Automated Decision Support Research Articles

Current application of artificial intelligence in laparoscopic cholecystectomy

Recent advances in artificial intelligence (AI) have sparked a surge in the application of computer vision (CV) in surgical video analysis. Surgical complications often occur due to lapses in judgment and decision-making. In laparoscopic cholecystectomy, achievement of the critical view of safety is commonly advocated to prevent bile duct injuries. However, bile duct injuries rates remain stable, probably due to inconsistent application or a poor understanding of critical view of safety. Advances in AI have made it possible to train algorithms that identify anatomy and interpret the surgical field. AI-based CV techniques may leverage surgical video data to develop real-time automated decision support tools and surgeon training systems. The effectiveness of CV application in surgical procedures is still under early evaluation. The review considers the commonly used deep learning algorithms in CV and describes their usage in detail in four application scenes, including phase recognition, anatomy detection, instrument detection and action recognition in laparoscopic cholecystectomy. The MedLine, Scopus, and IEEE Xplore databases were searched for publications up to 2024. The keywords used in the search were “laparoscopic cholecystectomy”, “artificial intelligence”. The currently described applications of CV in laparoscopic cholecystectomy are limited. Most current research focus on the identification of workflow and anatomical structure, while the identification of instruments and surgical actions is still awaiting further breakthroughs. Future research on the use of CV in laparoscopic cholecystectomy should focus on application in more scenarios, such as surgeon skill assessment and the development of more efficient models.

Adaptive pre-processing methods for increasing the accuracy of segmentation of dental X-RAY images

The subject of research in the article is the effectiveness of adaptive methods of preprocessing of medical images, in particular adaptive bilateral filter and modified CLAHE, in the tasks of segmentation of dental X-ray images. These methods make it possible to preserve important image details and effectively reduce noise, even in cases of high variability of images coming from different sources. The goal of the work is to study the impact of adaptive preprocessing methods on increasing the accuracy of segmentation of medical images and to determine the optimal combination of methods that provide the best results in segmentation tasks. The article addresses the following tasks: experimental comparison of adaptive preprocessing methods with traditional approaches, evaluation of segmentation efficiency using metrics such as Dice Score, Jacquard Coefficient (IoU Score), Precision and Sensitivity/Completeness (Recall)), as well as analysis of the effect of pre-processing on the quality of segmentation. The following methods are used: mathematical modeling, neural network training based on the U-Net model with a pre-trained timm-resnest101e encoder, image scaling to 512x512 pixels, training with a fixed learning rate of 0.001. The following results were obtained: the combined use of the adaptive bilateral filter and the modified CLAHE provided the highest segmentation quality indicators (Dice Score 0.9603 and Jacquard Coefficient (IoU Score) 0.94501), surpassing traditional methods. This proves the advantage of adaptive approaches in preserving the contours of objects and reducing noise. Conclusions: the application of adaptive preprocessing methods significantly improves the accuracy of segmentation of medical images. The combined approach including the adaptive bilateral filter and the modified CLAHE is the most effective for medical imaging tasks, which increases the accuracy of diagnosis and the reliability of automated decision support systems.

Interdisciplinary perspective on architectural programming: current status and future directions

