An agent-driven semantical identifier using radial basis neural networks and reinforcement learning

Due to the huge availability of documents in digital form, and the deception possibility raise bound to the essence of digital documents and the way they are spread, the authorship attribution problem has constantly increased its relevance. Nowadays, authorship attribution,for both information retrieval and analysis, has gained great importance in the context of security, trust and copyright preservation. This work proposes an innovative multi-agent driven machine learning technique that has been developed for authorship attribution. By means of a preprocessing for word-grouping and time-period related analysis of the common lexicon, we determine a bias reference level for the recurrence frequency of the words within analysed texts, and then train a Radial Basis Neural Networks (RBPNN)-based classifier to identify the correct author. The main advantage of the proposed approach lies in the generality of the semantic analysis, which can be applied to different contexts and lexical domains, without requiring any modification. Moreover, the proposed system is able to incorporate an external input, meant to tune the classifier, and then self-adjust by means of continuous learning reinforcement.Comment: Published on: Proceedings of the XV Workshop "Dagli Oggetti agli Agenti" (WOA 2014), Catania, Italy, Sepember. 25-26, 201

Is swarm intelligence able to create mazes?

In this paper, the idea of applying Computational Intelligence in the process of creation board games, in particular mazes, is presented. For two different algorithms the proposed idea has been examined. The results of the experiments are shown and discussed to present advantages and disadvantages

Cascade feed forward neural network-based model for air pollutants evaluation of single monitoring stations in urban areas

In this paper, air pollutants concentrations for N O2 , N O, N Ox and P M 10 in a single monitoring station are predicted using the data coming from other different monitoring stations located nearby. A cascade feed forward neural network based modeling is proposed. The main aim is to provide a methodology leading to the introduction of virtual monitoring station points consistent with the actual stations located in the city of Catania in Italy.

The Ontology for Agents, Systems and Integration of Services: OASIS version 2

Semantic representation is a key enabler for several application domains, and the multi-agent systems realm makes no exception. Among the methods for semantically representing agents, one has been essentially achieved by taking a behaviouristic vision, through which one can describe how they operate and engage with their peers. The approach essentially aims at defining the operational capabilities of agents through the mental states related with the achievement of tasks. The OASIS ontology -- An Ontology for Agent, Systems, and Integration of Services, presented in 2019 -- pursues the behaviouristic approach to deliver a semantic representation system and a communication protocol for agents and their commitments. This paper reports on the main modeling choices concerning the representation of agents in OASIS 2, the latest major upgrade of OASIS, and the achievement reached by the ontology since it was first introduced, in particular in the context of ontologies for blockchains.Comment: Already published on Intelligenza Artificiale, Vol. 17, no 1, pp. 51-62, 2023. DOI 10.3233/IA-23000

A Software Architecture Assisting Workflow Executions on Cloud Resources

An enterprise providing services handled by means of workflows needs to monitor and control their execution, gather usage data, determine priorities, and properly use computing cloud-related resources. This paper proposes a software architecture that connects unaware services to components handling workflow monitoring and management concerns. Moreover, the provided components enhance dependability of services while letting developers focus only on the business logic

Real-Time Cloud-based Game Management System via Cuckoo Search Algorithm

This paper analyses the idea of applying Swarm Intelligence in the process of managing the entire 2D board game in a real-time environment. For the proposed solution Game Management System is used as a cloud resource with a dedicated intelligent control agent. The described approach has been analysed on the basis of board games like mazes. The model and the control algorithm of the system is described and examined. The results of the experiments are presented and discussed to show possible advantages and disadvantages of the proposed method.

Implementing a GIS-Based Digital Atlas of Agricultural Plastics to Reduce Their Environmental Footprint: Part II, an Inductive Approach.

