DECO3R: A Differential Evolution-based algorithm for generating compact Fuzzy Rule-based Classification Systems

Self-Adaptive Optimization for Improved Data Sanitization and Restoration

Nowadays, Data Sanitization is considered as a highly demanded area for solving the issue of privacy preservation in Data mining. Data Sanitization, means that the sensitive rules given by the users with the specific modifications and then releases the modified database so that, the unauthorized users cannot access the sensitive rules. Promisingly, the confidentiality of data is ensured against the data mining methods. The ultimate goal of this paper is to build an effective sanitization algorithm for hiding the sensitive rules given by users/experts. Meanwhile, this paper concentrates on minimizing the four sanitization research challenges namely, rate of hiding failure, rate of Information loss, rate of false rule generation and degree of modification. Moreover, this paper proposes a heuristic optimization algorithm named Self-Adaptive Firefly (SAFF) algorithm to generate the small length key for data sanitization and also to adopt lossless data sanitization and restoration. The generated optimized key is used for both data sanitation as well as the data restoration process. The proposed SAFF-based algorithm is compared and examined against the other existing sanitizing algorithms like Fire Fly (FF), Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution algorithm (DE) algorithms and the results have shown the excellent performance of proposed algorithm. The proposed algorithm is implemented in JAVA. The data set used are Chess, Retail, T10, and T40.

Versatile Internet of Things for Agriculture: An eXplainable AI Approach

The increase of the adoption of IoT devices and the contemporary problem of food production have given rise to numerous applications of IoT in agriculture. These applications typically comprise a set of sensors that are installed in open fields and measure metrics, such as temperature or humidity, which are used for irrigation control systems. Though useful, most contemporary systems have high installation and maintenance costs, and they do not offer automated control or, if they do, they are usually not interpretable, and thus cannot be trusted for such critical applications. In this work, we design Vital, a system that incorporates a set of low-cost sensors, a robust data store, and most importantly an explainable AI decision support system. Our system outputs a fuzzy rule-base, which is interpretable and allows fully automating the irrigation of the fields. Upon evaluating Vital in two pilot cases, we conclude that it can be effective for monitoring open-field installations.

Efficient Image Retrieval by Fuzzy Rules from Boosting and Metaheuristic

Fast content-based image retrieval is still a challenge for computer systems. We present a novel method aimed at classifying images by fuzzy rules and local image features. The fuzzy rule base is generated in the first stage by a boosting procedure. Boosting meta-learning is used to find the most representative local features. We briefly explore the utilization of metaheuristic algorithms for the various tasks of fuzzy systems optimization. We also provide a comprehensive description of the current best-performing DISH algorithm, which represents a powerful version of the differential evolution algorithm with effective embedded mechanisms for stronger exploration and preservation of the population diversity, designed for higher dimensional and complex optimization tasks. The algorithm is used to fine-tune the fuzzy rule base. The fuzzy rules can also be used to create a database index to retrieve images similar to the query image fast. The proposed approach is tested on a state-of-the-art image dataset and compared with the bag-of-features image representation model combined with the Support Vector Machine classification. The novel method gives a better classification accuracy, and the time of the training and testing process is significantly shorter.