PurposeArchitectural programming, as a critical phase in construction projects, has been widely recognized for its importance and advantages throughout the construction process. With the rapid development of the socioeconomic landscape, architectural programming has garnered increasing attention from various other disciplines, becoming a key trend in interdisciplinary collaboration. This study aims to provide a comprehensive understanding of the current status and future directions of architectural programming from an interdisciplinary perspective through scientometric analysis and systematic review.Design/methodology/approachThis study first collected English journal articles on architectural programming published between 1975 and 2024 from the Web of Science and Scopus databases. After an initial screening of titles and abstracts, 515 articles were selected for scientometric analysis to reveal the current state and advantages of architectural programming research in multidisciplinary fields. Subsequently, a second screening of full-text articles identified 75 journal articles for systematic review, focusing on research topics and challenges in interdisciplinary applications.FindingsThe study reveals an exponential increase in the number of papers related to architectural programming between 1975 and 2024, particularly in the last decade. Six key research topics of architectural programming in multidisciplinary fields were identified: (1) performance optimization and evaluation, (2) digitalization and automation development, (3) project management and decision support, (4) improvement of human and social welfare, (5) sustainable resources and environment and (6) educational practices of architectural programming. Additionally, the study identified the main challenges in the interdisciplinary application of architectural programming, including (1) incompatibility among disciplines, (2) limitations of data and methodologies and (3) insufficient social engagement. To address these challenges, three potential future directions were proposed: (1) establishing interdisciplinary teams and platforms, (2) enhancing multi-source data integration and digital transformation and (3) improving governance mechanisms and educational training.Originality/valueBy combining quantitative and qualitative methods, this study provides a comprehensive review of architectural programming research and applications in multidisciplinary fields, offering theoretical foundations and practical references for the future development of architectural programming. This review not only aids in understanding the overall status of current architectural programming research but also offers valuable insights and recommendations for future research directions and practical applications.

Michael is better than Mehmet: exploring the perils of algorithmic biases and selective adherence to advice from automated decision support systems in hiring.

Artificial intelligence algorithms are increasingly adopted as decisional aides in many contexts such as human resources, often with the promise of being fast, efficient, and even capable of overcoming biases of human decision-makers. Simultaneously, this promise of objectivity and the increasing supervisory role of humans may make it more likely for existing biases in algorithms to be overlooked, as humans are prone to over-rely on such automated systems. This study therefore aims to investigate such reliance on biased algorithmic advice in a hiring context. Simulating the algorithmic pre-selection of applicants we confronted participants with biased or non-biased recommendations in a 1 × 2 between-subjects online experiment (n = 260). The findings suggest that the algorithmic bias went unnoticed for about 60% of the participants in the bias condition when explicitly asking for this. However, overall individuals relied less on biased algorithms making more changes to the algorithmic scores. Reduced reliance on the algorithms led to the increased noticing of the bias. The biased recommendations did not lower general attitudes toward algorithms but only evaluations for this specific hiring algorithm, while explicitly noticing the bias affected both. Individuals with a more negative attitude toward decision subjects were more likely to not notice the bias. This study extends the literature by examining the interplay of (biased) human operators and biased algorithmic decision support systems to highlight the potential negative impacts of such automation for vulnerable and disadvantaged individuals.

Unlocking Business Value: Integrating AI-Driven Decision-Making in Financial Reporting Systems

This research article investigates the synergies between artificial intelligence (AI), digital transformation (DT), and financial reporting systems within the business context. The central theme explores how organizations enhance their decision-making processes by integrating AI technologies into digital transformation initiatives, particularly in financial reporting. The focal point is comprehending how the synergy of these integrated systems can unlock substantial business value, instigate strategic innovation, and elevate overall financial analytics through the adoption of intelligent, data-driven decision-making methodologies. By harnessing advanced analytics, automation, and adaptive decision support capabilities, organizations navigate the complexities of a rapidly evolving business environment, in which neural networks emerge as a valuable tool for calibrating outcomes in the financial accounting environment, demonstrating effectiveness in processing complex financial data, identifying patterns, and making predictions, ushering in a new era of transformative possibilities. The introduction of a game theory payoff matrix in this AI decision-making tool adds a strategic framework for analyzing interactions among decision-makers, considering strategic choices and outcomes in a dynamic and competitive context.