Plastic pollution, largely perceived by the public as a major risk factor that strongly impacts sea life and preservation, has an even higher negative impact on terrestrial ecosystems. Indeed, quantitative data about plastic contamination on agricultural soils are progressively emerging in alarming ways. One of the main contributors to this pollution involves the mismanagement of agricultural plastic waste (APW), i.e., the residues from plastic material used to improve the productivity of agricultural crops, such as greenhouse covers, mulching films, irrigation pipes, etc. Wrong management of agricultural plastics during and after their working lives may pollute the agricultural soil and aquifers by releasing macro-, micro-, and nanoplastics, which could also enter into the human food chain. In this study, we aimed to develop a methodology for the spatial quantification of agricultural plastics to achieve sustainable post-consumer management. Through an inductive approach, based on statistical data from the agricultural census of the administrative areas of the Italian provinces, an agricultural plastic coefficient (APC) was proposed, implemented, and spatialized in a GIS environment, to produce a database of APW for each type of crop. The proposed methodology can be exported to other countries. It represents valuable support that could realize, in integration with other tools, an atlas of agricultural plastics, which may be a starting point to plan strategies and actions targeted to the reduction of the plastic footprint of agriculture

Composite alginate-hyaluronan sponges for the delivery of tranexamic acid in post-extractive alveolar wounds

The management of wounds in patients on anticoagulant therapy who require oral surgical procedures is problematic and often results in a non-satisfactory healing process. Here we report a method to prepare an advanced dressing able to avoid uncontrolled bleeding by occluding the post-extractive alveolar wounds, and simultaneously, capable of a fast release of tranexamic acid (TA). Composite alginate/hyaluronan (ALG/HA) sponge dressings loaded with TA were prepared by a straightforward internal gelation method followed by a freeze-drying step. Both blank and drug-loaded sponges were soft, flexible, elegant in appearance and non-brittle in nature. SEM analysis confirmed the porous nature of these dressings. The integration of HA influenced the microstructure, reducing the porosity, modifying the water uptake kinetic and increasing the resistance to compression. TA release from ALG/HA sponges showed a controlled release up to 3h and it was faster in the presence of HA. Finally, an in vitro clotting test performed on human whole blood confirmed that the TA-loaded sponges significantly reduce the blood clotting index (BCI) by 30% compared to ALG/HA20 sponges. These results suggest that, if placed in a socket cavity, these dressings could give a relevant help to the blood hemostasis after dental extractions, especially in patients with coagulation disorders

Rainfall classification and forecasting based on a novel voting adaptive dynamic optimization algorithm

Environmental issues of rainfall are basic in terms of understanding and management of ecosystems and natural resources. The rainfall patterns significantly affect soil moisture, vegetation growth and biodiversity in the ecosystems. In addition, proper classification of rainfall types helps in the evaluation of the risk of flood, drought, and other extreme weather events’ risk, which immensely affect the ecosystems and human societies. Rainfall classification can be improved by using machine learning and metaheuristic algorithms. In this work, an Adaptive Dynamic Puma Optimizer (AD-PO) algorithm combined with Guided Whale Optimization Algorithm (Guided WOA) introduces a potentially important improvement in rainfall classification approaches. These algorithms are to be combined to enable researchers to comprehend and classify rain events by their specific features, such as intensity, duration, and spatial distribution. A voting ensemble approach within the proposed (AD-PO-Guided WOA) algorithm increases its predictive performance because of the combination of predictions from several classifiers to localize the dominant rainfall class. The presented approach not only makes the classifying of rain faster and more accurate but also strengthens the robustness and trustworthiness of the classification in this regard. Comparison to other optimization algorithms validates the effectiveness of the AD-PO-Guided WOA algorithm in terms of performance metrics with an outstanding 95.99% accuracy. Furthermore, the second scenario is applied for forecasting based on the long short-term memory networks (LSTM) model optimized by the AD-PO-Guided WOA algorithm. The AD-PO-Guided WOA- LSTM algorithm produces rainfall prediction with an MSE of 0.005078. Wilcoxon rank test, descriptive statistics, and sensitivity analysis are applied to help evaluating and improving the quality and validity of the proposed algorithm. This intensive method facilitates rainfall classification and is a base for suggested measures that cut the hazards of extreme weather events on societies