Publications

Harmful Internet use (HIU) is a term coined for the unintended use of the\nInternet. In this study, we propose a more accurate HIU measuring method based\non the peer assessment and differential evolution approach. The sample data\ncomprises a juvenile population in Poland; 267 subjects assessed 1,513 peers.\nIn addition to classic statistical analysis, differential evolution has been\nemployed. Results indicate that there may be a substantially higher rate of HIU\nthan other studies have indicated. More accurate measurement of the adolescent\npopulation influx affected by HIU is needed for healthcare and welfare system\nplanning.\n Presented in Prague, Czech Republic, 20-22 July 2022.\n

Sound energy scheduling and allocation is of paramount significance for the current steel industry, and the quantitative prediction of energy media is being regarded as the prerequisite for such challenging tasks. In this paper, a long-term prediction for the energy flows is proposed by using a granular computing-based method that considers industrial-driven semantics and granulates the initial data based on the specificity of manufacturing processes. When forming information granules on a basis of experimental data, we propose to deal with the unequal-length temporal granules by exploiting dynamic time warping, which becomes instrumental to the realization of the prediction model. The model engages the fuzzy C -means clustering method. To quantify the performance of the proposed method, real-world industrial energy data coming from a steel plant in China are employed. The experimental results demonstrate that the proposed method is superior to some other data-driven methods and becomes capable of satisfying the requirements of the practically viable prediction.

During surgeries (especially in long ones), patients are subject to a substantial amount of drug dosage necessary to achieve the required neuromuscular blockade level. This paper aims at the development of a fuzzy controller that satisfies two important goals: 1) an optimization of the amount of drug (atracurium) required to induce an adequate level of relaxation and 2) a concomitant ability to explain the undertaken control decision at the level of natural language. For instance, statements of the form "Since the difference between the target and the current blockade level is near zero, a small quantity of drug infusion is currently being applied", where "near zero" and "small" are linguistic terms that are represented as fuzzy sets. In this sense, we can regard this controller as a construct that is human friendly and highly interpretable (transparent). To address the two objectives outlined above, we consider the use of a multiobjective evolutionary optimization. How the quality of the control action and the controller interpretability are formalized and captured in this optimization framework is presented. The effectiveness of the approach is demonstrated through a comprehensive suite of experiments involving 100 simulated patients (used for training) and 500 patients (forming the test set), validating the approach for application in the operating theater.

Neural networks (NNs) implemented at the transistor level are powerful adaptive systems. They can perform hundreds of operations in parallel but at the expense of a large number of building blocks. In the case of analog realization, an extremely low chip area and low power dissipation can be achieved. To accomplish this, the building blocks should be simple. This brief presents a new current-mode low-complexity flexible adaptive mechanism (ADM) with a strongly reduced leakage in analog memory. Input signals ranging from 0.5 to 20 μA are held for 10–50 ms, with the leakage rate from 0.2%/ms to 0.04%/ms, respectively, depending on temperature. A small storage capacitor of 200 fF enables a short write time (< 100 ns). A single ADMcell occupies 1400 μm2 when realized in the Taiwan Semiconductor Manufacturing Company Ltd. CMOS 0.18-μm technology. The potential application of this NN is envisioned in a mobile platform based on a wireless sensor network to be used for online analysis of electrocardiography signals.

Publisher Summary The chapter discusses the revealing modeling relationships between variables in exploratory data analysis. These relationships can be modeled through rules, linear or nonlinear functions, and neural networks, to name a few alternatives. The objective of fuzzy multimodels is to deliver an environment assuring a successful interaction among several relational or functional constructs and to allow for their efficient utilization. An interesting and useful scenario arises when the data are governed by relational and one-to-many mappings rather than being confined to purely functional mechanisms. Fuzzy multimodeling introduced in this study is concerned with the design and utilization of families of models rather than single models. Fuzzy multimodels comprise a collection of local models along with the relevant mechanisms of their triggering and aggregating aimed at assuring a suitable interaction among these models. The algorithmic details are laid down and illustrated through several detailed simulation studies.

Although many classification techniques exist to analyze patterns possessing straightforward characteristics, they tend to fail when the ratio of features to patterns is very large. This “curse of dimensionality” is especially prevalent in many complex, voluminous biomedical datasets acquired using the latest spectroscopic modalities. To address this pattern classification issue, we present a technique using an adaptive network of fuzzy logic connectives to combine class boundaries generated by sets of discriminant functions. We empirically evaluate the effectiveness of this classification technique by comparing it against two conventional benchmark approaches, both of which use feature averaging as a preprocessing phase.

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Fuzzy clustering has emerged as a fundamental technique of information granulation. In this study, we introduce and discuss multivariable encoding and decoding mechanisms (referred altogether as a reconstruction problem) expressed in the language of fuzzy sets and fuzzy relations. The underlying performance index associated with the problem helps quantify a reconstruction error that arises when transforming a numeric datum through fuzzy sets (relations) and then reconstructing it into an original numeric format. The clustering platform considered in this study concerns the well-known algorithm of Fuzzy C-Means (FCM). The main design aspects deal with the relationships between the number of clusters versus the reconstruction properties and the resulting reconstruction error. The impact of the fuzzification coefficient on the reconstruction quality is investigated. This finding is of interest, given the fact that predominantly all applications involving FCM use the value of the fuzzification coefficient equal to 2. In light of the completed experiments, we demonstrate that this selection may not be experimentally legitimate. We also carry out a comparative analysis of the reconstruction properties of the Boolean decoding that is induced by the fuzzy partition. Experimental investigations involve selected machine learning data. </para>