A Practical tutorial on Explainable AI Techniques

The past years have been characterized by an upsurge in opaque automatic decision support systems, such as Deep Neural Networks (DNNs). Although DNNs have great generalization and prediction abilities, it is difficult to obtain detailed explanations for their behaviour. As opaque Machine Learning models are increasingly being employed to make important predictions in critical domains, there is a danger of creating and using decisions that are not justifiable or legitimate. Therefore, there is a general agreement on the importance of endowing DNNs with explainability. EXplainable Artificial Intelligence (XAI) techniques can serve to verify and certify model outputs and enhance them with desirable notions such as trustworthiness, accountability, transparency and fairness. This guide is intended to be the go-to handbook for anyone with a computer science background aiming to obtain an intuitive insight from Machine Learning models accompanied by explanations out-of-the-box. The article aims to rectify the lack of a practical XAI guide by applying XAI techniques in particular day-to-day models, datasets and use-cases. In each chapter, the reader will find a description of the proposed method as well as one or several examples of use with Python notebooks. These can be easily modified in order to be applied to specific applications. We also explain what the prerequisites are for using each technique, what the user will learn about them, and which tasks they are aimed at.

Developing a framework for integrating blockchain technology into earthquake risk mitigation and disaster management strategies of smart cities

PurposeEarthquakes pose a significant challenge to human safety and the durability of infrastructure, highlighting the urgent need for innovative disaster management strategies. This study addresses the gap in current earthquake disaster management approaches, which are often related to issues of transparency, centralization and sluggish response times. By exploring the integration of blockchain technology into seismic hazard management, the purpose of the research is to overcome these limitations by offering a novel framework for integrating blockchain technology into earthquake risk mitigation and disaster management strategies of smart cities.Design/methodology/approachThis study develops an innovative approach to address these issues by introducing a blockchain-based seismic monitoring and automated decision support system for earthquake disaster management in smart cities. This research aims to capitalize on the benefits of blockchain technology, specifically its real-time data accessibility, decentralization and automation capabilities, to enhance earthquake disaster management. The methodology employed integrates seismic monitoring data into a blockchain framework, ensuring accurate, reliable and comprehensive information. Additionally, smart contracts are utilized to handle decision-making and enable rapid responses during earthquake disasters, offering an effective alternative to traditional approaches.FindingsThe study results highlight the system’s potential to foster reliability, decentralization and efficiency in earthquake disaster management, promoting enhanced collaboration among stakeholders and facilitating swift actions to minimize human and capital loss. This research lays the foundation for further exploration of blockchain technology’s practical applications in other disaster management contexts and its potential to transform traditional practices.Originality/valueCurrent methodologies, while contributing to the reduction of earthquake-related impacts, are often hindered by limitations such as lack of transparency, centralization and slow response times. In contrast, the adoption of blockchain technology can address these challenges and offer benefits over various aspects, including decentralized control, improved security, real-time data accessibility and enhanced inter-organizational collaboration.

A qualitative interview study to determine barriers and facilitators of implementing automated decision support tools for genomic data access

Data access committees (DAC) gatekeep access to secured genomic and related health datasets yet are challenged to keep pace with the rising volume and complexity of data generation. Automated decision support (ADS) systems have been shown to support consistency, compliance, and coordination of data access review decisions. However, we lack understanding of how DAC members perceive the value add of ADS, if any, on the quality and effectiveness of their reviews. In this qualitative study, we report findings from 13 semi-structured interviews with DAC members from around the world to identify relevant barriers and facilitators to implementing ADS for genomic data access management. Participants generally supported pilot studies that test ADS performance, for example in cataloging data types, verifying user credentials and tagging datasets for use terms. Concerns related to over-automation, lack of human oversight, low prioritization, and misalignment with institutional missions tempered enthusiasm for ADS among the DAC members we engaged. Tensions for change in institutional settings within which DACs operated was a powerful motivator for why DAC members considered the implementation of ADS into their access workflows, as well as perceptions of the relative advantage of ADS over the status quo. Future research is needed to build the evidence base around the comparative effectiveness and decisional outcomes of institutions that do/not use ADS into their workflows.

Version [1.1.0]- [DLDiagnosis: A mobile and web application for diseases classification using Deep Learning]

This paper presents version 1.1.0 of the DLDiagnosis software, which serves as an automated decision support tool for disease detection and classification through imaging analysis using deep learning. The latest update of DLDiagnosis includes significant enhancements, expanding the system to manage a total of eight different diseases. Three new diseases have been added: keratoconus, breast cancer, and pneumonia. This update focuses on refining user interface elements, identifying gaps in code logic, and improving mobile and web interface elements. Additionally, various functionalities have been improved, including reduced diagnostic latency, faster results, enhanced responsiveness, and a more consistent and intuitive diagnostic experience.

