The development of autonomous robotic systems offers significant potential for performing complex tasks with precision and consistency. Recent advances in Artificial Intelligence (AI) have enabled more capable intelligent automation systems, addressing increasingly complex challenges. However, this progress raises questions about human roles in such systems. Human-Centered AI (HCAI) aims to balance human control and automation, ensuring performance enhancement while maintaining creativity, mastery, and responsibility. For real-world applications, autonomous robots must balance task performance with reliability, safety, and trustworthiness. Integrating HCAI principles enhances human-robot collaboration and ensures responsible operation.This paper presents a bibliometric analysis of intelligent autonomous robotic systems, utilizing SciMAT and VOSViewer to examine data from the Scopus database. The findings highlight academic trends, emerging topics, and AI’s role in self-adaptive robotic behaviour, with an emphasis on HCAI architecture. These insights are then projected onto the IBM MAPE-K architecture, with the goal of identifying how these research results map into actual robotic autonomous systems development efforts for real-world scenarios.

The decentralized nature of federated learning (FL) poses critical challenges related to security: Clients participating in the process may not necessarily be trustworthy and could engage in adversarial attacks, potentially undermining the integrity and reliability of the global machine learning model. Security concerns have been extensively investigated in traditional FL, where collaboratively learned models are typically deep neural networks. However, this class of models does not meet the requirement of explainability, which is considered essential for the trustworthiness of AI systems. In this work, we present an analysis on security threats to FL of explainable models, namely fuzzy rule-based classifiers (FRBCs). We outline the types of attacks a malicious client may implement, and assess, through a preliminary experimental analysis, the impact they have on FL of FRBCs in terms of global model performance. We also compare these findings with the effects of the same or similar well-established attacks in traditional FL of neural network models. Finally, we provide insights to improve the security of FRBCs learned in a federated fashion.

The increasing complexity of cyber threats has necessitated the adoption of Intrusion Detection Systems (IDSs) which often rely on Artificial Intelligence (AI) models to detect malicious activities in network traffic. In distributed environments, interconnected private sub-networks pose additional challenges, as threats can spread while evading local detection. However, the use of AI models in these systems raises issues related to transparency, privacy, and data security. In this context, this study proposes a decentralized IDS based on Federated Learning (FL) and Explainable Artificial Intelligence (XAI) techniques. The network traffic classification model is based on a Multi-Layer Perceptron (MLP) neural network, while the SHapley Additive exPlanations (SHAP) method is employed to provide interpretable explanations of the system decisions. Fed-SHAP, based on the federated fuzzy c-means clustering, is used to generate a global SHAP background dataset to be adopted for generating consistent and reliable explanations. The system is evaluated on realistic scenarios with non-Independent and Identically Distributed (IID) network data. Experimental results demonstrate that the proposed federated approach maintains comparable accuracy with respect to centralized models while ensuring data protection and explanation consistency in federated environments.

The use of personal protective equipment (PPE) is essential to improve workplace safety. Despite specific regulations requiring the use of PPE, workers often neglect to wear it due to factors such as inattention, urgency, or convenience. Monitoring the correct use of PPE is especially critical in high-risk tasks. Computer vision technology can automate this process, leveraging deep neural models. This study investigates the performance of modern object detection models in identifying the correct use of PPE, focusing on their accuracy and execution speed. Specifically, the YOLOv11 and RT-DERT models are employed, trained on a real-world PPE dataset. Deployment on low-cost hardware, specifically an NVIDIA Jetson Nano, is evaluated using three deployment frameworks, namely PyTorch, OpenVINO, and TensorRT. The results show that YOLOv11n, with 2.6 million parameters, provides slightly lower average accuracy than more complex models. It stands out for its speed, reaching performances of 6.6 Frames Per Second (FPS) with PyTorch, 2.3 FPS with OpenVINO, and 10.6 FPS with TensorRT. On the other hand, YOLOv11l and YOLOv11x with, respectively, 46.5 and 86.7 million parameters offer higher accuracy, especially evident in small class identification, where simpler models tend to struggle. However, they show lower throughput, with 1.2 and 0.7 FPS on PyTorch. RT-DETR has competitive accuracy but lower performance on edge devices.

The use of personal protective equipment (PPE) is essential to strengthen the safety of workers in the workplace. Although there exist specific regulations requiring the use of PPEs, due to carelessness, haste or comfort, workers sometimes neglect to wear them. Thus, it is crucial to monitor the appropriate use of PPE, especially in dangerous processes. Computer vision technology can help perform this task automatically, exploiting appropriate deep neural models for recognizing PPE. YOLO (You Only Look Once) deep neural models ensure good accuracy against a limited complexity, which allows running them on devices with limited computational capacity. In this paper, we evaluate the performances, in terms of accuracy and processing speed, of the most popular models implemented in the version 5 of YOLO (YOLOv5) in the task of recognizing whether workers correctly wear PPE. To this aim, all models are trained on a PPE dataset on a dedicated server. Then, each model is deployed on low-cost hardware, which includes a Raspberry Pi4 Model B equipped with an Intel Neural Compute Stick 2, used as a processing unit. The outcomes show that YOLOv5n, with only 1.9 million parameters, is the fastest model which allows processing 7.9 frames per second, while YOLOv5l and YOLOv5x, respectively with 46.5 and 86.7 million parameters, are the most accurate but slowest models, processing 1.3 and 0.7 frames per second. We also compare the performance of the YOLOv5 models with the ones in version 4 of YOLO, showing how models in version 5 in general outperform the previous version with higher accuracy, especially in the detection of small objects.

Fuzzy Regression Trees (FRTs) are widely acknowledged as highly interpretable ML models, capable of dealing with noise and/or uncertainty thanks to the adoption of fuzziness. The accuracy of FRTs, however, strongly depends on the polynomial function adopted in the leaf nodes. Indeed, their modelling capability increases with the order of the polynomial, even if at the cost of greater complexity and reduced interpretability. In this paper we introduce the concept of Heterogeneous FRT: the order of the polynomial function is selected on each leaf node and can lead either to a zero-order or a first-order approximation. In our experimental assessment, the percentage of the two approximation orders is varied to cover the whole spectrum from pure zero-order to pure first-order FRTs, thus allowing an in-depth analysis of the trade-off between accuracy and interpretability. We present and discuss the results in terms of accuracy and interpretability obtained by the corresponding FRTs on nine benchmark datasets

The quest for trustworthiness in Artificial Intelligence (AI) is increasingly urgent, especially in the field of next-generation wireless networks. Future Beyond 5G (B5G)/6G networks will connect a huge amount of devices and will offer innovative services empowered with AI and Machine Learning tools. Nevertheless, private user data, which are essential for training such services, are not an asset that can be unrestrictedly shared over the network, mainly because of privacy concerns. To overcome this issue, Federated Learning (FL) has recently been proposed as a paradigm to enable collaborative model training among multiple parties, without any disclosure of private raw data. However, the initiative to natively integrate FL services into mobile networks is still far from being accomplished. In this paper we propose a novel FL-as-a-Service framework that provides the B5G/6G network with flexible mechanisms to allow end users to exploit FL services, and we describe its applicability to a Quality of Experience (QoE) forecasting service based on a vehicular networking use case. Specifically, we show how FL of eXplainable AI (XAI) models can be leveraged for the QoE forecasting task, and induces a benefit in terms of both accuracy, compared to local learning, and trustworthiness, thanks to the adoption of inherently interpretable models. Such considerations are supported by an extensive experimental analysis on a publicly available simulated dataset. Finally, we assessed how the learning process is affected by the system deployment and the performance of the underlying communication and computation infrastructure, through system-level simulations, which show the benefits of deploying the proposed framework in edge-based environments.

