Robust Expert System Research Articles

Heart Disease Prognosis Using D-GRU with Logistic Chaos Honey Badger Optimization in IoMT Framework

In recent years, heart disease has superseded several other contributory death factors. It is challenging to predict an individual’s risk of acquiring heart disease since it requires both expert knowledge and real-world experience. Developing an effective method for the prognosis of heart disease using Internet of Medical Things (IoMT) technology in healthcare organizations by collecting sensor data from patients’ bodies, utilizing robust expert systems, and incorporating vast healthcare data on cardiac disorders to alert physicians in critical situations is a challenging task. Several machine learning-based techniques for predicting and diagnosing cardiac disease have recently been demonstrated. However, these algorithms cannot effectively handle high-dimensionalinformation due to the need for an intelligent framework incorporating multiple sources to predict cardiac illness. This work proposes a unique model for heart disease prediction based on deep learning, Deep Gated Recurrent Units (D-GRU), which combines with Stacked Auto Encoders. A novel optimization algorithm, the Logistic Chaos Honey Badger Algorithm, is proposed for optimal feature selection. Publicly available heart disease-related datasets collected from UCI Repository, Cleveland Database, are used for training the proposed D-GRU model. The trained model is further tested on the data gathered from the sensors in the IoMT framework. The performance of the proposed model is compared against several deep learning models and existing works in the literature. The proposed D-GRU model outperforms the other models taken for comparison andexhibits performance supremacy with an accuracy of 95.15% in predicting heart diseases.

SentiMLBench: Benchmark Evaluation of Machine Learning Algorithms for Sentiment Analysis

Sentiment Analysis has been a topic of interest for researchers due to its increasing usage by Industry. To measure end-user sentiment., there is no clear verdict on which algorithms are better in real-time scenarios. A rigorous benchmark evaluation of various algorithms running across multiple datasets and different hardware architectures is required that can guide future researchers on potential advantages and limitations. In this paper, proposed SentiMLBench is a critical evaluation of key ML algorithms as standalone classifiers, a novel cascade feature selection (CFS) based ensemble technique in multiple benchmark environments each using a different twitter dataset and processing hardware. The best trained ensemble model with CFS enhancement surpasses current state-of-the-art models, according to experimental results. In a study, though ensemble model provides good accuracy, it falls short of neural networks accuracy by 2%. ML algorithms accuracy is poor as standalone classifiers across all three studies. The supremacy of neural networks is further stamped in study three where it outperforms other algorithms in accuracy by over 10%. Graphical processing unit provide speed and higher computational power at a fraction of a cost compared to a normal processor thereby providing critical architectural insights into developing a robust expert system for sentiment analysis.

Semantic relational machine learning model for sentiment analysis using cascade feature selection and heterogeneous classifier ensemble.

The exponential rise in social media via microblogging sites like Twitter has sparked curiosity in sentiment analysis that exploits user feedback towards a targeted product or service. Considering its significance in business intelligence and decision-making, numerous efforts have been made in this area. However, lack of dictionaries, unannotated data, large-scale unstructured data, and low accuracies have plagued these approaches. Also, sentiment classification through classifier ensemble has been underexplored in literature. In this article, we propose a Semantic Relational Machine Learning (SRML) model that automatically classifies the sentiment of tweets by using classifier ensemble and optimal features. The model employs the Cascaded Feature Selection (CFS) strategy, a novel statistical assessment approach based on Wilcoxon rank sum test, univariate logistic regression assisted significant predictor test and cross-correlation test. It further uses the efficacy of word2vec-based continuous bag-of-words and n-gram feature extraction in conjunction with SentiWordNet for finding optimal features for classification. We experiment on six public Twitter sentiment datasets, the STS-Gold dataset, the Obama-McCain Debate (OMD) dataset, the healthcare reform (HCR) dataset and the SemEval2017 Task 4A, 4B and 4C on a heterogeneous classifier ensemble comprising fourteen individual classifiers from different paradigms. Results from the experimental study indicate that CFS supports in attaining a higher classification accuracy with up to 50% lesser features compared to count vectorizer approach. In Intra-model performance assessment, the Artificial Neural Network-Gradient Descent (ANN-GD) classifier performs comparatively better than other individual classifiers, but the Best Trained Ensemble (BTE) strategy outperforms on all metrics. In inter-model performance assessment with existing state-of-the-art systems, the proposed model achieved higher accuracy and outperforms more accomplished models employing quantum-inspired sentiment representation (QSR), transformer-based methods like BERT, BERTweet, RoBERTa and ensemble techniques. The research thus provides critical insights into implementing similar strategy into building more generic and robust expert system for sentiment analysis that can be leveraged across industries.

