This paper proposes a new model based on Fuzzy k-Nearest Neighbors for classification with monotonic constraints, Monotonic Fuzzy k-NN (MonFkNN). Real-life data-sets often do not comply with monotonic constraints due to class noise. MonFkNN incorporates a new calculation of fuzzy memberships, which increases robustness against monotonic noise without the need for relabeling. Our proposal has been designed to be adaptable to the different needs of the problem being tackled. In several experimental studies, we show significant improvements in accuracy while matching the best degree of monotonicity obtained by comparable methods. We also show that MonFkNN empirically achieves improved performance compared with Monotonic k-NN in the presence of large amounts of class noise.

Due to the complexity of the supply chain with multiple conflicting objectives requiring a search for a set of trade-off solutions, there has been a range of studies applying multi-objective methods. In recent years, there has been a growing interest in the area of many-objective (four or more objectives) optimisation which handles difficulties that multi-objective methods are not able to overcome. In this study, we explore formulation of Supply Chain Management (SCM) problem in terms of the possibility of having conflicting objectives. Non-dominated Sorting Genetic Algorithm-III (NSGA-III) is used as a many-objective algorithm. First, to make an effective search and to reach solutions with better quality, parameters of algorithm are tuned. After parameter tuning, we used NSGA-III at its best performance and tested it on twenty four synthetic and real-world problem instances considering three performance metrics, hypervolume, generational distance and inverted generational distance.

Over the past 20 years, the development of offshore wind farms has become increasingly important across the world. One of the most crucial reasons for that is offshore wind turbines have higher average speeds than those onshore, producing more electricity. In this study, a new hybrid approach integrating Interval Rough Numbers (IRNs) into Best-Worst Method (BWM) and Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) is introduced for multi-criteria intelligent decision support to choose the best offshore wind farm site in a Turkey’s coastal area. Four alternatives in the Aegean Sea are considered based on a range of criteria. The results show the viability of the proposed approach which yields Bozcaada as the appropriate site, when compared to and validated using the other multi-criteria decision-making techniques from the literature, including IRN based MABAC, WASPAS, and MAIRCA.

Conserving landscape connections among favorable habitats is a widely used strategy to maintain populations in an increasingly fragmented world. A species can then exist as a metapopulation consisting of several subpopulations connected by dispersal. Our study focuses on the importance of human–wildlife coexistence areas in maintaining connectivity among primary habitats of small ungulates within and outside protected areas in a large landscape in central India. We used geospatial information and species presence data to model the suitable habitats, core habitats, and connectivity corridors for four antelope species in an ~89,000 km2 landscape. We found that about 63% of the core habitats, integrated across the four species, lie outside the protected areas. We then measured connectivity in two scenarios: the present setting, and a hypothetical future setting—where habitats outside protected areas are lost. We also modelled the areas with a high risk of human-influenced antelope mortality using eco-geographical variables and wildlife mortality records. Overall, we found that the habitats in multiple-use forests play a central role in maintaining the connectivity network for antelopes. Sizable expanses of privately held farmlands and plantations also contribute to the essential movement corridors. Some perilous patches with greater mortality risk for species require mitigation measures such as underpasses, overpasses, and fences. Greater conservation efforts are needed in the spaces of human–wildlife coexistence to conserve the habitat network of small ungulates.

Improving the efficiency of type-reduction algorithms continues to attract research interest. Recently, there has been some new type-reduction approaches claiming that they are more efficient than the well-known algorithms such as the enhanced Karnik-Mendel (EKM) and the enhanced iterative algorithm with stopping condition (EIASC). In a previous paper, we found that the computational efficiency of an algorithm is closely related to the platform, and how it is implemented. In computer science, the dependence on languages is usually avoided by focusing on the complexity of algorithms (using big O notation). In this article, the main contribution is the proposal of two novel type-reduction algorithms. Also, for the first time, a comprehensive study on both existing and new type-reduction approaches is made based on both algorithm complexity and practical computational time under a variety of programming languages. Based on the results, suggestions are given for the preferred algorithms in different scenarios depending on implementation platform and application context.