Federated Learning (FL) has been proposed as a privacy preserving paradigm for collaboratively training AI models: in an FL scenario data owners learn a shared model by aggregating locally-computed partial models, with no need to share their raw data with other parties. Although FL is today extensively studied, a few works have discussed federated approaches to generate explainable AI (XAI) models. In this context, we propose an FL approach to learn Takagi-Sugeno-Kang Fuzzy Rule-based Systems (TSK-FRBSs), which can be considered as XAI models in regression problems. In particular, a number of independent data owner nodes participate in the learning process, where each of them generates its own local TSK-FRBS by exploiting an ad-hoc defined procedure. Then, these models are forwarded to a server that is responsible for aggregating them and generating a global TSK-FRBS, which is sent back to the nodes. An appropriate aggregation strategy is proposed to preserve the explainability of the global TSK-FRBS. A thorough experimental analysis highlights that the proposed approach brings benefits, in terms of accuracy, to data owners participating in the federation preserving the privacy of the data. Indeed, the accuracy achieved by the global TSK-FRBS is higher than the ones of the TSK-FRBSs learned by exploiting only local training data.

Regression Trees (RTs) have been widely used in the last decades in various domains, also thanks to their inherent explainability. Fuzzy RTs (FRTs) extend RTs by using fuzzy sets and have proven to be particularly suitable for dealing with noisy and/or uncertain environments. The modelling capability of FRTs depends, among other factors, on the model used in the leaves for determining the output, and on the inference strategy. Nevertheless, the impact of such factors on FRTs accuracy and explainability has not been adequately investigated.In this paper, we extend a recently proposed learning scheme for FRTs by employing both linear models in the leaves and the maximum matching inference strategy. The former extension aims to increase accuracy, and the latter to improve explainability. We carried out an extensive experimental analysis by comparing the four FRT versions corresponding to any possible combination of the two extensions introduced in the paper. The results show that the best trade-off between accuracy and explainability is obtained by employing both of them.

In recent years, several clustering algorithms have been proposed with the aim of mining knowledge from streams of data generated at a high speed by a variety of hardware platforms and software applications. Among these algorithms, density-based approaches have proved to be particularly attractive, thanks to their capability of handling outliers and capturing clusters with arbitrary shapes. The streaming setting poses additional challenges that need to be addressed as well: data streams are potentially unbounded and affected by concept drift, i.e., a modification over time in the underlying data generation process. In this article, we propose temporal streaming fuzzy density-based spatial clustering of applications with noise (TSF-DBSCAN), a novel fuzzy clustering algorithm for streaming data. TSF-DBSCAN is an extension of the well-known DBSCAN algorithm, one of the most popular density-based clustering approaches. Fuzziness is introduced in TSF-DBSCAN to model the uncertainty about the distance threshold that defines the neighborhood of an object. As a consequence, TSF-DBSCAN identifies clusters with fuzzy overlapping borders. A fading model, which makes objects less relevant as they become more remote in time, endows TSF-DBSCAN with the capability of adapting to evolving data streams. The integration of the model in a two-stage approach ensures computational and memory efficiency: during the online stage, continuously arriving objects are organized in proper data structures that are later exploited in the offline stage to determine a fine-grained partition. An extensive experimental analysis on synthetic and real-world datasets shows that TSF-DBSCAN yields competitive performance when compared to other clustering algorithms recently proposed for streaming data.

In the last years, there has been an ever-increasing interest in profiling various aspects of city life, especially in the context of smart cities. This interest has become even more relevant recently when we have realized how dramatic events, such as the Covid-19 pandemic, can deeply affect the city life, producing drastic changes. Identifying and analyzing such changes, both at the city level and within single neighborhoods, may be a fundamental tool to better manage the current situation and provide sound strategies for future planning. Furthermore, such fine-grained and up-to-date characterization can represent a valuable asset for other tools and services, e.g., web mapping applications or real estate agency platforms. In this article, we propose a framework featuring a novel methodology to model and track changes in areas of the city by extracting information from online newspaper articles. The problem of uncovering clusters of news at specific times is tackled by means of the joint use of state-of-the-art language models to represent the articles, and of a density-based streaming clustering algorithm, properly shaped to deal with high-dimensional text embeddings. Furthermore, we propose a method to automatically label the obtained clusters in a semantically meaningful way, and we introduce a set of metrics aimed at tracking the temporal evolution of clusters. A case study focusing on the city of Rome during the Covid-19 pandemic is illustrated and discussed to evaluate the effectiveness of the proposed approach.

Explainable Artificial Intelligence (XAI) is expected to play a key role in the design phase of next generation cellular networks. As 5G is being implemented and 6G is just in the conceptualization stage, it is increasingly clear that AI will be essential to manage the ever-growing complexity of the network. However, AI models will not only be required to deliver high levels of performance, but also high levels of explainability. In this paper we show how fuzzy models may be well suited to address this challenge. We compare fuzzy and classical decision tree models with a Random Forest (RF) classifier on a Quality of Experience classification dataset. The comparison suggests that, in our setting, fuzzy decision trees are easier to interpret and perform comparably or even better than classical ones in identifying stall events in a video streaming application. The accuracy drop with respect to RF classifier, which is considered to be a black-box ensemble model, is counterbalanced by a significant gain in terms of explainability.

Tracking and profiling changes in the occurrence of notable events in a city, in terms of what happens in the different areas and how possible changes are perceived, is an important issue in the context of smart cities: in fact, it may be helpful in developing applications to help administrations and citizens alike. In this paper, we propose an approach to provide time-sensitive snapshots of events within the different areas of a city, and the city as a whole. To probe inside neighborhoods and communities, we propose to use articles in online newspapers, as they represent an accessible source of information on what notable events actually happen, and on the most relevant topics at a given moment in time. We adopt an approach to group up articles by means of clustering, and to automatically assign labels to clusters by analyzing their content. The outcomes of this procedure, repeated along a certain timespan, are able to describe the temporal evolution of notable events in specific city areas. In this paper we show the effectiveness of the proposed methodology by reporting a case study for the city of Rome, over an investigation span of few years, which includes also the Covid-19 pandemic period.

Density-based clustering algorithms represent a convenient approach when the number of clusters is not known in advance and their shapes are arbitrary. Nevertheless, they are highly sensitive to the input parameter setting, especially when clusters' borders are close to each other, or even overlap. In this paper we propose FDBSCAN-APT, a fuzzy extension of the DBSCAN algorithm. FDBSCAN-APT is able to discover clusters with fuzzy overlapping borders and relies on the automatic setting of input parameters thanks to the definition of a novel heuristic based on the statistical modelling of the density distribution of objects. An extensive experimental analysis carried out on synthetic datasets shows that FDBSCAN-APT always finds reasonable parameter configurations and produces good clustering results in a variety of challenging scenarios.

Abstract Traffic and air pollution caused by the increasing number of cars have become important issues in nowadays cities. A possible solution is to employ recommender systems for efficient ridesharing among users. These systems, however, typically do not allow specifying ordered stops, thus preventing a large amount of possible users from exploiting ridesharing, e.g. parents leaving kids at school while going to work. Indeed, if a parent desired to share a ride, he/she would need to indicate the following constraint in the path: the stop at school should precede the stop at work. In this paper, we propose a ridesharing recommender, which allows each user to specify an ordered list of stops and suggests efficient ride matches. The ride-matching criterion is based on a dissimilarity between the driver’s path and the shared path, computed as the shortest path on a directed acyclic graph with ordering constraints between the stops defined in the single paths. The dissimilarity value is the detour requested to the driver to visit also the stops of the paths involved in the ride-share, respecting the visiting order of the stops within each path. Results are presented on a case study involving the city of Pisa.

The exploitation of data streams, nowadays provided nonstop by a myriad of diverse applications, asks for specific analysis methods. In this paper, we propose SF-DBSCAN, a fuzzy version of the DBSCAN algorithm, aimed to perform unsupervised analysis of streaming data. Fuzziness is introduced by fuzzy borders of density-based clusters. We describe and discuss the proposed algorithm, which evolves the clusters at each occurrence of a new object. Three synthetic datasets are used to show the ability of SF-DBSCAN to successfully track changes of data distribution, thus properly addressing concept drift. SF-DBSCAN is compared with a basic, crisp streaming version of DBSCAN with regard to modelling effectiveness.

