Decision System Research Articles

Multimodal Perception and Decision-Making Systems for Complex Roads Based on Foundation Models

Multimodal Perception and Decision-Making Systems for Complex Roads Based on Foundation Models

Interdisciplinary Perspectives on Agent-Based Modeling in the Architecture, Engineering, and Construction Industry: A Comprehensive Review

This paper explores the transformative impact of agent-based modeling (ABM) on the architecture, engineering, and construction (AEC) industry, highlighting its indispensable role in revolutionizing project management, construction processes, safety protocols, and sustainability initiatives including energy optimization and occupants’ comfort. Through an in-depth review of 178 documents published between 1970 and 2024 on current practices and the integration of ABM with emerging digital technologies, this study underscores the critical importance of ABM in facilitating enhanced decision-making, resource optimization, and complex system simulations. For instance, ABM is shown to reduce project delays by up to 15% through enhanced resource allocation and improve safety outcomes by simulating worker behavior and identifying potential hazards in dynamic construction environments. The results reveal ABM’s potential to significantly improve construction methodologies, integrate technological advancements seamlessly, and contribute to the development of sustainable and resilient building practices. Furthermore, this paper identifies key areas for future research, including the exploration of ABM’s capabilities in conjunction with other digital innovations to unlock new avenues for efficiency and sustainability in construction. This study sets out a forward-looking agenda for providing this modeling approach to address contemporary challenges and harness opportunities for innovation and growth in the AEC sector.

Recommendation for Prospective Permanent Employees using the Simple Additive Weighting Method

The rapid development of technological progress has made the use of personal computer technology increase significantly, where this use has made computers into branches that can still be developed, one of which is creating a decision-making system. Decision Support System is a computer-based system that is intended to assist decision making by utilizing certain data and models to solve various semi-structured problems. The application of Decision Support Systems can be found in various fields, one of which is a decision support system for prospective employees. This study aims to design a system that can provide the best decision in determining permanent employees at J&T Express Kotanopan. The method used in this study is the SAW (Simple Additive Weighting) method, with a website-based decision support system that can be used without time and place constraints, it can help J&T Express in selecting permanent employees. The results of testing this method have an accuracy level of more than 90% based on the data tested. Based on the results of the highest value obtained using the SAW method, this study was successful in determining permanent employees at J&T Express Kotanopan

From operational design domain to test cases: A methodology to include harsh weather

Background To gain widespread use, assisted and automated driving (AAD) systems will have to cope with harsh weather conditions, such as rain, fog, and snow. This affects the development and testing of perception and decision-making systems. Since the weather cannot be controlled in field tests, the availability and use of virtual simulation and test facilities that can accurately reproduce harsh weather becomes vital. Test cases subjecting the system under test to harsh conditions, covering all expected weather phenomena in both typical and challenging scenarios, must be defined to evaluate all aspects of the system. Methods State-of-the-art in scenario-based and hash weather testing for AAD systems was analysed; based on the analysis, a team with diverse expertise in AAD development and testing defined a methodology for defining a set of harsh weather test cases. Results This paper proposes, and exemplifies the use of, a methodology to develop a representative set of test cases based on the defined operational design domain and use cases for an AAD system under development, considering the possibility of reproducing tests in different test environments with a focus on harsh weather. Conclusions We believe that our proposed methodology can accelerate the overall testing process and contribute to the difficult safety assurance challenges for automated vehicles.

Research on the Constructed-Wetland/Regulation-Tank System at Pontificia Universidad Javeriana's Bogotá Campus

The stormwater harvesting (SWH) project at Pontificia Universidad Javeriana (Bogotá) began in 2007, initiated by the Water and Environmental Engineering research group (Ciencia e Ingeniería del Agua y el Ambiente), as part of PUJB's Environmental Management Plan and in collaboration with the University's Office of Physical Resources. This process included assessments of water supply and demand, rainwater quality, and financial and construction considerations. The system—a constructed wetland/regulation tank (CWRT)—collects rainwater runoff from the university's parking building (3,776 m²), the soccer field, and surrounding areas (14,816 m²). Beyond its hydrological performance, especially during periods of water scarcity, the system also functions as a full-scale laboratory to study biodiversity and ecological impacts in hydrological and hydraulic processes. The monitoring program, focused on changes in rainwater quantity and quality, has inspired several master's and doctoral research projects. These studies explore critical topics in urban hydrology, including stormwater harvesting, urban sediment management, real-time decision-making systems, and environmental and human health. This paper outlines the development of the stormwater harvesting system, shares the main findings from research projects, and discusses ongoing projects.

