Search Results (8)

Predicting the energy consumption of buildings plays a critical role in supporting utility providers, users, and facility managers in minimizing energy waste and optimizing operational efficiency. However, this prediction becomes difficult because of the limited availability of supervised labeled data to train Artificial Intelligence (AI) models. This data availability becomes either expensive or difficult due to privacy protection. To overcome the scarcity of balanced labeled data, semi-supervised learning utilizes extensive unlabeled data. Motivated by this, we propose semi-supervised learning to train AI model. For the AI model, we employ the Belief Rule-Based Expert System (BRBES) because of its domain knowledge-based prediction and uncertainty handling mechanism. For improved accuracy of the BRBES, we utilize initial labeled data to optimize BRBES’ parameters and structure through evolutionary learning until its accuracy reaches the confidence threshold. As semi-supervised learning, we employ a self-training model to assign pseudo-labels, predicted by the BRBES, to unlabeled data generated through weak and strong augmentation. We reoptimize the BRBES with labeled and pseudo-labeled data, resulting in a semi-supervised BRBES. Finally, this semi-supervised BRBES explains its prediction to the end-user in nontechnical human language, resulting in a trust relationship. To validate our proposed semi-supervised explainable BRBES framework, a case study based on Skellefteå, Sweden, is used to predict and explain energy consumption of buildings. Experimental results show 20 ± 0.71% higher accuracy of the semi-supervised BRBES than state-of-the-art semi-supervised machine learning models. Moreover, the semi-supervised BRBES framework turns out to be 29 ± 0.67% more explainable than these semi-supervised machine learning models. Full article

Industrial Control Systems (ICSs) are vital components of industrial production, and their security posture significantly impacts operational safety. Given that ICSs frequently interact with external networks, cyberattacks can disrupt system symmetry, thereby affecting industrial processes. This paper aims to predict the network security posture of ICSs to ensure system symmetry. A prediction model for the network security posture of ICSs was established utilizing Evidence Reasoning (ER) and Explainable Belief Rule Base (BRB-e) technologies. Initially, an evaluation framework for the ICS architecture was constructed, integrating data from various layers using ER. The development of the BRB prediction model requires input from domain experts to set initial parameters; however, the subjective nature of these settings may reduce prediction accuracy. To address this issue, an ICS network security posture prediction model based on the Explainable Belief Rule Base (BRB-e) was proposed. The modeling criteria for explainability were defined based on the characteristics of the ICS network, followed by the design of the inference process for the BRB-e prediction model to enhance accuracy and precision. Additionally, a parameter optimization method for the explainable BRB-e prediction model is presented using a constrained Projection Equilibrium Optimization (P-EO) algorithm. Experiments utilizing industrial datasets were conducted to validate the reliability and effectiveness of the prediction model. Comparative analyses indicated that the BRB-e model demonstrates distinct advantages in both prediction accuracy and explainability when compared to other algorithms. Full article

The prediction of building energy consumption is beneficial to utility companies, users, and facility managers to reduce energy waste. However, due to various drawbacks of prediction algorithms, such as, non-transparent output, ad hoc explanation by post hoc tools, low accuracy, and the inability to deal with data uncertainties, such prediction has limited applicability in this domain. As a result, domain knowledge-based explainability with high accuracy is critical for making energy predictions trustworthy. Motivated by this, we propose an advanced explainable Belief Rule-Based Expert System (eBRBES) with domain knowledge-based explanations for the accurate prediction of energy consumption. We optimize BRBES’s parameters and structure to improve prediction accuracy while dealing with data uncertainties using its inference engine. To predict energy consumption, we take into account floor area, daylight, indoor occupancy, and building heating method. We also describe how a counterfactual output on energy consumption could have been achieved. Furthermore, we propose a novel Belief Rule-Based adaptive Balance Determination (BRBaBD) algorithm for determining the optimal balance between explainability and accuracy. To validate the proposed eBRBES framework, a case study based on Skellefteå, Sweden, is used. BRBaBD results show that our proposed eBRBES framework outperforms state-of-the-art machine learning algorithms in terms of optimal balance between explainability and accuracy by 85.08%. Full article

Berries have gained widespread recognition for their abundant natural antioxidant, anti-inflammatory, and immunomodulatory properties. However, there has been limited research conducted thus far to investigate the role of the active constituents of berries in alleviating contact hypersensitivity (CHS), the most prevalent occupational dermatological disease. Our study involved an ex vivo investigation aimed at evaluating the impact of black raspberry extract (BRB-E) and various natural compounds found in berries, such as protocatechuic acid (PCA), proanthocyanidins (PANT), ellagic acid (EA), and kaempferol (KMP), on mitigating the pathogenicity of CHS. We examined the efficacy of these natural compounds on the activation of dendritic cells (DCs) triggered by 2,4-dinitrofluorobenzene (DNFB) and lipopolysaccharide (LPS). Specifically, we measured the expression of activation markers CD40, CD80, CD83, and CD86 and the production of proinflammatory cytokines, including Interleukin (IL)-12, IL-6, TNF-α, and IL-10, to gain further insights. Potential mechanisms through which these phytochemicals could alleviate CHS were also investigated by investigating the role of phospho-ERK. Subsequently, DCs were co-cultured with T-cells specific to the OVA_323-339 peptide to examine the specific T-cell effector responses resulting from these interactions. Our findings demonstrated that BRB-E, PCA, PANT, and EA, but not KMP, inhibited phosphorylation of ERK in LPS-activated DCs. At higher doses, EA significantly reduced expression of all the activation markers studied in DNFB- and LPS-stimulated DCs. All compounds tested reduced the level of IL-6 in DNFB-stimulated DCs in Flt3L as well as in GM-CSF-derived DCs. However, levels of IL-12 were reduced by all the tested compounds in LPS-stimulated Flt3L-derived BMDCs. PCA, PANT, EA, and KMP inhibited the activated DC-mediated Interferon (IFN)-γ and IL-17 production by T-cells. Interestingly, PANT, EA, and KMP significantly reduced T-cell proliferation and the associated IL-2 production. Our study provides evidence for differential effects of berry extracts and natural compounds on DNFB and LPS-activated DCs revealing potential novel approaches for mitigating CHS. Full article

