily life, the field of intelligent vehicle control technology has garnered significant attention among automotive researchers. The dynamics of smart cars during driving often exhibit nonlinearity and are susceptible to external environmental disturbances, such as wind resistance and road surface inequality. Traditional PID controllers have limited ability to suppress these interferences, so this study uses fuzzy PID algorithms to solve the inherent challenges in intelligent vehicle control. Through comprehensive analysis of the current landscape and technological advancements in intelligent vehicle control systems, coupled with rigorous MATLAB/Simulink simulations and real-world testing, the study showed that the overshoot of the adaptive fuzzy PID was reduced by 86% compared to the traditional PID. Consequently, it markedly enhances the precision and stability of speed control in intelligent vehicles, providing a groundbreaking perspective and practical methods for the enhancement of intelligent vehicle control systems with considerable application potential.

Artificial intelligence application and high-performance work systems in the manufacturing sector: a moderated-mediating model

This empirical investigation examines the complex dynamics between Artificial Intelligence (AI), Potential Development (PD), Training Initiatives (TI), and High-Performance Work Systems (HPWS) within manufacturing firms to gain valuable insights into how AI technologies influence high-performance work systems through employee development and training. Using a purposive sampling technique, around two hundred employees from twenty-four manufacturing firms in the textile, automotive, steel, and pharmaceutical sectors participated in the self-administered survey. The empirical analysis of the data sets was conducted using the PLS-SEM approach. This result demonstrated positive associations between AI, PD, and HPWS, emphasizing the key role of AI in supporting employee development and improving high-performance work systems. Furthermore, training’s amplification effect on the relation between artificial intelligence and professional development highlighted the significance of employees’ upskilling for AI integration. Conversely, the mediating role of PD between AI adoption and HPWS effectiveness highlighted the significant role of employee professional development in achieving HPWS through AI integration within the systems. The study offered insight into the mediation of PD between AI and HPWS effectiveness, emphasizing its centrality in translating AI-driven advances into tangible organizational outcomes. The study findings have significant ramifications for both theory and practice. Theoretically, this research adds to an evolving dialogue surrounding AI’s effects on HR practices and organizational outcomes; practically speaking, organizations can utilize this research’s insights in strategically integrating AI technologies, designing tailored training programs for their employees, and creating an environment conducive to ongoing employee development.

A Hybrid Intelligent Tourism Recommendation System Using Big Data and AI for Enhanced User-Centric Suggestions

A Hybrid Intelligent Tourism Recommendation System Using Big Data and AI for Enhanced User-Centric Suggestions

Revolutionizing agri-food technology: Development and validation of the Portable Intelligent Oil Recognition System (PIORS)

The increasing demand for high-quality vegetable oils has amplified the need for efficient, and accurate, oil recognition systems. This paper introduces the Portable Intelligent Oil Recognition System (PIORS), a pioneering advancement in the field. PIORS stands as the first-ever portable, AI-powered device tailored for rapid and accurate oil type identification, capable of delivering results within a mere five seconds. Using advanced machine learning, PIORS can distinguish between various oil types such as sesame, black seed, flaxseed, and almond oils, ensuring the quality and safety of the final product. The device's innovative design integrates an advanced sensor array with seamless Bluetooth connectivity, offering real-time data synchronization with mobile applications. Several Machine learning models, including Support Vector Machines (SVM), AdaBoost, Random Forest, K-Nearest Neighbors (K-NN), Gradient-Boosting Decision Tree (GBDT), and Extreme Gradient Boosting (XGBoost) were implemented and thoroughly validated to obtain correct categorization. The results show that the XGBoost instantaneous classification algorithm continuously beat the competition, obtaining an astounding success rate of 97 percent in predicting and differentiating between the four oil classes. PIORS not only sets a new standard for speed and efficiency in oil quality assessment but also paves the way for groundbreaking applications in food safety, environmental monitoring, and quality control processes. This paper details the development, implementation, and validation of PIORS, showcasing its potential to revolutionize the agri-food industry with its cutting-edge, AI-driven approach to oil recognition.

Investigating Characteristics of Media Recommendation Solicitation in r/ifyoulikeblank

Despite the existence of search-based recommender systems like Google, Netflix, and Spotify, online users sometimes may turn to crowdsourced recommendations in places like the r/ifyoulikeblank subreddit. In this exploratory study, we probe why users go to r/ifyoulikeblank, how they look for recommendation, and how the subreddit users respond to recommendation requests. To answer, we collected sample posts from r/ifyoulikeblank and analyzed them using a qualitative approach. Our analysis reveals that users come to this subreddit for various reasons, such as exhausting popular search systems, not knowing what or how to search for an item, and thinking crowd have better knowledge than search systems. Examining users query and their description, we found novel information users provide during recommendation seeking using r/ifyoulikeblank. For example, sometimes they ask for artifacts recommendation based on the tools used to create them. Or, sometimes indicating a recommendation seeker's time constraints can help better suit recommendations to their needs. Finally, recommendation responses and interactions revealed patterns of how requesters and responders refine queries and recommendations. Our work informs future intelligent recommender systems design.

Design an intelligent system comprising a fuzzy controller and a single-inductance DC/DC converter to improve photovoltaic system performance

In photovoltaic systems, the DC/DC converter operates in an environment characterized by disturbances, imprecise and uncertain variations in climatic parameters, and sensitivity to intrinsic photovoltaic module (PVM) parameters. These factors can significantly affect the operational efficiency and performance of both the DC/DC converters and the connected load. The objective of this research is to design an intelligent system comprising a fuzzy controller and a single inductance DC/DC converter to enhance the performance of PV systems. Two distinct Maximum Power Point Tracking (MPPT) methods are proposed: the first employs a traditional technique, while the second leverages fuzzy logic. The developed system incorporates a photovoltaic module that supplies power to a load through a step-down chopper. The experimental setup comprises two primary sections, namely an acquisition section and a power interface one. Signals acquired from the photovoltaic module transmitted to the PC via the serial port using a PIC16F877A microcontroller. This microcontroller undertakes signal processing and control of the power interface for the chopper, employing a MOSFET power transistor. The outcomes obtained during the implementation underscore the necessity for the fuzzy controller in optimizing the overall system performance.

Personalized melanoma grading system: a presentation of a patient with four melanomas detected over two decades with evolving whole-body imaging and artificial intelligence systems

Personalized melanoma grading system: a presentation of a patient with four melanomas detected over two decades with evolving whole-body imaging and artificial intelligence systems