Leveraging Emerging Technologies for Enhanced Airport Ground Movement Management Efficiency and Reliability

The Executive summary serves to give the highlights of the research paper and an overview of the objectives, methods used, key findings and implications. It is a brief rundown of the whole research document and provides time-pressed readers with some insights onto the study without having to go into deeper details. Introduction: This research paper examines how blockchain, edge computing and autonomous systems can be integrated inside airports in order to enhance performance and reliability of airport ground movement management. There is need for innovative approaches towards optimizing ground movement in highly complex airports to ensure that they operate seamlessly in a growing air traffic scenario. Objectives: The main aim of this study is to explore how blockchain, edge computing and autonomous systems could change airport ground movement management entirely. These technologies will be exploited for improving data sharing, enhancing real-time processing capabilities as well as enabling automatic decision support services on the ground operations. Methodology: In this case a mixed method approach has been used which involves qualitative and quantitative analysis. Qualitative methods like surveys and questionnaires are done so as researchers get insights from different stakeholders, Integrating various stakeholders, including airlines, airports, and regulators, regarding their perspectives on integrating new technologies into ground movement management!!! Quantitative methods involve data analysis to assess the effectiveness and efficiency of proposed technological solutions. Additionally, simulations and case studies are conducted to evaluate the impact of these technologies on ground movement optimization. Key Findings: The research yields promising results, indicating significant improvements in routing efficiency, reduced taxiing times, and increased operational reliability. The integration of blockchain facilitates secure data sharing, while edge computing enables real-time processing, leading to faster decision-making in ground movement management! Autonomous systems contribute to optimizing resource routing and allocation decisions, further enhancing operational efficiency. Implications and Recommendations: The findings of the research carry both theoretical and practical implications. Theoretical implications include advancing knowledge in the field of airport ground movement management by exploring the potential of emerging technologies. On a practical level, the research provides valuable insights for airport stakeholders seeking to modernize their ground movement control systems?? Recommendations include addressing the complexity of integrating multiple emerging technologies and overcoming challenges related to data security and system interoperability. Conclusion: In conclusion, the research underscores the significance of integrating blockchain, edge computing, and autonomous systems into airport traffic management to enhance efficiency and reliability!!! By addressing key challenges in ground movement management, the study paves the way for more streamlined and resilient airport operations, ultimately benefiting the aviation industry as a whole.

IoT-AID: An Automated Decision Support Framework for IoT

IoT-AID: An Automated Decision Support Framework for IoT

A new online learning algorithm for streaming data and decision support with a Bayesian approach

ABSTRACT With the new revolution in data technology, many types of streaming data are automatically generated in our living environment. The vast amount of information carried by this streaming data demands decision support from new online learning algorithms. In this paper, we propose a new online learning algorithm to monitor changes in stream data/system status and provide automatic decision support using streaming data. The new online learning algorithm we propose consists of two components. Firstly, we adapt an existing Bayesian approach into an online learning algorithm for change detection, which serves as Component A. Then, we integrate this algorithm with a modified online learning method from repeated games (Play against Random Past), which forms Component B. Theoretical support for Component B is provided with a mathematical proof at the end. We demonstrate the algorithm's performance in this paper through simulations using both artificial data from a random process and data from the 2009 H1N1 pandemic.

Classification of pathologies on digital chest radiographs using machine learning methods

This article is devoted to the research and development of methods for classifying pathologies on digital chest radiographs using two different machine learning approaches: the eXtreme gradient boosting (XGBoost) algorithm and the deep convolutional neural network residual network (ResNet50). The goal of the study is to develop effective and accurate methods for automatically classifying various pathologies detected on chest X-rays. The study collected an extensive dataset of digital chest radiographs, including a variety of clinical cases and different classes of pathology. Developed and trained machine learning models based on the XGBoost algorithm and the ResNet50 convolutional neural network using pre-processed images. The performance and accuracy of both models were assessed on test data using quality metrics and a comparative analysis of the results was carried out. The expected results of the article are high accuracy and reliability of methods for classifying pathologies on chest radiographs, as well as an understanding of their effectiveness in the context of clinical practice. These results may have significant implications for improving the diagnosis and care of patients with chest diseases, as well as promoting the development of automated decision support systems in radiology.