Design and Implementation of a 7-Person Voting Device based on Quartus

As the complexity of digital system design continues to increase, efficient and precise decision support systems have become increasingly important. This paper presents the design and implementation of a 7-voter decision system based on Altera’s Quartus software. The voter is applied in the core decision-making process to ensure the accuracy and efficiency of decisions. The study begins by outlining the design principles of the 7-voter decision system, using a design philosophy that minimizes timing delays and optimizes hardware resource utilization. A complex majority voting logic circuit is implemented using high-level hardware description languages (VHDL/Verilog), ensuring the advanced nature and applicability of the voting circuit. In terms of the logical implementation of the voting circuit, the paper conducts an in-depth analysis of the optimization of combinational and sequential logic and standardizes the input and output to enhance the signal stability and the system’s resistance to interference. On the Quartus platform, the design process of the voting system is elaborated in detail, with careful consideration given to boundary conditions and hardware compatibility. The implementation of the voter has undergone rigorous simulation and testing. Strict parameter setting for simulations and waveform analysis confirm the correctness of the circuit, and fault simulation tests further confirm its reliability. Performance indicators such as response time and percentage of resource utilization have been quantitatively analyzed and meet the design objectives set beforehand. Overall, this research not only realizes a highperformance 7-voter decision system but also provides an effective case study for applications on the high-performance Quartus design platform. Future research can build on the current work to further optimize algorithms and hardware, explore the potential for the application of the voter in a broader range of fields, and adapt to the new features of the Quartus platform to achieve more powerful decision support functions.

Dual-tiered insights: cross-examining entities in free text electronic health records

Abstract Background Poor determination and processing of actions at the primary care level are found to be associated with unstructured and cluttered electronic health records (EHRs). Physicians recommend that EHRs must highlight entities, such as action items, findings, follow-ups, and reasons for medication changes. There’s consensus around the success of large language models (LLMs) in detecting clinical and non-clinical entities in EHRs. However, end-to-end systems make limited efforts to go beyond entity and relationship detection to support clinical decision-making. Methods We propose a two-tier system that generates case-based comparative reports for entities highlighted by physicians in EHRs. First, we fine-tuned a custom Bidirectional Encoder Representations from Transformers (BERT) model that detects the required entities within the EHRs. Second, a clustering-based service generates a report comparing the detected entities of a specific EHR with other EHRs sharing the same conditions determined by the healthcare professional. Results The system leverages the MIMIC IV deidentified free-text clinical notes dataset. The first-tier successfully classified entities with a macro-score average range of [0.87 - 0.94]. For the second tier, preliminary simulations on a set of test samples were made to cluster the entities. The simulations leverage a custom statistical tests engine offering a suite of entity-type specific tests. The results of these tests are used to generate the case-based detected entities comparative report. Conclusions Highlighting clinical and non-clinical entities bridges the communication gap between primary and secondary care. Clustering these entities extends the capability of LLMs in clinical support decision systems by providing healthcare professionals with the ability to run comparative analyses between EHRs. The comparative analysis reports provide a factual comparison between a selected number of detected entities and their thresholds. Key messages • Clustering clinical and non-clinical entities extends the capacity of named entity recognition in clinical decision-making systems. • Ability to generate a comparative analysis report based on figures extracted from recognised entities in free-text electronic health records.

A Review of Research Progress on the Impact of Information Asymmetry on Green Consumption

Abstract: This paper discusses the impact of information asymmetry on green consumption and sustainable consumption. It emphasizes the importance of information in promoting green consumer behavior and highlights the role of media publicity and evaluation systems in shaping consumer perception and decision-making. The paper also explores the influence of government policies and subsidies on green consumption, noting that government intervention can benefit the entire green product industry chain. The authors suggest that in order to reduce the impact of information asymmetry, comprehensive and high-quality information should be provided to consumers, and consumers should be educated on information filtering and processing. The paper concludes by emphasizing the need for further research on the quantitative assessment of green information dissemination, the influence of green information on consumer psychology and behavior, and the role of government information in promoting green consumption. It calls for a multi-party linkage mechanism to drive the sustainable development of green consumption.