Dispersal from one population to another is crucial for meta-population stability and survival. Long-distance dispersal events have been widely documented in male tigers (Panthera tigris), but similar events in female tigers are less known. We opportunistically recorded a long-distance dispersal event that ended with the establishment of a new home-range for a radio-collared sub-adult female tiger in central India. We analysed the animal’s movement patterns during the dispersal event and the subsequent home-range establishment. The average minimum distance and the average minimum daily displacements were 11.4 km and 4.5 km respectively. The total linear and cumulative displacements were 99.1 km and 340.2 km respectively, undertaken over 78 days. Using a Brownian bridge movement model, we showed that the tiger was not moving in a linear path, but showed exploratory movement. During this dispersal event, the tiger traversed an area of 2082 km2 (95% UD), including 19 distinct ‘stepping-stone’ habitat patches. Combining the Ornstein–Uhlenbeck movement behaviour model and an autocorrelated kernel density estimation model, we identified a newly established home range of 40.3 km2 at the end of the dispersal event. Our results describe the longest known female tiger dispersal event, highlighting the possibility that natural dispersal of female tigers can provide an additional option to assisted translocations for the species range expansion. This is relevant in current scenarios where tiger habitats remain fragmented and tiger population numbers are recovering due to effective in situ conservation efforts.

Electric vehicles are the key to facilitating the transition to low-carbon 'green' transport. However, there are concerns with their range and the location of the charging stations which delay a full-fledged adoption of their use. Hence, the electric charging infrastructure in a given region is critical to mitigating those concerns. In this study, an interval type-2 fuzzy set based multi-criteria decision-making method is introduced for selecting the best location for electric charging stations. This method is improved by Simulated Annealing obtaining the best configuration of the parameters of the interval type-2 membership functions along with two different aggregation operators; linguistic weighted sum and average. The proposed overall reusable multi-stage solution approach is applied to a real-world public transport problem of the municipal bus company in Istanbul. The results indicate that the approach indeed improves the model, capturing the associated uncertainties embedded in the interval type-2 membership functions better, leading to a more effective fuzzy system. The experts confirm those observations and that Simulated Annealing improved interval type-2 fuzzy method achieves more reliable results for selecting the best sites for the electric bus charging stations. (C) 2020 Elsevier Inc. All rights reserved.

Constrained interval type-2 (CIT2) fuzzy sets have been introduced to preserve interpretability when moving from type-1 to interval type-2 (IT2) membership functions. Although they can be used to produce type-2 fuzzy systems with enhanced explainability, so far, the latter comes at the expense of high computational cost. Specifically, the exhaustive type-reduction method for CIT2 Mamdani systems has been shown to be too slow to be used in practical applications and even the current approximation procedure is much slower than modern type-reduction algorithms used for IT2 fuzzy sets. In this article, a novel type-reduction procedure for CIT2 sets is presented, based on the concept of switch indices. The algorithm is applied on a real-world classification problem and compared to other type-reduction approaches used in IT2 and CIT2 systems. In the case studies presented, the new algorithm is significantly faster than the exhaustive and sampling CIT2 approaches while keeping the high level of interpretability of the type-reduction operation that characterizes CIT2 fuzzy sets.

Rocket launches are such rare events that their impacts on the environment or biodiversity are almost never studied. Here we report on changes in local insect diversity in the immediate aftermath of a rocket launch conducted on June 25th, 2016 from the Wenchang Satellite Launch Center, southern China. Rocket fuel emissions and disturbance associated with rocket launches may negatively influence local insect biodiversity. We compared insect community structure before and after the rocket launch in two different tropical tree plantations near the launch site. We studied insect species richness (total species numbers) and abundances (total individuals. of all species) using insect net and mercury lamp trapping in a total of six 20 × 20 m2 plots distributed three each in a mixed plantation of coconut (Cocos nucifera) with 10 native tree species, and a pure coconut plantation. Comparing insect species richness and abundances, we found that species richness and abundances were overall greater in the mixed species plantation compared to the coconut monoculture. We also found that species richness and abundance were overall lower after the launch event. However, there was no significant difference in the response of the two plantations, either in richness or abundance, to the launch disturbance. Therefore, the negative effect appeared indiscriminate with respect to plantation type, and the greater plant diversity or insect diversity in the mixed plantation did not protect against this disturbance. Therefore, the general impact of rocket launching disturbance may be similar to catastrophic disturbances (e.g., typhoons) that cause widespread mortality irrespective of species.