An Automated Decision Support System to Analyze Malignancy Patterns of Breast Masses Employing Medically Relevant Features of Ultrasound Images.

An automated computer-aided approach might aid radiologists in diagnosing breast cancer at a primary stage. This study proposes a novel decision support system to classify breast tumors into benign and malignant based on clinically important features, using ultrasound images. Nine handcrafted features, which align with the clinical markers used by radiologists, are extracted from the region of interest (ROI) of ultrasound images. To validate that these elected clinical markers have a significant impact on predicting the benign and malignant classes, ten machine learning (ML) models are experimented with resulting in test accuracies in the range of 96 to 99%. In addition, four feature selection techniques are explored where two features are eliminated according to the feature ranking score of each feature selection method. The Random Forest classifier is trained with the resultant four feature sets. Results indicate that even when eliminating only two features, the performance of the model is reduced for each feature selection technique. These experiments validate the efficiency and effectiveness of the clinically important features. To develop the decision support system, a probability density function (PDF) graph is generated for each feature in order to find a threshold range to distinguish benign and malignant tumors. Based on the threshold range of particular features, a decision support system is developed in such a way that if at least eight out of nine features are within the threshold range, the image will be denoted as true predicted. With this algorithm, a test accuracy of 99.38% and an F1 Score of 99.05% is achieved, which means that our decision support system outperforms all the previously trained ML models. Moreover, after calculating individual class-based test accuracies, for the benign class, a test accuracy of 99.31% has been attained where only three benign instances are misclassified out of 437 instances, and for the malignant class, a test accuracy of 99.52% has been attained where only one malignant instance is misclassified out of 210 instances. This system is robust, time-effective, and reliable as the radiologists' criteria are followed and may aid specialists in making a diagnosis.

A mathematical model for reliability management of agricultural machinery

The article examines the current and practically significant task of managing the reliability of agricultural machinery used in the production activities of Russian Federation penal system institutions. A model for optimal management of technical maintenance and repairs of technical equipment is proposed in order to increase their reliability. Additionally, the necessary optimality conditions for solving the optimization problem under consideration are formulated. An algorithm for the numerical solution of the formulated optimal control problem has been developed, and the results of testing the implemented algorithm are presented. The implementation of the proposed approach in the form of an automated decision support system serves to solve the problem of increasing the operational reliability of vehicles used in agricultural activities of correctional institutions.

Application of Neuronal Intelligent Prediction Algorithms in Automated Decision Support for Bridge Construction

Abstract By predicting the construction elevation of a cable-stayed bridge with the help of the BP neural network, it was found that the BP neural network is suitable for the prediction of bridge construction, and an accurate prediction value can be obtained. The maximum deviation of the prediction value is 41.6 mm, which is a large error. Therefore, the use of intelligent algorithms to predict the linear elevation of cable-stayed bridge construction and reduce the impact of error can provide an important reference basis for the next stage of elevation control of standing molds and guarantee the accuracy of bridge construction control.

Estimation of gestating sows’ welfare status based on machine learning methods and behavioral data

Estimating the welfare status at an individual level on the farm is a current issue to improve livestock animal monitoring. New technologies showed opportunities to analyze livestock behavior with machine learning and sensors. The aim of the study was to estimate some components of the welfare status of gestating sows based on machine learning methods and behavioral data. The dataset used was a combination of individual and group measures of behavior (activity, social and feeding behaviors). A clustering method was used to estimate the welfare status of 69 sows (housed in four groups) during different periods (sum of 2 days per week) of gestation (between 6 and 10 periods, depending on the group). Three clusters were identified and labelled (scapegoat, gentle and aggressive). Environmental conditions and the sows’ health influenced the proportion of sows in each cluster, contrary to the characteristics of the sow (age, body weight or body condition). The results also confirmed the importance of group behavior on the welfare of each individual. A decision tree was learned and used to classify the sows into the three categories of welfare issued from the clustering step. This classification relied on data obtained from an automatic feeder and automated video analysis, achieving an accuracy rate exceeding 72%. This study showed the potential of an automatic decision support system to categorize welfare based on the behavior of each gestating sow and the group of sows.

