Decision Algorithm Research Articles

Cantor: A Novel Dynamic Source‐Grid‐Load‐Storage Dispatching Model for Multiple Objectives in a Regional‐Level Power System

The alignment of the real‐time power supply–demand side becomes a crucial issue in the regional‐level power system due to the growing complexity of source‐grid‐load‐storage scenarios. However, traditional dispatching schemes with inflexible time points are hardly adaptive to the unpredictability of renewable energy generation and multiple‐type load demands. To address this issue, we propose Cantor, a novel dynamic source‐gird‐load‐storage dispatching model for multiple objectives in a regional‐level power system. Cantor incorporates two techniques: (1) An identification algorithm of dispatching time points (IDTP) is designed to satisfy the requirement of power supply–demand alignment dynamically. (2) The latest Twin Delayed Deep Deterministic Policy Gradient decision algorithm is improved to calculate optimal dispatching results in different IDTPs. Furthermore, our case studies highlight Cantor's effective adaptation to complex scenarios in a regional‐level power system dispatching, outperforming state‐of‐the‐art models in power supply reliability, environmental protection, and economic costs, as well as model‐solving efficiency. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

A Digital Twin Framework Utilizing Machine Learning for Robust Predictive Maintenance: Enhancing Tire Health Monitoring

Abstract We introduce a novel digital twin (DT) framework for predictive maintenance of long-term physical systems. Using monitoring tire health as an application, we show how the DT framework can be used to enhance automotive safety and efficiency, and how the technical challenges can be overcome using a three-step approach. Firstly, for managing the data complexity over a long operation span, we employ data reduction techniques to concisely represent physical tires using historical performance and usage data. Relying on these data, for fast real-time prediction, we train a transformer-based model offline on our concise dataset to predict future tire health over time, represented as Remaining Casing Potential (RCP). Based on our architecture, our model quantifies both epistemic and aleatoric uncertainty, providing reliable confidence intervals around predicted RCP. Secondly, to incorporate real-time data, we update the predictive model in the DT framework, ensuring its accuracy throughout its life span with the aid of hybrid modeling and the use of discrepancy function. Thirdly, to assist decision making in predictive maintenance, we implement a Tire State Decision Algorithm, which strategically determines the optimal timing for tire replacement based on RCP forecasted by our transformer model. This approach ensures our DT accurately predicts system health, continually refines its digital representation, and supports predictive maintenance decisions. Our framework effectively embodies a physical system, leveraging big data and machine learning (ML) for predictive maintenance, model updates, and decision-making.

Predictors of Gastrostomy Tube Placement in Head and Neck Cancer Patients Undergoing Radiation or Chemoradiotherapy: A Systematic Review.

Malnutrition is a major problem in head and neck cancer (HNC) with up to half of patients requiring gastrostomy tube (G-tube) placement. Predicting this need remains complex given mixed evidence surrounding its usage. A comprehensive search was performed to identify studies examining risk factors associated with G-tube placement following radiotherapy (RT) or concurrent chemoradiotherapy (CCRT)in HNC patients. Sixteen retrospective studies were included (n = 11 015). The overall prevalence of G-tube placement was 44% with 76% of patients receiving reactive G-tube placement. Pretreatment dysphagia, pretreatment BMI < 18.5, and tumors in the hypopharynx were significant predictive factors for prophylactic G-tube placement. Type of chemotherapy regimen, tumors in the nasopharynx, and cytokine changes were significant predictive factors for reactive G-tube placement. Several factors were identified that contribute to increased risk of G-tube placement and may guide current decision-making algorithms.

Application of Machine Learning for Target Selection and Acid Treatment Design

Acid treatment is commonly used to enhance the production capacity of wells drilled in carbonate deposits. However, field outcomes of this procedure may vary significantly. Current approaches to acid treatment design rely on advanced software tools that evaluate major acidizing factors. Machine learning is a valuable complement to the existing techniques: it facilitates the selection of target wells and aids in defining initial parameters for design engineering on reliable and effective software platforms. This study examines potential applications of machine learning in target selection based on the history of treatment outcomes influenced by the initial well conditions, operational conditions, treatment frequency, acid volumes, acid system types, pretreatment strategies, acid system diverters, and acid residence time. Acid treatment design requires complex laboratory work to investigate the kinetics of acid-rock interactions determined by the mineral composition of the rock formation and the chemical properties of the acid system, including the concentrations of its components. The problem of predicting the reaction kinetics of acid systems by processing an array of laboratory data using machine learning methods, specifically linear regression and random forest methods, was discussed. It was demonstrated that the incorporation of machine learning enables the development of robust decision-making algorithms that optimize acid treatment by considering its multifactorial effects. These algorithms significantly simplify the tasks of acid treatment design.