The paper presents a framework for characterizing and profiling city areas from available data provided by online web services and web sites. These data are points of interest (restaurants, services, hotels, schools, churches, shops, wi-fi access points, etc.) disseminated in the city, local news, traffic information, city events, lifestyle and human behaviors. The framework allows selecting the different data sources, preprocessing the data, extracting meaningful features, executing a clustering algorithm to determine the profiles of the single areas of the city, and visualizing the results on the city map. The definition of the areas is based on the construction of a virtual grid of squared cells on the city. We employed the framework for profiling areas of the metropolitan city of Milan, Italy. We tested different cell sizes and employed the k-means clustering algorithm to group similar areas of the city. We highlight how areas belonging to the same cluster, although located in different zones of the city, actually present similar characteristics. Such a framework can be of the utmost importance for several entities. By exploiting the profiles of the city areas, citizens can benefit from tailored services, enterprises can define ad hoc marketing strategies, and local governments can be supported in decision making.

Fuzzy associative classification has not been widely analyzed in the literature, although associative classifiers (ACs) have proved to be very effective in different real domain applications. The main reason is that learning fuzzy ACs is a very heavy task, especially when dealing with large datasets. To overcome this drawback, in this paper, we propose an efficient distributed fuzzy associative classification approach based on the MapReduce paradigm. The approach exploits a novel distributed discretizer based on fuzzy entropy for efficiently generating fuzzy partitions of the attributes. Then, a set of candidate fuzzy association rules is generated by employing a distributed fuzzy extension of the well-known FP-Growth algorithm. Finally, this set is pruned by using three purposely adapted types of pruning. We implemented our approach on the popular Hadoop framework. Hadoop allows distributing storage and processing of very large data sets on computer clusters built from commodity hardware. We have performed an extensive experimentation and a detailed analysis of the results using six very large datasets with up to 11 000 000 instances. We have also experimented different types of reasoning methods. Focusing on accuracy, model complexity, computation time, and scalability, we compare the results achieved by our approach with those obtained by two distributed nonfuzzy ACs recently proposed in the literature. We highlight that, although the accuracies result to be comparable, the complexity, evaluated in terms of number of rules, of the classifiers generated by the fuzzy distributed approach is lower than the one of the nonfuzzy classifiers.

Fuzzy decision trees (FDTs) have shown to be an effective solution in the framework of fuzzy classification. The approaches proposed so far to FDT learning, however, have generally neglected time and space requirements. In this paper, we propose a distributed FDT learning scheme shaped according to the MapReduce programming model for generating both binary and multiway FDTs from big data. The scheme relies on a novel distributed fuzzy discretizer that generates a strong fuzzy partition for each continuous attribute based on fuzzy information entropy. The fuzzy partitions are, therefore, used as an input to the FDT learning algorithm, which employs fuzzy information gain for selecting the attributes at the decision nodes. We have implemented the FDT learning scheme on the Apache Spark framework. We have used ten real-world publicly available big datasets for evaluating the behavior of the scheme along three dimensions: 1) performance in terms of classification accuracy, model complexity, and execution time; 2) scalability varying the number of computing units; and 3) ability to efficiently accommodate an increasing dataset size. We have demonstrated that the proposed scheme turns out to be suitable for managing big datasets even with a modest commodity hardware support. Finally, we have used the distributed decision tree learning algorithm implemented in the MLLib library and the Chi-FRBCS-BigData algorithm, a MapReduce distributed fuzzy rule-based classification system, for comparative analysis.

Boosting is a simple and effective procedure that combines several weak learners with the aim of generating a strong classifier. Multi-class boosting has been only recently studied in the context of crisp classifiers, showing encouraging performances. In this paper, we propose FDT-Boost, a boosting approach shaped according to the multi-class SAMME-AdaBoost scheme, that employs size-constrained fuzzy binary decision trees as weak classifiers. We test FDT-Boost on twenty-three classification benchmarks. By comparing our approach with FURIA, one of the most popular fuzzy classifiers, and with a fuzzy binary decision tree, we show that our approach is accurate, yet keeping low the model complexity in terms of total number of leaf nodes.

In this paper, we analyze and compare four multi-objective evolutionary granular rule-based classifiers. We learn concurrently the rule base, the most suitable number of granules and their parameters during the evolutionary process. Rule learning is performed by a method, which selects rules and conditions from an initial heuristically-generated rule base. The four classifiers differ for the type of granule, namely Type-1 and Type-2 fuzzy sets, and for the method used for generating the initial rule base, namely crisp and fuzzy decision tree learning algorithms. Results show that generating the initial rule base by using a fuzzy decision tree outperforms the use of the crisp decision tree. On the other hand, no statistical difference exists between the use of Type-1 and Type-2 fuzzy sets as granules.

Internet traffic classification has moved in the last years from traditional port and payload-based approaches towards methods employing statistical measurements and machine learning techniques. Despite the success achieved by these techniques, they are not able to explain the relation between the features, which describe the traffic flow, and the corresponding traffic classes. This relation can be extremely useful to network managers for quickly handling possible network drawback. In this paper, we propose to tackle the traffic classification problem by using multi-objective evolutionary fuzzy classifiers (MOEFCs). MOEFCs are characterised by good trade-offs between accuracy and interpretability. We adopt two Internet traffic datasets extracted from two real-world networks. We discuss the results obtained both by applying a cross validation on each single dataset, and by using a dataset as training set and the other as test set. We show that, in both cases, MOEFCs can achieve satisfactory accuracy in the face of low complexity and, therefore, high interpretability.

Classification techniques are becoming essential in the financial world for reducing risks and possible disasters. Managers are interested in not only high accuracy, but in interpretability and transparency as well. It is widely accepted now that the comprehension of how inputs and outputs are related to each other is crucial for taking operative and strategic decisions. Furthermore, inputs are often affected by contextual factors and characterized by a high level of uncertainty. In addition, financial data are usually highly skewed toward the majority class. With the aim of achieving high accuracies, preserving the interpretability, and managing uncertain and unbalanced data, this paper presents a novel method to deal with financial data classification by adopting type-2 fuzzy rule-based classifiers (FRBCs) generated from data by a multiobjective evolutionary algorithm (MOEA). The classifiers employ an approach, denoted as scaled dominance, for defining rule weights in such a way to help minority classes to be correctly classified. In particular, we have extended PAES-RCS, an MOEA-based approach to learn concurrently the rule and data bases of FRBCs, for managing both interval type-2 fuzzy sets and unbalanced datasets. To the best of our knowledge, this is the first work that generates type-2 FRBCs by concurrently maximizing accuracy and minimizing the number of rules and the rule length with the objective of producing interpretable models of real-world skewed and incomplete financial datasets. The rule bases are generated by exploiting a rule and condition selection (RCS) approach, which selects a reduced number of rules from a heuristically generated rule base and a reduced number of conditions for each selected rule during the evolutionary process. The weight associated with each rule is scaled by the scaled dominance approach on the fuzzy frequency of the output class, in order to give a higher weight to the minority class. As regards the data base learning, the membership function parameters of the interval type-2 fuzzy sets used in the rules are learned concurrently to the application of RCS. Unbalanced datasets are managed by using, in addition to complexity, selectivity and specificity as objectives of the MOEA rather than only the classification rate. We tested our approach, named IT2-PAES-RCS, on 11 financial datasets and compared our results with the ones obtained by the original PAES-RCS with three objectives and with and without scaled dominance, the FRBCs, fuzzy association rule-based classification model for high-dimensional dataset (FARC-HD) and fuzzy unordered rules induction algorithm (FURIA), the classical C4.5 decision tree algorithm, and its cost-sensitive version. Using nonparametric statistical tests, we will show that IT2-PAES-RCS generates FRBCs with, on average, accuracy statistically comparable with and complexity lower than the ones generated by the two versions of the original PAES-RCS. Further, the FRBCs generated by FARC-HD and FURIA and the decision trees computed by C4.5 and its cost-sensitive version, despite the highest complexity, result to be less accurate than the FRBCs generated by IT2-PAES-RCS. Finally, we will highlight how these FRBCs are easily interpretable by showing and discussing one of them.