ПРИМЕНЕНИЕ КРОССПЛАТФОРМЕННОГО ПРИЛОЖЕНИЯ GNUPLOT ДЛЯ ВИЗУАЛИЗАЦИИ ОБЛАСТЕЙ РАДИАЦИОННОГО ЗАРАЖЕНИЯ

The authors of the article have developed an automated decision support system for performing tasks to prevent and eliminate an emergency situation associated with the explosion of a «dirty» nuclear bomb for employees of the CCS of the territorial bodies of EMERCOM of Russia. An integral part of this system is a cross-platform application that allows you to visualize the pollution field during remote ecological mapping of the Earth's surface or object. The application is made in the C# programming language using the Gnuplot graphical utility. The Gnuplot utility is a cross-platform unified application for building all types of graphs. 
 At the moment, using the Gnuplot utility, a large library of programs has been developed with the ability to solve various mathematical problems, including problems on statistics, visualization and approximation of data, performed in different programming languages, such as C#, Python, etc.

The Conceptual Model of the BIM4NZEB-DS System for Selecting Technological Variants of Rational Passive Energy Efficiency Measures for a Sustainable Building


 
 
 
 Implementing sustainable solutions in building design is one of the significant elements in achieving the transition to sustainability. The diversity of structural elements, construction materials, and the various preferences of interested parties complicate the decision-making process. Building Information Modeling (BIM) provides a wide range of available technological solutions and methods for automated decision support and can make decision-making more efficient. This study presents the conceptual model for BIM4NZEB-DS system for the automated selection of rational variants of passive energy efficiency measures. The algorithm of this system will integrate the solutions for data transfer from the BIM model and multi-criteria methods for the analysis of variants. The assessment of variants is based on economic and environmental criteria. The purpose of the system is to increase the reliability of the assessment, optimise the technological and human resources required, and minimise the time of decision-making. The developed system will be validated using the BIM model of real-case building
 
 
 

Implementing an Automated Prediction Model to Improve Prescribing of HIV Preexposure Prophylaxis

Summary Antiretroviral preexposure prophylaxis (PrEP) is nearly 100% effective at decreasing HIV acquisition but is underused in priority populations. Primary care clinicians need tools to help them identify persons likely to benefit from PrEP use and prescribe it when appropriate. The researchers developed and validated an automated decision support tool with interactive alerts in the electronic health record to increase PrEP discussions and prescribing in primary care. They piloted the tool at three federally qualified health centers and assessed feasibility, acceptance by clinicians, and preliminary impact on PrEP care. Of 33,803 patients who visited the pilot clinics from July 2022 through January 2023, providers received PrEP alerts at the point of care for 2.2% of patients, demonstrating feasibility. Although numbers of PrEP prescriptions remained low, the proportion of all patients with new PrEP prescriptions was 4.5 times higher at pilot clinics compared with matched control clinics (0.09% vs. 0.02%). Implementation of the decision support tool was associated with a statistically nonsignificant 5.5% increase in HIV tests per 100 patients. In qualitative interviews, providers said the tool facilitated PrEP discussions with patients, particularly for those patients who would not have initiated discussions because of stigma. The researchers found that acceptance, use, and impact of machine-learning models for PrEP depends on collaborating with and building trust among providers, including blending a data-driven approach to identifying patients at increased risk for HIV acquisition with providers’ traditional decision-making framework. These approaches could be useful for health care organizations seeking to implement automated prediction models across all areas of medicine.