Translating Law and Code in Government: Algorithmic Decisions and Their Legal Effects in Canada

This article analyzes the translation of law into computer code and the use of automated decision-making systems in government to make legal distinctions. Specifically, how are algorithmic decisions tied to law, and what happens when legal effects are mediated through technologies? The sociology of translation and Bruno Latour's theory of law, as elaborated by Kyle McGee, provides the means to study associations between law and technology. I trace how the force of law can be extended when mediated through computer systems and analyze the associations of law and technology in Canada's government, through projects exemplifying the shift to “code-driven law.” These include the translation of “rules-as-code,” and several of the sociotechnical systems governing Canada's borders, demonstrating how design choices in government digital services inevitably shape the outcomes of public policy and can have legal effects. While Latour's legal scholarship avoided traditional questions of legitimacy, a key consideration for automated government systems is how legitimacy is constructed and contested. For rules-as-code, legitimate algorithmic outcomes should be traceable to law, but existing government systems commonly maintain legitimacy by identifying a human actor “in-the-loop” as the ultimate decision-maker, thereby obscuring how thoroughly imbricated human and algorithmic agency are in contemporary governance.

SoftLungX: leveraging transfer learning with convolutional neural networks for accurate respiratory disease classification in chest X-ray images

Medical imaging is an indispensable and very important step in the diagnosis and treatment of illnesses. However, due to large amounts of resources necessary for training the model, training from scratch may not invariably emerge as the optimal recourse. Transfer learning has emerged as a viable solution, where pretrained weights from ImageNet are initially utilized and fine-tuninned afterwards. This paper presents a novel approach employing transfer learning to classify both respiratory diseases and radiological findings from chest X-rays. The dataset comprises 191 660 X-ray images gathered from public databases and 752 chest X-ray images obtained from a retrospective study at the University Clinical Center of Kragujevac, forming the foundation of Big data analysis. It includes various conditions such as atelectasis, cardiomegaly, infiltration, pleural thickening, non-viral pneumonia, pneumothorax, COVID-19 viral pneumonia, tuberculosis, etc. as well as masses and nodules indicative of tumors and images of healthy subjects. Although there are existing solutions with some diseases, this is the first paper that inlcludes differentiation between healthy and diseased cases, as well as in case of present disease, as much as 18 different classes are inlcuded in the analysis, which has not been done so far. The proposed methodology included transfer learning with DenseNet121 as the backbone and CheXNeXt weights as initialisation scheme, upon which additional layers were added for fine tuning. The results demonstrate the model’s capacity to distinguish between healthy and diseased, with an average area under curve (AUC) of 0.99. The AUC ranges from 0.86 for nodules to 0.99 for pneumonia and 0.99 for COVID-19. Our method outperforms the state-of-the-art in accuracy across all 18 classes, with the exception of classes consolidation, mass, and nodule. These findings are promising for a tested clinical site, though further validation in clinical environment accros multiple sites may be necessary before using the model as a standalone decision system, rather than a decision support system for disease diagnosis based on chest X-rays.

Addressing status quo bias to turn a failing decision system introduction around: the case of DB Schenker

ABSTRACT A new decision system introduction gone wrong can be costly and have further negative consequences. One of the reasons why IT introductions can go wrong is user resistance – often caused by a status quo bias of the users towards the old system. An ironic aspect as decision systems are often especially designed to counter decision biases. This case study presents evidence that targeted countermeasures to status quo bias can help. Based on a review of the available literature, research-based countermeasures were identified and later complemented with practice-based countermeasures from a case study at DB Schenker. With the help of these countermeasures, DB Schenker managed to turn around a new financial reporting system introduction gone wrong.