One of the most appealing features of fuzzy rule-based classifiers is the capability of explaining how the conclusions are inferred. This feature is hard to preserve when fuzzy rules are extracted from a very large amount of data. In this paper, we propose a distributed version of PAES-RCS, a multiobjective evolutionary approach to learn concurrently the rule and data bases of fuzzy rule-based classifiers by maximizing accuracy and minimizing complexity. PAES-RCS has proven to be very efficient in obtaining satisfactory approximations of the Pareto front exploiting a limited number of iterations. We implemented the distributed version of PAES-RCS by using Apache Spark as data processing framework. We discuss the effectiveness of our approach in terms of classification rate and scalability by performing a number of experiments on three real-world big datasets. Further, we compare our approach with other well-known state-of-art algorithms in terms of both accuracy and complexity, and evaluate the achievable speedup on a small computer cluster. We show that the distributed version can efficiently extract compact rule bases with high accuracy and allows handling big datasets even with modest hardware support.

Ride-sharing practice represents one of the possible answers to the traffic congestion problem in today's cities. In this scenario, recommenders aim to determine similarity among different paths with the aim of suggesting possible ride shares. In this paper, we propose a novel dissimilarity function between pairs of paths based on the construction of a shared path, which visits all points of the two paths by respecting the order of sequences within each of them. The shared path is computed as the shortest path on a directed acyclic graph with precedence constraints between the points of interest defined in the single paths. The dissimilarity function evaluates how much a user has to extend his/her path for covering the overall shared path. After computing the dissimilarity between any pair of paths, we execute a fuzzy relational clustering algorithm for determining groups of similar paths. Within these groups, the recommenders will choose users who can be invited to share rides. We show and discuss the results obtained by our approach on 45 paths.

We present a system to detect incidents causing traffic congestion in the road network by analyzing real-time GPS data. These data are collected from tracking devices installed in the vehicles or from drivers' smartphones. After positioning the GPS coordinates on the road map, the system assigns a traffic state to each road segment based on the velocities of vehicles, and generates alerts for incident based on a spatiotemporal analysis of these states. The system is validated by using GPS data simulated in typical traffic conditions in the city of Pisa, Italy. The results show an incident detection rate of 91.6% and an average detection time shorter than 7 minutes.

Random forests are currently considered among the most accurate and efficient classifiers. Moreover, recently fuzzy implementations of random forests have been proposed to exploit the ability of fuzzy decision trees to cope with uncertain data. Whenever the size of training sets grows substantially, as it happens in the case of Big Data, ordinary implementations of classifiers become inadequate, and fuzzy random forests make no exception. In this paper, we consider a method, which generates fuzzy partitions of the continuous attributes along the decision tree learning, and we propose a distributed implementation of fuzzy random forests based on this method. The implementation relies on the MapReduce programming model and the Apache Hadoop framework. It is shown that such a model can easily accommodate an effective distribution strategy for the computation, yielding good scalability figures. The novel distributed algorithm makes fuzzy random forests able to deal with extremely large data sets, both in the learning and in the classification phases, thus fostering its applicability in the modern scenario of increasingly frequent data deluges.

In this paper, we propose an efficient distributed fuzzy associative classification model based on the MapReduce paradigm. The learning algorithm first mines a set of fuzzy association classification rules by employing a distributed version of a fuzzy extension of the well-known FP-Growth algorithm. Then, it prunes this set by using three purposely adapted types of pruning. We implemented the distributed fuzzy associative classifier using the Hadoop framework. We show the scalability of our approach by carrying out a number of experiments on a real-world big dataset. In particular, we evaluate the achievable speedup on a small computer cluster, highlighting that the proposed approach allows handling big datasets even with modest hardware support.

Random forests have proved to be very effective classifiers, which can achieve very high accuracies. Although a number of papers have discussed the use of fuzzy sets for coping with uncertain data in decision tree learning, fuzzy random forests have not been particularly investigated in the fuzzy community. In this paper, we first propose a simple method for generating fuzzy decision trees by creating fuzzy partitions for continuous variables during the learning phase. Then, we discuss how the method can be used for generating forests of fuzzy decision trees. Finally, we show how these fuzzy random forests achieve accuracies higher than two fuzzy rule-based classifiers recently proposed in the literature. Also, we highlight how fuzzy random forests are more tolerant to noise in datasets than classical crisp random forests.

Social networks have been recently employed as a source of information for event detection, with particular reference to road traffic congestion and car accidents. In this paper, we present a real-time monitoring system for traffic event detection from Twitter stream analysis. The system fetches tweets from Twitter according to several search criteria; processes tweets, by applying text mining techniques; and finally performs the classification of tweets. The aim is to assign the appropriate class label to each tweet, as related to a traffic event or not. The traffic detection system was employed for real-time monitoring of several areas of the Italian road network, allowing for detection of traffic events almost in real time, often before online traffic news web sites. We employed the support vector machine as a classification model, and we achieved an accuracy value of 95.75% by solving a binary classification problem (traffic versus nontraffic tweets). We were also able to discriminate if traffic is caused by an external event or not, by solving a multiclass classification problem and obtaining an accuracy value of 88.89%.

Recent studies have highlighted that a significant part of the electrical energy consumption in residential buildings is caused by an improper use of home appliances. The development of low-cost systems for profiling the consumption of electric appliances can play a key role in stimulating the users to adopt adequate policies for energy saving. In this paper, we describe a novel methodology for extracting the power consumption of each appliance deployed in a domestic environment from the aggregate measures collected by a single smart meter. In order to coarsely describe how each type of appliance works, we use finite-state machines (FSMs) based on fuzzy transitions. An ad-hoc disaggregation algorithm exploits a database of these FSMs for, at each meaningful variation in real and reactive aggregate powers, hypothesizing possible configurations of active appliances. This set of configurations is concurrently managed by the algorithm which, whenever requested, outputs the configuration with the highest confidence with respect to the sequence of detected events. We implemented a prototype of a monitoring system based on the proposed methodology and installed it in a real domestic scenario. We discuss an experiment in which 11 appliances were connected to the same circuit and the aggregate power consumption was measured by a smart meter for approximately 12 h. At the end of the experiment, only two possible configurations were outputs from the system, including the correct one.

Internet of Things (IoT) devices are typically powered by small batteries with a limited capacity. Thus, saving power as much as possible becomes crucial to extend their lifetime and therefore to allow their use in real application domains. Since radio communication is in general the main cause of power consumption, one of the most used approaches to save energy is to limit the transmission/reception of data, for instance, by means of data compression. However, the IoT devices are also characterized by limited computational resources which impose the development of specifically designed algorithms. To this aim, we propose to endow the lossless compression algorithm (LEC), previously proposed by us in the context of wireless sensor networks, with two simple adaptation schemes relying on the novel concept of appropriately rotating the prefix-free tables. We tested the proposed schemes on several datasets collected in several real sensor network deployments by monitoring four different environmental phenomena, namely, air and surface temperatures, solar radiation and relative humidity. We show that the adaptation schemes can achieve significant compression efficiencies in all the datasets. Further, we compare such results with the ones obtained by LEC and, by means of a non-parametric multiple statistical test, we show that the performance improvements introduced by the adaptation schemes are statistically significant.

Internet of Things (IoT) is expected to substantially support sustainable developments of future smart cities. However, the heterogeneity among connected objects (i.e., things) and the unreliable nature of their associated services may prevent IoT from playing this crucial role. To enable the possibility of dynamically (re-)configuring real world objects at run-time, we propose to represent them as Virtual Objects (VOs). VOs are semantic descriptions of physical objects and of the phenomena they observe and include software modules to expose the object functionalities as IoT services. In order to provide self configuration functionality at VO level, we propose to use a cognitive management framework that wisely tunes key application parameters. Finally, we present a practical environmental monitoring application exploiting wireless sensor nodes, to show how the proposed cognitive framework can be useful to select the most appropriate compression algorithm so as to reduce the overall energy consumption of the sampling devices.