As demonstrated by us earlier and by other researchers, a diet containing freeze-dried black raspberries (BRB) inhibits DNA damage and carcinogenesis in animal models. We tested the hypothesis that the inhibition of DNA damage by BRB is due, in part, to the enhancement of DNA repair capacity evaluated in the human HeLa cell extract system, an established in vitro system for the assessment of cellular DNA repair activity. The pre-treatment of intact HeLa cells with BRB extracts (BRBE) enhances the nucleotide excision repair (NER) of a bulky deoxyguanosine adduct derived from the polycyclic aromatic carcinogen benzo[a]pyrene (BP-dG) by ~24%. The NER activity of an oxidatively-derived non-bulky DNA lesion, guanidinohydantoin (Gh), is also enhanced by ~24%, while its base excision repair activity is enhanced by only ~6%. Western Blot experiments indicate that the expression of selected, NER factors is also increased by BRBE treatment by ~73% (XPA), ~55% (XPB), while its effects on XPD was modest (<14%). These results demonstrate that BRBE significantly enhances the NER yields of a bulky and a non-bulky DNA lesion, and that this effect is correlated with an enhancement of expression of the critically important NER factor XPA and the helicase XPB, but not the helicase XPD. Full article

Visual sentiment analysis has become more popular than textual ones in various domains for decision-making purposes. On account of this, we develop a visual sentiment analysis system, which can classify image expression. The system classifies images by taking into account six different expressions such as anger, joy, love, surprise, fear, and sadness. In our study, we propose an expert system by integrating a Deep Learning method with a Belief Rule Base (known as the BRB-DL approach) to assess an image’s overall sentiment under uncertainty. This BRB-DL approach includes both the data-driven and knowledge-driven techniques to determine the overall sentiment. Our integrated expert system outperforms the state-of-the-art methods of visual sentiment analysis with promising results. The integrated system can classify images with 86% accuracy. The system can be beneficial to understand the emotional tendency and psychological state of an individual. Full article

Sensor data are gaining increasing global attention due to the advent of Internet of Things (IoT). Reasoning is applied on such sensor data in order to compute prediction. Generating a health warning that is based on prediction of atmospheric pollution, planning timely evacuation of people from vulnerable areas with respect to prediction of natural disasters, etc., are the use cases of sensor data stream where prediction is vital to protect people and assets. Thus, prediction accuracy is of paramount importance to take preventive steps and avert any untoward situation. Uncertainties of sensor data is a severe factor which hampers prediction accuracy. Belief Rule Based Expert System (BRBES), a knowledge-driven approach, is a widely employed prediction algorithm to deal with such uncertainties based on knowledge base and inference engine. In connection with handling uncertainties, it offers higher accuracy than other such knowledge-driven techniques, e.g., fuzzy logic and Bayesian probability theory. Contrarily, Deep Learning is a data-driven technique, which constitutes a part of Artificial Intelligence (AI). By applying analytics on huge amount of data, Deep Learning learns the hidden representation of data. Thus, Deep Learning can infer prediction by reasoning over available data, such as historical data and sensor data streams. Combined application of BRBES and Deep Learning can compute prediction with improved accuracy by addressing sensor data uncertainties while utilizing its discovered data pattern. Hence, this paper proposes a novel predictive model that is based on the integrated approach of BRBES and Deep Learning. The uniqueness of this model lies in the development of a mathematical model to combine Deep Learning with BRBES and capture the nonlinear dependencies among the relevant variables. We optimized BRBES further by applying parameter and structure optimization on it. Air pollution prediction has been taken as use case of our proposed combined approach. This model has been evaluated against two different datasets. One dataset contains synthetic images with a corresponding label of PM_2.5 concentrations. The other one contains real images, PM_2.5 concentrations, and numerical weather data of Shanghai, China. We also distinguished a hazy image between polluted air and fog through our proposed model. Our approach has outperformed only BRBES and only Deep Learning in terms of prediction accuracy. Full article

Big Data applications have become increasingly popular with the emergence of cloud computing and the explosion of artificial intelligence. The increasing adoption of data-intensive machines and services is driving the need for more power to keep the data centers of the world running. It has become crucial for large IT companies to monitor the energy efficiency of their data-center facilities and to take actions on the optimization of these heavy electricity consumers. This paper proposes a Belief Rule-Based Expert System (BRBES)-based predictive model to predict the Power Usage Effectiveness (PUE) of a data center. The uniqueness of this model consists of the integration of a novel learning mechanism consisting of parameter and structure optimization by using BRBES-based adaptive Differential Evolution (BRBaDE), significantly improving the accuracy of PUE prediction. This model has been evaluated by using real-world data collected from a Facebook data center located in Luleå, Sweden. In addition, to prove the robustness of the predictive model, it has been compared with other machine learning techniques, such as an Artificial Neural Network (ANN) and an Adaptive Neuro Fuzzy Inference System (ANFIS), where it showed a better result. Further, due to the flexibility of the BRBES-based predictive model, it can be used to capture the nonlinear dependencies of many variables of a data center, allowing the prediction of PUE with much accuracy. Consequently, this plays an important role to make data centers more energy-efficient. Full article