Combined Open Reduction and Transantral Endoscopic Approach for Internal Fixation of Orbital Floor Fractures: A Tertiary Care Center Experience with Decision-Making Algorithm

Combined Open Reduction and Transantral Endoscopic Approach for Internal Fixation of Orbital Floor Fractures: A Tertiary Care Center Experience with Decision-Making Algorithm

ORAL HEALTH OF AGROINDUSTRY WORKERS: CHALLENGES OF THE 2030 AGENDA AND ESG CRITERIA, PROMOTING WELL-BEING AND INCLUSION TO INCREASE PRODUCTIVITY

Contemporary dentistry aims to replace private care with a health-related model. Agribusiness workers have difficulty accessing dental services. The dental care system has difficulties in acquiring, updating, referring, and monitoring patients. Artificial intelligence is a computer system that performs tasks that require human knowledge and skills, such as recognizing patterns and images, understanding open written and spoken language, perceiving relationships/connections, following decision algorithms, understanding concepts, and reasoning through the integration of new experiences, thus improving itself. This research aims to demonstrate the importance of a convolutional network for analyzing dental data that enables the diagnosis of different pathologies that may be found in agribusiness workers and the respective referral to services for recovery and control of the oral health of this target population. With the bibliometric review carried out, it was noticed that the construction and widespread use of convolutional networks in different dental specialties can contribute to the improvement of basic oral health care, in the private network and especially in the public network, thus benefiting agribusiness workers with an increase in quality of life, in oral health indexes and indicators, as well as in their productivity.

A Comparative Analysis of the Impact of Severe Acute Respiratory Syndrome Coronavirus 2 Infection on the Performance of Clinical Decision-Making Algorithms for Pulmonary Embolism

A Comparative Analysis of the Impact of Severe Acute Respiratory Syndrome Coronavirus 2 Infection on the Performance of Clinical Decision-Making Algorithms for Pulmonary Embolism

EUROVISCO Consensus Guidelines for the Use of Hyaluronic Acid Viscosupplementation in Knee Osteoarthritis Based on Patient Characteristics.

Viscosupplementation with hyaluronic acid (HA) is a commonly used intra-articular treatment for osteoarthritis (OA). We performed a Delphi consensus process to formulate guidelines for the use of intra-articular hyaluronic acid (IAHA) knee injection according to the patient's characteristics. The EUROVISCO group consists of 12 members who had expertise in clinical and/or research in the field of OA and IAHA treatment. This group drafted issues through an iterative process and subsequently voted according to a Delphi process on their level of agreement (LoA) on these recommendations. The scores were pooled to generate a median agreement score for each recommendation. The strength of the recommendation (SOR) was classified as strong if the median agreement score was ≥8. The level of consensus (LOC) was also obtained. The level of evidence was given for each recommendation. A total of 34 statements were evaluated by the expert group. A unanimous or high LoA was obtained in 16. IAHA can be considered irrespective of the age in patients with symptomatic knee OA. It can be used in patients with diabetes and/or moderate to severe obesity. It can also be used in knee OA patients with a history of gout, meniscocalcinosis and with mild-to-moderate varus/valgus malalignment. The group recommended against the use of VS in pregnant women and in OA flare. In summary, the working group provided strong recommendations for the use of IAHA injection that will facilitate individualized treatment decision algorithms in the management of knee OA.