In this paper, we exploit a multi-objective evolutionary algorithm (MOEA) to generate fuzzy rule-based classifiers (FRBCs) with different trade-offs between classification accuracy and rule base complexity. In order to learn the rule base we employ a rule and condition selection (RCS) approach which aims to select a reduced number of rules from a heuristically generated rule base and concurrently a reduced number of conditions for each selected rule. During the multi-objective evolutionary process, we generate the rule bases of the FRBCs by the RCS approach and concurrently learn the membership function parameters of the linguistic values used in the rules. The MOEA has been tested on fifteen classification benchmarks and compared with a similar technique proposed recently in the literature. We show how the FRBCs generated by our approach can achieve considerable accuracies, despite a low rule base complexity.

When considering data sets characterized by a large number of instances, the computational time required to apply Genetic Algorithms for generating Fuzzy Rule-Based Classifiers increases considerably, mainly due to the fitness evaluation. Another important problem associated to these kinds of data sets is an undesired increase of the obtained model complexity.

When dealing with datasets that are characterized by a large number of instances, multiobjective evolutionary learning (MOEL) of fuzzy rule-based systems (FRBSs) suffers from high computational costs, mainly because of the fitness evaluation. The use of a reduced set of representative instances in place of the overall training set (TS) would considerably lessen the computational effort. Even though a large number of papers have proposed instance selection approaches, mainly in classification problems, how this selection should be performed, especially in the context of regression, is still an open issue. In this paper, we tackle the instance selection problem in the framework of MOEL of FRBSs through a coevolutionary approach. In the execution of the MOEL, periodically, a single-objective genetic algorithm (SOGA) evolves a population of reduced TSs. The SOGA aims to maximize a purposely defined index which measures how much the Pareto fronts computed by using, respectively, the reduced TS and the overall TS are close to each other: The closer the fronts, the more the reduced TS is representative of the overall TS. During the execution of the MOEL, the rule base and the membership function parameters of the fuzzy sets are concurrently learned by maximizing the accuracy and minimizing the complexity. We tested our approach on 12 large datasets. We adopted reduced TSs composed of 5%, 10%, and 20% of the overall TS. Using nonparametric statistical tests, we verified that with 10% and 20% of the overall TS, the Pareto front approximations that are generated by our coevolutionary approach are comparable with the ones generated by applying the MOEL with the overall TS, although the coevolution allows us to save up to 86.36% of the execution time. In addition, the analysis of the behavior of three representative solutions on the test set highlights that the use of the reduced TSs does not affect the generalization capabilities of the generated FRBSs.

In the framework of multi-objective evolutionary fuzzy systems (MOEFSs), the search space grows as the number of features of the dataset increases, leading to a slow and possibly difficult convergence of the evolutionary algorithm. Furthermore, mainly due to the fitness evaluation, datasets with a large number of instances require very high computational costs. In this paper, we propose a co-evolutionary approach to generate sets of Mamdani fuzzy rule-based systems (MFRBSs) with different trade-offs between accuracy and interpretability. We aim to deal with high dimensional and large datasets and to learn together the rule base (RB) and the membership function parameters. To reduce the search space, we perform the multi-objective evolutionary learning of the RB by selecting reduced sets of rules and conditions from a previously generated RB. Further, to lessen the computational costs, during the multi-objective evolutionary learning process, periodically, a single-objective genetic algorithm evolves a population of reduced training sets. We show the preliminary results obtained by applying our approach to two real world high dimensional and large regression datasets.

Residential and business buildings account for a large fraction of the overall world energy consumption. Despite the high energy costs and the raising awareness about the impact on climate changes, a significant part of energy consumption in buildings is still due to an improper use of electrical appliances. In this paper we propose GreenBuilding, a sensor-based system for automated power management of electrical appliances in a building. We implemented GreenBuilding as a prototype system and deployed it in a real household scenario to perform a prolonged experimental analysis. The obtained results show that GreenBuilding is able to provide significant energy savings by using appropriate energy conservation strategies tailored to specific appliances.

Recent studies have highlighted that a significant part of the electrical energy consumption in residential and business buildings is due to an improper use of the electrical appliances. In this context, an automated power management system - capable of reducing energy wastes while preserving the perceived comfort level - would be extremely appealing. To this aim, we propose GreenBuilding, a sensor-based intelligent system that monitors the energy consumption and automatically controls the behavior of appliances used in a building. GreenBuilding has been implemented as a prototype and has been experimented in a real household scenario. The analysis of the experimental results highlights that GreenBuilding is able to provide significant energy savings.

Situation-aware service recommendation for mobile devices is aimed at proactively pushing personalized suggestions to users, presenting them unseen or unknown services. A challenging area in the field is that of recommendation schemes emerging from users' collective behavior. When we consider a mobile user, for instance, the recommendation process can be based on social events that can arise from collective positioning information. In this scenario, we discuss a collaborative multi-agent scheme for event detection, in which fuzzy representations are employed to cope with the approximation typical of implicit and aggregated information. More specifically, the first level of information processing is managed by marking agents leaving marks in the environment which are associated with users' positioning. The accumulation of marks enables a fuzzy information granulation process, managed by event agents, in which relevant events can emerge. Finally, a fuzzy inference level, managed by situation agents, deduces user situations from the underlying events.

In this work, an intelligent system for automatic detection of fault in PV fields is proposed. This system is based on a Takagi-Sugeno-Kahn Fuzzy Rule-Based System (TSK-FRBS), which provides an estimation of the instant power production of the PV field in normal functioning, i.e, when no faults occur. Then, the estimated power is compared with the real power and an alarm signal is generated if the difference between powers overcomes a threshold. The TSK-FRBS has been trained using data collected from a PV plant simulator, during normal functioning. Preliminary tests were carried out in a simulated framework, by reproducing both normal and fault conditions. Results show that the system can recognize more than 90% of fault conditions, even when noisy data are introduced.

A number of applications of wireless sensor networks require to know the location of the sensor nodes. Typically, however, mainly due to costs and limited capacity of the batteries powering the sensor nodes, only a few nodes of the network, denoted anchor nodes in the literature, are endowed with their exact positions. Thus, given a number of anchor nodes, the problem of estimating the locations of all the nodes of a wireless sensor network has attracted a large interest in the last years. The localization task is based on the estimated distances between pairs of nodes in range of each other and is particularly hard in the most appealing scenario, that is, when the network connectivity is quite low. In a recent paper, we have proposed to tackle the localization problem as a two-objective optimization task with the localization accuracy and the number of connectivity constraints that are not satisfied by the candidate geometry as the two objectives. In this paper, we aim to evaluate the behavior of five state-of-the-art multi-objective evolutionary algorithms (MOEAs) in solving the localization problem on different network topologies. We show that one of these MOEAs, namely PAES, statistically outperforms the others in terms of localization error.

Due to the need of limiting costs and energy consumption, in real deployments of wireless sensor networks the exact positions of only a restricted number of nodes are generally available. Since a lot of applications require to know where all the nodes have been placed, the issue of estimating the locations of the remaining nodes has attracted a lot of interest in the literature. In this paper, we discuss how the localization problem can be solved by using a two-objective evolutionary algorithm which concurrently aims to maximize the localization accuracy and minimize the number of connectivity constraints non-satisfied by the candidate geometry codified in the chromosomes. The proposed approach has been applied to different network configurations and compared in terms of normalized localization error with a state-of-the-art method based on semi-definite programming. The results show that our approach outperforms the compared method in all the configurations.