Preface.- Artificial Neural Networks: C.A. Czarnecki: Towards Intelligent Mobile Robots.- O. Ciftcioglu: Wavelet Transform by Soft Computing.- C.R. Parikh, M.J. Pont, Y. Li, N.B. Jones: Investigating the Performance of MLP Classifiers Where Limited Training Data Are Available for Some Classes.- R. Thawonmas: A Neural Network Model for Projection Pursuit.- Y. Li, M.J. Pont, C.R. Parikh, N.B. Jones: Comparing the Performance of Three Neural Classifiers for Use in Embedded Applications.- S.J. Wooding: On the Application of Self-Organising Maps to the Exploration of Product Performance Measures.- Y. Li, M.J. Pont, C.R. Parikh, N.B. Jones: Using a Combination of RBFN, MLP and kNN Classifiers for Engine Misfire Detection.- N. Crook, C. Dobbyn, T.o. Scheper: Chaos as a Desirable Stable State of Artificial Neural Networks.- Hybrid Systems: A.R. Graves, C.A. Czarnecki: A Framework for the Development of Hybrid AI Control Systems.- A. Gegov, G.S. Virk, D. Azzi, B.P. Haynes, K.I. Alkadhimi: Soft-Computing Based Predicitve Modelling of Building Management Systems.- A.R. Ferreira da Silva: A Hybrid Evolutionary Approach to Best Basis Discrimination.- O. Babka, L.S. Io, C. Lei, P.M. Wa: Comparing GA and NN Classification Methods.- Evolutionary Computing: J. Kibalik, J. Lazanksy: Partially Randomised Crossover Operators.- C. Bowerman, C.-F. Tsai: The Dynamic Setting of Genetic Algorithm Parameters.- J. Dvorak, M. Seda, T. Vlacil: Job Shop Scheduling with Transfer Batches.- J.W. Davidson, D. Savic, G.A. Walters: Symbolic and Numerical Regression: A Hybrid Technique for Polynomial Approximators.- A. Petrovski, J. McCall: Computational Optimisation of Cancer Chemotherapies Using Genetic Algorithms.- I.J. Griffiths, Q.H. Mehdi, N.E. Gougin: A Visual Development Environment for Coevolving Agent Behaviour.- C.P. Wong, M.J. Pont: An Overview of an Evolutionary Algorithm Pattern Language.- L. Slama, M. Balate: Improvement of Decoding Schemas of Genetic Programming for Function Identification.- J.A. Bland: Memory-Based Heuristic Search and Optimal Structural Design.- S.H. Shami, M.C. Sinclair: Co-evolutionary Agents for Telecommunication Network Restoration.- T. Watson, P. Messer: Mutation Genes in Dynamic Environments.- A. Cruz, S. Mukherjee: Genetic Operators for Test Pattern Generation in Programmable Logic Arrays.- P.W.H. Smith: Controlling Code Growth in Genetic Programming.- S. Areibi: The Effect of Clustering and Local Search on Genetic Algortihms.- M. Oates, D. Corne, R. Loader: Visualisation of Non-Ordinal Multi-Dimensional Landscapes.- M.N. Howell: Learning Rule Design by Genetic Programming for a Discrete Stochastic Learning Automata.- O.A. Basir: A Task-Driven Genetic Algorithm for Maximizing Task Reliability in Multi-Sensor Systems.- Fuzzy Systems: K. Chakrabarty: On Bags and Fuzzy Sets.- W. Mees: Fuzzy Decision Fusion for Automatic Scene Analysis in a Command and Control System.- S.H. Gwanmeh, K.O. Jones, D. Williams: Robustness Study of an On-Line Application of a Self-Learning Fuzzy Logic Controller.- M.J. Allen, I.J. Griffiths, I.M. Coulson, Q.M. Mehdi, N.E. Gough: Efficient Tracking of Coloured Objects Using Fuzzy-Tuned Scanpaths.- L. Collantes, R. Roy, J. Madill: Steelmaking Process Evaluation Using a Fuzzy Expert System.- A.R.P. Borges, C.H, Antunes: Fuzzy Decision Aid in Multiple Objective Linear Programming.- A. Lotfi, J.B. Hull: Indirect Learning Fuzzy Controllers.- C. Fayad, P. Webb: Fuzzy Logic Based Collision Avoidance Algorithm for a Mobile Robot .- H.M. Yang, C.J. Anumba: Collaborative Decision Making in Construction - Potential Application Area for Fuzzy Systems?