A novel decision-making agent-based multi-objective automobile insurance pricing algorithm with insurers and customers satisfaction

Automobile insurance is essential for customers to obtain an appropriate automobile insurance policy and plays a significant role in the insurance industry. However, pricing a reasonable automobile insurance policy for different customers requires considering several indicators, such as the number of historical accidents, the frequency of long-distance driving, and the mileage driven per month. Therefore, how to make a reasonable automobile insurance pricing policy according to the needs of different customers is an urgent issue. Existing pricing methods often face the challenges of inefficiencies and mispricing, making it difficult to satisfy both insurers and customers. To better address this problem, this paper proposes a decision-making agent-based multi-objective automobile insurance pricing (DMA-MoAIP) algorithm. The DMA-MoAIP algorithm provides a diverse set of pricing strategies that meet various requirements, and considers the satisfaction of both insurers and customers. Firstly, a decision-making agent (DMA) framework is proposed for insurers, which provides an accurate assessment of the risk level for each customer. Secondly, a data-driven scoring (DDS) mechanism is established to better measure the satisfaction of insurers and customers and form a score that corresponds to their satisfaction. Thirdly, a novel multi-objective particle swarm optimization (MoPSO) algorithm is designed to search for effective and diverse solutions in dealing with automobile insurance pricing problem. Experimental results show that DMA-MoAIP outperforms five state-of-the-art multi-objective algorithms (including NSGA-II and so on) in terms of convergence and solution diversity. Specifically, the solution diversity of DMA- MoAIP in automotive pricing has increased by an average of 17%, which can provide more pricing options to meet different customer needs. In practical applications, DMA-MoAIP provides three distinct pricing strategies: insurer-oriented, customer-oriented, and compromise pricing. These strategies underscore the importance of considering relevant driving behavior metrics, rendering them valuable for real-life applications.

An efficient multi-criteria cell selection handover mechanism for Vehicle-to-Everything (V2X)

The deployment of cost-effective small cells to create ultra-high-density (UDN) heterogeneous networks in 5 G networks has emerged as a potentially effective strategy for enhancing network coverage and optimising resource allocation. However, UDN makes network selection more challenging due to the densification of small cells in 5 G and their heterogeneity. This research presents an efficient small cell selection handover mechanism for 5 G V2X networks. The proposed mechanism uses a Multiple Criteria Decision Making (MCDM) technique for the handover best cell selection to improve the overall performance. The proposed handover mechanism is context sensitive and it adapts to changing network conditions, ensuring efficient handovers during high-speed vehicular movement. Furthermore, the mechanism incorporates the concept of small cell Stay Time, which may reduce unnecessary handovers. The simulation results reveal that the proposed mechanism outperforms traditional handover techniques and Handover Decision-making Algorithm (HDMA) mechanisms significantly in terms of reducing the number of frequent handovers, minimizing link failures, and minimizing ping-pong with an average of 66 % reduction for unnecessary handovers.

Managing Urticarial Vasculitis: A Clinical Decision-Making Algorithm Based on Expert Consensus.

Urticarial vasculitis (UV) is a rare and difficult-to-treat, small-vessel leukocytoclastic vasculitis presenting with recurrent long-lasting wheals. So far, no guidelines and treatment algorithms exist that could help clinicians with the management of UV. In this review, we describe evidence on systemic treatments used for UV and propose a clinical decision-making algorithm for UV management based on the Urticarial Vasculitis Activity Score assessed for 7 days (UVAS7). Patients with occasional UV-like urticarial lesions and patients with UV with skin-limited manifestations and/or mild arthralgia/malaise (total UVAS7 ≤7 of 70) can be initially treated using the step-wise algorithm for chronic urticaria including second-generation H1-antihistamines, omalizumab, and cyclosporine A. Patients with UV with more severe symptoms (UVAS7 >7), especially those with hypocomplementemic UV, may require a multidisciplinary approach, particularly if underlying diseases, for example, systemic lupus erythematosus, cancer, or infection, are present. Immunomodulatory therapy is based on clinical signs and symptoms, and the drug availability and safety profile, and includes systemic corticosteroids, dapsone, hydroxychloroquine, anti-interleukin-1 agents, and other therapies. The level of evidence for all UV treatments is low. Prospective studies with current and novel drugs are needed and could provide further insights into UV pathogenesis and treatment.