In this paper, we propose a three-objective evolutionary algorithm to generate a set of Mamdani fuzzy rule-based systems (MFRBSs) with different tradeoffs between accuracy, rule base (RB) complexity and partition integrity. The RB, the linguistic partition granularities and the membership function (MF) parameters are concurrently learnt during the evolutionary process. In particular, the granularity learning is performed by exploiting the concept of virtual RB and an appropriate mapping strategy, and the MF parameter tuning is achieved by a piecewise linear transformation. The RB complexity is measured as the total number of conditions in the antecedents of the rules and the partition integrity is evaluated by using a purposely-defined index, based on the piecewise linear transformation. We use a chromosome composed of three parts, which codify, respectively, the RB, and, for each variable, the number of fuzzy sets and the parameters of the piecewise linear transformation of the membership functions. Results on two real-world regression problems are shown and discussed.

When applied to high dimensional datasets, multi-objective evolutionary learning (MOEL) of fuzzy rule-based systems suffers from high computational costs, mainly due to the fitness evaluation. To use a reduced training set (TS) in place of the overall TS could considerably lessen the required effort. How this reduction should be performed, especially in the context of regression, is still an open issue. In this paper, we propose to adopt a co-evolutionary approach. In the execution of the MOEL, periodically, a single-objective genetic algorithm (SOGA) evolves a population of reduced TSs. The SOGA aims to maximize a purposely-defined index which measures how much a reduced TS is representative of the overall TS in the context of the MOEL. We tested our approach on a real world high dimensional dataset. We show that the Pareto fronts generated by applying the MOEL with the overall and the reduced TSs are comparable, although the use of the reduced TS allows saving on average the 75% of the execution time.

Situation awareness is a promising approach to recommend to a mobile user the most suitable resources for a specific situation. However, determining the correct user situation is not a simple task since users have different habits that may affect the way in which the situations arise. Thus, an appropriate tuning aimed at adapting the situation recognizer to the specific user is desirable to make a resource recommender more effective. In this paper, we show how this objective can be achieved by collecting data during the interaction of the user with the mobile device and using this context history to personalize the resource recommender by a genetic algorithm. To describe our approach, we adopt a recently proposed resource recommender which exploits fuzzy linguistic variables to manage the inherent vagueness of some contextual parameters. Experimental results on a real business case show that the responsiveness and modeling capabilities of the recommender increase, thus validating the proposed approach.

Nowadays, a huge quantity of resources for mobile users are made available on the most important marketplaces. Further, handheld devices can accommodate plenty of these resources, such as applications, documents and web pages, locally. Thus, to search for resources suitable for specific circumstances often requires a considerable effort and rarely brings to a completely satisfactory result. A tool able to recommend suitable resources at the right time in each situation would be of great help for the mobile users and would make the use of the handheld devices less boring and more attractive. To this aim, new levels of granularity, together with some degree of self-awareness, are needed to assist mobile users in managing and using resources. In this paper, we propose an efficient situation-aware resource recommender (SARR), which helps mobile users to timely locate resources proactively. Situations are determined by a semantic reasoner that exploits domain knowledge expressed in terms of ontologies and semantic rules. This reasoner works in synergy with a fuzzy engine, which is in charge of handling the vagueness of some conditions in the semantic rules, computing a certainty degree for each inferred situation. These degrees are used to rank the situations and consequently to assign a priority to the resources associated with the specific situations. The application of SARR to two real business cases is also shown and discussed.

In this paper, we introduce a new index for evaluating the interpretability of Mamdani fuzzy rule-based systems (MFRBSs). The index takes both the rule base complexity and the data base integrity into account. We discuss the use of this index in the multi-objective evolutionary generation of MFRBSs with different trade-offs between accuracy and interpretability. The rule base and the membership function parameters of the MFRBSs are learnt concurrently by exploiting an appropriate chromosome coding and purposely-defined genetic operators. Results on a real-world regression problem are shown and discussed.

Today's mobile Internet service portals offer thousands of services and mobile devices can host plenty of applications, documents and web URLs. Hence, for average mobile users there is an increasing cognitive burden in finding the most appropriate service among the many available. On the other hand, methodologies such as bookmarks and resource tagging require a great arranging effort to handle increasing resources. To help mobile users in managing and using this personal information space, new levels of granularity should be introduced in the organization of services, together with some degree of self-awareness. This paper proposes a situation-aware service recommender that helps locating services proactively. In the recommender, a semantic layer determines one or more user current situations by using domain knowledge expressed in terms of ontology and semantic rules. A fuzzy inference layer manages the vagueness of some contextual condition of these rules and outputs an uncertainty degree for each situation. Based on this degree, the recommender proposes a set of specific resources.

Energy is a primary constraint in the design and deployment of wireless sensor networks (WSNs) since sensor nodes are typically powered by batteries with a limited capacity. Since radio communication is, in general, the most energy hungry operation in a sensor node, most of the techniques proposed to extend the lifetime of a WSN have focused on limiting transmission/reception of data, for instance, through data compression. Since sensor nodes are equipped with limited computational and storage resources, enabling compression requires specifically designed algorithms. In this paper, we propose a lossy compressor based on a differential pulse code modulation scheme with quantization of the differences between consecutive samples. The quantization parameters, which allow achieving the desired trade-off between compression performance and information loss, are determined by a multi-objective evolutionary algorithm. Experiments carried out on three datasets collected by real WSN deployments show that our approach can achieve significant compression ratios despite negligible reconstruction errors.

Energy is a primary constraint in the design and deployment of wireless sensor networks (WSNs), since sensor nodes are typically powered by batteries with a limited capacity. Energy efficiency is generally achieved by reducing radio communication, for instance, limiting transmission/reception of data. Data compression can be a valuable tool in this direction. The limited resources available in a sensor node demand, however, the development of specifically designed compression algorithms. In this paper, we propose a simple lossless entropy compression (LEC) algorithm which can be implemented in a few lines of code, requires very low computational power, compresses data on the fly and uses a very small dictionary whose size is determined by the resolution of the analog-to-digital converter. We have evaluated the effectiveness of LEC by compressing four temperature and relative humidity data sets collected by real WSNs, and solar radiation, seismic and ECG data sets. We have obtained compression ratios up to 70.81% and 62.08% for temperature and relative humidity data sets, respectively, and of the order of 70% for the other data sets. Then, we have shown that LEC outperforms two specifically designed compression algorithms for WSNs. Finally, we have compared LEC with gzip, bzip2, rar, classical Huffman and arithmetic encodings.

In this paper, we propose a novel approach to system identification based on morphogenetic theory (MT). Given a context H defined by a set of M objects, each described by a set of N attributes, and a vector X of desired outputs for each object, MT combines notions from formal concept analysis and tensor calculus so as to generate a morphogenetic system (MS). The MS is defined by a set of weights s1, …, sN, one for each attribute. Given H and X, weights are computed so as to generate the projection Y of X on the space of the attributes with the minimum distance between Y and X. An MS can be represented as a neuron, morphogenetic neuron, with a number of synapses equal to the number of attributes and synaptic weights equal to s1, …, sN. Unlike traditional neural network paradigm, which adopts an iterative process to determine synaptic weights, in MT, weights are computed at once. We introduce a method to generate a morphogenetic neural network (MNN) for identification problems. The method is based on extending appropriately and iteratively the attribute space so as to reduce the error between desired output and computed output. By using four well-known datasets, we show that an MNN can identify an unknown system with a precision comparable with classical multilayer perceptron with complexity similar to the MNN but reducing drastically the time needed to generate the neural network. Furthermore, the structure of the MNN is generated automatically by the method and does not require a trial-and-error approach often applied in classical neural networks. © 2009 Wiley Periodicals, Inc.

