What is Computational Intelligence and where is it going?

What is Computational Intelligence (CI) and what are its relations with Artificial Intelligence (AI)? A brief survey of the scope of CI journals and books with ``computational intelligence'' in their title shows that at present it is an umbrella for three core technologies (neural, fuzzy and evolutionary), their applications, and selected fashionable pattern recognition methods. At present CI has no comprehensive foundations and is more a bag of tricks than a solid branch of science. The change of focus from methods to challenging problems is advocated, with CI defined as a part of computer and engineering sciences devoted to solution of non-algoritmizable problems. In this view AI is a part of CI focused on problems related to higher cognitive functions, while the rest of the CI community works on problems related to perception and control, or lower cognitive functions. Grand challenges on both sides of this spectrum are addressed

Application of an electronic nose coupled with fuzzy-wavelet network for the detection of meat spoilage

Food product safety is one of the most promising areas for the application of electronic noses. During the last twenty years, these sensor-based systems have made odour analyses possible. Their application into the area of food is mainly focused on quality control, freshness evaluation, shelf-life analysis and authenticity assessment. In this paper, the performance of a portable electronic nose has been evaluated in monitoring the spoilage of beef fillets stored either aerobically or under modified atmosphere packaging, at different storage temperatures. A novel multi-output fuzzy wavelet neural network model has been developed, which incorporates a clustering pre-processing stage for the definition of fuzzy rules. The dual purpose of the proposed modelling approach is not only to classify beef samples in the relevant quality class (i.e. fresh, semi-fresh and spoiled), but also to predict their associated microbiological population. Comparison results against advanced machine learning schemes indicated that the proposed modelling scheme could be considered as a valuable detection methodology in food microbiology

AUTOMATED OIL PALM FRIDT GRADING USING ARTIFICIAL INTELLIGENCE

This project deals with the grading of oil palm fruit based on ripeness of oil palm fruit. The current procedure in the palm oil mills is graded manual by human graders. The result from manual grading are very subjective and inconsistent as it varies and depends on techniques and experience of each human graders. Hence, it affects the quality and quantity of the oil that can be extracted. In this project, a new model of automated grading system for oil palm fruit is developed using the RGB color model and artificial fuzzy logic. The purpose of this grading system is to distinguish between the three different classes of oil palm fruit which are underripe, ripe and overripe. The ripeness or color ripening index was based on different color intensity. The grading system uses a computer and a CCD camera to analyze and interpret images correspondent to human eye and mind. The computer program is developed for the image processing part like the segmentation of colors, the calculation of the mean color intensity based on RGB color model and the decision making process using fuzzy logic to train the data and make the classification for the oil palm fruit. The program developed has been able to distinguish the three different classes of oil palm fruit automatically with 86.67% of overall efficiency. This project provides a very good technique to standardize the oil palm fruit grading system over a large area and the research will continue to normalize the system to be able to use under different source of lighting

Is Neuro-Symbolic AI Meeting its Promise in Natural Language Processing? A Structured Review

Advocates for Neuro-Symbolic Artificial Intelligence (NeSy) assert that combining deep learning with symbolic reasoning will lead to stronger AI than either paradigm on its own. As successful as deep learning has been, it is generally accepted that even our best deep learning systems are not very good at abstract reasoning. And since reasoning is inextricably linked to language, it makes intuitive sense that Natural Language Processing (NLP), would be a particularly well-suited candidate for NeSy. We conduct a structured review of studies implementing NeSy for NLP, with the aim of answering the question of whether NeSy is indeed meeting its promises: reasoning, out-of-distribution generalization, interpretability, learning and reasoning from small data, and transferability to new domains. We examine the impact of knowledge representation, such as rules and semantic networks, language structure and relational structure, and whether implicit or explicit reasoning contributes to higher promise scores. We find that systems where logic is compiled into the neural network lead to the most NeSy goals being satisfied, while other factors such as knowledge representation, or type of neural architecture do not exhibit a clear correlation with goals being met. We find many discrepancies in how reasoning is defined, specifically in relation to human level reasoning, which impact decisions about model architectures and drive conclusions which are not always consistent across studies. Hence we advocate for a more methodical approach to the application of theories of human reasoning as well as the development of appropriate benchmarks, which we hope can lead to a better understanding of progress in the field. We make our data and code available on github for further analysis.Comment: Surve

Using spatiotemporal patterns to qualitatively represent and manage dynamic situations of interest : a cognitive and integrative approach