Adaptive Prediction Structure for Learned Video Compression

Learned video compression has developed rapidly and shown competitive rate-distortion performance compared with the latest traditional video coding standard H.266 (VVC). However, existing works were restricted to fixed prediction direction and GoP size. The inflexibility on prediction structure hinders learned video compression towards optimal compression efficiency in diverse motion scenarios. In this paper, we propose to advance learned video compression with adaptive prediction structure decision. Specifically, we propose a unified compression framework that supports both forward prediction and bi-directional prediction. The framework can flexibly switch to different prediction direction to achieve better prediction performance. Meanwhile, we propose a low-complexity prediction structure decision algorithm, where prediction direction and GoP size are adaptively determined based on motion complexity to achieve optimal compression efficiency. Experimental results demonstrate that the proposed unified framework with adaptive decision algorithm improves compression efficiency of pure forward prediction-based or bi-directional prediction-based framework with neglectable ( \(0.9\%\) ) encoding time increment. Meanwhile, it achieves comparable compression performance with VVC and recent learned video coding methods.

Heuristic satisficing inferential decision making in human and robot active perception.

Inferential decision-making algorithms typically assume that an underlying probabilistic model of decision alternatives and outcomes may be learned a priori or online. Furthermore, when applied to robots in real-world settings they often perform unsatisfactorily or fail to accomplish the necessary tasks because this assumption is violated and/or because they experience unanticipated external pressures and constraints. Cognitive studies presented in this and other papers show that humans cope with complex and unknown settings by modulating between near-optimal and satisficing solutions, including heuristics, by leveraging information value of available environmental cues that are possibly redundant. Using the benchmark inferential decision problem known as "treasure hunt", this paper develops a general approach for investigating and modeling active perception solutions under pressure. By simulating treasure hunt problems in virtual worlds, our approach learns generalizable strategies from high performers that, when applied to robots, allow them to modulate between optimal and heuristic solutions on the basis of external pressures and probabilistic models, if and when available. The result is a suite of active perception algorithms for camera-equipped robots that outperform treasure-hunt solutions obtained via cell decomposition, information roadmap, and information potential algorithms, in both high-fidelity numerical simulations and physical experiments. The effectiveness of the new active perception strategies is demonstrated under a broad range of unanticipated conditions that cause existing algorithms to fail to complete the search for treasures, such as unmodelled time constraints, resource constraints, and adverse weather (fog).

THE NEED FOR LEGAL REGULATION OF THE ‟BLACK BOX” OF ARTIFICIAL INTELLIGENCE

This paper discusses the transparency and regulation of Artificial Intelligence (hereinafter the AI). It discusses the growing trend of using decision-making algorithms in various areas of life and the need for transparent and the understandable AI systems. The author highlights the concept of a ‟black box” where algorithms and their processes become hidden to users and developers, and emphasises the importance of explainability and accessibility of information about the operation of the AI. The article also addresses the right to explanation and possible strategies for ensuring transparency and accountability in algorithmic decision-making, including parameter documentation and system certification.

Artificial intelligence in the Internet of Things: Integrating and optimizing AI algorithms for real-time data processing and decision-making

Abstract. The introduction of Artificial Intelligence (AI) will provide great opportunities and pose significant challenges in Internet of Things (IoT) systems. IoT devices can create huge amount of data in real-time. Meanwhile, it is crucial to handle immediate processing and decision-making in IoT applications. However, IoT devices are usually constrained by limited computation power, memory, and energy compared to traditional devices, making the deployment of efficient AI algorithms a challenging task. In this paper, we present different algorithmic strategies to overcome the above problems, including model compression, quantization, and hardware accelerators. On the other hand, we also focus on the role of decentralization and edge computing architectures to increase the scalability and performance of AI-IoT systems. Finally, this paper also reviews energy-efficient AI algorithms and latency reduction methods to make the decision process real-time for various IoT applications.

Inner product reduction for fuzzy formal contexts

Formal concept analysis finds application across multiple domains, including knowledge representation, data mining, and decision analysis. Within this framework, the exploration of attribute reduction for fuzzy formal contexts represents a substantial area of research. We introduce a novel form of attribute reduction for fuzzy formal contexts named inner product reduction, and an algorithm for finding all inner product reducts is given by using the indiscernibility matrix, and a calculation example is given. Furthermore, for consistent fuzzy decision formal contexts, the definition and algorithm of inner product reduction are given. Finally, the concept and algorithm of inner product reduction are extended to general fuzzy decision formal contexts. Through experimental verification, the viability and efficacy of the inner product reduction algorithm for fuzzy formal contexts are verified.