In this paper, we propose the use of a multiobjective evolutionary approach to generate a set of linguistic fuzzy-rule-based systems with different tradeoffs between accuracy and interpretability in regression problems. Accuracy and interpretability are measured in terms of approximation error and rule base (RB) complexity, respectively. The proposed approach is based on concurrently learning RBs and parameters of the membership functions of the associated linguistic labels. To manage the size of the search space, we have integrated the linguistic two-tuple representation model, which allows the symbolic translation of a label by only considering one parameter, with an efficient modification of the well-known (2$+$2) Pareto Archived Evolution Strategy (PAES). We tested our approach on nine real-world datasets of different sizes and with different numbers of variables. Besides the (2$+$2)PAES, we have also used the well-known non-dominated sorting genetic algorithm (NSGA-II) and an accuracy-driven single-objective evolutionary algorithm (EA). We employed these optimization techniques both to concurrently learn rules and parameters and to learn only rules. We compared the different approaches by applying a nonparametric statistical test for pairwise comparisons, thus taking into consideration three representative points from the obtained Pareto fronts in the case of the multiobjective EAs. Finally, a data-complexity measure, which is typically used in pattern recognition to evaluate the data density in terms of average number of patterns per variable, has been introduced to characterize regression problems. Results confirm the effectiveness of our approach, particularly for (possibly high-dimensional) datasets with high values of the complexity metric.

We have developed a multi-classifier system for automatic classification of pulmonary nodules in lung CT (Computed Tomography) images. The system consists of a set of independent modules, each emulating a radiologist of a team, and a further module aimed at appropriately combining theradiologists' opinions. In the experiments we obtained a sensitivity of 95% against a specificity of 91.33%, adopting a combiner based on the decisio.n templates technique.

In this paper we propose a multi-objective genetic algorithm to generate Mamdani fuzzy rule-based systems with optimal trade-offs between complexity and accuracy. The main novelty of the algorithm is that both rule base and granularity of the uniform partitions defined on the input and output variables are learned concurrently. To this aim, we exploit a chromosome composed of two parts, which codify the numbers of fuzzy sets for each linguistic variable and the rule base, respectively. Rule bases defined on partitions with different granularity are handled by using an appropriate mapping strategy. The algorithm has been tested on a real word regression problem showing very promising results.

Multiobjective genetic fuzzy systems (MGFSs) have proved to be very effective in classification, regression and control tasks. However, large scale problems still present open and challenging research issues. Making identification of fuzzy rules faster can enlarge the range of applications of MGFSs. In this work we first analyze the time complexity for both the identification and the evaluation of Takagi-Sugeno fuzzy rule-based systems. Then we introduce a simple but effective idea for fast identification of consequent parameters, although in an approximated, suboptimal manner. In the experimental part we first test the correctness of the predicted asymptotical time complexity. Then we show the benefits through an example of multiobjective genetic learning of compact and accurate fuzzy systems, in which we saved 71.3% of time on a 7 input problem.

In this paper, we propose an approach to complexity reduction of Mamdani-type fuzzy rule-based systems (FRBSs) based on removing logical redundancies. We first generate an FRBS from data by applying a simplified version of the well-known Wang and Mendel method. Then, we represent the FRBS as a multi-valued logic relation. Finally, we apply MVSIS, a tool for circuit minimization and simulation, to minimize the relation and consequently to reduce complexity of the associated FRBS. Unlike similar previous approaches proposed in the literature, the use of MVSIS let us deal with nondeterminism, that is, let us manage rules with the same antecedent but different consequents. To allow nondeterminism guarantees to achieve a higher (or at least not worse) complexity reduction than the one achievable from removing nondeterminism as soon as it appears. We apply our approach to six popular benchmarks. Results show a considerable complexity reduction associated only sporadically with consistent accuracy degradation. Moreover, quite surprisingly, the complexity reduction often comes together with an improvement in the classification accuracy.

Saving energy is a very critical issue in wireless sensor networks (WSNs) since sensor nodes are typically powered by batteries with a limited capacity. Since the radio is the main cause of power consumption in a sensor node, transmission/reception of data should be limited as much as possible. To this aim, we propose a novel distributed approach to data aggregation based on fuzzy numbers and weighted average operators to reduce data communication in WSNs when we are interested in the estimation of an aggregated value such as maximum or minimum temperature measured in the network. The basic point of our approach is that each node maintains an estimate of the aggregated value. Based on this estimate, the node decides whether a new value measured by the sensor on board the node or received through a message has to be propagated along the network. We show how the lifetime of the network can be estimated through the datasheet of the sensor node and the number of received and transmitted messages. We discuss and evaluate the application of our approach to the monitoring of the maximum temperature in a 100-node simulated WSN and a 12-node real WSN. Finally, we compute the estimates of the lifetimes for both the networks.

Context adaptation is certainly a promising approach in the development of fuzzy rule based systems (FRBSs). First, an initial rule base is extracted from heuristic knowledge of the application domain. Meanings of linguistic terms are defined so as to guarantee high interpretability of the FRBSs. Then, meanings are adapted to a specific context through the use of operators that, using a set of known input–output patterns, appropriately modify the corresponding fuzzy sets. The choice of the specific operators and their parameters is context based and optimized so as to obtain a good interpretability–accuracy trade-off. In this paper, we propose a set of operators that, starting from a given FRBS, adapt the FRBS to the specific context by adjusting the universes of the input and output variables, and modifying the core, the support and the shape of the fuzzy sets which compose the partitions of these universes. The operators are defined so as to preserve ordering of the linguistic terms, universality of rules, and interpretability of partitions. The choice of the parameters used in the operators is performed by a genetic optimization process aimed at maximizing the accuracy and preserving the interpretability of the FRBS. We finally describe the application of our context adaptation approach to two Mamdani fuzzy systems developed, respectively, for two different domains, namely, regression and data modeling. © 2008 Wiley Periodicals, Inc.

In this paper, we propose a novel population- based multi-objective evolutionary algorithm (MOEA) for binary classifier optimization. The two objectives considered in the proposed MOEA are the false positive rate (FPR) and the true positive rate (TPR), which are the two measures used in the ROC analysis to compare different classifiers. The main feature of our MOEA is that the population evolves based on the properties of the convex hulls defined in the FPR-TPR space. We discuss the application of our MOEA to determine a set of fuzzy rule-based classifiers with different trade-offs between FPR and TPR in lung nodule detection from CT scans. We show how the Pareto front approximation generated by our MOEA is better than the one generated by NSGA-II, one of the most known and used population-based MOEAs.

In the framework of context adaptation of fuzzy systems, a typical requirement of a contextualized system is to maintain the same interpretability as the original one. Here, we propose a novel index based on a fuzzy ordering relation to provide a measure of interpretability. Our index assesses ordering, distinguishability and coverage at the same time. We use the proposed index and the mean square error as goals of a multi-objective genetic algorithm aimed at generating contextualized Mamdani fuzzy systems with different trade-offs between the two goals. Results obtained on a synthetic data set are also discussed.

An approach to select the most suitable fuzzy rule-based binary classifier to a specific application is proposed. First, an evolutionary three-objective optimization algorithm is applied to generate an approximation of a Pareto front composed of fuzzy rule-based binary classifiers with different trade-offs between accuracy and complexity. Accuracy is measured in terms of sensitivity and specificity, whereas complexity is computed as sum of the conditions which compose the antecedents of the rules included in the classifiers. Thus, low values of complexity correspond to fuzzy systems characterized by a low number of rules and a low number of input variables actually used in each rule. This ensures a high comprehensibility of the classifiers. Then, the most suitable classifier is selected by using the ROC convex hull method. We discuss the application of the proposed approach to generate a classifier for discriminating lung nodules from non-nodules in a computer aided diagnosis (CAD) system. Results obtained on a real data set extracted from lung CT images are also discussed

Context adaptation can be achieved by adjusting an initial normalized fuzzy rule-based system through the use of operators that appropriately change the representation of the linguistic variables. The choice of the specific operators and their parameters should be context-based and optimized so as to obtain a good interpretability-accuracy tradeoff. In this paper we propose a set of context adaptation operators that, starting from a given fuzzy system, adjust some of its component!, such as fuzzy set support and core, membership function shape, etc. We use a genetic tuning process for choosing the operator parameters. We finally describe the application of the proposed operators to Mamdani fuzzy systems with reference to two real examples.

