Juan M. Corchado

Using Heterogeneous Wireless Sensor Networks in a Telemonitoring System for Healthcare

Ambient intelligence has acquired great importance in recent years and requires the development of new innovative solutions. This paper presents a distributed telemonitoring system, aimed at improving healthcare and assistance to dependent people at their homes. The system implements a service-oriented architecture based platform, which allows heterogeneous wireless sensor networks to communicate in a distributed way independent of time and location restrictions. This approach provides the system with a higher ability to recover from errors and a better flexibility to change their behavior at execution time. Preliminary results are presented in this paper.

Current Topics in Artificial Intelligence

Diagnosis.- Employing Test Suites for Verilog Fault Localization.- Analyzing the Influence of Differential Constraints in Possible Conflict and ARR Computation.- On the Complexity of Program Debugging Using Constraints for Modeling the Programs Syntax and Semantics.- Evolutive Algorithms and Neural Networks.- An Analysis of Particle Properties on a Multi-swarm PSO for Dynamic Optimization Problems.- An Incremental Learning Method for Neural Networks Based on Sensitivity Analysis.- A Multi-objective Neuro-evolutionary Algorithm to Obtain Interpretable Fuzzy Models.- Improving Isolated Handwritten Word Recognition Using a Specialized Classifier for Short Words.- Knowledge Representation and Engineering.- Closeness and Distance Relations in Order of Magnitude Qualitative Reasoning via PDL.- Base Belief Change for Finitary Monotonic Logics.- Common Pitfalls in Ontology Development.- Machine Learning.- Obtaining Optimal Class Distribution for Decision Trees: Comparative Analysis of CTC and C4.5.- Selecting Few Genes for Microarray Gene Expression Classification.- Sequential Pattern Mining in Multi-relational Datasets.- CBR Outcome Evaluation for High Similar Cases: A Preliminary Approach.- On the Suitability of Combining Feature Selection and Resampling to Manage Data Complexity.- Automated Constraint Selection for Semi-supervised Clustering Algorithm.- Multiagents.- Empirical Hardness for Mixed Auctions.- Developing Strategies for the ART Domain.- A Multiagent Solution to Adaptively Classify SOAP Message and Protect against DoS Attack.- Natural Language.- Adding Morphological Information to a Connectionist Part-Of-Speech Tagger.- Planning.- A Look-Ahead B&B Search for Cost-Based Planning.- A Tabu Search Algorithm to Minimize Lateness in Scheduling Problems with Setup Times.- Improving Local Search for the Fuzzy Job Shop Using a Lower Bound.- Tutoring Systems.- Data-Driven Student Knowledge Assessment through Ill-Defined Procedural Tasks.- Uncertainty: Bayesian Networks.- Recursive Probability Trees for Bayesian Networks.- Vision.- Generating Saliency Maps Using Human Based Computation Agents.- A New Contiguity-Constrained Agglomerative Hierarchical Clustering Algorithm for Image Segmentation.- Applications.- Classifying Sleep Apneas Using Neural Networks and a Combination of Experts.- Expert System to Real Time Control of Machining Processes.- A Flexible System for Document Processing and Text Transcription.

Editorial: Hybrid learning machines

The concept of machine intelligence (MI) is complex, and thus many theories and definitions have emerged recently. Last few decades have seen a new era of machine intelligence focusing on the principles, theoretical aspects, and design methodology of algorithms gleaned from nature and biology. Examples are artificial neural networks inspired by mammalian neural systems, evolutionary computation inspired by natural selection in biology, simulated annealing inspired by thermodynamics principles, and swarm intelligence inspired by collective behavior of insects or microorganisms, etc. interacting locally with their environment causing coherent functional global patterns to emerge. These techniques have found their way in solving some real world problems in science, business, technology, and commerce. The integration of different learning and adaptation techniques, to overcome individual limitations and achieve synergetic effects through hybridization or fusion of these techniques, has in recent years contributed to a large number of new intelligent system designs. Despite the advances made, progress across the board has been moderate. One reason stems from the relatively slow pace at which work to understand biological intelligence has progressed. Another reason is rooted in the same inertia that has hampered the development of the research on intelligence previously—the reluctance to actively benefit from the achievements of the hybrid approaches. The 2nd International Workshop on Hybrid Artificial Intelligence Systems (HAIS 07–CAEPIA 2007) was held in conjunction with the Conference of the Spanish Association for Artificial Intelligence (CAEPIA) in Salamanca, Spain, from 12 to 16 November 2007. It was organized by the Biomedicine, Intelligent Systems and Educational Technology Group of the University of Salamanca. HAIS 2007 gathered individual researchers who see the need for synergy between various intelligent techniques. This special issue comprising 10 papers is focused on different hybrid learning approaches and its real world applications. Papers were selected on the basis of fundamental ideas/concepts rather than the thoroughness of techniques deployed. The papers are organized as follows. In the first paper, Gutiérrez et al. propose a hybrid neural network model using a possible combination of different transfer projection functions and kernel functions in the hidden layer of a feed-forward neural network. An evolutionary algorithm is adapted to this model and applied for learning the architecture, weights and node typology. Three different combined basis function models are proposed with all the different pairs that can be obtained. Combined functions using projection and kernel functions are found to be better than pure basis functions for the task of classification in several data sets.