Polish Speech and Text Emotion Recognition in a Multimodal Emotion Analysis System

Emotion recognition by social robots is a serious challenge because sometimes people also do not cope with it. It is important to use information about emotions from all possible sources: facial expression, speech, or reactions occurring in the body. Therefore, a multimodal emotion recognition system was introduced, which includes the indicated sources of information and deep learning algorithms for emotion recognition. An important part of this system includes the speech analysis module, which was decided to be divided into two tracks: speech and text. An additional condition is the target language of communication, Polish, for which the number of datasets and methods is very limited. The work shows that emotion recognition using a single source—text or speech—can lead to low accuracy of the recognized emotion. It was therefore decided to compare English and Polish datasets and the latest deep learning methods in speech emotion recognition using Mel spectrograms. The most accurate LSTM models were evaluated on the English set and the Polish nEMO set, demonstrating high efficiency of emotion recognition in the case of Polish data. The conducted research is a key element in the development of a decision-making algorithm for several emotion recognition modules in a multimodal system.

Increase of Reliability of Anomalies Detection on Images at Formation of Their Feature Vectors in Wavelet Bases

The method of detection of life rafts and lifeboats in the water area of seas and oceans after shipwrecks based on the recognition of anomalies in the processed images, which increases the probability of recognition of monitoring objects, is proposed. The approach to solving such a problem is substantiated. The formulation of the problem of object recognition from the perspective of binary classification in the detection of anomalies is presented. The analytical expression for the decision-making algorithm is obtained. The possibility of formalization of image matrices in the form of histograms of color (brightness) intensity distributions is considered. The contrast of the feature space on their basis is estimated. It is suggested that the contrast of feature spaces be increased due to the secondary processing of histograms of distributions in the basis of multiple-scale wavelet decomposition. The possibility of realization of wavelet transformations on the basis of Haar functions and Gauss wavelets of the 1st and 2nd orders is considered. The mechanism of formation of secondary feature vectors from three-dimensional wavelet transforms by averaging their coefficients along the time shift axis is substantiated. It is shown that at the same dimensionality of histograms of brightness distribution with newly formed feature vectors, the latter provide higher contrast of feature spaces. It is recommended to use a Gaussian wavelet of the 2nd order for the formalization of images in jpeg format, which provides, other things being equal, a greater magnitude of differences for images containing anomalies. An approach to probabilistic evaluation of the algorithm for automatic image recognition is developed. The analytical expression is obtained and its constituent elements are justified. Graphical dependences of the probability of correct detection (recognition) of anomalies, depending on the size in relation to the total area of the frame and the dispersion of the underlying background are given. The results of the experiment on image recognition with a lifeboat in the ocean water area are presented. The directions of further research are defined.

Football Match Analysis and Prediction Based on LightGBM Decision Algorithm

Football Match Analysis and Prediction Based on LightGBM Decision Algorithm

Adaptive Management of Multi-Scenario Projects in Cybersecurity: Models and Algorithms for Decision-Making

In recent years, cybersecurity management has increasingly required advanced methodologies capable of handling complex, evolving threat landscapes. Scenario network-based approaches have emerged as effective strategies for managing uncertainty and adaptability in cybersecurity projects. This article introduces a scenario network-based approach for managing cybersecurity projects, utilizing fuzzy linguistic models and a Takagi–Sugeno–Kanga fuzzy neural network. Drawing upon L. Zadeh’s theory of linguistic variables, the methodology integrates expert analysis, linguistic variables, and a continuous genetic algorithm to predict membership function parameters. Fuzzy production rules are employed for decision-making, while the Mamdani fuzzy inference algorithm enhances interpretability. This approach enables multi-scenario planning and adaptability across multi-stage cybersecurity projects. Preliminary results from a research prototype of an intelligent expert system—designed to analyze project stages and adaptively construct project trajectories—suggest the proposed approach is effective. In computational experiments, the use of fuzzy procedures resulted in an over 25% reduction in errors compared to traditional methods, particularly in adjusting project scenarios from pessimistic to baseline projections. While promising, this approach requires further testing across diverse cybersecurity contexts. Future studies will aim to refine scenario adaptation and optimize system response in high-risk project environments.