Effect of Management Information System on Growth of Agro-Firms in Anambra State

The study examined the effect of management information systems on the growth of agro-firms in Anambra State. The objectives were to: determine the effect of decision-making information systems on the Return on Investment (ROI) of agro-businesses in Anambra State, Nigeria, and ascertain the extent to which database management systems affect the ROI of agro-businesses in Anambra State, Nigeria. The study adopted a survey research design, with data generated from both primary and secondary sources. The data collection method was a questionnaire, which was randomly administered to staff of selected agro-firms. The population of the study was 6,727, while the sample size was 1,293, out of which 929 questionnaires were retrieved. The hypotheses were tested using the multiple regression analysis method at a 0.05% level of significance. The findings revealed that decision-making information systems had a significant effect on the ROI of agro-businesses in Anambra State, with a t-statistic of 7.743 and a probability value of 0.000. Additionally, database information systems had a significant effect on the ROI of agro-businesses in Anambra State, with a t-statistic of 2.476 and a probability value of 0.013. The study concluded that management information systems have a significant positive effect on agro-firms in Anambra State. It recommended that agricultural firms should invest in advanced decision support tools to aid data-driven decisions that maximize ROI. Furthermore, agro-business managers should focus on optimizing their database information systems to improve data organization and accessibility. Regular maintenance and updates of the database systems are crucial for ensuring accurate and timely information, which can positively impact ROI.

Automatic Recommender System of Development Platforms for Smart Contract-Based Health Care Insurance Fraud Detection Solutions: Taxonomy and Performance Evaluation.

Health care insurance fraud is on the rise in many ways, such as falsifying information and hiding third-party liability. This can result in significant losses for the medical health insurance industry. Consequently, fraud detection is crucial. Currently, companies employ auditors who manually evaluate records and pinpoint fraud. However, an automated and effective method is needed to detect fraud with the continually increasing number of patients seeking health insurance. Blockchain is an emerging technology and is constantly evolving to meet business needs. With its characteristics of immutability, transparency, traceability, and smart contracts, it demonstrates its potential in the health care domain. In particular, self-executable smart contracts are essential to reduce the costs associated with traditional paradigms, which are mostly manual, while preserving privacy and building trust among health care stakeholders, including the patient and the health insurance networks. However, with the proliferation of blockchain development platform options, selecting the right one for health care insurance can be difficult. This study addressed this void and developed an automated decision map recommender system to select the most effective blockchain platform for insurance fraud detection. This study aims to develop smart contracts for detecting health care insurance fraud efficiently. Therefore, we provided a taxonomy of fraud scenarios and implemented their detection using a blockchain platform that was suitable for health care insurance fraud detection. To automatically and efficiently select the best platform, we proposed and implemented a decision map-based recommender system. For developing the decision-map, we proposed a taxonomy of 102 blockchain platforms. We developed smart contracts for 12 fraud scenarios that we identified in the literature. We used the top 2 blockchain platforms selected by our proposed decision-making map-based recommender system, which is tailored for health care insurance fraud. The map used our taxonomy of 102 blockchain platforms classified according to their application domains. The recommender system demonstrated that Hyperledger Fabric was the best blockchain platform for identifying health care insurance fraud. We validated our recommender system by comparing the performance of the top 2 platforms selected by our system. The blockchain platform taxonomy that we created revealed that 59 blockchain platforms are suitable for all application domains, 25 are suitable for financial services, and 18 are suitable for various application domains. We implemented fraud detection based on smart contracts. Our decision map recommender system, which was based on our proposed taxonomy of 102 platforms, automatically selected the top 2 platforms, which were Hyperledger Fabric and Neo, for the implementation of health care insurance fraud detection. Our performance evaluation of the 2 platforms indicated that Fabric surpassed Neo in all performance metrics, as depicted by our recommender system. We provided an implementation of fraud detection based on smart contracts.

Employee Insights into Organizational Justice and Job Performance: The Case of Insurance Companies

