Data Mining for Browsing Patterns in Weblog Data by Art Neural Networks

Categorising visitors based on their interaction with a website is a key problem in Web content usage. The clickstreams generated by various users often follow distinct patterns, the knowledge of which may help in providing customised content. This paper proposes an approach to clustering weblog data, based on ART2 neural networks. Due to the characteristics of the ART2 neural network model, the proposed approach can be used for unsupervised and self-learning data mining, which makes it adaptable to dynamically changing websites

A New Charging and Billing Model and Architecture for the Ubiquitous Consumer Wireless World

In a Ubiquitous Consumer Wireless World (UCWW) environment the provision, administration and management of the authentication, authorization and accounting (AAA) policies and business services are provided by third-party AAA service providers (3P-AAA-SPs) who are independent of the wireless access network providers (ANPs). In this environment the consumer can freely choose any suitable ANP, based on his/her own preferences. This new AAA infrastructural arrangement necessitates assessing the impact and re-thinking the design, structure and location of ‘charging and billing’ (C&B) functions and services. This paper addresses C&B issues in UCWW, proposing potential architectural solutions for C&B realization. Implementation approaches of these novel solutions together with a software testbed for validation and performance evaluation are addressed

Intelligent Car Parking Locator Service

This paper presents an InfoStation-based multi-agent system facilitating a Car Parking Locator service provision within a University Campus. The system network architecture is outlined, illustrating its functioning during the service provision. A detailed description of the Car Parking Locator service is given and the system entities’ interaction is described. System implementation approaches are also considered

InfoStation-based Adaptable Provision of m Learning Services: Main Scenarios

This paper presents an adaptable InfoStation-based multi-agent system facilitating the mobile eLearning (mLearning) service provision within a University Campus. A horizontal view of the network architecture is presented. Main communications scenarios are considered by describing the detailed interaction of the system entities involved in the mLearning service provision. The mTest service is explored as a practical example. System implementation approaches are also considered

Designing a Heterogeneous Sensor Tier for the EMULSION IoT Platform

The design of a heterogeneous sensor tier for the generic, multi-service, cloud-based, IoT operational platform EMULSION is presented in this paper, along with typical hardware examples and deployment schemas involving different types of sensor sets and monitoring stations for detecting and notifying about the changes occurring in the physical world. Aided by the communication tier, the sensor tier deals also with issues of heterogeneity related to the integration of different IoT things, objects, devices, etc., into EMULSION. The elaborated multi-tiered structure of the platform, allowing much flexibility of the service provisioning along with easy scalability and expandability, is presented as well.Bulgarian National Science Fund (BNSF) under the Grant No. KP-06-IP-CHINA/1 (КП06-ИП-КИТАЙ/1) and the S&T Major Project of the Science and Technology Ministry of China, Grant No. 2017YFE0135700

The Use of a Modelling & Simulation Tier by the EMULSION IoT Platform

This paper presents some design aspects of the EMULSION IoT platform, developed as a typical example of the horizontal IoT platforms. The architectural overview and multi-tiered structure of the platform are described, with special attention being paid to its modelling & simulation tier as a novel architectural element proposed for inclusion in similar IoT platforms. Used to model cyber-physical-social (CPS) objects and IoT services, along with their attributes and temporal/spatial/event characteristics, this tier is also utilized to simulate the actual provision of IoT services in order to determine the optimal configuration of the platform in each particular use case, by solving complex optimization tasks. Examples of such tasks are presented in the paper along with some results obtained to date.Bulgarian National Science Fund (BNSF) under the Grant No. KP-06-IP-CHINA/1 (КП06-ИП-КИТАЙ/1) and the S&T Major Project of the Science and Technology Ministry of China, Grant No. 2017YFE0135700