In this paper, we propose a fuzzy logic-based approach which exploits remotely sensed multispectral measurements of the reflected sunlight to estimate the concentration of optically active constituents of the sea water. The relation between the concentrations of interest and the subsurface reflectances is modeled by a set of fuzzy rules extracted automatically from the data through a two-step procedure. First, a compact initial rule base is generated by projecting onto the input variables the clusters produced by a fuzzy clustering algorithm. Then, a genetic algorithm is applied to optimize the rules. Appropriate constraints maintain the semantic properties of the initial model during the genetic evolution. Results of the application of the fuzzy model obtained from data simulated with an ocean color model over the channels of the Medium Resolution Imaging Spectrometer are shown and discussed.

One of the critical aspects of clustering algorithms is the correct identification of the dissimilarity measure used to drive the partitioning of the data set. The dissimilarity measure induces the cluster shape and therefore determines the success of clustering algorithms. As cluster shapes change from a data set to another, dissimilarity measures should be extracted from data. To this aim, we exploit some pairs of points with known dissimilarity value to teach a dissimilarity relation to a feed-forward neural network. Then, we use the neural dissimilarity measure to guide an unsupervised relational clustering algorithm. Experiments on synthetic data sets and on the Iris data set show that the relational clustering algorithm based on the neural dissimilarity outperforms some popular clustering algorithms (with possible partial supervision) based on spatial dissimilarity.

Most clustering algorithms partition a data set based on a dissimilarity relation expressed in terms of some distance function. When the nature of this relation is conceptual rather than metric, distance functions may fail to adequately model dissimilarity. For this reason, we propose to extract dissimilarity relations directly from the data. We exploit some pairs of patterns with known dissimilarity to build a TS fuzzy system, which models the dissimilarity relation between any pair of patterns. The resulting dissimilarity matrix is input to a new unsupervised fuzzy relational clustering algorithm, which partitions the data set based on the proximity of the vectors containing the dissimilarity values between a pattern and all the patterns in the data set. Experimental results to confirm the validity of our approach are shown and discussed.

In this paper, we present a system based on an Unsupervised Fuzzy Divisive Hierarchical Clustering (UFDHC) algorithm to determine a hierarchy of profiles of web site typical users from the web access log. These profiles can be extremely useful, for instance, to customize the web site, or to send personalized advertisements. After eliminating categories that have not been accessed by a significant percentage of users and removing the occasional users, the access log data are input to the UFDHC algorithm which clusters the users of the web site into a hierarchy of groups characterized by a set of common interests and represented by a prototype, which defines the profile of the group typical member. To show the effectiveness of our system, we describe how the profiles determined by the UFDHC algorithm from access log data collected along a period of 15 days allow classifying approximately 95% of the users defined by access log data collected during subsequent 60 days.

In this paper, we describe a fuzzy model for the estimation of sea water optically active constituents concentration in case II water. The model is extracted automatically from the data through a two step procedure. First, a fuzzy clustering algorithm is applied to identify a compact initial rule-based model. Then, the model is optimized by means of a genetic algorithm which tunes the rules so as to minimize the error between desired and predicted outputs. Appropriate constraints maintain the semantic properties of the initial model during the genetic evolution. The fuzzy model has been tailored to the multispectral data format of the MEdium Resolution Imaging Spectrometer (MERIS) on board the ESA-ENVISAT satellite launched in March 2002.

Abstract While developing systems, software engineers generally have to deal with a large number of design alternatives. Current object‐oriented methods aim to eliminate design alternatives whenever they are generated. Alternatives, however, should be eliminated only when sufficient information to take such a decision is available. Otherwise, alternatives have to be preserved to allow further refinements along the development process. Too early elimination of alternatives results in loss of information and excessive restriction of the design space. This paper aims to enhance the current object‐oriented methods by modeling and controlling the design alternatives through the application of fuzzy‐logic‐based techniques. By using an example method, it is shown that the proposed approach increases the adaptability and reusability of design models. The method has been implemented and tested in our experimental CASE environment. Copyright © 2001 John Wiley & Sons, Ltd.

This paper describes a method for fuzzy classification and recognition of 2D shapes, such as handwritten characters, image contours, etc. A fuzzy model is derived for each considered shape from a fuzzy description of a set of instances of this shape. A fuzzy description of a shape instance, in its turn, exploits appropriate fuzzy partitions of the two dimensions of the shape. These fuzzy partitions allow us to identify, and automatically associate an importance degree with the relevant shape zones for classification and recognition purposes. Two significant applications of the method are described, namely, recognition of olfactory signals and recognition of isolated, handwritten characters. In the former case, results are shown concerning the recognition of three different types of waste waters, collected in three different dilutions. In the latter case, results are shown concerning the application of the method to a NIST database, containing the segmented handprinted characters of 500 writers.

Presents Beatrix, a system for off-line recognition of handwritten sentences. The system consists of four subsystems: the first pre-processes the input bitmap and splits each sentence into words; the second integrates neural and fuzzy logic techniques for individual character recognition; the third carries out a lexical and grammatical analysis of the recognised text; and the fourth uses the words recognised with a high confidence value to re-train the second subsystem on the specific handwriting. The analysis performed by the third subsystem produces hypotheses about words and sentences in order to correct errors made by the second subsystem. The fourth subsystem allows the system to self-adapt to the specific handwriting under examination; therefore, the system possesses the properties of both writer-independent and writer-dependent systems.

In this paper, we propose an evolutionary method for detecting the optimal number of clusters in a data set, and describe its application to classification of signals generated by olfactory sensors. The method is based on a new evolutionary search and optimization strategy. The strategy forces the formation and maintenance of subpopulations of solutions. Subpopulations co-evolve and converge towards different (sub-)optimal problem solutions. Only local chromosome interactions are allowed in order to avoid migration between subpopulations approximating different optimum points and to prevent the destruction of subpopulations. To this aim, specific selection and acceptance strategies have been defined. Experimental results obtained by applying the method to two test cases are also included.

In this paper we propose a new fuzzy linguistic method to recognize odorant samples. The method is applied to raw experimental data collected from a sensor array that comprises sixteen conducting polymer sensors with partially overlapping sensitivities. The sensors are exposed to odor samples and the percentage change in resistance is used for classification. The method describes the shape of each sensor response in terms of linguistic expressions derived from a fuzzy partition of the area occupied by the response. A purposely-defined weighted distance is used to compare the linguistic descriptions. Results on the application of the method to the classification of three chemicals in two different concentrations are presented.

In this paper we present SNIFFER, an electronic nose consisting of an array of conducting polymer sensors with partially overlapping sensitivities, and a pattern recognition system based on a new linguistic fuzzy classification method. The method describes the shape of the sensor responses in terms of linguistic expressions, which are derived from a fuzzy partition of the area occupied by each response. The sensors are exposed to odorants and the percentage change in resistance is used for classification purposes. Results of the application of SNIFFER to the recognition of different types of waste water produced both by the industrial activities of leather tanneries and by municipal sewers are presented.

Presents a fuzzy method for classification and recognition of separated handwritten characters. Linguistic expressions describing the individual characters are derived from a fuzzy model of a set of character samples. A small scale application of the method in which 26 lower-case cursive characters written by 30 different writers were analysed yielded 64% recognition rate. The method was also used to implement a character recogniser in a system for off-line recognition of cursive handwriting. In such a context, thanks to the use of a dictionary and a grammar parser, the recognition rate (at character level) rose to 96%.

We consider fuzzy implication operators which are extensions of the two valued logic implication operator and are non decreasing with respect to their second argument. Firstly, we analyze some features of these operators with regard to fuzzy reasoning with one rule. Then, as regards approximate reasoning with multiple rules, we demonstrate that, for an inference process using Sup-T composition in the context of Compositional Rule of Inference (CRI), a necessary condition to infer a reasonable conclusion is that the minimum be used as an aggregation operator. Finally, when the minimum is used as an aggregation operator we provide a sufficient condition to obtain a reasonable inference result.