This paper explores employee insights into organizational justice and job performance. It also examines the association between employee insights into organizational justice and their personal traits and analyzes the association between employee insights into organizational justice and job performance. This study used descriptive statistics as well as correlation analysis. The survey data were gathered from 250 employees of ten insurance companies (including both life and non-life) in Kathmandu Valley. The results indicate a significant and positive association between employee insights into organizational justice and job performance. It indicates that the justice of management matters a lot in enhancing employee job performance at work. The study's findings also reveal a substantial association between all employees' demographic characteristics and their insights into organizational justice. It indicates that when employers treat their employees fairly, they do well on the job. To develop a climate of organizational justice, organizations must ensure that rewards and resources are distributed fairly. It should prioritize clear compensation policies, fair reward systems, and performance-based incentives. It must also prioritize the establishment of fair and consistent decision-making systems by creating standardized systems for evaluations, promotions, and disciplinary actions. In addition, fostering respectful and equitable interpersonal interactions can also help to build an environment of organizational justice. For this, organizations must train managers and supervisors on effective communication skills, emphasizing the importance of treating employees with dignity and respect, promoting open-door policies in which employees feel comfortable discussing concerns, and ensuring that managers provide clear and honest clarification for decisions. All of these activities ultimately increase employees' willingness to optimize their potential for higher levels of job performance.

EU’s AI Regulation Approaches and Their Implication for Human Rights

The rapid development of Artificial Intelligence (AI) technologies presents significant opportunities, but also introduces critical challenges, particularly concerning the protection of human rights. In response, the European Union (EU) has developed comprehensive AI regulations, most notably the AI Act, positioning itself as a leader in global AI governance. However, questions remain regarding the effectiveness of these laws in safeguarding key human rights such as privacy, equality, autonomy, and freedom. This study aims to assess the sufficiency of the EU’s current AI regulations in protecting human rights, analyze the specific impacts of AI technologies on privacy and equality, and explore emerging legal trends in AI regulation within the EU and their broader global implications. Utilizing a mixed-method approach, this research integrates legislative analysis, comparative research, scientific forecasting, and interdisciplinary inquiry. The study critically examines the provisions of the AI Act and other relevant EU regulations, comparing them with international legal frameworks to identify gaps and future challenges. The findings reveal that, while the EU’s AI regulations offer foundational protections, there are significant deficiencies in addressing complex issues such as privacy, algorithmic bias, and transparency, particularly in high-risk areas like healthcare, law enforcement, and autonomous systems. The study also highlights that existing regulations are inadequate in managing the rapid integration of AI technologies across sectors and that marginalized groups are especially vulnerable to the risks posed by algorithmic decision-making systems. Furthermore, the EU’s AI regulatory framework is poised to become a global benchmark, but there is an urgent need for more adaptive and flexible legal mechanisms to keep pace with the dynamic nature of AI advancements. In conclusion, the study emphasizes the need for legal reforms to bridge the current gaps in privacy protection and address algorithmic bias, ensuring the robust protection of human rights in the age of AI.

Intermittent Preventive Treatment of Malaria in Pregnancy and the Impact on Neonates in African Countries as Assessed by Entropy Weight and TOPSIS Methods.

Background/Objectives: In regions of Africa with a high prevalence of malaria, pregnant women in their first or second trimester should be administered intermittent preventive treatment in pregnancy (IPTp). However, infants may contract malaria despite the IPTp therapy that their mothers have received. The objective of the present investigation was to assess the symptoms and various treatments for neonatal malaria. Methods: Entropy weight and TOPSIS were used to achieve the study goal. The TOPSIS multi-attribute decision-making system was used to assess newborn malaria symptoms and select the optimal treatment, even for mothers receiving IPTp medication during pregnancy. The entropy weight approach calculated TOPSIS attribute weights. The present research used UNICEF data for 14 African nations in 2023. Results: The results indicated that neonates whose mothers received IPTp therapy ultimately contracted malaria, with diarrhea being the primary symptom. It is important to note that health providers administer a combination of zinc and oral rehydration solution (ORS) to infants as the most effective treatment for malaria symptoms, thereby abandoning the first-line treatment for malaria, artemisinin-based combination therapy (ACT). Conclusions: The most effective treatment for neonatal malaria is a combination of zinc and ORS, although less than half of children in Africa have access to ORS. Therefore, the findings of this study may encourage African countries to prioritize co-pack therapy in their procurement and supply, healthcare provider training, and expenditures. This therapy will also help alleviate the symptoms of malaria in neonates.