The Terai ecoregion of the Himalayan foothills is among the most fire-affected ecosystems in the Indian subcontinent. Although most of the Terai has already been lost to agriculture and urbanization, the few remaining native habitats are strictly protected due to their high biodiversity and ecological importance. The use of fires to maintain vegetation and wildlife habitat in these protected areas is an integral part of forest management. Although fires are initiated by forest managers or local people, their eventual spread and behaviour are not controlled. We hypothesize that distributions of fires are determined by several direct and indirect drivers like fuel load, fuel moisture content, presence of natural or artificial fire breaks, and climatic attributes of precipitation and temperature. Using the moderate-resolution (375m) satellite sensor-based data we studied the environmental influence on the spatial-temporal patterns of fire events over 18 years (2000-2018) in a 519 km(2) protected area in northeastern India. The park has a mosaic of vegetation formations - including dry and swampy alluvial grassland, early successional woodland, and moist tropical forest. Despite high rainfall, there is an intense dry season that renders the herbaceous vegetation susceptible to fires. Using spatial and spatial-temporal Poisson regression models in a rigorous conditional autoregressive Bayesian framework, we found that net primary productivity (a proxy of vegetation type and fuel load), distance to roads (a measure of human influence), elevation (through its influence on floods and vegetation type), and river area extent (by determining the area under vegetation) had a significant influence on the spatial distributions of fires. The climatic signal on interannual variation in fires was weak, but dry season rainfall reduced fire incidence. The disproportionate distribution of fires adjacent to roads compared to the interior, and the repeated burning of some grassland patches need to be addressed in fire management.

The type-1 ordered weighted averaging (T1OWA) operator has demonstrated the capacity for directly aggregating multiple sources of linguistic information modeled by fuzzy sets rather than crisp values. Yager's ordered weighted averaging (OWA) operators possess the properties of idempotence, monotonicity, compensativeness, and commutativity. This article aims to address whether or not T1OWA operators possess these properties when the inputs and associated weights are fuzzy sets instead of crisp numbers. To this end, a partially ordered relation of fuzzy sets is defined based on the fuzzy maximum (join) and fuzzy minimum (meet) operators of fuzzy sets, and an alpha-equivalently-ordered relation of groups of fuzzy sets is proposed. Moreover, as the extension of orness and andness of an Yager's OWA operator, joinness and meetness of a T1OWA operator are formalized, respectively. Then, based on these concepts and the representation theorem of T1OWA operators, we prove that T1OWA operators hold the same properties as Yager's OWA operators possess, i.e., idempotence, monotonicity, compensativeness, and commutativity. Various numerical examples and a case study of diabetes diagnosis are provided to validate the theoretical analyses of these properties in aggregating multiple sources of uncertain information and improving integrated diagnosis, respectively.