Replication-based regularization approaches to diagnose Reinke's edema by using voice recordings

Reinke's edema is one of the most prevalent laryngeal pathologies. Its detection can be addressed by using computer-aided diagnosis systems based on features extracted from speech recordings. When extracting acoustic features from different voice recordings of a particular subject at a concrete moment, imperfections in technology and the very biological variability result in values that are close, but they are not identical. This suggests that the within-subject variability must be properly addressed in the statistical methodology. Regularization-based regression approaches can be used to reduce the classification errors by favoring the best predictors and penalizing the worst ones. Three replication-based regularization approaches for variable selection and classification have been specifically designed and implemented to take into account the underlying within-subject variability. In order to illustrate the applicability of these approaches, an experiment has been specifically conducted to discriminate Reinke's edema patients (30 subjects) from healthy people (30 subjects) in a hospital environment. The features have been extracted from four phonations of the sustained vowel /a/ recorded for each subject, leading to a database that has fed the proposed machine learning approaches. The proposed replication-based approaches have been proved to be reliable in terms of selected features and predictive ability, leading to a stable accuracy rate of 0.89 under a cross-validation framework. Also, a comparison with traditional independence-based regularization methods reports a great variability of the latter in terms of selected features and accuracy metrics. Therefore, the proposed approaches contribute to fill a gap in the scientific literature on statistical approaches considering within-subject variability and can be used to build a robust expert system.

An automated system for epilepsy detection using EEG brain signals based on deep learning approach

Epilepsy is a life-threatening and challenging neurological disorder, which is affecting a large number of people all over the world. For its detection, encephalography (EEG) is a commonly used clinical approach, but manual inspection of EEG brain signals is a time-consuming and laborious process, which puts a heavy burden on neurologists and affects their performance. Several automatic systems have been proposed using traditional approaches to assist neurologists, which perform well in detecting binary epilepsy scenarios e.g. normal vs. ictal, but their performance degrades in classifying ternary case e.g. ictal vs. normal vs. inter-ictal. To overcome this problem, we propose a system that is an ensemble of pyramidal one-dimensional convolutional neural network (P-1D-CNN) models. Though a CNN model learns the internal structure of data and outperforms hand-engineered techniques, the main issue is the large number of learnable parameters, whose learning requires a huge volume of data. To overcome this issue, P-1D-CNN works on the concept of refinement approach and it involves 61% fewer parameters compared to standard CNN models and as such it has better generalization. Further to overcome the limitations of the small amount of data, we propose two augmentation schemes. We tested the system on the University of Bonn dataset, a benchmark dataset; in almost all the cases concerning epilepsy detection, it gives an accuracy of 99.1 ± 0.9% and outperforms the state-of-the-art systems. In addition, while enjoying the strength of a CNN model, P-1D-CNN model requires 61% less memory space and its detection time is very short (< 0.000481 s), as such it is suitable for real-time clinical setting. It will ease the burden of neurologists and will assist the patients in alerting them before the seizure occurs. The proposed P-1D-CNN model is not only suitable for epilepsy detection, but it can be adopted in developing robust expert systems for other similar disorders.

Classification of Coffee Beans by GC-C-IRMS, GC-MS, and (1)H-NMR.