Les situations spatio-temporelles dynamiques sont des situations qui évoluent dans l’espace et dans le temps. L’être humain peut identifier des configurations de situations dans son environnement et les utilise pour prendre des décisions. Ces configurations de situations peuvent aussi être appelées « situations d’intérêt » ou encore « patrons spatio-temporels ». En informatique, les situations sont obtenues par des systèmes d’acquisition de données souvent présents dans diverses industries grâce aux récents développements technologiques et qui génèrent des bases de données de plus en plus volumineuses. On relève un problème important dans la littérature lié au fait que les formalismes de représentation utilisés sont souvent incapables de représenter des phénomènes spatiotemporels dynamiques et complexes qui reflètent la réalité. De plus, ils ne prennent pas en considération l’appréhension cognitive (modèle mental) que l’humain peut avoir de son environnement. Ces facteurs rendent difficile la mise en œuvre de tels modèles par des agents logiciels. Dans cette thèse, nous proposons un nouveau modèle de représentation des situations d’intérêt s’appuyant sur la notion des patrons spatiotemporels. Notre approche utilise les graphes conceptuels pour offrir un aspect qualitatif au modèle de représentation. Le modèle se base sur les notions d’événement et d’état pour représenter des phénomènes spatiotemporels dynamiques. Il intègre la notion de contexte pour permettre aux agents logiciels de raisonner avec les instances de patrons détectés. Nous proposons aussi un outil de génération automatisée des relations qualitatives de proximité spatiale en utilisant un classificateur flou. Finalement, nous proposons une plateforme de gestion des patrons spatiotemporels pour faciliter l’intégration de notre modèle dans des applications industrielles réelles. Ainsi, les contributions principales de notre travail sont : Un formalisme de représentation qualitative des situations spatiotemporelles dynamiques en utilisant des graphes conceptuels. ; Une approche cognitive pour la définition des patrons spatio-temporels basée sur l’intégration de l’information contextuelle. ; Un outil de génération automatique des relations spatiales qualitatives de proximité basé sur les classificateurs neuronaux flous. ; Une plateforme de gestion et de détection des patrons spatiotemporels basée sur l’extension d’un moteur de traitement des événements complexes (Complex Event Processing).Dynamic spatiotemporal situations are situations that evolve in space and time. They are part of humans’ daily life. One can be interested in a configuration of situations occurred in the environment and can use it to make decisions. In the literature, such configurations are referred to as “situations of interests” or “spatiotemporal patterns”. In Computer Science, dynamic situations are generated by large scale data acquisition systems which are deployed everywhere thanks to recent technological advances. Spatiotemporal pattern representation is a research subject which gained a lot of attraction from two main research areas. In spatiotemporal analysis, various works extended query languages to represent patterns and to query them from voluminous databases. In Artificial Intelligence, predicate-based models represent spatiotemporal patterns and detect their instances using rule-based mechanisms. Both approaches suffer several shortcomings. For example, they do not allow for representing dynamic and complex spatiotemporal phenomena due to their limited expressiveness. Furthermore, they do not take into account the human’s mental model of the environment in their representation formalisms. This limits the potential of building agent-based solutions to reason about these patterns. In this thesis, we propose a novel approach to represent situations of interest using the concept of spatiotemporal patterns. We use Conceptual Graphs to offer a qualitative representation model of these patterns. Our model is based on the concepts of spatiotemporal events and states to represent dynamic spatiotemporal phenomena. It also incorporates contextual information in order to facilitate building the knowledge base of software agents. Besides, we propose an intelligent proximity tool based on a neuro-fuzzy classifier to support qualitative spatial relations in the pattern model. Finally, we propose a framework to manage spatiotemporal patterns in order to facilitate the integration of our pattern representation model to existing applications in the industry. The main contributions of this thesis are as follows: A qualitative approach to model dynamic spatiotemporal situations of interest using Conceptual Graphs. ; A cognitive approach to represent spatiotemporal patterns by integrating contextual information. ; An automated tool to generate qualitative spatial proximity relations based on a neuro-fuzzy classifier. ; A platform for detection and management of spatiotemporal patterns using an extension of a Complex Event Processing engine

Artificial neural networks for predicting the generation of acetaldehyde in pet resin in the process of injection of plastic packages

The industrial production of preforms for the manufacture of PET bottles, during the plastic injection process, is essential to regulate the drying temperature of the PET resin, to control the generation of Acetaldehyde (ACH), which alters the flavor of carbonated or non-carbonated drinks, giving the drink a citrus flavor and putting in doubt the quality of packaged products. In this work, an Artificial Neural Network (ANN) of the Backpropagation type (Cascadeforwardnet) is specified to support the decision-making process in controlling the ideal drying temperature of the PET resin, allowing specialists to make the necessary temperature regulation decisions  for the best performance by decreasing ACH levels. The materials and methods were applied according to the manufacturer\u27s characteristics on the moisture in the PET resin grain, which may contain between 50 ppm and 100 ppm of ACH. Data were collected for the method analysis, according to temperatures and residence times used in the blow injection process in the manufacture of the bottle preform, the generation of ACH from the PET bottle after solid post-condensation stage reached residual ACH levels below (3-4) ppm, according to the desired specification, reaching levels below 1 ppm. The results found through the Computational Intelligence (IC) techniques applied by the ANNs, where they allowed the prediction of the ACH levels generated in the plastic injection process of the bottle packaging preform, allowing an effective management of the parameters of production, assisting in strategic decision making regarding the use of temperature control during the drying process of PET resin

Application Of AHP For Determining The Best Of Palm Oil Fresh Fruit Bunch

This study covers the importance of high quality of palm oil Fresh Fruit Bunches (FFB) to ensure high production in palm oil industry. The level of palm oil FFB maturity will affect to oil extraction rate (OER) which is the main key performance indicator for palm oil industry. The most important process to classify the palm oil FFB ripeness is the grading process. Therefore, the quality grading process of FFB needs to be conducted properly to ensure that high-quality palm oil FFB is selected for production. Usually, the grading process performed by some graders in each mill manually. A sample from each lorry was taken in the grading process. However, this method takes time and may lead to errors in the classification process, especially if the graders have less experience. One of the useful tools that can be employed to make decisions in classification process is Analytical Hierarchy Process (AHP). The main concern was to ensure the reliability of AHP technique achievable. The methodology in this study consists of five phases ie; data collection from expert grader and industries visited, identifying the most important criteria, analysis by AHP method, validation by TOPSIS technique and finally the ranked of the best criteria of high quality FFB. The Expert Choice Software and Microsoft Office Excel are tools that used to analyze the data collected from expert graders in the AHP and TOPSIS technique. The main objective of this study is to determine the best quality of FFB using AHP. The result found that the number of detached fruitlets is the most important criteria to determine the FFB ripeness with 0.560 priority vector followed by color with 0.219 priority vector compared to other criteria. The sensitivity analysis performed to ensure the results are consistent and reliable. It will help the graders to conduct a proper grading process at mills to increase the quality of OER