Hybrid multi-agent architecture as a real-time problem-solving model

This paper presents a multi-agent architecture that facilitates the development of real-time multi-agent systems based on the SIMBA approach. The approach allows the integration of unbounded deliberative processes with critical real-time tasks. CBP-BDI deliberative agents collaborate with ARTIS agents in order to solve real-time problems efficiently. The proposal has been successfully tested and evaluated in a case study based on the use of mobile robots for mail delivery.

Applying lazy learning algorithms to tackle concept drift in spam filtering

A great amount of machine learning techniques have been applied to problems where data is collected over an extended period of time. However, the disadvantage with many real-world applications is that the distribution underlying the data is likely to change over time. In these situations, a problem that many global eager learners face is their inability to adapt to local concept drift. Concept drift in spam is particularly difficult as the spammers actively change the nature of their messages to elude spam filters. Algorithms that track concept drift must be able to identify a change in the target concept (spam or legitimate e-mails) without direct knowledge of the underlying shift in distribution. In this paper we show how a previously successful instance-based reasoning e-mail filtering model can be improved in order to better track concept drift in spam domain. Our proposal is based on the definition of two complementary techniques able to select both terms and e-mails representative of the current situation. The enhanced system is evaluated against other well-known successful lazy learning approaches in two scenarios, all within a cost-sensitive framework. The results obtained from the experiments carried out are very promising and back up the idea that instance-based reasoning systems can offer a number of advantages tackling concept drift in dynamic problems, as in the case of the anti-spam filtering domain.

Agents and ambient intelligence: case studies

The significance that ambient intelligence (AmI) has acquired in recent years requires the development of innovative solutions. In this sense, the development of AmI-based systems requires the creation of increasingly complex and flexible applications. One of the most important aspects in AmI is the use of context-aware technologies in order to perceive stimuli from both the users and the environment. Thus, the information obtained must be managed by intelligent and self-adaptable technologies in order to provide an adequate interaction between the users and their environment. Agents and multi-agent systems are one of these technologies. The agents have characteristics such as autonomy, reasoning, reactivity, social abilities and pro-activity which make them appropriate for developing dynamic and distributed systems based on AmI, as they possess the capability of adapting themselves to the users and environmental characteristics. This paper presents several case studies where agents and context-aware technologies have been implemented to build AmI-based systems. These case studies expand the possibilities of AmI and get closer to its vision.

Review: Fight sample degeneracy and impoverishment in particle filters: A review of intelligent approaches

During the last two decades there has been a growing interest in Particle Filtering (PF). However, PF suffers from two long-standing problems that are referred to as sample degeneracy and impoverishment. We are investigating methods that are particularly efficient at Particle Distribution Optimization (PDO) to fight sample degeneracy and impoverishment, with an emphasis on intelligence choices. These methods benefit from such methods as Markov Chain Monte Carlo methods, Mean-shift algorithms, artificial intelligence algorithms (e.g., Particle Swarm Optimization, Genetic Algorithm and Ant Colony Optimization), machine learning approaches (e.g., clustering, splitting and merging) and their hybrids, forming a coherent standpoint to enhance the particle filter. The working mechanism, interrelationship, pros and cons of these approaches are provided. In addition, approaches that are effective for dealing with high-dimensionality are reviewed. While improving the filter performance in terms of accuracy, robustness and convergence, it is noted that advanced techniques employed in PF often causes additional computational requirement that will in turn sacrifice improvement obtained in real life filtering. This fact, hidden in pure simulations, deserves the attention of the users and designers of new filters.

Development of CBR-BDI Agents: A Tourist Guide Application

In this paper we present an agent-based application of a wireless tourist guide that combines the Beliefs-Desires-Intentions approach with learning capabilities of Case Base Reasoning techniques. This application shows how to develop adaptive agents with a goal driven design and a decision process built on a CBR architecture. The resulting agent architecture has been validated by real users who have used the tourist guide application, on a mobile device, and can be generalized for the development of other personalized services.

Applying wearable solutions in dependent environments

This paper proposes a multiagent system (MAS) that uses smart wearable devices and mobile technology for the care of patients in a geriatric home care facility. The system is based on an advanced ZigBee wireless sensor network (WSN) and includes location and identification microchips installed in patient clothing and caregiver uniforms. The use of radio-frequency identification and near-field communication technologies allows remote monitoring of patients, and makes it possible for them to receive treatment according to preventive medical protocol. The proposed MAS manage the infrastructure of services within the environment both efficiently and securely by reasoning, task-planning, and synchronizing the data obtained from the sensors. Additionally, this paper presents the design and implementation of the reasoning agent in the MAS. A system prototype was installed in a real environment and the results obtained are presented in this paper.

SpamHunting: An instance-based reasoning system for spam labelling and filtering

In this paper we show an instance-based reasoning e-mail filtering model that outperforms classical machine learning techniques and other successful lazy learners approaches in the domain of anti-spam filtering. The architecture of the learning-based anti-spam filter is based on a tuneable enhanced instance retrieval network able to accurately generalize e-mail representations. The reuse of similar messages is carried out by a simple unanimous voting mechanism to determine whether the target case is spam or not. Previous to the final response of the system, the revision stage is only performed when the assigned class is spam whereby the system employs general knowledge in the form of meta-rules.