OCI-CBR: A hybrid model for decision support in preference-aware investment scenarios

Pérez-Pons, María E.; Parra-Domínguez, Javier; Hernández, Guillermo; Bichindaritz, Isabelle; Corchado, Juan M.

doi:10.1016/j.eswa.2022.118568

Cited by 3 publications

(4 citation statements)

References 36 publications

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“…where x * ki represents the characteristic description data of the standardized case and x ki represents that of the original case. x l and var(x i ) are calculated using Equations ( 26) and (27).…”

Section: Weight Value Calculation Based On Pcamentioning

confidence: 99%

“…As a mature branch of artificial intelligence, case-based reasoning (CBR) has been widely applied in other fields [23]. CBR has greater classification performance compared with traditional data mining methods [24] and it has also shown excellent performance in fields like fault diagnosis [25][26][27], risk assessment [28,29], and forest fire prediction [30][31][32]. It should be noted that the weights of case characteristic attributes in CBR have a significant impact on the prediction performance of the model.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Coal Spontaneous Combustion Hazard Grades Based on Fuzzy Clustered Case-Based Reasoning

Pei,

Jia,

Liu

et al. 2024

Fire

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Accurate prediction of the coal spontaneous combustion hazard grades is of great significance to ensure the safe production of coal mines. However, traditional coal temperature prediction models have low accuracy and do not predict the coal spontaneous combustion hazard grades. In order to accurately predict coal spontaneous combustion hazard grades, a prediction model of coal spontaneous combustion based on principal component analysis (PCA), case-based reasoning (CBR), fuzzy clustering (FM), and the snake optimization (SO) algorithm was proposed in this manuscript. Firstly, based on the change rule of the concentration of signature gases in the process of coal warming, a new method of classifying the risk of spontaneous combustion of coal was established. Secondly, MeanRadius-SMOTE was adopted to balance the data structure. The weights of the prediction indicators were calculated through PCA to enhance the prediction precision of the CBR model. Then, by employing FM in the case base, the computational cost of CBR was reduced and its computational efficiency was improved. The SO algorithm was used to determine the hyperparameters in the PCA-FM-CBR model. In addition, multiple comparative experiments were conducted to verify the superiority of the model proposed in this manuscript. The results indicated that SO-PCA-FM-CBR possesses good prediction performance and also improves computational efficiency. Finally, the authors of this manuscript adopted the Random Balance Designs—Fourier Amplitude Sensitivity Test (RBD-FAST) to explain the output of the model and analyzed the global importance of input variables. The results demonstrated that CO is the most important variable affecting the coal spontaneous combustion hazard grades.

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Section: Weight Value Calculation Based On Pcamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of Coal Spontaneous Combustion Hazard Grades Based on Fuzzy Clustered Case-Based Reasoning

Pei,

Jia,

Liu

et al. 2024

Fire

View full text Add to dashboard Cite

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“…Most complex systems are affected by multiple elements; they generate, or are related to multiple data sources, they evolve over time, and in most cases, they contain a degree of expert knowledge (Pérez-Pons et al, 2023). In this regard, it seems clear that the combined use of symbolic systems capable of modelling knowledge together with connectionist techniques that analyze data at different levels or from different sources can offer global solutions.…”

Section: History and Evolution From Ai To Generative Aimentioning

confidence: 99%

Generative Artificial Intelligence: Fundamentals

Corchado,

López F.,

Núñez V.

et al. 2023

ADCAIJ

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Generative language models have witnessed substantial traction, notably with the introduction of refined models aimed at more coherent user-AI interactions—principally conversational models. The epitome of this public attention has arguably been the refinement of the GPT-3 model into ChatGPT and its subsequent integration with auxiliary capabilities such as search features in Microsoft Bing. Despite voluminous prior research devoted to its developmental trajectory, the model’s performance, and applicability to a myriad of quotidian tasks remained nebulous and task specific. In terms of technological implementation, the advent of models such as LLMv2 and ChatGPT-4 has elevated the discourse beyond mere textual coherence to nuanced contextual understanding and real-world task completion. Concurrently, emerging architectures that focus on interpreting latent spaces have offered more granular control over text generation, thereby amplifying the model’s applicability across various verticals. Within the purview of cyber defense, especially in the Swiss operational ecosystem, these models pose both unprecedented opportunities and challenges. Their capabilities in data analytics, intrusion detection, and even misinformation combatting is laudable; yet the ethical and security implications concerning data privacy, surveillance, and potential misuse warrant judicious scrutiny.