Many decision making processes are based on choosing options with maximum utility. Often utility assessments are associated with uncertainty, which may be mathematically modeled by intervals of utilities. Intervals of utilities may be mapped to single utility values by so-called type reduction methods which have been originally developed in the context of interval type-2 defuzzification: the method by Nie and Tan (NT), consistent linear type reduction (CLTR), consistent quadratic type reduction (CQTR), and the uncertainty weight method (UW). This paper considers the problem of comparing pairs of utility intervals using type reduction methods. Three different possible relations between pairs of intervals (disjoint, overlapping, and inclusive) are distinguished in an extensive experimental study, which yields recommendations for the choice of type reduction methods with respect to the level of risk that the decision maker is willing to take. If the focus is on mean utility, then we recommend the Nie-Tan method. For more cautious decision making, when very low utilities should be avoided, we recommend consistent linear type reduction with a high value of the cautiousness parameter or consistent quadratic type reduction. For more risky decision making with a strong focus on very high utilities we recommend consistent linear type reduction with a low value of the cautiousness parameter.

Soil phosphorus is a key driver of plant biodiversity and aboveground biomass (AGB) in tropical forests. A plant community’s ability to exploit such limiting resources may be better represented by functional diversity than species or phylogenetic diversity, and may therefore have the higher predictive power regarding how soil phosphorus influences AGB in tropical forests. However, nearly no studies have tested this in tropical high-altitude forest ecosystems. Here we aim to test: 1) the relative influence of three biodiversity metrics (functional diversity, species diversity, and phylogenetic diversity) on aboveground biomass in a tropical cloud forest and 2) the interrelationships among soil phosphorus, biodiversity, and AGB in this ecosystem. In a tropical cloud forest in Hainan Island, China, we measured 13 key functional traits for 195 species in 48 plots of size 20 × 20 m2 each. We also measured soil phosphorus in all plots and computed the community phylogeny. Using this dataset, we tested the interrelationships among soil phosphorus, species diversity, functional diversity, phylogenetic diversity, and AGB using Generalized Additive Modeling and Redundancy Analysis. Functional diversity was significantly positively related to AGB, whereas species diversity and phylogenetic diversity were not significantly related to AGB. Functional diversity explained 53% of AGB, while species diversity and phylogenetic diversity only explained 17% and 6%, respectively. Soil phosphorus explained 56% of the variation in functional diversity, but only explained 22%, 13% and 21% of the variation in species diversity, phylogenetic diversity and AGB, respectively. Functional diversity, rather than species and phylogenetic diversity, are the better predictors of AGB. The influence of functional diversity on AGB appears to be linked to how variation in soil phosphorus affects functional diversity. We suggest that functional diversity should be incorporated into models that are designed to test how soil resources influence ecosystem function in tropical forest ecosystems.

The importance of grasslands for the sustenance of global biodiversity is paramount. Grassland ecosystems support rich and unique diversity at all trophic levels, are remarkably productive, and resilient to environmental changes. Grasslands in the Indian subcontinent are among the most threatened due to habitat loss, sparking renewed interest in the ecology of the different grasslands found here. We studied land cover dynamics of woodland-grassland mixtures that are part of the Terai ecosystems located at the base of the Himalayan mountain ranges. The vegetation in this region is known to be extremely dynamic even within short time scales, but the patterns and processes associated with this dynamism are not well understood. We analyzed the landcover changes at eight protected wildlife conservation areas from the region (four from India and four from Nepal) that occurred over the last three decades. We used the random forest classifier and an ensemble-based classification technique to carry out supervised classification of the land cover, which was dominated by vegetation. Landsat data, verified with a set of ground measurements and Google earth imagery, were used to generate the landcover types. Using the time series of land cover data, we quantified the observed transitions over decadal timescales. We then used Linear Discriminant functions and Bayesian spatial models to determine the relative importance of environmental variables influencing land cover transitions. We found that the area occupied by grasslands have reduced across all the protected areas we studied. In the last 30 years, the overall natural grassland area decreased by 24 percent, while the agricultural area doubled. The woodland cover increased by 28 percent as a result of ecological succession. Distance from human settlements was found to be the most crucial factor affecting the transitions, followed by topography and distance to water bodies. The grasslands are being widely transformed or degraded to early successional woodland and farmlands, and show increased alien plant invasions. Human encroachment and an increase in human activities have a major influence on these transitions. The impact of these changes on biodiversity and ecosystem function needs to be studied and the urgent attention of managers to stop further degradation is needed.