HYBRID PID CONTROL ALGORITHMS FOR NONLINEAR PROCESS CONTROL

This paper presents modifications of the classical PID control algorithm, implemented by an Adaptive Neuro-Fuzzy Architecture (ANFA). The main goal here is to design a fuzzy PID controller with a flexible structure, adaptive tuning of its parameters and algorithm modifications, which leads to improvement of the system performance. Thus the controlling process and system are prevented from the undesired and non expected changes of the system input signals. The antecedent part of the applied fuzzy rules contains a linear function, similar to the modified discrete equation of the corresponding conventional PID controller. The simulations demonstrate satisfactory results of these performances and implementations applied to a nonlinear plant

RG Hyperparameter Optimization Approach for Improved Indirect Prediction of Blood Glucose Levels by Boosting Ensemble Learning

This paper proposes an RG hyperparameter optimization approach, based on a sequential use of random search (R) and grid search (G), for improving the blood glucose level prediction of boosting ensemble learning models. An indirect prediction of blood glucose levels in patients is performed, based on historical medical data collected by means of physical examination methods, using 40 human body’s health indicators. The conducted experiments with real clinical data proved that the proposed RG double optimization approach helps improve the prediction performance of four state-of-the-art boosting ensemble learning models enriched by it, achieving 1.47% to 24.40% MSE improvement and 0.75% to 11.54% RMSE improvement.National Key Research and Development Program of China under the Grant No. 2017YFE0135700 and the Bulgarian National Science Fund (BNSF) under the Grant No. КП-06-ИП-КИТАЙ/1 (КP-06-IP-CHINA/1

Closed-Loop Nash Equilibrium in the Class of Piecewise Constant Strategies in a Linear State Feedback Form for Stochastic LQ Games

In this paper, we examine a sampled-data Nash equilibrium strategy for a stochastic linear quadratic (LQ) differential game, in which admissible strategies are assumed to be constant on the interval between consecutive measurements. Our solution first involves transforming the problem into a linear stochastic system with finite jumps. This allows us to obtain necessary and sufficient conditions assuring the existence of a sampled-data Nash equilibrium strategy, extending earlier results to a general context with more than two players. Furthermore, we provide a numerical algorithm for calculating the feedback matrices of the Nash equilibrium strategies. Finally, we illustrate the effectiveness of the proposed algorithm by two numerical examples. As both situations highlight a stabilization effect, this confirms the efficiency of our approach.1 Decembrie 1918 Universit

Remote Sensing Image Detection Based on YOLOv4 Improvements

Remote sensing image target object detection and recognition are widely used both in military and civil fields. There are many models proposed for this purpose, but their effectiveness on target object detection in remote sensing images is not ideal due to the influence of climate conditions, obstacles and confusing objects presented in images, image clarity, and associated problems with small-target and multi-target detection and recognition. Therefore, how to accurately detect target objects in images is an urgent problem to be solved. To this end, a novel model, called YOLOv4_CE, is proposed in this paper, based on the classical YOLOv4 model with added improvements, resulting from replacing the backbone feature-extraction CSPDarknet53 network with a ConvNeXt-S network, replacing the Complete Intersection over Union (CIoU) loss with the Efficient Intersection over Union (EIoU) loss, and adding a coordinate attention mechanism to YOLOv4, as to improve its remote sensing image detection capabilities. The results, obtained through experiments conducted on two open data sets, demonstrate that the proposed YOLOv4_CE model outperforms, in this regard, both the original YOLOv4 model and four other state-of-the-art models, namely Faster R-CNN, Gliding Vertex, Oriented R-CNN, and EfficientDet, in terms of the mean average precision (mAP) and F1 score, by achieving respective values of 95.03% and 0.933 on the NWPU VHR-10 data set, and 95.89% and 0.937 on the RSOD data set.National Key Research and Development Program of China under Grant 2017YFE0135700; MES under Grant No. D01-168/28.07.2022 for NCDSC part of the Bulgarian National Roadmap on RIs; Telecommunications Research Centre (TRC), University of Limerick, Ireland