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“…As mentioned in Section 2.2, CBR techniques are widely used in the field of decision support on environmental systems, but also in other domains, such as the financial domain (Pérez-Pons et al, 2023), or other applications, such as fault detection (Nasiri and Khosravani, 2019), planning (Jiang et al, 2019) or classification (Gao and Gao, 2021).…”

Section: Case-based Reasoningmentioning

confidence: 99%

A methodology for the automation of building intelligent process control systems

Pascual Pañach

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(English) One of the major problems to design and implement control and supervision systems for real-world processes lies in the need to stablish an ad-hoc solution for each process, i.e., environmental systems in this thesis, more precisely Waste Water Treatment Plants (WWTPs). Despite WWTPs have similarities to some extent, they have particular characteristics that must be considered in the process control system design. This fact involves considerable amounts of time and resources from design stages to maintenance, including implementation and start-up stages. This thesis proposes an Intelligent Decision Support (IDS) methodology based on the interoperation of a data-driven technique –Cas e Bas ed Reas oning (CBR)– and a model-driven technique –Rule Bas ed Reas oning (RBR)– for control, supervision and decision support of real-world processes. Design stage of control and decision support tools tends to be somehow ad-hoc regarding to the nature of the processes involved. Hence, an automated approach is proposed for the sake of scalability to different types and configurations. The proposed hybrid scheme provides complementarity in the set-point generation for the process controllers, increasing the reliability of the Intelligent Process Control System (IPCS), which is the core component of the IDSS. The IDSS is flexible to allow coping with the evolution of dynamic systems, learning from recorded situations, through a new temporal reasoning approach. Furthermore, some innovative data imputation techniques are proposed for managing the missing values appearing in a complex process. The approach presented has been implemented in different real facilities in the Barcelona area (Catalonia, Spain). (Català) Un dels principals problemes alhora de dissenyar i implementar un sistema de supervisió i control per a un procés radica en la necessitat d’establir una solució ad-hoc per a cada instal·lació – Estacions Depuradores d’Aigües Residuals (EDAR) en el context d’aquesta tesi. Malgrat les similituds entre totes les plantes , hi ha característiques particulars de cada sistema que cal tenir en compte en el procés de disseny del sistema de control. Aquest fet implica importants quantitats de temps i recursos destinats tant al disseny com a la implementació, posada en marxa i manteniment de la solució proposada. Aquesta tesi proposa una metodologia per generar un sistema intel·ligent de suport a la decisió basat en la interoperació de tècniques basades en dades –Raonament Basat en Cas os (CBR)– i en models –Raonament Basat en Regles (RBR)– per al control, supervisió i suport a la decisió de sistemes mediambientals . El disseny d’eines per al control i la supervisió sol implicar solucions fetes a mida en funció de la naturalesa dels processos considerats en cada cas. Per a això, es proposa una solució que permet automatitzar aquest disseny i escalar-lo a diferents tipus de processos i configuracions. Així, es proposa una solució híbrida que proporciona redundància en la generació de consignes per al procés, augmentant així la fiabilitat del Sistema Intel·ligent de Control (IPCS, en angles), nucli del Sistema Intel·ligent de Suport a la Decisió (IDSS, en anglès ). Addicionalment, es tracta d’una solució flexible capaç d’aprendre de les experiències enregistrades, mitjançant una aproximació de raonament temporal. A més a més, es proposen algunes tècniques innovadores d’imputació de dades mancants que apareixen en un procés complex. La metodologia es materialitza en una eina instal·lada i testejada en diferents instal·lacions reals ubicades a la zona de Barcelona.

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OCI-CBR: A hybrid model for decision support in preference-aware investment scenarios

Cited by 3 publications

References 36 publications

Prediction of Coal Spontaneous Combustion Hazard Grades Based on Fuzzy Clustered Case-Based Reasoning

Prediction of Coal Spontaneous Combustion Hazard Grades Based on Fuzzy Clustered Case-Based Reasoning

Generative Artificial Intelligence: Fundamentals

A methodology for the automation of building intelligent process control systems

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