In a previous work using 1H-NMR we reported encouraging steps towards the construction of a robust expert system for the discrimination of coffees from Colombia versus nearby countries (Brazil and Peru), to assist the recent protected geographical indication granted to Colombian coffee in 2007. This system relies on fingerprints acquired on a 400 MHz magnet and is thus well suited for small scale random screening of samples obtained at resellers or coffee shops. However, this approach cannot easily be implemented at harbour's installations, due to the elevated operational costs of cryogenic magnets. This limitation implies shipping the samples to the NMR laboratory, making the overall approach slower and thereby more expensive and less attractive for large scale screening at harbours. In this work, we report on our attempt to obtain comparable classification results using alternative techniques that have been reported promising as an alternative to NMR: GC-MS and GC-C-IRMS. Although statistically significant information could be obtained by all three methods, the results show that the quality of the classifiers depends mainly on the number of variables included in the analysis; hence NMR provides an advantage since more molecules are detected to obtain a model with better predictions.

A novel texture feature based multiple classifier technique for roadside vegetation classification

This paper presents a novel texture feature based multiple classifier technique and applies it to roadside vegetation classification. It is well-known that automation of roadside vegetation classification is one of the important issues emerging strongly in improving the fire risk and road safety. Hence, the application presented in this paper is significantly important for identifying fire risks and road safety. The images collected from outdoor environments such as roadside, are affected for a high variability of illumination conditions because of different weather conditions. This paper proposes a novel texture feature based robust expert system for vegetation identification. It consists of five steps, namely image pre-processing, feature extraction, training with multiple classifiers, classification, validation and statistical analysis. In the initial stage, Co-occurrence of Binary Pattern (CBP) technique is applied in order to obtain the texture feature relevant to vegetation in the roadside images. In the training and classification stages, three classifiers have been fused to combine the multiple decisions. The first classifier is based on Support Vector Machine, the second classifier is based on feed forward back-propagation neural network (FF-BPNN) and the third classifier is based on -Nearest Neighbor (k-NN). The proposed technique has been applied and evaluated on two types of vegetation images i.e. dense and sparse grasses. The classification accuracy with a success of 92.72% has been obtained using 5-fold cross validation approach. An (Analysis of Variance) test has also been conducted to show the statistical significance of results.

Design, Development and Implementation of a Robust Decision Support Expert System (branDEC) in Multi Criteria Decision Making

In today's dynamic business scenario, in order to sustain within the global turmoil, decision plays a pivotal role. Today's market is completely driven with the choice of the consumer or the end user and toprolong this unprecedented uproar an adroit, firm and stable decision has to be taken. To acclimatize with the changing scenario, the situation demands for development of an expert system to expedite the decision making activity. Advent in MCDM (Multi Criteria Decision Making) techniques diffused with high end mathematical sub-layers isserving this purpose for the past decade. Literature survey reveals lack of availability of a robust expert system encompassing a numerous MCDM techniques, normalization techniques and weight determination techniques. Therefore this project is to mitigate this paucity and to develop a decision support expert system branDEC-V:1R:1 [bran is an eponym derived by taking the first letter of the authors name and DEC is short form of“decision”]which can simultaneously harness all the existing offline MCDM methods and which make a decision avoiding time and computational complexity.

Automatic assignment of 1H‐NMR spectra of small molecules

A novel data-evaluation procedure for the automatic atom to peak or multiplet assignment of 1H-NMR spectra of small molecules has been developed using a fast and robust expert system. The applicability and reliability of the method are demonstrated by comparison of a manually assigned database of 1H-NMR spectra with the assignments produced by the automatic procedure. The results of this analysis show an excellent success ratio, indicating that this new algorithm can have a major impact as a time saving tool for the organic chemist. A new graphical feature used to illustrate both the stability and quality of the elementary assignments is also introduced.

Optimization and Automation of Quantitative NMR Data Extraction

NMR is routinely used to quantitate chemical species. The necessary experimental procedures to acquire quantitative data are well-known, but relatively little attention has been applied to data processing and analysis. We describe here a robust expert system that can be used to automatically choose the best signals in a sample for overall concentration determination and determine analyte concentration using all accepted methods. The algorithm is based on the complete deconvolution of the spectrum which makes it tolerant of cases where signals are very close to one another and includes robust methods for the automatic classification of NMR resonances and molecule-to-spectrum multiplets assignments. With the functionality in place and optimized, it is then a relatively simple matter to apply the same workflow to data in a fully automatic way. The procedure is desirable for both its inherent performance and applicability to NMR data acquired for very large sample sets.