Constrained interval type-2 (CIT2) fuzzy sets are a class of type-2 fuzzy sets that has been recently proposed as a way to extend type-1 membership functions to interval type-2 (IT2) while keeping a semantic connection between the IT2 fuzzy set and the concept it models. Recent work has shown how their mathematical properties can be used to design CIT2 fuzzy logic systems that are able to provide explanations for their outputs. Although the CIT2 representation can be a valuable alternative to the IT2 one, no software library for their implementation is available for the research community. The aim of this paper is to introduce a new Java library, Juzzy Constrained, that has been developed as an extension of the popular type-1 and type-2 Java toolkit Juzzy, adding support for CIT2 sets and systems. Throughout the paper, the main classes and the structure of the new library are described, together with a working example that illustrates how to build a CIT2 fuzzy system from scratch and how it can be used to produce explanations for the output.

In recent year, there has been a growing need for intelligent systems that not only are able to provide reliable classifications but can also produce explanations for the decisions they make. The demand for increased explainability has led to the emergence of explainable artificial intelligence (XAI) as a specific research field. In this context, fuzzy logic systems represent a promising tool thanks to their inherently interpretable structure. The use of a rule-base and linguistic terms, in fact, have allowed researchers to create models that are able to produce explanations in natural language for each of the classifications they make. So far, however, designing systems that make use of interval type-2 (IT2) fuzzy logic and also give explanations for their outputs has been very challenging, partially due to the presence of the type-reduction step. In this paper, it will be shown how constrained interval type-2 (CIT2) fuzzy sets represent a valid alternative to conventional interval type-2 sets in order to address this issue. Through the analysis of two case studies from the medical domain, it is shown how explainable CIT2 classifiers are produced. These systems can explain which rules contributed to the creation of each of the endpoints of the output interval centroid, while showing (in these examples) the same level of accuracy as their IT2 counterpart.

Many research fields are now faced with huge volumes of data automatically generated by specialised equipment. Astronomy is a discipline that deals with large collections of images difficult to handle by experts alone. As a consequence, astronomers have been relying on the power of the crowds, as a form of citizen science, for the classification of galaxy images by amateur people. However, the new generation of telescopes that will produce images at a higher rate highlights the limitations of this approach, and the use of machine learning methods for automatic classification is considered essential. The goal of this paper is to shed light on the automated classification of galaxy images exploring two distinct machine learning strategies. First, following the classical approach consisting of feature extraction together with a classifier, we compare the state-of-the-art feature extractor for this problem, the WND-CHARM, with our proposal based on autoencoders for feature extraction on galaxy images. We then compare these results with an end-to-end classification using convolutional neural networks. To better leverage the available citizen science data, we also investigate a pre-training scheme that exploits both amateur- and expert-labelled data. Our experiments reveal that autoencoders greatly speed up feature extraction in comparison with WND-CHARM and both classification strategies, either using convolutional neural networks or feature extraction, reach comparable accuracy. The use of pre-training in convolutional neural networks, however, has allowed us to provide even better results.

Alpha-cut representation of fuzzy sets has been used as a basis for fuzzy numbers ranking in some applications but rarely used for defuzzification of rule-based systems or fuzzy controllers. Moreover, such alpha-cut defuzzification (called ACD here) is not yet formally linked to the membership function (MF) or to the common MF-based defuzzification methods, namely the centroid. The ACD can be considered as a generalisation of the similar algorithms in fuzzy numbers to any fuzzy set. A close-form formula for ACD is developed that involves both MF and its derivative, which shows that ACD reflects both static and dynamic aspects of a fuzzy set. Moreover, formal links between ACD and some MF-based defuzzification methods are shown. Through two groups of experiments, the utility of the new method is compared with centroid defuzzification. Particularly, we examined how the ACD significantly outperforms the centroid for noisy time-series prediction. Finally, the computation complexity of ACD is shown to be about the same as the centroid method, for convex fuzzy sets. Our tests suggest that ACD can be considered as a viable alternative defuzzification method for fuzzy system designers.