Technology is never neutral: Robodebt and a human rights analysis of automated decision-making on welfare recipients

ABSTRACT Government agencies have embraced automated decision-making systems, particularly in welfare, despite the risk of mass-replicated erroneous decision-making creating systemic issues. This article uses Robodebt as an Australian case study with global ramifications to argue that government agency use of erroneous automated decision-making systems in welfare raises human rights concerns. First, this article examines the shortcomings of administrative law in a digital welfare context. Second, the article closely analyses the International Covenant on Civil and Political Rights and International Covenant on Economic, Social and Cultural Rights and identifies several human rights concerns raised by Robodebt. This article then examines ex-ante impact assessment models and concludes by recommending an obligation on government agencies to conduct a human rights impact assessment before deploying an automated decision-making system. By demonstrating the human rights implications on welfare recipients arising from automated decision-making systems, this article justifies the use of international human rights law as a complementary framework.

VD furnace discharge temperature control method study

This paper designs a set of VD furnace temperature control decision-making system. In the refining process, the harsh environment and harsh conditions can be overcome, and the control and prediction of VD furnace outlet temperature can be realised. According to the known parameters of the current smelting information, a matching model is established, and a GM (1, N) model based on grey theory is designed to predict the temperature. At the same time, the control parameter optimisation model is designed, and the local optimisation method is used to optimise the control parameters to calculate the prediction accuracy. The experimental results show that the system can accurately control the refining temperature of the VD furnace and effectively improve the quality of steel. This is of great significance to improve steelmaking production efficiency and reduce costs.

SWOT analysis of the application of artificial intelligence in radiologic technology and radiology

Artificial intelligence (AI) is bringing changes to radiology and radiologic technology, enabling the development of programs and algorithms that facilitate diagnosis and decisions. It is essential to understand how AI can improve patient outcomes, increase the efficiency of investigations and reduce costs. Machine and deep learning have proven to be extremely useful in the detection and characterization of lesions, improving routine imaging techniques and facilitating the work of radiologists by reducing workload and improving the quality of reporting. The practical application of artificial intelligence in radiology and radiologic technology has been slowed down by the lack of integrated solutions and well-structured data archives, as well as challenges such as non-transparency of decision-making systems and large amounts of quality data needed to train artificial intelligence models. There are concerns about the potential impact of AI on the work of radiologists and radiologic technologists, which may hinder the development and implementation of this technology. Textual data derived from image reports can provide valuable healthcare insights. Natural language processing (NLP), a subset of artificial intelligence, offers promising solutions for handling unstructured text in these reports, opening a new era in extracting information from medical images and related reports. Due to the challenges in training experts for the application of AI in healthcare, a multidisciplinary approach will have to be used and investments in collaboration and education will have to be made. There are outstanding issues of liability and regulation regarding data storage and privacy, particularly in the case of cloud storage. The concern of the workforce and their lack of education about artificial intelligence represents an obstacle to its adoption, but also offers an opportunity for the technological advancement of the profession.

The Natural Pattern-based Fuzzy Investment System

Research background: The underlying research problem concerns simplifying nature’s infinite sustainability pattern into a usable system to prove self-care in the financial field. Many disciplines have discovered self-sustaining systems, implying one should also exist in the portfolio- or asset management field. Purpose of the article: This paper models and applies a self-sustaining investment system based on nature’s sustainability pattern to present a usable and easily adaptable financial self-care model that can contribute to societal and individual well-being. Methods: This interdisciplinary research incorporates fractal patterns from nature and integrates these into an innovative investment decision system via a working and utilizable model. The article approaches the problem in three ways. First, it applies an unprecedented, proprietary fuzzy heuristic approach to parse the Mandelbrot set hiding nature’s growth code. Second, it subjects the resulting model system to static, dynamic, and iterative methods. Finally, it tests the above in practice in a focus group research project based on individual decisions in a portfolio collision. Findings & value added: The paper brings manageable order to investment decision processes using a specific econophysics approach to provide a complex whole of frames, alternatives, and dynamic wealth management functions. The paper attempts to demonstrate the self-sustaining power of the natural order in investment portfolio returns, behavioral finance, and wealth management decisions. The theory of an efficient, well-functioning, self-sustaining investment decision system based on heuristic criteria is developable and has been proven. The study made the organizational dominance measurable and – through fuzzy logic – simplified the complexity of the Mandelbrot set. The research also shows the scalability of the fuzzy symmetry framework, implying that it is transferable to other disciplines.