A new Expert System for greenness identification in agricultural images

It is well-known that one important issue emerging strongly in agriculture is related with the automation of tasks, where camera-based sensors play an important role. They provide images that must be conveniently processed. The most relevant image processing procedures require the identification of green plants, in our experiments they comes from barley and maize fields including weeds, so that some type of action can be carried out, including site-specific treatments with chemical products or mechanical manipulations.The images come from outdoor environments, which are affected for a high variability of illumination conditions because of sunny or cloudy days or both with high rate of changes.Several indices have been proposed in the literature for greenness identification, but under adverse environmental conditions most of them fail or do not work properly. This is true even for camera devices with auto-image white balance.This paper proposes a new automatic and robust Expert System for greenness identification. It consists of two main modules: (1) decision making, based on image histogram analysis and (2) greenness identification, where two different strategies are proposed, the first based on classical greenness identification methods and the second inspired on the Fuzzy Clustering approach. The Expert System design as a whole makes a contribution, but the Fuzzy Clustering strategy makes the main finding of this paper. The system is tested for different images captured with several camera devices.

An automated method for the detection of pulmonary embolism in V/ Q-scans

In this paper, a fully automatic method for the diagnosis of pulmonary embolism (PE) from V/ Q-scans is presented. Image analysis is applied to the ventilation and the perfusion images obtained in the V/ Q-scan. The difference of the ventilation and the perfusion is calculated after transformation and hot-spot reduction of the images. From this difference image the integral of the underperfused areas are used as features. With the aid of these features a simple test for PE is devised. The method is evaluated on two sets of patients. One set comprises 102 patients who have undergone both V/ Q-scanning and angiography. The performance given as the area under the Receiver Operating Characteristic (ROC) curve is 0.85. Another set is made up by the 507 consecutive patients examined with V/ Q-scanning at Lund University Hospital in Sweden. In this case, the reference was the consensus opinion of two radiologists, the ROC-area is 0.67. A fully automatic and reasonably robust expert system is developed to aid the radiologist in the interpretation of V/ Q-scans for PE.

An Approach to the Urban Scene Understanding Problem Useful for Autonomous Navigation

This paper presents an approach to the urban scene understanding problem, providing a robust expert system able to take out natural elements such as trees or plants. The system is based on a wide range of natural features associated to seasons, weather, chromatic components, sunshine levels, etc. A particularity of the system is that it is only dealing with pixels, so video-rate processing can be achieved in an easy way, whereas others which deal with regions. Then, the proposed expert system works as a pre-processor and needs, indeed, knowledgement about the environment. Although, pixels candidates could be associated to regions in order to be useful for a further high level processing. In this sense, the system would be applied to a mobile robot in order to use the trees as landmarks for navigation.

Automatic generation of qualitative models of chemical process units

A library of general physical and chemical phenomena such as heat transfer and reaction has been developed in the qualitative process theory (QPT) representation of Forbus and used to build qualitative models of chemical process units. The QPT descriptions specify the conditions under which the phenomena become active and the constraints they contribute to the model, thus allowing the computer to automatically build and alter process unit models. In order to realistically model chemical plants, we introduce a Lagrangian approach to modelling the behavior of plug flow vessels and we present a set of techniques to focus the model building and solution mechanisms on specific aspects of unit behavior, reducing model complexity. These model generation and solution methods form a basis for developing flexible and robust expert systems that perform fault diagnosis, HAZOP studies and plant design.

Possible low-priced, robust expert systems using neural networks and minimal entropy coding

Abstract Within this paper the following research results are presented. A combination of minimal entropy coding and neural network technique is proposed for low-priced and robust expert system building. A “thesis of tractability” describing the size and complexity of ordinary human expertise when using the above-mentioned methods in knowledge engineering is given. Developmental and maintenance costs of expert and decision support systems can be cut down by more than 90 percent below the costs encountered when rule-based shells are used. A PC-based expert system shell (named ZEUS) incorporating the above-mentioned principles has been developed, and two expert systems have been made. Both cases are tractable and significant cost reduction has been verified in specific cases. Principles, techniques, and functionalities of the program ZEUS are described and evaluated.